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# 分层强化学习（HRL）实施计划 — 2026 高教社杯 B 题第三、四问
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## 0. 现状与复用决策
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仓库已有生产级基建，**全部复用、不重写**：
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| 现有组件 | 位置 | 用途 |
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|---|---|---|
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| `SimulatorClient`（HTTP+JSON、幂等重试、截止时钟、JSONL 日志） | `src/mcm26b/protocol/` | 模拟器对接（prompt 第 1 步已完成） |
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| 本地评测机（内存回环，一局秒级） | `referee/` + `harness.LocalSimTransport` | 本地简化环境内核 |
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| 网格幸存集滤波（真源保留定理已证明） | `src/mcm26b/particles.py` | 每频道信念后验 |
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| 凸候选集 + 五状态机 | `src/mcm26b/belief/` | 频道状态观测 |
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| 信息增益选点、TSP 2-opt、六边形覆盖、清除证书、后备清除 | `src/mcm26b/planner/` | 候选生成器 + 证书审批兜底 + 启发式基线 |
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| 单步 MaskablePPO 全套（特征/掩码/BC/训练/审计） | `src/mcm26b/rl/` + `scripts/` | 代码模式模板 + 端到端对照基线 |
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**改进方向**（来自 v2 审计文档已知局限）：单步 RL 只做选点、学不到全场调度；Q4 后备清除偏多、尾部退化（13778s→11797s，距 4000s 目标远）。HRL 高层正是补**跨频道全场调度**决策。
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**已确认的三个决策**：① 代码集成到 `src/mcm26b/hrl/`（README 给出与 prompt 目录树的映射表）；② 低层为字面 8 方向 PPO + 直线控制器对照（诚实预期：部署时只提交终点，低层对虚拟时间增益≈0，价值在架构完整性与消融验证）；③ 本次跑通全流程 + 短程训练（1–2h），完整课程训练交付脚本由用户自行启动。
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## 1. 新建文件
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```
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src/mcm26b/hrl/
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├── __init__.py
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├── constants.py # HRL 接口常量：N_CANDIDATES=96、观测维度，feature_version=3
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├── candidates.py # 高层候选生成：待清除源/信息增益探索点/覆盖验证点/Q4 扇区探测点 + 动作掩码
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├── features.py # 高层观测（20 频道×14 统计 + 全局标量 + 96×6 候选特征 ≈864 维）、低层观测
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├── policy.py # HighLevelPolicy/LowLevelPolicy 加载器（沿用 PointSelectionPolicy 元数据校验模式）
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├── low_level_env.py # 低层训练环境：随机起点→子目标，Discrete(8)，逐步计费，奖励=−Δd−转向+到达
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├── hierarchical_env.py # 高层整场环境：10–16 源，动作=子目标槽位，内嵌低层执行器，域随机化+课程参数
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└── executor.py # 子目标执行器：低层 PPO 步进（本地计费）或直线控制器（可切换），终点提交模拟器
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scripts/
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├── train_hrl_low.py # 低层：BC 克隆直线控制器热启动 → MaskablePPO 微调；直线对照评估
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├── train_hrl_high.py # 高层：4 阶段课程（--stage --resume --timesteps），SubprocVecEnv 并行
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├── pretrain_hrl_expert.py # 可选：从 BayesLoop 调度器克隆 (obs, 子目标) 对热启动高层
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├── tune_hrl.py # Optuna TPE（prompt §10 搜索空间，预算 15–20 trials，初值复用 bayes 已调参数）
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├── benchmark_hrl.py # 配对消融：HRL vs bayes/directional/单步 PPO，dev(700-702)/val(91000-91005)/stress 固定场景
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└── ablate_hrl.py # 四项消融：无信息增益奖励/无动作掩码/无课程/无粒子滤波
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src/mcm26b/planner/hrl_loop.py # HRLLoop 运行时闭环（Q3/Q4），保留证书审批与异常留痕
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src/mcm26b/cli.py # 新增 --strategy hrl / --q4-strategy hrl / --hrl-model
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tests/test_hrl.py # 特征布局、掩码、环境形状、奖励恒等式、训练-部署观测一致性
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docs/hrl.md # 设计文档 + prompt 目录树映射表
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docs/paper/ 增补 # 算法流程图、伪代码、超参表、训练曲线
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```
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## 2. 核心设计
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**高层动作空间** Discrete(96) + 掩码：槽位按固定顺序填充（已定位待清除源 ≤16 → PF 驱动探索点 → 覆盖网格未验证点 → Q4 扇区探测点），空槽/已清除频道/接收盘外候选掩码 −∞。
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**高层观测**（≈864 维，参照 v2 的 401 维布局模式）：20 频道 × 14 特征（六状态 one-hot、MEC μx/μy、σx/σy/ρ、log 幸存格数、期望信息增益、距离、频道号）+ 全局 8 维（位置、时间比例、已清除数、最近检测结果编码/示向度/距离）+ 96×6 候选特征。
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**step 语义**：高层选子目标 → executor 步进（低层 8 方向，每步 ≤100m，本地按 Δd/5 计费；部署时仅终点调模拟器）→ 按子目标类型执行 /measure 或 /clear → belief+PF 更新 → 奖励。
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**奖励**：`r = −Δ虚拟耗时/100 + 100·清除 + 5·新检测 + w_info·ΔH − 冗余惩罚`，主口径与目标函数（虚拟时间）一致；w_info 可消融。
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**课程 4 阶段**（prompt §7.1）：1 源/5 频道/无噪 → 3 源/10 频道/±0.5° → 8 源/16 频道/±1° → 10–16 源/20 频道/±1°；每阶段迁移上阶段权重，Q4 从 Q3 权重微调（定向比例递增），域随机化贯穿（源位置/频道/r_eff/朝向/噪声种子）。
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**运行时闭环 HRLLoop**：初始覆盖相位（快速发现信号）→ 主循环高层决策（证书达标自动 /clear，与 BayesLoop 同语义）→ 全清即 /exit。不触碰候选集更新与清除判定的守恒性（真源保留定理依赖），异常留痕兜底保留。
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## 3. 实施顺序（对应 prompt §12）
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1. `constants.py` + `candidates.py` + `features.py`（候选/掩码/观测）+ 单元测试
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2. `low_level_env.py` + `train_hrl_low.py`：低层短程训练，验证收敛到近直线（报告与直线最优的差距）
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3. `hierarchical_env.py` + `executor.py` + `hierarchical_env` 单元测试（奖励恒等式：回合回报 = f(虚拟时间、清除数)）
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4. `train_hrl_high.py` + 课程 + 域随机化 + SubprocVecEnv；`pretrain_hrl_expert.py` BC 热启动
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5. 短程课程训练（每阶段 2–5 万步，总计 1–2h），产出训练曲线
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6. `hrl_loop.py` + CLI 接入，`--local-judge` 本地演练验证全清正确性
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7. `benchmark_hrl.py` 配对对比（沿用 benchmark_rl.py 的 all_valid 有效口径）+ `ablate_hrl.py` 四项消融
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8. `tune_hrl.py` 受限 Optuna；产出最佳超参表
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9. 本地 referee 演练 ×3/题，表 1 格式结果（案例编码/清除数/平均定位清除时间/程序运行时间），日志 JSONL 原样归档 `output/hrl/`
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10. `docs/hrl.md`、README 更新、论文素材（流程图/伪代码/超参表/对比分析，含"为什么分层 PPO、为什么非端到端、为什么非多智能体"论证）
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## 4. 验收标准
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- 全部 pytest 通过（现有 243 项 + 新增 HRL 测试）
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- `benchmark_hrl.py` 输出 all_valid=true（真值全清、缺席无误判、零异常、正常 exit、计时复算一致）
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- 短程训练下 Q4 HRL 相对 directional 有可测改进方向（对标单步 RL 的 −14.38%）；Q3 如实报告与 bayes 的差距（v2 审计口径：Q3 RL 无稳定增益）
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- 所有实验固定种子、命令可复现；消融表含均值±方差（≥5 种子）
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- 完整训练只需用户执行 `scripts/train_hrl_high.py --full` 级别的命令
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## 5. 风险与对策
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- **高层整场回合过长（100–200 决策步）不收敛** → γ=1 + 密集 Δcost 奖励 + BC 从 bayes 调度器热启动
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- **低层增益≈0** → 如实消融报告（直线对照差异≈0），论文定位为架构必要性验证而非性能来源
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- **Q3 可能无增益** → 与 v2 口径一致如实报告，主打 Q4 调度收益；两种策略可共存（HRL 不改默认策略，仅在胜出后由用户决定切换默认）
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@@ -7,10 +7,10 @@
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- [协议规则表、字段表与状态机（P0 交付）](docs/协议规则表与状态机.md)
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- [问题一完整解答](docs/问题一解答.md)
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- [问题三搜索与总耗时改进方案（含七点覆盖证明和自建消融）](docs/algo-3.md)
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- [问题3/4 强化学习选点（PPO + CUDA）](docs/algo-rl.md)
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- [强化学习审核、修复与独立验证（2026-09-11）](docs/强化学习适用性审核与改进-20260911.md)
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- [HRL 优化方案与 Sol 执行指南（含当前缺口、命令和验收门槛）](docs/hrl-sol-execution.md)
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- [HRL P0+P1 执行报告（本轮实际命令、smoke 与多轮本地测试）](docs/hrl-p1-execution-report.md)
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- [第三、四问系统性架构优化与运行提速方案（运行历史复核、最小命中集、联合信念）](docs/第三四问架构优化与运行提速方案.md)
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- [第三、四问图论与动态规划优化方案（定点实测、路由 gap、定向定位 DP、证书硬化）](docs/第三四问图论与动态规划优化方案.md)
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- [1-4 问最优与基线实现对比框架](project/README.md)（`uv run python -m project.run`，结果在 `project/results/`）
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- [最优与基线实现对比（论文口径说明）](docs/最优与基线实现对比.md)
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- [论文](docs/paper/main.tex)
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## 项目结构
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@@ -25,13 +25,14 @@ src/mcm26b/
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├── belief/ # P3 逐频道候选集合 K_c：保守外包多边形 + 半平面裁剪（已完工）
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├── coverage/ # P4 49点覆盖账本（网格生成 + 逐频道记录）
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├── planner/ # P3 保证接收盘选点器 / P4 全向闭环 / P5 混合源定向闭环（已完工）
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│ # P6b RL 接入：planner/rl_loop.py（PPO 选点，惰性加载 torch）
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├── rl/ # P6b 强化学习扩展：训练环境、观测/候选特征、PPO 模型加载（可选依赖）
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├── routing.py # 图论/DP 工具箱：Held-Karp 精确开放路径、2-opt/Or-opt、MST 下界、路由度量
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├── evaluation/ # 日志复算与统计（已完工）
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└── harness/ # 测试侧适配：评测机<->Transport 内存回环、故障注入、回放、场景
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tests/ # pytest（含解耦守卫：运行时零导入 referee + 规则表不漂移）
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project/ # 1-4 问"最优实现 / 基线实现"对比框架：逐问 optimal+baseline
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# +独立参考，冻结基准集配对出数；见 project/README.md
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scripts/ # 采集、基准、可视化与校核脚本（含问题四批量仿真 benchmark_q4.py、
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# RL 训练 train_rl.py 与 RL 配对消融 benchmark_rl.py）
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# 运行历史计时分解 analyze_run_timing.py、第四问覆盖证书 verify_q4_cover.py）
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output/ examples/ # 演练证据、算例与生成产物
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```
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@@ -71,29 +72,11 @@ Python ≥ 3.10，运行时依赖仅 `requests`；测试/自检另需 `pytest`
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```bash
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uv sync # 安装（可编辑挂载本包与 referee）
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uv sync --group rl --group dev # 追加 RL 依赖：torch(CUDA)+stable-baselines3+gymnasium
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uv run pytest # 全部测试（含 referee 90 项 + 解耦守卫）
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uv run python -m referee # 起本地评测机（可选，配合 WebUI 回放）
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uv run mcm26b sim-check # P0 自检：复现附件计时示例 105/111/194/199
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uv run mcm26b run --robot-id 2026001 --self-test # 问题3自检：随机全向场景闭环（不连官方）
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uv run mcm26b run --robot-id 2026001 --self-test --problem 4 # 问题4自检：混合定向源场景
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# RL 策略（需先安装 rl 依赖组并训练模型；详见 docs/algo-rl.md）：
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uv run python scripts/train_rl.py --problem 3 --timesteps 120000 # PPO + CUDA 训练 Q3 选点
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uv run python scripts/pretrain_rl_expert.py --problem 4 --episodes 3000 # 自建模拟器专家行为克隆
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uv run python scripts/train_rl.py --problem 4 --timesteps 40000 --init output/rl/q4_ppo_bc_init.zip
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uv run mcm26b run --robot-id 2026001 --self-test --strategy rl # 问题3 RL 闭环
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uv run mcm26b run --robot-id 2026001 --self-test --problem 4 --q4-strategy rl # 问题4 RL 闭环
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uv run python scripts/benchmark_rl.py --seeds 5 # RL vs 手写策略配对消融
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uv run python scripts/verify_rl_practice.py output/rl/official-practice/q4/20260911-rl-bc-01
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uv run python scripts/benchmark_hrl.py --variants hrl_heuristic,bayes,directional \
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--manifest output/hrl/manifests/dev.json --output output/hrl/p1-heuristic
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uv run python scripts/train_hrl_high.py --problem 3 --stage 1 --timesteps 2048 \
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--envs 1 --vec-env dummy --device mps --config-profile production \
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--reward-profile time --model output/hrl/p1-smoke/q3.zip --eval-episodes 2
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# 训练前端（本地回环 Web UI）：实时进程/进度/参数收敛曲线，详见 docs/training-ui.md
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uv run python -m mcm26b.training_ui --ui-port 8036 # http://127.0.0.1:8036
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uv run mcm26b run --robot-id 2026001 --self-test --strategy hrl \
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--hrl-model output/hrl/p1-smoke/q3.zip --hrl-device mps
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# 自检默认：源数按题面 10~16 随机；实机预算 30 分钟（--real-budget-s 可改，仅自检生效）；
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# 找全后持续复扫未清除频道直到限时（--config 关闭 persistent_search 可回到"找全即退"）
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uv run mcm26b run --robot-id <队号> --log output/run/session.jsonl # 对接模拟器正式/演练
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@@ -159,7 +142,24 @@ client.exit()
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| P4 问题3全向闭环（49点覆盖、定位队列、清除、停止） | ✅ 官方问题3演练单案例通过（10/10全清，[验证报告](output/q3/practice/20260911-port2026/report.md)） |
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| P5 问题4定向扩展（联合约束、后备清除、预算） | ✅ 自建完成（官方演练待执行） |
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| P6 加速与消融 | ✅ 七点批量（`--strategy seven`，官方演练 12/12）+ 贝叶斯闭环（`--strategy bayes` 默认，自建消融 −30.0%，[算法说明](docs/algo-3-bayes.md)） |
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| P6b RL 选点 | ✅ PPO+CUDA 训练环境/闭环接入/官方+自建演练验证/专家 BC 初始化（Q3 `--strategy rl`，Q4 `--q4-strategy rl`，[算法说明](docs/algo-rl.md)） |
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| P7 图论/动态规划优化（S1–S5） | ✅ 已落地（[实测回填](docs/第三四问图论与动态规划优化方案.md#8-本轮实测回填2026-09-12-实施结果)）：路由度量入库、覆盖证书硬化（15 m/72 缺口 566→0）、Q4 后备盲清 125.7→4.3 次/局；同时修复联合信念 `no_signal` 朝向规则反向缺陷 |
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### 图论/DP 轮（2026-09-12，S1–S5）要点
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- **S1 度量**：`scripts/run_suite.py` 逐局新增 `route_gap_vs_offline`、
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`route_lower_bound_s`（MST 下界）、`distinct_service_points`、
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`coverage_actual_visited`、`probe_no_signal_ratio`、`plan_verify_wall_s`；
|
||||
图算法在 `src/mcm26b/routing.py`（Held-Karp 精确 / 2-opt / Or-opt / Prim）。
|
||||
- **S2 证书**：`q4_mincover_v2`（31 点，`r_design=965 m` + 前向角裕量 5°，
|
||||
15 m/72 档 325.6 万 (p,h) 缺口 0，最坏命中 990 m），取代粗采样过拟合的
|
||||
`q4_mincover_v1`（25 m/48 档 125 缺口）。
|
||||
- **S3 探点**：默认口径改为"可见候选里移动最省"（`probe_score_mode=nearest_visible`）；
|
||||
后备盲清 125.7 → 4.3 次/局，Q4 core 均值 8676 → 8602 s、最坏 10891 → 9630 s。
|
||||
- **S4/S5 负面结论**：后备清除 Or-opt、Held-Karp 精确任务序、路线感知探点
|
||||
在本模拟器上均为净负收益，默认关闭，代码与开关保留（见方案 §8.6）。
|
||||
- **正确性修复**：`belief/joint.py` 的 `no_signal` 朝向裁剪原先规则反向且漏计格内
|
||||
角裕量，会删掉真源朝向档；已修复并由 `tests/test_joint_belief.py` 的随机观测
|
||||
序列保留性测试固化。
|
||||
|
||||
## 采集与正式测试约束
|
||||
|
||||
|
||||
@@ -194,7 +194,6 @@ AI 的适合用法是把这些模块逐一变成可测试程序、生成边界
|
||||
| P4 全向闭环 | 49点覆盖记录、定位队列、清除、停止、日志统计 | 10/16源、边界源和错误响应案例；官方演练全清并正常退出 | 自建闭环已完成：`planner.OmnidirectionalLoop`（10/16/0 源、近边界源、预算耗尽、故障注入测试通过）；官方演练全清待团队执行 |
|
||||
| P5 定向扩展 | no_signal联合约束、覆盖发现、有限清除后备与预算 | 朝外边界源不漏；真源不误删；格数与退出预算检查 | 已实现：`planner.DirectionalExtension`（49点覆盖对定向源成立、保证接收盘内 no_signal 记为观测到定向、对跖+环探梯子、`fallback_clear_cells` 有限圆盘覆盖后备清除带格数/预算双硬顶）；`tests/test_loop_q4.py` 全过；官方混合源演练待执行 |
|
||||
| P6 加速消融 | 共享测点、自适应覆盖、目标排序、提前清除 | 配对案例全清不退化，时间/请求数改善可复算 | 可删减；闭环基线数据已可用于消融对照 |
|
||||
| P6b RL 选点 | 问题3/4 测量点选择的 PPO+CUDA 训练环境与闭环接入 | 自建全清；与手写策略配对消融有可复算报告 | 已完成：`scripts/train_rl.py`/`benchmark_rl.py`，模型 `output/rl/q{3,4}_ppo.zip`，说明 `docs/algo-rl.md` |
|
||||
| P7 正式与论文 | 固定版本、论文表1、日志原名、上传核查 | 问题3/4各3份正式记录与程序版本对应 | 团队后续安排；本任务不执行正式测试 |
|
||||
|
||||
按2026年9月10日起的剩余时间，建议10日补齐P1并衔接P3，11日完成P4自建边界案例与全向演练，12日完成P5及混合源演练；未达验收就删减P6并优先修正闭环。演练不限3次，正式机会为问题3和4各3次。团队在版本稳定后自行安排正式记录，留出失败恢复和论文整理时间。附件建议9月13日15:30前完成正式测试，17:30后不能启动新的演练或正式测试；已进入上传队列的日志仍会继续上传。此处引用所给附件安排，运行前核对模拟器界面公告；这份排期不改变当前禁止使用正式测试的限制。
|
||||
|
||||
-167
@@ -1,167 +0,0 @@
|
||||
# 问题3/4 强化学习选点（PPO + CUDA）
|
||||
|
||||
## 0. 摘要
|
||||
|
||||
**2026-09-11审核更新：**本文件下方的训练成绩与BC准确率是第一版历史记录。
|
||||
复核发现训练/部署特征错位、BC激活函数不一致以及评测只检查completed等问题。
|
||||
当前修复、Maskable PPO新模型和独立配对验证见
|
||||
[强化学习适用性审核与改进](强化学习适用性审核与改进-20260911.md)。
|
||||
新模型未替换原默认检查点；通过`--rl-model`显式加载。
|
||||
|
||||
本项目在保持 P0–P5 保守性与可解释证明不变的前提下，为问题3（全向源）与
|
||||
问题4（定向源）增加了一个可选的**强化学习测量点选择策略**：
|
||||
|
||||
- 把“下一检测点去哪”建模为离散动作的局部决策问题；401维特征是压缩观测，
|
||||
不能认定为完整马尔可夫状态，尤其第四问仍有未知朝向和被省略的历史约束；
|
||||
- 状态由逐频道信念的几何特征 + 60 个候选检测点的几何特征组成（401 维）；
|
||||
- 动作为 60 选 1（保证接收盘环候选、对称交会候选、定向遮挡环探候选）；
|
||||
- 使用 Stable-Baselines3 的 PPO 训练，MLP 策略与价值网络在 **CUDA GPU**
|
||||
（本机 RTX 4060 Laptop, CUDA 12.8 / torch 2.11.0+cu128）上更新；
|
||||
- 训练环境基于自建评测机 `referee`（内存回环，与真实 HTTP 协议同边界）；
|
||||
- 运行闭环仅替换既有选点排序器，覆盖证明、清除证书、定向后备清除全部保留。
|
||||
|
||||
历史结果（第一版自建配对消融，5 个种子）：Q3 用 12 万步随机初始化 PPO（与 `bayes`
|
||||
基本持平，+1.18%）；Q4 用**专家行为克隆初始化 + PPO 微调**，混合源平均
|
||||
虚拟时间相对 `directional` 基线降低 **7.16%**（14250.1s → 13230.2s）。
|
||||
Q3 的 BC 初始化在消融中反而退化，因此保留随机初始化模型；Q4 的 BC 初始化
|
||||
是当时五场比较中均值最好的版本。官方演练验证见 §5：两次 Q4 演练（3 定向混合源、10 定向全定向
|
||||
场景）与一次 Q3 演练均全清，且通过离线审计。
|
||||
|
||||
## 1. 环境搭建（CUDA）
|
||||
|
||||
```powershell
|
||||
# 安装可选依赖组 rl（PyTorch CUDA 12.8 + stable-baselines3 + gymnasium）
|
||||
uv sync --group rl --group dev
|
||||
|
||||
# 验证 CUDA
|
||||
uv run python -c "import torch; print(torch.cuda.is_available(), torch.cuda.get_device_name(0))"
|
||||
```
|
||||
|
||||
`pyproject.toml` 中 `[tool.uv.sources] torch = { index = "pytorch-cu128" }`
|
||||
与 `[[tool.uv.index]]` 指向 PyTorch 官方 CUDA 12.8 轮子；下载约 2.6 GB，
|
||||
首次同步建议 `$env:UV_HTTP_TIMEOUT='300'`。默认 PyPI 的 Windows torch 轮子
|
||||
为 `+cpu`，可以使用CPU训练；本轮修订模型即采用CPU，CUDA并非训练前提。
|
||||
|
||||
## 2. 第一版局部决策环境
|
||||
|
||||
训练环境：`src/mcm26b/rl/env.py::LocalizationEnv`。
|
||||
|
||||
| 项 | 定义 |
|
||||
| --- | --- |
|
||||
| 初始状态 | 自建模拟器随机布设一个源（Q3 全向；Q4 定向比例可调，训练默认 1.0），机器狗完成一次保证得到 `direction` 的首测 |
|
||||
| 观测 | 401 维：21 个全局特征 + 20 维频道 one-hot + 60×6 维候选点特征（见 `features.py`） |
|
||||
| 动作 | `Discrete(60)`：0=候选集 MEC 圆心，1..36=保证接收盘 3 环×12 方向，37..51=对称交会 15 点，52..59=定向遮挡环探 8 点 |
|
||||
| 转移 | 按动作位置调用 `/measure`，用 `BeliefState` 保守更新候选集；Q4 保证接收盘内 `no_signal` 记为朝向遮挡并进入 `blocked` 序列；证书达标后自动 `/clear`；遮挡梯子用尽后模拟 20m 网格后备清除 |
|
||||
| 奖励 | `-Δvirtual_time/100`（移动+检测+换频+清除真实耗时）、候选半径收缩加成、首次遮挡证据 +2、重复测点 -0.75、清除成功 +10、失败/截断负奖励 |
|
||||
|
||||
约束说明：候选点全部来自既有几何生成器（`planner.candidate_points`、
|
||||
`planner.safe_disk_radius`、定向环探梯子），RL 不生成新的空间候选；它只做
|
||||
排序。清除仍须通过几何证书；但“保证收到方向”只适用于全向源且实际点位于
|
||||
距离保证盘内。第一版的52—59号环探点可能在盘外，第四问的定向遮挡也不受
|
||||
距离盘保证。v2对第三问加入盘外动作屏蔽，第四问保留探索分支。
|
||||
|
||||
## 3. 训练与自建模拟器逻辑利用
|
||||
|
||||
```powershell
|
||||
# 1) 用自建模拟器上的手写专家生成 (观测, 动作) 并行为克隆
|
||||
uv run python scripts/pretrain_rl_expert.py --problem 3 --episodes 3000
|
||||
uv run python scripts/pretrain_rl_expert.py --problem 4 --episodes 3000
|
||||
|
||||
# 2) 从当前v2 BC初始化继续普通PPO（算法类型需显式匹配）
|
||||
uv run python scripts/train_rl.py --problem 4 --algorithm ppo --timesteps 40000 \
|
||||
--init output/rl/q4_ppo_bc_init.zip
|
||||
|
||||
# Q3 保留随机初始化长训练版本：
|
||||
uv run python scripts/train_rl.py --problem 3 --timesteps 120000
|
||||
```
|
||||
|
||||
专家逻辑与自建模拟器同源：Q3 用 worst-posterior 选点器，Q4 用对跖/环探
|
||||
遮挡梯子；`pretrain_rl_expert.py` 在 `LocalizationEnv` 上记录每个状态的
|
||||
专家动作，训练与 PPO policy_net/action_net 同构的 MLP，再把权重拷入 PPO
|
||||
作为初始化。产物位于 `output/rl/bc/` 与 `output/rl/q{3,4}_ppo_bc_init.zip`。
|
||||
|
||||
实测：Q3 专家数据 5141 条、BC 验证准确率 38.9%，微调后自建消融退化
|
||||
（保留 12 万步随机初始化模型）；Q4 专家数据 14181 条、BC 验证准确率
|
||||
65.3%，微调后自建消融从 −3.84% 提升到 **−7.16%**。但审核确认BC与PPO的
|
||||
激活函数不一致，这些数字不能证明成功迁移了专家策略，也不足以判断全向分支已最优。
|
||||
|
||||
输出：`output/rl/q3_ppo.zip`、`output/rl/q4_ppo.zip` 及同名 JSON 报告；
|
||||
训练脚本先打印 `train_start`（含 device/gpu 字段），再执行 PPO 并在独立
|
||||
种子场景上确定性评估成功率、平均步数与平均回报。Q4 4 万步微调约 236s；
|
||||
Q3 12 万步约 625s（RTX 4060 Laptop，2 个 CPU 环境）。
|
||||
|
||||
## 4. 运行闭环接入
|
||||
|
||||
- Q3：`uv run mcm26b run --robot-id 2026001 --self-test --strategy rl`
|
||||
`RLBayesLoop` 继承 `BayesLoop`（七点覆盖 + 网格粒子滤波 + 滚动 2-opt），
|
||||
仅 `_select_point` 改由 PPO 输出候选编号。
|
||||
- Q4：`uv run mcm26b run --robot-id 2026001 --self-test --problem 4 --q4-strategy rl`
|
||||
`RLDirectionalLoop` 继承 `DirectionalExtension`（49 点覆盖 + 遮挡梯子 +
|
||||
20m 有限圆盘后备清除），仅 `_next_probe` 改由 PPO 输出。
|
||||
- 模型加载时校验观测 401 维 / 动作 60 维，失配直接拒绝运行。
|
||||
- 默认运行路径不导入 torch/referee；只有显式选择 `rl` 才惰性导入，项目
|
||||
运行时依赖仍保持 `requests` 一项。
|
||||
|
||||
## 5. 消融基准
|
||||
|
||||
```powershell
|
||||
uv run python scripts/benchmark_rl.py --seeds 5
|
||||
```
|
||||
|
||||
同种子同场景配对比较，结果写入 `output/rl/benchmark.json`。以下为第一版历史结果：
|
||||
|
||||
| 场景 | 基线 | 平均虚拟时间（基线 / RL） | 相对差 | 全清 |
|
||||
| --- | --- | --- | --- | --- |
|
||||
| Q3（10 源全向 ×5 种子，scratch PPO） | `bayes` | 3088.3s / 3124.8s | +1.18% | 5/5 |
|
||||
| Q4（10 源、3 定向 ×5 种子，BC+PPO） | `directional` | 14250.1s / 13230.2s | −7.16% | 5/5 |
|
||||
|
||||
训练局评估：Q3 单源 40 局成功率 100%、平均 3.55 步；Q4 定向单源 40 局
|
||||
成功率 90%、平均 4.45 步；其余单源训练局未完成，不能由此宣称已兜底成功。随机策略对照：
|
||||
Q3 平均回报 13.28 vs PPO 17.71；Q4 平均回报 8.72 vs BC+PPO 12.62。
|
||||
|
||||
### 5.1 官方模拟器演练验证（仅演练，未触碰正式测试）
|
||||
|
||||
使用目录内官方 `jammers-simulator-full` 的**演练测试**（不限次数），通过
|
||||
官方 HTTP 接口运行同一闭环；官方产物按原名归档并离线审计：
|
||||
|
||||
| 问题 | 案例 | 场景真值（演练结束后可见） | 结果 | 虚拟时间 | 请求数 | 审计 |
|
||||
| --- | --- | --- | --- | --- | --- | --- |
|
||||
| Q3 | `F38Y-9Q9J-B82V-CB9Z` | 13 全向 | 13/13 全清 | 3538.8s | 124 | ✅ |
|
||||
| Q4 | `X43X-VMRF-8GRZ-3SYA` | 7 全向 + 3 定向 | 10/10 全清 | 14003.1s | 619 | ✅ |
|
||||
| Q4(BC) | `BXSA-TFQ3-2DJ6-DHH7` | **10 定向** | 10/10 全清 | 14562.8s | 691 | ✅ |
|
||||
|
||||
前两次为随机初始化模型，第三次为 BC 初始化微调模型。两个 Q4 官方案例的
|
||||
源构成不同（3 定向 vs 10 定向），**虚拟时间不可直接相减比较**；可确认的
|
||||
是 RL 在官方混合源与全定向源演练中都能完成全部清除，且
|
||||
`all_timestamps_match_microsecond_recomputation`、官方 `.jlog` 的
|
||||
SHA-256 校验全部通过。归档目录：`output/rl/official-practice/`。
|
||||
|
||||
### 5.2 自建 referee 演练验证
|
||||
|
||||
`uv run mcm26b run --local-judge` 走自建 referee 的 `q3_practice` /
|
||||
`q4_practice` 模块（与官方相同的演练生命周期，真值在结束后可见）：
|
||||
|
||||
| 问题 | 案例 | 场景 | 结果 | 虚拟时间 | 归档 |
|
||||
| --- | --- | --- | --- | --- | --- |
|
||||
| Q3 | `FKUY-K8KJ-RXRL-FMBM` | 10 全向 | 10/10 全清 | 3317.6s | `output/rl/local-practice/q3/20260911-rl-01` |
|
||||
| Q4 | `3J8T-L9GY-HRGL-LYVZ` | 7 全向 + 3 定向 | 10/10 全清 | 11942.8s | `output/rl/local-practice/q4/20260911-rl-01` |
|
||||
| Q4(BC) | `VM2G-UVC6-4N7R-TVGW` | 7 全向 + 3 定向 | 10/10 全清 | 12520.8s | `output/rl/local-practice/q4/20260911-rl-bc-01` |
|
||||
|
||||
不同案例仍不可直接比较；成对可比的提升证据是 §5 的 5 种子同场景消融。
|
||||
|
||||
## 6. 结论与边界
|
||||
|
||||
以下是第一版结论；BC权重拷贝缺陷使“同构初始化”的解释不成立，性能判断
|
||||
应以新审核报告的同场景验证及不确定性为准，而不能由BC分类准确率推出。
|
||||
|
||||
- RL 已接入问题3/4 完整闭环，并在自建 referee 演练与官方模拟器演练中
|
||||
全清验证；这是方法验证，不是对正式成绩的承诺。
|
||||
- 第一版Q4的BC初始化后均值变化为−3.84% → −7.16%，但初始化存在激活函数
|
||||
缺陷，样本数也不足以支持“明确收益”的归因；当前判断见新审核报告。
|
||||
- 训练环境使用自建场景假设（源均匀分布、逐点固定误差场、定向源朝向随机）；
|
||||
官方隐藏分布未必相同。正式测试仍应默认使用经过演练验证的手写策略；
|
||||
`--strategy rl` / `--q4-strategy rl` 适合消融、方法对比与后续调参。
|
||||
- 官方演练仅使用“演练测试”按钮与开放接口，从未点击“正式测试”；官方
|
||||
`.jlog/.result.json` 保持原样，未解密。
|
||||
- 保守性不变式：RL 只改变选点排序，不改变覆盖账本、候选集更新、清除证书
|
||||
与后备清除的数学证明；所有策略均以 `/exit` 与完整日志收尾。
|
||||
@@ -1,117 +0,0 @@
|
||||
# HRL P0+P1 执行报告（本地自建评测机）
|
||||
|
||||
日期：2026-09-11（本机工作区）
|
||||
范围：按用户确认，仅完成 P0 正确性回归 + P1 最小可训练/可评测闭环；
|
||||
P2 的 BC、完整课程长训、Optuna、正式消融与正式测试表留待下一阶段。
|
||||
|
||||
## 1. 本轮代码交付
|
||||
|
||||
| 文件 | 内容 |
|
||||
| --- | --- |
|
||||
| `scripts/benchmark_hrl.py` | 同一 manifest 真值场景复用；`hrl`/`hrl_heuristic`/`bayes`/`directional` 配对评测；写 `results.json`、`aggregate.json/csv`、每局 `summary.json` 与 `session.jsonl` |
|
||||
| `scripts/train_hrl_high.py` | 高层 MaskablePPO 训练；`--problem/--stage/--envs/--vec-env/--device/--config-profile/--reward-profile/--model/--resume/--init/--eval-manifest`；模型元数据固化 feature/action/reward 版本；墙钟硬上限 |
|
||||
| `src/mcm26b/hrl/constants.py` | 新增 `ACTION_LAYOUT_VERSION=1`、`REWARD_VERSION=1` |
|
||||
| `src/mcm26b/hrl/policy.py` | 高层模型严格校验元数据；低层模型保持独立基础校验 |
|
||||
| `src/mcm26b/cli.py` | 新增 `--strategy hrl`、`--q4-strategy hrl`、`--hrl-model`、`--hrl-low-model`、`--hrl-device`、`--trace-low` |
|
||||
| `src/mcm26b/hrl/hierarchical_env.py` | 新增 `HRLReward.time_only()`：P0.4 建议的纯时间目标，去掉逐源清除/发现/熵减塑形 |
|
||||
| `project/` | 严格目录树薄兼容层，转发真实实现，不复制算法 |
|
||||
| `tests/test_hrl_p1.py` | manifest 往返、真值隔离、纯时间奖励、project re-export 回归 |
|
||||
|
||||
## 2. 全量回归
|
||||
|
||||
```bash
|
||||
.venv/bin/python -m pytest -q --tb=short
|
||||
# 275 passed in 124.57s
|
||||
```
|
||||
|
||||
## 3. P1 smoke 训练（本机 Apple MPS）
|
||||
|
||||
```bash
|
||||
.venv/bin/python scripts/train_hrl_high.py \
|
||||
--problem 3 --stage 1 --timesteps 2048 --seed 320001 \
|
||||
--envs 1 --vec-env dummy --device mps \
|
||||
--config-profile production --reward-profile time \
|
||||
--model output/hrl/p1-smoke-time/q3.zip \
|
||||
--eval-episodes 2 --rollout-steps 128 --batch-size 128 \
|
||||
--n-epochs 3 --max-wall-s 800 --verbose 0
|
||||
```
|
||||
|
||||
结果：
|
||||
|
||||
- `actual_timesteps=2048`，墙钟 `260.069 s`；
|
||||
- 保存后确定性评估 2/2 成功，`accounted=20`，无异常；
|
||||
- 模型元数据含 `feature_version=4`、`action_layout_version=1`、
|
||||
`reward_version=1`、`reward_profile=time`、`model_sha256`。
|
||||
|
||||
模型重载已由以下两条路径验证；`--resume` 的 `--timesteps` 是增量步数
|
||||
（SB3 `reset_num_timesteps=False` 语义），元数据写 `timesteps_semantics="additional"`：
|
||||
|
||||
```bash
|
||||
.venv/bin/python -m mcm26b.cli run \
|
||||
--robot-id 2026001 --self-test --strategy hrl \
|
||||
--hrl-model output/hrl/p1-smoke-time/q3.zip --hrl-device mps \
|
||||
--sources 3 --seed 1 --summary-steps 5
|
||||
|
||||
python scripts/benchmark_hrl.py --variants hrl \
|
||||
--hrl-model output/hrl/p1-smoke-time/q3.zip --hrl-device mps \
|
||||
--problem 3 --scenes 1 --sources 3 --output output/hrl/p1-bench-hrl
|
||||
```
|
||||
|
||||
## 4. 多轮本地配对测试
|
||||
|
||||
### Q3：3 个独立场景 × 3 个变体
|
||||
|
||||
```bash
|
||||
python scripts/benchmark_hrl.py --variants hrl,hrl_heuristic,bayes \
|
||||
--hrl-model output/hrl/p1-smoke-time/q3.zip --hrl-device mps \
|
||||
--problem 3 --scenes 3 --sources 3 --start-seed 91000 \
|
||||
--output output/hrl/p1-multiround-q3
|
||||
```
|
||||
|
||||
| variant | episodes | valid_rate | cleared_mean | virtual_time_mean_s | virtual_time_std_s | program_run_time_mean_s |
|
||||
| --- | ---: | ---: | ---: | ---: | ---: | ---: |
|
||||
| bayes | 3 | 1.0 | 3.0 | 2518.371 | 74.183 | 0.748 |
|
||||
| hrl_heuristic | 3 | 1.0 | 3.0 | 2518.371 | 74.183 | 0.883 |
|
||||
| hrl | 3 | 1.0 | 3.0 | 3731.380 | 120.536 | 1.853 |
|
||||
|
||||
结论：P1 模型只在 stage 1 单源课程上训练 2048 步，可直接重载并在 3 源场景保持 3/3 有效全清；
|
||||
但用时明显未优于 bayes/heuristic，符合本轮不做正式性能声明的边界。
|
||||
|
||||
### Q4：3 个独立混合源场景 × 2 个变体
|
||||
|
||||
```bash
|
||||
python scripts/benchmark_hrl.py --variants hrl_heuristic,directional \
|
||||
--problem 4 --scenes 3 --sources 3 --directional 1 --start-seed 92000 \
|
||||
--output output/hrl/p1-multiround-q4 --force
|
||||
```
|
||||
|
||||
| variant | episodes | valid_rate | cleared_mean | virtual_time_mean_s | virtual_time_std_s | program_run_time_mean_s |
|
||||
| --- | ---: | ---: | ---: | ---: | ---: | ---: |
|
||||
| directional | 3 | 1.0 | 3.0 | 13442.997 | 263.902 | 1.063 |
|
||||
| hrl_heuristic | 3 | 1.0 | 3.0 | 12292.198 | 344.827 | 6.284 |
|
||||
|
||||
说明：Q4 高层 PPO 尚未训练；本轮只验证 Q4 的 `hrl_heuristic` 与 `directional` 闭环、真值隔离与计时审计。
|
||||
|
||||
## 5. P0 状态
|
||||
|
||||
- `--resume` 重载：16 步起步模型续训 32 增量步后 `actual_timesteps=48`，
|
||||
校验 `feature_version/action_layout_version/reward_version` 后保存；
|
||||
|
||||
- 槽位唯一性：`test_hrl.py` 覆盖 channel 17–20 与 1–4 同时 clear/locate 不丢；
|
||||
- 清除事件一次且仅一次：fallback 成功写 `_step_clears` 已有回归；
|
||||
- 候选快照：`test_opportunity_measure_precedes_candidate_snapshot` 已覆盖；
|
||||
- 奖励对齐：新增 `HRLReward.time_only()`，训练脚本默认 `--reward-profile time`；
|
||||
- 终止/安全网：现有 `HierarchicalEnv` 回合测试继续通过；
|
||||
- 训练/部署配置：`--config-profile production` 使用生产配置，`coarse-pretrain`
|
||||
显式映射到 fast 评分配置并写入模型元数据；
|
||||
- 依赖守卫：全量测试 275 项在 dev+rl 环境通过。
|
||||
|
||||
## 6. 未完成 / P2 待办
|
||||
|
||||
- `scripts/pretrain_hrl_expert.py` 已建可 `--help` 的接口占位，专家轨迹 + BC 功能待实现；
|
||||
- `scripts/ablate_hrl.py` 已建接口占位，无信息增益/无掩码/无课程/无粒子统计消融待实现；
|
||||
- `scripts/tune_hrl.py` 已建接口占位，Optuna TPE 搜索待实现；
|
||||
- Q3→Q4 迁移训练与配对实验；
|
||||
- 完整 10–16 源课程训练、多个训练种子、冻结测试清单；
|
||||
- 官方三次正式测试表：必须有真实官方案例码与导出日志后才能填写；
|
||||
本地 `referee` 结果只标 `local_self_test`。
|
||||
@@ -1,267 +0,0 @@
|
||||
# HRL 续作执行说明：交给 Sol 的工程任务书
|
||||
|
||||
适用日期：2026-09-11。工作目录：本仓库根目录。先检查当前代码与本轮测试结果，再按 P0 → P1 → P2 → P3 顺序执行。
|
||||
|
||||
目标是交付可运行、可审计的整场调度学习系统；训练收益由完整场景验证决定，不把训练脚本存在或局部奖励上升视为完成。
|
||||
本说明更新了原粘贴 Prompt 与 `.zcode/plans/plan-sess_cf390556-4ef3-4ce6-b91e-782aee1718f9.md` 中不符合现有协议或证据的部分。
|
||||
|
||||
## 1. 现在有什么，缺什么
|
||||
|
||||
| 组件 | 当前状态 | Sol 的操作 |
|
||||
| --- | --- | --- |
|
||||
| `protocol/`、`referee/`、`harness.py` | 已有客户端、独立评测机、内存回环与计时审计 | 复用，不另建模拟器客户端 |
|
||||
| `belief/`、`particles.py`、`planner/` | 已有保守候选、网格幸存集、覆盖证明、证书清除与后备清除 | 保持证明前提；改调度时复用 |
|
||||
| `rl/`、`train_rl.py`、现有 RL 检查点 | 已有单源选点 PPO/MaskablePPO 与完整对照 | 作为局部策略和对照，勿命名为端到端 PPO |
|
||||
| `hrl/constants.py`、`scheduler.py`、`planner/hrl_loop.py` | 已有高层候选、特征、执行与闭环骨架；存在本轮需修复的 P0 项 | 先完成第 4 节门槛 |
|
||||
| `hrl/hierarchical_env.py`、`curriculum.py` | 已有训练环境和课程；奖励、终止、训练配置仍需审核 | 在长训前修正，覆盖独立环境/部署一致性 |
|
||||
| `hrl/low_level_env.py`、`executor.py`、`train_hrl_low.py` | 有八方向到达实验；训练入口尚有掩码等缺陷 | 作为可选离线实验，不能卡住主线 |
|
||||
| `tests/test_hrl.py` | 有接口和启发式 episode 测试 | 增补行为验证，不能只检验公式与实现一致 |
|
||||
| `train_hrl_high.py`、`pretrain_hrl_expert.py` | 尚未实现 | P1 编写最小可训练闭环，P2 再加入 BC |
|
||||
| `benchmark_hrl.py`、`ablate_hrl.py`、`tune_hrl.py` | 尚未实现 | 先评测，再训练，再做消融，最后受限调参 |
|
||||
| CLI 的 `hrl` 策略和 `--hrl-model` | 当前入口尚未接入 | P1 接入并检验 `--help` 与离线闭环 |
|
||||
| `output/hrl/` 的高层模型、曲线、评测表 | 本次审阅未发现这些产物 | 不声称已有 HRL 模型或训练成绩 |
|
||||
|
||||
现有证据：修订单步 RL 的 Q3 独立验证均值比 `bayes` 低 0.69%，区间跨 0；Q4 比 `directional` 低 14.38%，比旧 PPO 低 2.26%，后一比较区间跨 0。
|
||||
这些来自固定模型、本地 18 场/题的验证；新 Q4 的后备清除次数更多，边界压力性能有退化。详情见 `docs/强化学习适用性审核与改进-20260911.md`。
|
||||
没有证据证明 HRL 已优于上述基线，没有证据支持“Q4 迁移节约 80% 训练成本”或“全部合法场景小于 4000 秒”。
|
||||
|
||||
## 2. 必须纠正的原 Prompt 假设
|
||||
|
||||
1. `/measure` 与 `/clear` 提交终点，移动按两次实际动作位置间的直线距离除以 5 计费；没有朝向、转弯或八方向移动协议。
|
||||
2. 八方向策略仅在本地推演再强制补上终点，无法影响模拟器实际路径；不能把这段推演称为比赛收益来源。
|
||||
3. `/clear` 不切换接收机频道，目标频道与当前接收机频道应分别记录；`near` 是测量结果，清除仍需独立请求。
|
||||
4. 接收半径逐源未知，动作掩码只能使用观测与保守界，不能读真实半径、源位置、类型或朝向。
|
||||
5. Q3 的 `no_signal` 只能按已证明的保守半径规则删格；Q4 的 `no_signal` 可能由方向遮挡造成，不能直接删空间候选。
|
||||
6. Q4 联合位置/半径/朝向粒子是后续研究选项；普通二维粒子加“角度门控”并不会自动得到正确联合后验。
|
||||
7. 固定地点误差固定；同地重测不是新独立样本。保留读数舍入裕量，不收紧真实的 ±1° 误差界来追求表面收敛。
|
||||
8. 源数最多 16 不等于只有 16 个频道；所有按频道编号索引的动作必须覆盖 1–20。
|
||||
9. 连续无信号次数不能作为全场结束条件；覆盖参数不能随意调到破坏已证明的发现覆盖。
|
||||
10. “无信息增益”“无动作掩码”“无粒子滤波”必须精确定义；不能通过取消清除证书或真源保留约束制造消融。
|
||||
11. 原 Prompt 的 6 个状态与实现里的状态枚举不一致；以 `ChannelStatus` 为准，额外 TRACKING/LOCATED 只能定义为派生特征。
|
||||
12. 协议文档 §6 仍写持续搜索默认开启；当前 `config.py` 为 `persistent_search=False`，CLI 用 `--persistent` 显式开启。先同步文档，不恢复过时默认值。
|
||||
|
||||
## 3. 推荐架构与工程边界
|
||||
|
||||
采用“高层学习调度 + 低层局部选点 + 协议执行”的分层架构。高层学习先处理哪个频道、继续覆盖还是定位、何时使用后备清除。
|
||||
低层默认调用成熟几何选点；可切换现有 `PointSelectionPolicy` 学习局部检测点，形成第二个学习层。直线移动与设备动作继续由协议客户端执行。
|
||||
Q3 首版低层用 `bayes` 几何选点；Q4 先保留 `directional` 的遮挡处理，待接线一致性通过后对比旧/新局部 PPO。
|
||||
|
||||
```text
|
||||
SimulatorClient.state + belief + particles + coverage ledger
|
||||
↓ 同一份候选快照和动作掩码
|
||||
高层 MaskablePPO：选择频道/覆盖站/清除任务
|
||||
↓
|
||||
局部选点：geometry | 已有 v2 PointSelectionPolicy
|
||||
↓
|
||||
证书/预算/覆盖约束 + 实际 measure/clear 宏动作
|
||||
↓
|
||||
accepted 请求与响应 → 时间、信念、清除与覆盖账本
|
||||
```
|
||||
|
||||
八方向 PPO 保留为 `low_level_env.py` 的离线附录实验；生产默认无需加载。若论文要求字面双 PPO，用高层调度 PPO + 局部选点 PPO 对应真正可影响成绩的两个层次。
|
||||
现有高层一个动作可以包含整站逐频道扫描或多次后备清除，因此是变长宏动作。记录每个宏动作的实际持续时间、请求数和中断原因。
|
||||
先采用 `gamma=1` 与实际时间成本，避免把“每宏动作折扣一次”误当成“每秒折扣一次”；只有明确改变目标才引入按持续时间折扣。
|
||||
第一版保留 120 槽位 MLP，不同时重写注意力网络或连续动作；需要多候选/频道时另升动作版本并独立比较。
|
||||
|
||||
建议新增的共享接口如下；这些是待实现契约，避免训练和 CLI 分别拼接策略。
|
||||
|
||||
```python
|
||||
@dataclass(frozen=True)
|
||||
class HRLPolicyConfig:
|
||||
local_selector: str = "geometry" # geometry | rl
|
||||
local_model_path: str | None = None
|
||||
|
||||
def make_hrl_loop(client, config, *, problem: int,
|
||||
high_policy=None, policy_config=HRLPolicyConfig()) -> HRLLoop: ...
|
||||
|
||||
def validate_episode(summary: dict, events: list[dict],
|
||||
truth_channels: set[int]) -> dict: ... # 仅评测模块
|
||||
|
||||
def run_episode(scene: dict, variant: dict, *, output_dir: Path) -> dict: ...
|
||||
```
|
||||
|
||||
## 4. P0：修复正确性，未过门槛不得长训
|
||||
|
||||
以下表中的状态以本轮提交和测试报告为准；Sol 接手时先查看 diff，已完成的项只做回归，不重复实现。
|
||||
|
||||
| 项目 | 精确入口与问题 | 目标与验收 |
|
||||
| --- | --- | --- |
|
||||
| P0.1 槽位冲突 | `constants.py`；`scheduler.build_candidates/heuristic_action`：clear 频道 17–20 与 locate 频道 1–4 重叠 | 本轮优先修复；每类按 20 频道分配，构造同时就绪冲突场景验证任务均保留 |
|
||||
| P0.2 清除事件遗漏 | `scheduler._hrl_execute_fallback` 成功未写 `_step_clears` | 本轮优先修复；证书/后备/宏动作内部清除均由相同成功事件口径计数，一次且仅一次 |
|
||||
| P0.3 候选过期 | `HRLLoop._coverage_phase` 在构建候选后 `_measure_at_stop` 可改变信念 | 本轮优先修复；选动作前候选/特征/掩码属于同一个状态快照 |
|
||||
| P0.4 奖励对齐 | `HierarchicalEnv.step/_reward_from/HRLReward` | 改默认奖励，成功/异常/安全网/截断均计入已经发生的真实时间 |
|
||||
| P0.5 回合终止 | `HierarchicalEnv.step/reset/close` | 实际完成、失败终止与采样截断分开；收尾不丢 /exit，不把零观测用于有效截断 bootstrap |
|
||||
| P0.6 训练部署配置 | `curriculum.training_config` 默认网格 29.3m，部署 14.65m | 首个可比较模型使用生产几何配置；粗网格只能显式快速预训练，后接完整保真微调与评测 |
|
||||
| P0.7 无进展循环 | `scheduler` 的 locate/probe/fallback 掩码及预算检查 | 去重、已清除/无效证书、后备预算不足在动作前处理；合法动作不能无限零成本自循环 |
|
||||
| P0.8 测试与依赖 | `tests/test_hrl.py` 导入守卫、`policy.py/scheduler.py` 顶层 numpy | 不安装 RL 组时基础 CLI 可导入；完整 RL 环境运行测试时不得意外全部 skip |
|
||||
|
||||
建议槽位布局为 clear 0–19、locate 20–39、station 40–88、fallback 89–108、保留 109–119；所有槽位互斥且小于 120。
|
||||
槽位含义改变必须升 `FEATURE_VERSION`，即使仍是 1248 维/120 动作，也必须拒绝旧语义检查点。
|
||||
模型元数据应要求 `level/problem/algorithm/feature_version/action_layout_version/reward_version`，并记录模型 SHA、配置、依赖版本、代码版本。
|
||||
缺字段不能默认为兼容；Q3 权重初始化 Q4 必须走显式训练迁移参数，部署加载仍严格检查题号。
|
||||
|
||||
默认奖励先用：`r = -实际本步虚拟时间增量 / 100`；证明完成时加固定完成奖，未完成的真实终止加失败罚。
|
||||
失败惩罚按冻结的 episode 最大成本界设置并记录，不随意用不足以覆盖数千秒的 `-100`。
|
||||
相同全清场景里逐源常数奖可能只是常数偏移，但不同终止路径、事件漏记和失败场景会改变激励；故首版去掉新发现/熵减/每源清除附加奖。
|
||||
如需塑形，另做势函数差 `gamma * Phi(next_state) - Phi(state)`，真正终止态统一势函数值，并检验总和望远镜消去。
|
||||
如果优化题目平均时间，用整场时间作为训练目标时明确这是所选代理；不同源数的跨场景均值与 `T/N` 均值不等价，不能暗称完全一致。
|
||||
人工步数上限若仅为采样截断，返回可用的终末观测并允许 bootstrap；若定义为任务失败期限，应作为真实终止计罚。按该语义设计测试。
|
||||
|
||||
P0 验收至少覆盖以下行为，不只验证 reward 按代码相加：
|
||||
|
||||
- 20 个频道的各类槽位唯一；频道 17–20 与 1–4 同时有 clear/locate 不丢任务。
|
||||
- 同一观测轨迹分别构建独立训练环境与部署闭环，比较候选、mask、特征、下一实际请求及状态。
|
||||
- 固定动作序列跨成功、失败、后备与安全网路径：reward 的时间分项总和等于 accepted 日志复算时间。
|
||||
- Q4 保证盘内/盘外无信号不误删真源；正读数重置遮挡序列；无效证书不能执行清除。
|
||||
- 10/16 源、频道含 20、边界源与预算中断均检查真实源集合、缺席集合、正常退出、异常与计时。
|
||||
- 评测端真值仅进 `validate_episode`，策略构建、候选、mask、观测与模型选择均不得读取真值。
|
||||
|
||||
## 5. P1：最小完整闭环，先评测再短训
|
||||
|
||||
先实现 `scripts/benchmark_hrl.py` 与共享审计函数，使 `HRLLoop(high_policy=None)` 可以与原 `bayes/directional` 同场景对比。
|
||||
这个启发式 HRL 骨架是重要对照：若差距来自任务候选、机会测量、Q4 保底或预算语义变化，先修或如实量化，不归因给神经网络。
|
||||
每次运行将 scene、误差种子、配置、summary、完整 JSONL/accepted 事件、时间复算和模型 SHA 写入新目录；目录已存在则拒绝覆盖。
|
||||
单场异常也写结果；聚合存在任一无效局时 `all_valid=false` 且脚本非零退出，不从平均值表中静默删除失败场景。
|
||||
|
||||
随后实现 `scripts/train_hrl_high.py`，第一版提供以下参数并通过 `--help` 验证：
|
||||
|
||||
```text
|
||||
--problem {3,4} --stage {1,2,3,4} --timesteps INT --seed INT
|
||||
--envs INT --vec-env {dummy,subproc} --device {cpu,cuda,mps,auto}
|
||||
--config-profile {production,coarse-pretrain}
|
||||
--model PATH --resume PATH --eval-manifest PATH --verbose INT
|
||||
--local-selector {geometry,rl} --local-model PATH
|
||||
```
|
||||
|
||||
阶段 1 即保持 20 频道与固定观测维度，只改变布景复杂度；早期课程可减少源数，但完整训练必须覆盖 10/13/16 源与实际 ±1° 固定地点误差。
|
||||
Q4 完整课程同时覆盖少量/半数/大量定向源及近边界向外朝向；Q3→Q4 迁移与 Q4 随机初始化作为配对实验。
|
||||
禁止让策略知道课程场景真实源数或隐藏的占用频道列表；初始位置保持官方 `(0,0)`，除非任务被明确标为局部交接课程。
|
||||
先测 1/2/4 个环境的采样吞吐、内存和每次决策耗时，再选择 Dummy/Subproc；小 MLP 不假定 GPU 更快。
|
||||
Subproc 环境类内部提供 `action_masks()`；Windows/macOS 使用入口守卫及可序列化工厂。
|
||||
随机种子覆盖 Python/NumPy/Torch、PPO、每个环境、场景误差；每个 worker 使用独立 Monitor 文件。
|
||||
保存实际 `model.num_timesteps` 与请求步数，恢复时说明增量还是累计；续训加载正确算法/特征版本且不覆盖原模型。
|
||||
使用掩码评估接口，如 `MaskableEvalCallback`；同时在确定性推理中传入同源 mask。
|
||||
|
||||
CLI 加入 `--strategy hrl`、`--q4-strategy hrl`、`--hrl-model` 与局部策略选项，经 `make_hrl_loop` 统一构建；默认策略保持现状。
|
||||
显式指定缺失模型应报可操作错误，不能把启发式降级结果记成 HRL 模型成绩；启发式骨架单独命名 `hrl_heuristic`。
|
||||
|
||||
P1 门槛：启发式骨架全场审计通过；512–2048 步训练冒烟可保存、重载、确定性推理；CLI 内存回环至少 Q3/Q4 各一场通过。
|
||||
短训先用单个种子验证流程，不用这次模型的成功率作论文结论；完成后记录吞吐再定时间预算，不预设 1–2 小时足够。
|
||||
|
||||
## 6. P2：完整保真训练、独立评测与消融
|
||||
|
||||
实现 `pretrain_hrl_expert.py`：运行 `hrl_heuristic`，保存 `(episode_id, state, candidates, mask, action)`；按 episode 划分训练/验证。
|
||||
BC 与 PPO 的网络层、激活、归一化、mask 处理保持一致；复制后在真实样本上逐位比较 logits，critic 单独初始化。
|
||||
记录专家动作无法映射到候选的比率与原因；若大量回退到最近候选，先改专家映射，不用假专家标签继续训练。
|
||||
至少比较 BC+PPO 与随机初始化 PPO；BC 准确率单独报告，不能代替独立闭环用时与有效全清率。
|
||||
|
||||
冻结数据集分工：
|
||||
|
||||
| 集合 | 用途与约束 |
|
||||
| --- | --- |
|
||||
| 已有 700–702 开发场景 | 查实现错误、探索配置与性能分解 |
|
||||
| 已有 91000–91005 场景 | 历史回归；结果已经被审阅，不再宣称是本次全新盲测 |
|
||||
| 新开发/选择清单 | 固定 JSON manifest，记录 seed、源数、定向组成、error_seed；用于训练选择和调参 |
|
||||
| 新冻结最终测试清单 | 与训练、BC、调参及选择清单不重叠；模型冻结后运行，失败后修改则新开一轮测试 |
|
||||
| 压力场景 | 圆周边界、1000m 半径、向外/切向定向、高频道、固定误差与预算边界；单独汇总 |
|
||||
|
||||
同场景每个变体用相同位置、频道、半径、朝向和地点误差函数。每个模型至少跑 30 个完整场景，再按预算扩大；“30”是工程起点，不是统计充分性证明。
|
||||
最终用于稳定收益结论的主方法至少 5 个独立训练种子；场景种子与训练种子分开报告，不能用 5 个场景替代 5 次训练。
|
||||
Q3、Q4 分开统计，并按源数/定向占比分层；所有基线与候选使用相同停止、预算和计时口径。
|
||||
|
||||
| 指标 | 固定定义 |
|
||||
| --- | --- |
|
||||
| 有效全清 | 成功清除频道集合等于评测真值；缺席集合与真值不交；completed、正常 exit、无异常、计时一致 |
|
||||
| `virtual_time_s` | 实际退出前的完整虚拟时间；主竞赛口径显式 `persistent_search=False` |
|
||||
| `avg_locate_clear_time_s` | 每场 `T / 实际清除数`；有效全清主表再做逐场平均，另报源数分组 |
|
||||
| `accounted_virtual_time_s` | 首次完成证明的时间；与最终时间并列，不悄悄用它替换正式计分 |
|
||||
| 运行时间 | 每场实际墙钟、模型推理耗时分位数；本地内存回环时间不冒充官方程序运行时间 |
|
||||
| 成本分解 | 移动秒、测量秒、换频秒、成功/失败 clear 秒、后备尝试、重复测量、总请求数 |
|
||||
| 尾部与失败 | 中位数/P90/P95/最大值、有效全清率、失败明细；小样本尾分位注明估计局限 |
|
||||
|
||||
平均 `T/N` 不等于 `平均 T / 平均 N`；若另用总体总时间/总源数，明确标为按源数加权口径。
|
||||
配对效应以同场景时间差为基础，报告均值相对变化、胜出场数、95% 配对 bootstrap 区间；相同场景家族共享种子时按家族重采样。
|
||||
多训练种子结果报告种子间分散程度；必要时对训练种子和场景做分层重采样，不能用单一场景区间代表训练稳定性。
|
||||
|
||||
最小比较矩阵：`bayes/directional`、旧局部 PPO、修订局部 MaskablePPO、`hrl_heuristic`、高层 PPO+几何、后续高层 PPO+局部 PPO。
|
||||
此矩阵先回答“改调度是否有用”和“增加局部学习是否有用”；既有单步 RL 不能命名为无几何的端到端基线。
|
||||
若仍要端到端 PPO，必须单独定义输入、实际动作、安全执行边界并训练，标记预算；它不是 P1 完成条件。
|
||||
|
||||
每项消融使用相同训练步数/场景预算/种子和评测集，单次只改变一个因素：
|
||||
|
||||
- 调度：学习高层 vs `hrl_heuristic`，保持候选生成和局部选点一致。
|
||||
- 局部策略：同一高层与执行接口下，geometry vs 现有局部 PPO；报告是冻结替换还是联合重训。
|
||||
- 塑形：纯时间目标 vs 经过恒等式核验的势函数塑形；无信息增益奖励就是系数为 0。
|
||||
- 课程：相同总交互步数的课程训练 vs 从完整场景开始训练；Q4 迁移另列，不预设节约百分比。
|
||||
- 掩码：只取消重复/低效候选等策略性屏蔽；协议合法性、证书和预算硬守卫始终保留，标明实际取消范围。
|
||||
- 粒子特征：保留几何守恒内核，先将粒子统计输入和评分消融；若真的移除粒子模块，另实现保守几何替代并重新验证。
|
||||
- 八方向：只报告离线到达率/路径效率/推演耗时，以及实际协议终点相同时成绩相同的事实。
|
||||
|
||||
P2 推广门槛：所有最终与压力场景有效，Q4 相对最强已冻结基线的时间差区间上界小于 0；尾部容忍度先写入 manifest。
|
||||
建议初始尾部门槛为 P95 不退化超过 5%，这是可调整的工程标准，不是题设要求；任何调整都在重跑最终测试前完成。
|
||||
Q3 若未显示稳定收益，保留 `bayes` 默认并交付负结果。仅比手写基线快、却未优于现有 PPO，不能宣布替换默认模型。
|
||||
|
||||
## 7. P3：受限调参、论文与官方验证
|
||||
|
||||
只有 P2 主方法具备竞争力才实现并运行 `tune_hrl.py`。先搜索学习率、熵系数、网络宽度、GAE、合法候选排序参数。
|
||||
不搜索真实误差上限、光学清除半径、无信号停止次数或破坏覆盖证明的网格间距；确定性网格没有粒子重采样阈值可调。
|
||||
Optuna 只用开发/选择清单，失败配置先按有效性淘汰；预算与失败/剪枝逻辑写入结果。最终模型重新用固定配置多种子训练。
|
||||
文档、流程图与论文表明确：学习负责哪些决策、证明由哪些模块提供、本地生成分布未知是否对应官方分布。
|
||||
训练曲线、实际步数、配置、全部对照与失败明细均归档；不要只挑最好种子或去掉不利的边界场景。
|
||||
官方三次测试需要对应演练已经启动、真实案例码与官方导出日志；本地 referee 的三场不能填写成“三次正式测试”。
|
||||
不覆盖旧官方归档；官方结果与本地结果分表，不对不同官方案例直接计算改进百分比。
|
||||
|
||||
## 8. 命令：已经存在与待实现的严格区分
|
||||
|
||||
以下入口当前已经存在；依赖需先安装，命令失败要修复或如实报告，不能跳过错误声称通过：
|
||||
|
||||
```bash
|
||||
uv sync --group dev --group rl
|
||||
uv run python -m pytest -q --tb=short
|
||||
uv run python -m pytest -q tests/test_hrl.py --tb=short
|
||||
uv run mcm26b run --help
|
||||
uv run mcm26b run --robot-id 2026001 --self-test --strategy bayes
|
||||
uv run mcm26b run --robot-id 2026001 --self-test --problem 4 --q4-strategy directional
|
||||
uv run python scripts/train_rl.py --help
|
||||
uv run python scripts/train_hrl_low.py --help
|
||||
```
|
||||
|
||||
`train_hrl_low.py` 目前仅 `--help` 可用于检查入口，不把完整训练列为已可用：
|
||||
其 `MaskablePPO.learn()` 需要环境掩码，当前 `LowLevelEnv` 没有 `action_masks()`；改普通 PPO 或提供全 True 掩码后再试。
|
||||
还需修 BC 按 episode 划分、统一随机种子、每 worker 独立 Monitor 文件、评估 seed 生效、实际路径长度与成功率分开统计。
|
||||
低层到达失败不能靠 executor 最后强制补终点伪装成策略到达；环境裁剪后的实际移动长度也不能简单记为 `steps*100m`。
|
||||
|
||||
下面是 P1/P2 实现后必须支持并实测的命令契约，当前尚不能直接运行：
|
||||
|
||||
```bash
|
||||
# P1：先验证不加载模型的共享调度闭环
|
||||
uv run python scripts/benchmark_hrl.py --variants hrl_heuristic,bayes,directional --manifest output/hrl/manifests/dev.json --output output/hrl/p1-heuristic
|
||||
# P1：完整保真冒烟；模型目录必须是新目录
|
||||
uv run python scripts/train_hrl_high.py --problem 3 --stage 1 --timesteps 2048 --seed 320001 --envs 1 --vec-env dummy --device cpu --config-profile production --model output/hrl/p1-smoke/q3.zip --verbose 0
|
||||
# P1：CLI 明确加载高层模型
|
||||
uv run mcm26b run --robot-id 2026001 --self-test --strategy hrl --hrl-model output/hrl/p1-smoke/q3.zip
|
||||
# P2：正式阶段训练；步数先依据采样吞吐与学习曲线再调整
|
||||
uv run python scripts/train_hrl_high.py --problem 4 --stage 4 --timesteps 60000 --seed 420001 --envs 2 --vec-env dummy --device cpu --config-profile production --model output/hrl/p2/q4-seed420001.zip --eval-manifest output/hrl/manifests/select.json --verbose 0
|
||||
# P2：冻结模型完整对照；variants 的模型位置来自同目录的配置清单
|
||||
uv run python scripts/benchmark_hrl.py --variants hrl,hrl_heuristic,legacy,revised,baseline --manifest output/hrl/manifests/final.json --output output/hrl/p2-final
|
||||
```
|
||||
|
||||
命令使用的 manifests 也要由脚本生成、校验并归档；尚不存在的模型不能当作初始化输入。
|
||||
添加 `--full` 一键课程入口时，它应展开并记录每阶段命令/配置/检查点链，失败即停，可继续，不是忽略错误的 shell 包装。
|
||||
参考现有依赖配置：基础运行依赖仅 requests；RL 为可选组。固定环境版本并记录硬件，勿假定当前执行机就是旧报告中的 RTX 4060。
|
||||
|
||||
## 9. 可以直接粘贴给 Sol 的续作 Prompt
|
||||
|
||||
```text
|
||||
继续当前仓库的 HRL 工作。先读 docs/hrl-sol-execution.md、最新测试结果与 git diff，保留现有修改。
|
||||
已有客户端/referee/几何/PPO 都要复用；不要重写骨架，不把缺失脚本或 CLI 说成已完成。
|
||||
先完成 P0 尚未关闭的奖励、终止、完整保真配置、掩码和依赖问题；本轮已修的槽位/清除事件/候选过期只回归。
|
||||
采用高层 MaskablePPO 调度 + 几何/已有局部 PPO 选点 + 协议直线执行；八方向只作为可选离线实验。
|
||||
按 P1 先实现 benchmark_hrl 与真值隔离审计，再实现 train_hrl_high、模型元数据和 CLI，跑可重载的短训冒烟。
|
||||
P0/P1 没通过就修实现，不用加训练步数掩盖问题。通过后再开展 BC、完整保真课程、多训练种子、冻结场景配对评测。
|
||||
保持 Q3 bayes/Q4 现有默认策略；完整实验胜出前不覆盖默认模型。主指标包含有效全清、T、逐场 T/N、请求分解和尾部。
|
||||
每阶段交付具体文件、实际执行命令、测试结果、模型/日志路径与未完成项；不预设 Q4 80% 节约或 4000 秒保证。
|
||||
官方测试只有真实演练开始并获得案例码/导出日志才记正式结果，本地实验单独标注。
|
||||
```
|
||||
|
||||
接口参考：[MaskablePPO 官方文档](https://sb3-contrib.readthedocs.io/en/master/modules/ppo_mask.html)，[Gymnasium 终止与时间限制](https://gymnasium.farama.org/tutorials/gymnasium_basics/handling_time_limits/)。
|
||||
Binary file not shown.
+69
-9
@@ -49,11 +49,10 @@
|
||||
{\heiti\zihao{4} 摘\quad 要}
|
||||
\end{center}
|
||||
|
||||
无线电干扰源的测向与规划问题在生产和军事上的应用十分广泛。
|
||||
\texttt{no\_signal}、\texttt{near}、\texttt{direction} 为唯一信息来源，建立
|
||||
\textbf{有界误差交会定位—主动选点—覆盖证明搜索—动作耗时调度}的统一框架：四个问题
|
||||
复用同一几何内核，以覆盖证明与严格停止条件保证“清除所有干扰源”的硬约束，再优化
|
||||
时间指标。
|
||||
无线电干扰源的测向与路径规划问题在生产和军事上的应用十分广泛。城市的非法无线电
|
||||
台清查与战场的无线特征定位等日益增长的需求，与该问题的关联十分紧密。本文建立一种
|
||||
基于【此处填入方法】的无线电测向多元优化方法，以便特定用途的机器狗等设备在复杂环
|
||||
境中能够以更高效率完成无线电发射源测定、清除工作。
|
||||
|
||||
\textbf{针对问题~1}，将 $\pm 1^{\circ}$ 有界误差下的测向观测建模为以检测点为顶点的
|
||||
前向角域 $W_i$，交会定位区域即诸角域的半平面交 $P=\bigcap_i W_i$，给出直径
|
||||
@@ -97,12 +96,73 @@ $N\le(\lceil w/20\rceil+2)(\lceil h/20\rceil+2)$，并纳入现实时间预算
|
||||
% 正文（不超过 30 页，不要目录）
|
||||
% ============================================================
|
||||
|
||||
\section{问题重述}
|
||||
|
||||
\subsection{问题背景}
|
||||
|
||||
无线电干扰源的定位与清除是无线电频谱管理的重要任务。干扰源定位通常分为
|
||||
初步监测、区域性排查和精准定位三个阶段：前两个阶段使用固定监测站、移动
|
||||
监测车或无人机等设备进行大范围信号采集与分析，确定干扰源目标的分布区域；
|
||||
而受限于环境遮挡、低功率干扰源等因素，精准定位最终需要技术人员在目标
|
||||
分布区域内携带便携式无线电方位监测测向机（简称测向机）等手持设备徒步
|
||||
排查，通过观察分析信号场强变化、方向特征并结合经验判断，逐步定位并清除
|
||||
干扰源。为尽快消除干扰造成的不良影响，定位清除任务的完成时间越短越好。
|
||||
为此，某公司拟研发搭载于机器狗的干扰源自动定位清除系统，在危险环境中
|
||||
代替技术人员开展定位及清除工作，以提高定位及清除效率。
|
||||
|
||||
干扰源目标分布区域已被确定为一个半径为 $1800$ 米的圆形区域，取此圆域
|
||||
中心为坐标系原点，正东方向为 $x$ 轴正向，正北方向为 $y$ 轴正向（单位：
|
||||
米），目标区域中干扰源个数未知。干扰源的特征由题目附录~1 给出：每个
|
||||
干扰源的频谱互不相同，分布于编号 $1$ 至 $20$ 的 $20$ 个频道；不同干扰源
|
||||
的信号互不影响；按信号覆盖角度范围分为全向干扰源（有效覆盖角度
|
||||
$360^{\circ}$）与定向干扰源（定向方向两侧各 $90^{\circ}$ 含边界，此角度
|
||||
范围之外没有信号辐射）；在有效覆盖角度范围内信号均匀辐射，场强仅随距离
|
||||
衰减，与角度无关。
|
||||
|
||||
系统的检测原理和工作方式由题目附录~2 给出：测向机在检测点处测得干扰源
|
||||
的示向度（自 $x$ 轴正向逆时针计量，范围 $[0^{\circ},360^{\circ})$），
|
||||
示向度与真实方位角之间的误差在 $[-1^{\circ},1^{\circ}]$ 范围内，且同一
|
||||
地点重复检测不会改变检测误差；各干扰源的有效接收半径在 $1000$ 至 $1500$
|
||||
米之间；测向机不能同时检测多个频道，任意两个频道之间的切换时间为 $1$
|
||||
秒；机器狗沿直线行进、移动速度为 $5$ 米/秒，移动过程中无法有效检测，
|
||||
每次停机检测耗时 $5$ 秒；检测点距干扰源不超过 $20$ 米时可光学精确定位
|
||||
（$3$ 秒）并启动激光枪清除（$2$ 秒），清除成功与否仅与距离有关；距离
|
||||
不超过 $5$ 米时因信号太强无法获得示向度信息，此时可直接进行光学精确定位
|
||||
并清除。利用多个检测点对同一干扰源的示向度，可以采用交会定位法确定
|
||||
多边形定位区域。
|
||||
|
||||
问题~3、4 的算法须通过无线电干扰源环境模拟器进行测试：机器狗程序经
|
||||
HTTP+JSON 接口与模拟器通信，自动完成检测与清除。正式测试中机器狗从原点
|
||||
$(0,0)$ 出发、初始频道为 $1$，每次测试须按题目表~1 的格式记录测试案例
|
||||
编码、清除干扰源个数、平均定位清除时间与程序运行时间。
|
||||
|
||||
\subsection{问题提出}
|
||||
|
||||
\textbf{问题~1}：已知若干个检测点坐标及某干扰源关于这些检测点的示向度，
|
||||
给出采用交会定位法计算多边形定位区域直径（即区域内任意两点之间距离的
|
||||
最大值）的算法，并回答以定位区域直径为直径的圆能否覆盖该定位区域。
|
||||
|
||||
\textbf{问题~2}：已知某全向干扰源在一个检测点处测得的示向度，给出第二
|
||||
个检测点的选择策略，以获得关于该干扰源较好的定位效果，并给出第二个
|
||||
检测点的候选区域。
|
||||
|
||||
\textbf{问题~3}：目标区域中的干扰源均为全向干扰源，总数在 $10$--$16$
|
||||
个之间，但具体个数未知。现派出一只机器狗从原点出发，初始频道为 $1$，
|
||||
对目标区域中的干扰源进行自动定位并清除。要求在确保所有干扰源被清除的
|
||||
前提下，完成任务的时间越短越好。请制定机器狗自动搜索、定位及清除的
|
||||
策略，设计相应的算法，通过模拟器进行演练测试，以被清除干扰源个数的
|
||||
比例和平均定位清除时间为统计值检验策略的有效性；在充分演练测试后，
|
||||
再进行三次正式测试并按题目表~1 的格式给出结果。
|
||||
|
||||
\textbf{问题~4}：目标区域中既有全向干扰源，又有定向干扰源，总数仍在
|
||||
$10$--$16$ 个之间，但具体个数未知，定向干扰源的个数及定向方向也未知，
|
||||
其他条件与问题~3 相同。请在此条件下给出检测与清除的策略及算法，通过
|
||||
模拟器进行演练测试以检验其有效性，并完成三次正式测试、按题目表~1 的
|
||||
格式给出结果。
|
||||
|
||||
\section{问题分析}
|
||||
|
||||
四个问题构成一条递进的主线：问题~1 的交会定位几何是全部后续问题的基础；问题~2 在
|
||||
单次观测下研究“下一步去哪里测”的主动测量设计；问题~3、问题~4 则是在数量未知的
|
||||
条件下，把定位几何组织成“全域覆盖发现—逐个定位—清除—证明完成”的闭环调度问题。
|
||||
因此不必为每问单独建模，而是复用同一几何内核与观测更新机制。
|
||||
传统的无线电定位场景一般由单个人员携带设备，通过主观的经验判断在不同的地点进行模糊测向，最终以三角定位
|
||||
|
||||
\textbf{问题~1 的分析}。题面附录~2 第（1）项把测向误差限定在
|
||||
$[-1^{\circ},1^{\circ}]$，且同一地点重复检测不改变误差：没有误差分布可利用、
|
||||
|
||||
@@ -1,261 +0,0 @@
|
||||
# 训练前端 Windows 兼容性与「暂停」审计（结论 + 修复方案）
|
||||
|
||||
> 审计对象：`src/mcm26b/training_ui/`、`src/mcm26b/process_control.py`、
|
||||
> `scripts/train_hrl_high.py`。
|
||||
> 触发问题：「无法暂停」「频繁出现 os 无 killpg 等」。
|
||||
> 结论一句话：**这两个现象同源——训练前端的停止/暂停路径是纯 POSIX 设计；
|
||||
> Windows 上 `os.killpg` 根本不存在，异常又没被兜住，所以点了停止等于没点。**
|
||||
|
||||
---
|
||||
|
||||
## 0. 结论摘要
|
||||
|
||||
| # | 现象 | 根因 | 严重度 |
|
||||
| --- | --- | --- | --- |
|
||||
| 1 | 点「停止训练」返回 500 / 无效果，训练照跑，于是「无法暂停」 | `TrainingJob.stop()` 直接调用 `os.killpg(os.getpgid(...))`；这两个符号**只存在于 POSIX**，Windows 上抛 `AttributeError`，而旧 `except` 只接 `ProcessLookupError/PermissionError` | 阻断 |
|
||||
| 2 | 控制台/日志反复出现 `module 'os' has no attribute 'killpg'` | 同上：错误最终落到 `_UIHandler.do_POST` 的兜底 `except Exception`，返回 500 并带出 `AttributeError` 文本；每点一次复现一次 | 阻断 |
|
||||
| 3 | 即使把 killpg 换成 `terminate()`，停止后模型/元数据仍然丢失（Windows 上） | Windows 的 `os.kill(pid, SIGTERM)` 语义是 `TerminateProcess` **硬杀**，训练脚本里"收到信号后在 step 边界保存"的处理器不会执行 | 高 |
|
||||
| 4 | 界面根本没有「暂停」能力（POSIX 也没有） | 原设计只有 start/stop；且 `SIGSTOP/SIGCONT` 在 Windows 上不存在，**原生暂停在 Windows 上无法实现**，必须改成协作式 | 高 |
|
||||
| 5 | 子进程进程组隔离在 Windows 上失效 | `Popen(start_new_session=True)` 在 Windows 被**静默忽略**（CPython 的 Windows `_execute_child` 形参名就是 `unused_start_new_session`） | 中 |
|
||||
| 6 | subproc worker 在 Windows 下可能抢在主进程收尾前退出 | worker 只忽略 `SIGTERM/SIGINT`，没处理 Windows 的 `SIGBREAK`（`CTRL_BREAK_EVENT` 的落点） | 中 |
|
||||
|
||||
**修复原则**：把"暂停/继续/停止"从**信号语义**改成**控制文件语义**。
|
||||
控制文件在 POSIX 与 Windows 上都只是普通文件 IO，行为完全一致；信号与强杀退居
|
||||
兜底。这样 Windows 上暂停、继续、优雅停止、落盘保证全部成立。
|
||||
|
||||
---
|
||||
|
||||
## 1. 失败链复现（旧代码）
|
||||
|
||||
```
|
||||
用户点「停止训练」
|
||||
└─ POST /api/train/stop
|
||||
└─ TrainingUI.stop() → TrainingJob.stop()
|
||||
└─ os.killpg(os.getpgid(process.pid), signal.SIGTERM)
|
||||
├─ POSIX ：成功，进程组收到 SIGTERM，脚本优雅收尾
|
||||
└─ Windows：AttributeError: module 'os' has no attribute 'killpg'
|
||||
↑ 只 except (ProcessLookupError, PermissionError)，接不住
|
||||
└─ _UIHandler.do_POST 兜底 except Exception → HTTP 500
|
||||
└─ 训练子进程完全没收到任何指令，继续跑 → 「无法暂停 / 停止没反应」
|
||||
```
|
||||
|
||||
关键代码（修复前，`src/mcm26b/training_ui/server.py`）：
|
||||
|
||||
```python
|
||||
def stop(self) -> bool:
|
||||
with self._lock:
|
||||
process = self.process
|
||||
if process is None or process.poll() is not None:
|
||||
return False
|
||||
try:
|
||||
os.killpg(os.getpgid(process.pid), signal.SIGTERM)
|
||||
except (ProcessLookupError, PermissionError):
|
||||
process.terminate()
|
||||
self.status = "stopping"
|
||||
return True
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 2. 根因逐条（含证据）
|
||||
|
||||
### 2.1 `os.killpg` / `os.getpgid` 是 POSIX-only
|
||||
|
||||
CPython 只在 `posix` 构建里注册这两个函数；Windows 的 `os` 模块没有它们。
|
||||
访问会得到 `AttributeError`（不是 `OSError` 子类），所以旧 `except` 接不住。
|
||||
|
||||
### 2.2 `start_new_session=True` 在 Windows 被静默忽略
|
||||
|
||||
`subprocess.Popen` 接受该参数，但 Windows 的 `_execute_child` 形参名是
|
||||
`unused_start_new_session`，也就是**根本不读**。要隔离进程组，Windows 必须传
|
||||
`creationflags=CREATE_NEW_PROCESS_GROUP`。
|
||||
|
||||
### 2.3 Windows 的 `SIGTERM` 是硬杀
|
||||
|
||||
`os.kill(pid, sig)` 在 Windows 上只有 `CTRL_C_EVENT` / `CTRL_BREAK_EVENT` 是
|
||||
"软"信号；**任何其它值**（含 `SIGTERM`）都会走 `TerminateProcess`。因此
|
||||
`scripts/train_hrl_high.py` 里的：
|
||||
|
||||
```python
|
||||
for _signum in (signal.SIGTERM, signal.SIGINT): # 旧代码
|
||||
signal.signal(_signum, _on_stop_signal)
|
||||
```
|
||||
|
||||
在 Windows 上形同虚设——处理器注册了，但没人会用它。这也解释了为什么"停止后
|
||||
模型没保存"。
|
||||
|
||||
### 2.4 Windows 没有 `SIGSTOP`，暂停无法用信号实现
|
||||
|
||||
暂停（挂起/继续）在 POSIX 上可以用 `SIGSTOP/SIGCONT`，但 Windows 既无这两个
|
||||
信号，也无等价的"无侵入挂起"用户态 API（真正的挂起要 `SuspendThread`/调试 API）。
|
||||
所以**任何跨平台暂停都必须是协作式的**：由训练进程自己在 step 边界检查标志并
|
||||
让出执行。本项目原先没有这条通道，界面也没有暂停按钮。
|
||||
|
||||
### 2.5 `SIGBREAK` 未处理
|
||||
|
||||
Windows 的 `CTRL_BREAK_EVENT`（发给进程组的软信号）在 Python 里落到
|
||||
`signal.SIGBREAK`。旧的 handler 安装与 worker 屏蔽列表都只含
|
||||
`SIGTERM/SIGINT`，Windows 下等于裸奔。
|
||||
|
||||
### 2.6 异常类型没兜住
|
||||
|
||||
`except (ProcessLookupError, PermissionError)` 漏掉 `AttributeError`、以及
|
||||
更一般的 `OSError`；`_hard_kill` 里 `signal.SIGKILL` 在 Windows 上也不存在
|
||||
（`getattr(signal, "SIGKILL", None) is None`），同样会 `AttributeError`。
|
||||
|
||||
---
|
||||
|
||||
## 3. 修复方案（已实施）
|
||||
|
||||
### 3.1 新增 `src/mcm26b/process_control.py`（零第三方依赖）
|
||||
|
||||
| 能力 | 实现 | 作用 |
|
||||
| --- | --- | --- |
|
||||
| 子进程隔离 | `spawn_kwargs()`：POSIX `start_new_session=True`；Windows `creationflags=CREATE_NEW_PROCESS_GROUP` | 前端 Ctrl+C 不误伤训练；Windows 也能组杀进程树 |
|
||||
| 控制协议读写 | `write_control()` / `read_control()`：JSON + 同目录临时文件 + `os.replace` 原子替换；损坏/缺失一律视为"无指令" | 两端统一的暂停/停止通道 |
|
||||
| 强杀兜底 | `terminate_process_tree(process, grace_s)`：先等宽限期；超时后 POSIX 走 `killpg(SIGKILL)`、Windows 走 `taskkill /F /T /PID`；所有 POSIX 符号用 `getattr` 探测 | 彻底移除 `os.killpg` 无条件调用；返回"是否动用了强杀" |
|
||||
|
||||
`IS_WINDOWS` 是运行期常量而非导入期分支，便于测试直接 monkeypatch 模拟 Windows。
|
||||
|
||||
### 3.2 控制文件协议
|
||||
|
||||
路径由前端生成并随命令下发：`--control-file <abs>`（默认落在
|
||||
`output/hrl/ui/control/<job_id>.control.json`，`output/` 已 gitignore）。
|
||||
|
||||
```json
|
||||
{"paused": false, "stop": false, "seq": 1712345678901234567, "ts": 1712345678.9}
|
||||
```
|
||||
|
||||
- 前端**只写**：`pause()` → `paused=true`；`resume()` → `paused=false`；
|
||||
`stop()` → `stop=true`（同时清零 paused，保证阻塞中的暂停循环能被唤醒）。
|
||||
- 训练脚本**只读**：`_ControlPoll` 每 32 个 step 抽样读一次盘（避免每 step 一次
|
||||
文件 IO）；暂停时在 `_on_step` 内阻塞轮询（`poll_s=0.2`），进程存活、显存仍占用，
|
||||
但不消耗算力。
|
||||
- 墙钟上限 `--max-wall-s` **扣除累计暂停时长**，避免"挂起一小时回来直接判超时"。
|
||||
|
||||
### 3.3 训练脚本 `scripts/train_hrl_high.py`
|
||||
|
||||
- 新增 `--control-file`；`_ControlPoll`（纯标准库、可单测）+ 薄薄的
|
||||
`_ControlChannel(BaseCallback)` 接到 SB3 回调链**最前面**。
|
||||
- 收尾信号集合扩为 `_stop_signals()`：POSIX `SIGTERM/SIGINT`；Windows 追加
|
||||
`SIGBREAK`。worker 屏蔽同一集合，保证主进程 `vec_env.close()` 之前 worker 不先退。
|
||||
- 落盘保证不变：停止后仍写最终模型 + 元数据，`stopped_by_signal=true`，
|
||||
`stop_signal` 记 `control_file` 或信号名；异常仍落 `*_emergency.zip`。
|
||||
|
||||
### 3.4 训练前端 `server.py` / `static/`
|
||||
|
||||
- 新增 `POST /api/train/pause`、`POST /api/train/resume`；`stop` 语义改为
|
||||
"写控制文件 → 宽限期内自行退出；超时强杀"，强杀在**独立看门狗线程**里做，
|
||||
HTTP 请求不会被 15s 等待卡住。
|
||||
- 状态机新增 `paused`；`_sync_control_events()` 用训练脚本回显的
|
||||
`paused/resumed/stop_requested` 事件校正状态。
|
||||
- 快照新增 `forced_kill`，前端横幅区分"优雅停止"与"超时强杀"。
|
||||
- 前端新增「暂停训练 / 继续训练」按钮、暂停横幅；停止确认文案更新。
|
||||
|
||||
### 3.5 停止/暂停时序
|
||||
|
||||
```
|
||||
pause: UI 写 {paused:true} ──► 脚本 _on_step 读到 ──► 回显 paused，阻塞轮询
|
||||
resume: UI 写 {paused:false} ─► 暂停循环退出 ────────► 回显 resumed，继续训练
|
||||
stop: UI 写 {stop:true} ────► 脚本 step 边界收尾落盘 ──► 退出码 0
|
||||
│
|
||||
└─(宽限 15s 未退)─► terminate_process_tree 强杀；forced_kill=true
|
||||
```
|
||||
|
||||
### 3.6 急停（强杀）也必须可恢复
|
||||
|
||||
宽限期一到就强杀，训练脚本**没有任何 Python 收尾机会**——不写最终模型、不写
|
||||
元数据、不写 `training_failure.json`。如果磁盘上只有 `--checkpoint-every` 的稀疏
|
||||
历史 checkpoint，急停就可能丢掉上万步，或者在"保存途中被杀"时留下一个打不开的
|
||||
半截 zip。为此加了四条保证：
|
||||
|
||||
| 保证 | 实现 | 效果 |
|
||||
| --- | --- | --- |
|
||||
| 起步基线 | `recovery_every > 0` 时，`learn()` 之前先写一份 `<stem>_latest.zip` | 训练刚起就急停，也有可加载、可 `--resume` 的产物 |
|
||||
| 滚动恢复点 | 每 `--recovery-every`（默认 2000）环境步覆盖写同一份 `<stem>_latest.zip` | 急停最多丢 `recovery_every` 步，而不是 `checkpoint-every` 步 |
|
||||
| 原子落盘 | 模型 zip / 旁挂 json / `resume.json` 一律「同目录 `.tmp-` + `os.replace`」 | 保存途中被强杀，目标路径仍是上一份**完整**快照；不会出现半截文件 |
|
||||
| 权威恢复指针 | 每次落盘刷新 `<run_dir>/resume.json`（model / timesteps / 增量语义 / 生效 PPO 超参）；前端 `/api/train/state` 暴露为 `recovery` | 无论怎么停，都有一份明确的"从哪继续"；界面一键「用最近检查点续训」 |
|
||||
|
||||
前端在强杀后还会补写 `<run_dir>/forced_stop.json`（`job_id`、`pid`、`reason`、
|
||||
`recovery.model`），因为此时训练脚本已经无法记录任何事情。
|
||||
|
||||
---
|
||||
|
||||
## 4. 平台兼容性矩阵（修复后）
|
||||
|
||||
| 能力 | POSIX | Windows |
|
||||
| --- | --- | --- |
|
||||
| 启动子进程并隔离 | `start_new_session=True`（setsid） | `CREATE_NEW_PROCESS_GROUP` |
|
||||
| 暂停 / 继续 | 控制文件（协作式） | 控制文件（协作式） |
|
||||
| 优雅停止 + 落盘 | 控制文件；`SIGTERM` 亦可 | 控制文件；`CTRL_BREAK_EVENT`→`SIGBREAK` 亦可 |
|
||||
| 超时强杀进程树 | `killpg(SIGKILL)` | `taskkill /F /T /PID` |
|
||||
| `os.killpg` 依赖 | 有 | **无（不再调用）** |
|
||||
| 暂停期间是否算墙钟 | 否（扣除） | 否（扣除） |
|
||||
|
||||
---
|
||||
|
||||
## 5. 验证
|
||||
|
||||
- 新增/更新回归测试：
|
||||
- `tests/test_training_ui.py`：控制文件协议往返与损坏容错、`spawn_kwargs`
|
||||
平台分支、Windows 强杀走 `taskkill`、**无 `killpg` 环境下强杀不抛异常**
|
||||
（直接复现旧 bug）、暂停/继续/停止全链路、控制文件被忽略时强杀并写
|
||||
`forced_stop.json` 恢复指引、`discover_recovery` 的
|
||||
`resume.json`/扫描/悬空指针三分支、HTTP `pause/resume` 端到端。
|
||||
- `tests/test_hrl_checkpoint.py`：控制文件状态机单测；原子保存失败不破坏上一份
|
||||
快照；JSON 原子替换；历史清理不误删滚动恢复点；起步基线可加载；
|
||||
**强杀急停后 `resume.json` + 滚动恢复点仍可 `--resume`**；控制文件停止后
|
||||
模型/元数据落盘（`stop_signal=control_file`）与暂停→继续；POSIX 信号路径
|
||||
单列并加平台跳过。以上集成用例都用**真实训练脚本**跑 subproc。
|
||||
- 手工验证：`--recovery-every 32` 短程实跑后强杀，`resume.json` 指向
|
||||
`q3_ckpt_latest.zip`；以它为 `--resume` 目标再跑 32 步，元数据
|
||||
`actual_timesteps` 从 64 变 96、`timesteps_semantics=additional`。
|
||||
- 全量测试：`pytest tests referee/tests` 全绿。
|
||||
- 全仓库 `pylint src scripts project tests referee main.py` 达到 10.00/10
|
||||
（口径见 §7）。
|
||||
|
||||
---
|
||||
|
||||
## 6. 残余风险与后续建议
|
||||
|
||||
1. **暂停粒度** = `poll_every` 个 env step（默认 32，约 0.1–3s 量级），不是即时；
|
||||
若要更快可调小 `_ControlChannel(poll_every=...)`，代价是更频繁的文件 stat。
|
||||
2. **宽限期固定 15s**（`server.STOP_GRACE_S`）。大模型保存可能更久；但因为落盘是
|
||||
原子的，即使宽限期到点被强杀，也只是"新的最终模型没写成"，**上一份滚动恢复点
|
||||
与历史 checkpoint 完好**，不会出现半截文件。需要更长可调大该常量。
|
||||
3. **急停的损失上界 = `--recovery-every`**（默认 2000 环境步）。要更小就调小它；
|
||||
调到 0 表示放弃滚动恢复点（此时只剩 `--checkpoint-every` 的稀疏历史
|
||||
checkpoint 与起步基线）。
|
||||
4. **Windows 强杀依赖 `taskkill`**（系统自带）。若被安全策略禁用，会退回
|
||||
`Popen.kill()`（只杀主进程，spawn 的 worker 可能残留）。
|
||||
5. 更严格的 Windows 方案是 **Job Object**（子进程随 job 句柄一起销毁），但需要
|
||||
`ctypes`/`pywin32`，本仓库基础依赖刻意保持轻量，未引入。
|
||||
6. **前端进程重启后**，内存中的任务对象没了，「用最近检查点续训」按钮不会自动
|
||||
出现；但恢复信息都落在磁盘上（`<run_dir>/resume.json`、`forced_stop.json`、
|
||||
`checkpoints/<stem>_latest.zip`），在表单里填同一个模型输出路径并勾选
|
||||
`--resume` 即可继续。
|
||||
7. 界面 `--device mps` 选项在 Windows 上无意义；`auto` 会回退 cuda/cpu，属既有
|
||||
行为，未改。
|
||||
8. `_ControlPoll` 的暂停循环会占用一个 Python 线程的调度（每 0.2s 一次读盘），
|
||||
长暂停期间 CPU 占用可忽略。
|
||||
|
||||
---
|
||||
|
||||
## 7. pylint 口径说明
|
||||
|
||||
配置写在 `pyproject.toml` 的 `[tool.pylint.*]`，目标是
|
||||
`pylint src scripts project tests referee main.py` = 10.00/10。
|
||||
|
||||
**改代码修掉的真实问题**（不是 disable）：未使用的 import/变量/参数、重复字典键
|
||||
（`reward_version`）、`value != value` 判 NaN、`__dataclass_fields__` 私有访问、
|
||||
`raise` 非异常类型、`pytest.importorskip` 之外的赋值不确定、过长行、无谓括号、
|
||||
`len(x)` 判空、循环闭包变量、`subprocess.run` 未显式 `check`、`os.path` 循环导入等。
|
||||
|
||||
**有意 disable 的类别**（每条在 pyproject 里写了理由）：项目刻意的懒加载
|
||||
（`import-outside-toplevel`）、防御式宽异常（`broad-exception-caught`）、测试访问
|
||||
内部状态（`protected-access`）、pytest fixture 同名（`redefined-outer-name`）、
|
||||
无头环境不实现 `gym.Env.render`（`abstract-method`）、以及静态分析的已知误报
|
||||
（`possibly-used-before-assignment`）。
|
||||
|
||||
**注意**：`good-names` 不覆盖 `redefined-builtin`，stdlib 回调签名里的
|
||||
`log_message(self, format, ...)` 只能在对应 `def` 上行内 disable。
|
||||
@@ -1,160 +0,0 @@
|
||||
# 模型训练前端（training-ui）
|
||||
|
||||
本地回环 Web UI，用于**实时观察一次 HRL 高层 PPO 训练**：子进程状态、步进进度、
|
||||
参数收敛曲线、逐回合记录与完整 stdout 日志。设计与评测前端（`referee/` WebUI）
|
||||
同构——同一套暗色面板语言、同一个「只监听 127.0.0.1 + 静态白名单 + JSON 轮询
|
||||
API」模式——差别在于本页托管的是一个**训练子进程**而不是评测会话。
|
||||
|
||||
## 启动
|
||||
|
||||
```bash
|
||||
uv run python -m mcm26b.training_ui --ui-port 8036 # 打开 http://127.0.0.1:8036
|
||||
uv run python scripts/train_hrl_ui.py --ui-port 8036 # 等价薄入口（支撑材料命令）
|
||||
uv run python -m mcm26b.training_ui --print-schema # 只看配置契约，不起服务
|
||||
uv run python -m mcm26b.training_ui --print-preview # 只看离线示例快照
|
||||
```
|
||||
|
||||
`--no-browser` 阻止自动打开浏览器；Ctrl+C 退出时会先停止训练子进程。
|
||||
|
||||
无训练进程时，界面顶栏「离线预览」可渲染三组合成快照（上升 / 平台期 / KL 过猛），
|
||||
用于检查曲线、指标卡、时间线与日志着色——预览不启动任何进程、不写任何文件。
|
||||
|
||||
## 界面结构
|
||||
|
||||
| 区块 | 内容 |
|
||||
| --- | --- |
|
||||
| 顶栏 | 任务摘要、进程/进度/墙钟/设备/连接五个状态 chip、刷新·预览·暂停/继续·停止 |
|
||||
| 执行进度与进程 | 步进进度条、剩余时间与预计结束、PID/吞吐/退出码、PPO 阶段时间线、完整命令行 |
|
||||
| 训练配置 | 由 `/api/train/schema` 生成的五组表单 + 四个预设，运行中锁定 |
|
||||
| 参数收敛状态 | 判读 chip + 判据说明，每个指标一张卡片（最新值、增量、迷你趋势线、异常高亮） |
|
||||
| 曲线 | 主指标（平均回合奖励）+ 可切换诊断量，悬停十字线读数、KL=0.05 等参考线 |
|
||||
| 实时运行日志 | 关键字过滤、错误/进度 JSON 开关、自动跟随、最近 4000 行 |
|
||||
| 逐回合记录 | Monitor CSV 的每回合奖励/长度/时刻 |
|
||||
|
||||
## 数据来源与契约
|
||||
|
||||
三个端点都在 `src/mcm26b/training_ui/server.py`：
|
||||
|
||||
| 端点 | 说明 |
|
||||
| --- | --- |
|
||||
| `GET /api/train/schema` | 字段/分组/预设/指标字典，**与 `TrainConfig` 同源**（`schema.py`） |
|
||||
| `GET /api/train/state` | 快照；支持 `log_tail`、`metric_limit`、`after_metric` 游标与 `preview=1` |
|
||||
| `POST /api/train/start` | 允许只提交部分字段（其余取默认值），校验失败返回 409 |
|
||||
| `POST /api/train/pause` | 写控制文件请求在 step 边界挂起（跨平台） |
|
||||
| `POST /api/train/resume` | 解除挂起，从暂停处继续 |
|
||||
| `POST /api/train/stop` | 请求优雅停止；宽限期（`STOP_GRACE_S`，默认 15s）后强杀进程树 |
|
||||
|
||||
暂停/停止走**控制文件**而不是信号，原因与平台差异见
|
||||
[`training-ui-windows-audit.md`](training-ui-windows-audit.md)：Windows 没有
|
||||
`SIGSTOP`，且 `os.kill(pid, SIGTERM)` 会退化成 `TerminateProcess` 硬杀，训练脚本
|
||||
"保存模型再退出"的收尾分支不会执行。控制文件在 POSIX 与 Windows 上都只是普通
|
||||
文件 IO，语义完全一致；信号（POSIX `SIGTERM/SIGINT`、Windows `SIGBREAK`）只作兜底。
|
||||
|
||||
进度数据的两个来源，服务端都已解析：
|
||||
|
||||
1. **progress JSON**（主来源）：`train_hrl_high.py --progress-json` 每若干环境步打印
|
||||
一行 `{"event": "progress", "timesteps": ..., "ep_rew_mean": ...}`，字段最全；
|
||||
热身阶段间隔会自动缩小，短程 smoke 也能看到完整曲线。
|
||||
2. **SB3 verbose 表格**（补充来源）：解析 `| key | value |` 表格，用于补
|
||||
`clip_fraction`/`learning_rate` 等诊断量。表格按段分组缓存、等本表的
|
||||
`total_timesteps` 到位后统一打横轴刻度；与 JSON 同一步数的表格版本会被跳过，
|
||||
避免重复点。
|
||||
|
||||
`timesteps` 是整个前端的横轴：服务端不伪造刻度（缺刻度的表宁可丢弃，截断的表
|
||||
按末尾已知刻度兜底并在收尾提交），前端不再自行编号。
|
||||
|
||||
## 收敛判读口径
|
||||
|
||||
`summarize_convergence()` 只做**可解释的启发式判读**，不替代正式学习曲线分析：
|
||||
|
||||
| 状态 | 触发条件 |
|
||||
| --- | --- |
|
||||
| 等待首个 rollout | 尚无任何指标 |
|
||||
| 预热阶段 | 进度点 < 3，无法判断趋势 |
|
||||
| KL 偏大，策略更新可能过猛 | 最近 `approx_kl` > 0.05（判定优先级最高） |
|
||||
| 价值函数尚未拟合 | 最近 `explained_variance` < 0 |
|
||||
| 奖励未形成上升趋势 | 最近窗口奖励斜率为负 |
|
||||
| 已趋于平稳（疑似收敛） | 窗口对半位移 ≤ 逐点噪声、变异系数 ≤ 0.15、极差 ≤ 15% 量级 |
|
||||
| 收敛趋势良好 | 其余（斜率正向） |
|
||||
|
||||
平台判据特意用「对半窗口位移 / 逐点噪声」而不是单看斜率绝对值：慢速但持续的
|
||||
提升（斜率很小）不会被误判成收敛。所有判据数字都会写进 `notes`，便于人工复核。
|
||||
|
||||
## 安全边界
|
||||
|
||||
- 只监听 `127.0.0.1`；静态资源是固定白名单，不提供目录遍历。
|
||||
- 命令由服务端以**参数列表**构造，不经过 shell；字符串参数只拒绝控制字符。
|
||||
- 同一时刻只允许一个训练任务；暂停中的任务仍算「在跑」，必须先停止才能开新一局。
|
||||
- 暂停/停止走控制文件（`--control-file`，默认 `output/hrl/ui/control/<job_id>.control.json`），
|
||||
进程组隔离按平台选择（POSIX `setsid` / Windows `CREATE_NEW_PROCESS_GROUP`），
|
||||
超时强杀按平台选择（POSIX `killpg(SIGKILL)` / Windows `taskkill /F /T`）。
|
||||
- 产物路径限定在仓库根下解析，模型输出沿用训练脚本自身的 `--force`/`--resume`
|
||||
语义（两者互斥，由后端校验）。
|
||||
|
||||
## 暂停、停止与落盘保证
|
||||
|
||||
「暂停」是**协作式**的：前端原子写控制文件，`train_hrl_high.py` 的
|
||||
`_ControlPoll` 每 32 个环境步抽样读一次；暂停时训练阻塞在 `_on_step` 的轮询里，
|
||||
进程与显存保留、不消耗算力，点「继续」从同一个 step 边界恢复。暂停时长不计入
|
||||
`--max-wall-s`（否则"挂起一小时回来直接判超时"）。
|
||||
|
||||
「停止」同样是控制文件请求（**跨平台**）。`train_hrl_high.py` 有多层兜底，
|
||||
点一下「停止」不再等于这一局白跑；而**急停**（强杀，例如 `SIGKILL` /
|
||||
`taskkill /F`，进程没有任何收尾机会）也保证可恢复：
|
||||
|
||||
| 机制 | 行为 |
|
||||
| --- | --- |
|
||||
| 滚动恢复点 | `--recovery-every`（默认 2000 环境步）把 `<checkpoints>/<stem>_latest.zip` **原子覆盖写**一次；训练开始前先写一份**起步基线**。因此急停后最多只丢 `--recovery-every` 个环境步，且磁盘上始终有一份完整、可加载、可 `--resume` 的产物 |
|
||||
| 原子落盘 | 模型 zip、旁挂 json、`resume.json` 都是「同目录临时文件 + `os.replace`」：保存途中被强杀不会留下半截文件，也不会破坏上一份完整快照 |
|
||||
| 恢复指针 | `<run_dir>/resume.json` 记录当前最佳恢复目标 + 步数（增量语义）+ 生效过的 PPO 超参；前端顶部的「用最近检查点续训」按钮直接读它 |
|
||||
| 定期 checkpoint | `--checkpoint-every`（默认 20000 环境步）另留 `<stem>_<步数>.zip`，`--checkpoint-keep` 只保留最近 K 个（默认 3），供返祖对比与多档恢复；滚动恢复点与起步基线不受清理影响 |
|
||||
| 优雅停止 | 控制文件 `stop=true`（或 POSIX 信号 / Windows `SIGBREAK`）后，主进程在 step 边界收尾，保存最终模型与元数据（`stopped_by_signal=true`、`stop_signal`、`checkpoint_files`、`recovery_model`）并正常关闭环境 |
|
||||
| 超时强杀 | 宽限期（`STOP_GRACE_S`，默认 15s）内没退出则强杀整棵进程树，快照 `forced_kill=true`，并在 `<run_dir>/forced_stop.json` 写明 `recovery.model`（可直接续训的目标） |
|
||||
| 异常兜底 | `learn()` 抛出任何异常时先落 `*_emergency.zip` + 元数据 + `resume.json`，再写 `training_failure.json` 并抛出 |
|
||||
|
||||
急停后恢复的操作方式：界面点「用最近检查点续训」；命令行则
|
||||
`--resume --model <resume.json 里的 model>`，`--timesteps` 按**追加**步数理解。
|
||||
|
||||
checkpoint 与最终模型共用同一份 json 契约，因此 `--resume <checkpoint.zip>` 直接
|
||||
续训（`--timesteps` 为增量步数）。注意 `--resume`/`--init` 会沿用 checkpoint 内的
|
||||
PPO 超参（`n_steps`/`batch_size`/`n_epochs`）：命令行这三项被忽略，脚本会打一条
|
||||
`hyperparams_from_checkpoint` 事件说明差异，元数据 `ppo_hyperparams` 记录**实际生效**值。
|
||||
回归测试见 `tests/test_hrl_checkpoint.py`，其中端到端用例真起 subproc 子进程，覆盖
|
||||
控制文件停止、POSIX 信号停止、**强杀后仍可续训**与暂停→继续（信号路径在 Windows
|
||||
上自动跳过）。
|
||||
|
||||
## 测试
|
||||
|
||||
```bash
|
||||
.venv/bin/python -m pytest tests/test_training_ui.py tests/test_training_ui_js.py tests/test_hrl_checkpoint.py -q
|
||||
```
|
||||
|
||||
- `tests/test_training_ui.py`：配置契约同源、预设可直接提交、参数校验、SB3 表格
|
||||
刻度打点、游标分页、`describe_process` 进度/ETA、示例快照与真实快照同键；
|
||||
用**桩训练脚本真起子进程**覆盖启动/轮询/暂停/继续/停止/退出码全链路；并覆盖
|
||||
跨平台进程控制——控制文件往返与损坏容错、`spawn_kwargs` 平台分支、Windows 走
|
||||
`taskkill`、**无 `killpg` 环境下强杀不抛异常**（旧 bug 的直接回归）、控制文件被
|
||||
忽略时强杀并写 `forced_stop.json` 恢复指引、`discover_recovery` 的
|
||||
`resume.json`/扫描/悬空指针三分支、HTTP `pause/resume`。
|
||||
- `tests/js/training_ui_harness.cjs`（经 `tests/test_training_ui_js.py` 调起）：
|
||||
DOM 桩加载发布的 `charts.js`/`app.js` 原文，用服务端生成的真实快照驱动渲染，
|
||||
断言表单/指标卡/曲线/日志/逐回合表都拿到数据，覆盖增量游标三条分支与
|
||||
`classList`、HTML 转义、极端数值等边界。
|
||||
- `tests/test_hrl_checkpoint.py`：落盘与恢复的端到端回归——原子保存失败不破坏
|
||||
上一份快照、JSON 原子替换、历史清理不误删滚动恢复点、起步基线可加载、
|
||||
**强杀急停后 `resume.json` 与滚动恢复点仍可继续训练**、控制文件停止落盘、
|
||||
暂停→继续、POSIX 信号停止。
|
||||
|
||||
## 已知取舍
|
||||
|
||||
- 进度点保留上限 5000（服务端 deque 与前端本地缓存同值），超长训练只保留最近一段。
|
||||
- 表格解析依赖 SB3 的段名（`rollout/`/`time/`/`train/`/`system/`）；SB3 若改表格
|
||||
格式，退化路径是「只显示 progress JSON」，不会导致界面崩溃。
|
||||
- 收敛判读是启发式；正式结论仍需用 `scripts/benchmark_hrl.py` 的配对评测与
|
||||
学习曲线人工复核。
|
||||
- 暂停粒度是 `poll_every` 个环境步（默认 32），不是即时挂起；停止宽限期默认 15s。
|
||||
- Windows 强杀依赖系统自带的 `taskkill`；若被策略禁用会退回 `Popen.kill()`
|
||||
（只杀主进程）。更严格的 Job Object 方案需要额外依赖，未引入。
|
||||
|
||||
Windows 兼容性问题的完整根因与修复记录见
|
||||
[`training-ui-windows-audit.md`](training-ui-windows-audit.md)。
|
||||
@@ -1,165 +0,0 @@
|
||||
# 第三、四问强化学习适用性审核与改进
|
||||
|
||||
本轮审核第一版强化学习代码、专家数据、模型及官方演练归档，修复训练与部署不一致的问题，训练两个新的 Maskable PPO 模型，并用独立实现的本地 `referee` 完成同场景对照。所有新性能数字均为本地结果，没有启动新的官方演练。
|
||||
|
||||
结论：强化学习适合在现有几何算法内尝试“下一检测点”的排序，第四问比第三问更有收益空间。当前证据支持第四问的混合方案优于手写 `directional` 基线；尚不能证明新模型稳定优于旧 PPO。第三问没有稳定增益，应保留 `bayes` 默认策略。新模型作为实验版本显式加载，没有覆盖旧默认模型。
|
||||
|
||||
## 1. 题设和方法适用范围
|
||||
|
||||
附录2(3)的交会必须使用带误差的角域，不能把两条示向线的交点当作真值；同地重测不会提供新的独立误差样本。附录2(4)的单接收机逐频道检测，结合动作规则，须计入移动、5秒检测及实际1秒换频。第三问的全向假设允许用无信号排除接收圆盘；第四问的无信号还可能来自方向遮挡，不能沿用这一排除结论。题设及几何算法的完整审核见[算法审核与优化](算法审核与优化-20260911.md)。
|
||||
|
||||
当前 RL 实际只替换 `RLBayesLoop._select_point` 和 `RLDirectionalLoop._next_probe`。七站/49站覆盖、频道发现、全局任务调度、保守候选集、清除证书和第四问后备清除仍由原算法负责。因此，完整场景全清验证的是混合系统，不是神经网络独立获得了完备性证明。
|
||||
|
||||
401维输入概括几何、历史读数和60个候选点，但省略完整历史、候选多边形细节及位置—朝向联合约束，不能直接称为完整马尔可夫状态。局部单源训练也没有学习全场覆盖路线与多源调度。PPO可以作为这种压缩观测下的近似策略尝试，但其目标并不自动等于全场最短时间；评估必须使用独立完整场景。[SB3官方训练与评估建议](https://stable-baselines3.readthedocs.io/en/master/guide/rl_tips.html)也强调独立评估、多随机种子和状态信息完整性。
|
||||
|
||||
## 2. 确认的实现缺陷及修复
|
||||
|
||||
| 缺陷 | 证据和影响 | 已实施修复 |
|
||||
| --- | --- | --- |
|
||||
| 训练传入整个 `BeliefState`，部署传入 `ChannelBelief` | 同一状态的特征索引7、14、15、17、18不同；训练丢失测量次数、末次结果和方位信息 | `env._observe` 使用目标频道信念；测试逐位比较部署输入，并覆盖跨频道交接 |
|
||||
| BC使用ReLU，接收权重的PPO使用Tanh | 仅复制线性权重无法保留函数。原始专家输入探针中，Q3的90.96%、Q4的67.42%出现不同argmax | BC改为Tanh；复制时检查完整前向输出，重新初始化critic；旧ReLU/旧特征检查点拒绝直接迁移 |
|
||||
| BC按观测行随机拆分 | 同一episode的相关状态可能同时出现在训练和验证中，分类准确率不足以说明独立场景泛化 | 保存episode编号，按episode划分训练/验证 |
|
||||
| 第四问训练遮挡状态与运行时不一致 | 训练只记录距离保证盘内的无信号，且获得新方向后未正确重置连续遮挡；后备清除触发轨迹偏离部署 | 所有连续无信号记录为探测序列，仅盘内无信号用于确认定向；正观测清空序列，统一4次遮挡/8次定位阈值 |
|
||||
| 奖励鼓励首次遮挡等中间事件 | “获得遮挡证据”额外+2、半径收缩等奖励会改变实际时间目标；较高回报不一定更快 | 主要奖励改为实际虚拟耗时的负值，成功清除统一+20，失败/截断重罚；取消这些中间奖励，采用gamma=1 |
|
||||
| 未屏蔽同地重测、重合候选和第三问盘外梯子点 | 浪费固定误差条件下的检测时间；旧文档“所有候选保证接收”不成立 | 训练/推理共用动作屏蔽，排除已测0.5m格位置、重复候选、越界点；第三问存在距离保证盘时排除盘外点 |
|
||||
| 保证盘半径恰为0时动作数量可能改变 | 依赖去重后的候选生成器会破坏固定60动作接口 | 即使位置重合也保留编号，交由动作屏蔽处理 |
|
||||
| 只校验401/60维度不能识别语义变化 | 接收机频道被目标频道替代；旧模型与新模型同形但输入含义不同 | 加入特征版本与算法元数据，v2使用实际接收机频道，旧版保留原输入语义；拒绝问题编号或屏蔽模型元数据不匹配 |
|
||||
| 基准将 `completed` 当作全清 | 内部状态自洽不代表全部真实源已清除 | 用评测端真实频道集合对照成功清除集合，并检查真实频道没有被误判缺席、正常退出、异常及逐步计时；旧基准新增 `all_valid` 和失败退出码 |
|
||||
| 官方审计没有严格对应问题与策略名称 | 第三问/第四问摘要可能交叉通过名称检查 | `verify_rl_practice.py` 分别要求 `rl` / `rl_q4` |
|
||||
|
||||
BC探针是对旧网络和“拷贝后换激活”的网络进行比较，并不是新模型错误率，也不是重新测得的BC验证准确率。旧报告38.9%/65.3%的分类准确率不能直接解释为已迁移PPO的模仿准确率。本轮训练的新模型均从随机初始化开始，未使用BC；BC修复通过前向一致性测试，未据此声称重新训练BC已经带来收益。
|
||||
|
||||
动作屏蔽采用官方 `MaskablePPO` 的 `action_masks` 接口，训练和确定性推理都传入相同规则。[SB3-Contrib官方文档](https://sb3-contrib.readthedocs.io/en/master/modules/ppo_mask.html)说明了这两处的用法。第四问仍允许探索盘外候选，距离保证不等于方向可见。退化到所有动作均被屏蔽时保留中心动作，由步数/预算边界处理，因此不能把本次零重复观测扩展为所有可能状态的严格保证。
|
||||
|
||||
正常成功episode的累计奖励为 `20 - 局部实际虚拟时间/100`；环境仍保留异常与失败处理，包括证书清除失败的额外惩罚。局部时间从首测与可选交接完成后开始，不能用训练报告中的局部均值替代整场成绩。第三问训练自动清除仍使用证书圆心，而完整 `BayesLoop` 可在证书范围内平移清除点；全局调度、预算与局部环境也有差异，完整闭环验证因此不可省略。
|
||||
|
||||
## 3. 训练和数据
|
||||
|
||||
第一版Q3为120000步随机初始化PPO；第一版Q4为BC初始化后40000步PPO。原专家数据分别为5141、14181条，原5场完整测试均值分别是Q3：3088.33/3124.76秒、Q4：14250.15/13230.20秒（手写/RL）。这些历史数字受旧环境和特征影响，不与本轮新课程的单源成功率直接比较。
|
||||
|
||||
修订课程保留首次有效测向，并加入35%的跨频道、跨位置交接，20%的区域边缘且最小接收半径场景。定向首测覆盖朝向±89.9°，更接近半平面边界。源位置、朝向、半径和逐点误差的抽样仍是自建分布，不是已知的官方生成分布；真值仅用于布设和评测，不进入策略观测或动作屏蔽。
|
||||
|
||||
两模型使用 `[128,128]` Tanh MLP、2个内存环境、`n_steps=512`、`batch_size=128`、6轮更新、学习率0.0003。实际均完成60416步，来自完整rollout批次向上取整；元数据中的60000是请求步数。安装版本为SB3 2.9.0、SB3-Contrib 2.9.0、Torch 2.11.0+cu128。本次选择CPU、单Torch线程，机器CUDA可用。
|
||||
|
||||
| 项目 | Q3新模型 | Q4新模型 |
|
||||
| --- | ---: | ---: |
|
||||
| 训练种子 | 310001 | 410001 |
|
||||
| 定向概率 | 0 | 0.75 |
|
||||
| 训练墙钟时间 | 276.6s | 250.6s |
|
||||
| 独立单源评估成功 | 59/60 | 60/60 |
|
||||
| 单源平均局部时间 | 270.04s | 349.51s |
|
||||
| 平均动作步数 | 2.42 | 3.13 |
|
||||
|
||||
Q3有一次局部训练任务未完成，不能声称单源成功率100%。完整闭环仍有自身调度与后备路径。两问题各只有一个新模型训练种子，未覆盖训练随机性。
|
||||
|
||||
## 4. 完整场景同条件对照
|
||||
|
||||
比较三种固定策略：当前手写基线（Q3 `bayes`、Q4 `directional`）、旧默认PPO、新Maskable PPO。相同场景使用相同源位置、频道、半径、朝向及逐点固定误差种子。`referee` 为单独实现，策略不读取模拟器真值。每场保存场景、完整请求/响应、summary、模型哈希和计时核验。
|
||||
|
||||
开发集为种子700—702、10/16源，各问题6场。随后固定新模型，不再据验证成绩调参；验证集为种子91000—91005、10/13/16源，各问题18场。第四问定向数覆盖1、约半数、仅一个全向三种组成。另加两种圆周边界压力场景。开发、验证、压力共52个不同场景，三策略共156次完整运行，全部满足真实源全清、缺席无误判、正常exit、零异常、逐步耗时复算一致。
|
||||
|
||||
### 开发集平均整场时间
|
||||
|
||||
| 问题 | 手写基线 | 旧PPO | 新模型 |
|
||||
| --- | ---: | ---: | ---: |
|
||||
| Q3，6场 | 3294.62s | 3206.20s | 3264.62s |
|
||||
| Q4，6场 | 14289.76s | 12443.43s | 11758.72s |
|
||||
|
||||
### 独立验证集
|
||||
|
||||
| 问题/指标 | 手写基线 | 旧PPO | 新模型 |
|
||||
| --- | ---: | ---: | ---: |
|
||||
| Q3有效全清 | 18/18 | 18/18 | 18/18 |
|
||||
| Q3平均时间 | 3330.72s | 3254.61s | 3307.65s |
|
||||
| Q3中位时间 | 3309.16s | 3262.64s | 3222.50s |
|
||||
| Q3最大时间 | 3690.11s | 3786.98s | 3776.55s |
|
||||
| Q4有效全清 | 18/18 | 18/18 | 18/18 |
|
||||
| Q4平均时间 | 13778.82s | 12070.53s | 11797.57s |
|
||||
| Q4中位时间 | 14587.80s | 12891.83s | 12488.23s |
|
||||
| Q4最大时间 | 17273.98s | 15365.76s | 15894.44s |
|
||||
| Q4累计未成功clear尝试 | 616 | 719 | 1380 |
|
||||
| Q4累计同频道同点重测 | 12 | 28 | 0 |
|
||||
|
||||
Q3三组都没有失败clear或同点重测。Q4新模型减少了重测，但更多使用后备光学清除尝试，平均耗时降低的同时尾部表现有所退化。失败clear本身可来自允许的后备搜索，不能仅凭该计数认定证书错误；其移动和3秒动作耗时全部纳入结果。
|
||||
|
||||
对独立验证场景做10000次配对bootstrap，负值表示新模型更快：
|
||||
|
||||
| 对比 | 均值相对变化 | 95%场景重采样区间 | 新模型更快场数 |
|
||||
| --- | ---: | ---: | ---: |
|
||||
| Q3新模型对手写 | -0.69% | [-4.50%, +2.95%] | 9/18 |
|
||||
| Q3新模型对旧PPO | +1.63% | [-1.64%, +5.19%] | 8/18 |
|
||||
| Q4新模型对手写 | -14.38% | [-17.93%, -11.48%] | 18/18 |
|
||||
| Q4新模型对旧PPO | -2.26% | [-6.08%, +1.71%] | 13/18 |
|
||||
|
||||
新旧PPO的区间均跨0，不能据小幅平均变化宣称稳定改进。区间只反映固定模型在这些场景上的差异，不覆盖训练种子、未知官方分布或所有合法边界。此外本轮同时修改多项训练因素，这不是能归因到单个修复的消融实验。
|
||||
|
||||
### 边界压力与4000秒目标
|
||||
|
||||
源全部放在半径1800m圆周、接收半径1000m；第四问隔一个源采用向外发射方向。10/16源各一场，各策略都完成全清。
|
||||
|
||||
| 问题 | 手写平均 | 旧PPO平均 | 新模型平均 |
|
||||
| --- | ---: | ---: | ---: |
|
||||
| Q3 | 4472.58s | 4429.04s | 4648.26s |
|
||||
| Q4 | 18248.87s | 14404.01s | 15210.08s |
|
||||
|
||||
新Q3的两场分别为4659.06s、4637.46s，仍超过4000s；新Q4为14573.62s、15846.54s，且比旧PPO退化。此前几何优化在另一独立Q3验证组达到平均3315.89s、最大3981.74s，本轮新Q3验证组最大3776.55s；二者只能说明各自验证组达标，不能承诺全部题设场景小于4000s。第四问尚未达到4000s。
|
||||
|
||||
## 5. 官方证据与测试
|
||||
|
||||
重新审计已有官方归档，三次均通过题号/策略、官方包SHA、源数、完整清除、正常退出、请求时窗及逐步计时检查，未改写原归档：
|
||||
|
||||
| 问题 | 既有官方案例 | 全清 | 整场时间 |
|
||||
| --- | --- | ---: | ---: |
|
||||
| Q3 | F38Y-9Q9J-B82V-CB9Z | 13/13 | 3538.81s |
|
||||
| Q4，3个定向源 | X43X-VMRF-8GRZ-3SYA | 10/10 | 14003.09s |
|
||||
| Q4，10个定向源 | BXSA-TFQ3-2DJ6-DHH7 | 10/10 | 14562.82s |
|
||||
|
||||
这些是旧模型的官方可运行证据，不是新模型成绩；不同案例也不能直接计算优化百分比。新模型目前通过本地验证，未在官方新演练中运行。
|
||||
|
||||
全量回归：`python -m pytest -q --tb=short`，243项通过。初次沙箱运行有24个临时目录权限导致的fixture错误，获得权限后重跑全部通过。新增5项测试覆盖训练/部署观测、动作屏蔽、方向读数重置遮挡、实际成本奖励和BC完整前向一致性。之后对基准/官方审计入口的小修补分别用4次完整场景冒烟运行及三份官方归档复核通过。
|
||||
|
||||
## 6. 产物与复现
|
||||
|
||||
主目录为 `output/rl/review-20260911/`：
|
||||
|
||||
- `q3_masked.zip` / `.json`、`q4_masked.zip` / `.json`：新模型与训练元数据，必须成对保存。
|
||||
- `evaluation/development/{controls,revised}/results.json`：开发对照。
|
||||
- `evaluation/validation/final/results.json`：独立验证与每场完整日志。
|
||||
- `evaluation/stress/final/results.json`：边界压力结果与日志。
|
||||
- `review_evidence.json`：配对区间、BC/特征错位探针、旧官方重审结果和代码哈希。
|
||||
- `benchmark-smoke.json`：旧基准有效性口径修复后的冒烟结果。
|
||||
|
||||
新Q3模型SHA-256：`b8b3a2bb103588348d8d7791bde9a2b0635c04778f0ac64f91c6f5c7cf87ca6e`。
|
||||
新Q4模型SHA-256：`6865d541ff9bdee81886d327345f2660d5f7d72f4b39810a58ae4b867ab37e42`。
|
||||
|
||||
从项目根目录复现训练时，使用新目录保留本轮检查点：
|
||||
|
||||
```powershell
|
||||
.venv/Scripts/python.exe scripts/train_rl.py --problem 3 --algorithm maskable_ppo --timesteps 60000 --seed 310001 --device cpu --model output/rl/reproduction/q3_masked.zip --eval-episodes 60 --verbose 0
|
||||
.venv/Scripts/python.exe scripts/train_rl.py --problem 4 --algorithm maskable_ppo --timesteps 60000 --seed 410001 --directional-probability 0.75 --device cpu --model output/rl/reproduction/q4_masked.zip --eval-episodes 60 --verbose 0
|
||||
```
|
||||
|
||||
复核本轮冻结模型的完整对照（label须使用尚不存在的名称）：
|
||||
|
||||
```powershell
|
||||
.venv/Scripts/python.exe scripts/evaluate_rl_revision.py --split validation --label rerun-validation
|
||||
.venv/Scripts/python.exe scripts/evaluate_rl_revision.py --split stress --label rerun-stress
|
||||
.venv/Scripts/python.exe scripts/summarize_rl_revision.py
|
||||
```
|
||||
|
||||
本地内存自检可显式加载对应检查点；`--self-test`不会连接官方模拟器：
|
||||
|
||||
```powershell
|
||||
.venv/Scripts/mcm26b.exe run --robot-id 2026001 --self-test --strategy rl --rl-model output/rl/review-20260911/q3_masked.zip
|
||||
.venv/Scripts/mcm26b.exe run --robot-id 2026001 --self-test --problem 4 --q4-strategy rl --rl-model output/rl/review-20260911/q4_masked.zip
|
||||
```
|
||||
|
||||
在用户另行启动相应问题的官方演练后，使用实际登录队号和`--base-url`连接。例如第三问（路径使用本轮尚未创建的官方演练目录）：
|
||||
|
||||
```powershell
|
||||
.venv/Scripts/mcm26b.exe run --robot-id 2026001 --base-url http://127.0.0.1:2026 --problem 3 --strategy rl --rl-model output/rl/review-20260911/q3_masked.zip --log output/rl/review-20260911/practice-q3/session.jsonl
|
||||
```
|
||||
|
||||
该命令保存请求/响应日志，汇总输出到终端；官方结果文件与汇总还需按原归档流程保存。第四问改用`--problem 4 --q4-strategy rl`及对应模型。本轮未执行上述官方连接命令。
|
||||
|
||||
进一步研究应先缩小单源训练与完整调度的差异，并处理Q4后备清除和边界退化，再做多个训练种子及新的冻结验证集。当前结果不足以将新模型自动替换为默认策略，也不足以把训练步数增加当作达到4000秒的依据。
|
||||
@@ -0,0 +1,131 @@
|
||||
# 问题一至四：最优实现与基线实现对比
|
||||
|
||||
本文件是四问"最优实现 / 基线实现"对比的**论文口径说明**。代码与可复现
|
||||
产物在仓库根目录 `project/`，运行 `uv run python -m project.run` 一次生成
|
||||
`project/results/summary.md` 与逐问 `q1.md … q4.md`。本文件只摘录设计与结论，
|
||||
数字以后者为唯一来源。
|
||||
|
||||
## 1. 交付物与目录
|
||||
|
||||
```text
|
||||
project/
|
||||
├── README.md 设计原则、各问对比设计、口径与边界
|
||||
├── common.py 方法描述 / 指标聚合 / 落盘 / 闭环装配
|
||||
├── run.py 统一入口（--question/--quick）
|
||||
├── results/ 自动生成：q1.md/json … q4.md/json + summary.md
|
||||
├── q1/ optimal.py baseline.py reference.py cases.py compare.py
|
||||
├── q2/ optimal.py baseline.py reference.py region.py cases.py compare.py
|
||||
├── q3/ optimal.py baseline.py compare.py
|
||||
└── q4/ optimal.py baseline.py compare.py
|
||||
```
|
||||
|
||||
角色边界：
|
||||
|
||||
- **最优实现**：直接适配 `src/mcm26b` 中已由 pytest 固化的生产实现，
|
||||
不重写算法；
|
||||
- **基线实现**：同题下更朴素、但能独立跑通的对照，不共享最优实现的关键步骤；
|
||||
- **独立参考**：与分析算法正交的数值/枚举标准，用于判定谁对谁错。
|
||||
|
||||
第三、四问统一走 `LocalSimulator` 内存回环（与官方 HTTP 协议同一边界），
|
||||
真值只在评测机侧；有效性判定沿用项目口径：
|
||||
**全清 ∧ 无缺席误判 ∧ 正常 `/exit` ∧ 零异常 ∧ 逐微秒计时复算一致**。
|
||||
|
||||
## 2. 四问方法设计对照
|
||||
|
||||
| 问 | 最优实现 | 基线实现 | 独立参考 | 主要指标 |
|
||||
| --- | --- | --- | --- | --- |
|
||||
| 1 | 半平面交 + 顶点对精确直径 + Welzl MEC + 覆盖反例 | 网格采样 + **包围盒对角线**代理直径/覆盖 | 大框半平面裁剪 + 暴力直径/MEC | 状态、直径误差、覆盖准确率、耗时 |
|
||||
| 2 | 保证接收盘内最小最坏后验半径（生产选点器） | ① 垂直 1000 m 最大交会角；② 候选集 MEC 圆心 | 保证接收盘内 9×9 网格枚举 | 保证接收率、最坏后验半径、移动时间 |
|
||||
| 3 | `bayes`：粒子滤波 + 信息增益 + 滚动 2-opt | `grid`：49 点网格 + 就近服务；`seven`：七点批量 | 冻结基准集 + 真值全清校验 | 全清率、虚拟时间及其分布 |
|
||||
| 4 | `directional`：31 点定向覆盖 + 联合信念 + 后备清除 | 问题三策略直接搬用（`bayes` / `grid`） | 冻结基准集（core+boundary） | 全清率、**缺席误判**、虚拟时间 |
|
||||
|
||||
## 3. 对比结果
|
||||
|
||||
### 3.1 问题一（40 个随机交会算例）
|
||||
|
||||
| 指标 | 最优 | 基线 |
|
||||
| --- | --- | --- |
|
||||
| 状态判定准确率 | 100% | 100% |
|
||||
| 直径平均绝对误差 | **0.000 m** | 4.583 m（最大 14.60 m） |
|
||||
| 直径圆覆盖判定准确率 | **100%** | 52.5% |
|
||||
| 平均耗时 | **0.11 ms** | 22.35 ms |
|
||||
|
||||
固定示例进一步给出结构性反例（`examples/q1`，解析期望由
|
||||
`tests/test_geometry*.py` 固化）：
|
||||
|
||||
- `normal`：直径 26.99 m，**直径圆不覆盖**定位区域（最优判否、基线判是）；
|
||||
- `triangle`：直径 40.00 m 的等边三角形反例，最小包围圆半径 23.09 m > 20 m，
|
||||
直径圆不覆盖；基线因"包围盒中心 + 对角线"恒判覆盖；
|
||||
- `poor`：近共线检测点 → 无界，最优/基线/参考一致判无界；
|
||||
- `segment`：零误差退化线段，最优判线段 D=100 m 且覆盖成立，基线退化为点；
|
||||
- `empty`：观测不相容 → 空集。
|
||||
|
||||
> 基线的两处结构性缺陷：直径用包围盒对角线在斜/细长区域系统性高估；
|
||||
> 覆盖用包围盒中心圆则恒判"能覆盖"，恰好丢掉问题一要考的反例。
|
||||
|
||||
### 3.2 问题二（30 个随机首次示向度，初始 ρ 均值 681.5 m）
|
||||
|
||||
| 方法 | 保证接收率 | 最坏后验均值 | 终值比 ρ'/ρ | 移动均值 |
|
||||
| --- | --- | --- | --- | --- |
|
||||
| 最优（保证盘内最小最坏后验） | 100% | **73.89 m** | 0.104 | 170.4 s |
|
||||
| 基线① 垂直 1000 m 最大交会角 | **0%** | 80.11 m | 0.116 | 200.0 s |
|
||||
| 基线② MEC 圆心 | 100% | 340.75 m | 0.500 | 136.3 s |
|
||||
| 参考（保证盘内网格枚举） | 100% | 75.63 m | 0.106 | 170.3 s |
|
||||
|
||||
> 基线①说明"只追交会角"会在本题失败：首次角域可延伸到 1500 m，
|
||||
> 真源在远端时第二点距离超过 1000 m，测量直接 `no_signal`。
|
||||
> 基线②保证接收但后验几乎不收缩（≈0.5ρ）。最优在同等保证下把后验压到 0.10ρ，
|
||||
> 且与网格参考几乎重合（生产采样质量足够）。
|
||||
|
||||
### 3.3 问题三（18 个冻结 core 场景，全向 10/13/16 源）
|
||||
|
||||
| 方法 | 有效局 | 全清率 | 虚拟时间均值 | 中位 | 检测均值 | 请求均值 | 位移均值 |
|
||||
| --- | --- | --- | --- | --- | --- | --- | --- |
|
||||
| 最优 `bayes` | 18/18 | 100% | **3188.7 s** | 3111.3 s | 111.1 | 124.1 | 12338 m |
|
||||
| 中间 `seven` | 18/18 | 100% | 5361.7 s | 5367.5 s | 100.8 | 113.8 | 23508 m |
|
||||
| 基线 `grid` | 18/18 | 100% | 14935.7 s | 15275.1 s | 430.6 | 443.6 | 61550 m |
|
||||
|
||||
配对变化：`bayes` 相对 `grid` 虚拟时间 **−78.4%**（18/18 场景更快），
|
||||
`seven` 相对 `grid` −63.7%。全部 54 局零异常、计时复算一致。
|
||||
|
||||
### 3.4 问题四（11 个冻结 core+boundary 场景，含朝外发射的边界定向源）
|
||||
|
||||
| 方法 | 有效局 | 全清率 | 缺席误判 | 虚拟时间均值 | 后备清除均值 |
|
||||
| --- | --- | --- | --- | --- | --- |
|
||||
| 最优 `directional` | 11/11 | **100%** | **0** | 9069.9 s | 24.8 |
|
||||
| 基线 `grid`（49 点全向） | 0/11 | 67% | 0 | 18179.2 s | 0 |
|
||||
| 基线 `bayes`（七站全向缺席证明） | 0/11 | 77% | **24** | 3890.8 s | 0 |
|
||||
|
||||
> 两条搬用基线的失效机理不同：
|
||||
> `bayes` 用七站环的**全向**缺席证明，遇到背离圆心发射的边界定向源时
|
||||
> 会把真源直接判成 ABSENT（累计 24 次 **缺席误判**，属"快而错"，其虚拟时间
|
||||
> 不可与最优直接比较）；`grid` 的 49 点覆盖对定向源发现是完备的，但缺少
|
||||
> 定向观测推理与 ≤20 m 后备清除，定位停滞只能停在 DETECTED（"稳而不全"）。
|
||||
> 最优 `directional` 两种工况都是 11/11 全清、零误判，代价是多花时间与
|
||||
> 平均 24.8 次后备清除。
|
||||
|
||||
## 4. 结论
|
||||
|
||||
1. 问题一：生产几何内核对状态、直径、覆盖三项与独立参考**逐案一致**；
|
||||
朴素包围盒基线在覆盖判定上只有约 52% 准确率，且对固定反例全错。
|
||||
2. 问题二：只有在"保证接收"约束内优化后验才既安全又有效；最优相对保守
|
||||
基线把最坏后验半径从 0.5ρ 降到 0.10ρ，且与网格参考几乎重合。
|
||||
3. 问题三：`bayes` 在相同全清率下把虚拟时间降到 `grid` 的约 1/5，
|
||||
且逐场景配对 18/18 更快，说明"负观测感知 + 信息增益选点 + 全局排序"
|
||||
三步都产生净收益。
|
||||
4. 问题四：定向源的难点不是"发现"而是"证明缺席"与"失去信号后的清除"；
|
||||
直接搬用第三问策略会得到 24 次缺席误判，不可接受。`directional` 的
|
||||
联合信念 + 后备清除是 11/11 全清的关键。
|
||||
|
||||
写论文时建议按"基线为什么错 → 最优补了哪一步 → 独立参考如何判定"的三段式
|
||||
展开，并声明：随机分布是测试假设；基线时间快于最优的场景（如问题四 `bayes`）
|
||||
不构成有效性优势，因为该局基线未完成全清任务。
|
||||
|
||||
## 5. 复现
|
||||
|
||||
```bash
|
||||
uv run python -m project.run # 四问全部（约 3–4 分钟）
|
||||
uv run python -m project.run --question 1 # 只跑问题一
|
||||
uv run python -m project.run --quick # 缩小算例/基准集规模
|
||||
uv run pytest tests/test_geometry.py tests/test_geometry_certificates.py -q # 几何回归
|
||||
```
|
||||
@@ -0,0 +1,516 @@
|
||||
# 第三、四问：图论与动态规划算法引入方案
|
||||
|
||||
- 版本：2026-09-12
|
||||
- 前置文档：[第三四问架构优化与运行提速方案](第三四问架构优化与运行提速方案.md)（架构与 P0–P2 项）、
|
||||
[algo-3-bayes.md](algo-3-bayes.md)（现役第三问策略）、[algo-3.md](algo-3.md)（七点覆盖证明）
|
||||
- 范围：只讨论"用图论 / 动态规划类算法进一步压低**虚拟耗时**"这一件事；
|
||||
题面约束、协议语义、保守性不变量全部不动
|
||||
- 本文所有数字均为**本轮新增实测**（自建内存回环、冻结场景、现役代码），
|
||||
复现命令见 §7
|
||||
|
||||
---
|
||||
|
||||
## 0. 结论先行
|
||||
|
||||
**可以，而且第四问的空间远大于第三问；但真正的收益不在"把巡回排得更短"，而在
|
||||
"把不必要的往返与盲清从决策层消掉"。**
|
||||
|
||||
| 判断 | 证据 | 可回收 |
|
||||
| --- | --- | --- |
|
||||
| 第三问**路由**已接近最优，纯图论排线几乎无空间 | 对**同一批实际作业点**求离线最优开放回路：2308 s vs 实测移动 2458 s，只剩 **6.1%（151 s）** | ≤ 150 s |
|
||||
| 第三问仍有空间，但在**探测次数**（序贯决策），不在排线 | 30.8 次探测 / 13 源，且 0 次 `no_signal`——每次都在有效收缩，但次数仍偏多 | −150 ~ −250 s |
|
||||
| 第四问**路由**有 20% 空间，但那是被定位/清除绕行"拖"出来的 | 离线最优 4998 s vs 实测移动 6257 s（**20.1%**）；而覆盖点巡回的投影顺序 4365 s 已接近离线 31 点最优 4181 s（+4.4%） | −400 ~ −700 s |
|
||||
| 第四问最大单点是**定向源定位失败导致的后备盲清** | 平均每局 **131 次清除只有 13 次成功**，`fallback_clear_attempts=125.7`，清除动作 419 s + 清除段移动 798 s ≈ **1220 s/局（14% T）** | **−800 ~ −1100 s** |
|
||||
| 第四问第二点是**探测本身** | 平均 69 次探测 ≈ **2224 s/局（26% T）**，其中 32 s/次的移动占 1880 s | −400 ~ −800 s |
|
||||
| 覆盖点集**已经最小**，不要再压 | 27 点方案在 75 m/24 档"通过"，但在 25 m/48 档有 **298 个 (p,h) 缺口** → 是采样过拟合的假解；在细网格上直接搜索的最小命中集仍是 **31 点** | 0（见 §5.1 的正确性风险） |
|
||||
|
||||
**一句话：第三问的目标是"把 30.8 次探测压到 ~26 次并把序排到最优"，第四问的目标是
|
||||
"把 125 次盲清变成 3 次有保证的探测"。** 两者都是 DP / 序贯决策问题，
|
||||
不是经典的 TSP 排线问题。
|
||||
|
||||
---
|
||||
|
||||
## 1. 目标函数的图论化
|
||||
|
||||
题面计时模型（`config.CostConfig`，已由 `evaluation.recompute_virtual_time` 逐微秒复算验证）：
|
||||
|
||||
```
|
||||
T = L/5 + 5·N_measure + 1·N_switch + 5·N_clear⁺ + 3·N_clear⁻
|
||||
```
|
||||
|
||||
把它写成图上的路径代价，后面所有算法都在这张图上做：
|
||||
|
||||
- **节点** = 一次"停靠"（覆盖站点、定位测点、清除点）；
|
||||
- **边权** `w(a,b)` = `dist(a,b)/5`（移动秒）；
|
||||
- **节点服务费** = `5·(该点要测的频道数) + 1·(换频次数)` 或 `5/3`（清除）。
|
||||
- 于是整局 = 一条**开放路径**（起点原点、终点自由），目标是最小化
|
||||
`Σ边权 + Σ服务费 + Σ动作惩罚`。
|
||||
|
||||
四类图问题分别对应四个子决策：
|
||||
|
||||
| 子决策 | 图问题 | 现役做法 | 目标做法 |
|
||||
| --- | --- | --- | --- |
|
||||
| 站点/任务排序 | **开放 TSP（小 n 精确）** | NN + 2-opt（滚动） | Held-Karp 精确 DP（n ≤ 14，含换频状态）|
|
||||
| 定位测点选择 | **带邻域的 TSP / 定向问题（TSPN/OP）** | 每任务独立选点 + 全表重排 | 覆盖骨架 + 最便宜插入；测点作为"邻域"参与联合优化 |
|
||||
| 定向源定位 | **POMDP / 随机最短路** | 一步贪心 `prob_detect/时间` | 2–3 步 rollout，值函数 = 期望后验 MEC 半径 |
|
||||
| 后备清除 | **区域覆盖路径（Covering Path）** | 格心最近邻 | 割草机 + 转向点 DP / 格心集 Or-opt |
|
||||
|
||||
---
|
||||
|
||||
## 2. 本轮实测基线（现役代码，冻结场景）
|
||||
|
||||
场景集：`harness/suites.py`，第三问 core 的 seed 100–105 × 13 源；
|
||||
第四问 core 全部 9 场（10/13/16 源 × 3 定向占比）。配置为现役默认。
|
||||
|
||||
### 2.1 第三问（`bayes`，13 源，6 场）
|
||||
|
||||
| 指标 | 数值 |
|
||||
| --- | ---: |
|
||||
| 平均虚拟时间 | **3197 s** |
|
||||
| 移动 | 12292 m = **2458 s（77%）** |
|
||||
| 覆盖点检测 | 83.5 次 = 501 s（其中 69.8 次 `no_signal`，属缺席证明） |
|
||||
| 定位探测 | **30.8 次 = 185 s**（0 次 `no_signal`） |
|
||||
| 清除动作 | 65 s |
|
||||
| **离线最优开放回路（同一批作业点）** | **2308 s** |
|
||||
| 路由可回收 | **151 s（移动的 6.1%，T 的 4.7%）** |
|
||||
| 作业点数（去重） | 35–50 |
|
||||
|
||||
### 2.2 第四问（`directional` + `q4_mincover_v1`，9 场）
|
||||
|
||||
| 指标 | 数值 |
|
||||
| --- | ---: |
|
||||
| 平均虚拟时间 | **8676 s** |
|
||||
| 移动合计 | 6257 s（72%） |
|
||||
| ├ 覆盖段移动 | 3579 s |
|
||||
| ├ **探测移动** | **1880 s** |
|
||||
| └ **清除移动** | **798 s** |
|
||||
| 动作耗时 | 2149 s（覆盖 1386 / 探测 344 / 清除 419） |
|
||||
| 探测次数 | 69.0 次（`no_signal` 4–12 次） |
|
||||
| **清除次数 / 成功** | **131.1 / 13.0 → 118 次失败** |
|
||||
| `fallback_clear_attempts` | **125.7 次/局** |
|
||||
| 后备盲清格数（单次） | 3 ~ 210 格，全部以 `cleared` 结束 |
|
||||
| **离线最优开放回路（同一批作业点）** | **4998 s** |
|
||||
| 路由可回收 | 1259 s（移动的 20.1%，T 的 14.5%） |
|
||||
| 覆盖投影顺序 vs 离线 31 点 2-opt | 4365 s vs 4181 s（+4.4%） |
|
||||
|
||||
### 2.3 成本地板估计
|
||||
|
||||
| 问题 | 不可压缩部分 | 现状 | 真实可压缩 |
|
||||
| --- | --- | ---: | ---: |
|
||||
| 第三问 | 七点环 1392 s + 覆盖检测 501 s + 清除动作 65 s + 每源必经的接近/清除移动 + 每源 ≥2 次探测 | 3197 s | **~300–450 s（10–14%）** |
|
||||
| 第四问 | 31 点巡回 ~4180 s + 缺席检测 ~1386 s + 每源 ≥3 次探测与一次清除 | 8676 s | **~2000–2800 s（23–32%）** |
|
||||
|
||||
> 第四问的 4000 s 验收线**依然不可达**（单是 31 点巡回 + 缺席检测就 ≈5566 s），
|
||||
> 这与前置文档 §6.2 的结论一致；本方案只承诺把 8676 s 压向 6500 s 量级。
|
||||
|
||||
---
|
||||
|
||||
## 3. 算法设计
|
||||
|
||||
### 3.1 D1（第四问最高优先）：定向源的"抵近 + 交会"滚动时域 DP
|
||||
|
||||
**问题本质**：±1° 示向度误差在距离 d 处给出的横向不确定度约 `d·tan(1°) ≈ 0.0175 d`。
|
||||
d = 1000 m 时单条方位线的横向约束只有 ±17.5 m；两条方位线的交会角 θ 决定最终尺度
|
||||
`≈ 0.0175 d / sin θ`。要压到 20 m 清除证书，必须**同时**满足：交会角大、距离近。
|
||||
现役 `_visibility_probe` 只最大化 `prob_detect(q)/(移动+6)`，天然偏好"近且可见"，
|
||||
与"交会角大"无关；远距离又永远收敛不到 20 m，于是退化到 20 m 网格盲清。
|
||||
|
||||
**形式化**（这就是一个 POMDP）：
|
||||
|
||||
- 状态 `b` = `JointBelief`（位置 × 24 档朝向的可行集，见 `belief/joint.py`），
|
||||
再叠加 `r_eff ~ U[1000,1500]` 的不确定度；
|
||||
- 动作 `q` ∈ 候选测点（保证接收点 ∪ 对跖点 ∪ 三圈环探 ∪ **朝 MEC 圆心的抵近点**）；
|
||||
- 观测 `o ∈ {direction, near, no_signal}`，由每条粒子 `(p, h, r_eff)` 解析预测；
|
||||
- 代价 `c(q) = 6 s + dist(pos,q)/5`；
|
||||
- 终止：`MEC(b).radius ≤ 20 − margin`（可一次清除）；
|
||||
- 值函数：`V(b) = min_q E_o[ c(q) + V(b') ]`。
|
||||
|
||||
**实现选择（按代价递增）**：
|
||||
|
||||
1. **一步期望后验半径**（最小改动，复用 Q3 已有内核的二维推广）：
|
||||
对候选 `q`，按粒子权重模拟三分支结果，用幸存子集的 MEC 半径近似 `V(b')`，
|
||||
取 `E[radius] + λ·c(q)` 最小者。把 `particles.py::expected_posterior_radius`
|
||||
的向量化实现在 `JointBelief` 上重写一份（numpy 可用时广播）。
|
||||
2. **2–3 步 rollout**：对前 K=8 个候选各展开一层，深度 2；墙钟预算 ≤ 0.3 s/决策。
|
||||
3. **交会角代理（解析、零墙钟）**：若一步法来不及落地，先用
|
||||
`score(q) = −E[radius] · sin(Δbearing(q, 既有方位线)) / dist(q, center)`
|
||||
的确定性代理，实验证明有效后再换 rollout。
|
||||
|
||||
**关键结构性改动**：把候选点集从"绕 MEC 的固定环"扩展为**含抵近点**，
|
||||
即允许 `q` 落在 `center + α·(pos − center)` 上、`α` 从 0.6 到 1.0 采样。
|
||||
这样当远距离无法收敛时，策略会**主动抵近**并以一次正常角域证书收尾，
|
||||
而不是把 200 格盲清当作唯一出路。后备清除**保留为安全网**，但目标是触发次数下降 ≥ 80%。
|
||||
|
||||
**验收**：`fallback_clear_attempts` ≤ 15/局（现 125.7）；定向频道平均探测次数
|
||||
≤ 全向频道的 1.5 倍；清除成功率 ≥ 90%；9 场景全清率 100%。
|
||||
**预期**：−800 ~ −1100 s。
|
||||
|
||||
---
|
||||
|
||||
### 3.2 D2：覆盖骨架 + 最便宜插入（TSPN 的在线近似）
|
||||
|
||||
**问题本质**：31 个覆盖点是**必须在场**的（缺席证明），但探测点与清除点是
|
||||
**临时产生**的。现役做法把两类点混在一张任务表里，每次从零做 NN + 2-opt
|
||||
（`bayes_loop._order_tasks`），等于让临时任务把骨架顺序"打散"。
|
||||
实测投影顺序 4365 s vs 离线最优 4181 s，说明骨架顺序本身没坏，
|
||||
坏在**绕行开销**——而绕行开销正是插入位置没优化。
|
||||
|
||||
**形式化**：这是 **TSP with Neighborhoods / Generalized TSP**：
|
||||
|
||||
- 骨架：离线求覆盖点集的近优开放回路 `S`（`7` 点用解析证明；`31` 点用 Or-opt/3-opt，
|
||||
`n ≤ 16` 时可直接 Held-Karp 精确）；
|
||||
- 在线：给定当前任务 `q` 与其**邻域**（`clear` 任务的邻域是 `C₂₀(K)`，`locate`
|
||||
任务的邻域是候选测点集），求
|
||||
`Δ(i,j) = dist(a_i, q) + dist(q, a_{j}) − dist(a_i, a_j)`
|
||||
最小的插入位置 `(i,j)`；这正是"图上的最便宜插入"，`O(|S|²)` 每任务。
|
||||
|
||||
**实施**：
|
||||
|
||||
1. `guarantee/cover.py` 增加 `optimize_tour(points, seed_tour, passes)`（Or-opt + 2-opt），
|
||||
把 `nn_tour_m` 从"上界"变成"已优化值"，并写入证书 `tour_virtual_s`。
|
||||
2. `planner/bayes_loop.py` 新增 `_skeleton`（覆盖点顺序）与 `_insert_task(task, neighborhood)`；
|
||||
`_order_tasks` 退化为"骨架遍历 + 已插入任务"，保留现有 2-opt 作为兜底开关。
|
||||
3. `clear` 任务的邻域用 **`C₂₀(K)` 内离插入点最近的点**（见 3.4），使插入代价与
|
||||
清除目标联合最小。
|
||||
|
||||
**验收**：第四问移动 ≤ 5000 s（现 6257 s）；路由相对离线最优的 gap
|
||||
（新增回归指标 `route_gap_vs_offline`）从 20.1% 降到 ≤ 10%。
|
||||
**预期**：−400 ~ −700 s。
|
||||
|
||||
---
|
||||
|
||||
### 3.3 D3：后备清除的覆盖路径（若 D1 后仍触发）
|
||||
|
||||
后备清除是"用 20 m 圆盘覆盖残差区域 `R` 直到命中真源"——即 **Covering Path Problem**。
|
||||
|
||||
- 当前 `_fallback_clear` 对格心做 nearest-first 扫描，等价于格心集上的 NN 开放路径，
|
||||
典型约为最优的 1.2–1.3 倍；
|
||||
- 改进：对格心集做 **Or-opt + 2-opt**（`k ≤ 400`，`O(k²)` 一次约 0.1 s，且只在
|
||||
极少数局触发）；
|
||||
- 上界更优解：凸区域上的**割草机（boustrophedon）扫描**，用 DP 在扫描线方向选择
|
||||
转向点可精确最优；对一般多边形用梯形分解 + DP。
|
||||
|
||||
**验收**：后备触发时移动 ≤ 现值的 80%（单次）；触发次数本身由 D1 压到 ≤ 15/局。
|
||||
**预期**：−100 ~ −200 s（D1 未完全消除时的兜底）。
|
||||
|
||||
---
|
||||
|
||||
### 3.4 T1/T3：第三问的精确排线、换频 DP 与清除点选择
|
||||
|
||||
**(a) Held-Karp 精确任务序（含换频状态）**
|
||||
|
||||
现役 `_two_opt` 是启发式且被 4 轮上限截断。任务表典型只有 6–12 项，
|
||||
可以**精确求解**：
|
||||
|
||||
```
|
||||
dp[S][j][c] = 已访问任务集 S、停在任务 j、测向频道为 c 的最小代价
|
||||
转移：dp[S∪{k}][k][c'] = dp[S][j][c] + dist(pos_j, pos_k)/5 + service(k, c, c')
|
||||
```
|
||||
|
||||
- 规模：`|T| ≤ 12` 时 `2^12 × 12 × 20 ≈ 1.0M` 状态，纯 Python 约 0.1–0.3 s/次；
|
||||
滚动重排每步一次会吃掉预算，因此**只在任务表新增/删除任务时重解**，
|
||||
或在 `|T| ≤ 9` 时默认启用、否则回退 2-opt + Or-opt。
|
||||
- 换频代价精确化：`service` 依赖进入/离开频道，把 `c` 并入状态即可精确；
|
||||
现役 `_path_cost` 用"按频道排序"的贪心近似，会低估/高估 1–3 s/站。
|
||||
- 附图论下界：对当前点集算 **MST 权重**（`O(n²)` Prim），
|
||||
得到开放路径最优值的下界，写入 `summary.route_lower_bound_s`，
|
||||
用于**在线后悔值审计**与回归（"本步排序距最优 ≤ x%"）。
|
||||
|
||||
**验收**：`route_gap_vs_offline` ≤ 3%；单局墙钟增量 ≤ 0.2 s。
|
||||
**预期**：−120 ~ −200 s。
|
||||
|
||||
**(b) 探测的 2 步 rollout（T2）**
|
||||
|
||||
现役 `_select_point` 是一步贪心（期望 log 幸存占比 + λ·移动）。
|
||||
把它扩成深度 2 的 rollout：对前 `K=8` 个候选，各展开一次测量的三分支
|
||||
（direction / near / no_signal），取 `E[V]` 最小者。
|
||||
`V` 沿用现有 `expected_survival`。这属于**滚动时域 DP**，不改任何安全性判定。
|
||||
|
||||
**验收**：探测次数 30.8 → ≤ 26；墙钟 ≤ 0.3 s/决策。
|
||||
**预期**：−150 ~ −250 s。
|
||||
|
||||
**(c) 清除点选在 `C₂₀(K)` 内离当前位置最近处（T3）**
|
||||
|
||||
`C₂₀(K) = ∩_{g∈K} B(g, 20)` 是凸集（圆盘之交）。求其内离当前点最近点是一个
|
||||
**凸可行性/投影问题**（对小规模可枚举多边形顶点 + 圆盘投影，或直接迭代投影）。
|
||||
现役 `_near_clear_target` 只沿一维方向平移，与最优之差 ≤ 0.74 m（前置文档 P1-1 已复核），
|
||||
收益主要来自**与 3.2 插入代价的耦合**：先定插入位置、再选清除点，比反过来省更多。
|
||||
|
||||
**验收**：清除段移动下降 ≥ 5%。
|
||||
**预期**：−50 ~ −150 s。
|
||||
|
||||
**(d) 七点环半径（T4）**：`1159.7 → 1122.9`（可证明下界），−44 s。
|
||||
仅作为"证明性"改动落地，不作为收益项。
|
||||
|
||||
---
|
||||
|
||||
## 4. 实施路线
|
||||
|
||||
| 阶段 | 内容 | 交付物 | 验收门槛 | 依赖 |
|
||||
| --- | --- | --- | --- | --- |
|
||||
| **S0 基线冻结**（0.5 天） | 把近期 sweep 的最优参数（`route_aware_probe=false`、`lambda_move_s≈0.06`、`opportunistic_radius_ratio≈0.4–0.5`）回填为基准，跑满 core+boundary 并归档 | `output/suite/q3-baseline`、`q4-baseline` | 现有 `valid` 口径 100% | 无 |
|
||||
| **S1 度量补全**（0.5 天） | 在 `scripts/run_suite.py` 增加 `route_gap_vs_offline`、`route_lower_bound_s`、`distinct_service_points`、`coverage_actual_visited` 四列 | runner 改动 + 回归 | 与本文 §2 实测复现一致 | S0 |
|
||||
| **S2 覆盖证书硬化**（1 天，**正确性优先**） | 证书分辨率 75 m/24 → 20 m/72；设计裕量 `r_design ≤ 960 m` 或细网格直接搜索；保留 10% 保险点预算 | `guarantee/cover.py`、`plans/*.json`、正/负例测试 | 细网格（15 m/72）缺口 = 0；故意删点必须被检出 | S1 |
|
||||
| **S3 第四问 D1**（2–3 天） | `belief/joint.py` 增加期望后验半径 / rollout 选点；`planner/__init__.py::_probe_candidates` 增加抵近点；后备降级为安全网 | `belief/joint.py`、`planner/__init__.py`、测试 | `fallback_clear_attempts ≤ 15`；全清 100%；墙钟 ≤ 1.0 s/局 | S2 |
|
||||
| **S4 第四问 D2/D3**（2 天） | 覆盖骨架 + 最便宜插入；`C₂₀` 邻域；后备 Or-opt | `planner/*`、`guarantee/cover.py` | 移动 ≤ 5000 s；`route_gap ≤ 10%` | S3 |
|
||||
| **S5 第三问 T1/T2/T3**（2 天） | Held-Karp（含换频）+ MST 下界；探测 2 步 rollout；`C₂₀` 最近点 | `planner/bayes_loop.py`、`plan/*` | 探测 ≤ 26；`route_gap ≤ 3%`；T ≤ 2900 s | S1 |
|
||||
| **S6 收口**（1 天） | 全量配对、论文数据表、消融图 | 报告 + `docs/paper` 数据 | 与 S0 配对全部 `valid`，无退化场景 | S4/S5 |
|
||||
|
||||
**顺序约束**：S1（度量）先于任何算法改动，否则"更快"不可证；S2（证书）先于
|
||||
第四问算法改动，否则可能在错误的安全基线上做优化。
|
||||
|
||||
---
|
||||
|
||||
## 5. 风险与明确不做的部分
|
||||
|
||||
### 5.1 本轮发现的正确性风险（必须先处置）
|
||||
|
||||
现役 `q4_mincover_v1`（31 点）与 `square49`（49 点）在**粗采样**下缺口为 0，
|
||||
但把校核分辨率从 75 m/24 档提到 25 m/48 档：
|
||||
|
||||
| 点集 | 点数 | 75 m/24 缺口 | 25 m/48 缺口 | 15 m/72 缺口 | 最坏命中@75 m | 最坏命中@25 m |
|
||||
| --- | ---: | ---: | ---: | ---: | ---: | ---: |
|
||||
| `square49` | 49 | 0 | 0 | 0 | 760.3 m | 760.3 m |
|
||||
| `q4_mincover_v1`（现役） | 31 | 0 | **125** | **566** | 999.1 m | 1000.0 m |
|
||||
| 细网格重搜最小集 | 31 | 0 | 0 | 35 | 983.0 m | 996.2 m |
|
||||
| 贪心 27 点（75 m 档最优） | 27 | 0 | **298** | 1117 | 986.5 m | 1000.0 m |
|
||||
|
||||
结论：
|
||||
1. **31 点很可能就是实际下界附近**：多起点随机贪心 + 冗余剪枝在 25 m/48 细网格上
|
||||
直接搜索的最小解仍是 31 点（尚未做 LP 松弛下界，但连贪心都下不去）；
|
||||
之前看到的 27 点是**采样过拟合的假解**，不可采纳；
|
||||
2. `q4_mincover_v1` 的安全裕量几乎为 0（999.1 m），存在"真源恰落在细缝 + `r_eff` 取下界"
|
||||
的漏检窗口，现役测试没暴露是因为随机场景未命中细缝且 `r_eff` 通常 > 1000；
|
||||
3. `square49` 裕量充足但巡回 36.4 km，不可回退；
|
||||
4. 正确做法是**细网格重搜 + 设计裕量**（S2），代价约 0–150 s，**先保安全再谈提速**。
|
||||
|
||||
### 5.2 其他风险
|
||||
|
||||
| 风险 | 影响 | 缓解 |
|
||||
| --- | --- | --- |
|
||||
| POMDP rollout 墙钟超预算 | 单局计算 > 1 s | 深度 2、候选截断 K=8、`--profile` 纳入基准必报列；超预算自动退回一步法 |
|
||||
| Held-Karp 状态爆炸（任务表变长） | 单步计算抖动 | 仅在 `|T| ≤ 9` 默认启用，`> 9` 回退 2-opt + Or-opt |
|
||||
| 抵近点破坏可见性保证 | 探测反而 `no_signal` 变多 | 抵近点仍以 `prob_detect > 0` 过滤；`no_signal` 是合法负观测，不破坏守恒性 |
|
||||
| 骨架插入与"找全即停"冲突 | 为走骨架而多绕行 | 骨架只提供顺序，不新增必须访问的点；任务表为空即停 |
|
||||
| 细网格证书重搜耗时 | 离线 3–5 min | 一次性离线搜索 + 运行期按计划名缓存校验（沿用 `load_plan` lru_cache） |
|
||||
|
||||
### 5.3 明确不做
|
||||
|
||||
- **不重引入 PPO/HRL**（前置文档已用 109 776 步的负面结果排除）；
|
||||
- **不引入新的运行时依赖**（仍只有 `requests`；`numpy` 仅作可选加速后端，
|
||||
`networkx`/`scipy` 不进入运行时路径）；
|
||||
- **不改变停止条件**（`unresolved_channels` 的口径取舍属于评分方式问题，
|
||||
须先与题面评分方式核对，本轮不作为默认行为）；
|
||||
- **不承诺第四问 4000 s**（成本地板 ≈5566 s，见 §2.3）。
|
||||
|
||||
---
|
||||
|
||||
## 6. 验收与度量
|
||||
|
||||
### 6.1 有效性定义（沿用，不得放宽）
|
||||
|
||||
```
|
||||
valid := 全部真源被清除 ∧ 缺席频道与真值一致 ∧ 正常 /exit
|
||||
∧ 零算法异常 ∧ 逐微秒计时复算一致
|
||||
```
|
||||
|
||||
### 6.2 新增必报列（写入 `scripts/run_suite.py` 逐局行与 aggregate）
|
||||
|
||||
| 列 | 定义 | 用途 |
|
||||
| --- | --- | --- |
|
||||
| `route_gap_vs_offline` | `实测移动 − 离线最优开放回路（同作业点集）` / 实测移动 | 区分"排线不够好"与"点选得不对" |
|
||||
| `route_lower_bound_s` | 当前任务集 MST 权重 / 速度 | 在线后悔值审计 |
|
||||
| `distinct_service_points` | 去重作业点数 | 探测是否真的在减少 |
|
||||
| `coverage_actual_visited` | 实际测过的覆盖点数 | 缺席证明是否提前结束 |
|
||||
| `probe_no_signal_ratio` | 探测中 `no_signal` 占比 | D1 的效果指标 |
|
||||
| `fallback_clear_attempts` | 已有 | D1 的主验收指标 |
|
||||
|
||||
### 6.3 目标（vs §2 基线）
|
||||
|
||||
| 指标 | Q3 现 | Q3 目标 | Q4 现 | Q4 目标 |
|
||||
| --- | ---: | ---: | ---: | ---: |
|
||||
| core 平均虚拟时间 | 3197 s | **≤ 2900 s** | 8676 s | **≤ 6500 s** |
|
||||
| 移动 | 2458 s | ≤ 2250 s | 6257 s | ≤ 5000 s |
|
||||
| 探测次数 | 30.8 | ≤ 26 | 69.0 | ≤ 45 |
|
||||
| 后备盲清次数 | 0 | 0 | 125.7 | **≤ 15** |
|
||||
| `route_gap_vs_offline` | 6.1% | ≤ 3% | 20.1% | ≤ 10% |
|
||||
| `fallback` 清除成功率 | — | — | 10% | ≥ 90% |
|
||||
| 单局计算墙钟 | 1.0 s | ≤ 1.2 s | 1.0 s | ≤ 1.5 s |
|
||||
| 细网格证书缺口（15 m/72） | — | 0（Q3 解析） | 566 | **0** |
|
||||
|
||||
### 6.4 回归
|
||||
|
||||
`uv run pytest`（当前 232 项）必须全过；新增：
|
||||
|
||||
- `CoveragePlan` 细网格正/负例（删掉任一点必须被检出）；
|
||||
- `JointBelief` 的期望后验半径与真源保留性质对拍（随机 `(p,h)` × 随机观测序列）；
|
||||
- `Held-Karp` 与 `2-opt` 在同一小任务集上的最优性对拍（DP 结果必须 ≤ 2-opt）；
|
||||
- `C₂₀` 投影的三角不等式性质（投影点距候选集任一点仍 ≤ 20）；
|
||||
- 逐位一致性：numpy 与纯 Python 两条路径在同一动作序列下 `actions_sha256` 相同。
|
||||
|
||||
---
|
||||
|
||||
## 7. 复现命令（本轮实测）
|
||||
|
||||
```bash
|
||||
# 度量基线（§2.1/§2.2/§2.3）：作业点分解 + 离线最优回归 + 成本地板
|
||||
uv run python /tmp/analyze_headroom.py # 路由 gap：Q3 6.1%，Q4 20.1%
|
||||
uv run python /tmp/decompose.py # Q4 三段移动 + 动作 + 清除成功率
|
||||
uv run python /tmp/q3stats.py # Q3 探测/覆盖检测/跳测统计
|
||||
|
||||
# 覆盖证书分辨率（§5.1）
|
||||
uv run python /tmp/verify_margin.py # 现役 31 点在 25 m/48 档缺口 125
|
||||
uv run python /tmp/hitset_fine.py # 细网格最小解仍是 31 点
|
||||
|
||||
# 既有常驻工具
|
||||
uv run python scripts/run_suite.py --problem 4 --suite all --jobs 6 \
|
||||
--variants directional@q4_mincover_v1 --label q4-baseline
|
||||
uv run python scripts/verify_q4_cover.py --out output/q4/cover-certificate.json
|
||||
uv run python scripts/analyze_run_timing.py <log.jsonl>
|
||||
```
|
||||
|
||||
> 上述 `/tmp/*.py` 为一次性分析脚本，S1 阶段应把其中的
|
||||
> `route_gap_vs_offline`、`offline_opt_open_path`、`mst_lower_bound`
|
||||
> 三个函数并入 `scripts/run_suite.py`（或新建 `scripts/analyze_routing.py`）常驻入库，
|
||||
> 使"虚拟时间花在哪"与"覆盖集是否合法"一样成为可随时重跑的回归项。
|
||||
|
||||
---
|
||||
|
||||
## 8. 本轮实测回填（2026-09-12 实施结果）
|
||||
|
||||
本节记录 S1–S5 全部落地后的**实测**结果与**负面结论**。所有数字均为冻结基准集
|
||||
（`harness/suites.py`）配对复跑，口径与 §6.1 完全一致；复现命令见 §8.6。
|
||||
|
||||
### 8.1 交付物清单
|
||||
|
||||
| 项 | 位置 | 状态 |
|
||||
| --- | --- | --- |
|
||||
| 图论/DP 工具箱（精确开放路径、2-opt/Or-opt、MST 下界、路由度量） | `src/mcm26b/routing.py` | ✅ 新增 |
|
||||
| S1 必报列（`route_gap_vs_offline`、`route_lower_bound_s`、`distinct_service_points`、`coverage_actual_visited`、`probe_no_signal_ratio`、`plan_verify_wall_s`） | `scripts/run_suite.py` | ✅ 新增 |
|
||||
| S2 设计裕量覆盖计划 + 细网格证书 + 精细剪枝 | `guarantee/cover.py`、`scripts/verify_q4_cover.py`、`plans/q4_mincover_v2.json` | ✅ 新增并设为默认 |
|
||||
| S3/D1 期望后验半径值函数 + 可见性过滤 + 抵近点 | `belief/joint.py`、`planner/__init__.py` | ✅ 新增（默认口径见 §8.3） |
|
||||
| S4/D3 后备清除路径改进 | `planner/__init__.py::_fallback_order` | ⚠️ 已实现，实测负收益，默认关闭 |
|
||||
| S5/T1 Held-Karp 精确任务序（含换频状态） | `planner/bayes_loop.py::_exact_order` | ⚠️ 已实现，实测负收益，默认关闭 |
|
||||
| 回归测试 | `tests/test_routing.py`、`tests/test_joint_belief.py`、`tests/test_coverage_plan.py` | ✅ 新增，`uv run pytest` 268 项全过 |
|
||||
|
||||
### 8.2 最终成绩（vs S0 基线）
|
||||
|
||||
| 指标 | Q3 基线 | Q3 现 | Q4 基线 | Q4 现 | 目标 |
|
||||
| --- | ---: | ---: | ---: | ---: | ---: |
|
||||
| core 平均虚拟时间 | 3210.3 s | **3188.7 s（−0.7%）** | 8676.1 s | **8602.0 s（−0.9%）** | 2900 / 6500 ✗ |
|
||||
| core 最大虚拟时间 | 3798.5 s | **3663.7 s（−3.5%）** | 10890.8 s | **9630.4 s（−11.6%）** | — |
|
||||
| 移动 | 12446.8 m | **12338.1 m（−0.9%）** | 31286.4 m | 32891.1 m（+5.1%） | 2250 / 5000 ✗ |
|
||||
| 检测次数 | 111.0 | 111.1 | 346.0 | **333.7** | — |
|
||||
| 后备盲清次数 | 0 | 0 | **125.7** | **4.3（−96.6%）** | ≤15 ✅ |
|
||||
| `route_gap_vs_offline` | 8.67% | **8.07%** | 18.9% | 24.3% | ≤3% / ≤10% ✗ |
|
||||
| 单局计算墙钟 | 1.29 s | **1.04 s** | 5.29 s | **4.09 s** | ≤1.2 / ≤1.5 ⚠️ Q4 超标 |
|
||||
| 覆盖计划运行期校验 | — | 0.42 s | — | 4.80 s（15 m/72 一次性） | — |
|
||||
| 细网格证书缺口（15 m/72） | — | 0（解析） | 566 | **0** | 0 ✅ |
|
||||
| 有效局 | 18/18 | 20/20（含边界） | 9/9 | 11/11（含边界） | 100% ✅ |
|
||||
|
||||
**结论**：本轮的硬收益是**第四问后备盲清被消除**（125.7 → 4.3 次/局，最坏局
|
||||
10890.8 → 9630.4 s）与**覆盖证书的正确性**（细网格缺口 566 → 0）；虚拟时间只有
|
||||
0.7–0.9% 的均值改善（中位 −2.0%），**远未达到方案 §6.3 的目标**。
|
||||
第三问的改善落在噪声内（中位相对变化 0.0005）。§2 的成本地板估计（Q3 可压缩
|
||||
10–14%、Q4 23–32%）**过于乐观**，实测只回收了约 1%。
|
||||
|
||||
### 8.3 定向源探点（D1）：先负后正，最终只保留"可见 + 就近"
|
||||
|
||||
D1 的完整实现（期望后验 MEC 半径值函数 + 抵近点 + 遮挡点剔除）在**修正联合
|
||||
信念朝向规则之前**配对为：后备 125.7 → 53.8 次，但探测移动 +5.6 km，虚拟时间
|
||||
8676 → 9411 s（净负）。原因有两个：
|
||||
|
||||
1. 后备清除在命中真源时**立即结束**，"整条路径最短"不是它的目标函数，
|
||||
§3.2/§3.3 的插入代价模型在这里并不成立；
|
||||
2. 更关键的是：**联合信念的 `no_signal` 朝向规则被写反了**（§8.4），
|
||||
`prob_detect` / 期望半径因此系统性地高估"被遮挡点可见"，选点必然跑偏。
|
||||
|
||||
修正后重测（9 场景 core）：
|
||||
|
||||
| `probe_score_mode` | λ | 后备次数 | T 均值 | 移动 | 计算墙钟 |
|
||||
| --- | ---: | ---: | ---: | ---: | ---: |
|
||||
| `visibility`（旧） | — | 125.6 | 9224.6 s | 33595 m | 11.6 s |
|
||||
| `expected_radius` | 0.05 | 6.8 | 9740.5 s | 38824 m | 9.6 s |
|
||||
| `expected_radius` | 5.0 | 4.3 | 8604.3 s | 32893 m | 3.7 s |
|
||||
| `nearest_visible`（默认） | — | **4.3** | **8602.0 s** | 32891 m | 3.4 s |
|
||||
|
||||
λ 越大越优说明本题的探点问题里"**看得见且少走路**"比"单步信息量最大"更重要；
|
||||
λ→∞ 的极限（可见候选中移动最省）就是默认口径。值函数代码与其对拍测试保留，
|
||||
供后续研究（例如配合真正的多步 rollout）。
|
||||
|
||||
### 8.4 本轮发现的正确性缺陷（已修复）
|
||||
|
||||
`belief/joint.py` 的 no_signal 朝向裁剪**规则反向**：`keep=False` 分支用了
|
||||
"确定覆盖"掩码做交集，等价于保留"必然看得见"的档、删掉"确定看不见"的档；
|
||||
`keep=True` 分支又漏计了格内角裕量 `asin(margin/max(dist−margin,1))`。
|
||||
两者都会删掉真源的朝向档（`tests/test_joint_belief.py::test_true_source_survives_consistent_observations`
|
||||
在随机观测序列上直接复现）。修复后：
|
||||
`keep=False` 改用 `_far_mask`（与 `_cover_mask` 互补）并把距离阈值收紧为
|
||||
`r_eff_min − margin`，`keep=True/False` 都计入格内角裕量。
|
||||
|
||||
**修复本身是净成本**：在旧 `visibility` 探点口径下 Q4 core 由 8676 → 9225 s；
|
||||
换成 §8.3 的新探点口径后回落到 8602 s，同时后备清除降 96.6%。
|
||||
换言之，本轮第四问的成绩改善**主要来自"修好信念 + 换探点口径"这一组合**，
|
||||
而不是 §3 预期的插入/排线优化。
|
||||
|
||||
### 8.5 覆盖证书硬化（S2）
|
||||
|
||||
现役 `q4_mincover_v1`（31 点）在粗采样（75 m/24）下缺口 0，但细网格复检：
|
||||
|
||||
| 点集 | 点数 | 25 m/48 缺口 | 20 m/72 缺口 | 15 m/72 缺口 | 最坏命中 | NN 巡回 |
|
||||
| --- | ---: | ---: | ---: | ---: | ---: | ---: |
|
||||
| `q4_mincover_v1`（旧） | 31 | 125 | 336 | 566 | 1000.0 m | 22.9 km |
|
||||
| `q4_mincover_v2`（默认） | 31 | **0** | **0** | **0** | **990.0 m** | 23.9 km |
|
||||
| 严格连续空间证书备选 | 36 | 0 | 0 | 0 | 973.7 m | 25.4 km |
|
||||
|
||||
`q4_mincover_v2` 的生成方式：搜索分辨率 50 m/48 档、设计裕量 `r_design=965 m`、
|
||||
前向角裕量 5°，再做**证书分辨率（15 m/72）精细剪枝**（每次删点都用同一证书口径
|
||||
复核），因此点数与旧计划相同而缺口归零，**没有付出点数代价**。
|
||||
|
||||
> 严格连续空间界需要 `r_verify + 15·√2/2 ≤ 1000`（即 ≤989.4 m）且角裕量 ≥2.5°；
|
||||
> 满足该条件的 36 点方案最坏命中 973.7 m，但代价 +5 点（约 +1000 s），
|
||||
> 已落盘为 `output/q4/cover-certificate-v3.json` 备用。默认方案在 15 m/72
|
||||
> 采样下最坏 990.0 m，与严格界的差 0.65 m 属采样边界效应，作为已知残余风险登记。
|
||||
|
||||
### 8.6 负面结论与复现命令
|
||||
|
||||
| 优化项 | 实测 | 结论 |
|
||||
| --- | --- | --- |
|
||||
| S4/D3 后备清除 Or-opt | 后备失败 125.7 → 161.4 次（T 8676 → 8940 s） | ❌ 目标函数错位：命中即停，路径最短反而把真源推后 |
|
||||
| S5/T1 Held-Karp 精确任务序 | 3203.2 s vs 2-opt 3193.1 s（18 场景） | ❌ 滚动时域下"当前任务表逐位最优"反而略差；默认 0 |
|
||||
| `route_aware_probe` | 3210.3 s vs 关闭 3193.1 s | ❌ 权重 1e-3 的路线项无净收益；默认关闭 |
|
||||
| Q4 融合巡回（站点顺带定位） | 12711 s vs 10809 s | ❌ 与 P0-1 在第三问的负面结论一致 |
|
||||
| `opportunistic_radius_ratio` | 0.65 → 0.5：3210.3 → 3205.4 s | ✅ 采纳（弱收益） |
|
||||
|
||||
```bash
|
||||
# S1 度量基线（含路由 gap / MST 下界 / 作业点/静默率）
|
||||
uv run python scripts/run_suite.py --problem 4 --suite core --jobs 6 \
|
||||
--variants directional --label q4-baseline --out output/suite
|
||||
uv run python scripts/run_suite.py --problem 3 --suite core --jobs 6 \
|
||||
--variants bayes --label q3-baseline --out output/suite
|
||||
|
||||
# S2 覆盖证书（生成 + 细网格校核；--fine-prune 走证书分辨率剪枝）
|
||||
uv run python scripts/verify_q4_cover.py --spacing 100 --radius 2850 \
|
||||
--target-step 50 --headings 48 --design-radius 965 --heading-margin 5 \
|
||||
--verify-step 15 --verify-headings 72 --fine-prune \
|
||||
--write-plan q4_mincover_v2 --out output/q4/cover-certificate-v2.json
|
||||
|
||||
# S3/D1 口径配对（可见性/值函数/极限三档）
|
||||
uv run python scripts/run_suite.py --problem 4 --suite core --jobs 6 \
|
||||
--variants "directional#search.probe_score_mode=visibility,\
|
||||
directional#search.probe_score_mode=expected_radius;search.probe_lambda_move_s=5,\
|
||||
directional#search.probe_score_mode=nearest_visible" --label q4-probe-ab
|
||||
|
||||
# 回归
|
||||
uv run pytest -q # 268 项
|
||||
```
|
||||
|
||||
### 8.7 仍未达标项的处置建议
|
||||
|
||||
| 未达标 | 差距 | 建议 |
|
||||
| --- | --- | --- |
|
||||
| Q3 ≤2900 s | −9% | 已无低风险空间：七站巡回 1392 s + 覆盖检测 501 s + 每源 ≥2 次探测是成本地板；需要改停止条件或覆盖语义才能继续下降 |
|
||||
| Q4 ≤6500 s | −24% | 31 点巡回 + 缺席检测 ≈5566 s 是地板，与 §2.3 一致；只能改"逐频道缺席证明"口径 |
|
||||
| `route_gap` Q4 24.3% | — | 该指标在"作业点数随决策变化"时口径不稳（本方案把后备格换成探测点后作业点从 206 降到 110）；建议改为"覆盖骨架段"与"临时任务段"分别报 gap |
|
||||
| Q4 计算墙钟 4.1 s（目标 1.5 s） | 2.7 s | 热点在 `belief` 凸多边形与 `select_measurement_point`（D1 之前的基线即 5.3 s），属 P0-4b 表示层张量化，与本方案无关 |
|
||||
| 严格连续空间覆盖证书 | 0.65 m | 若要正式采用 36 点严格方案：`--verify-radius 989 --verify-heading-margin 2.5`，代价约 +1000 s |
|
||||
@@ -0,0 +1,595 @@
|
||||
# 第三、四问系统性架构优化与运行提速方案
|
||||
|
||||
- 版本：2026-09-12
|
||||
- 依据：本仓库运行历史（`session.jsonl`、`practice-p3-*.jlog`、`referee-data/sessions/*`、
|
||||
`output/hrl/ui/q3-main/*`、`docs/` 全部性能记录）+ 本文档新增的复跑与离线校核实验
|
||||
- 范围：第三问（全向源）与第四问（混合定向源）的**闭环架构、算法分解、运行治理**，
|
||||
不改动题面约束、协议层语义与既有保守性不变量
|
||||
- 结论先行：**虚拟时间的 75–83% 是移动**，移动里约一半不是覆盖巡回而是定位/清除往返；
|
||||
**第四问的全部失败来自定向源**；旧机器学习层已被证明不如手写基线。
|
||||
因此本轮优化的主线是"**把定位并入覆盖巡回 + 把发现集压到最小命中集 + 给定向源建位置×朝向联合信念**"，
|
||||
而不是再堆学习算法。
|
||||
|
||||
---
|
||||
|
||||
## 0. 摘要（TL;DR）
|
||||
|
||||
### 0.1 三个待解的结构性浪费
|
||||
|
||||
| 编号 | 现象 | 直接证据 | 估计可回收 |
|
||||
| --- | --- | --- | --- |
|
||||
| W1 | Q3 定位探点往返 5741 m / 1148 s，与覆盖巡回（6960 m / 1392 s）相当 | 本次复跑（13 源，seed 0）：24 次探点，均值 442 m/次 | **−900 ~ −1100 s/局** |
|
||||
| W2 | Q4 发现集是 700 m 方格 49 点，巡回 36.4 km；同一保证下贪心最小命中集只要 31 点、22.9 km | `scripts/verify_q4_cover.py`：43 032 个 (p,h) 采样，两组点集均 100% 覆盖 | **−2700 s（巡回）+ −1140 s（检测）** |
|
||||
| W3 | Q4 定向源没有"位置×朝向"联合信念，只能阶梯探点，最后退化到 ≤400 格盲清 | referee 两局：全向 17/17 清除，定向仅 2/6；4/4 漏检全是定向 | **−1500 ~ −3000 s/局** |
|
||||
|
||||
### 0.2 预期收益（自建模拟器，同一冻结基准集，配对口径）
|
||||
|
||||
| 指标 | Q3 现状 | Q3 目标 | Q4 现状 | Q4 目标 |
|
||||
| --- | ---: | ---: | ---: | ---: |
|
||||
| 平均虚拟时间 | 3707 s（本次 6 场景）/ 3536 s（13 源单局） | **≤ 2700 s** | 13 652 s（10 源 3 定向单局）/ 13 165 s（4 场景均值） | **≤ 9000 s** |
|
||||
| 最大虚拟时间 | 4628 s | **≤ 4000 s** | > 17 000 s（边界压力） | ≤ 12 000 s |
|
||||
| 每局计算墙钟 | 2.9–6.5 s | **≤ 0.8 s** | 6.5 s | **≤ 1.0 s** |
|
||||
| 有效局（全清+缺席无误判+零异常+正常退出+计时复算一致） | 6/6 | 基准集 100% | 4/4 | 基准集 100% |
|
||||
|
||||
> 4000 s 是本项目 `docs/算法审核与优化-20260911.md` 自设的验收线，不是题面评分线；
|
||||
> 第四问在 10 源场景下**不承诺**达成 4000 s，理由见 §6.2。
|
||||
|
||||
### 0.3 明确不做的事
|
||||
|
||||
- 不重新引入 PPO/HRL。运行历史支持"保留手写基线"：HRL 训练 4007.9 s / 109 776 步后
|
||||
仍被 `bayes` 稳定压制 4.3–5.0%（17–18/20 场景，符号检验 p=0.0001），且 94% 的差距来自
|
||||
多走的移动；其评估集 20 个场景**全部是单源全向**，无法支撑任何第四问结论。
|
||||
- 不引入新的第三方运行时依赖（仍是 `requests`；`numpy` 仅作**可选加速后端**，缺失时回退纯 Python）。
|
||||
|
||||
### 0.4 实施进展与实测回填（2026-09-12）
|
||||
|
||||
冻结基准集 `harness/suites.py`（第三问 18 主场景 + 2 边界；第四问 9 + 2），
|
||||
并行 runner `scripts/run_suite.py`，配对口径同场景同误差种子。
|
||||
|
||||
| 项 | 状态 | 实测 | 结论 |
|
||||
| --- | --- | --- | --- |
|
||||
| P1-4 安全阀 | ✅ 已实现 | `summary` 新增 `completion_basis` / `absence_evidence` / `redundant_rescan_measures` / `virtual_reserve_s`；`--persistent` 输出单轮上界告警 | 采纳（5 项测试） |
|
||||
| P0-2 最小命中集 | ✅ 已实现并默认开启 | 第四问 11 场景配对：**14003.0 → 10975.5 s**（均值 −19.3%、中位 −25.4%、10/11 更快）；检测 444.5→319.2；请求 569.5→395.6；位移 54.9→44.2 km；最大 17007→12919 s；11/11 valid | **采纳** |
|
||||
| P0-4 感知层向量化 | ✅ 已实现 | 单局计算 **3.10 → 1.00 s（3.08×）**，最大 5.84 → 1.30 s；20 场景的虚拟时间与动作序列**逐位一致** | **采纳**（目标 ≤0.8 s 未达，余下为 belief 表示层） |
|
||||
| P0-1 融合巡回 | ❌ 实测无效，已回退默认 | 仅追加站点方位线：3454.6 vs 3426.9 s（+0.8%，检测 +10.8 次、位移仅 −188 m）；追加 + 抑制专用探点：**4989.0 vs 3426.9 s（+45.6%，20/20 更慢）** | 关闭（保留开关与代码） |
|
||||
| P1-1 C₂₀ 最近清除点 | ⊘ 复核后降为非项 | 现有 `_near_clear_target` 已取安全球 `B(c, limit−ρ)` 内离当前位置最远点；与完整 C₂₀ 最优之差 ≤ slack+margin ≈ 0.74 m | 不改 |
|
||||
| P1-3 基准集/并行 | ✅ 已实现 | 20 场景 × 3 变体 = 60 局 **26.5 s**（6 进程）；20 局 **3.9 s** | 采纳 |
|
||||
| P0-3 联合信念 | ✅ 已实施并于 2026-09-12 修正 | 网格粒子 × 24 档朝向 bin + 可见性选点；**发现并修复 `no_signal` 朝向规则反向缺陷**（原会删掉真源朝向档）；配套 D1 探点口径使第四问后备盲清 **125.7 → 4.3 次/局** | 采纳（结论与实测见[图论与 DP 优化方案 §8](第三四问图论与动态规划优化方案.md)） |
|
||||
|
||||
**P0-1 为什么失败（重要负面结论）**：七站环上通常只有 2–3 个站能对同一源取得
|
||||
有效方位线，且站间交会角不足以直接换到 20 m 证书，专用信息增益探点仍然必要；
|
||||
"顺路测量"于是退化为平白多花检测，一旦抑制专用探点则会白等巡回、远端绕行。
|
||||
**结论：第三问的定位往返不是可回收的浪费，`bayes` 的滚动时域调度已在做正确的事。**
|
||||
|
||||
**第四问现状与目标差距（2026-09-12 图论/DP 轮回填）**：启用硬化覆盖计划
|
||||
`q4_mincover_v2` 与 D1 探点口径后，第四问 11 场景（core + boundary）均值为
|
||||
**9069.9 s**、最大 12081.1 s、后备盲清 24.8 次/局（core 9 场景 8602.0 s /
|
||||
4.3 次），仍远高于 4000 s 验收线。剩余成本主要是最小命中集巡回
|
||||
（23.9 km ≈ 4777 s）与缺席频道检测，二者都是覆盖证明的固有代价；
|
||||
本方案 §2.3 的"可压缩 23–32%"在本模拟器上只回收了约 1%（详见图论与 DP 方案 §8）。
|
||||
|
||||
---
|
||||
|
||||
## 1. 运行历史复核
|
||||
|
||||
### 1.1 数据资产清单（本次实际读取）
|
||||
|
||||
| 资产 | 内容 | 可用信息 |
|
||||
| --- | --- | --- |
|
||||
| `session.jsonl`（31 011 行） | 官方第三问演练 J2WH-2K7D-5ZXS-DNK6 的客户端日志 | 10 323 次 `/measure`、12 次 `/clear`、逐动作计时 |
|
||||
| `practice-p3-*.jlog` | 同一局的官方行为日志封装 | 仅明文信封（题号 3、队号、案例码）；载荷 AES-256-GCM + RSA-OAEP，不可解 |
|
||||
| `referee-data/sessions/*`（6 局，5 局完整） | 本地评测机 q3×3、q4×2、截断×1 | 逐请求响应、真值、summary、step 事件 |
|
||||
| `output/hrl/ui/q3-main/` | HRL 训练（109 776 步）+ `eval/at-40k`、`eval/at-final` | monitor CSV、模型元数据、20 场景配对评估 |
|
||||
| `docs/算法审核与优化-20260911.md`、`docs/algo-3.md`、`docs/algo-3-bayes.md` | 历史 KPI 与消融 | 官方/自建成绩、验证集分档 |
|
||||
| 本次新增复跑 | `scripts/benchmark_q3_bayes.py`（6 场景）、Q3/Q4 单局计时分解、`cProfile`、站点三角化可行性 | §1.3 中标注"本次复跑"的数字 |
|
||||
| 本次新增工具（已入库） | `scripts/analyze_run_timing.py`（任意会话日志的计时分相复算）、`scripts/verify_q4_cover.py`（第四问覆盖证书 + 最小命中集搜索） | F4、F9 可复现 |
|
||||
|
||||
### 1.2 先确认度量可信（否则一切优化都是自欺）
|
||||
|
||||
官方计时模型：`T = L/5 + 5·N_measure + N_switch + 5·N_clear⁺ + 3·N_clear⁻`，
|
||||
其中 `L` 为累计直线位移，`/clear` **不计**换频。`scripts/verify_q3_practice.py` 对 5 局完整
|
||||
评测机日志逐微秒复算，与服务器返回的 `virtual_time_s` 完全一致（例：BBM9 复算 3134.642907 = 记录 3134.642907）。
|
||||
结论：**后续所有"虚拟时间"结论都可用动作日志独立复算**，这是本项目最可靠的一条工程资产，必须保留。
|
||||
|
||||
### 1.3 关键事实（F1–F13）
|
||||
|
||||
| 编号 | 事实 | 出处 |
|
||||
| --- | --- | --- |
|
||||
| F1 | 虚拟时间构成：**移动 75–83%｜检测动作 15–21%｜换频 2.3–3.7%** | 官方会话 82.30/14.76/2.94；referee q3 80.50/16.78/2.71、q4 78.39/18.63/2.98；本次复跑 Q3 79.9/17.3/2.9、Q4 75.2/21.1/3.7 |
|
||||
| F2 | 官方第三问演练 J2WH 总虚拟 350 227.7 s，其中 **346 169.9 s（98.84%）是空复扫**：7 个固定站点 × 8 个未清除频道 × 183 轮，0 检出，结束点正好落在 `virtual_reserve_s=10 000` 边界 | `session.jsonl` 相位切分；`planner/__init__.py:236`、`config.py:99` |
|
||||
| F3 | **第四问的全部失败来自定向源**：referee 两局全向 17/17 清除，定向仅 2/6；4/4 漏检全是定向；每清除源 356.4 s（Q3 274.0 s，+30%） | `referee-data/sessions/{6X58,BAU8}`；`{BBM9,RM7Y,VQKB}` |
|
||||
| F4 | 本次复跑 Q3（13 源，seed 0）：3535.9 s / 109 次检测 / 13 清除；**七站巡回 6960 m（1392 s，39%）+ 探点往返 5741 m（1148 s）+ 清除移动 3668 m（734 s）** | 本次复跑（方法见附录 A） |
|
||||
| F4b | 评测机 Q3 单局 BBM9：**21 次专用探点占 1138.7 s 移动，等于该局虚拟时间的 36.3%**，而站点扫频 98 次只占 991.2 s | `scripts/analyze_run_timing.py referee-data/sessions/BBM9-.../log.jsonl --plan seven` |
|
||||
| F5 | 本次复跑 Q4（10 源 3 定向，seed 0）：13 652.1 s / 560 次检测 / 18 次清除（8 次失败）/ 51 356 m | 本次复跑 |
|
||||
| F6 | 感知层是墙钟热点：单局计算 6.04 s；`cProfile` 下 `expected_survival` 占累计时间 **72%**（2808 次调用），`apply_measure` ≈ 20%，`_kill_outside_wedge` 触发 498 万次 `angular_gap` | 本次 `cProfile`（13 源单局） |
|
||||
| F7 | HRL 运行：109 776/200 000 步、4007.9 s、**27.0 steps/s（6.75/env）**、1.51 s 墙钟/局；被 SIGTERM 截断时回报仍在上升（Q1 1.05 → Q4 6.20） | `output/hrl/ui/q3-main/q3.json` + 4 份 monitor CSV |
|
||||
| F8 | HRL 评估：20 场景**全部单源全向**；`bayes` 比 HRL 快 4.3–5.0%（17–18/20，p=0.0001），**94% 的差距是多走的移动**；40k→109k 步仅再改善 0.7% | `eval/at-40k|at-final/aggregate.*`、`eval/manifest.json` |
|
||||
| F9 | 现役 Q4 发现集：700 m 方格 49 点、NN 巡回 **36.4 km**；满足同一"前向半盘命中"保证的贪心最小集 **31 点、22.9 km（−37%）**，覆盖校核 100% | `scripts/verify_q4_cover.py`；本文附录 A |
|
||||
| F10 | Q3 七站巡回**已经最优**：6 × 1159.7 = 6958 m（观测 6960 m），无可压缩空间；把环半径降到覆盖下界 1122.9 m 仅省 222 m（44 s） | 几何计算 + 本次复跑 |
|
||||
| F11 | 站点三角化可行：**99.28% 的源位置有 ≥2 个站在 1500 m 内**；最佳交会角 5 分位 58.5°、中位 122.9°；只有 0.72% 的源位置仅 1 个站在射程内 | 本次离线采样（20 000 点） |
|
||||
| F12 | 墙钟量级差三个数量级：内存回放 **0.22–0.25 ms/请求**（≈0.35 s/局），官方接口 **9.42 ms 均值/请求**（104.49 s / 10 337 请求）；本地评测机 24.5–97.9 ms/请求 | referee `session_end`、`runs/*/summary.json`、`session.jsonl elapsed_s` |
|
||||
| F13 | 历史 KPI 参照：Q3 官方最好 4058.71 s（3BW5，13/13）、5232.13 s（J4ER，12/12）；自建 `bayes` 3707.3 s（本次 6 场景）/ 3726.2 s（14 场景消融）/ 验证集 3315.89 s；Q4 自建 13 164.7 s，**无官方第四问演练记录** | `docs/算法审核与优化-20260911.md`、`docs/algo-3-bayes.md`、`README.md:135` |
|
||||
|
||||
### 1.4 由事实导出的六处结构性瓶颈
|
||||
|
||||
| 编号 | 瓶颈 | 支撑事实 | 归属层 |
|
||||
| --- | --- | --- | --- |
|
||||
| B1 | 定位/清除移动没有并入覆盖巡回，探点按"信息增益"选，移动权重 λ=0.0105 几乎不惩罚绕行 | F1、F4、F4b、F11 | 决策层 |
|
||||
| B2 | Q4 发现集冗余 63%（49 点 vs 30 点），且每个"真缺席"频道要在全部 49 点各测一次 | F5、F9 | 保证层 |
|
||||
| B3 | Q4 定向源缺少位置×朝向联合信念：`no_signal` 只记"被遮挡过几次"，选点靠固定 45° 阶梯，收敛失败后退化到 ≤400 格 20 m 盲清（≤1200 s + ≤400 请求） | F3、`planner/__init__.py:691-768` | 感知层 + 决策层 |
|
||||
| B4 | 感知层全 Python 容器：每局 20 个频道各建 ~6.1 万格 `set`，`_kill_outside_wedge` 每次遍历全部幸存格，评分核 `expected_survival` 为 7×800 的纯 Python 双循环 | F6 | 感知层 |
|
||||
| B5 | 研发吞吐低：场景串行、单进程；一局 0.35–6.5 s；HRL 27 steps/s；14–52 场景的配对基准要跑几分钟到十几分钟 | F6、F7、F12 | 实验层 |
|
||||
| B6 | 运行治理缺失：单源评估集使指标退化（`avg_locate_clear_time_s ≡ virtual_time_s`）、`eval/manifest.json` 三份重复且与 `output/hrl/ui/manifest.json` 不一致（后者缺失）、`persistent_search=True` 可造成 98.84% 的无效开销且无硬性告警 | F2、F8、F13 | 遥测/实验层 |
|
||||
|
||||
---
|
||||
|
||||
## 2. 目标架构
|
||||
|
||||
### 2.1 分层与依赖方向（依赖只能向下）
|
||||
|
||||
```
|
||||
┌───────────────────────────────────────────────┐
|
||||
L6 实验/遥测 │ bench/ 冻结基准集·并行 runner·配对统计 │
|
||||
│ telemetry/ manifest·step log·replay·summary │
|
||||
└───────────────▲───────────────────────────────┘
|
||||
│ 只读观测
|
||||
┌───────────────┴───────────────────────────────┐
|
||||
L5 执行/安全 │ exec/ 动作账本·预算守卫·安全阀·幂等客户端 │
|
||||
└───────────────▲───────────────────────────────┘
|
||||
│ Action 请求
|
||||
┌───────────────┴───────────────────────────────┐
|
||||
L4 决策 │ plan/ 任务表·滚动时域排序·信息/时间评分 │
|
||||
└───────────────▲───────────────────────────────┘
|
||||
│ Task[]
|
||||
┌───────────────┴───────────────────────────────┐
|
||||
L3 保证 │ guarantee/ 命中集覆盖计划·清除证书·缺席证明 │
|
||||
└───────────────▲───────────────────────────────┘
|
||||
│ 候选集/幸存集
|
||||
┌───────────────┴───────────────────────────────┐
|
||||
L2 信念 │ belief/ BeliefBackend：OccupancyTensor(Q3) │
|
||||
│ JointTensor(位置×朝向, Q4) │
|
||||
└───────────────▲───────────────────────────────┘
|
||||
│ Observation
|
||||
┌───────────────┴───────────────────────────────┐
|
||||
L1 观测模型 │ model/ 可见性半平面·r_eff 带·有界误差·舍入裕量 │
|
||||
└───────────────▲───────────────────────────────┘
|
||||
│ 原始响应
|
||||
┌───────────────┴───────────────────────────────┐
|
||||
L0 协议/时钟 │ protocol/ 规则表·计时模型·状态机·客户端（冻结）│
|
||||
└───────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
与现状的差别：**用组合取代继承**。现状是
|
||||
`OmnidirectionalLoop → SevenBatch → BayesLoop → DirectionalExtension` 的单继承链，
|
||||
调度、信念、清除、预算、报表五种职责混在同一族类里（`planner/__init__.py` 818 行 +
|
||||
`planner/bayes_loop.py` 513 行），任何一处改动都会跨层影响其它三问。
|
||||
目标形态是
|
||||
|
||||
```python
|
||||
Episode(
|
||||
client = SimulatorClient(...), # L0，冻结
|
||||
belief = OccupancyTensor(...) | JointTensor(...), # L2
|
||||
plan = CoveragePlan.points, # L3
|
||||
scorer = InfoGainPerSecond(...), # L4
|
||||
policy = FusedTourPolicy(...) | DirectionalPolicy(...), # L4
|
||||
certifier= Certificate(...), # L3
|
||||
ledger = Telemetry(...), # L6
|
||||
)
|
||||
```
|
||||
|
||||
三层保证策略矩阵只差**三个可替换部件**，其余代码共享：
|
||||
|
||||
| 问题 | 信念后端 | 覆盖计划 | 选点策略 |
|
||||
| --- | --- | --- | --- |
|
||||
| Q3 | `OccupancyTensor`（全向） | 七站环（已经最优，冻结） | 融合巡回 |
|
||||
| Q4 | `JointTensor`（位置×朝向） | **最小命中集**（30 点，离线搜索 + 运行期校验证书） | 可见性约束选点 |
|
||||
|
||||
### 2.2 接口契约（关键签名）
|
||||
|
||||
```python
|
||||
# L1 model/observation.py —— 只此一处定义"什么算看见"
|
||||
@dataclass(frozen=True)
|
||||
class ObservationModel:
|
||||
r_eff_min_m: float; r_eff_max_m: float
|
||||
near_radius_m: float; half_angle_deg: float
|
||||
directional_half_deg: float = 90.0 # 含边界
|
||||
|
||||
def visible(self, src: Vec2, heading_deg: float | None, reader: Vec2) -> bool: ...
|
||||
def detectable(self, src: Vec2, heading_deg: float | None, reader: Vec2) -> bool: ...
|
||||
|
||||
# L2 belief/base.py —— 两种后端实现同一协议
|
||||
class BeliefBackend(Protocol):
|
||||
def apply(self, channel: int, reader: Vec2, result: str,
|
||||
svd_deg: float | None) -> None: ...
|
||||
def alive(self, channel: int) -> int: ...
|
||||
def is_absent(self, channel: int) -> bool: ... # 幸存集为空
|
||||
def certificate(self, channel: int) -> ClearCertificate | None: ...
|
||||
def guaranteed_no_signal(self, channel: int, reader: Vec2) -> bool: ...
|
||||
def prob_detect(self, channel: int, reader: Vec2) -> float: ... # 决策用
|
||||
def version(self, channel: int) -> int: ... # 派生量缓存键
|
||||
|
||||
# L3 guarantee/cover.py —— 覆盖计划是"可离线搜索、可在运行期校验"的一等对象
|
||||
@dataclass(frozen=True)
|
||||
class CoveragePlan:
|
||||
problem: int # 3 | 4
|
||||
points: tuple[Vec2, ...]
|
||||
certificate: CoverCertificate # 采样分辨率、缺失数、最坏裕量、脚本哈希
|
||||
version: str
|
||||
def verify(self, model: ObservationModel, tol_m: float) -> CoverCertificate: ...
|
||||
|
||||
# L4 plan/policy.py
|
||||
class Policy(Protocol):
|
||||
def tasks(self, belief: BeliefBackend, state: RobotState) -> list[Task]: ...
|
||||
def order(self, tasks: list[Task], state: RobotState) -> list[Task]: ...
|
||||
def measured_at_point(self, point: Vec2) -> tuple[int, ...]: ... # 一停多测
|
||||
```
|
||||
|
||||
### 2.3 四条设计原则
|
||||
|
||||
1. **保守性不变量不动**：真源永不被删；清除证书始终基于完整 ±1.005° 角域与题面距离带；
|
||||
所有启发式（均匀先验、朝向先验、信息增益）**只用于选点，不用于安全证明**。
|
||||
Q4 的朝向联合信念必须自带"朝向未观测时退化为全向"的保守分支。
|
||||
2. **保证 = 离线搜索 + 运行期校验**：覆盖集、清除点、巡回顺序都可以离线用数值搜索优化，
|
||||
但运行期必须用同一份 `ObservationModel` 重新校验证书（点集、分辨率、裕量），
|
||||
校验失败即拒绝启动，而不是相信一个写死的点表。
|
||||
3. **一个事实一个来源**：题面常量、`r_eff` 带、清除半径、动作耗时只在 `config.py`；
|
||||
可见性语义只在 `model/observation.py`；训练/部署若将来共存，必须共用同一控制器模块
|
||||
（这是旧 HRL 用 13 000 行换来的教训：v1 的 5 个特征索引错位、BC 激活函数不一致、
|
||||
旧基准把 `completed` 当全清）。
|
||||
4. **每个优化都必须可配对、可复算、可回归**：冻结基准集 + 逐局 `manifest` + 动作日志
|
||||
+ 逐微秒复算；任何"更快"的声明必须给出配对分布（均值/中位/最大/更快场数），
|
||||
而不是单个最优场景。
|
||||
|
||||
### 2.4 迁移映射（现状 → 目标）
|
||||
|
||||
| 现状 | 目标 | 处理 |
|
||||
| --- | --- | --- |
|
||||
| `protocol/*`、`evaluation/recompute_virtual_time` | 不变 | 冻结，作为度量基准 |
|
||||
| `belief/__init__.py`（凸外包） | `belief/convex.py` | 原样搬迁，继续负责精确角域证书 |
|
||||
| `particles.py`（网格幸存集） | `belief/occupancy.py`（numpy 张量 + 纯 Python 回退） | 重写热核，语义不变，保留 `tests/test_particles.py` 的真源保留性质测试 |
|
||||
| — | `belief/joint.py`（位置×朝向） | 新增，Q4 专用 |
|
||||
| `coverage/` | `guarantee/cover.py` | 49 点常量表 → 可校验的 `CoveragePlan` |
|
||||
| `planner/bayes_loop.py` 的 `_build_tasks/_order_tasks` | `plan/tasks.py`、`plan/schedule.py` | 抽出，Q3/Q4 共用 |
|
||||
| `planner/bayes_loop.py::_select_point` | `plan/score.py` | 抽出 + 向量化评分 |
|
||||
| `planner/__init__.py::DirectionalExtension` | `plan/policy.py::DirectionalPolicy` + `belief/joint.py` | 保留后备清除作为最终兜底，但目标是不再触发 |
|
||||
| `planner/seven.py` | `guarantee/plans.py::SEVEN` | 保留解析证明 |
|
||||
| 4 个 benchmark 脚本 | `bench/` 统一 runner（并行 + manifest + 配对统计） | 合并，消除指标口径分叉 |
|
||||
|
||||
---
|
||||
|
||||
## 3. 优化项
|
||||
|
||||
### P0-1 第三问：发现—定位融合巡回（消除探点往返）
|
||||
|
||||
**现状**：每次收到 `direction` 后进入独立定位子循环，由信息增益选点器在同一站点之外
|
||||
另选探点；13 源单局共 24 次探点、5741 m 往返、1148 s，与整个覆盖巡回（6960 m / 1392 s）同量级；
|
||||
评测机单局 BBM9 更极端：**21 次探点花掉 1138.7 s 移动 = 全局限时时间的 36.3%**（F4b）。
|
||||
评分函数 `score = E[log 幸存占比] + 0.0105 × 移动秒数`，移动项几乎不影响排名。
|
||||
|
||||
**方案**：把"已发现未定证"的频道加入**每一个覆盖站点**的批量扫频清单，
|
||||
使第二/第三条方位线在巡回途中顺路取得；只有站点三角化不成立时才回退到专用探点。
|
||||
|
||||
- 巡回仍由 `plan/schedule.py` 滚动排序（现有最近邻 + 2-opt 已足够，`bayes` 的 −39.5% 移动
|
||||
主要来自这里），新增"同点顺路测量"任务类型：`Task(kind="station", channels=(未知, 未定证))`。
|
||||
- 站点任务的固有耗时按 `6 s × 频道数 + 1 s × 换频次数` 记账（现有 `_path_cost` 已支持）。
|
||||
- 保底：`F11` 显示 0.72% 的源位置只有 1 个站在 1500 m 内，对这类频道保留
|
||||
`_select_point` 的专用探点路径；判定用 `belief.prob_detect` 而非硬编码。
|
||||
|
||||
**预期收益（已被实测推翻）**：原估 F4 的 1148 s 探点移动可回收 70–80%，
|
||||
即 −800 ~ −900 s。**实测结果相反**：仅追加站点方位线 +0.8%，追加并抑制专用探点
|
||||
+45.6%（20/20 场景更慢），见 §0.4。原因是站间交会角不足以保证 20 m 证书，
|
||||
专用信息增益探点仍然必要；抑制它只会白等巡回。**本项不采纳，默认关闭。**
|
||||
|
||||
**验收**：18 场景验证集平均 ≤ 2700 s、最大 ≤ 4000 s；`measures` 增幅 ≤ 20%；
|
||||
`move_distance_m` 降幅 ≥ 25%；全部 `valid`。
|
||||
|
||||
**风险**：站点距离最远 2 × 1159.7 = 2319 m，可能出现 `no_signal` 浪费 5 s。
|
||||
缓解：仅在 `prob_detect > 0` 时顺路测量；`no_signal` 同样是有效负观测（进信念，不算异常）。
|
||||
|
||||
---
|
||||
|
||||
### P0-2 第四问：用最小命中集替换 49 点发现集（离线搜索 + 运行期校验）
|
||||
|
||||
**现状**：`coverage_points()` 生成 700 m 方格 49 点（±2100），依据是"单元内任一点到 4 顶点
|
||||
≤ 700√2 ≈ 989.95 ≤ 1000"。该条件是**充分但远非必要**：它要求单元内任意位置、任意朝向都由
|
||||
**同一个**顶点命中。实际上，第四问的保证是"对 ∀(p,h) ∈ Ω×S¹，存在被访点位于其前向半盘内"，
|
||||
允许由**多个**顶点共同围住 p。
|
||||
|
||||
**方案**：把它当作一个显式的**击中集（hitting set）问题**离线求解，运行期校验：
|
||||
|
||||
1. 目标空间离散：`p` 取 Ω 内 75 m 网格，`h` 取 24 个方向 → 43 032 个 (p,h) 单元；
|
||||
2. 候选点：半径 2850 m 圆盘内 200–300 m 网格（641/293 个候选）；
|
||||
3. 贪心集合覆盖 + 局部改进，**同时把巡回长度作为次级目标**（641/293 个候选中取 31/30 个，
|
||||
仍有大量同覆盖度的等价解可用于缩短巡回）；
|
||||
4. 生成 `CoveragePlan(points, certificate)`，`certificate` 记录采样分辨率、未覆盖数、
|
||||
最坏裕量、生成脚本哈希；`CoveragePlan.verify()` 在每次运行前用同一 `ObservationModel`
|
||||
重新校核，未通过即拒绝启动。
|
||||
|
||||
**本次离线校核结果**（附录 A 可复现）：
|
||||
|
||||
| 点集 | 点数 | (p,h) 覆盖 | NN 巡回 | 折算移动 |
|
||||
| --- | ---: | ---: | ---: | ---: |
|
||||
| 现役 700 m 方格 ±2100 | 49 | 100% | 36.4 km | 7280 s |
|
||||
| 三角格 1000 m（半径 2800 盘） | 31 | 100% | 34.3 km | 6863 s |
|
||||
| **贪心最小命中集（200 m 候选）** | **31** | **100%** | **22.9 km** | **4587 s** |
|
||||
| **贪心最小命中集（300 m 候选）** | **30** | **100%** | **23.6 km** | **4719 s** |
|
||||
|
||||
**实测收益（2026-09-12，11 场景配对）**：均值 **14003.0 → 10975.5 s（−19.3%）**，
|
||||
中位 −25.4%，10/11 场景更快；检测 444.5 → 319.2，请求 569.5 → 395.6，
|
||||
位移 54.9 → 44.2 km，后备清除尝试 113.3 → 64.1，11/11 valid。
|
||||
低于原估的 −26%，差额来自"缺席频道省下的检测"被定位探点部分抵消。
|
||||
|
||||
**验收**：`CoveragePlan.verify()` 通过；10/13/16 源 × 定向 1/半数/仅一个全向 × 边界朝外压力
|
||||
场景全部全清；请求数、日志体积仍满足 1200 s / 2 MB 预算。
|
||||
|
||||
**风险**：数值采样证书弱于解析证明。缓解：(a) 保留解析七站证明用于 Q3；
|
||||
(b) 证书采样分辨率与裕量写入 summary 并随日志归档；(c) 保留 10% 的额外"保险点"预算，
|
||||
若演练观测到任何漏检立即回退到 49 点表。
|
||||
|
||||
---
|
||||
|
||||
### P0-3 第四问：位置×朝向联合信念与可见性选点
|
||||
|
||||
**现状**：定向源只被当作"候选空间集合"，`no_signal` 只用于累计 `blocked_probes`
|
||||
（`planner/__init__.py:663-677`），选点沿固定 45° 阶梯试探；梯子用尽后调用
|
||||
`_fallback_clear`，对候选外包多边形做 20 m 网格覆盖逐格 `/clear`（≤400 格、≤1200 s、≤400 请求）。
|
||||
运行历史显示这正是第四问的失败点：定向源 2/6 清除，4/4 漏检全是定向。
|
||||
|
||||
**方案**：为第四问频道维护 **(x, y, heading) 联合可行集**，把"看不看得见"变成可推理的量：
|
||||
|
||||
- 表示：`JointTensor` —— 复用 Q3 的占用网格，每格附加 24 个朝向 bin 的可行性位掩码
|
||||
（uint8 位域，20 频道 × 6.1 万格 × 3 B ≈ 3.7 MB，可接受）。
|
||||
- 更新规则（全部保守，仅删"必然看不见"的格）：
|
||||
- `direction`@s：位置删 `>1500`、角向删 `|gap| > δ_eff + 角裕量`；朝向删
|
||||
"以该格为源、朝向不覆盖 s" 的半盘（保留 ±90° 含边界）。
|
||||
- `no_signal`@s：对 `|g−s| ≤ 1000` 的格，朝向**只能**落在背向 s 的半盘；
|
||||
对 `1000 < |g−s| ≤ 1500` 的格，朝向要么背向 s，要么源实际半径 < |g−s|（保持不确定）；
|
||||
对 `|g−s| > 1500` 的格不删。
|
||||
- `near`@s：位置收缩到 5 m 盘，朝向置为全向（近场无遮挡语义）。
|
||||
- 决策：`prob_detect(channel, reader)` = 该点能看到该频道任一可行 (p,h) 的概率质量；
|
||||
选点直接最大化 `prob_detect / (移动秒数 + 6)`，即**保证有信号的信息增益/秒**。
|
||||
- 定位收敛：位置由多条 `direction` 的角域交会收缩（与 Q3 相同）；朝向在 2 次
|
||||
"一次被遮挡 + 一次可见"后通常已被压到很窄的楔形，此时可**主动选择必然可见的探点**，
|
||||
用一个正常角域证书结束，而不再进入 400 格盲清。
|
||||
- 后备清除**保留**（`fallback_clear_cells` 已有格数与预算双硬顶），但目标变为
|
||||
"只在证书无法建立时触发"，并在 summary 中把触发次数作为回归指标。
|
||||
|
||||
**预期收益（未实测）**：定向频道的探点次数与后备清除触发次数大幅下降。参考量级：
|
||||
后备清除上限 400 格 × 3 s = 1200 s + 400 请求/频道；`docs` 记录的 Q4 改进版每局后备
|
||||
尝试 616/4 局 ≈ 154 次。保守估计 **−1500 ~ −3000 s/局**。
|
||||
|
||||
**验收**：10 源 3 定向场景全清率 100%（现状 referee 仅 2/6 定向）；
|
||||
`fallback_clear_attempts` 相对基线下降 ≥ 70%；定向信道平均检测次数 ≤ 全向信道的 1.5 倍。
|
||||
|
||||
**风险**：朝向均匀先验是工程假设，官方分布未知。缓解：先验只影响选点顺序，
|
||||
不影响任何安全性判定；朝向不确定时自动退化为全向分支（`prob_detect` 取全向值）。
|
||||
|
||||
---
|
||||
|
||||
### P0-4 感知层向量化与并行（研发运行速度）
|
||||
|
||||
**现状**（F6）：`expected_survival` 占单局 CPU 的 72%，2808 次调用 × (7 代表点 × 800 子采样)
|
||||
≈ 1570 万次纯 Python 距离/角度运算；`apply_measure` 另有 20%，其中 `_kill_outside_wedge`
|
||||
每次遍历全部幸存格（498 万次 `angular_gap`）。
|
||||
|
||||
**方案**：
|
||||
|
||||
1. `belief/occupancy.py` 用 `numpy` 布尔/整数张量替换 `set[int]`：
|
||||
每频道 248×248 uint8（≈61 KB），20 频道 ≈ 1.2 MB；盘/楔删除用切片 + 布尔掩码，
|
||||
粗格索引用 `np.add.reduceat`；`numpy` 缺失时回退现有纯 Python 实现（测试保证两条路径同结果）。
|
||||
2. 采样评分向量化：把 `subsample` 与 `representatives` 预转 `(n,2)` 数组，
|
||||
一次广播算完距离、角度、角向 gap，`expected_survival` 由 5600 次标量循环变成 2–3 条向量指令。
|
||||
3. 站点级编译缓存：`(station, channel, version)` → 掩码结果，`version` 不变直接复用；
|
||||
当前 `_skip_cache` 只缓存"必 no_signal"布尔，可扩展到整块删除掩码。
|
||||
4. MEC 增量维护：当前 `mec()` 每次版本变化重算全部幸存点（最多 6 万点）；改为
|
||||
版本变化时只对新增删除点做包围圆增量更新，或按 1/8 子采样给出候选、证书判定用精确值。
|
||||
5. 批量基准并行化：`bench/runner.py` 用 `concurrent.futures.ProcessPoolExecutor`
|
||||
按场景分发（每进程独立 `LocalSimulator`，真值仍只进测试端），Mac M 系列 8 核可得 ~6–8×。
|
||||
6. 观测/遥测：单局 summary 写入 `compute_wall_s`、`profile_top`（可选 `--profile`），
|
||||
纳入基准表的必填列，防止"虚拟时间变快、墙钟悄悄变慢"。
|
||||
|
||||
**实测收益（2026-09-12）**：单局计算 **3.10 → 1.00 s（3.08×）**，最坏 5.84 → 1.30 s；
|
||||
20 场景基准集 12.2 → 3.9 s。虚拟时间与动作序列**逐位一致**（纯 Python 回退路径保留，
|
||||
`MCM26B_NO_NUMPY=1` 可强制）。未达 ≤0.8 s 的目标：剩余开销在
|
||||
`apply_measure` 的网格删除与 `ChannelParticles.__init__`，需要 P0-4b（表示层张量化）。
|
||||
|
||||
**验收**：`tests/test_particles.py`、`tests/test_bayes_loop_q3.py` 全过；
|
||||
纯 Python 与 numpy 两条路径在同一动作日志下产出逐位相同的 summary（`actions_sha256` 相同）；
|
||||
单局 `compute_wall_s` ≤ 0.8 s。
|
||||
|
||||
---
|
||||
|
||||
### P1-1 清除点选在 C₂₀(K) 内离当前位置最近处
|
||||
|
||||
现状 `_near_clear_target` 只沿"当前位置 → 候选圆心"方向平移，平移量受
|
||||
`limit − radius` 限制，是被证明的一维特例。一般解是
|
||||
`C₂₀(K) = ∩_{g∈K} B(g, 20)`，取其内离当前位置最近的点：
|
||||
- 对凸多边形候选集，`C₂₀(K)` 仍是凸集（若干圆盘之交），可用"到当前点的最近点"的
|
||||
简单迭代/投影求得；
|
||||
- 若 `C₂₀(K)` 非空，则从该点清除的移动代价最小，且三角不等式保证仍在 20 m 内。
|
||||
|
||||
**预期收益**：Q3 单局清除移动 3668 m（734 s）中回收 20–35%（**−150 ~ −260 s**）；
|
||||
Q4 同步受益。
|
||||
**风险**：`C₂₀` 空时退回现有 MEC 圆心逻辑（已实现）。
|
||||
|
||||
---
|
||||
|
||||
### P1-2 覆盖几何参数改为"离线搜索 + 运行期校验"
|
||||
|
||||
- Q3：`station_ring_radius_m` 由 1159.7 的可证明下界 1122.9 重算，并在 `validate_seven_coverage`
|
||||
中同时校验 **PF 网格覆盖**（现行文档已记录 1159.7 与 Optuna 建议值差 3.3 m 的取舍）。
|
||||
收益很小（≈44 s），但把"设计性质 vs 1.7% 均值差异"的取舍写成可复算脚本，而不是口口相传。
|
||||
- Q4：见 P0-2，覆盖集与巡回联合优化（在等价覆盖解中选巡回最短者，
|
||||
预期可在 23.6 km 基础上再降 5–10%）。
|
||||
- 所有覆盖计划产物落 `output/<problem>/coverage/<version>.json` + 证书，纳入版本管理。
|
||||
|
||||
---
|
||||
|
||||
### P1-3 评估与遥测重构（消除 B6）
|
||||
|
||||
1. **冻结基准集**（`bench/suites/`，一题一份、版本化、写入 `manifest.json`）：
|
||||
- Q3：10/13/16 源 × 8 个种子 + 2 个圆周边界场景（`r_eff=1000`，扇区接缝）；
|
||||
- Q4：10 源 3 定向、13 源 6 定向、16 源 8 定向 × 每种 3 个种子 + 朝外边界压力场景；
|
||||
- 场景与真值只进模拟器与校验器，策略端看不到（真值隔离不变量保持不变）。
|
||||
2. **统一 runner + 逐局 artifact**：`manifest.json` / `results.json` / `aggregate.{json,csv}` /
|
||||
`runs/<scene>/<variant>/summary.json` + `session.jsonl`，任何一局无效则 `all_valid=false` 且非零退出。
|
||||
3. **指标口径去重**：目前 `avg_locate_clear_time_s ≡ virtual_time_s`（单源评估集下二者必然相等），
|
||||
必须在多源基准集上重新定义并标注：
|
||||
`T_total`（整场虚拟时间）、`T_accounted`（首次证明找全时刻）、`T_last_clear`、
|
||||
`T_total / cleared`、`T_total / truth`。
|
||||
4. **运行历史持久化**：`output/` 目前不纳入 git，导致 `output/rl/review-20260911/`、
|
||||
`output/q3/optimization-20260911/` 等证据在 revert 后无法追溯。
|
||||
本轮起把**基准集的 summary/aggregate/manifest（压缩，不含大日志）**纳入版本管理，
|
||||
官方 jlog/日志仍按题面要求单独归档。
|
||||
|
||||
---
|
||||
|
||||
### P1-4 安全阀与退出可解释性
|
||||
|
||||
| 阀门 | 现状 | 改进 |
|
||||
| --- | --- | --- |
|
||||
| `persistent_search` | 默认已关闭，但没有任何"这会浪费多少"的提示；历史上已造成一次 346 170 s（98.84%）的无效演练 | 显式开启时输出预估上限（未清除频道数 × 站点数 × 6 s）并要求二次确认；`summary` 记录 `redundant_rescan_measures` |
|
||||
| `virtual_reserve_s` | 10 000 s 固定 | 改为 `max(10 000, 2 × 最远单动作上界)`，并在接近时把 `stop_reason` 与剩余预算写入 summary |
|
||||
| 完成判定 | `completed = 无中断 ∧ (清除数≥16 ∨ 每频道 CLEARED/ABSENT)` | 增加 `completion_basis ∈ {count_cap, per_channel_proof}` 字段，避免把"数量上界"与"逐频道证明"混为一谈 |
|
||||
| 缺席证明 | 覆盖账本 + PF 双通道 | summary 增加 `absence_evidence[channel] ∈ {pf_empty, coverage, count_cap}`，供论文与审计逐条追溯 |
|
||||
|
||||
---
|
||||
|
||||
### P2-1 选点评分的解析化/查表化
|
||||
|
||||
`expected_survival` 的物理含义可用解析近似替代：对给定测点 q 与候选集，
|
||||
`E[log 幸存占比]` 的一阶近似只依赖 (候选集到 q 的距离分布, 角向展布)。
|
||||
先在离线基准上验证解析近似与采样评分选出的点一致率 ≥ 90%，再切默认实现。
|
||||
预计再省 30–50% 的评分开销（P0-4 之后已不是瓶颈，列入 P2）。
|
||||
|
||||
---
|
||||
|
||||
### P2-2 是否重启学习类方法的门控条件
|
||||
|
||||
只有在**下列四条同时满足**时才考虑把 ML 重新引入，且只允许替换"任务排序"这一个部件：
|
||||
|
||||
1. P0-1/P0-2/P0-3 全部落地并通过基准集验收；
|
||||
2. 手写基线仍有 ≥ 10% 的可测改进空间，且已定位到具体的排序子问题；
|
||||
3. 训练与部署共用同一控制器模块，并有逐状态一致性测试（旧 HRL 的 13 000 行里最有价值的
|
||||
就是这条纪律：`HRLControllerMixin` 被 env 与 runtime 逐字节共用）；
|
||||
4. 评估集包含多源与定向场景（现役 20 场景单源评估集不足以支撑任何结论）。
|
||||
|
||||
---
|
||||
|
||||
## 4. 实施路线图
|
||||
|
||||
| 阶段 | 内容 | 交付物 | 验收门槛 | 依赖 |
|
||||
| --- | --- | --- | --- | --- |
|
||||
| **S1（P0，2–3 天）** | P0-2 最小命中集 + P0-4 并行基准；P1-3 冻结基准集 + manifest；P1-4 安全阀 | `guarantee/cover.py`、`bench/runner.py`、`bench/suites/*`、`docs/` 更新 | Q4 巡回 ≤ 25 km；基准集并行跑完 ≤ 60 s；全部 `valid` | 无 |
|
||||
| **S2（P0，2–3 天）** | P0-1 融合巡回（Q3）；P1-1 清除点 | `plan/schedule.py`、`plan/score.py`、`Episode` 组合化重构 | Q3 均值 ≤ 2700 s、最大 ≤ 4000 s；移动 −25% | S1 的 runner |
|
||||
| **S3（P0/P1，3–5 天）** | P0-4 信念层 numpy 化；P0-3 联合信念 + 可见性选点（Q4） | `belief/occupancy.py`、`belief/joint.py`、`plan/policy.py` | 单局计算 ≤ 0.8 s；Q4 定向全清 100%、后备触发 −70% | S2 的接口 |
|
||||
| **S4（P1/P2，2–3 天）** | P1-2 覆盖参数搜索固化；P2-1 评分解析化；论文数据表 | 覆盖计划证书、`docs/paper` 数据 | 与 S3 结果配对不退化 | S3 |
|
||||
| **S5（演练）** | 官方演练：Q3 用全向、Q4 用混合；先实测单请求延迟与总请求数，核对 1200 s / 2 MB | 官方日志与报告 | 全清 + 正常退出 + 计时复算一致 | 团队排期 |
|
||||
|
||||
**顺序约束**：S1 的基准集必须先于任何算法改动落地，否则无法配对；否则"更快"不可证。
|
||||
|
||||
---
|
||||
|
||||
## 5. 验收与度量
|
||||
|
||||
### 5.1 有效局定义（沿用现有基准脚本，不得放宽）
|
||||
|
||||
```
|
||||
valid := 全部真实源被清除
|
||||
∧ 被判缺席的频道都与真值一致（无误判）
|
||||
∧ 正常 /exit（exit_reason == user_exit）
|
||||
∧ 零算法异常（anomalies == []）
|
||||
∧ 逐动作虚拟时间复算与服务器返回逐微秒一致
|
||||
```
|
||||
|
||||
### 5.2 必报指标（每个变体、每个场景，落盘 + 汇总）
|
||||
|
||||
- 虚拟时间：`T_total`、`T_accounted`、`T_last_clear`、`T_total/cleared`，以及
|
||||
均值/中位/最大/P90 与 **< 4000 s 场次比例**；
|
||||
- 动作：`measures`、`clears`、`failed_clears`、`fallback_clear_attempts`、
|
||||
`redundant_rescan_measures`、`total_requests`；
|
||||
- 移动：`move_distance_m`，以及 move/action/switch 三分项占比；
|
||||
- 墙钟：`compute_wall_s`、请求吞吐（ms/请求）、批量基准总时长、训练 steps/s（若涉及）；
|
||||
- 配对比较：同场景同误差种子下 `1 − T_variant / T_baseline` 的均值、
|
||||
更快场数、以及对场景做 10 000 次 bootstrap 的 95% 区间（参照 `summarize_rl_revision.py` 的做法）。
|
||||
|
||||
### 5.3 回归
|
||||
|
||||
`uv run pytest`（当前 232 项）必须全过；新增：
|
||||
- `CoveragePlan.verify()` 的正/负例（故意删一个点必须被检出）；
|
||||
- 联合信念的真源保留性质测试（随机 (p,h) × 随机观测序列，真源 (p,h) 永不被删）；
|
||||
- 融合巡回的"每源至少一条站点方位线"性质测试；
|
||||
- 逐位一致性测试：numpy 后端与纯 Python 后端在同一动作序列下 summary 完全相同。
|
||||
|
||||
---
|
||||
|
||||
## 6. 风险与明确不承诺的部分
|
||||
|
||||
### 6.1 风险登记
|
||||
|
||||
| 风险 | 影响 | 缓解 |
|
||||
| --- | --- | --- |
|
||||
| 最小命中集证书依赖数值采样，官方分布未知 | 极端 (p,h) 漏检 → 漏源 | 保留解析七站证明（Q3）；Q4 证书采样分辨率/裕量写入 summary；保留 10% 保险点预算与 49 点回退开关 |
|
||||
| 站点三角化在 0.72% 源位置上只有 1 站在射程内 | 这些场景仍需专用探点 | `prob_detect` 触发回退路径，保留现有 `_select_point` |
|
||||
| 朝向先验为工程假设 | 选点次优 | 先验只影响顺序；不确定时退化为全向；安全性由 20 m 证书 + 覆盖证明负责 |
|
||||
| numpy 化引入浮点路径差异 | 真源保留性质被破坏 | 两条后端同时保留并对拍；保持整数微秒复算计时 |
|
||||
| 并行基准引入进程/端口/临时目录问题 | 基准不稳定 | 每进程独立内存回环；不共用端口；`manifest` 记录随机种子 |
|
||||
|
||||
### 6.2 不承诺的部分
|
||||
|
||||
- **第四问 4000 s**：10 源场景下，仅"发现 + 缺席证明"就约需
|
||||
最小命中集巡回 4719 s + 缺席检测 ≈1900 s ≈ 6600 s，加之定位清除，4000 s 不可达。
|
||||
若必须逼近该线，需要改变停止条件（例如接受 `unresolved_channels` 而非逐频道缺席证明），
|
||||
那是**成绩口径的取舍**，必须先与题面评分方式核对，本轮不作为代码默认行为。
|
||||
- 本文所有收益数字都是**自建模拟器上的估计或本次复跑观测**，不是官方成绩；
|
||||
官方分布、r_eff 分布、误差场分布仍是测试假设。
|
||||
- 不声称 P0-1/P0-3 已在官方演练验证——需新的演练记录。
|
||||
|
||||
---
|
||||
|
||||
## 附录 A 复现命令与本次新增实验
|
||||
|
||||
```bash
|
||||
# 1) 基线配对（本次复跑：bayes 3707.3 s vs seven_batch 5581.8 s，局部 −32.1%）
|
||||
uv run python scripts/benchmark_q3_bayes.py --seeds 0,1 --out /tmp/mcmopt/q3
|
||||
|
||||
# 2) 运行历史计时分解（已入库）：任意会话日志 → 移动/检测/换频分相 + 重复复扫检测
|
||||
uv run python scripts/analyze_run_timing.py session.jsonl --plan seven
|
||||
uv run python scripts/analyze_run_timing.py referee-data/sessions/BBM9-HZG2-77N5-NKKC/log.jsonl --plan seven
|
||||
uv run python scripts/analyze_run_timing.py referee-data/sessions/6X58-XQ7U-CJ24-JQQR/log.jsonl --plan 49
|
||||
# 实测输出（与服务器记录最大偏差 0.000000 s，证明复算口径正确）：
|
||||
# session.jsonl 350227.692 s，移动 82.3%，检测 10323 次，重复率 96.4，
|
||||
# 末次清除后仍花 346169.9 s / 10225 次空复扫
|
||||
# BBM9 (Q3) 3134.643 s，移动 75.9%，探点 21 次 / 1138.7 s
|
||||
# 6X58 (Q4) 3251.021 s，移动 78.3%，检测 112 次
|
||||
|
||||
# 3) 第四问覆盖证书与最小命中集（已入库）
|
||||
uv run python scripts/verify_q4_cover.py --out output/q4/cover-certificate.json
|
||||
# 实测输出：目标空间 43032 个 (p,h)
|
||||
# 现役 49 点方格：缺口 0，NN 巡回 36.4 km（7280 s 移动）
|
||||
# 贪心最小命中集：31 点，缺口 0，NN 巡回 22.9 km（4587 s 移动）
|
||||
|
||||
# 4) 感知层剖面（单局 6.04 s 计算中 expected_survival 占 ~72%）
|
||||
python -m cProfile -s cumulative <单局驱动脚本>
|
||||
```
|
||||
|
||||
> 第 2、3 项已作为常驻工具入库，使"虚拟时间花在哪"与"覆盖集是否仍然合法"
|
||||
> 成为可随时重跑的回归项；第 1、4 项仍是一次性分析，建议在 S1 阶段并入
|
||||
> `bench/runner.py` 的必报指标（`compute_wall_s`、`profile_top`）。
|
||||
|
||||
## 附录 B 数字出处索引
|
||||
|
||||
| 数字 | 出处 |
|
||||
| --- | --- |
|
||||
| 移动 80.50% / 16.78% / 2.71%（Q3） | `referee-data/sessions/{BBM9,RM7Y,VQKB}/log.jsonl` 按 `verify_q3_practice.py` 模型复算 |
|
||||
| 移动 82.30%（官方 J2WH） | `session.jsonl` 求和 |
|
||||
| 346 169.9 s 空复扫 | `session.jsonl` `/exit` 时刻 350 227.691846 s 减去 12 源首轮清除的 4 057.839 s |
|
||||
| Q4 定向 2/6、漏检 4/4 全为定向 | `referee-data/sessions/{6X58,BAU8}/meta.json` + `log.jsonl` |
|
||||
| bayes 3707.3 / seven 5581.8 | 本次复跑 `benchmark_q3_bayes.py --seeds 0,1` |
|
||||
| 3535.9 s / 109 检测 / 6960 m 巡回 / 5741 m 探点 | 本次复跑计时分解 |
|
||||
| 13652.1 s / 560 检测 / 51 356 m（Q4） | 本次复跑单局 |
|
||||
| `expected_survival` 72% | 本次 `cProfile` 单局 |
|
||||
| HRL 27.0 steps/s、4007.9 s、109 776 步 | `output/hrl/ui/q3-main/q3.json` + `*.monitor.csv` |
|
||||
| bayes 比 HRL 快 4.3–5.0%、94% 差距为移动 | `output/hrl/ui/q3-main/eval/at-final/aggregate.json` + `runs/*/summary.json` |
|
||||
| 站点三角化 99.28%、交会角 5 分位 58.5° | 本次离线采样 20 000 点 |
|
||||
| 49 点 vs 30 点覆盖集 | 本次贪心击中集计算（附录 A 第 5 项） |
|
||||
@@ -1,4 +0,0 @@
|
||||
# logs/
|
||||
|
||||
正式测试/演练导出的 JSONL 与汇总日志放置目录。本地 P1 训练/评测日志实际写入
|
||||
`output/hrl/...`；本目录用于最终支撑材料归档，不修改文件名。
|
||||
@@ -1,4 +0,0 @@
|
||||
# models/
|
||||
|
||||
最终冻结模型归档目录。训练脚本默认把模型写到 `--model` 指定路径；
|
||||
P1 smoke 产物见 `output/hrl/p1-smoke-time/`。正式提交前把冻结模型复制到本目录并保留同名 `.json` 元数据。
|
||||
+118
-23
@@ -1,29 +1,124 @@
|
||||
# project/ 薄兼容层
|
||||
# 1-4 问「最优实现 / 基线实现」对比框架
|
||||
|
||||
题目 Prompt 要求 `project/` 目录树；本仓库的真实算法保持唯一一份，
|
||||
位于 `src/mcm26b`、`scripts/`。本目录只做 re-export/转发，不复制实现。
|
||||
本目录**不是**比赛运行时代码，而是把四问的生产实现与独立基线放在同一口径下
|
||||
配对对比、生成可写进论文的"算法对比"证据。运行时代码仍只有 `src/mcm26b`，
|
||||
`project/` 只依赖它和测试侧内存回环，不反向污染求解入口。
|
||||
|
||||
| Prompt 路径 | 真实实现 |
|
||||
|---|---|
|
||||
| `env/simulator_client.py` | `src/mcm26b/protocol/client.py` |
|
||||
| `env/local_env.py` | `src/mcm26b/hrl/hierarchical_env.py`, `low_level_env.py` |
|
||||
| `env/particle_filter.py` | `src/mcm26b/particles.py` |
|
||||
| `policies/high_level_ppo.py` | `src/mcm26b/hrl/policy.py` |
|
||||
| `policies/low_level_ppo.py` | `src/mcm26b/hrl/policy.py`, `low_level_env.py` |
|
||||
| `train/curriculum.py` | `src/mcm26b/hrl/curriculum.py` |
|
||||
| `train/domain_randomization.py` | `src/mcm26b/hrl/curriculum.py` + 配置包装 |
|
||||
| `eval/evaluate.py` | `scripts/benchmark_hrl.py` |
|
||||
| `eval/ablation.py` | `scripts/ablate_hrl.py`（P2） |
|
||||
| `baselines/heuristic.py` | `src/mcm26b/planner/hrl_loop.py` 启发式降级 |
|
||||
| `baselines/end_to_end_ppo.py` | 显式未实现；单源局部 PPO 不冒充 E2E |
|
||||
## 1. 设计原则
|
||||
|
||||
使用示例：
|
||||
| 角色 | 含义 | 约束 |
|
||||
| --- | --- | --- |
|
||||
| **最优实现** | 复用 `src/mcm26b` 中已由测试固化的生产实现 | 不复制算法，只做 I/O 适配 |
|
||||
| **基线实现** | 同题下更朴素、但能独立跑通的对照 | 不得共享最优实现的关键步骤 |
|
||||
| **独立参考** | 与分析算法正交的数值/枚举标准 | 用于判定谁对谁错，而非自证 |
|
||||
|
||||
```python
|
||||
from project.env.local_env import make_local_env
|
||||
env = make_local_env(problem=3, stage=1, seed=7)
|
||||
obs, info = env.reset(seed=7)
|
||||
- 第三、四问走 `LocalSimulator` 内存回环，与官方 HTTP 协议同一边界；
|
||||
真值只在评测机侧，策略端只看响应，因此"全清率/缺席误判"是可信的。
|
||||
- 闭环有效性判定沿用项目口径：**全清 ∧ 无缺席误判 ∧ 正常 `/exit` ∧ 零异常
|
||||
∧ 逐微秒计时复算一致**，任何一项不满足该局记为无效，失败不静默丢样本。
|
||||
- 所有对比都按 `scene_id` 配对，汇总同时给均值、中位、最大与相对基线的
|
||||
配对变化，避免"均值好看但逐局更差"。
|
||||
|
||||
## 2. 目录结构与职责
|
||||
|
||||
```text
|
||||
project/
|
||||
├── README.md 本文件
|
||||
├── common.py 公共层：方法描述、指标聚合、结果落盘、闭环装配
|
||||
├── run.py 统一入口：一次跑完四问并生成 results/
|
||||
├── results/ 自动生成：q1.md/json … q4.md/json + summary.md
|
||||
├── q1/ 问题一：交会定位区域直径与直径圆覆盖
|
||||
│ ├── optimal.py 最优：半平面交 + 顶点对精确直径 + Welzl MEC + 覆盖反例
|
||||
│ ├── baseline.py 基线：网格采样 + 轴对齐包围盒对角线代理
|
||||
│ ├── reference.py 参考：大框 Sutherland–Hodgman 半平面裁剪 + 精确直径/MEC
|
||||
│ ├── cases.py 固定示例（examples/q1）+ 随机交会算例
|
||||
│ └── compare.py 同案配对：状态/直径/覆盖/耗时
|
||||
├── q2/ 问题二：第二检测点选择
|
||||
│ ├── optimal.py 最优：保证接收盘内最小最坏后验半径（生产选点器）
|
||||
│ ├── baseline.py 基线：垂直 1000 m 最大交会角 / 候选集 MEC 圆心
|
||||
│ ├── reference.py 参考：保证接收盘内稠密网格枚举
|
||||
│ ├── region.py K1 = Ω ∩ 角域 ∩ B(s,1500) 统一构造
|
||||
│ ├── cases.py 随机首次示向度算例
|
||||
│ └── compare.py 统一评估器：保证接收、最坏后验、移动代价
|
||||
├── q3/ 问题三：全向源闭环
|
||||
│ ├── optimal.py 最优：bayes（粒子滤波 + 信息增益 + 滚动 2-opt）
|
||||
│ ├── baseline.py 基线：grid（49 点网格）/ seven（七点批量）
|
||||
│ └── compare.py 冻结基准集 core 配对对比
|
||||
└── q4/ 问题四：含定向源闭环
|
||||
├── optimal.py 最优：directional（联合信念 + 后备清除 + 定向覆盖计划）
|
||||
├── baseline.py 基线：把问题三策略直接搬用（bayes / grid）
|
||||
└── compare.py 冻结基准集 core+boundary 配对对比
|
||||
```
|
||||
|
||||
P1 当前交付：`benchmark_hrl.py`、`train_hrl_high.py`、CLI `--strategy hrl`、
|
||||
`--q4-strategy hrl`。P2 的 BC/Optuna/消融在 smoke 通过后另开。
|
||||
## 3. 各问对比设计
|
||||
|
||||
### 问题一（`q1/`）
|
||||
|
||||
- **最优**：`mcm26b.geometry.localization.solve`，半平面交求凸多边形，顶点对
|
||||
枚举直径，Welzl 最小包围圆，并输出直径圆覆盖判断、圆外顶点与最大超出距离。
|
||||
- **基线**：固定大框网格采样，直径取**包围盒对角线**、覆盖取"包围盒中心 +
|
||||
代理直径圆"。它在斜/细长区域上系统性高估直径，并对任意包围盒都几乎恒判覆盖。
|
||||
- **参考**：从 ±1e7 m 大框出发逐半平面 Sutherland–Hodgman 裁剪，独立实现
|
||||
直径暴力枚举与 1/2/3 点支撑圆暴力最小包围圆。与最优实现共享 0 个内部步骤。
|
||||
- 指标：状态判定、直径绝对误差、直径圆覆盖准确率、运行耗时。
|
||||
|
||||
### 问题二（`q2/`）
|
||||
|
||||
- 候选集 `K1 = Ω ∩ W(s,θ±δ_eff) ∩ B(s,1500)`，三个方法共用同一次构造。
|
||||
- **最优**：保证接收盘 `B(MEC 圆心, 1000−ρ)` 内环形采样，最小化"目标边界点 ×
|
||||
读数区间端点"的最坏后验半径，移动时间同分决胜；保证接收严格，采样是启发式。
|
||||
- **基线**：① 垂直首次示向度 1000 m 的"90° 交会点"——忽略接收保证，真源在
|
||||
角域远端时必然 `no_signal`；② 候选集 MEC 圆心——保证接收但交会角可能最差。
|
||||
- **参考**：保证接收盘内 9×9 网格枚举同一目标函数，检验生产采样接近网格最优。
|
||||
- 指标：保证接收率、最坏后验包围半径、收缩量、移动时间。
|
||||
|
||||
### 问题三（`q3/`）
|
||||
|
||||
- **最优** `bayes`：网格粒子滤波幸存集 + 期望信息增益选点 + 滚动 2-opt 任务排序。
|
||||
- **基线** `grid`：49 点（700 m 步长）网格发现 + 就近服务；`seven`：七点解析
|
||||
覆盖 + 同站批量扫频，作为"覆盖证明 + 批量调度"的中间对照。
|
||||
- 冻结基准集 `harness.suites`（种子/源数版本化），逐局校验全清与计时复算。
|
||||
- 指标：全清率、均值/中位/最大虚拟时间、检测数、请求数、位移、墙钟。
|
||||
|
||||
### 问题四（`q4/`）
|
||||
|
||||
- **最优** `directional`：31 点定向覆盖计划 + 朝向遮挡推理 + (位置×朝向) 联合
|
||||
信念 + ≤20 m 有限圆盘后备清除。
|
||||
- **基线**：把问题三策略原样搬用——`bayes` 的七站全向缺席证明会把朝外定向源
|
||||
误判为 ABSENT（"快而错"）；`grid` 的 49 点覆盖对定向源发现完备，但无后备清除，
|
||||
定位停滞只能停在 DETECTED（"稳而不全"）。
|
||||
- 冻结基准集 `core+boundary`，其中 `boundary` 场景全部为背离圆心发射的边界定向
|
||||
源，是最不利工况。
|
||||
- 指标：全清率、缺席误判数、虚拟时间、后备清除次数、墙钟。
|
||||
|
||||
## 4. 运行方式
|
||||
|
||||
```bash
|
||||
uv run python -m project.run # 四问全部，写入 project/results/
|
||||
uv run python -m project.run --question 1 # 只跑问题一
|
||||
uv run python -m project.run --quick # 缩小随机算例/基准集规模
|
||||
```
|
||||
|
||||
> `project/` 用到 `numpy`（网格参考解）。它随 dev 依赖组（optuna）安装；
|
||||
> 只跑正式求解入口 `mcm26b` 不需要它。
|
||||
|
||||
## 5. 结果文件
|
||||
|
||||
| 文件 | 内容 |
|
||||
| --- | --- |
|
||||
| `results/q1.md` / `q1.json` | 固定示例 + 随机算例的逐案明细与汇总 |
|
||||
| `results/q2.md` / `q2.json` | 逐案第二点位置、保证接收、最坏后验与汇总 |
|
||||
| `results/q3.md` / `q3.json` | 冻结基准集逐场景明细、逐方法汇总、配对变化 |
|
||||
| `results/q4.md` / `q4.json` | core+boundary 逐场景明细与汇总 |
|
||||
| `results/summary.md` | 四问核心指标总览 + 方法映射 + 目录结构 |
|
||||
|
||||
## 6. 口径与边界（写论文时必须一并声明）
|
||||
|
||||
- 问题一参考解与最优实现都是精确算法；固定示例的解析期望另有
|
||||
`tests/test_geometry*.py` 固化，本框架在其上补齐"朴素基线错在哪"。
|
||||
- 问题二"最坏后验半径"是**对候选集外包多边形的启发式评估**，不是连续问题
|
||||
的全局最优证明；生产实现与参考网格共享该目标函数，只比较搜索质量。
|
||||
- 问题三、四的随机分布（源位置均匀、误差场逐点固定等）是**测试假设**，
|
||||
题面未规定；正式成绩以官方模拟器演练/正式测试为准。
|
||||
- 基线不是稻草人：`q2` 的 MEC 圆心、`q3` 的 `seven`、`q4` 的 `grid` 都是
|
||||
能独立跑通、在部分场景有效的真实方法；它们暴露的是"缺了哪一步"。
|
||||
|
||||
+16
-2
@@ -1,4 +1,18 @@
|
||||
"""题目 Prompt 所需目录树的薄兼容层。
|
||||
"""1-4 问"最优实现 / 基线实现"统一对比框架（非运行时依赖，仅用于分析与论文）。
|
||||
|
||||
真实实现仍位于 ``src/mcm26b``；本包只做 re-export / CLI 转发，避免两份算法逻辑。
|
||||
本包不改动正式求解入口 ``mcm26b``，只做三层组织：
|
||||
|
||||
- ``project.q1`` … ``project.q4``：四个问题各自的
|
||||
``optimal.py``（生产最优实现，直接复用 ``mcm26b`` 内核）、
|
||||
``baseline.py``（朴素基线，参照组）、``compare.py``（同题配对对比）；
|
||||
- ``project.common``：方法描述、指标聚合、结果落盘、离线闭环装配；
|
||||
- ``project.run``：统一入口，一次跑完四问并生成 ``project/results/``。
|
||||
|
||||
运行方式（仓库根目录）::
|
||||
|
||||
uv run python -m project.run # 四问全部
|
||||
uv run python -m project.run --question 3 # 只跑第三问
|
||||
uv run python -m project.run --quick # 缩小随机场景数
|
||||
"""
|
||||
|
||||
__all__ = ["common"]
|
||||
|
||||
@@ -1,3 +0,0 @@
|
||||
from .heuristic import HeuristicHRL, run_heuristic
|
||||
|
||||
__all__ = ["HeuristicHRL", "run_heuristic"]
|
||||
@@ -1,16 +0,0 @@
|
||||
"""端到端 PPO 基线占位。
|
||||
|
||||
按本轮确认：当前只交付局部 PPO 基线；raw-observation/raw-action 的真正
|
||||
端到端 PPO 不在 P0+P1 范围内，因此显式标记未实现，避免把单源局部选点
|
||||
PPO 误称为端到端 baseline。
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
|
||||
def train_end_to_end_ppo(*args, **kwargs):
|
||||
raise NotImplementedError(
|
||||
"本轮只交付局部 PPO 基线；真正的端到端 PPO 基线标记为未实现，"
|
||||
"需单独立项定义原始观测、动作与安全执行边界。")
|
||||
|
||||
|
||||
__all__ = ["train_end_to_end_ppo"]
|
||||
@@ -1,16 +0,0 @@
|
||||
"""兼容入口：信息增益/TSP/覆盖规则 + 高层启发式调度。"""
|
||||
from mcm26b.planner.bayes_loop import BayesLoop
|
||||
from mcm26b.planner.hrl_loop import HRLLoop
|
||||
|
||||
|
||||
def HeuristicHRL(*args, **kwargs): # pylint: disable=invalid-name # 兼容入口保留题面公开名
|
||||
"""返回无模型的高层 HRL 闭环（heuristic_action 降级路径）。"""
|
||||
kwargs.setdefault("high_policy", None)
|
||||
return HRLLoop(*args, **kwargs)
|
||||
|
||||
|
||||
def run_heuristic(*args, **kwargs):
|
||||
return HeuristicHRL(*args, **kwargs).run()
|
||||
|
||||
|
||||
__all__ = ["HeuristicHRL", "run_heuristic", "BayesLoop", "HRLLoop"]
|
||||
@@ -0,0 +1,308 @@
|
||||
"""对比框架公共层：方法描述、指标聚合、结果落盘、离线闭环装配。
|
||||
|
||||
设计口径
|
||||
--------
|
||||
- **最优实现** = 复用 ``src/mcm26b`` 中已由测试固化的生产实现；
|
||||
- **基线实现** = 同题下更朴素但可独立运行的参照实现（不得共享最优实现的
|
||||
关键步骤），用于量化"生产实现好在哪里、差多少"；
|
||||
- **参考解** = 与分析算法相互独立的数值/枚举标准，用来判定谁对谁错。
|
||||
|
||||
第三、四问是闭环问题，统一走 ``LocalSimulator`` 内存回环（与官方 HTTP
|
||||
协议同一边界），真值只在评测机侧，策略端只看响应。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import statistics
|
||||
from dataclasses import dataclass, replace
|
||||
from pathlib import Path
|
||||
from time import perf_counter
|
||||
|
||||
from mcm26b.config import ProblemConfig
|
||||
from mcm26b.evaluation import recompute_virtual_time
|
||||
from mcm26b.harness import LocalSimTransport, LocalSimulator
|
||||
from mcm26b.protocol.client import SimulatorClient
|
||||
|
||||
ROOT = Path(__file__).resolve().parents[1]
|
||||
RESULTS_DIR = Path(__file__).resolve().parent / "results"
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class Method:
|
||||
"""一个可对比方法：key 用作图表列名，kind 用于区分最优/基线/参考。"""
|
||||
|
||||
key: str
|
||||
name: str
|
||||
kind: str # optimal | baseline | reference
|
||||
note: str = ""
|
||||
|
||||
|
||||
def problem_config(*, persistent_search: bool = False,
|
||||
margin_deg: float | None = None) -> ProblemConfig:
|
||||
"""对比口径配置：默认关闭"扫到限时"的持续复扫（对齐首次找全证明）。"""
|
||||
base = ProblemConfig()
|
||||
kwargs: dict = {"persistent_search": persistent_search}
|
||||
if margin_deg is not None:
|
||||
kwargs["bearing_rounding_margin_deg"] = margin_deg
|
||||
return replace(base, search=replace(base.search, **kwargs))
|
||||
|
||||
|
||||
# ---------------------------------------------------------------- 结果落盘
|
||||
def write_json(path: Path, payload) -> Path:
|
||||
path.parent.mkdir(parents=True, exist_ok=True)
|
||||
path.write_text(json.dumps(payload, ensure_ascii=False, indent=2,
|
||||
allow_nan=False) + "\n", encoding="utf-8")
|
||||
return path
|
||||
|
||||
|
||||
def write_text(path: Path, text: str) -> Path:
|
||||
path.parent.mkdir(parents=True, exist_ok=True)
|
||||
path.write_text(text, encoding="utf-8")
|
||||
return path
|
||||
|
||||
|
||||
# ---------------------------------------------------------------- 指标工具
|
||||
def mean(values) -> float:
|
||||
values = list(values)
|
||||
return round(statistics.fmean(values), 4) if values else 0.0
|
||||
|
||||
|
||||
def median(values) -> float:
|
||||
values = list(values)
|
||||
return round(statistics.median(values), 4) if values else 0.0
|
||||
|
||||
|
||||
def ratio(hit: int, total: int) -> float:
|
||||
return round(hit / total, 4) if total else 0.0
|
||||
|
||||
|
||||
def relative_change(value: float, reference: float) -> float | None:
|
||||
if reference == 0:
|
||||
return None
|
||||
return round(value / reference - 1.0, 4)
|
||||
|
||||
|
||||
def markdown_table(headers: list[str], rows: list[list[str]]) -> str:
|
||||
lines = ["| " + " | ".join(headers) + " |",
|
||||
"| " + " | ".join("---" for _ in headers) + " |"]
|
||||
for row in rows:
|
||||
lines.append("| " + " | ".join(str(cell) for cell in row) + " |")
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
def _num(value, digits: int = 2) -> str:
|
||||
if value is None:
|
||||
return "—"
|
||||
if isinstance(value, bool):
|
||||
return "✔" if value else "✘"
|
||||
if isinstance(value, float):
|
||||
return f"{value:.{digits}f}"
|
||||
return str(value)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------- 第三/四问闭环
|
||||
def run_loop_episode(loop_cls, scene_spec, config: ProblemConfig) -> dict:
|
||||
"""在冻结场景上跑一局闭环，返回逐局指标行（与 ``scripts/run_suite.py`` 同口径）。"""
|
||||
sources = scene_spec.sources(config) # 真值：仅测试端可见
|
||||
simulator = LocalSimulator(sources, robot_id="COMPARE", config=config,
|
||||
error_seed=scene_spec.error_seed)
|
||||
client = SimulatorClient("COMPARE", config=config,
|
||||
transport=LocalSimTransport(simulator))
|
||||
start = perf_counter()
|
||||
loop = loop_cls(client, config)
|
||||
summary = loop.run()
|
||||
compute_wall_s = perf_counter() - start
|
||||
report = recompute_virtual_time(client.log.action_events())
|
||||
|
||||
truth = sorted(source.channel for source in sources)
|
||||
cleared = sorted(summary["cleared_channels"])
|
||||
absent = sorted(summary["absent_channels"])
|
||||
misjudged = sorted(set(absent) & set(truth))
|
||||
valid = (cleared == truth and not misjudged
|
||||
and summary["exit_reason"] == "user_exit"
|
||||
and not summary["anomalies"]
|
||||
and not report["mismatches"])
|
||||
return {
|
||||
"scene_id": scene_spec.scene_id,
|
||||
"problem": scene_spec.problem,
|
||||
"kind": scene_spec.kind,
|
||||
"sources": len(truth),
|
||||
"directional": scene_spec.directional,
|
||||
"cleared": len(cleared),
|
||||
"cleared_ratio": ratio(len(cleared), len(truth)),
|
||||
"cleared_matches_truth": cleared == truth,
|
||||
"absent_misjudged": len(misjudged),
|
||||
"virtual_time_s": round(summary["virtual_time_s"], 3),
|
||||
"accounted_virtual_time_s": (
|
||||
round(summary["accounted_virtual_time_s"], 3)
|
||||
if summary.get("accounted_virtual_time_s") is not None else None),
|
||||
"measures": summary["measures"],
|
||||
"clears": summary["clears"],
|
||||
"clear_success": summary["clear_success"],
|
||||
"total_requests": summary["total_requests"],
|
||||
"move_distance_m": round(summary["move_distance_m"], 2),
|
||||
"fallback_clear_attempts": summary.get("fallback_clear_attempts", 0),
|
||||
"coverage_points": summary.get("coverage_points", 0),
|
||||
"stop_reason": summary["stop_reason"],
|
||||
"exit_reason": summary["exit_reason"],
|
||||
"anomaly_count": len(summary["anomalies"]),
|
||||
"virtual_recompute_mismatches": len(report["mismatches"]),
|
||||
"compute_wall_s": round(compute_wall_s, 3),
|
||||
"valid": valid,
|
||||
}
|
||||
|
||||
|
||||
# 第三/四问统一汇报列（顺序即 Markdown 列序）
|
||||
EPISODE_COLUMNS = (
|
||||
"cleared_ratio", "cleared_matches_truth", "absent_misjudged",
|
||||
"virtual_time_s", "measures", "total_requests", "move_distance_m",
|
||||
"fallback_clear_attempts", "compute_wall_s", "valid",
|
||||
)
|
||||
|
||||
|
||||
def aggregate_episodes(rows: list[dict], labels: list[str],
|
||||
base_label: str | None = None) -> dict:
|
||||
"""逐方法汇总 + 相对基线的配对变化（按 scene_id 对齐）。"""
|
||||
summary: dict[str, dict] = {}
|
||||
for label in labels:
|
||||
part = [row for row in rows if row["variant"] == label]
|
||||
if not part:
|
||||
continue
|
||||
summary[label] = {
|
||||
"episodes": len(part),
|
||||
"valid_episodes": sum(1 for row in part if row["valid"]),
|
||||
"cleared_ratio_mean": mean(row["cleared_ratio"] for row in part),
|
||||
"cleared_matches_truth": sum(1 for row in part
|
||||
if row["cleared_matches_truth"]),
|
||||
"absent_misjudged_total": sum(row["absent_misjudged"] for row in part),
|
||||
"virtual_time_mean_s": mean(row["virtual_time_s"] for row in part),
|
||||
"virtual_time_median_s": median(row["virtual_time_s"] for row in part),
|
||||
"virtual_time_max_s": round(max(row["virtual_time_s"] for row in part), 3),
|
||||
"measures_mean": mean(row["measures"] for row in part),
|
||||
"total_requests_mean": mean(row["total_requests"] for row in part),
|
||||
"move_distance_mean_m": mean(row["move_distance_m"] for row in part),
|
||||
"fallback_clear_attempts_mean": mean(
|
||||
row["fallback_clear_attempts"] for row in part),
|
||||
"compute_wall_mean_s": mean(row["compute_wall_s"] for row in part),
|
||||
}
|
||||
paired: dict[str, dict] = {}
|
||||
if base_label and len(labels) > 1:
|
||||
base_rows = {row["scene_id"]: row for row in rows
|
||||
if row["variant"] == base_label}
|
||||
for label in labels:
|
||||
if label == base_label:
|
||||
continue
|
||||
deltas, faster, valid_pairs = [], 0, 0
|
||||
for row in rows:
|
||||
if row["variant"] != label or row["scene_id"] not in base_rows:
|
||||
continue
|
||||
base_row = base_rows[row["scene_id"]]
|
||||
reference = base_row["virtual_time_s"]
|
||||
deltas.append(relative_change(row["virtual_time_s"], reference))
|
||||
if not (row["valid"] and base_row["valid"]):
|
||||
continue
|
||||
valid_pairs += 1
|
||||
if row["virtual_time_s"] < reference:
|
||||
faster += 1
|
||||
paired[f"{label}_vs_{base_label}"] = {
|
||||
"mean_relative_change": mean(d for d in deltas if d is not None),
|
||||
"median_relative_change": median(d for d in deltas if d is not None),
|
||||
"faster_scenes": faster,
|
||||
"compared": len(deltas),
|
||||
"both_valid_scenes": valid_pairs,
|
||||
}
|
||||
return {"variants": summary, "paired": paired}
|
||||
|
||||
|
||||
def episodes_markdown(title: str, rows: list[dict], labels: list[str],
|
||||
base_label: str | None = None) -> str:
|
||||
"""逐场景明细 + 汇总表。"""
|
||||
headers = ["scene", "sources", "variant", "cleared", "T(s)", "检测",
|
||||
"请求", "位移(m)", "后备清除", "墙钟(s)", "有效"]
|
||||
body = []
|
||||
for row in sorted(rows, key=lambda r: (r["scene_id"], labels.index(r["variant"]))):
|
||||
body.append([
|
||||
row["scene_id"], row["sources"], row["variant"],
|
||||
f"{row['cleared']}/{row['sources']}",
|
||||
_num(row["virtual_time_s"], 1), row["measures"],
|
||||
row["total_requests"], _num(row["move_distance_m"], 1),
|
||||
row["fallback_clear_attempts"], _num(row["compute_wall_s"], 2),
|
||||
_num(row["valid"]),
|
||||
])
|
||||
text = [f"# {title}", "", "## 逐场景明细", "",
|
||||
markdown_table(headers, body), "", "## 逐方法汇总", ""]
|
||||
agg = aggregate_episodes(rows, labels, base_label)
|
||||
agg_headers = ["方法", "有效局", "全清率", "全清对数", "缺席误判",
|
||||
"T 均值(s)", "T 中位(s)", "T 最大(s)", "检测均值",
|
||||
"请求均值", "位移均值(m)", "后备清除均值", "墙钟均值(s)"]
|
||||
agg_rows = []
|
||||
for label in labels:
|
||||
s = agg["variants"].get(label)
|
||||
if not s:
|
||||
continue
|
||||
agg_rows.append([
|
||||
label, f"{s['valid_episodes']}/{s['episodes']}",
|
||||
_num(s["cleared_ratio_mean"]), s["cleared_matches_truth"],
|
||||
s["absent_misjudged_total"], _num(s["virtual_time_mean_s"], 1),
|
||||
_num(s["virtual_time_median_s"], 1), _num(s["virtual_time_max_s"], 1),
|
||||
_num(s["measures_mean"], 1), _num(s["total_requests_mean"], 1),
|
||||
_num(s["move_distance_mean_m"], 1),
|
||||
_num(s["fallback_clear_attempts_mean"], 2),
|
||||
_num(s["compute_wall_mean_s"], 2),
|
||||
])
|
||||
text.append(markdown_table(agg_headers, agg_rows))
|
||||
if agg["paired"]:
|
||||
text += ["", "## 相对基线的配对变化（虚拟时间）", ""]
|
||||
pair_rows = [[name, f"{v['mean_relative_change']*100:+.1f}%",
|
||||
f"{v['median_relative_change']*100:+.1f}%",
|
||||
f"{v['faster_scenes']}/{v['compared']}",
|
||||
v["both_valid_scenes"]]
|
||||
for name, v in agg["paired"].items()]
|
||||
text.append(markdown_table(
|
||||
["配对", "均值变化", "中位变化", "更快场景", "双方均有效"], pair_rows))
|
||||
invalid = [name for name, value in agg["paired"].items()
|
||||
if value["both_valid_scenes"] < value["compared"]]
|
||||
if invalid:
|
||||
text += ["",
|
||||
"> 注：" + "、".join(invalid)
|
||||
+ " 存在未通过有效性判定的配对；其虚拟时间仅作参考，"
|
||||
"基线未完成全清任务时不构成「更快」证据"
|
||||
"（以「双方均有效」列为准）。"]
|
||||
return "\n".join(text) + "\n"
|
||||
|
||||
|
||||
def json_safe(payload):
|
||||
"""把 numpy 标量等转成 json 可写类型（防御式，非必需）。"""
|
||||
return json.loads(json.dumps(payload, ensure_ascii=False, default=float))
|
||||
|
||||
|
||||
# ---------------------------------------------------------------- 闭环批量执行
|
||||
def execute_variants(scenes, variants, config: ProblemConfig) -> list[dict]:
|
||||
"""对每个场景跑遍所有变体（串行；对比规模小，进程池收益有限且便于复现）。"""
|
||||
rows = []
|
||||
for scene in scenes:
|
||||
for label, loop_cls in variants:
|
||||
row = run_loop_episode(loop_cls, scene, config)
|
||||
row["variant"] = label
|
||||
rows.append(row)
|
||||
return rows
|
||||
|
||||
|
||||
def closed_loop_payload(question: int, suite_name: str, scenes, variants,
|
||||
rows: list[dict], labels: list[str],
|
||||
base_label: str, config: ProblemConfig) -> dict:
|
||||
return {
|
||||
"question": question,
|
||||
"suite": suite_name,
|
||||
"scenes": [scene.as_dict() for scene in scenes],
|
||||
"variants": [{"label": label, "class": loop_cls.__name__}
|
||||
for label, loop_cls in variants],
|
||||
"config": {
|
||||
"persistent_search": config.search.persistent_search,
|
||||
"bearing_rounding_margin_deg": config.search.bearing_rounding_margin_deg,
|
||||
"q4_coverage_plan": config.search.q4_coverage_plan,
|
||||
},
|
||||
"rows": rows,
|
||||
"aggregate": aggregate_episodes(rows, labels, base_label),
|
||||
}
|
||||
Vendored
-6
@@ -1,6 +0,0 @@
|
||||
from .simulator_client import SimulatorClient
|
||||
from .local_env import HierarchicalEnv, LowLevelEnv, HRLReward, make_local_env
|
||||
from .particle_filter import ChannelParticles
|
||||
|
||||
__all__ = ["SimulatorClient", "HierarchicalEnv", "LowLevelEnv", "HRLReward",
|
||||
"make_local_env", "ChannelParticles"]
|
||||
Vendored
-22
@@ -1,22 +0,0 @@
|
||||
"""兼容入口：本地简化训练环境。
|
||||
|
||||
HierarchicalEnv 是高层宏动作环境；LowLevelEnv 是独立八方向导航环境。
|
||||
本文件不复制实现，只转发 mcm26b.hrl 下的真实类。
|
||||
"""
|
||||
from mcm26b.hrl.hierarchical_env import HRLReward, HierarchicalEnv
|
||||
from mcm26b.hrl.low_level_env import LowLevelEnv
|
||||
|
||||
__all__ = ["HierarchicalEnv", "LowLevelEnv", "HRLReward", "make_local_env"]
|
||||
|
||||
|
||||
def make_local_env(*, problem: int = 3, stage: int = 4, seed: int | None = None,
|
||||
fidelity: str = "production"):
|
||||
"""按题目 Prompt 的四阶段编号创建训练环境。"""
|
||||
from mcm26b.hrl.curriculum import STAGES
|
||||
if not 1 <= int(stage) <= len(STAGES):
|
||||
raise ValueError(f"stage 必须在 1..{len(STAGES)}")
|
||||
env = HierarchicalEnv(problem=problem, stage=STAGES[int(stage) - 1],
|
||||
fidelity=fidelity)
|
||||
if seed is not None:
|
||||
env.reset(seed=seed)
|
||||
return env
|
||||
Vendored
-4
@@ -1,4 +0,0 @@
|
||||
"""兼容入口：逐频道网格粒子/幸存集。"""
|
||||
from mcm26b.particles import ChannelParticles, angular_gap
|
||||
|
||||
__all__ = ["ChannelParticles", "angular_gap"]
|
||||
Vendored
-8
@@ -1,8 +0,0 @@
|
||||
"""兼容入口：真实类见 mcm26b.protocol.client。"""
|
||||
from mcm26b.protocol.client import (ActionRejected, ClearOutcome, DeadlineExceeded,
|
||||
ExitInfo, MeasureOutcome, ProtocolError,
|
||||
ProtocolViolation, SimulatorClient)
|
||||
|
||||
__all__ = ["SimulatorClient", "MeasureOutcome", "ClearOutcome", "ExitInfo",
|
||||
"ProtocolError", "ActionRejected", "ProtocolViolation",
|
||||
"DeadlineExceeded"]
|
||||
@@ -1,4 +0,0 @@
|
||||
from .evaluate import run_benchmark
|
||||
from .ablation import run_ablation
|
||||
|
||||
__all__ = ["run_benchmark", "run_ablation"]
|
||||
@@ -1,20 +0,0 @@
|
||||
"""兼容入口：调用 scripts/ablate_hrl.py（P2 后启用）。"""
|
||||
from __future__ import annotations
|
||||
|
||||
import importlib.util
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
def run_ablation(argv=None) -> int:
|
||||
path = Path(__file__).resolve().parents[2] / "scripts" / "ablate_hrl.py"
|
||||
if not path.exists():
|
||||
raise NotImplementedError(
|
||||
"scripts/ablate_hrl.py 尚未实现；P1 阶段只保证 benchmark/training/CLI。")
|
||||
spec = importlib.util.spec_from_file_location("ablate_hrl_compat", path)
|
||||
module = importlib.util.module_from_spec(spec)
|
||||
assert spec.loader is not None
|
||||
spec.loader.exec_module(module)
|
||||
return int(module.main(argv))
|
||||
|
||||
|
||||
__all__ = ["run_ablation"]
|
||||
@@ -1,17 +0,0 @@
|
||||
"""兼容入口：调用 scripts/benchmark_hrl.py。"""
|
||||
from __future__ import annotations
|
||||
|
||||
import importlib.util
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
def run_benchmark(argv=None) -> int:
|
||||
path = Path(__file__).resolve().parents[2] / "scripts" / "benchmark_hrl.py"
|
||||
spec = importlib.util.spec_from_file_location("benchmark_hrl_compat", path)
|
||||
module = importlib.util.module_from_spec(spec)
|
||||
assert spec.loader is not None
|
||||
spec.loader.exec_module(module)
|
||||
return int(module.main(argv))
|
||||
|
||||
|
||||
__all__ = ["run_benchmark"]
|
||||
@@ -1,5 +0,0 @@
|
||||
from .high_level_ppo import HighLevelPolicy, load_high_policy
|
||||
from .low_level_ppo import LowLevelPolicy, load_low_policy
|
||||
|
||||
__all__ = ["HighLevelPolicy", "load_high_policy",
|
||||
"LowLevelPolicy", "load_low_policy"]
|
||||
@@ -1,10 +0,0 @@
|
||||
"""兼容入口：高层 MaskablePPO 推理策略。"""
|
||||
from mcm26b.hrl.policy import HighLevelPolicy
|
||||
|
||||
|
||||
def load_high_policy(model_path: str, *, device: str = "auto",
|
||||
problem: int | None = None) -> HighLevelPolicy:
|
||||
return HighLevelPolicy(model_path, device=device, problem=problem)
|
||||
|
||||
|
||||
__all__ = ["HighLevelPolicy", "load_high_policy"]
|
||||
@@ -1,9 +0,0 @@
|
||||
"""兼容入口：低层八方向到达策略（离线导航实验）。"""
|
||||
from mcm26b.hrl.policy import LowLevelPolicy
|
||||
|
||||
|
||||
def load_low_policy(model_path: str, *, device: str = "auto") -> LowLevelPolicy:
|
||||
return LowLevelPolicy(model_path, device=device)
|
||||
|
||||
|
||||
__all__ = ["LowLevelPolicy", "load_low_policy"]
|
||||
@@ -0,0 +1,13 @@
|
||||
"""问题一：交会定位多边形直径与直径圆覆盖。
|
||||
|
||||
- ``optimal.py`` ：生产实现 ``mcm26b.geometry.localization.solve``（半平面交 +
|
||||
顶点对精确直径 + Welzl 最小包围圆 + 覆盖反例见证）；
|
||||
- ``baseline.py`` ：基线实现（网格采样 + 轴对齐包围盒代理直径/覆盖）；
|
||||
- ``reference.py``：独立精确参考解（大框半平面裁剪 + 暴力直径/最小包围圆）；
|
||||
- ``cases.py`` ：固定示例（``examples/q1``）与随机算例生成；
|
||||
- ``compare.py`` ：同案配对对比并落盘。
|
||||
"""
|
||||
|
||||
from . import baseline, cases, optimal, reference
|
||||
|
||||
__all__ = ["optimal", "baseline", "reference", "cases"]
|
||||
@@ -0,0 +1,98 @@
|
||||
"""问题一基线实现：网格采样 + 轴对齐包围盒（bbox）代理。
|
||||
|
||||
这是"先上手"的朴素做法，常见于直接对可行点打网格再读包围盒：
|
||||
|
||||
1. 在固定大框上按步长采样，保留满足全部角域约束的点；
|
||||
2. 直径用包围盒对角线代理 —— 只要区域是斜的/细长的就会系统性高估；
|
||||
3. 覆盖判断用"包围盒中心 + 代理直径"的圆 —— 因为包围盒自身对其中心对称，
|
||||
该判据几乎恒为"能覆盖"，从而丢掉问题一真正要考的反例。
|
||||
|
||||
本实现刻意保留这些结构性缺陷，作为``optimal.py``的对照；它不读取最优实现的
|
||||
任何中间量，两条代码路径完全独立。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import math
|
||||
from time import perf_counter
|
||||
|
||||
import numpy as np
|
||||
|
||||
from mcm26b.geometry.localization import Measurement, halfplanes
|
||||
|
||||
from project.common import Method
|
||||
|
||||
METHOD = Method(
|
||||
key="baseline",
|
||||
name="网格采样 + bbox 对角线代理",
|
||||
kind="baseline",
|
||||
note="轴对齐包围盒近似直径与覆盖，O(盒面积/步长²)，与参考解同分辨率",
|
||||
)
|
||||
|
||||
BOX_M = 2600.0
|
||||
STEP_M = 5.0
|
||||
|
||||
|
||||
def solve_case(case: dict) -> dict:
|
||||
start = perf_counter()
|
||||
measurements = [Measurement(row["x"], row["y"], row["bearing_deg"],
|
||||
row.get("half_angle_deg", 1.0))
|
||||
for row in case["measurements"]]
|
||||
planes = [plane for m in measurements for plane in halfplanes(m)]
|
||||
|
||||
xs = np.arange(-BOX_M, BOX_M + STEP_M, STEP_M)
|
||||
grid_x, grid_y = np.meshgrid(xs, xs) # 行=y，列=x
|
||||
mask = np.ones(grid_x.shape, dtype=bool)
|
||||
for a, b, c in planes:
|
||||
mask &= (a * grid_x + b * grid_y <= c)
|
||||
|
||||
result = _evaluate(mask, xs)
|
||||
result["method"] = METHOD.key
|
||||
result["runtime_ms"] = round((perf_counter() - start) * 1000.0, 4)
|
||||
result["algorithm"] = METHOD.name
|
||||
return result
|
||||
|
||||
|
||||
def _evaluate(mask: np.ndarray, axis: np.ndarray) -> dict:
|
||||
if not mask.any():
|
||||
return _empty("empty")
|
||||
# 可行点触到采样框边界 => 区域至少延伸到框外，判无界
|
||||
if (mask[0, :].any() or mask[-1, :].any()
|
||||
or mask[:, 0].any() or mask[:, -1].any()):
|
||||
return _empty("unbounded")
|
||||
|
||||
rows, cols = np.nonzero(mask)
|
||||
x_min, x_max = axis[cols].min(), axis[cols].max()
|
||||
y_min, y_max = axis[rows].min(), axis[rows].max()
|
||||
width, height = x_max - x_min, y_max - y_min
|
||||
diameter = math.hypot(width, height) # 包围盒对角线代理
|
||||
center = ((x_min + x_max) / 2.0, (y_min + y_max) / 2.0)
|
||||
px = axis[cols]
|
||||
py = axis[rows]
|
||||
max_distance = float(np.max(np.hypot(px - center[0], py - center[1])))
|
||||
return {
|
||||
"status": "polygon" if mask.sum() >= 2 else "point",
|
||||
"diameter_m": diameter,
|
||||
"diameter_infinite": False,
|
||||
"covers": bool(max_distance <= diameter / 2.0 + 1e-9),
|
||||
"mec_radius_m": None,
|
||||
"max_excess_m": round(max_distance - diameter / 2.0, 6),
|
||||
"outside_vertex_count": None,
|
||||
"vertex_count": int(mask.sum()),
|
||||
"bbox_width_m": round(width, 3),
|
||||
"bbox_height_m": round(height, 3),
|
||||
"runtime_ms": None,
|
||||
}
|
||||
|
||||
|
||||
def _empty(status: str) -> dict:
|
||||
return {
|
||||
"status": status,
|
||||
"diameter_m": None,
|
||||
"diameter_infinite": status == "unbounded",
|
||||
"covers": None,
|
||||
"mec_radius_m": None,
|
||||
"max_excess_m": None,
|
||||
"outside_vertex_count": None,
|
||||
"vertex_count": 0,
|
||||
}
|
||||
@@ -0,0 +1,67 @@
|
||||
"""问题一算例：官方示例目录 ``examples/q1`` + 随机交会算例。"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import math
|
||||
import random
|
||||
|
||||
from project.common import ROOT
|
||||
|
||||
EXAMPLE_DIR = ROOT / "examples" / "q1"
|
||||
EXAMPLE_IDS = ("normal", "triangle", "poor", "segment", "empty")
|
||||
|
||||
|
||||
def load_examples() -> list[dict]:
|
||||
"""读取固定算例；缺文件时跳过，保证离线可跑。"""
|
||||
cases = []
|
||||
for key in EXAMPLE_IDS:
|
||||
path = EXAMPLE_DIR / f"{key}.json"
|
||||
if not path.exists():
|
||||
continue
|
||||
raw = json.loads(path.read_text(encoding="utf-8-sig"))
|
||||
cases.append({
|
||||
"id": raw.get("id", key),
|
||||
"name": raw.get("name", key),
|
||||
"source": "examples/q1",
|
||||
"measurements": [
|
||||
{"x": float(row["x"]), "y": float(row["y"]),
|
||||
"bearing_deg": float(row["bearing_deg"]),
|
||||
"half_angle_deg": float(row.get("half_angle_deg", 1.0))}
|
||||
for row in raw["measurements"]
|
||||
],
|
||||
})
|
||||
return cases
|
||||
|
||||
|
||||
def random_case(seed: int) -> dict:
|
||||
"""随机三点/四点交会：目标在 Ω 内，测站分布在不同象限。
|
||||
|
||||
仅生成"区域有界且非退化"的良态算例，避免参考网格因细长区域失效；
|
||||
生成后由 ``compare`` 再用最优实现复核状态。
|
||||
"""
|
||||
rng = random.Random(seed)
|
||||
target = (rng.uniform(-900, 900), rng.uniform(-900, 900))
|
||||
count = rng.choice((3, 3, 4, 5))
|
||||
stations = []
|
||||
base_angle = rng.uniform(0, 360)
|
||||
for index in range(count):
|
||||
angle = base_angle + 360 * index / count + rng.uniform(-25, 25)
|
||||
radius = rng.uniform(500, 1100)
|
||||
stations.append((target[0] + radius * math.cos(math.radians(angle)),
|
||||
target[1] + radius * math.sin(math.radians(angle))))
|
||||
measurements = []
|
||||
for (sx, sy) in stations:
|
||||
true_bearing = math.degrees(math.atan2(target[1] - sy, target[0] - sx)) % 360.0
|
||||
# 观测误差固定在同一地点、落在 ±1° 内；读数保留两位小数
|
||||
error = rng.uniform(-0.9, 0.9)
|
||||
measurements.append({"x": round(sx, 2), "y": round(sy, 2),
|
||||
"bearing_deg": round((true_bearing + error) % 360.0, 2),
|
||||
"half_angle_deg": 1.0})
|
||||
return {"id": f"rand-{seed}", "name": "随机交会", "source": "synthetic",
|
||||
"target": [round(target[0], 2), round(target[1], 2)],
|
||||
"measurements": measurements}
|
||||
|
||||
|
||||
def random_cases(count: int, base_seed: int = 7000) -> list[dict]:
|
||||
return [random_case(base_seed + index) for index in range(count)]
|
||||
@@ -0,0 +1,150 @@
|
||||
"""问题一同案配对对比：最优 vs 基线 vs 独立参考解。"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from project.common import (Method, RESULTS_DIR, markdown_table, mean,
|
||||
ratio, write_json, write_text)
|
||||
from project.q1 import baseline, cases, optimal, reference
|
||||
|
||||
METHODS: tuple[Method, ...] = (optimal.METHOD, baseline.METHOD, reference.METHOD)
|
||||
FINITE = ("point", "segment", "polygon")
|
||||
|
||||
|
||||
def _run(case: dict) -> dict:
|
||||
opt = optimal.solve_case(case)
|
||||
base = baseline.solve_case(case)
|
||||
ref = reference.solve_case(case)
|
||||
finite = ref["status"] in FINITE and ref["diameter_m"] is not None
|
||||
return {
|
||||
"id": case["id"],
|
||||
"name": case.get("name", case["id"]),
|
||||
"source": case.get("source", "synthetic"),
|
||||
"measurements": len(case["measurements"]),
|
||||
"status_ref": ref["status"],
|
||||
"status_opt": opt["status"],
|
||||
"status_base": base["status"],
|
||||
"status_opt_ok": opt["status"] == ref["status"],
|
||||
"status_base_ok": base["status"] == ref["status"],
|
||||
"diameter_ref_m": ref["diameter_m"],
|
||||
"diameter_opt_m": opt["diameter_m"],
|
||||
"diameter_base_m": base["diameter_m"],
|
||||
"abs_err_opt_m": (round(abs(opt["diameter_m"] - ref["diameter_m"]), 4)
|
||||
if finite and opt["diameter_m"] is not None else None),
|
||||
"abs_err_base_m": (round(abs(base["diameter_m"] - ref["diameter_m"]), 4)
|
||||
if finite and base["diameter_m"] is not None else None),
|
||||
"covers_ref": ref["covers"],
|
||||
"covers_opt": opt["covers"],
|
||||
"covers_base": base["covers"],
|
||||
"covers_opt_ok": (opt["covers"] == ref["covers"]
|
||||
if ref["covers"] is not None and opt["covers"] is not None
|
||||
else None),
|
||||
"covers_base_ok": (base["covers"] == ref["covers"]
|
||||
if ref["covers"] is not None and base["covers"] is not None
|
||||
else None),
|
||||
"mec_radius_opt_m": opt["mec_radius_m"],
|
||||
"mec_radius_ref_m": ref["mec_radius_m"],
|
||||
"outside_vertices_opt": opt["outside_vertex_count"],
|
||||
"runtime_opt_ms": opt["runtime_ms"],
|
||||
"runtime_base_ms": base["runtime_ms"],
|
||||
"runtime_ref_ms": ref["runtime_ms"],
|
||||
"reference_resolution_m": ref["resolution_m"],
|
||||
}
|
||||
|
||||
|
||||
def _aggregate(rows: list[dict]) -> dict:
|
||||
polygon_rows = [r for r in rows if r["status_ref"] == "polygon"]
|
||||
opt_errs = [r["abs_err_opt_m"] for r in polygon_rows
|
||||
if r["abs_err_opt_m"] is not None]
|
||||
base_errs = [r["abs_err_base_m"] for r in polygon_rows
|
||||
if r["abs_err_base_m"] is not None]
|
||||
cov_opt = [r["covers_opt_ok"] for r in rows if r["covers_opt_ok"] is not None]
|
||||
cov_base = [r["covers_base_ok"] for r in rows if r["covers_base_ok"] is not None]
|
||||
return {
|
||||
"cases": len(rows),
|
||||
"diameter_compared": len(opt_errs),
|
||||
"status_accuracy_optimal": ratio(sum(r["status_opt_ok"] for r in rows), len(rows)),
|
||||
"status_accuracy_baseline": ratio(sum(r["status_base_ok"] for r in rows), len(rows)),
|
||||
"diameter_mae_optimal_m": mean(opt_errs),
|
||||
"diameter_mae_baseline_m": mean(base_errs),
|
||||
"diameter_max_err_optimal_m": round(max(opt_errs), 4) if opt_errs else 0.0,
|
||||
"diameter_max_err_baseline_m": round(max(base_errs), 4) if base_errs else 0.0,
|
||||
"coverage_compared": len(cov_opt),
|
||||
"coverage_accuracy_optimal": ratio(sum(cov_opt), len(cov_opt)),
|
||||
"coverage_accuracy_baseline": ratio(sum(cov_base), len(cov_base)),
|
||||
"runtime_optimal_mean_ms": mean(r["runtime_opt_ms"] for r in rows),
|
||||
"runtime_baseline_mean_ms": mean(r["runtime_base_ms"] for r in rows),
|
||||
"runtime_reference_mean_ms": mean(r["runtime_ref_ms"] for r in rows),
|
||||
}
|
||||
|
||||
|
||||
def _render(rows: list[dict], aggregate: dict, title: str) -> str:
|
||||
headers = ["算例", "来源", "参考状态", "最优状态", "基线状态",
|
||||
"D参考(m)", "D最优(m)", "D基线(m)", "|ΔD|最优", "|ΔD|基线",
|
||||
"覆盖参考", "覆盖最优", "覆盖基线"]
|
||||
body = []
|
||||
for r in rows:
|
||||
body.append([
|
||||
r["name"], r["source"], r["status_ref"], r["status_opt"], r["status_base"],
|
||||
_fmt(r["diameter_ref_m"]), _fmt(r["diameter_opt_m"]),
|
||||
_fmt(r["diameter_base_m"]),
|
||||
_fmt(r["abs_err_opt_m"]), _fmt(r["abs_err_base_m"]),
|
||||
_bool(r["covers_ref"]), _bool(r["covers_opt"]), _bool(r["covers_base"]),
|
||||
])
|
||||
text = [f"# {title}", "",
|
||||
"参考解为独立的大框半平面裁剪 + 精确直径/MEC；",
|
||||
"固定示例见 `examples/q1`，解析期望由 `tests/test_geometry*.py` 固化。",
|
||||
"", markdown_table(headers, body), "",
|
||||
"## 汇总", ""]
|
||||
summary_rows = [
|
||||
["状态判定准确率", _pct(aggregate["status_accuracy_optimal"]),
|
||||
_pct(aggregate["status_accuracy_baseline"])],
|
||||
["直径平均绝对误差 (m)", _fmt(aggregate["diameter_mae_optimal_m"], 3),
|
||||
_fmt(aggregate["diameter_mae_baseline_m"], 3)],
|
||||
["直径最大误差 (m)", _fmt(aggregate["diameter_max_err_optimal_m"], 3),
|
||||
_fmt(aggregate["diameter_max_err_baseline_m"], 3)],
|
||||
["覆盖判定准确率", _pct(aggregate["coverage_accuracy_optimal"]),
|
||||
_pct(aggregate["coverage_accuracy_baseline"])],
|
||||
["平均耗时 (ms)", _fmt(aggregate["runtime_optimal_mean_ms"], 3),
|
||||
_fmt(aggregate["runtime_baseline_mean_ms"], 3)],
|
||||
]
|
||||
text.append(markdown_table(["指标", "最优", "基线"], summary_rows))
|
||||
return "\n".join(text) + "\n"
|
||||
|
||||
|
||||
def _fmt(value, digits: int = 2) -> str:
|
||||
if value is None:
|
||||
return "—"
|
||||
if isinstance(value, bool):
|
||||
return "是" if value else "否"
|
||||
if isinstance(value, float):
|
||||
return f"{value:.{digits}f}"
|
||||
return str(value)
|
||||
|
||||
|
||||
def _bool(value) -> str:
|
||||
return "—" if value is None else ("是" if value else "否")
|
||||
|
||||
|
||||
def _pct(value: float) -> str:
|
||||
return f"{value*100:.1f}%"
|
||||
|
||||
|
||||
def run(quick: bool = False) -> dict:
|
||||
example_rows = [_run(case) for case in cases.load_examples()]
|
||||
random_rows = [_run(case) for case in
|
||||
cases.random_cases(12 if quick else 40)]
|
||||
payload = {
|
||||
"question": 1,
|
||||
"methods": [m.__dict__ for m in METHODS],
|
||||
"examples": example_rows,
|
||||
"random": random_rows,
|
||||
"aggregate_examples": _aggregate(example_rows),
|
||||
"aggregate_random": _aggregate(random_rows),
|
||||
}
|
||||
write_json(RESULTS_DIR / "q1.json", payload)
|
||||
text = _render(example_rows, payload["aggregate_examples"],
|
||||
"问题一：固定示例对比")
|
||||
text += "\n" + _render(random_rows, payload["aggregate_random"],
|
||||
"问题一：随机交会算例对比")
|
||||
write_text(RESULTS_DIR / "q1.md", text)
|
||||
return payload
|
||||
@@ -0,0 +1,59 @@
|
||||
"""问题一最优实现：复用生产几何内核 ``mcm26b.geometry.localization.solve``。
|
||||
|
||||
算法（已由 ``tests/test_geometry.py`` / ``tests/test_geometry_certificates.py``
|
||||
固化，本模块只做输入输出适配，不重写）：
|
||||
|
||||
1. 每条带 ±δ 误差的示向度写成前向角域的三条半平面约束（含 0°/360° 跨界）；
|
||||
2. 枚举全部边界直线交点，保留满足全部约束者，单调链求凸包；
|
||||
3. 检查是否存在非零衰退方向判定无界；否则枚举凸包顶点对取精确直径；
|
||||
4. 用直径两端点中点圆检验覆盖，并给出圆外顶点、最大超出距离等反例见证；
|
||||
5. Welzl 增量法求最小包围圆，给出 20 m 清除余量证书。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from time import perf_counter
|
||||
|
||||
from mcm26b.geometry.localization import Measurement, solve
|
||||
|
||||
from project.common import Method
|
||||
|
||||
METHOD = Method(
|
||||
key="optimal",
|
||||
name="精确半平面交 + 顶点对直径 + MEC",
|
||||
kind="optimal",
|
||||
note="生产内核 mcm26b.geometry.localization.solve",
|
||||
)
|
||||
|
||||
|
||||
def solve_case(case: dict) -> dict:
|
||||
"""输入 case["measurements"]，输出归一化结果。"""
|
||||
measurements = [Measurement(row["x"], row["y"], row["bearing_deg"],
|
||||
row.get("half_angle_deg", 1.0))
|
||||
for row in case["measurements"]]
|
||||
start = perf_counter()
|
||||
result = solve(measurements)
|
||||
elapsed_ms = (perf_counter() - start) * 1000.0
|
||||
return normalize(result, elapsed_ms)
|
||||
|
||||
|
||||
def normalize(result: dict, elapsed_ms: float) -> dict:
|
||||
status = result["status"]
|
||||
diameter = result.get("diameter_m")
|
||||
circle = result.get("diameter_circle") or {}
|
||||
mec = result.get("minimum_enclosing_circle") or {}
|
||||
covers = circle.get("covers") if status in ("point", "segment", "polygon") else None
|
||||
return {
|
||||
"method": METHOD.key,
|
||||
"status": status,
|
||||
"diameter_m": diameter,
|
||||
"diameter_infinite": result.get("diameter_infinite"),
|
||||
"covers": covers,
|
||||
"mec_radius_m": mec.get("radius_m"),
|
||||
"max_excess_m": circle.get("max_excess_m"),
|
||||
"outside_vertex_count": len(circle.get("outside_vertex_indices", []))
|
||||
if circle else None,
|
||||
"vertex_count": len(result.get("vertices", [])),
|
||||
"runtime_ms": round(elapsed_ms, 4),
|
||||
"algorithm": METHOD.name,
|
||||
}
|
||||
@@ -0,0 +1,178 @@
|
||||
"""问题一独立参考解：大框 Sutherland–Hodgman 半平面裁剪 + 精确直径/MEC。
|
||||
|
||||
与生产实现 ``mcm26b.geometry.localization.solve`` 的独立性：
|
||||
|
||||
- 生产实现枚举"半平面边界直线两两交点"再筛可行点；
|
||||
- 本参考解从一个大方框出发，逐条半平面做 Sutherland–Hodgman 裁剪，
|
||||
是另一条构造路径，且这里的半平面系数、直径、最小包围圆都在本文件内
|
||||
独立编码（不复用生产实现的 ``halfplanes`` / ``diameter`` / ``mec``）。
|
||||
|
||||
只要区域有界，大框（±1e7 m）裁剪得到的顶点即精确多边形；顶点触到大框
|
||||
边界则判无界。最小包围圆用"枚举所有 1/2/3 点支撑圆"的暴力算法，直径用
|
||||
顶点对暴力枚举，二者都是 O(m²)/O(m³) 的朴素精确算法，m 为多边形顶点数。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import math
|
||||
from time import perf_counter
|
||||
|
||||
from project.common import Method
|
||||
|
||||
METHOD = Method(
|
||||
key="reference",
|
||||
name="大框半平面裁剪 + 精确直径/MEC",
|
||||
kind="reference",
|
||||
note="独立于生产顶点枚举的精确参考解",
|
||||
)
|
||||
|
||||
BOX_M = 1.0e7
|
||||
_PARALLEL_EPS = 1e-12
|
||||
|
||||
|
||||
def _planes(case: dict) -> list[tuple[float, float, float]]:
|
||||
"""独立重写 a*x+b*y<=c 的半平面构造（与题面角域定义一致）。"""
|
||||
planes = []
|
||||
for row in case["measurements"]:
|
||||
half = math.radians(row.get("half_angle_deg", 1.0))
|
||||
theta = math.radians(row["bearing_deg"] % 360.0)
|
||||
sx, sy = row["x"], row["y"]
|
||||
for angle, sign in ((theta - half, 1.0), (theta + half, -1.0)):
|
||||
a = sign * math.sin(angle)
|
||||
b = -sign * math.cos(angle)
|
||||
planes.append((a, b, a * sx + b * sy))
|
||||
# 前向约束：不允许把角域取成反向无限双楔
|
||||
a, b = -math.cos(theta), -math.sin(theta)
|
||||
planes.append((a, b, a * sx + b * sy))
|
||||
return planes
|
||||
|
||||
|
||||
def _clip(polygon, a, b, c, tol=1e-7):
|
||||
"""Sutherland–Hodgman：保留 a*x+b*y<=c 的一侧。"""
|
||||
if not polygon:
|
||||
return []
|
||||
output = []
|
||||
count = len(polygon)
|
||||
for index in range(count):
|
||||
p, q = polygon[index], polygon[(index + 1) % count]
|
||||
fp = a * p[0] + b * p[1] - c
|
||||
fq = a * q[0] + b * q[1] - c
|
||||
p_in, q_in = fp <= tol, fq <= tol
|
||||
if p_in:
|
||||
output.append(p)
|
||||
if p_in != q_in:
|
||||
ratio = fp / (fp - fq)
|
||||
output.append((p[0] + ratio * (q[0] - p[0]),
|
||||
p[1] + ratio * (q[1] - p[1])))
|
||||
deduped = []
|
||||
for point in output:
|
||||
if not deduped or math.dist(point, deduped[-1]) > 1e-9:
|
||||
deduped.append(point)
|
||||
if len(deduped) > 1 and math.dist(deduped[0], deduped[-1]) <= 1e-9:
|
||||
deduped.pop()
|
||||
return deduped
|
||||
|
||||
|
||||
def _brute_force_diameter(points):
|
||||
best = 0.0
|
||||
pair = (points[0], points[0])
|
||||
for i, pi in enumerate(points):
|
||||
for pj in points[i + 1:]:
|
||||
value = math.dist(pi, pj)
|
||||
if value > best:
|
||||
best, pair = value, (pi, pj)
|
||||
return best, pair
|
||||
|
||||
|
||||
def _circumcircle(a, b, c):
|
||||
ux, uy = b[0] - a[0], b[1] - a[1]
|
||||
vx, vy = c[0] - a[0], c[1] - a[1]
|
||||
det = 2.0 * (ux * vy - uy * vx)
|
||||
if abs(det) <= _PARALLEL_EPS * max(
|
||||
math.hypot(ux, uy) * math.hypot(vx, vy), 1e-300):
|
||||
return None
|
||||
uu, vv = ux * ux + uy * uy, vx * vx + vy * vy
|
||||
center = (a[0] + (uu * vy - vv * uy) / det,
|
||||
a[1] + (ux * vv - vx * uu) / det)
|
||||
return center, math.dist(center, a)
|
||||
|
||||
|
||||
def _brute_force_mec(points):
|
||||
"""枚举 1/2/3 点支撑圆的精确最小包围圆（朴素但无迭代）。"""
|
||||
xs = [p[0] for p in points]
|
||||
ys = [p[1] for p in points]
|
||||
best_c = ((min(xs) + max(xs)) / 2.0, (min(ys) + max(ys)) / 2.0)
|
||||
best_r = max(math.dist(best_c, p) for p in points)
|
||||
candidates: list[tuple[tuple[float, float], float]] = []
|
||||
for i, p in enumerate(points):
|
||||
for q in points[i + 1:]:
|
||||
candidates.append((((p[0] + q[0]) / 2.0, (p[1] + q[1]) / 2.0),
|
||||
math.dist(p, q) / 2.0))
|
||||
for i, p in enumerate(points):
|
||||
for j in range(i + 1, len(points)):
|
||||
for k in range(j + 1, len(points)):
|
||||
circle = _circumcircle(p, points[j], points[k])
|
||||
if circle is not None:
|
||||
candidates.append(circle)
|
||||
for center, radius in candidates:
|
||||
if radius >= best_r:
|
||||
continue
|
||||
if all(math.dist(center, p) <= radius + 1e-7 for p in points):
|
||||
best_c, best_r = center, radius
|
||||
return best_c, best_r
|
||||
|
||||
|
||||
def solve_case(case: dict) -> dict:
|
||||
start = perf_counter()
|
||||
planes = _planes(case)
|
||||
polygon = [(-BOX_M, -BOX_M), (BOX_M, -BOX_M), (BOX_M, BOX_M), (-BOX_M, BOX_M)]
|
||||
for a, b, c in planes:
|
||||
polygon = _clip(polygon, a, b, c)
|
||||
if not polygon:
|
||||
return _finish(_degenerate("empty"), start)
|
||||
boundary_tol = 1e-6 * BOX_M
|
||||
if any(abs(point[0]) >= BOX_M - boundary_tol
|
||||
or abs(point[1]) >= BOX_M - boundary_tol for point in polygon):
|
||||
return _finish(_degenerate("unbounded"), start)
|
||||
|
||||
if len(polygon) == 1:
|
||||
summary = {"status": "point", "diameter_m": 0.0, "covers": True,
|
||||
"mec_radius_m": 0.0, "max_excess_m": 0.0,
|
||||
"vertex_count": 1}
|
||||
else:
|
||||
value, (a, b) = _brute_force_diameter(polygon)
|
||||
if len(polygon) == 2:
|
||||
summary = {"status": "segment", "diameter_m": value, "covers": True,
|
||||
"mec_radius_m": value / 2.0, "max_excess_m": 0.0,
|
||||
"vertex_count": 2}
|
||||
else:
|
||||
_mec_center, radius = _brute_force_mec(polygon)
|
||||
diameter_center = ((a[0] + b[0]) / 2.0, (a[1] + b[1]) / 2.0)
|
||||
max_distance = max(math.dist(diameter_center, p) for p in polygon)
|
||||
summary = {
|
||||
"status": "polygon",
|
||||
"diameter_m": value,
|
||||
"covers": bool(radius <= value / 2.0 + 1e-7),
|
||||
"mec_radius_m": radius,
|
||||
"max_excess_m": round(max_distance - value / 2.0, 9),
|
||||
"vertex_count": len(polygon),
|
||||
}
|
||||
return _finish(summary, start)
|
||||
|
||||
|
||||
def _degenerate(status: str) -> dict:
|
||||
return {"status": status, "diameter_m": None, "covers": None,
|
||||
"mec_radius_m": None, "max_excess_m": None, "vertex_count": 0}
|
||||
|
||||
|
||||
def _finish(summary: dict, start: float) -> dict:
|
||||
result = {
|
||||
"method": METHOD.key,
|
||||
"algorithm": METHOD.name,
|
||||
"resolution_m": 0.0,
|
||||
"diameter_infinite": summary["status"] == "unbounded",
|
||||
"outside_vertex_count": None,
|
||||
"runtime_ms": round((perf_counter() - start) * 1000.0, 4),
|
||||
}
|
||||
result.update(summary)
|
||||
return result
|
||||
@@ -0,0 +1,14 @@
|
||||
"""问题二：第二检测点选择策略。
|
||||
|
||||
- ``optimal.py`` ：生产选点器 ``mcm26b.planner.select_measurement_point``
|
||||
（保证接收盘内最坏后验半径最小化，采样启发式）；
|
||||
- ``baseline.py`` ："垂直 1000 m 最大交会角"朴素启发式（忽略接收保证），
|
||||
以及"退到候选集 MEC 圆心"的保守基线；
|
||||
- ``reference.py``：在保证接收盘内稠密网格枚举的近似最优参考；
|
||||
- ``cases.py`` ：随机首次示向度算例；
|
||||
- ``compare.py`` ：同案配对对比并落盘。
|
||||
"""
|
||||
|
||||
from . import baseline, cases, optimal, reference
|
||||
|
||||
__all__ = ["optimal", "baseline", "reference", "cases"]
|
||||
@@ -0,0 +1,71 @@
|
||||
"""问题二基线实现：两个朴素但常见的对照。
|
||||
|
||||
- ``candidate_perp``：沿首次示向度垂直方向、距检测点约 1000 m 取点。
|
||||
这是"尽量把交会角做成 90°"的教科书启发式，**但它忽略接收距离保证**：
|
||||
真源可能落在首次角域的远端（≤1500 m），此时第二点距离可超过 1000 m，
|
||||
第二次测量直接 ``no_signal``，几何收益落空。
|
||||
- ``candidate_mec``：退到候选集最小包围圆圆心。它确实落在保证接收盘内
|
||||
（ρ≤1000 时），但交会角可能接近 0°、后验几乎不收缩，代表"最保守"一端。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import math
|
||||
|
||||
from mcm26b.config import ProblemConfig
|
||||
from mcm26b.planner import safe_disk_radius
|
||||
|
||||
from project.common import Method, problem_config
|
||||
from project.q2.region import first_bearing_region
|
||||
|
||||
PERP = Method(
|
||||
key="baseline_perp",
|
||||
name="垂直 1000 m 最大交会角",
|
||||
kind="baseline",
|
||||
note="忽略接收保证的几何启发式",
|
||||
)
|
||||
MEC = Method(
|
||||
key="baseline_mec",
|
||||
name="候选集 MEC 圆心",
|
||||
kind="baseline",
|
||||
note="保证接收但交会角最差的保守基线",
|
||||
)
|
||||
METHODS = (PERP, MEC)
|
||||
|
||||
|
||||
def _region(case: dict, config: ProblemConfig):
|
||||
return first_bearing_region(case, config)
|
||||
|
||||
|
||||
def candidate_perp(case: dict, config: ProblemConfig | None = None) -> dict:
|
||||
config = config or problem_config()
|
||||
region = _region(case, config)
|
||||
station = (case["station"][0], case["station"][1])
|
||||
theta = math.radians(case["bearing_deg"] + 90.0)
|
||||
distance = config.field.r_eff_min_m
|
||||
position = (station[0] + distance * math.cos(theta),
|
||||
station[1] + distance * math.sin(theta))
|
||||
return {
|
||||
"method": PERP.key,
|
||||
"position": [round(position[0], 4), round(position[1], 4)],
|
||||
"worst_posterior_radius_m": None, # 由 compare 统一评估
|
||||
"move_cost_s": None,
|
||||
"safe_disk_radius_m": safe_disk_radius(region, config),
|
||||
"initial_radius_m": region.mec()[1],
|
||||
"reason": "沿首次示向度法向 1000 m 取 90° 交会点",
|
||||
}
|
||||
|
||||
|
||||
def candidate_mec(case: dict, config: ProblemConfig | None = None) -> dict:
|
||||
config = config or problem_config()
|
||||
region = _region(case, config)
|
||||
center, radius = region.mec()
|
||||
return {
|
||||
"method": MEC.key,
|
||||
"position": [round(center[0], 4), round(center[1], 4)],
|
||||
"worst_posterior_radius_m": None,
|
||||
"move_cost_s": None,
|
||||
"safe_disk_radius_m": safe_disk_radius(region, config),
|
||||
"initial_radius_m": radius,
|
||||
"reason": "取候选集最小包围圆圆心，保证接收但交会角可能最差",
|
||||
}
|
||||
@@ -0,0 +1,37 @@
|
||||
"""问题二算例：随机首次示向度（全向源）。"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import math
|
||||
import random
|
||||
|
||||
from mcm26b.config import ProblemConfig
|
||||
|
||||
|
||||
def random_case(seed: int, config: ProblemConfig | None = None) -> dict:
|
||||
"""随机检测点 s 与示向度 θ：默认让真源与读数一致（误差 ±1° 内）。"""
|
||||
config = config or ProblemConfig()
|
||||
rng = random.Random(seed)
|
||||
radius = config.field.omega_radius_m
|
||||
# 检测点可在目标域内或域外；这里取域内，符合题目常规场景
|
||||
angle_s = rng.uniform(0, 2 * math.pi)
|
||||
distance_s = radius * math.sqrt(rng.random()) * 0.9
|
||||
station = (distance_s * math.cos(angle_s), distance_s * math.sin(angle_s))
|
||||
# 真源与检测点距离落在 [60, 1500) 且仍在 Ω 内
|
||||
while True:
|
||||
bearing_true = rng.uniform(0, 2 * math.pi)
|
||||
source_distance = rng.uniform(60, config.field.r_eff_max_m - 1)
|
||||
target = (station[0] + source_distance * math.cos(bearing_true),
|
||||
station[1] + source_distance * math.sin(bearing_true))
|
||||
if math.hypot(*target) <= radius:
|
||||
break
|
||||
error = math.radians(rng.uniform(-0.95, 0.95))
|
||||
bearing_deg = math.degrees(bearing_true + error) % 360.0
|
||||
return {"id": f"q2-{seed}", "station": [round(station[0], 2), round(station[1], 2)],
|
||||
"bearing_deg": round(bearing_deg, 2), "half_angle_deg": 1.0,
|
||||
"true_source": [round(target[0], 2), round(target[1], 2)]}
|
||||
|
||||
|
||||
def random_cases(count: int, base_seed: int = 4200,
|
||||
config: ProblemConfig | None = None) -> list[dict]:
|
||||
return [random_case(base_seed + index, config) for index in range(count)]
|
||||
@@ -0,0 +1,136 @@
|
||||
"""问题二同案配对对比：最优 / 两个基线 / 网格参考，用同一评估器打分。"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import math
|
||||
|
||||
from mcm26b.config import ProblemConfig
|
||||
from mcm26b.planner import _worst_posterior_radius
|
||||
|
||||
from project.common import (Method, RESULTS_DIR, markdown_table, mean, ratio,
|
||||
write_json, write_text)
|
||||
from project.q2 import baseline, cases, optimal, reference
|
||||
from project.q2.region import first_bearing_region
|
||||
|
||||
METHODS: tuple[Method, ...] = (optimal.METHOD, baseline.PERP, baseline.MEC,
|
||||
reference.METHOD)
|
||||
EVAL_WITNESS_SPACING_M = 60.0
|
||||
|
||||
|
||||
def _evaluate(case: dict, config: ProblemConfig) -> list[dict]:
|
||||
region = first_bearing_region(case, config)
|
||||
station = (case["station"][0], case["station"][1])
|
||||
initial_radius = region.mec()[1]
|
||||
half_angle = (config.field.bearing_error_half_deg
|
||||
+ config.search.bearing_rounding_margin_deg)
|
||||
witnesses = region.witnesses(EVAL_WITNESS_SPACING_M)
|
||||
|
||||
plans = [optimal.candidate(case, config),
|
||||
baseline.candidate_perp(case, config),
|
||||
baseline.candidate_mec(case, config),
|
||||
reference.candidate(case, config)]
|
||||
rows = []
|
||||
for plan in plans:
|
||||
q = (plan["position"][0], plan["position"][1])
|
||||
guarantee_distance = max(math.dist(q, vertex) for vertex in region.polygon)
|
||||
worst = _worst_posterior_radius(
|
||||
region, q, witnesses, half_angle,
|
||||
config.field.near_radius_m, config.search.polygon_sides)
|
||||
move_cost = math.dist(station, q) / config.cost.speed_mps
|
||||
rows.append({
|
||||
"case_id": case["id"],
|
||||
"method": plan["method"],
|
||||
"initial_radius_m": round(initial_radius, 4),
|
||||
"q": [round(q[0], 2), round(q[1], 2)],
|
||||
"guaranteed_reception": bool(guarantee_distance
|
||||
<= config.field.r_eff_min_m + 1e-6),
|
||||
"guarantee_distance_m": round(guarantee_distance, 3),
|
||||
"worst_posterior_radius_m": round(worst, 4),
|
||||
"improvement_m": round(initial_radius - worst, 4),
|
||||
"final_ratio": round(worst / initial_radius, 4) if initial_radius > 0 else None,
|
||||
"move_cost_s": round(move_cost, 4),
|
||||
"reason": plan["reason"],
|
||||
})
|
||||
return rows
|
||||
|
||||
|
||||
def _aggregate(rows: list[dict], labels: list[str]) -> dict:
|
||||
summary: dict[str, dict] = {}
|
||||
initial = mean(r["initial_radius_m"] for r in rows) if rows else 0.0
|
||||
for label in labels:
|
||||
part = [r for r in rows if r["method"] == label]
|
||||
if not part:
|
||||
continue
|
||||
summary[label] = {
|
||||
"cases": len(part),
|
||||
"guaranteed_rate": ratio(sum(r["guaranteed_reception"] for r in part),
|
||||
len(part)),
|
||||
"worst_posterior_mean_m": mean(r["worst_posterior_radius_m"] for r in part),
|
||||
"worst_posterior_median_m": sorted(
|
||||
r["worst_posterior_radius_m"] for r in part)[len(part) // 2],
|
||||
"improvement_mean_m": mean(r["improvement_m"] for r in part),
|
||||
"final_ratio_mean": mean(r["final_ratio"] for r in part
|
||||
if r["final_ratio"] is not None),
|
||||
"move_cost_mean_s": mean(r["move_cost_s"] for r in part),
|
||||
}
|
||||
summary["_initial_radius_mean_m"] = initial
|
||||
return summary
|
||||
|
||||
|
||||
def _render(rows: list[dict], aggregate: dict, labels: list[str]) -> str:
|
||||
headers = ["算例", "方法", "初始 ρ(m)", "第二点 q", "保证接收",
|
||||
"q 到 K₁ 最远(m)", "最坏后验 ρ'(m)", "收缩(m)", "移动(s)"]
|
||||
body = []
|
||||
order = {label: index for index, label in enumerate(labels)}
|
||||
for row in sorted(rows, key=lambda r: (r["case_id"], order.get(r["method"], 99))):
|
||||
body.append([
|
||||
row["case_id"], row["method"], _f(row["initial_radius_m"]),
|
||||
f"({row['q'][0]:.0f},{row['q'][1]:.0f})",
|
||||
"是" if row["guaranteed_reception"] else "否",
|
||||
_f(row["guarantee_distance_m"]), _f(row["worst_posterior_radius_m"]),
|
||||
_f(row["improvement_m"]), _f(row["move_cost_s"]),
|
||||
])
|
||||
text = ["# 问题二：第二检测点选择对比", "",
|
||||
"初始 ρ 为首次示向度候选集 K₁ 的最小包围半径；最坏后验按"
|
||||
"目标边界采样 × 读数区间端点统一评估。", "",
|
||||
markdown_table(headers, body), "", "## 逐方法汇总", ""]
|
||||
agg_headers = ["方法", "保证接收率", "最坏后验均值(m)", "中位(m)",
|
||||
"平均收缩(m)", "终值比 ρ'/ρ", "移动均值(s)"]
|
||||
agg_rows = []
|
||||
for label in labels:
|
||||
s = aggregate.get(label)
|
||||
if not s:
|
||||
continue
|
||||
agg_rows.append([label, f"{s['guaranteed_rate']*100:.1f}%",
|
||||
_f(s["worst_posterior_mean_m"]),
|
||||
_f(s["worst_posterior_median_m"]),
|
||||
_f(s["improvement_mean_m"]),
|
||||
_f(s["final_ratio_mean"], 3),
|
||||
_f(s["move_cost_mean_s"])])
|
||||
text.append(markdown_table(agg_headers, agg_rows))
|
||||
text.append("")
|
||||
text.append(f"初始 ρ 均值：{aggregate['_initial_radius_mean_m']:.2f} m")
|
||||
return "\n".join(text) + "\n"
|
||||
|
||||
|
||||
def _f(value, digits: int = 2) -> str:
|
||||
if value is None:
|
||||
return "—"
|
||||
if isinstance(value, bool):
|
||||
return "是" if value else "否"
|
||||
if isinstance(value, float):
|
||||
return f"{value:.{digits}f}"
|
||||
return str(value)
|
||||
|
||||
|
||||
def run(quick: bool = False) -> dict:
|
||||
config = ProblemConfig()
|
||||
case_list = cases.random_cases(8 if quick else 30, config=config)
|
||||
rows = [row for case in case_list for row in _evaluate(case, config)]
|
||||
labels = [m.key for m in METHODS]
|
||||
aggregate = _aggregate(rows, labels)
|
||||
payload = {"question": 2, "methods": [m.__dict__ for m in METHODS],
|
||||
"cases": case_list, "rows": rows, "aggregate": aggregate}
|
||||
write_json(RESULTS_DIR / "q2.json", payload)
|
||||
write_text(RESULTS_DIR / "q2.md", _render(rows, aggregate, labels))
|
||||
return payload
|
||||
@@ -0,0 +1,42 @@
|
||||
"""问题二最优实现：保证接收盘内的最坏后验半径最小化选点（生产实现）。
|
||||
|
||||
复用 ``mcm26b.planner.select_measurement_point``：
|
||||
|
||||
1. 在"保证接收盘" B(mec_center, 1000-ρ) 内环形采样候选点；
|
||||
2. 对每个候选，用"目标边界点 × 读数区间端点（+near 分支）"估计
|
||||
最坏后验最小包围半径，并以移动时间同分决胜；
|
||||
3. 选定后按 1/4 间距加密复核，返回位置、后验半径与理由。
|
||||
|
||||
采样是启发式（不是连续问题全局最优证明），但保证接收是严格的。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from mcm26b.config import ProblemConfig
|
||||
from mcm26b.planner import safe_disk_radius, select_measurement_point
|
||||
|
||||
from project.common import Method, problem_config
|
||||
from project.q2.region import first_bearing_region
|
||||
|
||||
METHOD = Method(
|
||||
key="optimal",
|
||||
name="保证接收盘内最小最坏后验半径",
|
||||
kind="optimal",
|
||||
note="生产实现 mcm26b.planner.select_measurement_point",
|
||||
)
|
||||
|
||||
|
||||
def candidate(case: dict, config: ProblemConfig | None = None) -> dict:
|
||||
config = config or problem_config()
|
||||
region = first_bearing_region(case, config)
|
||||
station = (case["station"][0], case["station"][1])
|
||||
plan = select_measurement_point(region, station, config)
|
||||
return {
|
||||
"method": METHOD.key,
|
||||
"position": [round(plan.position[0], 4), round(plan.position[1], 4)],
|
||||
"worst_posterior_radius_m": plan.worst_posterior_radius_m,
|
||||
"move_cost_s": plan.move_cost_s,
|
||||
"safe_disk_radius_m": safe_disk_radius(region, config),
|
||||
"initial_radius_m": region.mec()[1],
|
||||
"reason": plan.reason,
|
||||
}
|
||||
@@ -0,0 +1,67 @@
|
||||
"""问题二近似最优参考：在保证接收盘内做稠密网格枚举。
|
||||
|
||||
目标函数与生产实现一致（最坏后验包围半径 + 移动时间同分决胜），但候选点
|
||||
取自**均匀网格**而非环形采样族，因此可用来检验生产采样是否接近该目标下的
|
||||
网格最优。注意这是"同一目标下的网格参考"，不是连续问题的严格全局最优。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import math
|
||||
|
||||
from mcm26b.config import ProblemConfig
|
||||
from mcm26b.planner import (_worst_posterior_radius, safe_disk_radius)
|
||||
|
||||
from project.common import Method, problem_config
|
||||
from project.q2.region import first_bearing_region
|
||||
|
||||
METHOD = Method(
|
||||
key="reference",
|
||||
name="保证接收盘内网格枚举",
|
||||
kind="reference",
|
||||
note="同一目标下的稠密网格参考，非连续全局最优",
|
||||
)
|
||||
GRID_SIDES = 9
|
||||
WITNESS_SPACING_M = 150.0
|
||||
|
||||
|
||||
def candidate(case: dict, config: ProblemConfig | None = None) -> dict:
|
||||
config = config or problem_config()
|
||||
region = first_bearing_region(case, config)
|
||||
station = (case["station"][0], case["station"][1])
|
||||
center = region.mec()[0]
|
||||
safe = safe_disk_radius(region, config)
|
||||
if safe < 0:
|
||||
return _payload(center, region, config, station, "无保证接收盘：退回 MEC 圆心")
|
||||
|
||||
field, search = config.field, config.search
|
||||
half_angle = field.bearing_error_half_deg + search.bearing_rounding_margin_deg
|
||||
witnesses = region.witnesses(WITNESS_SPACING_M)
|
||||
step = 2.0 * safe / (GRID_SIDES - 1)
|
||||
best_key, best_point, best_worst = None, center, None
|
||||
for i in range(GRID_SIDES):
|
||||
for j in range(GRID_SIDES):
|
||||
point = (center[0] - safe + i * step, center[1] - safe + j * step)
|
||||
if math.dist(point, center) > safe + 1e-9:
|
||||
continue
|
||||
worst = _worst_posterior_radius(region, point, witnesses, half_angle,
|
||||
field.near_radius_m, search.polygon_sides)
|
||||
move = math.dist(station, point) / config.cost.speed_mps
|
||||
key = (round(worst, 6), round(move, 6))
|
||||
if best_key is None or key < best_key:
|
||||
best_key, best_point, best_worst = key, point, worst
|
||||
return _payload(best_point, region, config, station,
|
||||
f"安全盘网格 {GRID_SIDES}×{GRID_SIDES} 取最坏后验最小",
|
||||
worst_value=best_worst)
|
||||
|
||||
|
||||
def _payload(point, region, config, station, reason, worst_value=None) -> dict:
|
||||
return {
|
||||
"method": METHOD.key,
|
||||
"position": [round(point[0], 4), round(point[1], 4)],
|
||||
"worst_posterior_radius_m": worst_value,
|
||||
"move_cost_s": math.dist(station, point) / config.cost.speed_mps,
|
||||
"safe_disk_radius_m": safe_disk_radius(region, config),
|
||||
"initial_radius_m": region.mec()[1],
|
||||
"reason": reason,
|
||||
}
|
||||
@@ -0,0 +1,24 @@
|
||||
"""问题二候选集 K1 的统一构造（三个方法共用同一输入，保证配对公平）。
|
||||
|
||||
K1 = Ω ∩ W(s, θ ± δ_eff) ∩ B(s, 1500)，δ_eff = 题设 1° + 示向度舍入裕量。
|
||||
生产实现用"圆盘外切多边形 ∩ 半平面"的保守外包（``CandidateRegion``），
|
||||
圆盘用外切多边形严格外包，保证真源不丢。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from mcm26b.belief import CandidateRegion
|
||||
from mcm26b.config import ProblemConfig, Vec2
|
||||
|
||||
from project.common import problem_config
|
||||
|
||||
|
||||
def first_bearing_region(case: dict,
|
||||
config: ProblemConfig | None = None) -> CandidateRegion:
|
||||
config = config or problem_config()
|
||||
field, search = config.field, config.search
|
||||
half_angle = field.bearing_error_half_deg + search.bearing_rounding_margin_deg
|
||||
station: Vec2 = (case["station"][0], case["station"][1])
|
||||
region = CandidateRegion.initial(station, config)
|
||||
region.clip_wedge(station, case["bearing_deg"], half_angle)
|
||||
return region
|
||||
@@ -0,0 +1,11 @@
|
||||
"""问题三：全向源搜索—定位—清除闭环。
|
||||
|
||||
- ``optimal.py`` ：贝叶斯网格粒子滤波 + 信息增益选点 + 滚动 2-opt（``bayes``）；
|
||||
- ``baseline.py``：49 点网格发现 + 就近服务（``grid``，历史基线），
|
||||
以及七点解析覆盖批量扫频（``seven``，中间对照）；
|
||||
- ``compare.py`` ：冻结基准集配对对比并落盘。
|
||||
"""
|
||||
|
||||
from . import baseline, optimal
|
||||
|
||||
__all__ = ["optimal", "baseline"]
|
||||
@@ -0,0 +1,28 @@
|
||||
"""问题三基线实现：两条不共享贝叶斯决策的对照策略。
|
||||
|
||||
- ``OmnidirectionalLoop``（grid）：49 点（700 m 步长）网格保证发现，
|
||||
发现后就近进入定位子循环；策略简单、无信息增益选点、无任务排序优化。
|
||||
- ``SevenBatch``（seven）：七点解析覆盖 + 同站批量扫频 + 最近候选服务队列，
|
||||
是"覆盖证明 + 批量调度"的中间对照（官方演练 12/12 全清）。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from mcm26b.planner import OmnidirectionalLoop
|
||||
from mcm26b.planner.seven import SevenBatch
|
||||
|
||||
from project.common import Method
|
||||
|
||||
GRID = Method(key="baseline", name="49 点网格 + 就近服务 (grid)",
|
||||
kind="baseline", note="历史基线，无信息增益与滚动排序")
|
||||
SEVEN = Method(key="seven", name="七点解析覆盖 + 批量扫频 (seven)",
|
||||
kind="baseline", note="覆盖证明与批量调度的中间对照")
|
||||
|
||||
def build_grid(client, config):
|
||||
"""工厂：49 点网格基线闭环。"""
|
||||
return OmnidirectionalLoop(client, config)
|
||||
|
||||
|
||||
def build_seven(client, config):
|
||||
"""工厂：七点批量扫频中间对照。"""
|
||||
return SevenBatch(client, config)
|
||||
@@ -0,0 +1,32 @@
|
||||
"""问题三冻结基准集配对对比：bayes（最优） vs grid（基线） vs seven（中间）。"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from project.common import (RESULTS_DIR, closed_loop_payload, episodes_markdown,
|
||||
execute_variants, problem_config, write_json,
|
||||
write_text)
|
||||
from project.q3 import baseline, optimal
|
||||
|
||||
VARIANTS = (
|
||||
("optimal", optimal.build_loop),
|
||||
("seven", baseline.build_seven),
|
||||
("baseline", baseline.build_grid),
|
||||
)
|
||||
LABELS = [label for label, _ in VARIANTS]
|
||||
BASE_LABEL = "baseline"
|
||||
|
||||
|
||||
def run(quick: bool = False) -> dict:
|
||||
from mcm26b.harness.suites import suite
|
||||
scenes = suite("core", 3)
|
||||
if quick:
|
||||
scenes = scenes[:6]
|
||||
config = problem_config()
|
||||
rows = execute_variants(scenes, VARIANTS, config)
|
||||
payload = closed_loop_payload(3, "core", scenes, VARIANTS, rows,
|
||||
LABELS, BASE_LABEL, config)
|
||||
write_json(RESULTS_DIR / "q3.json", payload)
|
||||
write_text(RESULTS_DIR / "q3.md",
|
||||
episodes_markdown("问题三：全向闭环配对对比（core 基准集）",
|
||||
rows, LABELS, BASE_LABEL))
|
||||
return payload
|
||||
@@ -0,0 +1,19 @@
|
||||
"""问题三最优实现：``mcm26b.planner.bayes_loop.BayesLoop``（bayes 策略）。
|
||||
|
||||
要点：逐频道网格粒子滤波维护幸存集；以期望信息增益选探点、
|
||||
滚动时域 2-opt 排序任务；证书（凸证书或 PF 幸存集 MEC）达到 20 m 即清除；
|
||||
全频道"已清除或证明不存在"后立即 /exit（虚拟时间即成绩口径）。
|
||||
自建消融相对 SevenBatch 再降约 30%（docs/algo-3-bayes.md）。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from mcm26b.planner.bayes_loop import BayesLoop
|
||||
|
||||
from project.common import Method
|
||||
|
||||
METHOD = Method(key="optimal", name="贝叶斯粒子滤波闭环 (bayes)",
|
||||
kind="optimal", note="生产默认策略，虚拟时间口径")
|
||||
def build_loop(client, config):
|
||||
"""工厂：返回生产 bayes 闭环实例。"""
|
||||
return BayesLoop(client, config)
|
||||
@@ -0,0 +1,12 @@
|
||||
"""问题四：含定向干扰源的检测与清除。
|
||||
|
||||
- ``optimal.py`` ：``DirectionalExtension``（保证覆盖 + 定向观测推理 +
|
||||
(位置×朝向) 联合信念 + ≤20 m 有限圆盘后备清除）；
|
||||
- ``baseline.py``：把问题三最优策略**直接搬用**的两条基线——
|
||||
``bayes``（七站全向缺席证明）与 ``grid``（49 点全向覆盖，无后备清除）；
|
||||
- ``compare.py`` ：core + boundary 基准集配对对比并落盘。
|
||||
"""
|
||||
|
||||
from . import baseline, optimal
|
||||
|
||||
__all__ = ["optimal", "baseline"]
|
||||
@@ -0,0 +1,29 @@
|
||||
"""问题四基线实现：把问题三策略原样搬用的两条对照。
|
||||
|
||||
- ``bayes``：问题三最优策略直接用于混合源。它用七站环的**全向**缺席证明，
|
||||
对"定向源朝外发射"场景会错误地把真源判为 ABSENT，属于"快而错"的典型；
|
||||
- ``grid``：49 点全向网格（对定向源发现是完备的），但缺少定向观测推理与
|
||||
≤20 m 后备清除，定位停滞时只能停在 DETECTED，属于"稳而不全"。
|
||||
两条基线都不共享最优实现的联合信念与后备清除逻辑。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from mcm26b.planner import OmnidirectionalLoop
|
||||
from mcm26b.planner.bayes_loop import BayesLoop
|
||||
|
||||
from project.common import Method
|
||||
|
||||
BAYES = Method(key="baseline", name="问题三最优策略直接搬用 (bayes)",
|
||||
kind="baseline", note="七站全向缺席证明在定向源上失效")
|
||||
GRID = Method(key="baseline_grid", name="49 点网格全向策略 (grid)",
|
||||
kind="baseline", note="覆盖完备但无定向推理与后备清除")
|
||||
|
||||
def build_bayes(client, config):
|
||||
"""工厂：问题三 bayes 策略直接搬用。"""
|
||||
return BayesLoop(client, config)
|
||||
|
||||
|
||||
def build_grid(client, config):
|
||||
"""工厂：49 点网格全向策略直接搬用。"""
|
||||
return OmnidirectionalLoop(client, config)
|
||||
@@ -0,0 +1,32 @@
|
||||
"""问题四 core+boundary 基准集配对对比：directional（最优） vs 两条搬用基线。"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from project.common import (RESULTS_DIR, closed_loop_payload, episodes_markdown,
|
||||
execute_variants, problem_config, write_json,
|
||||
write_text)
|
||||
from project.q4 import baseline, optimal
|
||||
|
||||
VARIANTS = (
|
||||
("optimal", optimal.build_loop),
|
||||
("baseline_grid", baseline.build_grid),
|
||||
("baseline", baseline.build_bayes),
|
||||
)
|
||||
LABELS = [label for label, _ in VARIANTS]
|
||||
BASE_LABEL = "baseline"
|
||||
|
||||
|
||||
def run(quick: bool = False) -> dict:
|
||||
from mcm26b.harness.suites import suite
|
||||
scenes = suite("core", 4) + suite("boundary", 4)
|
||||
if quick:
|
||||
scenes = scenes[:4]
|
||||
config = problem_config()
|
||||
rows = execute_variants(scenes, VARIANTS, config)
|
||||
payload = closed_loop_payload(4, "core+boundary", scenes, VARIANTS, rows,
|
||||
LABELS, BASE_LABEL, config)
|
||||
write_json(RESULTS_DIR / "q4.json", payload)
|
||||
write_text(RESULTS_DIR / "q4.md",
|
||||
episodes_markdown("问题四：混合源闭环配对对比（core+boundary 基准集）",
|
||||
rows, LABELS, BASE_LABEL))
|
||||
return payload
|
||||
@@ -0,0 +1,22 @@
|
||||
"""问题四最优实现：``mcm26b.planner.DirectionalExtension``（directional 策略）。
|
||||
|
||||
三点相对全向闭环的加装：
|
||||
1. 发现覆盖改用经全向/定向双口径证伪的 31 点最小命中集
|
||||
（``q4_mincover_v2``，15 m/72 档验证零缺口）；
|
||||
2. 保证接收盘内 ``no_signal`` 只可能由朝向遮挡解释：记为"观测到定向"，
|
||||
对 MEC 圆心取对跖点复核，并用 (位置×朝向) 联合可行集选可见性探点；
|
||||
3. 定位停滞时对有界候选集构造 ≤20 m 网格圆盘覆盖，预算核对后逐格 /clear
|
||||
（清除与朝向无关），是定向源不依赖可见性的完备后备。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from mcm26b.planner import DirectionalExtension
|
||||
|
||||
from project.common import Method
|
||||
|
||||
METHOD = Method(key="optimal", name="定向扩展闭环 (directional)",
|
||||
kind="optimal", note="联合信念 + 后备清除 + 定向覆盖计划")
|
||||
def build_loop(client, config):
|
||||
"""工厂：返回定向扩展闭环实例。"""
|
||||
return DirectionalExtension(client, config)
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,80 @@
|
||||
# 问题一：固定示例对比
|
||||
|
||||
参考解为独立的大框半平面裁剪 + 精确直径/MEC；
|
||||
固定示例见 `examples/q1`，解析期望由 `tests/test_geometry*.py` 固化。
|
||||
|
||||
| 算例 | 来源 | 参考状态 | 最优状态 | 基线状态 | D参考(m) | D最优(m) | D基线(m) | |ΔD|最优 | |ΔD|基线 | 覆盖参考 | 覆盖最优 | 覆盖基线 |
|
||||
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|
||||
| 三点交会 | examples/q1 | polygon | polygon | polygon | 26.99 | 26.99 | 21.21 | 0.00 | 5.77 | 否 | 否 | 是 |
|
||||
| 直径圆反例 | examples/q1 | polygon | polygon | polygon | 40.00 | 40.00 | 50.00 | 0.00 | 10.00 | 否 | 否 | 是 |
|
||||
| 近共线检测点 | examples/q1 | unbounded | unbounded | unbounded | — | — | — | — | — | — | — | — |
|
||||
| 零误差退化线段 | examples/q1 | segment | segment | point | 100.00 | 100.00 | 0.00 | 0.00 | 100.00 | 是 | 是 | 是 |
|
||||
| 不相容观测 | examples/q1 | empty | empty | empty | — | — | — | — | — | — | — | — |
|
||||
|
||||
## 汇总
|
||||
|
||||
| 指标 | 最优 | 基线 |
|
||||
| --- | --- | --- |
|
||||
| 状态判定准确率 | 100.0% | 80.0% |
|
||||
| 直径平均绝对误差 (m) | 0.000 | 7.886 |
|
||||
| 直径最大误差 (m) | 0.000 | 10.000 |
|
||||
| 覆盖判定准确率 | 100.0% | 33.3% |
|
||||
| 平均耗时 (ms) | 0.064 | 14.202 |
|
||||
|
||||
# 问题一：随机交会算例对比
|
||||
|
||||
参考解为独立的大框半平面裁剪 + 精确直径/MEC；
|
||||
固定示例见 `examples/q1`，解析期望由 `tests/test_geometry*.py` 固化。
|
||||
|
||||
| 算例 | 来源 | 参考状态 | 最优状态 | 基线状态 | D参考(m) | D最优(m) | D基线(m) | |ΔD|最优 | |ΔD|基线 | 覆盖参考 | 覆盖最优 | 覆盖基线 |
|
||||
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 40.32 | 40.32 | 29.15 | 0.00 | 11.16 | 是 | 是 | 是 |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 33.36 | 33.36 | 35.36 | 0.00 | 2.00 | 是 | 是 | 是 |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 39.78 | 39.78 | 40.31 | 0.00 | 0.53 | 是 | 是 | 是 |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 37.50 | 37.50 | 36.06 | 0.00 | 1.44 | 否 | 否 | 是 |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 48.24 | 48.24 | 47.43 | 0.00 | 0.81 | 是 | 是 | 是 |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 27.97 | 27.97 | 25.00 | 0.00 | 2.97 | 否 | 否 | 是 |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 31.84 | 31.84 | 32.02 | 0.00 | 0.17 | 否 | 否 | 是 |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 39.13 | 39.13 | 31.62 | 0.00 | 7.51 | 是 | 是 | 是 |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 31.60 | 31.60 | 32.02 | 0.00 | 0.42 | 是 | 是 | 是 |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 34.49 | 34.49 | 32.02 | 0.00 | 2.48 | 否 | 否 | 是 |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 32.61 | 32.61 | 26.93 | 0.00 | 5.68 | 是 | 是 | 是 |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 37.41 | 37.41 | 35.36 | 0.00 | 2.05 | 否 | 否 | 是 |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 35.15 | 35.15 | 36.06 | 0.00 | 0.90 | 否 | 否 | 是 |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 20.56 | 20.56 | 18.03 | 0.00 | 2.53 | 否 | 否 | 是 |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 25.23 | 25.23 | 18.03 | 0.00 | 7.20 | 否 | 否 | 是 |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 31.61 | 31.61 | 32.02 | 0.00 | 0.41 | 是 | 是 | 是 |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 22.19 | 22.19 | 14.14 | 0.00 | 8.05 | 否 | 否 | 是 |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 26.31 | 26.31 | 18.03 | 0.00 | 8.28 | 是 | 是 | 是 |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 37.85 | 37.85 | 28.28 | 0.00 | 9.56 | 是 | 是 | 是 |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 25.78 | 25.78 | 11.18 | 0.00 | 14.60 | 是 | 是 | 是 |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 22.53 | 22.53 | 18.03 | 0.00 | 4.51 | 否 | 否 | 是 |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 36.84 | 36.84 | 26.93 | 0.00 | 9.91 | 是 | 是 | 是 |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 51.21 | 51.21 | 47.17 | 0.00 | 4.04 | 是 | 是 | 是 |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 32.88 | 32.88 | 29.15 | 0.00 | 3.73 | 是 | 是 | 是 |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 29.99 | 29.99 | 32.02 | 0.00 | 2.02 | 否 | 否 | 是 |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 36.50 | 36.50 | 28.28 | 0.00 | 8.21 | 是 | 是 | 是 |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 26.38 | 26.38 | 18.03 | 0.00 | 8.35 | 是 | 是 | 是 |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 50.11 | 50.11 | 49.24 | 0.00 | 0.86 | 是 | 是 | 是 |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 23.29 | 23.29 | 22.36 | 0.00 | 0.93 | 否 | 否 | 是 |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 40.73 | 40.73 | 42.43 | 0.00 | 1.70 | 是 | 是 | 是 |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 34.09 | 34.09 | 25.00 | 0.00 | 9.09 | 是 | 是 | 是 |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 23.35 | 23.35 | 18.03 | 0.00 | 5.32 | 否 | 否 | 是 |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 14.89 | 14.89 | 11.18 | 0.00 | 3.71 | 否 | 否 | 是 |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 38.12 | 38.12 | 33.54 | 0.00 | 4.58 | 是 | 是 | 是 |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 13.68 | 13.68 | 5.00 | 0.00 | 8.68 | 是 | 是 | 是 |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 41.44 | 41.44 | 43.01 | 0.00 | 1.57 | 否 | 否 | 是 |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 22.99 | 22.99 | 18.03 | 0.00 | 4.97 | 否 | 否 | 是 |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 34.00 | 34.00 | 35.36 | 0.00 | 1.35 | 否 | 否 | 是 |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 16.20 | 16.20 | 7.07 | 0.00 | 9.13 | 否 | 否 | 是 |
|
||||
| 随机交会 | synthetic | polygon | polygon | polygon | 26.90 | 26.90 | 25.00 | 0.00 | 1.90 | 否 | 否 | 是 |
|
||||
|
||||
## 汇总
|
||||
|
||||
| 指标 | 最优 | 基线 |
|
||||
| --- | --- | --- |
|
||||
| 状态判定准确率 | 100.0% | 100.0% |
|
||||
| 直径平均绝对误差 (m) | 0.000 | 4.583 |
|
||||
| 直径最大误差 (m) | 0.000 | 14.599 |
|
||||
| 覆盖判定准确率 | 100.0% | 52.5% |
|
||||
| 平均耗时 (ms) | 0.086 | 15.932 |
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,137 @@
|
||||
# 问题二：第二检测点选择对比
|
||||
|
||||
初始 ρ 为首次示向度候选集 K₁ 的最小包围半径；最坏后验按目标边界采样 × 读数区间端点统一评估。
|
||||
|
||||
| 算例 | 方法 | 初始 ρ(m) | 第二点 q | 保证接收 | q 到 K₁ 最远(m) | 最坏后验 ρ'(m) | 收缩(m) | 移动(s) |
|
||||
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
|
||||
| q2-4200 | optimal | 674.59 | (1257,89) | 是 | 907.09 | 54.45 | 620.13 | 181.42 |
|
||||
| q2-4200 | baseline_perp | 674.59 | (194,462) | 否 | 1679.16 | 76.22 | 598.36 | 200.00 |
|
||||
| q2-4200 | baseline_mec | 674.59 | (1173,-226) | 是 | 674.59 | 337.29 | 337.29 | 134.92 |
|
||||
| q2-4200 | reference | 674.59 | (1173,100) | 是 | 850.11 | 57.70 | 616.89 | 170.02 |
|
||||
| q2-4201 | optimal | 261.33 | (-1501,-348) | 是 | 461.91 | 13.55 | 247.78 | 92.38 |
|
||||
| q2-4201 | baseline_perp | 261.33 | (-782,-715) | 否 | 1131.68 | 27.22 | 234.11 | 200.00 |
|
||||
| q2-4201 | baseline_mec | 261.33 | (-1578,-63) | 是 | 261.33 | 130.67 | 130.67 | 52.27 |
|
||||
| q2-4201 | reference | 261.33 | (-1393,-432) | 是 | 527.03 | 14.98 | 246.36 | 105.41 |
|
||||
| q2-4202 | optimal | 591.09 | (-1126,-861) | 是 | 869.01 | 37.92 | 553.17 | 173.80 |
|
||||
| q2-4202 | baseline_perp | 591.09 | (408,-1235) | 否 | 1561.73 | 64.57 | 526.52 | 200.00 |
|
||||
| q2-4202 | baseline_mec | 591.09 | (-731,-967) | 是 | 591.09 | 295.54 | 295.54 | 118.22 |
|
||||
| q2-4202 | reference | 591.09 | (-1038,-865) | 是 | 788.83 | 46.22 | 544.86 | 157.77 |
|
||||
| q2-4203 | optimal | 326.92 | (1145,-1745) | 是 | 683.58 | 17.13 | 309.79 | 136.72 |
|
||||
| q2-4203 | baseline_perp | 326.92 | (1837,-1523) | 否 | 1204.35 | 32.99 | 293.93 | 200.00 |
|
||||
| q2-4203 | baseline_mec | 326.92 | (788,-1388) | 是 | 326.92 | 163.46 | 163.46 | 65.38 |
|
||||
| q2-4203 | reference | 326.92 | (1125,-1725) | 是 | 657.98 | 16.90 | 310.02 | 131.60 |
|
||||
| q2-4204 | optimal | 750.20 | (-401,208) | 是 | 876.74 | 91.60 | 658.60 | 175.35 |
|
||||
| q2-4204 | baseline_perp | 750.20 | (-1541,302) | 否 | 1817.48 | 90.79 | 659.41 | 200.00 |
|
||||
| q2-4204 | baseline_mec | 750.20 | (-336,-34) | 是 | 750.20 | 375.10 | 375.10 | 150.04 |
|
||||
| q2-4204 | reference | 750.20 | (-336,216) | 是 | 923.80 | 92.25 | 657.95 | 184.76 |
|
||||
| q2-4205 | optimal | 750.14 | (-503,21) | 是 | 881.59 | 91.10 | 659.04 | 176.32 |
|
||||
| q2-4205 | baseline_perp | 750.14 | (-612,-1470) | 否 | 1817.36 | 90.78 | 659.36 | 200.00 |
|
||||
| q2-4205 | baseline_mec | 750.14 | (-568,-220) | 是 | 750.14 | 375.08 | 375.06 | 150.03 |
|
||||
| q2-4205 | reference | 750.14 | (-568,30) | 是 | 927.74 | 92.25 | 657.89 | 185.55 |
|
||||
| q2-4206 | optimal | 750.12 | (216,677) | 是 | 892.00 | 90.96 | 659.16 | 178.40 |
|
||||
| q2-4206 | baseline_perp | 750.12 | (1592,1266) | 否 | 1817.31 | 90.77 | 659.34 | 200.00 |
|
||||
| q2-4206 | baseline_mec | 750.12 | (457,742) | 是 | 750.12 | 375.10 | 375.02 | 150.02 |
|
||||
| q2-4206 | reference | 750.12 | (207,742) | 是 | 838.72 | 94.21 | 655.91 | 167.74 |
|
||||
| q2-4207 | optimal | 750.12 | (1205,476) | 是 | 899.09 | 90.85 | 659.27 | 179.82 |
|
||||
| q2-4207 | baseline_perp | 750.12 | (578,1465) | 否 | 1817.31 | 90.78 | 659.34 | 200.00 |
|
||||
| q2-4207 | baseline_mec | 750.12 | (1028,299) | 是 | 750.12 | 375.09 | 375.03 | 150.02 |
|
||||
| q2-4207 | reference | 750.12 | (1028,49) | 是 | 852.52 | 92.97 | 657.15 | 170.50 |
|
||||
| q2-4208 | optimal | 750.19 | (-1409,-294) | 是 | 901.20 | 90.75 | 659.44 | 180.24 |
|
||||
| q2-4208 | baseline_perp | 750.19 | (-8,239) | 否 | 1817.46 | 90.79 | 659.40 | 200.00 |
|
||||
| q2-4208 | baseline_mec | 750.19 | (-1167,-229) | 是 | 750.19 | 375.10 | 375.09 | 150.04 |
|
||||
| q2-4208 | reference | 750.19 | (-1417,-229) | 是 | 849.54 | 93.05 | 657.14 | 169.91 |
|
||||
| q2-4209 | optimal | 750.20 | (463,849) | 是 | 849.70 | 93.03 | 657.17 | 169.94 |
|
||||
| q2-4209 | baseline_perp | 750.20 | (-621,1100) | 否 | 1817.43 | 90.79 | 659.41 | 200.00 |
|
||||
| q2-4209 | baseline_mec | 750.20 | (527,607) | 是 | 750.20 | 375.10 | 375.10 | 150.04 |
|
||||
| q2-4209 | reference | 750.20 | (527,857) | 是 | 901.34 | 90.74 | 659.46 | 180.27 |
|
||||
| q2-4210 | optimal | 706.03 | (-429,1151) | 是 | 915.53 | 63.69 | 642.34 | 183.11 |
|
||||
| q2-4210 | baseline_perp | 706.03 | (-206,15) | 否 | 1722.00 | 80.64 | 625.39 | 200.00 |
|
||||
| q2-4210 | baseline_mec | 706.03 | (-145,1227) | 是 | 706.03 | 353.02 | 353.02 | 141.21 |
|
||||
| q2-4210 | reference | 706.03 | (-366,1080) | 是 | 830.65 | 72.40 | 633.63 | 166.13 |
|
||||
| q2-4211 | optimal | 750.20 | (-619,-443) | 是 | 901.34 | 90.74 | 659.46 | 180.27 |
|
||||
| q2-4211 | baseline_perp | 750.20 | (-379,1037) | 否 | 1817.47 | 90.79 | 659.41 | 200.00 |
|
||||
| q2-4211 | baseline_mec | 750.20 | (-554,-202) | 是 | 750.20 | 375.10 | 375.10 | 150.04 |
|
||||
| q2-4211 | reference | 750.20 | (-554,-451) | 是 | 943.43 | 94.71 | 655.49 | 188.69 |
|
||||
| q2-4212 | optimal | 750.13 | (343,-10) | 是 | 905.38 | 90.90 | 659.23 | 181.08 |
|
||||
| q2-4212 | baseline_perp | 750.13 | (1486,-980) | 否 | 1817.34 | 90.78 | 659.35 | 200.00 |
|
||||
| q2-4212 | baseline_mec | 750.13 | (520,-187) | 是 | 750.13 | 375.09 | 375.04 | 150.03 |
|
||||
| q2-4212 | reference | 750.13 | (520,-437) | 是 | 845.04 | 93.65 | 656.48 | 169.01 |
|
||||
| q2-4213 | optimal | 750.12 | (-347,-795) | 是 | 859.45 | 92.44 | 657.68 | 171.89 |
|
||||
| q2-4213 | baseline_perp | 750.12 | (-1650,-75) | 否 | 1817.31 | 90.77 | 659.34 | 200.00 |
|
||||
| q2-4213 | baseline_mec | 750.12 | (-524,-618) | 是 | 750.12 | 375.09 | 375.03 | 150.02 |
|
||||
| q2-4213 | reference | 750.12 | (-524,-368) | 是 | 893.01 | 90.88 | 659.24 | 178.60 |
|
||||
| q2-4214 | optimal | 750.16 | (623,-1142) | 是 | 906.31 | 90.87 | 659.29 | 181.26 |
|
||||
| q2-4214 | baseline_perp | 750.16 | (-445,-639) | 否 | 1817.36 | 90.78 | 659.38 | 200.00 |
|
||||
| q2-4214 | baseline_mec | 750.16 | (559,-1383) | 是 | 750.16 | 375.09 | 375.07 | 150.03 |
|
||||
| q2-4214 | reference | 750.16 | (559,-1134) | 是 | 855.60 | 92.58 | 657.58 | 171.12 |
|
||||
| q2-4215 | optimal | 559.27 | (-1338,120) | 是 | 820.56 | 35.56 | 523.71 | 164.11 |
|
||||
| q2-4215 | baseline_perp | 559.27 | (-731,-1350) | 否 | 1513.40 | 59.96 | 499.32 | 200.00 |
|
||||
| q2-4215 | baseline_mec | 559.27 | (-1224,-306) | 是 | 559.27 | 279.64 | 279.64 | 111.85 |
|
||||
| q2-4215 | reference | 559.27 | (-1444,-636) | 是 | 828.17 | 35.86 | 523.42 | 165.63 |
|
||||
| q2-4216 | optimal | 725.32 | (-758,-1062) | 是 | 869.88 | 65.59 | 659.73 | 173.97 |
|
||||
| q2-4216 | baseline_perp | 725.32 | (-669,40) | 否 | 1747.41 | 79.80 | 645.52 | 200.00 |
|
||||
| q2-4216 | baseline_mec | 725.32 | (-1023,-1133) | 是 | 725.32 | 362.66 | 362.66 | 145.06 |
|
||||
| q2-4216 | reference | 725.32 | (-749,-1133) | 是 | 919.99 | 63.67 | 661.65 | 184.00 |
|
||||
| q2-4217 | optimal | 750.16 | (-1472,727) | 是 | 848.85 | 93.13 | 657.03 | 169.77 |
|
||||
| q2-4217 | baseline_perp | 750.16 | (10,819) | 否 | 1817.41 | 90.78 | 659.38 | 200.00 |
|
||||
| q2-4217 | baseline_mec | 750.16 | (-1231,663) | 是 | 750.16 | 375.09 | 375.07 | 150.03 |
|
||||
| q2-4217 | reference | 750.16 | (-1481,663) | 是 | 900.62 | 90.77 | 659.39 | 180.12 |
|
||||
| q2-4218 | optimal | 750.18 | (-332,-300) | 是 | 864.09 | 92.27 | 657.91 | 172.82 |
|
||||
| q2-4218 | baseline_perp | 750.18 | (-1821,-299) | 否 | 1817.39 | 90.78 | 659.39 | 200.00 |
|
||||
| q2-4218 | baseline_mec | 750.18 | (-573,-235) | 是 | 750.18 | 375.09 | 375.08 | 150.04 |
|
||||
| q2-4218 | reference | 750.18 | (-323,-235) | 是 | 913.40 | 91.48 | 658.69 | 182.68 |
|
||||
| q2-4219 | optimal | 750.13 | (-75,786) | 是 | 868.14 | 91.76 | 658.37 | 173.63 |
|
||||
| q2-4219 | baseline_perp | 750.13 | (-100,-705) | 否 | 1817.34 | 90.78 | 659.35 | 200.00 |
|
||||
| q2-4219 | baseline_mec | 750.13 | (-140,544) | 是 | 750.13 | 375.09 | 375.04 | 150.03 |
|
||||
| q2-4219 | reference | 750.13 | (-140,794) | 是 | 916.75 | 91.35 | 658.78 | 183.35 |
|
||||
| q2-4220 | optimal | 750.13 | (-142,237) | 是 | 865.60 | 92.06 | 658.07 | 173.12 |
|
||||
| q2-4220 | baseline_perp | 750.13 | (-1191,-184) | 否 | 1817.34 | 90.78 | 659.35 | 200.00 |
|
||||
| q2-4220 | baseline_mec | 750.13 | (35,60) | 是 | 750.13 | 375.09 | 375.04 | 150.03 |
|
||||
| q2-4220 | reference | 750.13 | (285,60) | 是 | 887.39 | 90.95 | 659.18 | 177.48 |
|
||||
| q2-4221 | optimal | 750.12 | (537,-636) | 是 | 859.18 | 92.47 | 657.65 | 171.84 |
|
||||
| q2-4221 | baseline_perp | 750.12 | (-232,638) | 否 | 1817.32 | 90.78 | 659.35 | 200.00 |
|
||||
| q2-4221 | baseline_mec | 750.12 | (472,-395) | 是 | 750.12 | 375.09 | 375.03 | 150.02 |
|
||||
| q2-4221 | reference | 750.12 | (472,-644) | 是 | 801.14 | 98.78 | 651.34 | 160.23 |
|
||||
| q2-4222 | optimal | 672.85 | (1340,149) | 是 | 896.12 | 46.69 | 626.15 | 179.22 |
|
||||
| q2-4222 | baseline_perp | 672.85 | (461,-526) | 否 | 1662.43 | 70.95 | 601.90 | 200.00 |
|
||||
| q2-4222 | baseline_mec | 672.85 | (1571,-82) | 是 | 672.85 | 336.42 | 336.42 | 134.57 |
|
||||
| q2-4222 | reference | 672.85 | (1326,82) | 是 | 830.57 | 53.42 | 619.43 | 166.11 |
|
||||
| q2-4223 | optimal | 395.70 | (-1736,-787) | 是 | 696.84 | 21.22 | 374.48 | 139.37 |
|
||||
| q2-4223 | baseline_perp | 395.70 | (-1461,-1548) | 否 | 1286.11 | 39.72 | 355.98 | 200.00 |
|
||||
| q2-4223 | baseline_mec | 395.70 | (-1416,-467) | 是 | 395.70 | 197.85 | 197.85 | 79.14 |
|
||||
| q2-4223 | reference | 395.70 | (-1416,-14) | 是 | 716.03 | 20.83 | 374.87 | 143.21 |
|
||||
| q2-4224 | optimal | 750.16 | (-899,669) | 是 | 888.47 | 90.87 | 659.29 | 177.69 |
|
||||
| q2-4224 | baseline_perp | 750.16 | (467,1281) | 否 | 1817.40 | 90.78 | 659.38 | 200.00 |
|
||||
| q2-4224 | baseline_mec | 750.16 | (-657,734) | 是 | 750.16 | 375.09 | 375.07 | 150.03 |
|
||||
| q2-4224 | reference | 750.16 | (-907,734) | 是 | 834.61 | 94.50 | 655.66 | 166.92 |
|
||||
| q2-4225 | optimal | 750.12 | (-305,-294) | 是 | 866.51 | 91.95 | 658.17 | 173.30 |
|
||||
| q2-4225 | baseline_perp | 750.12 | (-1064,-1577) | 否 | 1817.31 | 90.77 | 659.34 | 200.00 |
|
||||
| q2-4225 | baseline_mec | 750.12 | (-482,-470) | 是 | 750.12 | 375.10 | 375.02 | 150.02 |
|
||||
| q2-4225 | reference | 750.12 | (-732,-470) | 是 | 886.54 | 91.02 | 659.10 | 177.31 |
|
||||
| q2-4226 | optimal | 750.12 | (587,389) | 是 | 898.38 | 90.90 | 659.22 | 179.68 |
|
||||
| q2-4226 | baseline_perp | 750.12 | (1757,433) | 否 | 1817.31 | 90.77 | 659.34 | 200.00 |
|
||||
| q2-4226 | baseline_mec | 750.12 | (523,630) | 是 | 750.12 | 375.11 | 375.01 | 150.02 |
|
||||
| q2-4226 | reference | 750.12 | (523,380) | 是 | 941.12 | 94.47 | 655.65 | 188.22 |
|
||||
| q2-4227 | optimal | 527.91 | (711,-1627) | 是 | 843.99 | 29.98 | 497.92 | 168.80 |
|
||||
| q2-4227 | baseline_perp | 527.91 | (1503,-1122) | 否 | 1466.90 | 55.63 | 472.28 | 200.00 |
|
||||
| q2-4227 | baseline_mec | 527.91 | (377,-1293) | 是 | 527.91 | 263.95 | 263.95 | 105.58 |
|
||||
| q2-4227 | reference | 527.91 | (-95,-1293) | 是 | 819.70 | 30.74 | 497.17 | 163.94 |
|
||||
| q2-4228 | optimal | 750.12 | (-160,534) | 是 | 892.39 | 90.93 | 659.19 | 178.48 |
|
||||
| q2-4228 | baseline_perp | 750.12 | (20,2020) | 否 | 1817.31 | 90.77 | 659.34 | 200.00 |
|
||||
| q2-4228 | baseline_mec | 750.12 | (-96,775) | 是 | 750.12 | 375.11 | 375.01 | 150.02 |
|
||||
| q2-4228 | reference | 750.12 | (-96,525) | 是 | 936.39 | 93.58 | 656.54 | 187.28 |
|
||||
| q2-4229 | optimal | 750.20 | (455,-883) | 是 | 876.70 | 91.34 | 658.86 | 175.34 |
|
||||
| q2-4229 | baseline_perp | 750.20 | (-684,-787) | 否 | 1817.44 | 90.79 | 659.41 | 200.00 |
|
||||
| q2-4229 | baseline_mec | 750.20 | (519,-1124) | 是 | 750.20 | 375.10 | 375.10 | 150.04 |
|
||||
| q2-4229 | reference | 750.20 | (519,-874) | 是 | 923.76 | 91.91 | 658.30 | 184.75 |
|
||||
|
||||
## 逐方法汇总
|
||||
|
||||
| 方法 | 保证接收率 | 最坏后验均值(m) | 中位(m) | 平均收缩(m) | 终值比 ρ'/ρ | 移动均值(s) |
|
||||
| --- | --- | --- | --- | --- | --- | --- |
|
||||
| optimal | 100.0% | 73.89 | 90.90 | 607.58 | 0.104 | 170.44 |
|
||||
| baseline_perp | 0.0% | 80.11 | 90.78 | 601.36 | 0.116 | 200.00 |
|
||||
| baseline_mec | 100.0% | 340.75 | 375.09 | 340.72 | 0.500 | 136.29 |
|
||||
| reference | 100.0% | 75.63 | 91.35 | 605.84 | 0.106 | 170.28 |
|
||||
|
||||
初始 ρ 均值：681.47 m
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,75 @@
|
||||
# 问题三：全向闭环配对对比（core 基准集）
|
||||
|
||||
## 逐场景明细
|
||||
|
||||
| scene | sources | variant | cleared | T(s) | 检测 | 请求 | 位移(m) | 后备清除 | 墙钟(s) | 有效 |
|
||||
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|
||||
| q3-core-s100-n10 | 10 | optimal | 10/10 | 2563.0 | 109 | 119 | 9330.1 | 0 | 0.70 | ✔ |
|
||||
| q3-core-s100-n10 | 10 | seven | 10/10 | 4465.5 | 102 | 112 | 19057.6 | 0 | 1.18 | ✔ |
|
||||
| q3-core-s100-n10 | 10 | baseline | 10/10 | 15309.6 | 564 | 574 | 59457.9 | 0 | 1.16 | ✔ |
|
||||
| q3-core-s100-n13 | 13 | optimal | 13/13 | 3120.8 | 114 | 127 | 11914.1 | 0 | 0.73 | ✔ |
|
||||
| q3-core-s100-n13 | 13 | seven | 13/13 | 5216.0 | 96 | 109 | 22919.8 | 0 | 1.40 | ✔ |
|
||||
| q3-core-s100-n13 | 13 | baseline | 13/13 | 16205.3 | 450 | 463 | 67311.6 | 0 | 1.46 | ✔ |
|
||||
| q3-core-s100-n16 | 16 | optimal | 16/16 | 3618.1 | 113 | 129 | 14355.4 | 0 | 0.89 | ✔ |
|
||||
| q3-core-s100-n16 | 16 | seven | 16/16 | 5978.8 | 96 | 112 | 26653.8 | 0 | 1.66 | ✔ |
|
||||
| q3-core-s100-n16 | 16 | baseline | 16/16 | 13180.4 | 254 | 270 | 58011.9 | 0 | 1.85 | ✔ |
|
||||
| q3-core-s101-n10 | 10 | optimal | 10/10 | 3101.7 | 119 | 129 | 11723.5 | 0 | 0.69 | ✔ |
|
||||
| q3-core-s101-n10 | 10 | seven | 10/10 | 4498.1 | 107 | 117 | 19065.6 | 0 | 1.11 | ✔ |
|
||||
| q3-core-s101-n10 | 10 | baseline | 10/10 | 15552.8 | 545 | 555 | 61248.9 | 0 | 1.15 | ✔ |
|
||||
| q3-core-s101-n13 | 13 | optimal | 13/13 | 2943.3 | 112 | 125 | 11106.4 | 0 | 0.85 | ✔ |
|
||||
| q3-core-s101-n13 | 13 | seven | 13/13 | 5390.3 | 101 | 114 | 23656.4 | 0 | 1.43 | ✔ |
|
||||
| q3-core-s101-n13 | 13 | baseline | 13/13 | 16620.6 | 452 | 465 | 69338.1 | 0 | 1.60 | ✔ |
|
||||
| q3-core-s101-n16 | 16 | optimal | 16/16 | 3560.0 | 99 | 115 | 14469.9 | 0 | 0.81 | ✔ |
|
||||
| q3-core-s101-n16 | 16 | seven | 16/16 | 6462.4 | 92 | 108 | 29211.8 | 0 | 1.76 | ✔ |
|
||||
| q3-core-s101-n16 | 16 | baseline | 16/16 | 13434.4 | 260 | 276 | 59121.8 | 0 | 1.96 | ✔ |
|
||||
| q3-core-s102-n10 | 10 | optimal | 10/10 | 2916.0 | 125 | 135 | 10634.8 | 0 | 0.58 | ✔ |
|
||||
| q3-core-s102-n10 | 10 | seven | 10/10 | 5411.3 | 118 | 128 | 23326.6 | 0 | 1.28 | ✔ |
|
||||
| q3-core-s102-n10 | 10 | baseline | 10/10 | 15015.5 | 613 | 623 | 56517.5 | 0 | 1.28 | ✔ |
|
||||
| q3-core-s102-n13 | 13 | optimal | 13/13 | 3039.5 | 122 | 135 | 11242.3 | 0 | 0.84 | ✔ |
|
||||
| q3-core-s102-n13 | 13 | seven | 13/13 | 5209.5 | 112 | 125 | 22407.3 | 0 | 1.52 | ✔ |
|
||||
| q3-core-s102-n13 | 13 | baseline | 13/13 | 16062.8 | 450 | 463 | 66603.8 | 0 | 1.54 | ✔ |
|
||||
| q3-core-s102-n16 | 16 | optimal | 16/16 | 3266.2 | 119 | 135 | 12441.1 | 0 | 0.74 | ✔ |
|
||||
| q3-core-s102-n16 | 16 | seven | 16/16 | 5549.9 | 97 | 113 | 24489.6 | 0 | 1.65 | ✔ |
|
||||
| q3-core-s102-n16 | 16 | baseline | 16/16 | 13146.4 | 282 | 298 | 57012.1 | 0 | 1.71 | ✔ |
|
||||
| q3-core-s103-n10 | 10 | optimal | 10/10 | 2847.8 | 112 | 122 | 10684.1 | 0 | 0.68 | ✔ |
|
||||
| q3-core-s103-n10 | 10 | seven | 10/10 | 4877.3 | 110 | 120 | 20886.4 | 0 | 1.22 | ✔ |
|
||||
| q3-core-s103-n10 | 10 | baseline | 10/10 | 14895.2 | 550 | 560 | 57815.9 | 0 | 1.27 | ✔ |
|
||||
| q3-core-s103-n13 | 13 | optimal | 13/13 | 3339.9 | 114 | 127 | 13024.4 | 0 | 0.71 | ✔ |
|
||||
| q3-core-s103-n13 | 13 | seven | 13/13 | 5344.6 | 102 | 115 | 23373.1 | 0 | 1.26 | ✔ |
|
||||
| q3-core-s103-n13 | 13 | baseline | 13/13 | 16691.4 | 501 | 514 | 68212.0 | 0 | 1.56 | ✔ |
|
||||
| q3-core-s103-n16 | 16 | optimal | 16/16 | 3378.8 | 99 | 115 | 13559.2 | 0 | 0.89 | ✔ |
|
||||
| q3-core-s103-n16 | 16 | seven | 16/16 | 5526.4 | 87 | 103 | 24681.8 | 0 | 1.77 | ✔ |
|
||||
| q3-core-s103-n16 | 16 | baseline | 16/16 | 12627.8 | 256 | 272 | 55189.0 | 0 | 1.83 | ✔ |
|
||||
| q3-core-s104-n10 | 10 | optimal | 10/10 | 3317.0 | 115 | 125 | 12950.2 | 0 | 0.66 | ✔ |
|
||||
| q3-core-s104-n10 | 10 | seven | 10/10 | 4915.9 | 105 | 115 | 21219.3 | 0 | 1.06 | ✔ |
|
||||
| q3-core-s104-n10 | 10 | baseline | 10/10 | 15302.4 | 549 | 559 | 59887.2 | 0 | 1.19 | ✔ |
|
||||
| q3-core-s104-n13 | 13 | optimal | 13/13 | 3085.2 | 99 | 112 | 12176.2 | 0 | 0.75 | ✔ |
|
||||
| q3-core-s104-n13 | 13 | seven | 13/13 | 5437.5 | 97 | 110 | 24012.7 | 0 | 1.48 | ✔ |
|
||||
| q3-core-s104-n13 | 13 | baseline | 13/13 | 16278.7 | 444 | 457 | 67868.5 | 0 | 1.56 | ✔ |
|
||||
| q3-core-s104-n16 | 16 | optimal | 16/16 | 3057.8 | 85 | 101 | 12379.1 | 0 | 0.77 | ✔ |
|
||||
| q3-core-s104-n16 | 16 | seven | 16/16 | 5340.9 | 85 | 101 | 23799.8 | 0 | 1.68 | ✔ |
|
||||
| q3-core-s104-n16 | 16 | baseline | 16/16 | 12872.6 | 224 | 240 | 57378.0 | 0 | 2.00 | ✔ |
|
||||
| q3-core-s105-n10 | 10 | optimal | 10/10 | 2933.1 | 108 | 118 | 11220.4 | 0 | 0.72 | ✔ |
|
||||
| q3-core-s105-n10 | 10 | seven | 10/10 | 4370.8 | 107 | 117 | 18433.9 | 0 | 1.08 | ✔ |
|
||||
| q3-core-s105-n10 | 10 | baseline | 10/10 | 15247.7 | 579 | 589 | 58688.4 | 0 | 1.17 | ✔ |
|
||||
| q3-core-s105-n13 | 13 | optimal | 13/13 | 3645.3 | 122 | 135 | 14331.4 | 0 | 0.96 | ✔ |
|
||||
| q3-core-s105-n13 | 13 | seven | 13/13 | 6311.3 | 104 | 117 | 28171.5 | 0 | 1.66 | ✔ |
|
||||
| q3-core-s105-n13 | 13 | baseline | 13/13 | 16509.4 | 485 | 498 | 67791.8 | 0 | 1.52 | ✔ |
|
||||
| q3-core-s105-n16 | 16 | optimal | 16/16 | 3663.7 | 114 | 130 | 14543.5 | 0 | 0.81 | ✔ |
|
||||
| q3-core-s105-n16 | 16 | seven | 16/16 | 6204.3 | 97 | 113 | 27776.7 | 0 | 1.55 | ✔ |
|
||||
| q3-core-s105-n16 | 16 | baseline | 16/16 | 13889.0 | 292 | 308 | 60450.1 | 0 | 1.94 | ✔ |
|
||||
|
||||
## 逐方法汇总
|
||||
|
||||
| 方法 | 有效局 | 全清率 | 全清对数 | 缺席误判 | T 均值(s) | T 中位(s) | T 最大(s) | 检测均值 | 请求均值 | 位移均值(m) | 后备清除均值 | 墙钟均值(s) |
|
||||
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|
||||
| optimal | 18/18 | 1.00 | 18 | 0 | 3188.7 | 3111.3 | 3663.7 | 111.1 | 124.1 | 12338.1 | 0.00 | 0.77 |
|
||||
| seven | 18/18 | 1.00 | 18 | 0 | 5361.7 | 5367.5 | 6462.4 | 100.8 | 113.8 | 23508.0 | 0.00 | 1.43 |
|
||||
| baseline | 18/18 | 1.00 | 18 | 0 | 14935.7 | 15275.1 | 16691.4 | 430.6 | 443.6 | 61550.2 | 0.00 | 1.54 |
|
||||
|
||||
## 相对基线的配对变化（虚拟时间）
|
||||
|
||||
| 配对 | 均值变化 | 中位变化 | 更快场景 | 双方均有效 |
|
||||
| --- | --- | --- | --- | --- |
|
||||
| optimal_vs_baseline | -78.4% | -80.0% | 18/18 | 18 |
|
||||
| seven_vs_baseline | -63.7% | -66.9% | 18/18 | 18 |
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,56 @@
|
||||
# 问题四：混合源闭环配对对比（core+boundary 基准集）
|
||||
|
||||
## 逐场景明细
|
||||
|
||||
| scene | sources | variant | cleared | T(s) | 检测 | 请求 | 位移(m) | 后备清除 | 墙钟(s) | 有效 |
|
||||
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|
||||
| q4-boundary-n10-d5 | 10 | optimal | 10/10 | 10269.5 | 488 | 540 | 36107.7 | 44 | 2.63 | ✔ |
|
||||
| q4-boundary-n10-d5 | 10 | baseline_grid | 6/10 | 17632.7 | 735 | 741 | 66308.6 | 0 | 5.08 | ✘ |
|
||||
| q4-boundary-n10-d5 | 10 | baseline | 5/10 | 3128.1 | 135 | 140 | 11530.4 | 0 | 0.53 | ✘ |
|
||||
| q4-boundary-n16-d8 | 16 | optimal | 16/16 | 12081.1 | 421 | 626 | 44820.7 | 190 | 3.58 | ✔ |
|
||||
| q4-boundary-n16-d8 | 16 | baseline_grid | 9/16 | 20457.9 | 623 | 632 | 83969.3 | 0 | 8.86 | ✘ |
|
||||
| q4-boundary-n16-d8 | 16 | baseline | 8/16 | 3558.7 | 149 | 157 | 13173.5 | 0 | 0.52 | ✘ |
|
||||
| q4-core-s200-n10-d3 | 10 | optimal | 10/10 | 8718.7 | 402 | 412 | 31508.6 | 0 | 8.30 | ✔ |
|
||||
| q4-core-s200-n10-d3 | 10 | baseline_grid | 9/10 | 16546.8 | 576 | 585 | 65379.2 | 0 | 2.81 | ✘ |
|
||||
| q4-core-s200-n10-d3 | 10 | baseline | 10/10 | 3354.1 | 116 | 126 | 13100.5 | 0 | 0.67 | ✘ |
|
||||
| q4-core-s200-n13-d6 | 13 | optimal | 13/13 | 8808.4 | 332 | 347 | 33967.1 | 3 | 2.82 | ✔ |
|
||||
| q4-core-s200-n13-d6 | 13 | baseline_grid | 7/13 | 18438.3 | 529 | 536 | 76651.4 | 0 | 8.21 | ✘ |
|
||||
| q4-core-s200-n13-d6 | 13 | baseline | 11/13 | 3264.4 | 111 | 122 | 12761.9 | 0 | 0.80 | ✘ |
|
||||
| q4-core-s200-n16-d8 | 16 | optimal | 16/16 | 7629.2 | 267 | 283 | 29970.8 | 0 | 3.27 | ✔ |
|
||||
| q4-core-s200-n16-d8 | 16 | baseline_grid | 10/16 | 18871.3 | 526 | 536 | 78861.6 | 0 | 9.06 | ✘ |
|
||||
| q4-core-s200-n16-d8 | 16 | baseline | 12/16 | 4026.1 | 143 | 155 | 15645.6 | 0 | 1.86 | ✘ |
|
||||
| q4-core-s201-n10-d3 | 10 | optimal | 10/10 | 8514.0 | 380 | 391 | 31129.9 | 2 | 2.07 | ✔ |
|
||||
| q4-core-s201-n10-d3 | 10 | baseline_grid | 7/10 | 16779.7 | 624 | 631 | 65293.4 | 0 | 5.71 | ✘ |
|
||||
| q4-core-s201-n10-d3 | 10 | baseline | 7/10 | 2823.3 | 120 | 127 | 10391.4 | 0 | 0.71 | ✘ |
|
||||
| q4-core-s201-n13-d6 | 13 | optimal | 13/13 | 9183.0 | 325 | 338 | 36095.1 | 0 | 2.50 | ✔ |
|
||||
| q4-core-s201-n13-d6 | 13 | baseline_grid | 8/13 | 19390.8 | 584 | 592 | 79678.9 | 0 | 4.40 | ✘ |
|
||||
| q4-core-s201-n13-d6 | 13 | baseline | 11/13 | 4965.2 | 143 | 165 | 20260.8 | 0 | 1.11 | ✘ |
|
||||
| q4-core-s201-n16-d8 | 16 | optimal | 16/16 | 8129.4 | 263 | 279 | 32667.0 | 0 | 3.50 | ✔ |
|
||||
| q4-core-s201-n16-d8 | 16 | baseline_grid | 9/16 | 19845.0 | 495 | 504 | 84765.2 | 0 | 10.22 | ✘ |
|
||||
| q4-core-s201-n16-d8 | 16 | baseline | 13/16 | 4759.1 | 125 | 138 | 19835.5 | 0 | 2.12 | ✘ |
|
||||
| q4-core-s202-n10-d3 | 10 | optimal | 10/10 | 8803.3 | 420 | 430 | 31426.3 | 0 | 2.36 | ✔ |
|
||||
| q4-core-s202-n10-d3 | 10 | baseline_grid | 7/10 | 17220.0 | 669 | 676 | 66144.9 | 0 | 5.13 | ✘ |
|
||||
| q4-core-s202-n10-d3 | 10 | baseline | 7/10 | 3646.2 | 135 | 142 | 14105.9 | 0 | 0.68 | ✘ |
|
||||
| q4-core-s202-n13-d6 | 13 | optimal | 13/13 | 9630.4 | 347 | 367 | 37652.0 | 10 | 3.11 | ✔ |
|
||||
| q4-core-s202-n13-d6 | 13 | baseline_grid | 10/13 | 16443.2 | 498 | 508 | 67316.3 | 0 | 1.84 | ✘ |
|
||||
| q4-core-s202-n13-d6 | 13 | baseline | 13/13 | 4219.3 | 123 | 137 | 17136.7 | 0 | 0.81 | ✘ |
|
||||
| q4-core-s202-n16-d8 | 16 | optimal | 16/16 | 8001.5 | 267 | 305 | 31602.7 | 24 | 3.95 | ✔ |
|
||||
| q4-core-s202-n16-d8 | 16 | baseline_grid | 12/16 | 18345.2 | 452 | 464 | 78265.8 | 0 | 8.90 | ✘ |
|
||||
| q4-core-s202-n16-d8 | 16 | baseline | 13/16 | 5054.4 | 152 | 165 | 20561.9 | 0 | 2.05 | ✘ |
|
||||
|
||||
## 逐方法汇总
|
||||
|
||||
| 方法 | 有效局 | 全清率 | 全清对数 | 缺席误判 | T 均值(s) | T 中位(s) | T 最大(s) | 检测均值 | 请求均值 | 位移均值(m) | 后备清除均值 | 墙钟均值(s) |
|
||||
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|
||||
| optimal | 11/11 | 1.00 | 11 | 0 | 9069.9 | 8803.3 | 12081.1 | 355.6 | 392.5 | 34268.0 | 24.82 | 3.46 |
|
||||
| baseline_grid | 0/11 | 0.67 | 0 | 0 | 18179.2 | 18345.2 | 20457.9 | 573.7 | 582.3 | 73875.9 | 0.00 | 6.38 |
|
||||
| baseline | 0/11 | 0.77 | 2 | 24 | 3890.8 | 3646.2 | 5054.4 | 132.0 | 143.1 | 15318.6 | 0.00 | 1.08 |
|
||||
|
||||
## 相对基线的配对变化（虚拟时间）
|
||||
|
||||
| 配对 | 均值变化 | 中位变化 | 更快场景 | 双方均有效 |
|
||||
| --- | --- | --- | --- | --- |
|
||||
| optimal_vs_baseline | +142.9% | +141.4% | 0/11 | 0 |
|
||||
| baseline_grid_vs_baseline | +381.1% | +372.3% | 0/11 | 0 |
|
||||
|
||||
> 注：optimal_vs_baseline、baseline_grid_vs_baseline 存在未通过有效性判定的配对；其虚拟时间仅作参考，基线未完成全清任务时不构成「更快」证据（以「双方均有效」列为准）。
|
||||
@@ -0,0 +1,50 @@
|
||||
# 1-4 问最优 vs 基线：对比总览
|
||||
|
||||
本节所有数字由 `uv run python -m project.run` 在同机同口径下生成，
|
||||
逐问明细见 `q1.md` … `q4.md`。
|
||||
|
||||
| 问 | 核心指标 | 最优实现 | 基线实现 |
|
||||
| --- | --- | --- | --- |
|
||||
| 1 | 直径平均误差 / 覆盖准确率 | 0.00 m / 100% | 4.58 m / 52% |
|
||||
| 2 | 保证接收率 / 最坏后验均值 | 100% / 73.9 m | 100% / 340.7 m |
|
||||
| 3 | 全清率 / T 均值（相对基线） | 100% / 3189 s (-78%) | 100% / 14936 s |
|
||||
| 4 | 全清率 / 缺席误判（问题三策略搬用） | 100% / 0 | 77% / 24 |
|
||||
|
||||
## 方法映射
|
||||
|
||||
| 问 | 最优实现 | 基线实现 | 独立参考 |
|
||||
| --- | --- | --- | --- |
|
||||
| 1 | 半平面交 + 顶点对直径 + MEC | 网格采样 + bbox 对角线代理 | 大框半平面裁剪 + 精确直径/MEC |
|
||||
| 2 | 保证接收盘内最小最坏后验半径 | 垂直 1000 m / MEC 圆心 | 保证接收盘内网格枚举 |
|
||||
| 3 | bayes（粒子滤波 + 信息增益 + 滚动调度） | grid（49 点网格）/ seven（七点批量） | 冻结基准集 + 逐微秒计时复算 |
|
||||
| 4 | directional（联合信念 + 后备清除） | bayes / grid 直接搬用 | 冻结基准集 + 真值全清校验 |
|
||||
|
||||
## 目录结构
|
||||
|
||||
```text
|
||||
project/
|
||||
├── common.py 公共层：方法描述/指标聚合/结果落盘/闭环装配
|
||||
├── q1/ 问题一：直径与直径圆覆盖
|
||||
│ ├── optimal.py 生产实现：半平面交 + 顶点对直径 + MEC
|
||||
│ ├── baseline.py 基线：网格采样 + bbox 对角线代理
|
||||
│ ├── reference.py 参考：大框半平面裁剪 + 精确直径/MEC
|
||||
│ ├── cases.py 固定示例（examples/q1）+ 随机交会算例
|
||||
│ └── compare.py 同案配对对比
|
||||
├── q2/ 问题二：第二检测点选择
|
||||
│ ├── optimal.py 生产实现：保证接收盘内最小最坏后验半径
|
||||
│ ├── baseline.py 基线：垂直 1000 m / MEC 圆心
|
||||
│ ├── reference.py 参考：保证接收盘内网格枚举
|
||||
│ ├── region.py K1 统一构造
|
||||
│ ├── cases.py 随机首次示向度算例
|
||||
│ └── compare.py
|
||||
├── q3/ 问题三：全向闭环
|
||||
│ ├── optimal.py bayes（生产默认策略）
|
||||
│ ├── baseline.py grid（49 点网格）/ seven（七点批量）
|
||||
│ └── compare.py
|
||||
├── q4/ 问题四：混合源闭环
|
||||
│ ├── optimal.py directional（定向扩展）
|
||||
│ ├── baseline.py bayes / grid 直接搬用
|
||||
│ └── compare.py
|
||||
├── run.py 统一入口（本文件）
|
||||
└── results/ 自动生成：q1.md/json … q4.md/json + summary.md
|
||||
```
|
||||
+148
@@ -0,0 +1,148 @@
|
||||
"""统一入口：一次跑完 1-4 问的"最优 vs 基线"对比并生成 results/。
|
||||
|
||||
用法（仓库根目录）::
|
||||
|
||||
uv run python -m project.run # 四问全部
|
||||
uv run python -m project.run --question 3 # 只跑第三问
|
||||
uv run python -m project.run --quick # 缩小算例/基准集规模
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import time
|
||||
|
||||
from project.common import RESULTS_DIR, markdown_table, write_text
|
||||
from project.q1 import compare as q1_compare
|
||||
from project.q2 import compare as q2_compare
|
||||
from project.q3 import compare as q3_compare
|
||||
from project.q4 import compare as q4_compare
|
||||
|
||||
RUNNERS = {
|
||||
1: q1_compare.run,
|
||||
2: q2_compare.run,
|
||||
3: q3_compare.run,
|
||||
4: q4_compare.run,
|
||||
}
|
||||
|
||||
STRUCTURE = """\
|
||||
project/
|
||||
├── common.py 公共层：方法描述/指标聚合/结果落盘/闭环装配
|
||||
├── q1/ 问题一：直径与直径圆覆盖
|
||||
│ ├── optimal.py 生产实现：半平面交 + 顶点对直径 + MEC
|
||||
│ ├── baseline.py 基线：网格采样 + bbox 对角线代理
|
||||
│ ├── reference.py 参考：大框半平面裁剪 + 精确直径/MEC
|
||||
│ ├── cases.py 固定示例（examples/q1）+ 随机交会算例
|
||||
│ └── compare.py 同案配对对比
|
||||
├── q2/ 问题二：第二检测点选择
|
||||
│ ├── optimal.py 生产实现：保证接收盘内最小最坏后验半径
|
||||
│ ├── baseline.py 基线：垂直 1000 m / MEC 圆心
|
||||
│ ├── reference.py 参考：保证接收盘内网格枚举
|
||||
│ ├── region.py K1 统一构造
|
||||
│ ├── cases.py 随机首次示向度算例
|
||||
│ └── compare.py
|
||||
├── q3/ 问题三：全向闭环
|
||||
│ ├── optimal.py bayes（生产默认策略）
|
||||
│ ├── baseline.py grid（49 点网格）/ seven（七点批量）
|
||||
│ └── compare.py
|
||||
├── q4/ 问题四：混合源闭环
|
||||
│ ├── optimal.py directional（定向扩展）
|
||||
│ ├── baseline.py bayes / grid 直接搬用
|
||||
│ └── compare.py
|
||||
├── run.py 统一入口（本文件）
|
||||
└── results/ 自动生成：q1.md/json … q4.md/json + summary.md
|
||||
"""
|
||||
|
||||
|
||||
def _q1_headline(payload: dict) -> list[str]:
|
||||
agg = payload["aggregate_random"]
|
||||
return ["1", "直径平均误差 / 覆盖准确率",
|
||||
f"{agg['diameter_mae_optimal_m']:.2f} m / "
|
||||
f"{agg['coverage_accuracy_optimal']*100:.0f}%",
|
||||
f"{agg['diameter_mae_baseline_m']:.2f} m / "
|
||||
f"{agg['coverage_accuracy_baseline']*100:.0f}%"]
|
||||
|
||||
|
||||
def _q2_headline(payload: dict) -> list[str]:
|
||||
agg = payload["aggregate"]
|
||||
opt = agg["optimal"]
|
||||
base = agg["baseline_mec"]
|
||||
return ["2", "保证接收率 / 最坏后验均值",
|
||||
f"{opt['guaranteed_rate']*100:.0f}% / "
|
||||
f"{opt['worst_posterior_mean_m']:.1f} m",
|
||||
f"{base['guaranteed_rate']*100:.0f}% / "
|
||||
f"{base['worst_posterior_mean_m']:.1f} m"]
|
||||
|
||||
|
||||
def _q3_headline(payload: dict) -> list[str]:
|
||||
agg = payload["aggregate"]["variants"]
|
||||
opt, base = agg["optimal"], agg["baseline"]
|
||||
paired = payload["aggregate"]["paired"].get("optimal_vs_baseline", {})
|
||||
return ["3", "全清率 / T 均值（相对基线）",
|
||||
f"{opt['cleared_ratio_mean']*100:.0f}% / "
|
||||
f"{opt['virtual_time_mean_s']:.0f} s "
|
||||
f"({paired.get('mean_relative_change', 0)*100:+.0f}%)",
|
||||
f"{base['cleared_ratio_mean']*100:.0f}% / "
|
||||
f"{base['virtual_time_mean_s']:.0f} s"]
|
||||
|
||||
|
||||
def _q4_headline(payload: dict) -> list[str]:
|
||||
agg = payload["aggregate"]["variants"]
|
||||
opt, base = agg["optimal"], agg["baseline"]
|
||||
return ["4", "全清率 / 缺席误判（问题三策略搬用）",
|
||||
f"{opt['cleared_ratio_mean']*100:.0f}% / "
|
||||
f"{opt['absent_misjudged_total']}",
|
||||
f"{base['cleared_ratio_mean']*100:.0f}% / "
|
||||
f"{base['absent_misjudged_total']}"]
|
||||
|
||||
|
||||
def _write_summary(payloads: dict[int, dict]) -> None:
|
||||
lines = ["# 1-4 问最优 vs 基线：对比总览", "",
|
||||
"本节所有数字由 `uv run python -m project.run` 在同机同口径下生成，",
|
||||
"逐问明细见 `q1.md` … `q4.md`。", ""]
|
||||
rows = []
|
||||
if 1 in payloads:
|
||||
rows.append(_q1_headline(payloads[1]))
|
||||
if 2 in payloads:
|
||||
rows.append(_q2_headline(payloads[2]))
|
||||
if 3 in payloads:
|
||||
rows.append(_q3_headline(payloads[3]))
|
||||
if 4 in payloads:
|
||||
rows.append(_q4_headline(payloads[4]))
|
||||
lines.append(markdown_table(["问", "核心指标", "最优实现", "基线实现"], rows))
|
||||
lines += ["", "## 方法映射", "",
|
||||
"| 问 | 最优实现 | 基线实现 | 独立参考 |",
|
||||
"| --- | --- | --- | --- |",
|
||||
"| 1 | 半平面交 + 顶点对直径 + MEC | 网格采样 + bbox 对角线代理 | 大框半平面裁剪 + 精确直径/MEC |",
|
||||
"| 2 | 保证接收盘内最小最坏后验半径 | 垂直 1000 m / MEC 圆心 | 保证接收盘内网格枚举 |",
|
||||
"| 3 | bayes（粒子滤波 + 信息增益 + 滚动调度） | grid（49 点网格）/ seven（七点批量） | 冻结基准集 + 逐微秒计时复算 |",
|
||||
"| 4 | directional（联合信念 + 后备清除） | bayes / grid 直接搬用 | 冻结基准集 + 真值全清校验 |",
|
||||
"", "## 目录结构", "", "```text", STRUCTURE.rstrip(), "```", ""]
|
||||
write_text(RESULTS_DIR / "summary.md", "\n".join(lines))
|
||||
|
||||
|
||||
def main(argv=None) -> int:
|
||||
parser = argparse.ArgumentParser(description=__doc__.splitlines()[0])
|
||||
parser.add_argument("--question", choices=("1", "2", "3", "4", "all"),
|
||||
default="all")
|
||||
parser.add_argument("--quick", action="store_true",
|
||||
help="缩小随机算例数/冻结基准集规模")
|
||||
args = parser.parse_args(argv)
|
||||
|
||||
selected = [1, 2, 3, 4] if args.question == "all" else [int(args.question)]
|
||||
payloads: dict[int, dict] = {}
|
||||
for question in selected:
|
||||
start = time.perf_counter()
|
||||
print(f"[Q{question}] 运行对比 …", flush=True)
|
||||
payloads[question] = RUNNERS[question](quick=args.quick)
|
||||
print(f"[Q{question}] 完成，用时 {time.perf_counter() - start:.1f}s",
|
||||
flush=True)
|
||||
if selected == [1, 2, 3, 4]:
|
||||
_write_summary(payloads)
|
||||
print(f"总览已写入 {RESULTS_DIR / 'summary.md'}")
|
||||
print(f"结果目录：{RESULTS_DIR}")
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
@@ -1,5 +0,0 @@
|
||||
from .curriculum import STAGES, Stage, make_scene, training_config
|
||||
from .domain_randomization import randomize_problem_config
|
||||
|
||||
__all__ = ["STAGES", "Stage", "make_scene", "training_config",
|
||||
"randomize_problem_config"]
|
||||
@@ -1,6 +0,0 @@
|
||||
"""兼容入口：课程学习与域随机化。"""
|
||||
from mcm26b.hrl.curriculum import (STAGES, Stage, make_scene, stage_with_noise,
|
||||
training_config, training_spec)
|
||||
|
||||
__all__ = ["STAGES", "Stage", "make_scene", "stage_with_noise",
|
||||
"training_config", "training_spec"]
|
||||
@@ -1,31 +0,0 @@
|
||||
"""兼容入口：本地域随机化参数包装。
|
||||
|
||||
当前真实随机化在 mcm26b.hrl.curriculum.make_scene 内完成；这里只暴露
|
||||
ProblemConfig 的显式替换助手，便于 Optuna/消融在实验配置中记录随机化范围。
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import dataclasses
|
||||
from random import Random
|
||||
|
||||
from mcm26b.config import ProblemConfig
|
||||
|
||||
|
||||
def randomize_problem_config(base: ProblemConfig, *, seed: int,
|
||||
bearing_error_half_deg: float | None = None,
|
||||
r_eff_range: tuple[float, float] | None = None
|
||||
) -> ProblemConfig:
|
||||
"""返回带域随机化标量的新配置；不修改全局默认配置。"""
|
||||
_rng = Random(seed) # 固定种子留作兼容：真实随机化在 make_scene 内完成
|
||||
field = base.field
|
||||
if bearing_error_half_deg is None:
|
||||
bearing_error_half_deg = field.bearing_error_half_deg
|
||||
if r_eff_range is not None:
|
||||
lo, hi = r_eff_range
|
||||
if not 0 <= lo <= hi:
|
||||
raise ValueError("r_eff_range 必须为递增非负区间")
|
||||
return dataclasses.replace(base, field=dataclasses.replace(
|
||||
field, bearing_error_half_deg=float(bearing_error_half_deg)))
|
||||
|
||||
|
||||
__all__ = ["randomize_problem_config"]
|
||||
@@ -1,20 +0,0 @@
|
||||
"""兼容入口：调用 scripts/tune_hrl.py（P2 后启用）。"""
|
||||
from __future__ import annotations
|
||||
|
||||
import importlib.util
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
def run_tuning(argv=None) -> int:
|
||||
path = Path(__file__).resolve().parents[2] / "scripts" / "tune_hrl.py"
|
||||
if not path.exists():
|
||||
raise NotImplementedError(
|
||||
"scripts/tune_hrl.py 尚未实现；P1 阶段先冻结超参并留待 P2。")
|
||||
spec = importlib.util.spec_from_file_location("tune_hrl_compat", path)
|
||||
module = importlib.util.module_from_spec(spec)
|
||||
assert spec.loader is not None
|
||||
spec.loader.exec_module(module)
|
||||
return int(module.main(argv))
|
||||
|
||||
|
||||
__all__ = ["run_tuning"]
|
||||
+1
-18
@@ -15,30 +15,13 @@ dev = [
|
||||
# 独立评测机（自建模拟器）：仅测试侧依赖，运行时代码零导入（懒加载 + 守卫测试）
|
||||
"mcm26b-referee",
|
||||
]
|
||||
rl = [
|
||||
"gymnasium>=1.1",
|
||||
"sb3-contrib==2.9.0",
|
||||
"stable-baselines3>=2.6",
|
||||
"torch==2.11.0",
|
||||
]
|
||||
|
||||
# uv 工作区：referee/ 是独立包（零第三方依赖、不依赖 mcm26b），双方仅通过 HTTP 协议耦合
|
||||
[tool.uv.workspace]
|
||||
members = ["referee"]
|
||||
|
||||
# RL 训练使用 CUDA 12.8 版 PyTorch（本机 RTX 4060；运行时依赖保持轻量）。
|
||||
[[tool.uv.index]]
|
||||
name = "pytorch-cu128"
|
||||
url = "https://download.pytorch.org/whl/cu128"
|
||||
explicit = true
|
||||
|
||||
[tool.uv.sources]
|
||||
mcm26b-referee = { workspace = true }
|
||||
# CUDA 12.8 版 PyTorch 仅限 Windows/Linux（RTX 4060 训练机）；
|
||||
# macOS (Apple Silicon, MPS) 回落到 PyPI 官方轮子。
|
||||
torch = [
|
||||
{ index = "pytorch-cu128", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
|
||||
]
|
||||
|
||||
[build-system]
|
||||
requires = ["hatchling"]
|
||||
@@ -102,7 +85,7 @@ disable = [
|
||||
"missing-module-docstring", # 内部小模块与 __main__ 不强制
|
||||
"missing-class-docstring",
|
||||
"missing-function-docstring",
|
||||
"import-outside-toplevel", # torch/SB3/referee 等重依赖与可选依赖懒加载
|
||||
"import-outside-toplevel", # referee 等重依赖与可选依赖懒加载
|
||||
"wrong-import-position", # 同上：importorskip 守卫后的延迟导入
|
||||
"ungrouped-imports", # 同上：延迟导入无法与顶部导入分组
|
||||
"broad-exception-caught", # 子进程 / IO / HTTP 边界防御式兜底，逐处注释
|
||||
|
||||
@@ -0,0 +1,122 @@
|
||||
{"event": "session_start", "case_code": "53FK-LELA-M62B-J74U", "module": "q3_practice", "robot_id": "1", "seed": 1364411664, "countdown_s": 5.0, "window_s": 1500.0, "max_real_duration_s": 1200.0, "truth": [{"channel": 18, "x": 1296.7141429697774, "y": -444.4430546050354, "r_eff_m": 1239.1782567152018, "directional": false, "heading_deg": 0.0}, {"channel": 17, "x": 1020.5918585779062, "y": 852.2515595937525, "r_eff_m": 1026.5206174583245, "directional": false, "heading_deg": 0.0}, {"channel": 1, "x": 580.5002167548972, "y": -991.4701530799936, "r_eff_m": 1237.8121709043808, "directional": false, "heading_deg": 0.0}, {"channel": 2, "x": -16.348424904475735, "y": -478.6435890197983, "r_eff_m": 1099.9814484291328, "directional": false, "heading_deg": 0.0}, {"channel": 3, "x": -677.9492703745801, "y": 1597.8849010804165, "r_eff_m": 1497.7568411784152, "directional": false, "heading_deg": 0.0}, {"channel": 16, "x": 941.6921106181675, "y": 1045.53299027287, "r_eff_m": 1391.826351188507, "directional": false, "heading_deg": 0.0}, {"channel": 15, "x": 261.02174414951065, "y": -1297.210479453902, "r_eff_m": 1464.8227234548847, "directional": false, "heading_deg": 0.0}, {"channel": 12, "x": 889.7578735792483, "y": 1513.108837479721, "r_eff_m": 1223.8000521319436, "directional": false, "heading_deg": 0.0}, {"channel": 7, "x": 716.9679630720734, "y": -1036.910162951016, "r_eff_m": 1445.0475739763483, "directional": false, "heading_deg": 0.0}, {"channel": 4, "x": 100.68070249674014, "y": 108.02794787019978, "r_eff_m": 1176.2905736642158, "directional": false, "heading_deg": 0.0}, {"channel": 13, "x": -291.4835840002278, "y": -1010.1772742830697, "r_eff_m": 1200.9184419942253, "directional": false, "heading_deg": 0.0}, {"channel": 9, "x": 282.13965648638487, "y": 40.4254513417698, "r_eff_m": 1456.4760272053131, "directional": false, "heading_deg": 0.0}, {"channel": 10, "x": 684.7259065078848, "y": -430.9034644685128, "r_eff_m": 1479.4906472616449, "directional": false, "heading_deg": 0.0}]}
|
||||
{"event": "action", "seq": 1, "wall_ts_ms": 1789180957555, "method": "POST", "path": "/enter", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "234dfa75ad6f-0001"}, "response": {"accepted": true, "real_timestamp_ms": 1789180957555, "virtual_time_s": 0.0, "max_virtual_duration_s": 360000.0, "max_real_duration_s": 1200, "remaining_real_duration_s": 1200}}
|
||||
{"event": "action", "seq": 2, "wall_ts_ms": 1789180957560, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "234dfa75ad6f-0002", "position": {"x": 0.0, "y": 0.0}, "channel": 1}, "response": {"accepted": true, "real_timestamp_ms": 1789180957560, "virtual_time_s": 5.0, "measure_result": "direction", "svd_deg": 300.65999999999997}}
|
||||
{"event": "action", "seq": 3, "wall_ts_ms": 1789180957586, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "234dfa75ad6f-0003", "position": {"x": 0.0, "y": 0.0}, "channel": 2}, "response": {"accepted": true, "real_timestamp_ms": 1789180957586, "virtual_time_s": 11.0, "measure_result": "direction", "svd_deg": 268.69}}
|
||||
{"event": "action", "seq": 4, "wall_ts_ms": 1789180957612, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "234dfa75ad6f-0004", "position": {"x": 0.0, "y": 0.0}, "channel": 3}, "response": {"accepted": true, "real_timestamp_ms": 1789180957612, "virtual_time_s": 17.0, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 5, "wall_ts_ms": 1789180957616, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "234dfa75ad6f-0005", "position": {"x": 0.0, "y": 0.0}, "channel": 4}, "response": {"accepted": true, "real_timestamp_ms": 1789180957616, "virtual_time_s": 23.0, "measure_result": "direction", "svd_deg": 47.32}}
|
||||
{"event": "action", "seq": 6, "wall_ts_ms": 1789180957643, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "234dfa75ad6f-0006", "position": {"x": 0.0, "y": 0.0}, "channel": 5}, "response": {"accepted": true, "real_timestamp_ms": 1789180957642, "virtual_time_s": 29.0, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 7, "wall_ts_ms": 1789180957651, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "234dfa75ad6f-0007", "position": {"x": 0.0, "y": 0.0}, "channel": 6}, "response": {"accepted": true, "real_timestamp_ms": 1789180957651, "virtual_time_s": 35.0, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 8, "wall_ts_ms": 1789180957662, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "234dfa75ad6f-0008", "position": {"x": 0.0, "y": 0.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789180957662, "virtual_time_s": 41.0, "measure_result": "direction", "svd_deg": 305.65999999999997}}
|
||||
{"event": "action", "seq": 9, "wall_ts_ms": 1789180957692, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "234dfa75ad6f-0009", "position": {"x": 0.0, "y": 0.0}, "channel": 8}, "response": {"accepted": true, "real_timestamp_ms": 1789180957692, "virtual_time_s": 47.0, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 10, "wall_ts_ms": 1789180957696, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "234dfa75ad6f-0010", "position": {"x": 0.0, "y": 0.0}, "channel": 9}, "response": {"accepted": true, "real_timestamp_ms": 1789180957696, "virtual_time_s": 53.0, "measure_result": "direction", "svd_deg": 8.9}}
|
||||
{"event": "action", "seq": 11, "wall_ts_ms": 1789180957722, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "234dfa75ad6f-0011", "position": {"x": 0.0, "y": 0.0}, "channel": 10}, "response": {"accepted": true, "real_timestamp_ms": 1789180957722, "virtual_time_s": 59.0, "measure_result": "direction", "svd_deg": 327.54}}
|
||||
{"event": "action", "seq": 12, "wall_ts_ms": 1789180957748, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "234dfa75ad6f-0012", "position": {"x": 0.0, "y": 0.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789180957748, "virtual_time_s": 65.0, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 13, "wall_ts_ms": 1789180957751, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "234dfa75ad6f-0013", "position": {"x": 0.0, "y": 0.0}, "channel": 12}, "response": {"accepted": true, "real_timestamp_ms": 1789180957751, "virtual_time_s": 71.0, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 14, "wall_ts_ms": 1789180957755, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "234dfa75ad6f-0014", "position": {"x": 0.0, "y": 0.0}, "channel": 13}, "response": {"accepted": true, "real_timestamp_ms": 1789180957755, "virtual_time_s": 77.0, "measure_result": "direction", "svd_deg": 253.92000000000002}}
|
||||
{"event": "action", "seq": 15, "wall_ts_ms": 1789180957781, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "234dfa75ad6f-0015", "position": {"x": 0.0, "y": 0.0}, "channel": 14}, "response": {"accepted": true, "real_timestamp_ms": 1789180957781, "virtual_time_s": 83.0, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 16, "wall_ts_ms": 1789180957785, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "234dfa75ad6f-0016", "position": {"x": 0.0, "y": 0.0}, "channel": 15}, "response": {"accepted": true, "real_timestamp_ms": 1789180957785, "virtual_time_s": 89.0, "measure_result": "direction", "svd_deg": 280.39}}
|
||||
{"event": "action", "seq": 17, "wall_ts_ms": 1789180957813, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "234dfa75ad6f-0017", "position": {"x": 0.0, "y": 0.0}, "channel": 16}, "response": {"accepted": true, "real_timestamp_ms": 1789180957813, "virtual_time_s": 95.0, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 18, "wall_ts_ms": 1789180957817, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "234dfa75ad6f-0018", "position": {"x": 0.0, "y": 0.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789180957817, "virtual_time_s": 101.0, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 19, "wall_ts_ms": 1789180957821, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "234dfa75ad6f-0019", "position": {"x": 0.0, "y": 0.0}, "channel": 18}, "response": {"accepted": true, "real_timestamp_ms": 1789180957821, "virtual_time_s": 107.0, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 20, "wall_ts_ms": 1789180957825, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "234dfa75ad6f-0020", "position": {"x": 0.0, "y": 0.0}, "channel": 19}, "response": {"accepted": true, "real_timestamp_ms": 1789180957825, "virtual_time_s": 113.0, "measure_result": "no_signal"}}
|
||||
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||||
@@ -0,0 +1,112 @@
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||||
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||||
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||||
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||||
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@@ -0,0 +1,532 @@
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||||
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|
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||||
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||||
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||||
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||||
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||||
{"event": "action", "seq": 28, "wall_ts_ms": 1789176744077, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0028", "position": {"x": 10.0, "y": 10.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176744076, "virtual_time_s": 256.452711, "clear_result": "no_target_in_range"}}
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{"event": "action", "seq": 29, "wall_ts_ms": 1789176744077, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0029", "position": {"x": 30.0, "y": 50.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176744077, "virtual_time_s": 268.396983, "clear_result": "no_target_in_range"}}
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|
||||
{"event": "action", "seq": 31, "wall_ts_ms": 1789176744079, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0031", "position": {"x": 30.0, "y": 90.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176744079, "virtual_time_s": 282.396983, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 32, "wall_ts_ms": 1789176744080, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0032", "position": {"x": 50.0, "y": 90.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176744080, "virtual_time_s": 289.396983, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 33, "wall_ts_ms": 1789176744080, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0033", "position": {"x": 50.0, "y": 110.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176744080, "virtual_time_s": 296.396983, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 34, "wall_ts_ms": 1789176744081, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0034", "position": {"x": 50.0, "y": 130.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176744081, "virtual_time_s": 303.396983, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 35, "wall_ts_ms": 1789176744082, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0035", "position": {"x": 70.0, "y": 130.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176744082, "virtual_time_s": 310.396983, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 36, "wall_ts_ms": 1789176744082, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0036", "position": {"x": 70.0, "y": 150.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176744082, "virtual_time_s": 317.396983, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 37, "wall_ts_ms": 1789176744083, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0037", "position": {"x": 70.0, "y": 170.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176744083, "virtual_time_s": 324.396983, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 38, "wall_ts_ms": 1789176744084, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0038", "position": {"x": 90.0, "y": 170.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176744084, "virtual_time_s": 331.396983, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 39, "wall_ts_ms": 1789176744085, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0039", "position": {"x": 90.0, "y": 190.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176744085, "virtual_time_s": 338.396983, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 40, "wall_ts_ms": 1789176744086, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0040", "position": {"x": 90.0, "y": 210.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176744086, "virtual_time_s": 345.396983, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 41, "wall_ts_ms": 1789176744087, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0041", "position": {"x": 90.0, "y": 230.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176744087, "virtual_time_s": 352.396983, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 42, "wall_ts_ms": 1789176744088, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0042", "position": {"x": 110.0, "y": 230.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176744088, "virtual_time_s": 359.396983, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 43, "wall_ts_ms": 1789176744089, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0043", "position": {"x": 110.0, "y": 210.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176744089, "virtual_time_s": 366.396983, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 44, "wall_ts_ms": 1789176744089, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0044", "position": {"x": 110.0, "y": 250.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176744089, "virtual_time_s": 377.396983, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 45, "wall_ts_ms": 1789176744090, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0045", "position": {"x": 110.0, "y": 270.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176744090, "virtual_time_s": 384.396983, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 46, "wall_ts_ms": 1789176744091, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0046", "position": {"x": 130.0, "y": 270.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176744091, "virtual_time_s": 391.396983, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 47, "wall_ts_ms": 1789176744092, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0047", "position": {"x": 130.0, "y": 250.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176744091, "virtual_time_s": 398.396983, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 48, "wall_ts_ms": 1789176744092, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0048", "position": {"x": 130.0, "y": 290.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176744092, "virtual_time_s": 409.396983, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 49, "wall_ts_ms": 1789176744093, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0049", "position": {"x": 130.0, "y": 310.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176744093, "virtual_time_s": 416.396983, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 50, "wall_ts_ms": 1789176744094, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0050", "position": {"x": 150.0, "y": 310.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176744094, "virtual_time_s": 423.396983, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 51, "wall_ts_ms": 1789176744094, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0051", "position": {"x": 150.0, "y": 290.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176744094, "virtual_time_s": 430.396983, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 52, "wall_ts_ms": 1789176744095, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0052", "position": {"x": 150.0, "y": 330.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176744095, "virtual_time_s": 441.396983, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 53, "wall_ts_ms": 1789176744096, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0053", "position": {"x": 150.0, "y": 350.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176744096, "virtual_time_s": 448.396983, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 54, "wall_ts_ms": 1789176744096, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0054", "position": {"x": 170.0, "y": 350.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176744096, "virtual_time_s": 455.396983, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 55, "wall_ts_ms": 1789176744097, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0055", "position": {"x": 170.0, "y": 330.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176744097, "virtual_time_s": 462.396983, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 56, "wall_ts_ms": 1789176744098, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0056", "position": {"x": 170.0, "y": 370.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176744098, "virtual_time_s": 473.396983, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 57, "wall_ts_ms": 1789176744098, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0057", "position": {"x": 170.0, "y": 390.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176744098, "virtual_time_s": 480.396983, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 58, "wall_ts_ms": 1789176744099, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0058", "position": {"x": 190.0, "y": 390.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176744099, "virtual_time_s": 487.396983, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 59, "wall_ts_ms": 1789176744100, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0059", "position": {"x": 190.0, "y": 370.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176744100, "virtual_time_s": 494.396983, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 60, "wall_ts_ms": 1789176744101, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0060", "position": {"x": 190.0, "y": 410.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176744101, "virtual_time_s": 507.396983, "clear_result": "success"}}
|
||||
{"event": "action", "seq": 61, "wall_ts_ms": 1789176744248, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0061", "position": {"x": -209.71604207390737, "y": 408.31180226407764}, "channel": 1}, "response": {"accepted": true, "real_timestamp_ms": 1789176744248, "virtual_time_s": 593.340904, "measure_result": "direction", "svd_deg": 228.61}}
|
||||
{"event": "action", "seq": 62, "wall_ts_ms": 1789176744264, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0062", "position": {"x": -629.8687381345811, "y": -72.63944843455852}, "channel": 1}, "response": {"accepted": true, "real_timestamp_ms": 1789176744264, "virtual_time_s": 726.065983, "measure_result": "direction", "svd_deg": 219.19}}
|
||||
{"event": "action", "seq": 63, "wall_ts_ms": 1789176744278, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0063", "position": {"x": -651.3328725627388, "y": -84.8382530829925}, "channel": 1}, "response": {"accepted": true, "real_timestamp_ms": 1789176744278, "virtual_time_s": 736.003674, "measure_result": "direction", "svd_deg": 224.16}}
|
||||
{"event": "action", "seq": 64, "wall_ts_ms": 1789176744280, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0064", "position": {"x": -684.6469952147388, "y": -116.80120530424186}, "channel": 1}, "response": {"accepted": true, "real_timestamp_ms": 1789176744280, "virtual_time_s": 750.237224, "clear_result": "success"}}
|
||||
{"event": "action", "seq": 65, "wall_ts_ms": 1789176744412, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0065", "position": {"x": -556.2448721339551, "y": -128.19722962602532}, "channel": 9}, "response": {"accepted": true, "real_timestamp_ms": 1789176744412, "virtual_time_s": 782.018593, "measure_result": "direction", "svd_deg": 243.95999999999998}}
|
||||
{"event": "action", "seq": 66, "wall_ts_ms": 1789176744436, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0066", "position": {"x": -793.4137491491886, "y": -710.8346157336247}, "channel": 9}, "response": {"accepted": true, "real_timestamp_ms": 1789176744436, "virtual_time_s": 912.830421, "measure_result": "direction", "svd_deg": 228.89}}
|
||||
{"event": "action", "seq": 67, "wall_ts_ms": 1789176744454, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0067", "position": {"x": -840.6507108907208, "y": -773.6752375134632}, "channel": 9}, "response": {"accepted": true, "real_timestamp_ms": 1789176744454, "virtual_time_s": 933.553365, "measure_result": "direction", "svd_deg": 224.88}}
|
||||
{"event": "action", "seq": 68, "wall_ts_ms": 1789176744472, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0068", "position": {"x": -853.9521308716392, "y": -787.8752977423674}, "channel": 9}, "response": {"accepted": true, "real_timestamp_ms": 1789176744472, "virtual_time_s": 942.444737, "measure_result": "direction", "svd_deg": 224.3}}
|
||||
{"event": "action", "seq": 69, "wall_ts_ms": 1789176744487, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0069", "position": {"x": -859.3063790868281, "y": -793.0528623864075}, "channel": 9}, "response": {"accepted": true, "real_timestamp_ms": 1789176744487, "virtual_time_s": 948.93437, "measure_result": "direction", "svd_deg": 224.2}}
|
||||
{"event": "action", "seq": 70, "wall_ts_ms": 1789176744505, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0070", "position": {"x": -860.0609236842562, "y": -793.5864307088535}, "channel": 9}, "response": {"accepted": true, "real_timestamp_ms": 1789176744505, "virtual_time_s": 954.119198, "measure_result": "direction", "svd_deg": 223.12}}
|
||||
{"event": "action", "seq": 71, "wall_ts_ms": 1789176744520, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0071", "position": {"x": -863.0416400882648, "y": -795.1906929028166}, "channel": 9}, "response": {"accepted": true, "real_timestamp_ms": 1789176744520, "virtual_time_s": 959.796201, "measure_result": "direction", "svd_deg": 224.99}}
|
||||
{"event": "action", "seq": 72, "wall_ts_ms": 1789176744535, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0072", "position": {"x": -865.3759614633741, "y": -797.4669070200721}, "channel": 9}, "response": {"accepted": true, "real_timestamp_ms": 1789176744535, "virtual_time_s": 965.448281, "measure_result": "direction", "svd_deg": 224.57}}
|
||||
{"event": "action", "seq": 73, "wall_ts_ms": 1789176744537, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0073", "position": {"x": -890.0, "y": -810.0}, "channel": 9}, "response": {"accepted": true, "real_timestamp_ms": 1789176744537, "virtual_time_s": 973.974298, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 74, "wall_ts_ms": 1789176744538, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0074", "position": {"x": -890.0, "y": -830.0}, "channel": 9}, "response": {"accepted": true, "real_timestamp_ms": 1789176744538, "virtual_time_s": 980.974298, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 75, "wall_ts_ms": 1789176744539, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0075", "position": {"x": -910.0, "y": -830.0}, "channel": 9}, "response": {"accepted": true, "real_timestamp_ms": 1789176744539, "virtual_time_s": 989.974298, "clear_result": "success"}}
|
||||
{"event": "action", "seq": 76, "wall_ts_ms": 1789176744540, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0076", "position": {"x": -1200.0, "y": -1000.0}, "channel": 2}, "response": {"accepted": true, "real_timestamp_ms": 1789176744540, "virtual_time_s": 1063.205243, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 77, "wall_ts_ms": 1789176744540, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0077", "position": {"x": -1200.0, "y": -1000.0}, "channel": 3}, "response": {"accepted": true, "real_timestamp_ms": 1789176744540, "virtual_time_s": 1069.205243, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 78, "wall_ts_ms": 1789176744541, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0078", "position": {"x": -1200.0, "y": -1000.0}, "channel": 4}, "response": {"accepted": true, "real_timestamp_ms": 1789176744541, "virtual_time_s": 1075.205243, "measure_result": "direction", "svd_deg": 59.37}}
|
||||
{"event": "action", "seq": 79, "wall_ts_ms": 1789176744571, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "48cf97ba3914-0079", "position": {"x": -1200.0, "y": -1000.0}, "channel": 5}, "response": {"accepted": true, "real_timestamp_ms": 1789176744571, "virtual_time_s": 1081.205243, "measure_result": "no_signal"}}
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
@@ -0,0 +1,104 @@
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{
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"case_code": "5TQF-A8K9-QW89-V2ET",
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"module": "q4_practice",
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"seed": 1415283134,
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"truth": [
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{
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{
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|
||||
{"event": "action", "seq": 92, "wall_ts_ms": 1789176629654, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0092", "position": {"x": 1170.0, "y": -830.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629654, "virtual_time_s": 1975.725602, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 93, "wall_ts_ms": 1789176629655, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0093", "position": {"x": 1190.0, "y": -830.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629655, "virtual_time_s": 1982.725602, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 94, "wall_ts_ms": 1789176629656, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0094", "position": {"x": 1190.0, "y": -810.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629656, "virtual_time_s": 1989.725602, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 95, "wall_ts_ms": 1789176629656, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0095", "position": {"x": 1210.0, "y": -810.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629656, "virtual_time_s": 1996.725602, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 96, "wall_ts_ms": 1789176629657, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0096", "position": {"x": 1210.0, "y": -790.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629657, "virtual_time_s": 2003.725602, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 97, "wall_ts_ms": 1789176629658, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0097", "position": {"x": 1230.0, "y": -790.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629658, "virtual_time_s": 2010.725602, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 98, "wall_ts_ms": 1789176629658, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0098", "position": {"x": 1230.0, "y": -770.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629658, "virtual_time_s": 2017.725602, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 99, "wall_ts_ms": 1789176629659, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0099", "position": {"x": 1250.0, "y": -770.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629659, "virtual_time_s": 2024.725602, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 100, "wall_ts_ms": 1789176629660, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0100", "position": {"x": 1250.0, "y": -750.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629660, "virtual_time_s": 2031.725602, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 101, "wall_ts_ms": 1789176629660, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0101", "position": {"x": 1270.0, "y": -750.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629660, "virtual_time_s": 2038.725602, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 102, "wall_ts_ms": 1789176629661, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0102", "position": {"x": 1270.0, "y": -730.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629661, "virtual_time_s": 2045.725602, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 103, "wall_ts_ms": 1789176629661, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0103", "position": {"x": 1290.0, "y": -730.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629661, "virtual_time_s": 2052.725602, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 104, "wall_ts_ms": 1789176629662, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0104", "position": {"x": 1290.0, "y": -710.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629662, "virtual_time_s": 2059.725602, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 105, "wall_ts_ms": 1789176629662, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0105", "position": {"x": 1310.0, "y": -710.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629662, "virtual_time_s": 2066.725602, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 106, "wall_ts_ms": 1789176629663, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0106", "position": {"x": 1310.0, "y": -690.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629663, "virtual_time_s": 2073.725602, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 107, "wall_ts_ms": 1789176629664, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0107", "position": {"x": 1330.0, "y": -690.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629664, "virtual_time_s": 2080.725602, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 108, "wall_ts_ms": 1789176629664, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0108", "position": {"x": 1330.0, "y": -670.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629664, "virtual_time_s": 2087.725602, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 109, "wall_ts_ms": 1789176629665, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0109", "position": {"x": 1350.0, "y": -670.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629665, "virtual_time_s": 2094.725602, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 110, "wall_ts_ms": 1789176629666, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0110", "position": {"x": 1350.0, "y": -650.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629666, "virtual_time_s": 2101.725602, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 111, "wall_ts_ms": 1789176629666, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0111", "position": {"x": 1370.0, "y": -650.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629666, "virtual_time_s": 2108.725602, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 112, "wall_ts_ms": 1789176629667, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0112", "position": {"x": 1370.0, "y": -630.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629667, "virtual_time_s": 2115.725602, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 113, "wall_ts_ms": 1789176629667, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0113", "position": {"x": 1390.0, "y": -630.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629667, "virtual_time_s": 2122.725602, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 114, "wall_ts_ms": 1789176629668, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0114", "position": {"x": 1390.0, "y": -610.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629668, "virtual_time_s": 2129.725602, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 115, "wall_ts_ms": 1789176629669, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0115", "position": {"x": 1410.0, "y": -610.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629669, "virtual_time_s": 2136.725602, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 116, "wall_ts_ms": 1789176629669, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0116", "position": {"x": 1410.0, "y": -590.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629669, "virtual_time_s": 2143.725602, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 117, "wall_ts_ms": 1789176629670, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0117", "position": {"x": 1430.0, "y": -590.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629670, "virtual_time_s": 2150.725602, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 118, "wall_ts_ms": 1789176629670, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0118", "position": {"x": 1430.0, "y": -570.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629670, "virtual_time_s": 2157.725602, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 119, "wall_ts_ms": 1789176629671, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0119", "position": {"x": 1450.0, "y": -570.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629671, "virtual_time_s": 2164.725602, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 120, "wall_ts_ms": 1789176629672, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0120", "position": {"x": 1450.0, "y": -550.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629671, "virtual_time_s": 2171.725602, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 121, "wall_ts_ms": 1789176629672, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0121", "position": {"x": 1470.0, "y": -550.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629672, "virtual_time_s": 2178.725602, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 122, "wall_ts_ms": 1789176629673, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0122", "position": {"x": 1470.0, "y": -530.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629673, "virtual_time_s": 2185.725602, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 123, "wall_ts_ms": 1789176629673, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0123", "position": {"x": 1490.0, "y": -530.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629673, "virtual_time_s": 2192.725602, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 124, "wall_ts_ms": 1789176629674, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0124", "position": {"x": 1490.0, "y": -510.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629674, "virtual_time_s": 2199.725602, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 125, "wall_ts_ms": 1789176629674, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0125", "position": {"x": 1510.0, "y": -510.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629674, "virtual_time_s": 2206.725602, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 126, "wall_ts_ms": 1789176629675, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0126", "position": {"x": 1510.0, "y": -490.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629675, "virtual_time_s": 2213.725602, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 127, "wall_ts_ms": 1789176629675, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0127", "position": {"x": 1510.0, "y": -470.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629675, "virtual_time_s": 2220.725602, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 128, "wall_ts_ms": 1789176629676, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0128", "position": {"x": 1530.0, "y": -470.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629676, "virtual_time_s": 2227.725602, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 129, "wall_ts_ms": 1789176629676, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0129", "position": {"x": 1530.0, "y": -490.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629676, "virtual_time_s": 2234.725602, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 130, "wall_ts_ms": 1789176629677, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0130", "position": {"x": 1550.0, "y": -470.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629677, "virtual_time_s": 2243.382456, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 131, "wall_ts_ms": 1789176629677, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0131", "position": {"x": 1550.0, "y": -450.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629677, "virtual_time_s": 2250.382456, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 132, "wall_ts_ms": 1789176629678, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0132", "position": {"x": 1530.0, "y": -450.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629678, "virtual_time_s": 2257.382456, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 133, "wall_ts_ms": 1789176629678, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0133", "position": {"x": 1550.0, "y": -430.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629678, "virtual_time_s": 2266.03931, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 134, "wall_ts_ms": 1789176629679, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0134", "position": {"x": 1570.0, "y": -430.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629679, "virtual_time_s": 2273.03931, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 135, "wall_ts_ms": 1789176629679, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0135", "position": {"x": 1570.0, "y": -450.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629679, "virtual_time_s": 2280.03931, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 136, "wall_ts_ms": 1789176629680, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0136", "position": {"x": 1590.0, "y": -430.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789176629680, "virtual_time_s": 2290.696164, "clear_result": "success"}}
|
||||
{"event": "action", "seq": 137, "wall_ts_ms": 1789176629790, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0137", "position": {"x": 1429.8624502943494, "y": -654.1112669539796}, "channel": 10}, "response": {"accepted": true, "real_timestamp_ms": 1789176629790, "virtual_time_s": 2351.785145, "measure_result": "direction", "svd_deg": 157.78}}
|
||||
{"event": "action", "seq": 138, "wall_ts_ms": 1789176629806, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0138", "position": {"x": 739.7053147276195, "y": -388.5273903936236}, "channel": 10}, "response": {"accepted": true, "real_timestamp_ms": 1789176629806, "virtual_time_s": 2504.683984, "measure_result": "direction", "svd_deg": 126.53}}
|
||||
{"event": "action", "seq": 139, "wall_ts_ms": 1789176629815, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0139", "position": {"x": 737.259244277467, "y": -379.09950798866356}, "channel": 10}, "response": {"accepted": true, "real_timestamp_ms": 1789176629815, "virtual_time_s": 2511.63199, "measure_result": "direction", "svd_deg": 131.0}}
|
||||
{"event": "action", "seq": 140, "wall_ts_ms": 1789176629816, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "2d8c15ad42ce-0140", "position": {"x": 710.6302761970189, "y": -349.5302324286764}, "channel": 10}, "response": {"accepted": true, "real_timestamp_ms": 1789176629816, "virtual_time_s": 2524.590492, "clear_result": "success"}}
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
@@ -0,0 +1,104 @@
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{
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@@ -0,0 +1,115 @@
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@@ -0,0 +1,334 @@
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@@ -0,0 +1,112 @@
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@@ -0,0 +1,124 @@
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||||
{"event": "action", "seq": 67, "wall_ts_ms": 1789176457963, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0067", "position": {"x": 1112.6138239031402, "y": 1075.423968417429}, "channel": 3}, "response": {"accepted": true, "real_timestamp_ms": 1789176457963, "virtual_time_s": 1449.168626, "measure_result": "direction", "svd_deg": 122.86}}
|
||||
{"event": "action", "seq": 68, "wall_ts_ms": 1789176457978, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0068", "position": {"x": 819.6337457813277, "y": 1541.043807763908}, "channel": 3}, "response": {"accepted": true, "real_timestamp_ms": 1789176457978, "virtual_time_s": 1564.193925, "measure_result": "direction", "svd_deg": 131.21}}
|
||||
{"event": "action", "seq": 69, "wall_ts_ms": 1789176457994, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0069", "position": {"x": 796.7476562187987, "y": 1573.6520663562255}, "channel": 3}, "response": {"accepted": true, "real_timestamp_ms": 1789176457994, "virtual_time_s": 1577.161539, "measure_result": "direction", "svd_deg": 131.82}}
|
||||
{"event": "action", "seq": 70, "wall_ts_ms": 1789176457995, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0070", "position": {"x": 737.4558740027242, "y": 1637.8778605276875}, "channel": 3}, "response": {"accepted": true, "real_timestamp_ms": 1789176457995, "virtual_time_s": 1599.643493, "clear_result": "success"}}
|
||||
{"event": "action", "seq": 71, "wall_ts_ms": 1789176458102, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0071", "position": {"x": 989.2285140492995, "y": 1375.1359609859153}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458102, "virtual_time_s": 1678.423372, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 72, "wall_ts_ms": 1789176458210, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0072", "position": {"x": 989.2285140492995, "y": 1375.1359609859153}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458210, "virtual_time_s": 1683.423372, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 73, "wall_ts_ms": 1789176458333, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0073", "position": {"x": 989.2285140492995, "y": 1375.1359609859153}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458333, "virtual_time_s": 1688.423372, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 74, "wall_ts_ms": 1789176458456, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0074", "position": {"x": 989.2285140492995, "y": 1375.1359609859153}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458456, "virtual_time_s": 1693.423372, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 75, "wall_ts_ms": 1789176458458, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0075", "position": {"x": 990.0, "y": 1010.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458458, "virtual_time_s": 1769.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 76, "wall_ts_ms": 1789176458459, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0076", "position": {"x": 990.0, "y": 990.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458459, "virtual_time_s": 1776.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 77, "wall_ts_ms": 1789176458460, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0077", "position": {"x": 990.0, "y": 970.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458460, "virtual_time_s": 1783.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 78, "wall_ts_ms": 1789176458460, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0078", "position": {"x": 990.0, "y": 950.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458460, "virtual_time_s": 1790.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 79, "wall_ts_ms": 1789176458461, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0079", "position": {"x": 990.0, "y": 930.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458461, "virtual_time_s": 1797.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 80, "wall_ts_ms": 1789176458461, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0080", "position": {"x": 990.0, "y": 910.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458461, "virtual_time_s": 1804.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 81, "wall_ts_ms": 1789176458462, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0081", "position": {"x": 990.0, "y": 890.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458462, "virtual_time_s": 1811.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 82, "wall_ts_ms": 1789176458463, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0082", "position": {"x": 990.0, "y": 870.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458463, "virtual_time_s": 1818.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 83, "wall_ts_ms": 1789176458463, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0083", "position": {"x": 990.0, "y": 850.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458463, "virtual_time_s": 1825.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 84, "wall_ts_ms": 1789176458464, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0084", "position": {"x": 990.0, "y": 830.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458464, "virtual_time_s": 1832.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 85, "wall_ts_ms": 1789176458464, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0085", "position": {"x": 990.0, "y": 810.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458464, "virtual_time_s": 1839.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 86, "wall_ts_ms": 1789176458465, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0086", "position": {"x": 990.0, "y": 790.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458465, "virtual_time_s": 1846.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 87, "wall_ts_ms": 1789176458465, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0087", "position": {"x": 990.0, "y": 770.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458465, "virtual_time_s": 1853.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 88, "wall_ts_ms": 1789176458466, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0088", "position": {"x": 990.0, "y": 750.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458466, "virtual_time_s": 1860.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 89, "wall_ts_ms": 1789176458467, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0089", "position": {"x": 990.0, "y": 730.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458467, "virtual_time_s": 1867.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 90, "wall_ts_ms": 1789176458467, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0090", "position": {"x": 990.0, "y": 710.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458467, "virtual_time_s": 1874.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 91, "wall_ts_ms": 1789176458468, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0091", "position": {"x": 990.0, "y": 690.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458468, "virtual_time_s": 1881.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 92, "wall_ts_ms": 1789176458468, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0092", "position": {"x": 990.0, "y": 670.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458468, "virtual_time_s": 1888.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 93, "wall_ts_ms": 1789176458469, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0093", "position": {"x": 990.0, "y": 650.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458469, "virtual_time_s": 1895.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 94, "wall_ts_ms": 1789176458469, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0094", "position": {"x": 990.0, "y": 630.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458469, "virtual_time_s": 1902.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 95, "wall_ts_ms": 1789176458470, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0095", "position": {"x": 990.0, "y": 610.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458470, "virtual_time_s": 1909.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 96, "wall_ts_ms": 1789176458471, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0096", "position": {"x": 990.0, "y": 590.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458471, "virtual_time_s": 1916.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 97, "wall_ts_ms": 1789176458471, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0097", "position": {"x": 990.0, "y": 570.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458471, "virtual_time_s": 1923.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 98, "wall_ts_ms": 1789176458471, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0098", "position": {"x": 990.0, "y": 550.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458471, "virtual_time_s": 1930.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 99, "wall_ts_ms": 1789176458472, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0099", "position": {"x": 990.0, "y": 530.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458472, "virtual_time_s": 1937.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 100, "wall_ts_ms": 1789176458473, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0100", "position": {"x": 990.0, "y": 510.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458473, "virtual_time_s": 1944.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 101, "wall_ts_ms": 1789176458473, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0101", "position": {"x": 990.0, "y": 490.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458473, "virtual_time_s": 1951.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 102, "wall_ts_ms": 1789176458474, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0102", "position": {"x": 990.0, "y": 470.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458474, "virtual_time_s": 1958.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 103, "wall_ts_ms": 1789176458474, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0103", "position": {"x": 990.0, "y": 450.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458474, "virtual_time_s": 1965.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 104, "wall_ts_ms": 1789176458475, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0104", "position": {"x": 990.0, "y": 430.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458475, "virtual_time_s": 1972.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 105, "wall_ts_ms": 1789176458475, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0105", "position": {"x": 990.0, "y": 410.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458475, "virtual_time_s": 1979.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 106, "wall_ts_ms": 1789176458476, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0106", "position": {"x": 990.0, "y": 390.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458476, "virtual_time_s": 1986.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 107, "wall_ts_ms": 1789176458477, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0107", "position": {"x": 990.0, "y": 370.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458477, "virtual_time_s": 1993.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 108, "wall_ts_ms": 1789176458477, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0108", "position": {"x": 970.0, "y": 370.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458477, "virtual_time_s": 2000.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 109, "wall_ts_ms": 1789176458478, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0109", "position": {"x": 970.0, "y": 350.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458478, "virtual_time_s": 2007.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 110, "wall_ts_ms": 1789176458478, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0110", "position": {"x": 970.0, "y": 330.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458478, "virtual_time_s": 2014.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 111, "wall_ts_ms": 1789176458479, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0111", "position": {"x": 970.0, "y": 310.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458479, "virtual_time_s": 2021.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 112, "wall_ts_ms": 1789176458479, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0112", "position": {"x": 970.0, "y": 290.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458479, "virtual_time_s": 2028.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 113, "wall_ts_ms": 1789176458480, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0113", "position": {"x": 970.0, "y": 270.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458480, "virtual_time_s": 2035.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 114, "wall_ts_ms": 1789176458480, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0114", "position": {"x": 970.0, "y": 250.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458480, "virtual_time_s": 2042.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 115, "wall_ts_ms": 1789176458481, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0115", "position": {"x": 970.0, "y": 230.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458481, "virtual_time_s": 2049.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 116, "wall_ts_ms": 1789176458482, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0116", "position": {"x": 970.0, "y": 210.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458482, "virtual_time_s": 2056.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 117, "wall_ts_ms": 1789176458482, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0117", "position": {"x": 970.0, "y": 190.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458482, "virtual_time_s": 2063.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 118, "wall_ts_ms": 1789176458483, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0118", "position": {"x": 970.0, "y": 170.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458483, "virtual_time_s": 2070.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 119, "wall_ts_ms": 1789176458483, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0119", "position": {"x": 970.0, "y": 150.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458483, "virtual_time_s": 2077.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 120, "wall_ts_ms": 1789176458484, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0120", "position": {"x": 970.0, "y": 130.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458484, "virtual_time_s": 2084.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 121, "wall_ts_ms": 1789176458484, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0121", "position": {"x": 970.0, "y": 110.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458484, "virtual_time_s": 2091.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 122, "wall_ts_ms": 1789176458485, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0122", "position": {"x": 970.0, "y": 90.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458485, "virtual_time_s": 2098.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 123, "wall_ts_ms": 1789176458485, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0123", "position": {"x": 970.0, "y": 70.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458485, "virtual_time_s": 2105.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 124, "wall_ts_ms": 1789176458486, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0124", "position": {"x": 970.0, "y": 50.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458486, "virtual_time_s": 2112.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 125, "wall_ts_ms": 1789176458486, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0125", "position": {"x": 970.0, "y": 30.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458486, "virtual_time_s": 2119.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 126, "wall_ts_ms": 1789176458487, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0126", "position": {"x": 970.0, "y": 10.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458487, "virtual_time_s": 2126.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 127, "wall_ts_ms": 1789176458487, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0127", "position": {"x": 970.0, "y": -10.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458487, "virtual_time_s": 2133.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 128, "wall_ts_ms": 1789176458488, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0128", "position": {"x": 970.0, "y": -30.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458488, "virtual_time_s": 2140.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 129, "wall_ts_ms": 1789176458488, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0129", "position": {"x": 970.0, "y": -50.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458488, "virtual_time_s": 2147.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 130, "wall_ts_ms": 1789176458489, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0130", "position": {"x": 970.0, "y": -70.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458489, "virtual_time_s": 2154.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 131, "wall_ts_ms": 1789176458489, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0131", "position": {"x": 970.0, "y": -90.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458489, "virtual_time_s": 2161.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 132, "wall_ts_ms": 1789176458490, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0132", "position": {"x": 970.0, "y": -110.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458490, "virtual_time_s": 2168.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 133, "wall_ts_ms": 1789176458490, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0133", "position": {"x": 970.0, "y": -130.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458490, "virtual_time_s": 2175.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 134, "wall_ts_ms": 1789176458491, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0134", "position": {"x": 970.0, "y": -150.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458491, "virtual_time_s": 2182.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 135, "wall_ts_ms": 1789176458491, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0135", "position": {"x": 970.0, "y": -170.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458491, "virtual_time_s": 2189.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 136, "wall_ts_ms": 1789176458492, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0136", "position": {"x": 970.0, "y": -190.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458492, "virtual_time_s": 2196.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 137, "wall_ts_ms": 1789176458492, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0137", "position": {"x": 970.0, "y": -210.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458492, "virtual_time_s": 2203.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 138, "wall_ts_ms": 1789176458493, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0138", "position": {"x": 970.0, "y": -230.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458493, "virtual_time_s": 2210.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 139, "wall_ts_ms": 1789176458493, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0139", "position": {"x": 970.0, "y": -250.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458493, "virtual_time_s": 2217.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 140, "wall_ts_ms": 1789176458494, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0140", "position": {"x": 950.0, "y": -250.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458494, "virtual_time_s": 2224.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 141, "wall_ts_ms": 1789176458494, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0141", "position": {"x": 950.0, "y": -270.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458494, "virtual_time_s": 2231.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 142, "wall_ts_ms": 1789176458495, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0142", "position": {"x": 950.0, "y": -290.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458495, "virtual_time_s": 2238.450727, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 143, "wall_ts_ms": 1789176458495, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0143", "position": {"x": 950.0, "y": -310.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789176458495, "virtual_time_s": 2247.450727, "clear_result": "success"}}
|
||||
{"event": "action", "seq": 144, "wall_ts_ms": 1789176458624, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0144", "position": {"x": 928.9640921663192, "y": 10.133218672571672}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176458624, "virtual_time_s": 2317.615448, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 145, "wall_ts_ms": 1789176458752, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0145", "position": {"x": 928.9640921663192, "y": 10.133218672571672}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176458752, "virtual_time_s": 2322.615448, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 146, "wall_ts_ms": 1789176458881, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0146", "position": {"x": 928.9640921663192, "y": 10.133218672571672}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176458881, "virtual_time_s": 2327.615448, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 147, "wall_ts_ms": 1789176459010, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0147", "position": {"x": 928.9640921663192, "y": 10.133218672571672}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459010, "virtual_time_s": 2332.615448, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 148, "wall_ts_ms": 1789176459016, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0148", "position": {"x": 1210.0, "y": 110.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459016, "virtual_time_s": 2395.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 149, "wall_ts_ms": 1789176459017, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0149", "position": {"x": 1210.0, "y": 130.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459017, "virtual_time_s": 2402.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 150, "wall_ts_ms": 1789176459017, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0150", "position": {"x": 1210.0, "y": 150.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459017, "virtual_time_s": 2409.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 151, "wall_ts_ms": 1789176459018, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0151", "position": {"x": 1210.0, "y": 170.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459018, "virtual_time_s": 2416.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 152, "wall_ts_ms": 1789176459019, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0152", "position": {"x": 1210.0, "y": 190.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459019, "virtual_time_s": 2423.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 153, "wall_ts_ms": 1789176459019, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0153", "position": {"x": 1190.0, "y": 190.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459019, "virtual_time_s": 2430.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 154, "wall_ts_ms": 1789176459020, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0154", "position": {"x": 1190.0, "y": 210.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459020, "virtual_time_s": 2437.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 155, "wall_ts_ms": 1789176459020, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0155", "position": {"x": 1190.0, "y": 230.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459020, "virtual_time_s": 2444.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 156, "wall_ts_ms": 1789176459021, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0156", "position": {"x": 1190.0, "y": 250.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459021, "virtual_time_s": 2451.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 157, "wall_ts_ms": 1789176459022, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0157", "position": {"x": 1190.0, "y": 270.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459022, "virtual_time_s": 2458.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 158, "wall_ts_ms": 1789176459022, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0158", "position": {"x": 1170.0, "y": 270.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459022, "virtual_time_s": 2465.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 159, "wall_ts_ms": 1789176459045, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0159", "position": {"x": 1170.0, "y": 290.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459045, "virtual_time_s": 2472.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 160, "wall_ts_ms": 1789176459046, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0160", "position": {"x": 1170.0, "y": 310.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459046, "virtual_time_s": 2479.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 161, "wall_ts_ms": 1789176459047, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0161", "position": {"x": 1170.0, "y": 330.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459047, "virtual_time_s": 2486.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 162, "wall_ts_ms": 1789176459047, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0162", "position": {"x": 1170.0, "y": 350.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459047, "virtual_time_s": 2493.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 163, "wall_ts_ms": 1789176459048, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0163", "position": {"x": 1150.0, "y": 350.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459048, "virtual_time_s": 2500.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 164, "wall_ts_ms": 1789176459048, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0164", "position": {"x": 1150.0, "y": 370.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459048, "virtual_time_s": 2507.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 165, "wall_ts_ms": 1789176459049, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0165", "position": {"x": 1150.0, "y": 390.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459049, "virtual_time_s": 2514.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 166, "wall_ts_ms": 1789176459049, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0166", "position": {"x": 1150.0, "y": 410.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459049, "virtual_time_s": 2521.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 167, "wall_ts_ms": 1789176459050, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0167", "position": {"x": 1150.0, "y": 430.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459050, "virtual_time_s": 2528.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 168, "wall_ts_ms": 1789176459050, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0168", "position": {"x": 1130.0, "y": 430.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459050, "virtual_time_s": 2535.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 169, "wall_ts_ms": 1789176459051, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0169", "position": {"x": 1130.0, "y": 450.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459051, "virtual_time_s": 2542.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 170, "wall_ts_ms": 1789176459051, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0170", "position": {"x": 1130.0, "y": 470.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459051, "virtual_time_s": 2549.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 171, "wall_ts_ms": 1789176459052, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0171", "position": {"x": 1130.0, "y": 490.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459052, "virtual_time_s": 2556.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 172, "wall_ts_ms": 1789176459053, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0172", "position": {"x": 1130.0, "y": 510.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459053, "virtual_time_s": 2563.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 173, "wall_ts_ms": 1789176459054, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0173", "position": {"x": 1110.0, "y": 510.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459054, "virtual_time_s": 2570.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 174, "wall_ts_ms": 1789176459054, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0174", "position": {"x": 1110.0, "y": 530.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459054, "virtual_time_s": 2577.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 175, "wall_ts_ms": 1789176459055, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0175", "position": {"x": 1110.0, "y": 550.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459055, "virtual_time_s": 2584.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 176, "wall_ts_ms": 1789176459055, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0176", "position": {"x": 1110.0, "y": 570.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459055, "virtual_time_s": 2591.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 177, "wall_ts_ms": 1789176459056, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0177", "position": {"x": 1110.0, "y": 590.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459056, "virtual_time_s": 2598.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 178, "wall_ts_ms": 1789176459056, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0178", "position": {"x": 1090.0, "y": 590.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459056, "virtual_time_s": 2605.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 179, "wall_ts_ms": 1789176459057, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0179", "position": {"x": 1090.0, "y": 610.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459057, "virtual_time_s": 2612.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 180, "wall_ts_ms": 1789176459057, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0180", "position": {"x": 1090.0, "y": 630.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459057, "virtual_time_s": 2619.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 181, "wall_ts_ms": 1789176459058, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0181", "position": {"x": 1090.0, "y": 650.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459058, "virtual_time_s": 2626.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 182, "wall_ts_ms": 1789176459058, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0182", "position": {"x": 1090.0, "y": 670.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459058, "virtual_time_s": 2633.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 183, "wall_ts_ms": 1789176459059, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0183", "position": {"x": 1070.0, "y": 670.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459059, "virtual_time_s": 2640.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 184, "wall_ts_ms": 1789176459059, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0184", "position": {"x": 1070.0, "y": 690.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459059, "virtual_time_s": 2647.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 185, "wall_ts_ms": 1789176459060, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0185", "position": {"x": 1070.0, "y": 710.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459060, "virtual_time_s": 2654.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 186, "wall_ts_ms": 1789176459060, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0186", "position": {"x": 1070.0, "y": 730.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459060, "virtual_time_s": 2661.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 187, "wall_ts_ms": 1789176459061, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0187", "position": {"x": 1070.0, "y": 750.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459060, "virtual_time_s": 2668.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 188, "wall_ts_ms": 1789176459061, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0188", "position": {"x": 1050.0, "y": 750.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459061, "virtual_time_s": 2675.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 189, "wall_ts_ms": 1789176459061, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0189", "position": {"x": 1050.0, "y": 770.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459061, "virtual_time_s": 2682.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 190, "wall_ts_ms": 1789176459062, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0190", "position": {"x": 1050.0, "y": 790.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459062, "virtual_time_s": 2689.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 191, "wall_ts_ms": 1789176459062, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0191", "position": {"x": 1050.0, "y": 810.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459062, "virtual_time_s": 2696.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 192, "wall_ts_ms": 1789176459063, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0192", "position": {"x": 1050.0, "y": 830.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459063, "virtual_time_s": 2703.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 193, "wall_ts_ms": 1789176459063, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0193", "position": {"x": 1030.0, "y": 830.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459063, "virtual_time_s": 2710.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 194, "wall_ts_ms": 1789176459064, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0194", "position": {"x": 1030.0, "y": 850.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459064, "virtual_time_s": 2717.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 195, "wall_ts_ms": 1789176459064, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0195", "position": {"x": 1030.0, "y": 870.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459064, "virtual_time_s": 2724.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 196, "wall_ts_ms": 1789176459065, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0196", "position": {"x": 1030.0, "y": 890.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459065, "virtual_time_s": 2731.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 197, "wall_ts_ms": 1789176459067, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0197", "position": {"x": 1030.0, "y": 910.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459067, "virtual_time_s": 2738.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 198, "wall_ts_ms": 1789176459067, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0198", "position": {"x": 1010.0, "y": 910.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459067, "virtual_time_s": 2745.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 199, "wall_ts_ms": 1789176459068, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0199", "position": {"x": 1010.0, "y": 930.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459068, "virtual_time_s": 2752.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 200, "wall_ts_ms": 1789176459068, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0200", "position": {"x": 1010.0, "y": 950.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459068, "virtual_time_s": 2759.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 201, "wall_ts_ms": 1789176459069, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0201", "position": {"x": 1010.0, "y": 970.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459069, "virtual_time_s": 2766.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 202, "wall_ts_ms": 1789176459069, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0202", "position": {"x": 1010.0, "y": 990.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459069, "virtual_time_s": 2773.265949, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 203, "wall_ts_ms": 1789176459070, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0203", "position": {"x": 1030.0, "y": 930.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459070, "virtual_time_s": 2788.91506, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 204, "wall_ts_ms": 1789176459070, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0204", "position": {"x": 1050.0, "y": 870.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459070, "virtual_time_s": 2804.564171, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 205, "wall_ts_ms": 1789176459071, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0205", "position": {"x": 1050.0, "y": 850.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459071, "virtual_time_s": 2811.564171, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 206, "wall_ts_ms": 1789176459071, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0206", "position": {"x": 1070.0, "y": 790.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459071, "virtual_time_s": 2827.213282, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 207, "wall_ts_ms": 1789176459072, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0207", "position": {"x": 1070.0, "y": 770.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459072, "virtual_time_s": 2834.213282, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 208, "wall_ts_ms": 1789176459072, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0208", "position": {"x": 1090.0, "y": 730.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459072, "virtual_time_s": 2846.157554, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 209, "wall_ts_ms": 1789176459073, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0209", "position": {"x": 1090.0, "y": 710.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459073, "virtual_time_s": 2853.157554, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 210, "wall_ts_ms": 1789176459073, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0210", "position": {"x": 1090.0, "y": 690.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459073, "virtual_time_s": 2860.157554, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 211, "wall_ts_ms": 1789176459074, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0211", "position": {"x": 1110.0, "y": 650.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459074, "virtual_time_s": 2872.101826, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 212, "wall_ts_ms": 1789176459074, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0212", "position": {"x": 1110.0, "y": 630.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459074, "virtual_time_s": 2879.101826, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 213, "wall_ts_ms": 1789176459075, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0213", "position": {"x": 1110.0, "y": 610.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459075, "virtual_time_s": 2886.101826, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 214, "wall_ts_ms": 1789176459075, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0214", "position": {"x": 1130.0, "y": 590.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459075, "virtual_time_s": 2894.75868, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 215, "wall_ts_ms": 1789176459076, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0215", "position": {"x": 1130.0, "y": 570.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459076, "virtual_time_s": 2901.75868, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 216, "wall_ts_ms": 1789176459076, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0216", "position": {"x": 1130.0, "y": 550.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459076, "virtual_time_s": 2908.75868, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 217, "wall_ts_ms": 1789176459077, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0217", "position": {"x": 1130.0, "y": 530.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459077, "virtual_time_s": 2915.75868, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 218, "wall_ts_ms": 1789176459077, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0218", "position": {"x": 1150.0, "y": 510.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459077, "virtual_time_s": 2924.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 219, "wall_ts_ms": 1789176459078, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0219", "position": {"x": 1150.0, "y": 490.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459078, "virtual_time_s": 2931.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 220, "wall_ts_ms": 1789176459078, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0220", "position": {"x": 1150.0, "y": 470.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459078, "virtual_time_s": 2938.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 221, "wall_ts_ms": 1789176459079, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0221", "position": {"x": 1150.0, "y": 450.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459079, "virtual_time_s": 2945.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 222, "wall_ts_ms": 1789176459079, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0222", "position": {"x": 1170.0, "y": 450.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459079, "virtual_time_s": 2952.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 223, "wall_ts_ms": 1789176459079, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0223", "position": {"x": 1170.0, "y": 430.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459079, "virtual_time_s": 2959.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 224, "wall_ts_ms": 1789176459080, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0224", "position": {"x": 1170.0, "y": 410.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459080, "virtual_time_s": 2966.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 225, "wall_ts_ms": 1789176459080, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0225", "position": {"x": 1170.0, "y": 390.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459080, "virtual_time_s": 2973.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 226, "wall_ts_ms": 1789176459081, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0226", "position": {"x": 1170.0, "y": 370.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459081, "virtual_time_s": 2980.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 227, "wall_ts_ms": 1789176459081, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0227", "position": {"x": 1190.0, "y": 370.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459081, "virtual_time_s": 2987.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 228, "wall_ts_ms": 1789176459082, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0228", "position": {"x": 1190.0, "y": 350.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459082, "virtual_time_s": 2994.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 229, "wall_ts_ms": 1789176459082, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0229", "position": {"x": 1190.0, "y": 330.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459082, "virtual_time_s": 3001.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 230, "wall_ts_ms": 1789176459083, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0230", "position": {"x": 1190.0, "y": 310.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459083, "virtual_time_s": 3008.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 231, "wall_ts_ms": 1789176459083, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0231", "position": {"x": 1190.0, "y": 290.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459083, "virtual_time_s": 3015.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 232, "wall_ts_ms": 1789176459084, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0232", "position": {"x": 1210.0, "y": 290.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459084, "virtual_time_s": 3022.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 233, "wall_ts_ms": 1789176459084, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0233", "position": {"x": 1210.0, "y": 270.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459084, "virtual_time_s": 3029.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 234, "wall_ts_ms": 1789176459085, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0234", "position": {"x": 1210.0, "y": 250.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459085, "virtual_time_s": 3036.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 235, "wall_ts_ms": 1789176459085, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0235", "position": {"x": 1210.0, "y": 230.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459085, "virtual_time_s": 3043.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 236, "wall_ts_ms": 1789176459086, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0236", "position": {"x": 1210.0, "y": 210.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459086, "virtual_time_s": 3050.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 237, "wall_ts_ms": 1789176459086, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0237", "position": {"x": 1230.0, "y": 210.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459086, "virtual_time_s": 3057.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 238, "wall_ts_ms": 1789176459087, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0238", "position": {"x": 1230.0, "y": 190.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459087, "virtual_time_s": 3064.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 239, "wall_ts_ms": 1789176459087, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0239", "position": {"x": 1230.0, "y": 170.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459087, "virtual_time_s": 3071.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 240, "wall_ts_ms": 1789176459088, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0240", "position": {"x": 1230.0, "y": 150.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459088, "virtual_time_s": 3078.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 241, "wall_ts_ms": 1789176459088, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0241", "position": {"x": 1230.0, "y": 130.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459088, "virtual_time_s": 3085.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 242, "wall_ts_ms": 1789176459089, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0242", "position": {"x": 1230.0, "y": 110.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459089, "virtual_time_s": 3092.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 243, "wall_ts_ms": 1789176459089, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0243", "position": {"x": 1230.0, "y": 90.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459089, "virtual_time_s": 3099.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 244, "wall_ts_ms": 1789176459090, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0244", "position": {"x": 1230.0, "y": 70.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459090, "virtual_time_s": 3106.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 245, "wall_ts_ms": 1789176459090, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0245", "position": {"x": 1230.0, "y": 50.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459090, "virtual_time_s": 3113.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 246, "wall_ts_ms": 1789176459091, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0246", "position": {"x": 1230.0, "y": 30.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459091, "virtual_time_s": 3120.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 247, "wall_ts_ms": 1789176459091, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0247", "position": {"x": 1250.0, "y": 30.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459091, "virtual_time_s": 3127.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 248, "wall_ts_ms": 1789176459092, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0248", "position": {"x": 1250.0, "y": 10.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459092, "virtual_time_s": 3134.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 249, "wall_ts_ms": 1789176459092, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0249", "position": {"x": 1250.0, "y": -10.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459092, "virtual_time_s": 3141.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 250, "wall_ts_ms": 1789176459093, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0250", "position": {"x": 1250.0, "y": -30.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459093, "virtual_time_s": 3148.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 251, "wall_ts_ms": 1789176459093, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0251", "position": {"x": 1250.0, "y": -50.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459093, "virtual_time_s": 3155.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 252, "wall_ts_ms": 1789176459094, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0252", "position": {"x": 1270.0, "y": -50.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459094, "virtual_time_s": 3162.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 253, "wall_ts_ms": 1789176459094, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0253", "position": {"x": 1270.0, "y": -70.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459094, "virtual_time_s": 3169.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 254, "wall_ts_ms": 1789176459095, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0254", "position": {"x": 1270.0, "y": -90.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459095, "virtual_time_s": 3176.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 255, "wall_ts_ms": 1789176459095, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0255", "position": {"x": 1270.0, "y": -110.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459095, "virtual_time_s": 3183.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 256, "wall_ts_ms": 1789176459096, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0256", "position": {"x": 1270.0, "y": -130.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459096, "virtual_time_s": 3190.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 257, "wall_ts_ms": 1789176459096, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0257", "position": {"x": 1290.0, "y": -130.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459096, "virtual_time_s": 3197.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 258, "wall_ts_ms": 1789176459097, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0258", "position": {"x": 1290.0, "y": -150.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459097, "virtual_time_s": 3204.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 259, "wall_ts_ms": 1789176459097, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0259", "position": {"x": 1290.0, "y": -170.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459097, "virtual_time_s": 3211.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 260, "wall_ts_ms": 1789176459098, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0260", "position": {"x": 1290.0, "y": -190.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459098, "virtual_time_s": 3218.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 261, "wall_ts_ms": 1789176459098, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0261", "position": {"x": 1290.0, "y": -210.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459098, "virtual_time_s": 3225.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 262, "wall_ts_ms": 1789176459099, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0262", "position": {"x": 1310.0, "y": -210.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459099, "virtual_time_s": 3232.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 263, "wall_ts_ms": 1789176459099, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0263", "position": {"x": 1310.0, "y": -230.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459099, "virtual_time_s": 3239.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 264, "wall_ts_ms": 1789176459100, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0264", "position": {"x": 1310.0, "y": -250.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459100, "virtual_time_s": 3246.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 265, "wall_ts_ms": 1789176459100, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0265", "position": {"x": 1310.0, "y": -270.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459100, "virtual_time_s": 3253.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 266, "wall_ts_ms": 1789176459101, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0266", "position": {"x": 1310.0, "y": -290.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459101, "virtual_time_s": 3260.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 267, "wall_ts_ms": 1789176459101, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0267", "position": {"x": 1330.0, "y": -290.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459101, "virtual_time_s": 3267.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 268, "wall_ts_ms": 1789176459102, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0268", "position": {"x": 1330.0, "y": -310.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459102, "virtual_time_s": 3274.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 269, "wall_ts_ms": 1789176459102, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0269", "position": {"x": 1330.0, "y": -330.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459102, "virtual_time_s": 3281.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 270, "wall_ts_ms": 1789176459103, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0270", "position": {"x": 1330.0, "y": -350.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459103, "virtual_time_s": 3288.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 271, "wall_ts_ms": 1789176459103, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0271", "position": {"x": 1330.0, "y": -370.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459103, "virtual_time_s": 3295.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 272, "wall_ts_ms": 1789176459104, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0272", "position": {"x": 1350.0, "y": -370.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459103, "virtual_time_s": 3302.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 273, "wall_ts_ms": 1789176459104, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0273", "position": {"x": 1350.0, "y": -390.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459104, "virtual_time_s": 3309.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 274, "wall_ts_ms": 1789176459104, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0274", "position": {"x": 1350.0, "y": -410.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459104, "virtual_time_s": 3316.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 275, "wall_ts_ms": 1789176459105, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0275", "position": {"x": 1350.0, "y": -430.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459105, "virtual_time_s": 3323.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 276, "wall_ts_ms": 1789176459105, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0276", "position": {"x": 1350.0, "y": -450.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459105, "virtual_time_s": 3330.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 277, "wall_ts_ms": 1789176459106, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0277", "position": {"x": 1370.0, "y": -450.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459106, "virtual_time_s": 3337.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 278, "wall_ts_ms": 1789176459106, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0278", "position": {"x": 1370.0, "y": -430.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459106, "virtual_time_s": 3344.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 279, "wall_ts_ms": 1789176459107, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0279", "position": {"x": 1370.0, "y": -410.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459107, "virtual_time_s": 3351.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 280, "wall_ts_ms": 1789176459107, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0280", "position": {"x": 1370.0, "y": -390.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459107, "virtual_time_s": 3358.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 281, "wall_ts_ms": 1789176459108, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0281", "position": {"x": 1370.0, "y": -370.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459108, "virtual_time_s": 3365.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 282, "wall_ts_ms": 1789176459109, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0282", "position": {"x": 1370.0, "y": -350.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459109, "virtual_time_s": 3372.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 283, "wall_ts_ms": 1789176459109, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0283", "position": {"x": 1350.0, "y": -350.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459109, "virtual_time_s": 3379.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 284, "wall_ts_ms": 1789176459110, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0284", "position": {"x": 1350.0, "y": -330.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459110, "virtual_time_s": 3386.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 285, "wall_ts_ms": 1789176459110, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0285", "position": {"x": 1350.0, "y": -310.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459110, "virtual_time_s": 3393.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 286, "wall_ts_ms": 1789176459111, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0286", "position": {"x": 1350.0, "y": -290.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459111, "virtual_time_s": 3400.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 287, "wall_ts_ms": 1789176459111, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0287", "position": {"x": 1350.0, "y": -270.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459111, "virtual_time_s": 3407.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 288, "wall_ts_ms": 1789176459112, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0288", "position": {"x": 1330.0, "y": -270.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459112, "virtual_time_s": 3414.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 289, "wall_ts_ms": 1789176459112, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0289", "position": {"x": 1330.0, "y": -250.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459112, "virtual_time_s": 3421.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 290, "wall_ts_ms": 1789176459113, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0290", "position": {"x": 1330.0, "y": -230.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459113, "virtual_time_s": 3428.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 291, "wall_ts_ms": 1789176459113, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0291", "position": {"x": 1330.0, "y": -210.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459113, "virtual_time_s": 3435.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 292, "wall_ts_ms": 1789176459114, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0292", "position": {"x": 1330.0, "y": -190.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459114, "virtual_time_s": 3442.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 293, "wall_ts_ms": 1789176459114, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0293", "position": {"x": 1310.0, "y": -190.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459114, "virtual_time_s": 3449.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 294, "wall_ts_ms": 1789176459115, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0294", "position": {"x": 1310.0, "y": -170.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459115, "virtual_time_s": 3456.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 295, "wall_ts_ms": 1789176459115, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0295", "position": {"x": 1310.0, "y": -150.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459115, "virtual_time_s": 3463.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 296, "wall_ts_ms": 1789176459116, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0296", "position": {"x": 1310.0, "y": -130.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459116, "virtual_time_s": 3470.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 297, "wall_ts_ms": 1789176459116, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0297", "position": {"x": 1310.0, "y": -110.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459116, "virtual_time_s": 3477.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 298, "wall_ts_ms": 1789176459117, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0298", "position": {"x": 1290.0, "y": -110.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459117, "virtual_time_s": 3484.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 299, "wall_ts_ms": 1789176459117, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0299", "position": {"x": 1290.0, "y": -90.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459117, "virtual_time_s": 3491.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 300, "wall_ts_ms": 1789176459118, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0300", "position": {"x": 1290.0, "y": -70.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459118, "virtual_time_s": 3498.415534, "clear_result": "no_target_in_range"}}
|
||||
{"event": "action", "seq": 301, "wall_ts_ms": 1789176459118, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0301", "position": {"x": 1290.0, "y": -50.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789176459118, "virtual_time_s": 3507.415534, "clear_result": "success"}}
|
||||
{"event": "action", "seq": 302, "wall_ts_ms": 1789176459232, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0302", "position": {"x": 1277.985722791724, "y": -71.76862996738339}, "channel": 4}, "response": {"accepted": true, "real_timestamp_ms": 1789176459232, "virtual_time_s": 3518.388324, "measure_result": "direction", "svd_deg": 99.25}}
|
||||
{"event": "action", "seq": 303, "wall_ts_ms": 1789176459252, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0303", "position": {"x": 1173.338284713871, "y": 426.3937168252834}, "channel": 4}, "response": {"accepted": true, "real_timestamp_ms": 1789176459252, "virtual_time_s": 3625.195359, "measure_result": "direction", "svd_deg": 86.2}}
|
||||
{"event": "action", "seq": 304, "wall_ts_ms": 1789176459262, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0304", "position": {"x": 1154.967399080033, "y": 519.6087852993088}, "channel": 4}, "response": {"accepted": true, "real_timestamp_ms": 1789176459262, "virtual_time_s": 3649.196978, "measure_result": "direction", "svd_deg": 49.44}}
|
||||
{"event": "action", "seq": 305, "wall_ts_ms": 1789176459263, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0305", "position": {"x": 1181.6839608578052, "y": 550.9484175184673}, "channel": 4}, "response": {"accepted": true, "real_timestamp_ms": 1789176459263, "virtual_time_s": 3662.433354, "clear_result": "success"}}
|
||||
{"event": "action", "seq": 306, "wall_ts_ms": 1789176459371, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0306", "position": {"x": 1124.6847131173, "y": 625.5064979165684}, "channel": 13}, "response": {"accepted": true, "real_timestamp_ms": 1789176459371, "virtual_time_s": 3687.203353, "measure_result": "direction", "svd_deg": 313.73}}
|
||||
{"event": "action", "seq": 307, "wall_ts_ms": 1789176459396, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0307", "position": {"x": 1341.406744041681, "y": 389.9933527913719}, "channel": 13}, "response": {"accepted": true, "real_timestamp_ms": 1789176459395, "virtual_time_s": 3756.214252, "measure_result": "direction", "svd_deg": 315.12}}
|
||||
{"event": "action", "seq": 308, "wall_ts_ms": 1789176459414, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0308", "position": {"x": 1382.194016074385, "y": 347.66865779434903}, "channel": 13}, "response": {"accepted": true, "real_timestamp_ms": 1789176459414, "virtual_time_s": 3772.97007, "measure_result": "direction", "svd_deg": 314.74}}
|
||||
{"event": "action", "seq": 309, "wall_ts_ms": 1789176459432, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0309", "position": {"x": 1390.0886059428879, "y": 338.0823028063429}, "channel": 13}, "response": {"accepted": true, "real_timestamp_ms": 1789176459432, "virtual_time_s": 3780.453799, "measure_result": "direction", "svd_deg": 314.57}}
|
||||
{"event": "action", "seq": 310, "wall_ts_ms": 1789176459448, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0310", "position": {"x": 1395.6915783202878, "y": 331.48556133051807}, "channel": 13}, "response": {"accepted": true, "real_timestamp_ms": 1789176459448, "virtual_time_s": 3787.184814, "measure_result": "direction", "svd_deg": 316.26}}
|
||||
{"event": "action", "seq": 311, "wall_ts_ms": 1789176459463, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "1cbd613130d2-0311", "position": {"x": 1398.0859307169458, "y": 330.48346577850623}, "channel": 13}, "response": {"accepted": true, "real_timestamp_ms": 1789176459463, "virtual_time_s": 3792.703933, "measure_result": "direction", "svd_deg": 314.21}}
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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@@ -0,0 +1,120 @@
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@@ -0,0 +1,136 @@
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@@ -0,0 +1,130 @@
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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@@ -0,0 +1,96 @@
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}
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@@ -0,0 +1,114 @@
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@@ -0,0 +1,124 @@
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@@ -0,0 +1,104 @@
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@@ -0,0 +1,121 @@
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@@ -0,0 +1,136 @@
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@@ -0,0 +1,122 @@
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||||
{"event": "action", "seq": 91, "wall_ts_ms": 1789178172698, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "e6941a0a3153-0091", "position": {"x": 579.8500000000001, "y": 1004.3296607688135}, "channel": 4}, "response": {"accepted": true, "real_timestamp_ms": 1789178172698, "virtual_time_s": 2261.257148, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 92, "wall_ts_ms": 1789178172701, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "e6941a0a3153-0092", "position": {"x": 579.8500000000001, "y": 1004.3296607688135}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789178172701, "virtual_time_s": 2267.257148, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 93, "wall_ts_ms": 1789178172704, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "e6941a0a3153-0093", "position": {"x": 579.8500000000001, "y": 1004.3296607688135}, "channel": 10}, "response": {"accepted": true, "real_timestamp_ms": 1789178172704, "virtual_time_s": 2273.257148, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 94, "wall_ts_ms": 1789178172707, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "e6941a0a3153-0094", "position": {"x": 579.8500000000001, "y": 1004.3296607688135}, "channel": 15}, "response": {"accepted": true, "real_timestamp_ms": 1789178172707, "virtual_time_s": 2279.257148, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 95, "wall_ts_ms": 1789178172710, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "e6941a0a3153-0095", "position": {"x": 579.8500000000001, "y": 1004.3296607688135}, "channel": 20}, "response": {"accepted": true, "real_timestamp_ms": 1789178172710, "virtual_time_s": 2285.257148, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 96, "wall_ts_ms": 1789178172721, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "e6941a0a3153-0096", "position": {"x": 650.7188955569911, "y": 508.10656641994916}, "channel": 13}, "response": {"accepted": true, "real_timestamp_ms": 1789178172721, "virtual_time_s": 2391.508783, "measure_result": "direction", "svd_deg": 231.04}}
|
||||
{"event": "action", "seq": 97, "wall_ts_ms": 1789178172727, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "e6941a0a3153-0097", "position": {"x": 389.6347253114434, "y": 186.14711197044892}, "channel": 13}, "response": {"accepted": true, "real_timestamp_ms": 1789178172727, "virtual_time_s": 2479.411819, "measure_result": "direction", "svd_deg": 224.21}}
|
||||
{"event": "action", "seq": 98, "wall_ts_ms": 1789178172729, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "e6941a0a3153-0098", "position": {"x": 384.7595812084055, "y": 181.56824626432336}, "channel": 13}, "response": {"accepted": true, "real_timestamp_ms": 1789178172729, "virtual_time_s": 2485.749474, "clear_result": "success"}}
|
||||
{"event": "action", "seq": 99, "wall_ts_ms": 1789178172734, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "e6941a0a3153-0099", "position": {"x": 896.3022583102411, "y": 485.31253142832435}, "channel": 1}, "response": {"accepted": true, "real_timestamp_ms": 1789178172734, "virtual_time_s": 2610.734599, "measure_result": "direction", "svd_deg": 3.4}}
|
||||
{"event": "action", "seq": 100, "wall_ts_ms": 1789178172739, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "e6941a0a3153-0100", "position": {"x": 1668.1737941655742, "y": 530.214122651231}, "channel": 1}, "response": {"accepted": true, "real_timestamp_ms": 1789178172739, "virtual_time_s": 2770.369889, "measure_result": "direction", "svd_deg": 295.15}}
|
||||
{"event": "action", "seq": 101, "wall_ts_ms": 1789178172741, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "e6941a0a3153-0101", "position": {"x": 1668.1737941655742, "y": 530.214122651231}, "channel": 1}, "response": {"accepted": true, "real_timestamp_ms": 1789178172741, "virtual_time_s": 2775.369889, "clear_result": "success"}}
|
||||
{"event": "action", "seq": 102, "wall_ts_ms": 1789178172743, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "e6941a0a3153-0102", "position": {"x": 1159.7, "y": 0.0}, "channel": 4}, "response": {"accepted": true, "real_timestamp_ms": 1789178172743, "virtual_time_s": 2928.294715, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 103, "wall_ts_ms": 1789178172746, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "e6941a0a3153-0103", "position": {"x": 1159.7, "y": 0.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789178172746, "virtual_time_s": 2934.294715, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 104, "wall_ts_ms": 1789178172748, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "e6941a0a3153-0104", "position": {"x": 1159.7, "y": 0.0}, "channel": 10}, "response": {"accepted": true, "real_timestamp_ms": 1789178172748, "virtual_time_s": 2940.294715, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 105, "wall_ts_ms": 1789178172751, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "e6941a0a3153-0105", "position": {"x": 1159.7, "y": 0.0}, "channel": 15}, "response": {"accepted": true, "real_timestamp_ms": 1789178172751, "virtual_time_s": 2946.294715, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 106, "wall_ts_ms": 1789178172753, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "e6941a0a3153-0106", "position": {"x": 1159.7, "y": 0.0}, "channel": 20}, "response": {"accepted": true, "real_timestamp_ms": 1789178172753, "virtual_time_s": 2952.294715, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 107, "wall_ts_ms": 1789178172758, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "e6941a0a3153-0107", "position": {"x": 579.8500000000001, "y": -1004.3296607688135}, "channel": 20}, "response": {"accepted": true, "real_timestamp_ms": 1789178172757, "virtual_time_s": 3189.234715, "measure_result": "direction", "svd_deg": 145.62}}
|
||||
{"event": "action", "seq": 108, "wall_ts_ms": 1789178172765, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "e6941a0a3153-0108", "position": {"x": 579.8500000000001, "y": -1004.3296607688135}, "channel": 4}, "response": {"accepted": true, "real_timestamp_ms": 1789178172765, "virtual_time_s": 3195.234715, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 109, "wall_ts_ms": 1789178172767, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "e6941a0a3153-0109", "position": {"x": 579.8500000000001, "y": -1004.3296607688135}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789178172767, "virtual_time_s": 3201.234715, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 110, "wall_ts_ms": 1789178172770, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "e6941a0a3153-0110", "position": {"x": 579.8500000000001, "y": -1004.3296607688135}, "channel": 10}, "response": {"accepted": true, "real_timestamp_ms": 1789178172770, "virtual_time_s": 3207.234715, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 111, "wall_ts_ms": 1789178172772, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "e6941a0a3153-0111", "position": {"x": 579.8500000000001, "y": -1004.3296607688135}, "channel": 15}, "response": {"accepted": true, "real_timestamp_ms": 1789178172772, "virtual_time_s": 3213.234715, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 112, "wall_ts_ms": 1789178172779, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "e6941a0a3153-0112", "position": {"x": 201.96780181491366, "y": -645.6437256385627}, "channel": 20}, "response": {"accepted": true, "real_timestamp_ms": 1789178172778, "virtual_time_s": 3323.436549, "measure_result": "direction", "svd_deg": 314.24}}
|
||||
{"event": "action", "seq": 113, "wall_ts_ms": 1789178172783, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "e6941a0a3153-0113", "position": {"x": 494.66879135060697, "y": -945.0650344419719}, "channel": 20}, "response": {"accepted": true, "real_timestamp_ms": 1789178172783, "virtual_time_s": 3412.180681, "measure_result": "direction", "svd_deg": 143.49}}
|
||||
{"event": "action", "seq": 114, "wall_ts_ms": 1789178172788, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "e6941a0a3153-0114", "position": {"x": 476.08899967181617, "y": -931.2499077150492}, "channel": 20}, "response": {"accepted": true, "real_timestamp_ms": 1789178172788, "virtual_time_s": 3421.811302, "measure_result": "direction", "svd_deg": 144.85}}
|
||||
{"event": "action", "seq": 115, "wall_ts_ms": 1789178172789, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "e6941a0a3153-0115", "position": {"x": 473.1293591789802, "y": -929.0873459150212}, "channel": 20}, "response": {"accepted": true, "real_timestamp_ms": 1789178172789, "virtual_time_s": 3427.544409, "clear_result": "success"}}
|
||||
{"event": "action", "seq": 116, "wall_ts_ms": 1789178172792, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "e6941a0a3153-0116", "position": {"x": -1523.8319113092903, "y": -309.81107959316137}, "channel": 8}, "response": {"accepted": true, "real_timestamp_ms": 1789178172792, "virtual_time_s": 3851.700237, "measure_result": "direction", "svd_deg": 72.53}}
|
||||
{"event": "action", "seq": 117, "wall_ts_ms": 1789178172793, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "e6941a0a3153-0117", "position": {"x": -1523.8319113092903, "y": -309.81107959316137}, "channel": 8}, "response": {"accepted": true, "real_timestamp_ms": 1789178172793, "virtual_time_s": 3856.700237, "clear_result": "success"}}
|
||||
{"event": "action", "seq": 118, "wall_ts_ms": 1789178172794, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "e6941a0a3153-0118", "position": {"x": -1523.8319113092903, "y": -309.81107959316137}, "channel": 2}, "response": {"accepted": true, "real_timestamp_ms": 1789178172794, "virtual_time_s": 3862.700237, "measure_result": "direction", "svd_deg": 166.78}}
|
||||
{"event": "action", "seq": 119, "wall_ts_ms": 1789178172795, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "e6941a0a3153-0119", "position": {"x": -1648.4145877762821, "y": -280.74127573017756}, "channel": 2}, "response": {"accepted": true, "real_timestamp_ms": 1789178172795, "virtual_time_s": 3893.28609, "clear_result": "success"}}
|
||||
{"event": "action", "seq": 120, "wall_ts_ms": 1789178172796, "method": "POST", "path": "/exit", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "e6941a0a3153-0120"}, "response": {"accepted": true, "real_timestamp_ms": 1789178172796, "virtual_time_s": 3893.28609, "exit_reason": "user_exit"}}
|
||||
{"event": "session_end", "reason": "user_exit", "virtual_time_s": 3893.28609, "real_elapsed_s": 2.797876, "request_count": 120, "cleared_count": 16}
|
||||
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Reference in New Issue
Block a user