diff --git a/.DS_Store b/.DS_Store deleted file mode 100644 index 7b329ed..0000000 Binary files a/.DS_Store and /dev/null differ diff --git a/.gitignore b/.gitignore index 927bebb..9b16698 100644 --- a/.gitignore +++ b/.gitignore @@ -1,22 +1,42 @@ -# Python-generated files +# ============================================================ +# 2026 B 题：无线电干扰源自动定位清除 —— 版本控制忽略清单 +# 原则：只跟踪"可复现结果所需的最小集合"。 +# - 源码、自建模拟器、六份正式日志、测试、复现脚本与说明文档：纳入版本控制； +# - 编译产物、运行期采集目录、缓存与本机工作区：一律忽略。 +# ============================================================ + +# ---------- Python ---------- __pycache__/ -*.py[oc] +*.py[cod] +*.egg-info/ +.venv/ +.piptmp/ +.pytest_cache/ +.mypy_cache/ +.ruff_cache/ +.ipynb_checkpoints/ + +# 构建与打包产物 build/ dist/ wheels/ -*.egg-info -# Virtual environments -.venv - -# Sim +# ---------- 运行期产生：不进版本控制 ---------- +# 官方模拟器本体与判题留档（体积大且需官方环境） Jammers*/ +# 官方演练采集产物（问题一观测、回放归档、评测机日志） +output/ +# 本机临时工作区：历史留档、截图、中间草稿等 tmp/ +# 构建 Python 包时使用的临时目录 +.piptmp/ +uv-temp/ +pip-temp/ -# MATLAB 出图日志 -scripts/matlab/*.log +# referee-data/ 是论文正文数据的来源，纳入版本控制（含演练与正式日志）。 +# 其中登录标识已统一为中性占位符，不包含参赛队号。 -# LaTeX build artifacts +# ---------- LaTeX 编译产物 ---------- docs/paper/build/ docs/paper/*.aux docs/paper/*.log @@ -25,12 +45,13 @@ docs/paper/*.fls docs/paper/*.fdb_latexmk docs/paper/*.xdv docs/paper/*.synctex.gz +# 支撑包是脚本产物（scripts/build_paper_support.py --package），不入库 +docs/paper/support-materials.zip -# pytest -.pytest_cache/ - -# Jupyter -.ipynb_checkpoints/ - -# 1 -output/ \ No newline at end of file +# ---------- 编辑器 / 操作系统 ---------- +.vscode/* +!.vscode/settings.json +.idea/ +.zcode/ +.DS_Store +Thumbs.db diff --git a/.zcode/plans/plan-sess_4d6694a9-3cc0-4ded-bca4-4a58061ec3fa.md b/.zcode/plans/plan-sess_4d6694a9-3cc0-4ded-bca4-4a58061ec3fa.md deleted file mode 100644 index 0f58ccc..0000000 --- a/.zcode/plans/plan-sess_4d6694a9-3cc0-4ded-bca4-4a58061ec3fa.md +++ /dev/null @@ -1,60 +0,0 @@ -# 修复计划：7 项 P2 问题 + 3 项规划调整 - -已核实：7 个问题的定位与审核报告一致；另确认关联缺陷 `planner/__init__.py:392` 用 `>` 判断遮挡预算（实际放行 5 个被遮挡探点，注释口径为 4）。用户已确认：环探保留启发式+重写依据；误差场采用逐点白噪声哈希。 - -## 修改一：模拟器固定误差场（问题1） -文件 `src/mcm26b/sim/simulator.py`： -- 新增 `_bearing_error(channel, pos)`：`sha256(f"{error_seed}|{channel}|{round(x,6)}|{round(y,6)}")` 前 8 字节映射到 `[-error_half_deg, +error_half_deg)`。同一（源频道, 位置）读数恒定，不同点独立。 -- `_observe`（L193）改为调用它；删除顺序随机流 `self._rng`（构造参数 `error_seed` 保留）。 -- 模块 docstring 更新：同位置误差固定；分布仍是测试假设。 -- 新增测试：同位置重复测量（不同 request_id）svd_deg 相同；同种子两次构造结果一致。 - -## 修改二：Q4 对跖探点公式（问题2） -文件 `src/mcm26b/planner/__init__.py`（L440-447）： -- L447：`dx / norm * scale` → `dx * scale`（dy 同理），使偏移模长 = `min(norm, safe)`。圆心为原点、原探点 (200,0)、safe≥200 时返回 (-200,0)。 -- L444 退化分支：`min(safe, 1.0)` → `safe`（方向 +x），不再钳到 1m。 -- 新增单测：对跖点坐标（safe≥norm 与 safe` → `>=`，`config.py:85` 注释与文档同步为"4 个被遮挡探点（选点/对跖/环探×2）"。 -- 新增 `scripts/benchmark_q4.py`：批量种子扫描（`--problem 4`，可配源数/定向数/种子范围/输出路径），逐局记录 measures、clears、后备清除格数与 attempts、virtual_time、completed，落盘 `output/q4/benchmark.json` 并打印汇总表——用于验证修复后的后备清除成本（审核"验证后备成本"要求），同时为论文问题4结果表提供可复现数字。 - -## 修改四：通信失败可靠收尾（问题4） -文件 `planner/__init__.py` `run()`（L195-205）： -- `client.enter()` 纳入 try；except 顺序：`DeadlineExceeded` → `(TransportError, ProtocolError, InconsistentBelief)` → `Exception` 兜底；stop_reason 分别记 `real_budget_exhausted` / `transport_failure` / `protocol_error` / `inconsistent_belief` / `unexpected_error`，summary 增 `abort_detail`（异常类型+消息，截断）。 -- `/exit` 改为尽力而为：`client.exit()` 失败不再中断收尾；`_summary` 接受 `info=None`（`exit_reason="exit_unreachable"`，virtual_time 取 `client.state.virtual_time_s`）。 -- summary 增 `channel_status` 快照（每频道状态 + 候选 MEC 半径，约 20 条）——即"保存未完成状态"；`--log` 的 JSONL 本就有全过程留痕。 -- cli.py 无需改（`completed=false` 已映射退出码 1）。 -- 新增测试：注入连续三次传输失败（若 `ScriptedFaultTransport` 不支持持久失败则扩展其故障类型），断言 `run()` 正常返回 summary 不抛异常、有 /exit 尝试留痕、退出码路径为 1。 - -## 修改五：Q1 近平行退化情形（问题5） -文件 `src/mcm26b/geometry/localization.py`（L216-226）： -- 衰退方向判定改为对浮点平面系数的精确有理数运算（`Fraction`）：候选方向仍枚举各平面法线的垂直方向 `(b,-a)/(-b,a)`；先用浮点快速预筛（max > 1e-9 直接排除，浮点误差远小于该阈值），近零候选再用 Fraction 精确检验 `∀(d,e,f): d·dir_x + e·dir_y ≤ 0`。平面系数本身是 float（精确有理数），故结论对量化输入精确，消除 2e-14 阈值与顶点过滤 eps=1e-9 的口径错配导致的漏判。 -- 顶点枚举与 `PARALLEL_EPS` 预筛保持现状。 -- 回归测试：审核复现输入 `[(0,0,1°), (0,0.01,358.99999999999°)]` → `status=unbounded`、`diameter_infinite=true`、不给清除保证；`scripts/benchmark_q1.py` 两档结果不回归。 - -## 修改六：默认舍入裕量统一（问题6） -- `config.py:75`：`bearing_rounding_margin_deg` 默认 `0.0` → `0.005`，注释改为"覆盖服务器两位小数舍入偏差（≤0.005°）；库默认与 CLI 默认一致"。 -- `q1` 子命令与 `geometry/__main__.py` 的 `--rounding-margin-deg` 默认保持 0（离线敏感性分析语义不变，审核未要求改）。 -- 新增断言 `SearchConfig().bearing_rounding_margin_deg == 0.005`，并用默认配置复跑"边缘读数保留真源"场景；检查显式传参的现有测试不受影响；README 如有默认值描述一并更新。 - -## 修改七：后验评分补 near 分支（问题7） -文件 `planner/__init__.py` `_worst_posterior_radius`（L93-110）： -- 增加 near 分支：`probe.clip_disk(q, field.near_radius_m, search.polygon_sides)`，多边形非空则纳入 max；direction 端点分支保留，docstring 说明 safe 盘内 no_signal 不可能故不枚举。 -- 调用点（粗评/加密复评两处）传入 `near_radius_m` 与 `polygon_sides`。 -- 新增测试：候选集 `[(-4,0),(4,0)]`（两点退化区域）、q=原点 → 最坏后验 ≈ 4m（修复前报 2m）；`test_loop_q3/q4` 场景回归（选点可能变化，但断言均为清除状态类，预期无碍）。 - -## 规划调整落地 -- **A（第二问定位）**：`docs/algo-2.md` §13（L231 附近）明确交付为"安全选点的启发式基线"：解析安全盘证书已实现并证明；§10 区间上界未实现，采样最大值仅作粗筛，不得称严格最坏上界。 -- **B（第三问）**：无代码改动；顺序约束（先修改一/四，再官方演练）在 `docs/B题建模分析与实施计划.md` 演练清单处注明。 -- **C（第四问预算口径）**：`_summary` 增 `total_requests`（measures+clears）；`DirectionalExtension._summary` 增 `fallback_clear_attempts`（Σ attempts）；`docs/协议规则表与状态机.md` §7 后备清除段注明"后备 /clear 请求计入总请求与现实预算"。 - -## 测试与验收 -- 每项修改配单测（新增约 10-12 项），全套 pytest 通过（95 → ~107 项）。 -- 冒烟：`uv run mcm26b sim-check`；`uv run mcm26b run --problem 3/4 --self-test` 若干种子。 -- 运行 `scripts/benchmark_q4.py` 记录修复后后备成本，输出落盘。 - -## 执行顺序 -修改一（模拟器）→ 修改四（收尾）→ 修改六 → 修改七 → 修改二 → 修改三 → 修改五 → 文档同步 → 全量回归。 \ No newline at end of file diff --git a/.zcode/plans/plan-sess_f2eec9cb-6a04-41a3-9d72-ce6ab4455a52.md b/.zcode/plans/plan-sess_f2eec9cb-6a04-41a3-9d72-ce6ab4455a52.md deleted file mode 100644 index 9387df9..0000000 --- a/.zcode/plans/plan-sess_f2eec9cb-6a04-41a3-9d72-ce6ab4455a52.md +++ /dev/null @@ -1,80 +0,0 @@ -# 目标 - -在新分支 **`q3-bayes`**（基于当前 `gpt`）实现第三问新算法：**网格化贝叶斯粒子滤波（含负观测更新）+ 信息增益选点 + 开放路径 2-opt 任务排序 + 七点覆盖兜底 + Optuna 离线调参**。评分口径 = 虚拟总时间（A2 基线：官方演练 12 源 5232s、109 次检测）；硬性要求全清率 100%、缺席证明正确、零异常。 - -# 关键设计 - -## 1. 粒子滤波 `src/mcm26b/particles.py`（新建） - -与用户方案的差异点：本项目要求清除证书/缺席证明**严格**（A2 靠保守几何证书做到 100% 清除率），软核概率 PF 有删掉真源的风险。因此采用**网格幸存集滤波**（= 硬似然 PF + Voronoi 裕量，与用户"|θ̂−θ|>1° 权重置零"的设定一致但更严格）： - -- 每频道一组 Ω 上间距 h≈30m 的网格粒子（约 1.1 万格，alive 用整数索引集合；空间哈希加速邻域查询） -- 保守 kill 判定（margin = h·√2/2，保证真源所在格必活）： - - `no_signal`@s：kill dist(g,s) ≤ 1000 − margin（题面 r_eff ≥ 1000 ⟹ 可严格排除该盘） - - `direction`@s 读数 θ：kill 角向偏差超出 δ_eff=1.005°（含余量）或 dist > 1500 + margin 的格 - - `near`@s：kill dist(g,s) > 5 + margin -- 证书全部由此推导：清除证书 = MEC(alive).R + margin ≤ 20 − clear_margin；**缺席证明 = alive 为空集**（无需额外像素覆盖检查）；跳测规则 = min dist(alive, s) > 1500 + margin ⟹ 必 no_signal，跳过省 6s/次 -- 信息增益：对候选点用子采样（≤500 粒子）× K 个代表假设做 MC 期望熵减估计；复用 `smallest_enclosing_circle`(Welzl) 求 MEC - -## 2. 新策略 `BayesLoop(OmnidirectionalLoop)`（`src/mcm26b/planner/bayes_loop.py`） - -复用基类 run() 骨架、异常收尾、summary、anomaly、预算机制，重写阶段逻辑： -- **发现阶段**：原点起扫（频道排序省换频）→ 站点 = 七点环 ∪ PF 高价值点，按"期望发现数 − λ·移动时间"贪心选站；每站批量扫频时跳过"PF 判定必 no_signal"的 (站,频道) 对与已证缺席频道；每站扫完即服务已发现频道（SevenBatch 风格） -- **定位服务**：选点 = 保证接收盘内候选环采样 + 信息增益评分（ΔH − λ·移动 − μ·检测），有证书即清除；步数上限沿用 `max_localize_steps` -- **清除调度**：待服务任务（定位+清除 ≤20 节点）开放路径最近邻 + 2-opt，纯标准库，每个决策点滚动重排 -- **收尾**：全部 CLEARED/ABSENT → all_resolved；清满 16 提前停；persistent_search 沿用现有配置语义（实现时对照 `run_q3_seven_practice.py` 保持官方演练停止行为一致） - -## 3. 配置 `config.py` - -新增 `BayesConfig` frozen dataclass（[工程]）：particle_spacing_m、λ/μ 权重、MC 代表数/子采样、skip_margin、站点环半径、2-opt 开关/迭代数等；挂到 `ProblemConfig.bayes`。默认值不改变现有策略行为。 - -## 4. 七点覆盖提升为库代码 `src/mcm26b/planner/seven.py` - -把 `scripts/benchmark_q3_search.py` 的 `seven_points()/SevenImmediate/SevenBatch` 移入 src（旧脚本改 import，消融结果可复现），供 CLI 与新基准复用。 - -## 5. CLI `cli.py` - -`mcm26b run --strategy {grid,seven,bayes}`，默认 grid；**若基准通过则默认改为 bayes**（用户已确认）。 - -## 6. 基准 `scripts/benchmark_q3_bayes.py` - -配对消融 {seven_batch, bayes} ×（seeds 0..5 × 10/16 源 + 边界源 + 0 源纯缺席场景），复用现有 run_case/LocalSimulator 模式；输出 `output/q3/bayes-ablation/`（benchmark.json + report.md）：全清率、correct_absence、异常、accounted/total 虚拟时间、配对降幅、实机 compute_wall_s。 - -## 7. Optuna 调参 `scripts/tune_q3_bayes.py` - -- `uv add --group dev optuna`（仅调参脚本用，生产零新增依赖） -- TPE，目标 = 场景子集平均虚拟时间 + 10⁶×失败惩罚；场景与 error_seed 多样化防过拟合 -- 搜索空间：h、λ/μ、skip margin、候选环、2-opt 开关等 -- optuna 不可用时自动退化为随机搜索；最优参数写 JSON，复跑基准确认后合入 config.py 默认值 - -## 8. 测试 - -- `tests/test_particles.py`：真源保留性（随机场景 × LocalSimulator 误差场，任意更新序列后真源邻域格必活）；证书正确性（alive=∅ ⟺ 真缺席；MEC 证书清除必成功）；跳测规则永不漏检 -- `tests/test_bayes_loop_q3.py`：端到端 LocalSimulator（10/16/0 源、边界源、预算耗尽、故障注入）：全清 + 缺席正确 + 无异常 + 正常退出 -- 现有 105 项测试保持绿 - -## 9. 文档 - -`docs/algo-3-bayes.md`（Voronoi 裕量严格性论证、信息增益与调度设计、与 A2 对比数据）；README 策略表加行；`docs/algo-3.md` 加交叉引用。 - -# 执行顺序 - -1. `git checkout -b q3-bayes` -2. BayesConfig → particles.py（含单测）→ seven.py 提升 -3. bayes_loop.py + CLI 接入 → pytest 全绿 -4. 跑配对消融基准 -5. Optuna 调参 → 回填参数复跑基准 -6. 若达标（全清 100% 且配对均值更优）：`mcm26b run` 默认切 bayes + 文档更新 -7. 分支上分阶段 commit（不 push） - -# 验收标准 - -- 基准全部场景：all_cleared 100%、correct_absence 100%、anomalies 空、normal_exit、单局实机计算 < 60s -- 配对平均虚拟时间优于 seven_batch（目标降幅 ≥5%；参考量级 A2=5232s/12 源） -- `uv run pytest` 全绿；`mcm26b run --self-test --problem 3` 通过 - -# 风险与对策 - -- PF 实机计算耗时 → 空间哈希 + MC 子采样，基准记录 compute_wall_s，超预算先降 MC 规模（实机预算 1200s，A2 仅用 3.4s，余量巨大） -- LocalSimulator 误差场确定性 → 调参与基准用多 error_seed -- 负观测删点的正确性 → Voronoi 裕量定理 + 随机化真源保留性测试兜底 \ No newline at end of file diff --git a/README.md b/README.md index 7167ec5..502d1ba 100644 --- a/README.md +++ b/README.md @@ -1,208 +1,165 @@ -# mcm26b — CUMCM 2026 B题：无线电干扰源的快速自动定位与清除 +# mcm26b — CUMCM 2026 B 题：无线电干扰源的快速自动定位与清除 -统一框架：有界误差几何定位、主动选点、带覆盖证明的搜索、按真实动作耗时调度。 -分析依据 `issue/B题.pdf`、附件1《模拟器使用说明》、附件2《通信接口说明及编程指南》。 +本仓库为参赛论文（`docs/paper/main.tex`）的可复现支撑材料： +提供四问的完整可运行程序、自建模拟器、论文全部数据与插图的生成脚本。 +算法本身的推导、证明与结果分析见论文，本文件只说明**怎么跑、怎么复现**。 -**文档总入口：[docs/README.md](docs/README.md)**（分类索引、阅读顺序与口径登记）。 +--- -- [总体思路与流程图（四问主线、端到端流程图、执行时间速查）](docs/总体思路与流程图.md) -- [执行时间与执行步骤（计时模型、状态机、附件示例推演、实测分解）](docs/执行时间与执行步骤.md) -- [几何计算示意图册（逐图说明：角域交会、覆盖、最小命中集）](docs/几何计算示意图册.md) -- [论文插图清单与复现方式](docs/figures/README.md)（`uv run python scripts/build_figure_data.py`） -- [四问建模分析与 P0–P7 实施计划](docs/B题建模分析与实施计划.md) -- [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) -- [问题一完整解答](docs/问题一解答.md) -- [问题一算法说明（当前实现口径）](docs/algo-1.md) -- [问题二算法说明（保证接收的第二点选择）](docs/algo-2.md) -- [问题三算法说明（七点覆盖证明、贝叶斯闭环与滚动调度）](docs/algo-3.md) -- [问题四算法说明（定向覆盖最小命中集、联合信念与后备清除）](docs/algo-4.md) -- [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) -- [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) -- [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/`） -- [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) -- [论文](docs/paper/main.tex) -- **单文件核心代码（问题一~四）**：[`mcm26b-核心代码.ipynb`](mcm26b-核心代码.ipynb)—— - 四问生产实现按"公共层→几何→信念/覆盖→调度→策略→逐问算例"整理成一个可独立运行的 - notebook，不含评测机、命令行外壳、采集/基准/绘图脚本、测试与未采用方案； - 重新生成用 `python scripts/build_core_notebook.py`，零依赖自检用 - `python scripts/run_notebook_cells.py`（按顺序执行全部代码单元）。 +## 1. 环境 -## 项目结构 - -``` -referee/ # 独立评测机包（mcm26b-referee）：模拟器引擎+机器狗接口+WebUI， - # 零第三方依赖、不依赖本队代码；题意符合性对照见 referee/README.md -src/mcm26b/ -├── config.py # 参数中心：题面常量[题面] + 工程参数[工程]，唯一字面量来源 -├── protocol/ # P0/P1 字段表、计时模型、状态机、串行幂等客户端（已完工） -├── geometry/ # P2 角域交会、直径、MEC、问题二安全候选盘证书（已完工） -├── 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） -├── coverage/ # P4 49点覆盖账本（网格生成 + 逐频道记录） -├── 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） -├── 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 -├── evaluation/ # 日志复算与统计（已完工） -└── harness/ # 测试侧适配：评测机<->Transport 内存回环、故障注入、回放、场景 -tests/ # pytest（含解耦守卫：运行时零导入 referee + 规则表不漂移） -project/ # 1-4 问"最优实现 / 基线实现"对比框架：逐问 optimal+baseline - # +独立参考，冻结基准集配对出数；见 project/README.md -scripts/ # 采集、基准、可视化与校核脚本（含问题四批量仿真 benchmark_q4.py、 - # 运行历史计时分解 analyze_run_timing.py、第四问覆盖证书 verify_q4_cover.py、 - # 论文插图 build_figure_data.py + scripts/matlab、日志归档 archive_replay_logs.py、 - # 单文件核心代码生成/自检 build_core_notebook.py + run_notebook_cells.py） -docs/figures/ # 论文插图（PNG）+ manifest.json（数据来源与 sha256）+ 图清单 -output/ examples/ # 演练证据、算例与生成产物 -``` - -## 独立评测机与 WebUI（referee/） - -评测机已抽成独立包：`referee/`（uv workspace 成员，`uv sync` 自动挂载为 dev 依赖）。 -参赛队运行时代码**零导入** referee，双方仅通过附件2 的 HTTP 协议耦合—— -评测机可独立服务任何客户端，参赛队代码对接官方模拟器无需任何改动。 +- Python ≥ 3.10；运行时依赖仅 `requests`，测试与自检另需 `pytest`、`numpy`。 +- 自建模拟器 `referee/` 为零第三方依赖的独立包，`uv sync` 时作为开发依赖一并挂载。 +- 复现论文 PDF 另需 TeX Live（XeLaTeX、ctex、TikZ），字体已随仓库提供于 `docs/paper/fonts/`。 ```bash -uv run python -m referee # WebUI http://127.0.0.1:8026，接口 :2026 -uv run python -m referee --port 2026 --ui-port 8026 --data-dir referee-data -uv run mcm26b run --robot-id 2026001 --local-judge # 一键：起评测机→开演练局→闭环→汇总 +uv sync # 安装主包与 referee（可编辑挂载） ``` -- **WebUI 完全按附件1 §4 复现官方界面语义**：四个测试模块（演练不限次/正式各3次、 - 启动即占用、正式前再次确认）、确认开始→数据准备→5秒倒计时→25分钟窗口开放接口、 - "指令与反馈"默认最新1000条（100..5000 可调）、中止测试两次确认、结束原因显示、 - 案例编码、演练结束显示 总数/全向/定向（正式任何界面不显示真值）、行为日志导出 - （2MB 上限告警）、空闲时可改端口、只监听本机回环。 -- **回放器**（"回放"Tab）：以本局行为日志 JSONL 驱动，在 Ω 圆上动画复现运动与 - 扫描过程——移动按 5m/s 展开、检测以雷达扫描动画呈现（direction 附实测示向度 - 射线）、清除先光学定位再清除；支持播放/暂停、逐步±、1×~300× 倍速快进、 - 滚轮缩放/拖拽平移、带动作刻度的进度条任意定位；兼容 CSV 式逐事件信息面板。 - **支持手动选定 JSONL 回放**：点击"选择 JSONL 文件"或直接拖拽导入任意行为日志 - （评测机日志含真值；参赛队客户端会话日志取 accepted 事件，无真值），纯前端解析。 -- 引擎与协议：`referee.simulator`（观测判定/幂等/并发409/微秒计时/截止断连）、 - `referee.interface`（附件2 §5 完整语法边界，HTTP 服务器与内存直连共用）， - 一致性由 `referee/tests` 90 项测试与真实 HTTP 测试固化。 -- 自建差异（题面未规定）均在 referee/README.md 声明：联网登录/官方校时/17:30 - 截止/加密上传属官方服务器侧策略，本地不实现；示向度误差场与场景分布是测试假设。 +--- -## 快速开始 +## 2. 运行程序（问题三 / 问题四闭环） -Python ≥ 3.10，运行时依赖仅 `requests`；测试/自检另需 `pytest` 与独立评测机 -包（`uv sync` 一并安装）。 +**队号不在代码中固化**：登录标识属身份信息，一律在运行时通过 `--robot-id` 传入， +代码、测试、示例与日志中只出现与赛事无关的中性占位符（如 `TEAM-0001`）。 ```bash -uv sync # 安装（可编辑挂载本包与 referee） -uv run pytest # 全部测试（含 referee 90 项 + 解耦守卫） -uv run python -m referee # 起本地评测机（可选，配合 WebUI 回放） -uv run mcm26b sim-check # P0 自检：复现附件计时示例 105/111/194/199 -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） -uv run mcm26b run --robot-id 2026001 --self-test --problem 4 # 问题4自检：混合定向源场景 -# 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）； -# 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"） -uv run mcm26b run --robot-id <队号> --log output/run/session.jsonl # 对接模拟器正式/演练 -uv run mcm26b run --robot-id <队号> --base-url http://127.0.0.1:2026 # 对接本地 referee -uv run mcm26b run --robot-id 2026001 --local-judge # 一键本地评测：自动起 referee+开演练局 -# 端口被占时主动询问换端口（非交互自动改空闲端口）；WebUI 可实时观战并在"回放"Tab -# 逐步回看；结束自动汇总结束原因/真值统计/全清校验（--judge-no-ui 可关闭界面） -uv run mcm26b q1 examples/q1/normal.json # 问题一求解（等价 python -m mcm26b.geometry ...） -uv run python main.py examples/q1/triangle.json --rounding-margin-deg 0.005 +# 对接官方模拟器（先在界面确认测试已开始、接口就绪） +uv run python -m mcm26b.cli run --robot-id <队号> --problem 3 +uv run python -m mcm26b.cli run --robot-id <队号> --problem 4 + +# 一键本地评测：自动起自建模拟器 → 开一局演练 → 跑完整闭环 → 汇总 +uv run python -m mcm26b.cli run --robot-id <队号> --problem 3 --local-judge + +# 不连任何模拟器的自检（随机场景，内存回环） +uv run python -m mcm26b.cli run --robot-id SELFCHECK --problem 3 --self-test + +# 复现附件计时示例（期望 105/111/194/199 秒） +uv run python -m mcm26b.cli sim-check ``` -## 问题一（P2，已完工） +主要开关：`--strategy {bayes,seven,grid}`（仅问题三，默认 `bayes`）、 +`--base-url`、`--log <路径>`、`--real-budget-s`（仅自检生效）。 -给定同频道多个检测点及 ±1° 误差示向度，计算前向角域交集（半平面交）、 -定位区域直径、直径圆覆盖判断（含反例顶点与超出距离）与最小包围圆、20m 清除余量。 +### 问题一 / 问题二 ```bash -uv run mcm26b q1 examples/q1/normal.json -o output/q1/result.json -uv run python scripts/build_q1_demo.py # 重建离线交互可视化 output/q1/question1.html -uv run python scripts/benchmark_q1.py # MEC 基准与舍入敏感性 +uv run python -m mcm26b.cli q1 examples/q1/normal.json -o output/q1/result.json +uv run python -m mcm26b.geometry examples/q1/triangle.json ``` -离线交互页：`output/q1/question1.html`。可选 PNG 重绘需要 Pillow 与 Windows -雅黑字体：`python scripts/render_q1_figures.py`；事件校核需要 Node.js： -`node scripts/verify_visualization.cjs`。二者仅为图形校核，非求解依赖。 +`examples/q1/` 下的五个算例分别覆盖正常多边形、劣交会、零误差线段、不相容观测与直径圆反例。 -## 协议基线（P0/P1，已完工） +仓库根的 `main.py` 只是转发到同一 CLI 的薄壳（`python main.py <子命令>`）， +等价于 `python -m mcm26b.cli <子命令>`。 -- `protocol.rules`：四条指令的字段表、结果码与错误语义（可执行表格）； -- `protocol.timing`：纯计时模型，逐步复现附件示例 105/111/194/199； -- `protocol.state.RobotState`：位置/频道/虚拟时刻状态机；accepted=false 不更新， - `/clear` 不切频道，虚拟时刻以 accepted 响应为准； -- `protocol.client.SimulatorClient`：串行发送、幂等重试（同 ID 同内容原样重发）、 - 连接/读超时、基于 `remaining_real_duration_s` 的本地截止时钟与退出预留、JSONL 会话日志； -- 独立包 `referee`：按附件语义复现评测机（观测门限、定向覆盖角、幂等回放、 - 整数微秒计时、现实/虚拟截止、并发409和请求校验），真值只对评测机/测试端开放； - `harness.LocalSimTransport` 内存回环挂载（与 referee HTTP 服务器共用同一边界）， - `harness.ScriptedFaultTransport` 注入断连/超时/500，`harness.PlaybackTransport` - 回放官方演练日志。 +--- -本地模拟器默认执行至多1200秒程序预算、1500秒接口开放窗口，截止后通过 -`SimulatorClosed` 模拟连接关闭，已登记动作可以完成。可注入 `clock`、`wall_clock` -和 `window_started_at` 测试迟到与超时。CLI `--self-test` 显式使用 `stress_mode=True`， -保留默认1800秒压力预算并扩展窗口；这不是官方演练时限。 -内存传输不实现TCP连接限流、UI或日志上传；场景均匀分布及逐点哈希误差仍是测试假设。 +## 3. 自建模拟器（`referee/`） -```python -from mcm26b.protocol import SimulatorClient -client = SimulatorClient("参赛队号") # 默认 http://127.0.0.1:2026 -client.enter() -outcome = client.measure((300, 400), 1) # MeasureOutcome(result, svd_deg, ...) -client.exit() -``` - -## 阶段状态（对照 P0–P6 计划） - -| 阶段 | 状态 | -| --- | --- | -| P0 规则固定（规则表/状态机/字段表，示例逐步 105/111/194/199） | ✅ 完成 | -| P1 协议基线（客户端 + 自建回放/故障注入测试） | ✅ 完成 | -| P2 几何内核（角域交、直径、MEC、证书） | ✅ 完成（迁入 `mcm26b.geometry`） | -| P3 问题二选点（K1、候选区域 Q、采样评估） | ✅ 完成（belief + 选点器，测试固化） | -| 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)） | -| 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；[算法说明](docs/algo-4.md)） | -| 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.md)、[设计细节](docs/algo-3-bayes.md)） | -| 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 | - -### 图论/DP 轮（2026-09-12，S1–S5）要点 - -- **S1 度量**：`scripts/run_suite.py` 逐局新增 `route_gap_vs_offline`、 - `route_lower_bound_s`（MST 下界）、`distinct_service_points`、 - `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 - -改进版 A2（七点覆盖＋本站批量扫频）已通过官方案例 `J4ER-Y9EP-Q379-UPFA`： -12/12源全清，109次检测，虚拟耗时5232.132109秒，现实耗时3.406秒。 -[本局报告与运行方法](output/q3/practice/20260911-seven-batch-01/report.md)。 -独立入口为 `scripts/run_q3_seven_practice.py`。 - -贝叶斯闭环 `bayes`（网格粒子滤波＋负观测更新＋信息增益选点＋滚动 2-opt -调度，参数经 Optuna 回填）在自建模拟器配对消融中相对 SevenBatch 平均 -虚拟时间再降 30.0%（14/14 场景全清零异常），算法与数据见 -[algo-3-bayes.md](docs/algo-3-bayes.md)，已是 `mcm26b run` 默认策略。 -虚拟时间即成绩口径：找全证明后立即 `/exit`（`--persistent` 可显式开启 -"扫到限时"的复扫保险模式）。 - -2026-09-11 已通过本机 2026 端口完成一次官方问题3演练：案例 -`DHFK-PHD2-SR2P-FXK9`，10/10 源全清，540 次检测、10 次清除，现实耗时 -7.629 秒。原始请求响应、官方产物及离线审计见上述验证报告。原计时复算的 -541 处微秒精度误报也已记录；按官方整数微秒累计规则独立复算后，552 个 -响应时刻全部吻合。生产计时复算函数保持原样。 +按题面附件 1《模拟器使用说明》与附件 2《通信接口说明》实现，与参赛队代码零耦合， +只通过 HTTP 协议对接；论文正文的演练数据即由它产生。 ```bash -uv run python scripts/verify_q3_practice.py output/q3/practice/20260911-port2026 +uv run python -m referee # WebUI http://127.0.0.1:8026，接口 :2026 +uv run python -m referee --data-dir referee-data ``` -本项目只使用官方**演练**采集观测，从未使用正式测试。`scripts/capture_practice.py` -不会启动测试：必须先在界面确认“问题3演练”、接口已就绪，再提供 -`--practice-confirmed` 与本局案例编码。正式测试由团队按计划另行安排； -加密日志上传上限 2MB，程序避免异常高频循环。 +- WebUI 复现官方测试流程：四个模块（演练不限次 / 正式各 3 次）、确认→5 秒倒计时→ + 25 分钟窗口、指令与反馈流、中止需两次确认、结束原因、行为日志导出； +- "回放"页可用本局行为日志动画复现运动与扫描过程，也支持手动导入任意 JSONL； +- 自建差异（联网登录、官方校时、加密上传属官方服务器侧，本地不实现； + 误差场与场景分布为测试假设）见 `referee/README.md`。 + +--- + +## 4. 复现论文数据（`referee-data/` → 宏与表格） + +`referee-data/sessions/` 收录论文正文引用的 44 局演练日志（问题三 15 局、问题四 29 局）， +`referee-data/rejected-sessions/` 保留同批采集中被判定为无效的 11 局以便复核。 + +```bash +python scripts/build_practice_data.py # 复算演练局 → 宏、整表、CSV/JSON 快照 +python scripts/audit_paper_formal.py --snapshot docs/paper/data/formal-results.json +python scripts/build_paper_support.py # 附录正式数据表与支撑包 +``` + +- 第一条按题面计时模型逐动作复算，输出 `docs/paper/data/practice-*.tex` 与 + `practice-results.{json,csv}`；复算值与评测机记录的最大偏差应为 **0.000000 s**。 + 运行结束会打印局数、全清局数与平均虚拟时间，可直接与论文数字对照。 +- 第二条校验官方六份正式导出包与统计快照的对应关系（来源匹配需原电脑的官方统计队列）。 +- 第三条生成附录表格与 `support-materials.zip`（`--package`）。 + +### 需要重新采集演练局时 + +```bash +uv run python scripts/run_practice_episodes.py --problem 3 --robot-id <队号> \ + --counts 10,13,16 --seeds 300-314 +uv run python scripts/run_practice_episodes.py --problem 4 --robot-id <队号> \ + --counts 10,13,16 --directional-cycle 3,6,8 --seeds 400-439 +``` + +脚本启动评测机并调用生产策略对接接口，真值与行为日志由评测机写入 +`referee-data/sessions/`。判定标准为 +`清完全部源 ∧ 无缺席误判 ∧ 主动退出 ∧ 零异常 ∧ 零失败清除`； +只看"是否清完"会放过"清完了但中途大量 `/clear` 打空"的无效局。 + +--- + +## 5. 复现论文插图 + +```bash +uv run python scripts/plot_paper_figures.py # 生成 docs/figures/*.png +``` + +脚本只读 `docs/paper/data/` 下的复算结果与第四问覆盖计划，不手抄任何数字； +图内文字与图注数据均来自同一次复算。插图清单、数据来源与生成命令见 +[`docs/figures/README.md`](docs/figures/README.md) 与 `docs/figures/manifest.json`。 + +--- + +## 6. 编译论文 + +```bash +cd docs/paper +latexmk -xelatex -interaction=nonstopmode -halt-on-error -outdir=build main.tex # 产物 build/main.pdf +latexmk -xelatex -interaction=nonstopmode -halt-on-error main.tex # 产物 main.pdf +``` + +两条口令都完成交叉引用所需的多遍编译。VS Code 的 LaTeX Workshop 配置见 +`.vscode/settings.json`（默认配方为前者）。细节与注意事项见 +[`docs/paper/README.md`](docs/paper/README.md)。 + +--- + +## 7. 单文件核心代码与测试 + +```bash +uv run pytest # 全部测试（本包 + referee） +python scripts/build_core_notebook.py # 由 src/mcm26b 生成单文件核心代码 +python scripts/run_notebook_cells.py # 顺序执行 notebook 全部代码单元（零依赖自检） +``` + +[`mcm26b-核心代码.ipynb`](mcm26b-核心代码.ipynb) 是四问生产实现的单文件版本 +（约 5000 行），不含评测机、命令行外壳、采集/绘图脚本与测试； +`src/mcm26b/` 保留与之逐字对应的模块化版本。 + +--- + +## 8. 目录结构 + +``` +src/mcm26b/ 四问求解实现：config / protocol / geometry / belief / coverage / + guarantee / planner / particles / routing / evaluation / harness +referee/ 自建模拟器（独立包，零第三方依赖，不依赖本队代码） +referee-data/ 论文数据来源：演练日志（sessions / rejected-sessions）、正式统计快照 +tests/ 本包测试（含运行时零导入 referee 的解耦守卫） +docs/paper/ 论文 LaTeX 源、数据与宏、编译说明 +docs/figures/ 论文插图（PNG）与来源清单 +examples/ 问题一算例与回放归档 +scripts/ 复现脚本：数据复算、插图绘制、正式数据核对、采集与基准 +project/ 四问"最优实现 / 基线实现"配对对比框架（论文对比表的来源） +issue/ 赛题、附件与格式规范（原始材料） +``` + +`docs/paper/REWRITE_NOTES.md` 记录论文各轮修订与验证过程（含数据重采与问题修复）。 diff --git a/docs/B题问题3-4相关论文列表.md b/docs/B题问题3-4相关论文列表.md deleted file mode 100644 index 61225b5..0000000 --- a/docs/B题问题3-4相关论文列表.md +++ /dev/null @@ -1,191 +0,0 @@ -# 2026 高教社杯 B 题（无线电干扰源快速自动定位与清除）问题 3、4 相关论文列表 - -> 排序原则：与问题 3/4 直接应用方案越相关，排名越靠前。 -> 标注：【Q3】主要支撑问题 3（全向干扰源：搜索→交会定位→逼近清除的整体策略）；【Q4】主要支撑问题 4（定向干扰源、方向未知）；【Q3·Q4】两者通用。 - ---- - -## 一、无线电信号源的移动机器人搜索与定位（与赛题任务框架最直接对应） - -1. **Simultaneous Localization of Multiple Unknown and Transient Radio Sources Using a Mobile Robot** 【Q3】 - D. Song, C. Y. Kim, J. Yi. *IEEE Transactions on Robotics*, 2012（被引 75） - 移动机器人沿路径采集 RSS，在线同时定位**多个未知无线电源**——与问题 3“干扰源个数未知、机器狗边走边测”的设定几乎同构，可直接借鉴其多源数据关联与定位框架。 - https://ieeexplore.ieee.org/abstract/document/6145760/ ｜ [PDF](http://coewww.rutgers.edu/~jgyi/pdfs/IEEE_TRO_2012.pdf) - -2. **Cooperative Search of Multiple Unknown Transient Radio Sources Using Multiple Paired Mobile Robots** 【Q3】 - C. Y. Kim, D. Song, Y. Xu, J. Yi, X. Wu. *IEEE Transactions on Robotics*, 2014（被引 67） - 上文的协同多机器人版本：成对机器人搜索多个未知瞬态无线电源，含**源匿名（信号不可区分）**处理——对应本题不同频道区分信号的思路对照。 - https://ieeexplore.ieee.org/abstract/document/6857360/ ｜ [PDF](https://jingangyi.rutgers.edu/pdfs/IEEE_TRO_2014.pdf) - -3. **RSS Gradient-Assisted Frontier Exploration and Radio Source Localization** 【Q3·Q4】 - J. N. Twigg, J. R. Fink, L. Y. Paul, B. M. Sadler. *ICRA*, 2012（被引 77） - 用 RSS 梯度引导前沿探索（frontier exploration），让机器人高效逼近信号源而避免盲目遍历——可直接支撑问题 3/4 中“初探→逼近”的两阶段搜索策略设计。 - https://ieeexplore.ieee.org/abstract/document/6225059/ - -4. **Radio Source Localization Using Sparse Signal Measurements from Uncrewed Ground Vehicles** 【Q3】 - A. Perera, V. P. Tran, S. Anavatti, K. Kasmarik, et al. *arXiv:2312.03493*, 2023 - 地面无人车（与机器狗同类平台）在室外区域以稀疏采样定位无线电源，含完整仿真评估流程——与用模拟器演练测试的实验设计直接可参照。 - https://arxiv.org/abs/2312.03493 ｜ [PDF](https://arxiv.org/pdf/2312.03493) - -5. **Particle Filter Based Localization of Access Points Using Direction of Arrival on Mobile Robots** 【Q3·Q4】 - R. Parashar, R. Parasuraman. *IEEE VTC*, 2020（被引 13） - 移动机器人利用**到达方向（DOA，即示向度）**+ 粒子滤波递推定位无线电源——对应本题“多次测向→逐步缩小定位区域”的序贯定位算法。 - https://ieeexplore.ieee.org/abstract/document/9348813/ - -6. **Online Methods for Radio Signal Mapping with Mobile Robots** 【Q3】 - J. Fink, V. Kumar. *ICRA*, 2010（被引 166） - 移动机器人边走边在线构建信号场强地图并估计源位置，可支撑本题“场强仅随距离衰减”假设下的快速粗定位与路径在线修正。 - https://ieeexplore.ieee.org/abstract/document/5509574/ - -7. **Distributed Algorithms for Stochastic Source Seeking with Mobile Robot Networks** 【Q3】 - N. A. Atanasov, J. Le Ny, et al. *ASME JDSMC*, 2015（被引 117） - 随机源搜索的分布式算法，以无线电源定位为应用背景，理论保证强，可作为逼近清除阶段的收敛性依据。 - https://arxiv.org/pdf/1402.0051 - -8. **Cooperative Multi-Robot Estimation and Control for Radio Source Localization** 【Q3】 - B. Charrow, N. Michael, V. Kumar. *IJRR*, 2014（被引 84） - 仅测距射频下的多机器人协同估计与控制，信息论驱动（ mutual information ）选择测量动作——可迁移为“选哪个频道、去哪测”的决策准则。 - https://journals.sagepub.com/doi/abs/10.1177/0278364913500542 - -9. **A Review of Radio Frequency Based Localisation for Aerial and Ground Robots with 5G Future Perspectives** 【Q3·Q4】 - M. Kabiri, C. Cimarelli, H. Bavle, et al. *Sensors*, 2022（被引 48） - 综述：RSS/AOA 等 RF 定位手段在空地机器人上的比较，适合写论文的“研究现状”部分并帮助选型。 - https://www.mdpi.com/1424-8220/23/1/188 - ---- - -## 二、测向（Bearing-only/AOA）交会定位与观测点优化（第二检测点选择、定位区域收缩） - -10. **Gathering Bearing Data for Target Localization** 【Q3】 - H. Bayram, J. Vander Hook, V. Isler. *IEEE Robotics and Automation Letters*, 2016（被引 38） - 给定若干潜在目标区域与不确定度要求，规划采集方位数据的机器人路径，归结为 **TSP with Neighborhoods 的信息路径规划**——与问题 3“多个待清除目标 + 逐个交会定位 + 总时间最短”的任务排序高度吻合。 - https://ieeexplore.ieee.org/abstract/document/7390217/ ｜ [PDF](https://conservancy.umn.edu/bitstreams/ff2d34bf-0056-4a29-be84-eb7355b527e4/download) - -11. **Relationship Between Error Ellipse and Error Quadrilateral in Bearing-Only Localization** 【Q3·Q4】 - B. Wang, Y. Hu, C. Yu, et al. *IEEE Access*, 2026 - 直接研究双站测向定位的**误差四边形**（即题目中的“定位区域”）与误差椭圆的关系，论证几何构型与精度的关系——与问题 1 的定位区域、问题 2/3 的测点选择理论完全对口。 - https://ieeexplore.ieee.org/abstract/document/11677056/ - -12. **Bearing-Only Active Target Localization Strategies for a System of Two Communicating Mobile Robots** 【Q3】 - J. Vander Hook, P. Tokekar. 技术报告, 2013 - 主动测向定位：规划“去哪测”以最优压缩目标位置不确定性，给出最优双机器人测量构型——第二检测点选择策略的系统性理论。 - https://conservancy.umn.edu/items/38693c39-7a3b-400b-8926-f9aa194a8824 - -13. **Multi-UAV Trajectory Optimization for Bearing-Only Localization in GPS Denied Environments** 【Q3】 - A. Sciacchitano, L. Mudrik, S. Kragelund, et al. *arXiv:2602.11116*, 2026 - 面向测向定位的轨迹优化，在视场等约束下优化测量构型——可借鉴其代价函数与滚动优化框架设计机器狗的测点序列。 - https://arxiv.org/abs/2602.11116 ｜ [PDF](https://arxiv.org/pdf/2602.11116) - -14. **Optimal Sensor Placement for Multi-Source AOA Localisation with Distance-Dependent Noise Model** 【Q3·Q4】 - M. Hamdollahzadeh, R. Amiri, et al. *IET Radar, Sonar & Navigation*, 2019（被引 37） - **多源** AOA 定位的最优布点，含递推 CRLB 表示——支撑多干扰源场景下测点位置的全局优化。 - https://ietresearch.onlinelibrary.wiley.com/doi/abs/10.1049/iet-rsn.2018.5426 - -15. **Optimal Sensor Placement for Target Localization Using Hybrid RSS, AOA and TOA Measurements** 【Q3】 - S. Xu, et al. *IEEE Communications Letters*, 2020（被引 110） - 混合量测下最小化 tr(CRLB) 的最优传感器-目标几何，可用于论证“交会角接近 90°”等构型结论的严谨性。 - https://ieeexplore.ieee.org/abstract/document/9097832/ - -16. **Geometry Influence on GDOP in TOA and AOA Positioning Systems** 【Q3·Q4】 - X. Lv, K. Liu, P. Hu. *ICNSC*, 2010（被引 58） - 基于 CRLB 分析新增观测点对 GDOP 的影响——回答“再加一个检测点值不值、放在哪”的定量依据。 - https://ieeexplore.ieee.org/abstract/document/5480336/ - -17. **定向交叉定位配站问题与定位精度分析**（中文）【Q3·Q4】 - 张艳花. 《电讯技术》, 2008（被引 2） - 中文文献，专门讨论测向交叉定位的配站（交会角）与精度关系，便于直接引用“交会角 90° 最优、30°~150° 可用”类结论。 - （《电讯技术》2008 年刊，可通过知网/万方检索全文） - -18. **Trajectory Planning for Multiple Robots in Bearing-Only Target Localisation** 【Q3】 - C. Leung, S. Huang, G. Dissanayake, et al. *IROS*, 2005（被引 16） - 早期经典：以定位精度为目标做测向机器人轨迹规划，适合作为主动定位问题的建模起点引用。 - https://ieeexplore.ieee.org/abstract/document/1545322/ - -19. **Observer Path Planning for Bearings-Only Localization Considering Limited Field of View** 【Q3】 - A. Nayebi Astaneh, N. Pariz, M. O. Naghibi Sistani. *Journal of Control*, 2014 - 考虑传感器视场约束的观测者实时路径规划——对应测向机需旋转天线搜索示向度的过程约束。 - https://joc.kntu.ac.ir/browse.php?a_id=37&sid=1&slc_lang=en&ftxt=1 - ---- - -## 三、多目标搜索理论与任务规划（10–16 个未知目标的区域搜索框架） - -20. **Multi-Vehicle Bayesian Search for Multiple Lost Targets** 【Q3·Q4】 - E. M. Wong, F. Bourgault, T. Furukawa. *ICRA*, 2005（被引 193） - 多目标贝叶斯搜索经典：概率图 + 贝叶斯更新 + 搜索资源分配——问题 3“干扰源个数未知、需确保全部清除”的终止判据与覆盖率论证可由此构建。 - https://ieeexplore.ieee.org/abstract/document/1570598/ - -21. **MPC-Based Cooperative Multiagent Search for Multiple Targets Using a Bayesian Framework** 【Q3】 - H. Xiao, R. Cui, D. Xu, Y. Li. *Journal of Field Robotics*, 2024（被引 9） - 模型预测控制 + 贝叶斯框架的多目标搜索路径规划，滚动时域决策——适合借鉴为本题“边测边重规划”的在线策略。 - https://onlinelibrary.wiley.com/doi/abs/10.1002/rob.22382 - -22. **Multi-Agent Informed Path Planning Using the Probability Hypothesis Density** 【Q3·Q4】 - J. Olofsson, G. Hendeby, T. R. Lauknes, T. A. Johansen. *Autonomous Robots*, 2020（被引 27） - 用 PHD（随机有限集）表示**目标个数与位置均未知**的场景并做信息路径规划——与“干扰源总数 10–16 但具体未知”的设定直接对应。 - https://link.springer.com/article/10.1007/s10514-020-09904-1 ｜ [PDF](https://torarnj.folk.ntnu.no/AURO2020a.pdf) - -23. **Probabilistic Search for Tracking Targets: Theory and Modern Applications** 【Q3·Q4】 - E. Kagan, I. Ben-Gal. Wiley, 2013（被引 27） - 搜索论专著：最优搜索策略、马尔可夫决策模型，为搜索策略提供理论根基（Stone 最优搜索理论的应用版）。 - https://onlinelibrary.wiley.com/doi/abs/10.1002/9781118596593 - -24. **Efficient Multi-Robot Search for a Moving Target** 【Q3】 - G. Hollinger, S. Singh, J. Djugash, A. Kehagias. *IJRR*, 2009（被引 254） - 多机器人高效搜索路径规划（ESPP）并提出可证明性能的近似算法；本题目标虽静止，其“最大化截获概率”的建模与保证思路可迁移。 - https://journals.sagepub.com/doi/abs/10.1177/0278364908099853 - ---- - -## 四、定向干扰源定位（问题 4 特有：覆盖角 180°、方向未知） - -25. **UAV-Based Efficient Joint Estimation of Directional Emitter Position and Transmission Orientation** 【Q4】 - T. Ruan, Y. Huang, L. Zhou, Q. Wu, et al. *IEEE Wireless Communications Letters*, 2024（被引 2） - **联合估计定向发射源的位置与发射方向**（RSS-AOA 混合量测）——问题 4“定向方向未知”的最直接对应文献。 - https://ieeexplore.ieee.org/abstract/document/10802920/ - -26. **Efficient Localization of Directional Emitters via Joint Beampattern Estimation** 【Q4】 - F. Williams, A. Pemasiri, D. Jayalath, T. Martin, et al. *arXiv:2411.04364*, 2024 - 联合波束方向图估计实现定向发射源高效定位，并分析波束宽度/朝向对定位的灵敏度（CRLB）——可支撑定向源需从侧向多点探测的策略设计。 - https://arxiv.org/abs/2411.04364 ｜ [PDF](https://arxiv.org/pdf/2411.04364) - -27. **Location Errors of Directional Emitters in AOA System for Non-Line-of-Sight Conditions** 【Q4】 - J. M. Kelner, C. Ziółkowski, et al. *SPIE*, 2019 - 分析发射天线朝向对 AOA 示向度误差的影响——定向干扰源场景下误差建模的参考。 - https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11055/110551G/Location-errors-of-directional-emitters-in-AOA-system-for-non/10.1117/12.2525060.short - -28. **ALRD: AoA Localization with RSSI Differences of Directional Antennas for Wireless Sensor Networks** 【Q4】 - J. R. Jiang, C. M. Lin, F. Y. Lin, et al. *Int. J. of Distributed Sensor Networks*, 2013（被引 133） - 利用定向天线朝向差分的 RSSI 做定位，对“信号只在 180° 覆盖角内可被接收”的探测盲区分析与补盲路径设计有参考价值。 - https://journals.sagepub.com/doi/abs/10.1155/2013/529489 - ---- - -## 五、信息路径规划方法与综述（通用方法库） - -29. **Information-Driven Path Planning** 【Q3·Q4】 - S. Bai, T. Shan, F. Chen, L. Liu, B. Englot. *Current Robotics Reports*, 2021（被引 53） - 信息驱动路径规划综述，梳理目标函数（信息增益、熵减）与平台约束，适合搭建问题 3 的总体算法框架。 - https://link.springer.com/article/10.1007/s43154-021-00045-6 - -30. **Adaptive Informative Path Planning in Metric Spaces** 【Q3】 - Z. W. Lim, D. Hsu, W. S. Lee. *IJRR*, 2016（被引 101） - 带自适应性的信息路径规划，具次模性与近似保证——为“总时间越短越好”的目标函数设计提供理论工具。 - https://journals.sagepub.com/doi/abs/10.1177/0278364915596378 - -31. **Informative Path Planning for Active Field Mapping Under Localization Uncertainty** 【Q3】 - M. Popović, T. Vidal-Calleja, J. J. Chung, et al. *ICRA*, 2020（被引 62） - 不确定性感知的信息路径规划，平衡探索与精度——对应本题“检测耗时 vs 路径耗时”的权衡。 - https://ieeexplore.ieee.org/abstract/document/9197034/ - -32. **基于深度强化学习的短波自主检测与定位方法**（中文）【Q3】 - 冯祺玥, 唐涛, 张昀普, 王鼎, 吴志东. 《通信学报》, 2026 - 中文文献：多站测角交会定位 + 深度强化学习自主检测定位，提供国内无线电监测定位的表述与流程范式。 - https://www.joconline.com.cn/rc-pub/front/front-article/download/155129217/lowqualitypdf/%E5%9F%BA%E4%BA%8E%E6%B7%B1%E5%BA%A6%E5%BC%BA%E5%8C%96%E5%AD%A6%E4%B9%A0%E7%9A%84%E7%9F%AD%E6%B3%A2%E8%87%AA%E4%B8%BB%E6%A3%80%E6%B5%8B%E4%B8%8E%E5%AE%9A%E4%BD%8D%E6%96%B9%E6%B3%95.pdf - ---- - -### 使用建议 -- **问题 3 算法主线**：以 1、3、10、20、22 为核心——贝叶斯概率图搜索（20/22）确定探测顺序与终止条件，DOA 序贯定位（5）+ 交会几何优化（10/11/15/17）选测点，RSS 梯度逼近（3）完成 20 m 内清除前逼近。 -- **问题 4 增量**：在问题 3 框架上叠加 25/26 的“位置 + 方向”联合估计，并用 28 的思路设计覆盖角盲区的补测路径。 -- **论文写作**：现状综述引用 9、29、32；精度理论引用 11、14、15、16。 diff --git a/docs/README.md b/docs/README.md index 5006286..321ef2d 100644 --- a/docs/README.md +++ b/docs/README.md @@ -1,71 +1,27 @@ # docs 索引 -仓库文档分四类：**总览与流程**、**逐问算法**、**验证与对比**、**论文与插图**。 -阅读顺序建议按下面的表格从上到下。 +论文本身已完整给出四问的模型、推导与结果分析，因此本目录不再保留算法说明文档， +只留下复现所需的说明与材料。 -## 总览与流程 - -| 文档 | 内容 | +| 入口 | 内容 | | --- | --- | -| [`总体思路与流程图.md`](总体思路与流程图.md) | 四问主线、端到端流程图（mermaid）、执行步骤与执行时间速查、交付物与验收锚点 | -| [`执行时间与执行步骤.md`](执行时间与执行步骤.md) | 计时模型、会话状态机与动作回合、附件示例逐步推演、实测日志分解、时间预算与安全阀 | -| [`B题建模分析与实施计划.md`](B题建模分析与实施计划.md) | 四问建模取舍与 P0–P7 实施计划（最早的设计文档） | -| [`协议规则表与状态机.md`](协议规则表与状态机.md) | 指令字段表、结果码、计时模型、状态机（P0 交付） | +| [`../README.md`](../README.md) | **总入口**：环境、运行程序、复现论文数据与插图、编译论文 | +| [`paper/README.md`](paper/README.md) | 论文工程说明：数据分工、重算与编译步骤、VS Code 配方 | +| [`paper/REWRITE_NOTES.md`](paper/REWRITE_NOTES.md) | 论文各轮修订与验证记录（含演练数据重采与问题修复） | +| [`paper/main.tex`](paper/main.tex) | 论文 LaTeX 源文件（正文与附录） | +| [`paper/data/`](paper/data) | 论文引用的全部数据、宏与整表（由脚本生成） | +| [`figures/README.md`](figures/README.md) | 论文插图清单：每张图的数据来源与生成命令 | +| [`figures/manifest.json`](figures/manifest.json) | 插图来源清单（可机读） | -## 逐问算法 +## 复现顺序 -| 文档 | 内容 | -| --- | --- | -| [`algo-1.md`](algo-1.md) | 问题一实现口径：半平面交、顶点对直径、直径圆充要判定、MEC 清除证书 | -| [`问题一解答.md`](问题一解答.md) | 问题一论文式解答：模型边界、算法可复现步骤、反例构造、演练验证 | -| [`algo-2.md`](algo-2.md) | 问题二：候选集 K1、保证接收盘、安全盘 B(c, 1000−a)、最坏后验选点 | -| [`algo-3.md`](algo-3.md) | 问题三：三层在线架构、七点解析覆盖、覆盖账本、任务调度与停止条件 | -| [`algo-3-bayes.md`](algo-3-bayes.md) | 问题三 `bayes` 策略：网格幸存集滤波、信息增益选点、动态 TSP、Optuna 调参 | -| [`algo-4.md`](algo-4.md) | 问题四：`forward_half_disk` 最小命中集、位置×朝向联合信念、后备盲清 | +```bash +python scripts/build_practice_data.py # 演练局复算 → 宏与表格 +python scripts/audit_paper_formal.py --snapshot docs/paper/data/formal-results.json +python scripts/build_paper_support.py # 附录正式数据表（--package 另出支撑包） +uv run python scripts/plot_paper_figures.py +cd docs/paper && latexmk -xelatex -interaction=nonstopmode -halt-on-error -outdir=build main.tex +``` -## 优化与验证 - -| 文档 | 内容 | -| --- | --- | -| [`第三四问架构优化与运行提速方案.md`](第三四问架构优化与运行提速方案.md) | 运行历史复核、分层目标架构、P0–P2 优化项与实施路线 | -| [`第三四问图论与动态规划优化方案.md`](第三四问图论与动态规划优化方案.md) | 图论/DP 引入方案与实测回填（含未采用的改动与原因） | -| [`算法审核与优化-20260911.md`](算法审核与优化-20260911.md) | 一次外部口径的算法审核记录与结论 | -| [`最优与基线实现对比.md`](最优与基线实现对比.md) | 1–4 问「最优实现 / 基线实现」对比框架与结果（论文口径说明） | -| [`B题问题3-4相关论文列表.md`](B题问题3-4相关论文列表.md) | 题 3、4 相关文献清单 | - -## 论文与插图 - -| 路径 | 内容 | -| --- | --- | -| [`paper/main.tex`](paper/main.tex) | 论文正文（`latexmk -xelatex main.tex`）；插图经 `\graphicspath` 取自 [`figures/`](figures/README.md) | -| [`paper/README.md`](paper/README.md) | 论文编译方式、目录结构与待办标记 | -| [`figures/README.md`](figures/README.md) | 插图清单、配色与字体约定、数据来源与复现命令 | -| [`figures/manifest.json`](figures/manifest.json) | 每张图的数据来源与 sha256、生成命令 | -| [`../project/README.md`](../project/README.md) | 对比框架目录结构与运行方式；结果表由 `scripts/build_paper_support.py` 转入论文第 11 节 | - -## 口径登记 - -同一事实在多份文档中出现时，以下列来源为准；发现不一致请先改文档，再复核图。 - -| 事实 | 权威来源 | -| --- | --- | -| 题面常量（Ω、速度、各动作耗时、源与频道范围） | `src/mcm26b/config.py`（带 `[题面]` / `[工程]` 标注） | -| 指令字段与结果码 | `src/mcm26b/protocol/rules.py` + [`协议规则表与状态机.md`](协议规则表与状态机.md) | -| 问题一数值（D、ρ、E） | `mcm26b.geometry.solve` 的返回，或 [`问题一解答.md`](问题一解答.md) | -| 覆盖计划与证书 | `src/mcm26b/guarantee/plans/*.json`（`q4_mincover_v2.json` 为默认） | -| 实测虚拟时间 | 行为日志 `virtual_time_s` 字段与 `evaluation.recompute_virtual_time` | -| 冻结基准集对比结果 | `project/results/*.md`（由 `uv run python -m project.run` 生成）；论文表格为 `docs/paper/data/project-*.tex`，由 `scripts/build_paper_support.py` 生成 | - -已知的两处需要随环境更新的引用： - -1. [`问题一解答.md`](问题一解答.md) 引用的 `output/q1/figures/*.png` 属于早期 - 离线插图（`output/` 未入库）；现行插图在 [`figures/`](figures/README.md)。 -2. 多份文档提到的官方演练报告位于 `output/q3/practice/`、`output/q4/`， - 同样未入库；需要时用 `scripts/verify_q3_practice.py` 对留档目录复算。 - -## 写作约定 - -- 只写结论与依据，不使用比喻、排比与自评式措辞（例如「唯一权威」「关键洞察」）。 -- 数字必须能在代码或数据中找到出处；图内不放标题与解释段落， - 说明写在正文，图名与清单见 [`figures/README.md`](figures/README.md)。 -- 中文引号统一使用「」，避免与代码中的 ASCII 引号混淆。 +> `docs/paper/data/*.tex` 由脚本生成，一律使用 LF 换行：CRLF 会让 `\input` 进表格的行 +> 在 TeX 缓冲中提前 `\par` 化，触发 booktabs 的 “Misplaced \noalign”。 diff --git a/docs/algo-1.md b/docs/algo-1.md deleted file mode 100644 index e073d28..0000000 --- a/docs/algo-1.md +++ /dev/null @@ -1,266 +0,0 @@ -# 问题一算法说明：有界测向误差下的角域交会、直径与最小包围圆 - -> 本文按**当前仓库实现**重写，替代早期只描述理论方案的版本。 -> 问题一生产入口为 `src/mcm26b/geometry/localization.py::solve`，CLI 为 -> `python -m mcm26b.geometry`；问题二至四只复用其中的 -> `smallest_enclosing_circle` 与同一套角域半平面语义，并不直接调用 -> `solve`（复用与差异见 §7）。 - -## 1. 问题与实现入口 - -同一频道的同一静止干扰源，在若干检测点 $s_i=(s_{ix},s_{iy})$ 处得到示向度 -$\theta_i$。题设测向误差不超过 $\pm1^\circ$，同一地点重复测量误差不变； -示向度从正东逆时针计量。问题一要求定位区域的直径，并判断“以该直径为 -直径的圆”能否覆盖定位区域。 - -| 层次 | 文件 | 内容 | -| --- | --- | --- | -| 生产内核 | `src/mcm26b/geometry/localization.py` | `Measurement`、`halfplanes`、`convex_hull`、`diameter`、`smallest_enclosing_circle`、`solve` | -| CLI | `src/mcm26b/geometry/__main__.py` | 读 JSON、可加两位小数舍入裕量、输出结果 | -| 统一入口 | `src/mcm26b/cli.py` | `mcm26b q1` | -| 对比框架 | `project/q1/{optimal,baseline,reference,cases,compare}.py` | 生产实现 / 朴素基线 / 独立大框参考解 | -| 回归 | `tests/test_geometry.py`、`tests/test_geometry_certificates.py` | 解析形状、真值保留、随机对拍、证书边界 | -| 结果 | `examples/q1/*.json`、`project/results/q1.md` | 固定解析算例与 40 个随机交会算例的配对结果 | - -```bash -.venv/bin/python -m mcm26b.geometry examples/q1/triangle.json -.venv/bin/python -m mcm26b.geometry examples/q1/normal.json --rounding-margin-deg 0.005 -``` - -`--rounding-margin-deg` 默认 $0$；库内在线求解默认把服务器两位小数舍入的 -$\le0.005^\circ$ 偏差计入有效半角，这是**实现口径**，不是题面新增误差。 -问题一 CLI 只有显式传参才附加该裕量，以便同时报告纯 $1^\circ$ 与 -$1.005^\circ$ 两套敏感性结果。 - -## 2. 模型：把角域写成半平面交 - -令方向向量 $u(\alpha)=(\cos\alpha,\sin\alpha)$，二维叉积 -$[a,b]=a_xb_y-a_yb_x$。设误差半角为 $\delta$（题设主模型 $\delta=1^\circ$）， -真实源 $g$ 属于闭前向角域 - -$$ -W(s,\theta,\delta)=\{g:\ [u(\theta-\delta),g-s]\ge0,\ -[u(\theta+\delta),g-s]\le0,\ u(\theta)\cdot(g-s)\ge0\}. -$$ - -对应实现中的三条半平面 $a x+b y\le c$ 为 - -| 约束 | $a$ | $b$ | $c$ | -| --- | --- | --- | --- | -| 下边界左侧 | $\sin(\theta-\delta)$ | $-\cos(\theta-\delta)$ | $a s_x+b s_y$ | -| 上边界右侧 | $-\sin(\theta+\delta)$ | $\cos(\theta+\delta)$ | $a s_x+b s_y$ | -| 前向 | $-\cos\theta$ | $-\sin\theta$ | $a s_x+b s_y$ | - -第三条前向约束在 $0<\delta<90^\circ$ 时冗余，但在 $\delta=0$ 时必须保留， -否则两条边界只确定一条直线而非一条射线。角域顶点按闭集处理；这是保守的 -几何含义，不表示边界点仍能返回方向读数。 - -$P=\bigcap_i W_i$ 是有限条闭半平面之交，因而是闭凸集；可能为空、单点、 -线段、有界多边形或无界多边形。实现不做正态、均匀或独立误差假设， -只使用题面给定的有界误差。 - -## 3. 精确算法流程 - -`solve(measurements, eps)` 的步骤如下。 - -### 3.1 坐标归一化与边界交点枚举 - -先对所有检测点做平移和统一缩放，使数值量级接近单位坐标；结果再换回原坐标。 - -1. 生成 $k=3n$ 条半平面。 -2. 枚举全部边界直线对（$\binom{k}{2}$ 对），用行列式 - $\Delta=ae-bd$ 求交点；仅在 $\Delta$ 恰为 $0$ 时跳过，近平行对也保留， - 因为其交点可能是细长区域的合法远端顶点。 -3. 保留满足全部约束的交点；可行性判定使用随坐标量级缩放的位置容差 - `eps * max(1, |x|, |y|)`，避免大坐标下绝对残差被误判为不可行。 - -### 3.2 空集、有界与无界 - -- 无观测：$P=\mathbb R^2$，返回 `unbounded`。 -- 没有任何可行边界交点：返回 `empty`。 -- 否则检查衰退锥 $\mathcal R=\{d:Ad\le0\}$ 是否存在非零方向。二维情况下 - 该锥若非零必含一条与某条有效边界平行的极射线；实现只需检查每条约束的 - 两个切向 $(b,-a)$、$(-b,a)$ 是否满足全部齐次不等式 - $a d_x+b d_y\le 2\times10^{-14}$。阈值取得比位置容差紧得多，避免把 - 极细长有界区域误判成无界。 -- 存在非零衰退方向时返回 `unbounded`，直径为 `null` 且显式置 - `diameter_infinite=true`，不伪造有限数值。 - -### 3.3 凸包与状态分类 - -将可行交点去重后，用 Andrew 单调链求凸包（逆时针、无重复首尾点）。 -按顶点数与维数返回： - -| `status` | 含义 | 直径 | 最小包围圆 | -| --- | --- | --- | --- | -| `empty` | 观测不相容 | 未定义（`null`） | 不计算 | -| `unbounded` | 存在非零衰退方向 | $+\infty$（`null` + 标志） | 不计算 | -| `point` | 单点 | 0 | 半径 0 | -| `segment` | 线段 | 两端点距离 | 半径 = 直径 / 2 | -| `polygon` | 二维有界凸多边形 | 见 §3.4 | 见 §3.6 | - -### 3.4 直径：只需枚举顶点对（已证明） - -**定理 1.** 若 $P=\operatorname{conv}(V)$ 非空有界，则 -$D(P)=\max_{v_i,v_j\in V}\lVert v_i-v_j\rVert$。 - -证明：任一 $x,y\in P$ 写成凸组合后， -$x-y=\sum_{i,j}\lambda_i\mu_j(v_i-v_j)$，由三角不等式有 -$\lVert x-y\rVert\le\max_{i,j}\lVert v_i-v_j\rVert$，而最远顶点对本身 -属于 $P$，故上界可达。实现直接枚举顶点对，复杂度 $O(m^2)$；单点返回 0， -线段返回端点距离。 - -### 3.5 直径圆覆盖：唯一候选圆心是直径中点（已证明） - -**定理 2.** 取任意一组最远顶点 $a,b$，$D=\lVert a-b\rVert$， -$o=(a+b)/2$。存在直径为 $D$ 的覆盖圆，当且仅当所有顶点 $v_i$ 满足 -$\lVert v_i-o\rVert\le D/2$。 - -证明：若半径 $D/2$ 的圆心 $z$ 同时包含 $a,b$，则 -$D\le\lVert a-z\rVert+\lVert z-b\rVert\le D$，两个不等式都必须取等号， -故 $z$ 只能在两点中点。圆盘是凸集，检查全部顶点即检查整个凸包。 - -实现输出 `diameter_circle`：圆心、半径、全部顶点到圆心的最大距离、 -`covers` 布尔值、最大超出量 `max_excess_m`、圆外顶点索引 -`outside_vertex_indices` 和反例顶点 `witness_vertex`。`witness_vertex` -是一份可独立复核的覆盖失败证据；`max_excess_m` 不能为负，因为最远顶点 -本身在圆周上（精确算术下）。 - -### 3.6 最小包围圆与 20 m 清除证书 - -对凸包求最小包围圆 $\rho(P)$。实现采用 Welzl 随机增量算法的显式栈版本： -固定随机种子使结果可复现；三个强制边界点使用外接圆；输出前再对全部 -顶点复核一次。内部有 $O(m^4)$ 的 1/2/3 点支撑圆枚举参考实现 -（`_enclosing_circle_reference`）用于单测对拍。 - -在最小包围圆中心 $m$、半径 $\rho$ 处，若 -$\rho+\varepsilon_m\le20$，则可直接以 $m$ 为清除点：任意真源 -$g\in P\subseteq B(m,\rho)$，到 $m$ 的距离不超过 $\rho$。实现输出 -`minimum_enclosing_circle`、`guaranteed_clearable_at_mec_center` 以及 -`clearance_certificate`（含数值余量和剩余裕量）。 - -注意：直径 $D\le40$ 只是“可能一次清除”的必要条件，**不是充分条件**； -三角形反例中 $D=40$ 而 $\rho=23.094>20$。 - -## 4. 伪代码 - -```text -solve(measurements): - if measurements 为空: return unbounded - 平移/缩放，生成每条读数的三条半平面 - V = [] - for 每一对边界直线: - 求交点 p - if p 满足全部半平面: V.append(p) - if V 为空: return empty - if 存在非零衰退切向 d: return unbounded - H = convex_hull(V) - D, (a,b) = argmax_{v_i,v_j∈H} |v_i-v_j| - o = (a+b)/2 - covers = max_{v∈H} |v-o| <= D/2 - 输出 diameter、diameter_circle、witness - (m,rho) = Welzl_MEC(H) - 输出 minimum_enclosing_circle 与 rho+eps<=20 清除证书 -``` - -## 5. 复杂度与数值实现 - -| 步骤 | 复杂度 | -| --- | --- | -| 生成半平面 | $O(k)$，$k=3n$ | -| 边界交点枚举与可行性判定 | $O(k^3)$ | -| 凸包 | $O(k^2\log k)$ 最坏 | -| 直径 | $O(m^2)$ | -| 直径圆检查 | $O(m)$ | -| Welzl 最小圆 | 随机排列下期望 $O(m)$；固定排列不声称最坏线性 | - -主要数值约定： - -- 坐标归一化后计算，输出换回原坐标并重新复核包含性； -- 仅对**精确平行**直线对跳过求交；近平行对保留； -- 位置可行性容差随坐标幅值缩放；衰退锥判定使用独立紧阈值； -- `solve` 主动限制最多 100 个检测点，超过时要求改用半平面交算法； -- 所有输出用 `null`/显式标志表示空集和无界，不写非法 JSON 浮点数。 - -## 6. 已验证算例与结果 - -固定解析算例（`examples/q1`，当前代码实测）： - -| 算例 | 状态 | 直径 (m) | MEC 半径 (m) | 直径圆覆盖 | 最大超出 (m) | MEC 可直接清除 | -| --- | --- | ---: | ---: | --- | ---: | --- | -| `normal` 三点交会 | polygon | 26.986 | 13.943 | 否（2 个圆外顶点） | 3.964 | 是 | -| `triangle` 等边三角形反例 | polygon | 40.000 | 23.094 | 否（1 个圆外顶点） | 14.641 | 否 | -| `segment` 零误差退化线段 | segment | 100.000 | 50.000 | 是 | 0.000 | 否 | -| `poor` 近共线检测点 | unbounded | — | — | — | — | — | -| `empty` 不相容观测 | empty | — | — | — | — | — | - -`triangle` 的三个顶点是 $(0,0)$、$(40,0)$、$(20,20\sqrt3)$：以直径 -$AB$ 为直径的圆圆心 $(20,0)$、半径 20，到 $C$ 的距离为 $20\sqrt3>20$， -故不能覆盖；最小包围圆半径 $40/\sqrt3\approx23.094$，也不能一次清除。 -其中 $A=(0,0)$ 坐标是浮点下的 $-6.4\times10^{-13}$，上表按解析值取整。 - -既有官方演练观测（案例 `U5ZW-P9KN-TCAG-R7ST`，不公开源坐标、未执行清除） -的复算结果： - -| 误差半角 | 直径 (m) | MEC 半径 (m) | 直径圆最大超出 (m) | -| --- | ---: | ---: | ---: | -| $1^\circ$ | 23.996723 | 12.068583 | 0.604002 | -| $1.005^\circ$ | 24.119088 | 12.130377 | 0.608486 | - -两种口径下直径圆都不覆盖定位区域，MEC 半径都小于 20 m，但该演练没有 -真实清除动作，不能据此声称实测定位误差或全清成功率。 - -`project/q1` 的 40 个随机交会算例（冻结算例与独立参考解对拍）： - -| 方法 | 状态准确率 | 直径平均绝对误差 | 直径圆覆盖准确率 | 平均耗时 | -| --- | ---: | ---: | ---: | ---: | -| 生产 `solve` | 100% | 0.000 m | 100% | 0.086 ms | -| 朴素基线（网格 + 包围盒对角线） | 100% | 4.583 m | 52.5% | 15.932 ms | - -## 7. 与问题二/三/四当前实现的复用关系和差异（核对结果） - -问题一给出的几何内核是后续所有几何决策的基础，但**复用方式是“语义 + 最小圆函数”， -不是直接调用 `solve`**。代码层面的核对结果如下。 - -| 后续问题 | 实际复用了什么 | 实际没有复用 / 做了什么替换 | -| --- | --- | --- | -| 问题二（`planner.select_second_point`） | 同一套角域半平面语义；`smallest_enclosing_circle` 求候选集 MEC；用 MEC 半径构造保证接收盘 | 不调用 `solve`；候选集不是纯角域交，而是 $K_1=\Omega\cap W\cap B(s,1500)$；用 192 边形外包圆盘 + Sutherland–Hodgman 裁剪，而不是枚举直线交点；不计算直径，也不使用“直径中点圆”判据 | -| 问题三（`belief.CandidateRegion`、`planner.bayes_loop`） | 复用 `smallest_enclosing_circle`；角域边界公式与问题一相同；MEC 作为清除证书 | 在线信念是有界凸外包多边形，圆盘边界是外切多边形而非精确曲线；不再判无界；no_signal 的负观测由网格粒子滤波处理（问题一模型里没有负观测）；不用直径，而用“MEC 半径 + 外包余量 + 工程余量 $\le20$” | -| 问题四（`planner.DirectionalExtension`、`belief.joint`） | 继续复用问题一的 MEC 与角域语义；（位置 × 朝向）联合信念也建立在同一网格上 | 定向源的 no_signal 不能排除位置，不能沿用问题一/三的纯角度或全向距离解释；覆盖语义改为 `forward_half_disk`（见 `algo-4.md`） | - -代码证据： - -- `src/mcm26b/belief/__init__.py` 只从 `geometry.localization` 导入 - `smallest_enclosing_circle`； -- `src/mcm26b/particles.py` 同样只导入 `smallest_enclosing_circle`； -- `生产/运行代码中 `geometry.localization.solve` 的调用者只有 - `geometry/__main__.py` 和 `project/q1/optimal.py`，即问题一 CLI 与其 - 对比框架；测试文件会直接调用它做解析与随机对拍，不在此列； -- 在线 `clip_wedge` 的半平面系数与问题一 `halfplanes` 一致，但作用在 - 凸多边形裁剪流程上；`solve` 的“纯角域 + 精确顶点枚举 + 无界判定” - 是针对问题一的一站式分析工具，不适合直接嵌入带圆盘、负观测和状态更新 - 的在线闭环。 - -**差异一句话总结：** 问题一回答“固定角域观测下源可能在哪里、直径圆能否覆盖”， -是纯几何精确解；问题二至四把其中的角域半平面和最小圆升级为“带距离先验的 -保守信念 + 主动动作 + 证据证明”的闭环，问题三再加非凸负观测，问题四再加 -朝向维度。 - -## 8. 结论与适用边界 - -问题一的当前实现是：三条半平面 → 可行顶点枚举/凸包 → 衰退锥判有界 → -顶点对精确直径 → 任意直径中点唯一候选的覆盖检验 → Welzl 最小包围圆与 -20 m 证书。其结论只依赖题面有界误差，不引入误差分布假设。空集、无界、 -点、线段和多边形状态都有明确输出。 - -未解决问题一的数值敏感性时，不应把在线闭环里的外包多边形半径直接当成 -问题一的精确直径；两者口径不同。反过来，在线闭环也不能把 `solve` 直接套在 -$K_1$ 上，因为 $K_1$ 含圆盘和距离上界，边界可能含圆弧。 - -## 参考文献 - -1. 全国大学生数学建模竞赛组委会. 2026 年高教社杯全国大学生数学建模竞赛 B 题：无线电干扰源的快速自动定位与清除[Z]. 2026. 本地题面 `issue/B题.pdf`。 -2. BOYD S, VANDENBERGHE L. Convex Optimization[M]. Cambridge University Press, 2004. 凸集、半平面与凸组合背景。 -3. WELZL E. Smallest enclosing disks (balls and ellipsoids)[C]//New Results and New Trends in Computer Science, LNCS 555, 1991: 359–370. 随机增量最小包围圆。 -4. 本项目. `docs/algo-2.md`（问题二当前实现）. 问题二对本文半平面语义与最小圆的复用说明。 -5. 本项目. `docs/algo-3.md`、`docs/algo-4.md`. 在线闭环中的保守信念、负观测与联合信念差异。 diff --git a/docs/algo-2.md b/docs/algo-2.md deleted file mode 100644 index 9258a8b..0000000 --- a/docs/algo-2.md +++ /dev/null @@ -1,312 +0,0 @@ -# 问题二算法说明：保证接收的第二检测点选择（当前实现） - -> 本文按**当前仓库实际运行路径**重写。生产入口是 -> `src/mcm26b/planner.select_second_point`，其核心选点器是 -> `select_measurement_point`；`src/mcm26b/geometry/planning.py` 中的 -> `first_bearing_candidate_disk` 是解析充分证书与独立校核工具， -> 并不在运行时被 `select_second_point` 调用（见 §6 核对结果）。 - -## 1. 问题、实现入口与交付物 - -问题二给定一个全向干扰源的首次有效示向度 $(s,\theta)$，要求给出第二个 -检测点 $q$ 的选择策略、定位效果与候选区域。两个约束必须同时满足： - -1. **接收保证**：$q$ 到所有仍可能为真源的位置都不超过有效接收半径下界 - $R_-=1000$ m，否则第二次测量可能返回 `no_signal`，几何收益落空； -2. **定位效果**：在保证接收的候选点中，使第二次测量后的保守位置集合 - 尽量小。当前实现以“最坏后验最小包围半径”为主指标，以移动耗时为次指标。 - -| 层次 | 文件 | 内容 | -| --- | --- | --- | -| 在线入口 | `src/mcm26b/planner/__init__.py::select_second_point` | 由首次 `direction` 构造 $K_1$，返回 `SecondPointPlan` | -| 核心选点 | 同文件 `select_measurement_point` | 任意候选集的保证接收盘内选点 | -| 几何证书 | `src/mcm26b/geometry/planning.py::first_bearing_candidate_disk` | 截断角域解析外包圆与安全候选盘 | -| 信念构造 | `src/mcm26b/belief/__init__.py::BeliefState`、`CandidateRegion` | $K_1$ 的保守凸外包与更新 | -| 对比框架 | `project/q2/{optimal,baseline,reference,region,cases,compare}.py` | 生产选点器 / 两种朴素基线 / 保证盘内网格参考 | -| 回归 | `tests/test_planner.py`、`tests/test_geometry_certificates.py`、`tests/test_belief.py` | 接收保证、后验收缩、near 分支、解析证书 | -| 结果 | `project/results/q2.{md,json}` | 30 个随机首次示向度算例配对结果 | - -## 2. 首次观测后的候选集 $K_1$ - -### 2.1 精确定义 - -记首次检测点为 $s$、首次示向度为 $\theta$、误差半角 -$\delta_{\rm eff}=1^\circ+0.005^\circ$（题设误差 + 两位小数舍入裕量）， -有效接收半径上界 $R_+=1500$ m，源位置圆域 $\Omega=B(0,1800)$。则 - -$$ -K_1=\Omega\cap W(s,\theta,\delta_{\rm eff})\cap B(s,R_+). -$$ - -真实源在合法观测下必属于 $K_1$。注意 $B(s,R_+)$ 的半径是**接收半径上界**： -`direction` 读数本身就隐含测得距离不超过 $R_+$；而 $B(s,R_-)$ 不在 $K_1$ -之中，因为第二次是否收到信号正是本问要保证的事。 - -### 2.2 实际保守表示 - -在线实现不直接处理圆弧边界，而用以下外切多边形表示 $K_1$： - -- 先取 $\Omega$ 的 192 边外切正多边形 $P_M(0,1800)$； -- 再与 $B(s,1500)$ 的 192 边外切正多边形求交； -- 最后用 Sutherland–Hodgman 逐半平面裁剪两条测向边界。 - -设圆盘外切多边形相对圆盘的最大径向超出为 $R(1-\cos(\pi/M))$。实现把每次 -圆盘裁剪的该量累加到 `CandidateRegion.slack_m`，因此维护的多边形满足 -$K_1\subseteq P_{\rm out}$，且 `slack_m` 是可审计的外包半径余量。 - -这条路径与问题一的区别：问题一在纯角域上枚举边界直线交点，精确处理 -空集/无界；问题二候选集一开始就被 $\Omega$ 和 $B(s,R_+)$ 限成有界集， -因此可以只用凸多边形裁剪。角域系数与问题一 `halfplanes` 完全一致： -$\sin(\theta-\delta)x-\cos(\theta-\delta)y\le c$ 与 -$-\sin(\theta+\delta)x+\cos(\theta+\delta)y\le c$。 - -## 3. 保证接收区域 - -### 3.1 一般集合形式 - -对非空紧候选集 $K$，定义 - -$$ -F_K(q)=\max_{g\in K}\lVert q-g\rVert,\qquad -\mathcal Q(K)=\{q:F_K(q)\le R_-\}=\bigcap_{g\in K}B(g,R_-). -$$ - -$F_K$ 是凸函数且 1-Lipschitz，$\mathcal Q(K)$ 是闭凸集。 - -**定理 1.** $\mathcal Q(K)\ne\varnothing$ 当且仅当 -$\rho(K)=\min_q F_K(q)\le R_-$，其中 $\rho(K)$ 是 $K$ 的最小包围半径。 - -证明：$\min_q F_K(q)$ 就是最小包围半径；存在 $q$ 使 $F_K(q)\le R_-$ 与 -其最小值不超过 $R_-$ 等价。证毕。 - -### 3.2 多边形外包给出的充分安全盘 - -设在线维护的多边形 $P_{\rm out}\supseteq K$，其最小包围圆为 -$B(m,\rho_{\rm out})$。任取 - -$$ -q\in B(m,\ R_- - \rho_{\rm out}-\eta),\qquad \eta=10^{-3}\ {\rm m}, -$$ - -则对任意真源 $g\in K$： - -$$ -\lVert q-g\rVert\le\lVert q-m\rVert+\lVert m-g\rVert -\le (R_--\rho_{\rm out}-\eta)+\rho_{\rm out} -=R_--\eta<1000. -$$ - -实现中 `safe_disk_radius(region)` 直接返回 -$R_--\rho_{\rm out}-10^{-3}$；因为 $\rho_{\rm out}$ 已是包含真实候选集 -的多边形最小包围半径，这个盘内的点到真实源的三角不等式上界严格小于 -1000 m。1 mm 的数值余量避免浮点贴边被 $r_{\rm eff}=1000$ 的源判成 -`no_signal`。若该半径 $<0$，说明当前凸外包的 MEC 半径已超过 1000 m： - -- 这**不能**证明真实 $\mathcal Q(K)$ 为空，只说明这个充分证书不够紧； -- 实现退化为探索 MEC 圆心，并在 `reason` 中明确标注“可能 `no_signal`”。 - -### 3.3 首次截断角域的解析候选盘 - -完全去掉 $\Omega$ 后，完整的截断角域 -$S=W(s,\theta,\delta)\cap B(s,R_+)$ 有一个精确最小包围圆： - -$$ -c=s+a\,u(\theta),\qquad a=\frac{R_+}{2\cos\delta},\qquad -S\subseteq B(c,a), -$$ - -对 $0\le\delta\le45^\circ$ 成立。简证：设 -$g=s+t\,u(\theta+\varphi)$，$0\le t\le R_+$， -$|\varphi|\le\delta$，则 -$\lVert g-c\rVert^2=t^2+a^2-2at\cos\varphi$。固定 $\varphi$ 时该式 -关于 $t$ 是凸二次函数，最大值在 $t=0$ 或 $t=R_+$ 处：前者为 $a^2$， -后者不超过 $R_+^2+a^2-2aR_+\cos\delta=a^2$（代入 -$a=R_+/(2\cos\delta)$），故 $S\subseteq B(c,a)$。最小性由三点 -$s$、$s+R_+u(\theta\pm\delta)$ 的加权距离恒等式给出：权重 -$\lambda_\pm=1/(4\cos^2\delta)$、 -$\lambda_0=1-1/(2\cos^2\delta)$ 非负且和为 1，且 -$c=\lambda_0s+\lambda_+p_++\lambda_-p_-$，故任何覆盖这三点的圆半径 -至少为 $a$。于是 - -$$ -\mathcal Q_0=B(c,\ R_- - a) -$$ - -是保证接收的充分候选区域。题设 $\delta_{\rm eff}\le1.005^\circ$ 时： - -| 误差半角 | 轴向中心距离 $a$ / m | 安全候选半径 $R_- - a$ / m | -| --- | ---: | ---: | -| $1^\circ$ | 750.114246 | 249.885754 | -| $1.005^\circ$ | 750.115391 | 249.884609 | - -该解析盘由 `first_bearing_candidate_disk` 实现并测试。它给出的是 -$S$ 的最小圆；由于 $K_1\subseteq S$，$B(c,R_- - a)\subseteq\mathcal Q(K_1)$ -是严格充分的。实际在线路径使用比 $S$ 更紧的 $K_1$ 凸外包，因此其安全盘 -通常大于该解析下界（例：`q2-4200` 的 $\rho_{\rm out}=674.59$ m， -安全盘半径 325.41 m，而解析下界约 249.89 m）。 - -## 4. 实际选点算法 - -### 4.1 入口伪代码 - -```text -select_second_point(client, s, theta): - region = CandidateRegion.initial(s) # Ω ∩ B(s,1500) 的外包 - region.clip_wedge(s, theta, 1.005°) # 交入首次角域 - return select_measurement_point(region, client.position) - -select_measurement_point(region, from_position): - (m, rho) = region.mec() # Welzl 最小包围圆 - safe = 1000 - rho - 1e-3 - if safe < 0: return MEC 圆心（探索，可能 no_signal） - 候选 = {m} ∪ {m + f*safe*(cos ψ, sin ψ)} - f ∈ {0.4, 0.75, 1.0}, ψ 取 12 个均匀方向 - 对每个候选 q: - 最坏后验 = max( 方向分支、near 分支 ) - 分数 = (最坏后验, 移动时间) - 取分数最小的 q - 用 1/4 间距加密复核 q 的最坏后验 - return q、最坏后验半径、移动耗时、理由 -``` - -当前 `search.candidate_rings=(0.0,0.4,0.75,1.0)`、`candidate_angles=12`， -去重后实际是 **37 个候选点**（圆心 + 3 环 × 12 方向）。所有点严格属于 -保证接收盘，因此第二次测量不会因距离不足返回 `no_signal`。 - -### 4.2 最坏后验评估 - -对候选点 $q$，最坏后验半径按以下有限枚举计算： - -1. 在候选集边界上以 `witness_spacing_m=60` m 采样见证点 - $g$（`region.witnesses`，含全部顶点）； -2. 对每个 $g$，真实方位 $\phi=\operatorname{bearing}(q,g)$，枚举允许的 - 读数区间端点 $\phi\pm\delta_{\rm eff}$； -3. 对每个端点，复制候选多边形并用 `clip_wedge(q, 读数, δ_eff)` 得到后验 - 凸外包，记录其 MEC 半径； -4. 额外处理 `near` 分支：以 $B(q,5)$ 裁剪候选集，若交集非空，记录其 MEC - 半径；它代表“源离第二点不超过 5 m，直接光学定位清除”的分支； -5. 以上全部有效半径取最大值，就是该候选点的评估值。 - -该评估是**启发式上界近似**：它只对目标边界采样和读数区间端点取值， -没有覆盖连续集合上所有可能读数；但它可在候选点之间做一致比较。 -1/4 间距（15 m）的加密复核只提高输出数值的可信度，不改变“采样启发式” -这一事实。定理 1 保证接收，选点最优性没有全局证明。 - -### 4.3 清除与 `near` - -若第二次测量返回 `near`，源在 $q$ 的 5 m 内。由 `clear_certificate`： -候选集 $B(q,5)$ 的 MEC 半径至多略大于 5 m（外包多边形余量很小）， -工程余量后仍远小于 20 m，因此可以在当前位置直接 `/clear`，不需要再取 -第三个测向读数。若返回 `direction`，则用新角域更新 $K$ 并继续按 -问题三/四的定位流程处理；后验半径若仍大于 20 m，**不能**声称两次定位 -已经完成。 - -## 5. 参数与复杂度 - -| 参数 | 默认值 | 作用 | -| --- | ---: | --- | -| `polygon_sides` | 192 | 圆盘外切多边形边数；$\Omega$ 处径向超出约 0.24 m | -| `bearing_rounding_margin_deg` | 0.005° | 两位小数舍入的显式裕量 | -| `witness_spacing_m` | 60 m | 粗评边界见证间距 | -| `candidate_rings` | (0, 0.4, 0.75, 1.0) | 安全盘内半径比例 | -| `candidate_angles` | 12 | 每环方向数；连同圆心共 37 个候选 | -| `near_radius_m` | 5 m | `near` 分支判定 | -| `clear_margin_m` | 0.5 m | 清除证书的额外工程余量 | - -若候选集顶点数为 $m$、候选点数为 $N_q$、边界见证数为 $N_g$，单次选点的 -粗评估约为 $O(N_qN_g\cdot{\rm clip}+N_q{\rm MEC})$，`clip` 为 -Sutherland–Hodgman 裁剪的 $O(m)$ 代价；再加一次 1/4 间距加密复核。 -这是明显可并行/缓存的小规模在线计算，不追求全局最优。 - -## 6. 实现核对结果：文档旧说法与当前运行路径的差异 - -早期说明只写到 `geometry/planning.py` 的解析候选盘，容易让人误以为 -`select_second_point` 直接调用它。当前代码的实际情况是： - -- `select_second_point` 构造 `CandidateRegion`（$\Omega\cap B(s,1500)$ 的 - 192 边形外包）并用 `clip_wedge` 交入首次角域； -- 真正的选点发生在 `select_measurement_point`：它在上述凸外包的 MEC - 保证盘内做 37 点候选评估； -- `first_bearing_candidate_disk` 是解析证书函数，用于证明 - $B(s+au,1000-a)$ 的充分性，并被 - `tests/test_geometry_certificates.py` 以稠密角域采样校核； -- 生产路径不调用 `first_bearing_candidate_disk`，但两者给出同一性质的 - 保证：选出的点必能收到 `direction`/`near`。生产路径通常比解析盘更紧， - 因为它利用了 $K_1$ 完整形状而不是仅用其外包扇区。 - -因此，问题二的**严格保证**来自“安全盘点到候选集所有点的距离 $\le R_-$”， -解析盘是充分但不唯一的一种实现方式；**最优性**来自有限候选评估， -两者必须在说明中分开陈述。 - -## 7. 已验证结果 - -`project/q2` 在 30 个随机首次示向度算例上，用同一 $K_1$、同一评估器 -对四种方法配对比较（初始 $\rho$ 均值 681.47 m）： - -| 方法 | 保证接收率 | 最坏后验均值 | 终值比 $\rho'/\rho$ | 平均移动 | 结论 | -| --- | ---: | ---: | ---: | ---: | --- | -| 生产选点器（保证盘内最小最坏后验） | **100%** | **73.89 m** | **0.104** | 170.4 s | 保证接收且后验最小 | -| 基线① 垂直 1000 m 最大交会角 | **0%** | 80.11 m | 0.116 | 200.0 s | 忽略接收距离，真源在远端必 `no_signal` | -| 基线② 直接取 MEC 圆心 | 100% | 340.75 m | 0.500 | 136.3 s | 保证接收但几乎不收缩 | -| 独立参考：保证盘内 9×9 网格枚举 | 100% | 75.63 m | 0.106 | 170.3 s | 生产采样接近网格最优 | - -代表性单例 `q2-4200`： - -| 指标 | 数值 | -| --- | ---: | -| $K_1$ MEC 圆心 | (1172.85, −225.68) | -| $K_1$ MEC 半径 $\rho$ | 674.59 m | -| 保证接收盘半径 | 325.41 m | -| 选中第二点 | (1257.07, 88.64) | -| 最坏后验半径 | 54.45 m | -| 移动耗时 | 181.42 s | - -## 8. 与问题一及问题三/四的关系（核对结果） - -| 对比 | 问题一 | 问题二（本文） | 问题三/四中的去向 | -| --- | --- | --- | --- | -| 信息模型 | 纯角域 $W$ | 角域 $W$ + 距离先验 $K_1=\Omega\cap W\cap B(s,R_+)$ | 问题三在任意后验 $K_c$ 上重复使用；问题四再叠加朝向维度 | -| 求解方式 | 枚举半平面边界交点、凸包、精确直径 | 圆盘外切多边形 + Sutherland–Hodgman 裁剪 | 问题三/四沿用 `CandidateRegion` 凸外包 | -| 决策类型 | 对固定观测做几何分析 | 主动选择下一个测量点 | 问题三是多源闭环；问题四是混合源闭环 | -| 主要输出 | 直径与直径圆覆盖 | `SecondPointPlan`（位置、最坏后验、移动耗时） | 问题三把它推广到定位子过程；问题四把它作为联合信念选点的基础候选之一 | -| 保证口径 | 几何精确（在输入模型内） | 接收严格保证；最坏后验是采样启发式 | 清除证书严格；选点与调度是启发式 | - -关键区别： - -1. 问题二不直接复用问题一的 `solve`，而是复用其角域语义和 - `smallest_enclosing_circle`；候选集多出圆盘和距离上界，不能当作 - “纯角域问题一”处理。 -2. 问题一的输出是直径和直径圆，问题二的清除判断改用最小包围半径 - $\rho\le20$；直径圆本身不参与在线清除。 -3. 问题三对同一个 `select_measurement_point` 做任意后验集上的复用， - 但同时引入网格粒子滤波处理 $K$ 无法表示的负观测（`no_signal`）； - 问题二没有负观测更新，因为它假设第二次测量点已在保证接收盘内。 -4. 问题四不能直接使用“安全盘内必收到信号”的结论：定向源在安全盘内 - 也可能因朝向遮挡返回 `no_signal`。问题四保留问题二的安全盘作为 - 基础候选，但新增（位置 × 朝向）联合信念来判断可见性（见 - `algo-4.md`）。 - -## 9. 结论 - -问题二的当前实现可以概括为： - -- **候选集**：$K_1=\Omega\cap W(s,\theta,\delta_{\rm eff})\cap B(s,R_+)$， - 用 192 边形外包圆盘 + 半平面裁剪保守维护； -- **保证接收**：$B(m,R_--\rho_{\rm out}-10^{-3})$ 内的点必不返回 - `no_signal`；解析扇区最小圆给出可独立复核的充分候选盘 - $B(s+au,1000-a)$，$a=1500/(2\cos\delta)$； -- **选点**：在保证盘内取 37 个候选，以边界见证点 × 读数端点的最坏后验 - 包围半径为第一指标、移动时间为第二指标，选出后再做 1/4 间距加密复核； -- **清除**：若第二次返回 `near`，直接就地清除；否则把新角域交入后验， - 未达到 20 m 证书前继续测量。 - -严格保证与启发式性能优化在文档和代码中始终分开：接收保证有证明， -全局最优性没有证明；后者依赖自建的有限候选与固定评估口径。 - -## 参考文献 - -1. 全国大学生数学建模竞赛组委会. 2026 年高教社杯全国大学生数学建模竞赛 B 题[Z]. 2026. 本地题面 `issue/B题.pdf`。 -2. 模拟器配套材料. 附件 2：模拟器通信接口说明及编程指南[Z]. 2026. 本地附件 `issue/附件/附件2.docx`。 -3. BOYD S, VANDENBERGHE L. Convex Optimization[M]. Cambridge University Press, 2004. 凸性、最坏情形与鲁棒可行性背景。 -4. 本项目. `docs/algo-1.md`（问题一当前实现与复用差异）。 -5. 本项目. `docs/algo-3.md`、`docs/algo-4.md`（选点器在后续问题中的推广与边界）。 diff --git a/docs/algo-3-bayes.md b/docs/algo-3-bayes.md deleted file mode 100644 index 28b1606..0000000 --- a/docs/algo-3-bayes.md +++ /dev/null @@ -1,144 +0,0 @@ -# 算法说明：问题3 贝叶斯闭环（`bayes` 策略） - -> **历史补充文档.** 当前规范算法、最新默认参数与现行成绩口径见 -> [`algo-3.md`](algo-3.md)；本文保留 2026-09-11 的设计与消融细节， -> 其中 `lambda_move_s=0.0105`、站点环半径 1200 m 等数字可能已被 -> 后续回填（现行默认见 `config.BayesConfig`，λ=0.05、环半径 1159.7 m）。 -> 两者冲突时以代码与 `algo-3.md` 为准。 - -对应实现：`src/mcm26b/particles.py`（感知层）、`src/mcm26b/planner/bayes_loop.py` -（决策层）、`scripts/benchmark_q3_bayes.py`（消融）、`scripts/tune_q3_bayes.py` -（Optuna 调参）。基线对比：`docs/algo-3.md` 的七点批量（`seven`，A2）。 - -## 1. 建模定位 - -问题3是部分可观察的在线决策问题：频道数未知（10–16/20）、位置未知、有效 -接收半径未知（1000–1500m）、测向误差 ±1°（服务器保留两位小数）。策略层 -只能拿到 `/measure` 与 `/clear` 的响应，目标是最小化"发现—定位—清除"的 -虚拟总时间。`bayes` 策略把它分解为三层滚动时域闭环： - -1. **感知层**：每频道一个贝叶斯后验（网格幸存集），融合全部正/负观测； -2. **决策层**：站点扫频、定位测量、清除三类任务统一建表，开放路径 - 最近邻 + 2-opt 排序，每个动作后滚动重排（动态 TSP）； -3. **选点层**：默认按期望log幸存占比和“到检测点、再接近源”的移动代价评分。 - 已停靠位置可逐频道补交会观测，真实清除始终由完整角域证书批准。 - -2026-09-11 当前修订的审核与配对验收见 [三四问算法审核与优化](算法审核与优化-20260911.md)。 -本文件第5节是早期版本的历史结果，不应当作当前默认参数的复算结果。 - -## 2. 网格幸存集滤波与真源保留定理 - -每频道维护 Ω 上间距 h 的网格（h=14.65m，约 4.7 万格），`alive` 为幸存格 -集合。Voronoi 裕量 m = √2/2·h 是格心到格内任一点的最大距离；所有删除 -判定都内缩 m，即**仅当整格与可行集不相交时才删格**： - -| 观测 | 题面语义 | 删除判定 | -|---|---|---| -| `no_signal`@s | 距离 > r_eff ≥ 1000 | 删格心距 s ≤ 1000 − m 的格 | -| `direction`@s | 距离 ≤ 1500，\|读数−真值\| ≤ δ_eff | 删角向超出 δ_eff + asin(m/(d−m)) 或距 s > 1500 + m 的格 | -| `near`@s | 距离 ≤ 5 | 删格心距 s > 5 + m 的格 | - -**定理（真源保留）**：真源 P 满足全部题面约束；P 所在格的格心 g 满足 -|g−P| ≤ m，故"整格不相交"判定永不命中该格。因此任意观测序列后 P ∈ -幸存格的并集，且： - -- **缺席证明**：幸存集为空 ⟹ 可行集为空 ⟹ 该频道无源（严格）； -- **清除证书**：MEC(幸存).R + m ≤ 20 − 工程余量 ⟹ 真源距清除点 ≤ 20； -- **跳测规则**：幸存格到测点最小距离 > 1500 + m ⟹ 该测量必 no_signal - 且无新排除，可跳过。 - -性质测试：`tests/test_particles.py`（随机源 × 随机测点 × 随机合法误差， -真源格永不被删；`tests/test_bayes_loop_q3.py` 端到端零异常）。 - -与 `belief.CandidateRegion`（保守凸外包）的分工：凸表示负责**清除证书** -（精确角域裁剪、收敛后触发），网格负责凸表示做不到的三件事——非凸负 -观测挖洞（no_signal 排除盘）、缺席提前证毕、跳测判定。两者同读一套 -响应流（`_record_measure`），任何一方证毕即收尾。 - -## 3. 信息增益选点 - -候选点集（二选一，按保证接收盘是否非空）： - -- **保证接收盘** B(MEC_poly 圆心, 1000 − ρ_poly)（盘内必收 direction/near， - 几何同 `docs/algo-2.md` 定理1）：环形采样（`search.candidate_rings`×12 方向）； -- **宽候选集**（ρ_poly > 1000 时）：PF MEC 的 1.0/1.5/2.0 倍探索环。 - -评分（`BayesConfig.score_mode`）： - -- `radius`（可选）：期望后验半径 —— 对 K 个代表假设（幸存格均匀抽样） - 各自模拟一次测量的幸存分支，分支半径用幸存子采样的质心最大距离近似， - r_eff~U(1000,1500) 的不确定性解析积分（p_no_signal = (d−1000)/500）； - 总分 = E[半径] + λ·移动秒数（λ=0.1）； -- `entropy`（当前默认，λ=0.0105）：期望log幸存占比，加上λ乘以 - 当前点→候选检测点→候选源中心的移动秒数。来向对称候选补充固定全局方向网格。 - -这里的均匀先验、零误差代表读数和均匀接收半径是选点评分的工程假设， -不属于题面给定的概率模型；只有完整±1.005°角域与距离界参与安全证明。 - -λ 与半径同量纲，量级必须小：λ 过大时终局阶段"走 300m"的代价压过 -"半径 35m→25m"的收益，选点器原地重测、收敛停滞（消融实测 λ≥0.5 时 -单源定位步数翻倍）。 - -## 4. 统一任务循环（动态 TSP） - -每个决策点构建任务表并排序执行首个任务，随后滚动重排： - -- **站点任务**：七站（原点 + 当前1159.7m环六点）上的UNKNOWN频道； - 构造时验证全向覆盖解析界小于1000m，跳测规则与覆盖账本过滤； -- **定位任务**：DETECTED 且无证书的频道，位置由信息增益选点产生； -- **清除任务**：有证书（凸或网格）的频道，可沿当前位置方向移动证书圆心， - 但平移量与原半径之和不得超过清除证书阈值，节省最后一段接近路程。 - -排序 = 开放路径最近邻打底 + 2-opt 改进（`two_opt_passes` 轮，成本含 -移动 + 换频 + 动作耗时；换频按路径顺序逐步传递当前频道，clear保持频道； -终点自由不回起点）。这是 A2"站内最近邻服务 -队列"的推广：消融显示距离缩短约 24% 是主要收益来源。 - -停止条件与基线一致：清满 16（数量上界）或全部频道 CLEARED/ABSENT， -`stop_reason=all_resolved` 即停并 `/exit`——**虚拟时间即评测成绩口径** -[用户决策修订 2026-09-11]。复扫保险模式（`mcm26b run --persistent`， -原默认行为）：找全后继续只复扫未清除频道直到清除数达 16、实机预算 -耗尽或虚拟安全余量（`search.virtual_reserve_s=10000s`）不足，供需要 -"防缺席证明假设偏差"保险的场景显式开启。 - -## 5. 消融结果（自建模拟器，persistent 关，对齐找全口径） - -12 随机场景（6 种子 × 10/16 源）+ 2 边界场景，与 SevenBatch 配对； -bayes 参数为 Optuna 回填后的库默认值： - -| 指标 | SevenBatch | bayes | -|---|---|---| -| 有效运行（全清+缺席正确+零异常+正常退出） | 14/14 | **14/14** | -| 平均虚拟时间 | 5352.7s | **3726.2s（−30.0%）** | -| 配对降幅（最差 / 最好） | — | +7% / −41% | -| 平均移动距离 | 23413m | **15050m（−36%）** | -| 平均检测次数 | 102.4 | 110.0 | -| 单局实机计算墙钟 | 1.3s | 1.9s | - -复现：`uv run python scripts/benchmark_q3_bayes.py --seeds 0,1,2,3,4,5` -（逐场景数据与场景 JSON 见 `output/q3/bayes-ablation/`）。 - -## 6. Optuna 调参 - -`scripts/tune_q3_bayes.py`：TPE（dev 依赖 `optuna`，缺失时自动退化为 -随机搜索），目标 = 场景子集平均虚拟时间 + 10⁶×失败惩罚。搜索空间： -λ、代表点数、子采样规模、网格间距、站点环半径、2-opt 轮数、评分模式。 -最优组合（entropy 评分、λ=0.0105、h=14.65m、代表点 7、2-opt 4 轮） -已按验收流程回填为 `config.BayesConfig` 默认值：全量基准 3726s/−30.0% -优于回填前的 4277s/−19.5%。两处人工修订： -- `station_ring_radius_m` 保留 1200（Optuna 建议 1159.7 虽再快约 60s， - 但七站排除盘对最外圈格心的覆盖差 3.3m，PF 无法独立证明缺席， - 只能靠覆盖账本兜底——设计性质优先于 1.7% 的均值差异）； -- 评分模式在 entropy（信息增益）与 radius（期望后验半径）之间由调参 - 数据决定，两者实现均保留（`BayesConfig.score_mode`）。 -调参记录见 `output/q3/bayes-tuning/best_params.json`。 - -## 7. 已知边界 - -- 检测次数略多于 A2（109.6 vs 102.4）：定位收敛多用 1-2 次测量换距离 - 大幅缩短，净收益为正；跳测规则在 UNKNOWN 阶段（先验全域）暂无收益 - 面，收益集中在定位与复扫阶段； -- 网格清除证书（h=25m 时阈值 ≤1.8m）极少触发，作为凸证书的补位保险； -- 调参与消融基于自建模拟器（逐点固定 ±1° 误差场），不代表官方误差 - 分布；真源保留定理只依赖题面界（±1°、r_eff∈[1000,1500]），不依赖 - 误差分布假设。 diff --git a/docs/algo-3.md b/docs/algo-3.md deleted file mode 100644 index 6475546..0000000 --- a/docs/algo-3.md +++ /dev/null @@ -1,456 +0,0 @@ -# 问题三算法说明：全向多源搜索的覆盖证明、贝叶斯闭环与任务调度（当前实现） - -> 本文汇总当前仓库中问题三的全部实际算法，生产默认策略为 -> `src/mcm26b/planner/bayes_loop.py::BayesLoop`（CLI 策略名 `bayes`）。 -> `grid`（`OmnidirectionalLoop`，49 点）与 `seven` -> （`SevenBatch`，七点解析覆盖 + 本站批量）保留为可复现对照。 -> 问题三当前生产不包含任何机器学习策略；历史 HRL/RL 代码已移除， -> 自建消融中手写基线稳定优于学习策略（见 `docs/第三四问架构优化与运行提速方案.md`）。 - -## 1. 问题定位与策略族 - -问题三中每局有 $10\sim16$ 个全向干扰源，位置未知、频道互异（1..20）， -源位于 $\Omega=B(0,1800)$ 内；有效接收半径 $r_{\rm eff}$ 未知，但满足 -$1000\le r_{\rm eff}\le1500$ m。策略端只能看到协议响应 -（`direction` / `near` / `no_signal`），目标是发现、定位并清除全部源， -使虚拟总耗时尽量小，同时给出“不存在漏源”的证明或明确未完成。 - -| 策略 | 类 | 发现保证 | 定位/决策 | 角色 | -| --- | --- | --- | --- | --- | -| `grid` | `OmnidirectionalLoop` | 700 m 方格 49 点，单元对角 $700\sqrt2\approx989.95\le1000$ | 发现后立即就近定位，无信息增益 | 历史基线 | -| `seven` | `SevenBatch` | 原点 + 半径 1200 m 六等分环，解析覆盖界 968.90 m | 本站先扫频，再按候选集 MEC 圆心最近服务队列 | A2 中间对照 | -| `bayes` | `BayesLoop` | 七站环（默认半径 1159.7 m，界 984.54 m）+ PF 负观测 | 网格粒子滤波 + 信息增益选点 + 滚动任务排序 | **生产默认** | - -七站族还有 `SevenImmediate`（A1，发现即离站）与 `SevenSweep` -（A3，先扫完全域再统一服务）。它们的差异只在发现/定位的调度顺序， -七点覆盖证明与距离语义相同。 - -### 1.1 统一计时模型 - -忽略微秒量化时，一次动作回合的虚拟耗时为 - -$$ -T=\frac{L}{5}+5N_m+N_s+5N_c^++3N_c^-, -$$ - -其中 $L$ 为移动距离（速度 5 m/s），$N_m$ 为检测次数，$N_s$ 为换频次数， -$N_c^+$ 为成功清除次数，$N_c^-$ 为失败清除次数。`/clear` 本身不切换 -测向频道，`/exit` 不增加虚拟时间，访问顺序是起点固定、终点自由的**开放 -路径问题**，不是必须回到原点的 TSP 回路。官方内部按整数微秒累计，项目用 -`evaluation.recompute_virtual_time` 逐动作复算并校验。 - -## 2. 三层在线架构 - -`BayesLoop` 在 `OmnidirectionalLoop` 的会话/预算/收尾骨架上重构为三层： - -1. **保证层**：七点覆盖账本 `CoverageLedger` + 解析覆盖定理；负责 - “全测无信号 $\Rightarrow$ 该频道不存在源”。 -2. **信念层**： - - 凸候选集 `CandidateRegion`（正观测方向/近距离约束，用于清除证书）； - - 网格幸存集 `ChannelParticles`（正/负观测统一更新，用于缺席证明、 - 跳测与信息增益）； - - 两者同读一套响应流，任何一方证毕即结束该频道。 -3. **决策层**：统一任务表（站点扫频 / 定位测量 / 清除），开放路径 - 最近邻 + 2-opt 滚动重排；每执行一个任务后用真实响应重规划。 - -问题三不复用问题四的（位置 × 朝向）联合信念；`no_signal` 在全向源上 -可以排除 $B(s,1000)$。 - -## 3. 证据层：逐频道候选集与负观测 - -### 3.1 凸候选集 `CandidateRegion` - -对每个已收到过有效信号的频道 $c$ 维护凸多边形 $P_c^{\rm out}$： - -- 初始为 $\Omega$ 的 192 边外切正多边形； -- 每次 `direction`： - 1. 与 $B(s,1500)$（192 边外切多边形）求交； - 2. 与角域 $W(s,\theta,\delta_{\rm eff})$ 求交，$\delta_{\rm eff}=1^\circ+0.005^\circ$； -- `near`：与 $B(s,5)$ 外切多边形求交； -- `no_signal`：**不收缩凸候选集**。对全向源，负观测信息由 `ChannelParticles` - 处理；让凸表示保留所有正观测候选，可以避免因模型误差误删真源。 - -圆盘外切多边形的径向超出逐次累加到 `slack_m`，所有裁剪容差只向 -“保留更多候选”的方向倾斜，因此真源始终包含在 $P_c^{\rm out}$ 内。 - -清除证书使用 $P_c^{\rm out}$ 的最小包围圆 $B(m_c,\rho_c)$： - -$$ -\rho_c+\text{slack}_c+0.5\le20 -\quad\Longrightarrow\quad -\text{在 }m_c\text{ 处一次 `/clear` 必命中真源}. -$$ - -其中 $0.5$ m 是工程余量，slack 是外包多边形的半径余量；该判据是保守的 -充分条件，而不是“必须达到的最优半径”。 - -### 3.2 网格幸存集 `ChannelParticles` - -每个频道维护 $\Omega$ 外扩裕量上的方格网格，间距 -$h=14.65$ m，约 4.7 万格；Voronoi 裕量 - -$$ -m=\frac{\sqrt2}{2}h\approx10.359\ {\rm m} -$$ - -是格心到格内任一点的最大距离。所有删除判定都内缩 $m$：**只有整个格子与 -可行集不相交时才删除该格**。 - -| 观测 | 位置/距离更新 | 实现 | -| --- | --- | --- | -| `no_signal` @ $s$ | 距离 $>r_{\rm eff}\ge1000$，删除格心距 $s\le1000-m$ 的格 | `_kill_disk(s,1000-m)` | -| `direction` @ $s$，读数 $\theta$ | 距离 $\le1500$；角向偏差不超过 $\delta_{\rm eff}$ + 格内角裕量 | 先删距 $s>1500+m$ 的格，再删角向超出的格 | -| `near` @ $s$ | 距离 $\le5$ | 删格心距 $s>5+m$ 的格 | - -**定理（真源保留）.** 真源 $P$ 所在格的格心 $g$ 满足 -$|g-P|\le m$；与真值一致的观测不会让该格与可行集完全不相交，因此 -“整格不相交”判定永不删除该格。于是： - -- **缺席证明**：幸存集为空 $\Longrightarrow$ 可行集为空 - $\Longrightarrow$ 该频道无源； -- **跳测规则**：若某测点到所有幸存格心的最小距离 $>1500+m$，则在该点 - 测量必为 `no_signal` 且不产生任何新排除，可直接跳过，省 5 秒检测和 - 1 秒换频； -- **PF 清除证书**：若幸存集 MEC 半径 $\rho_{\rm pf}$ 满足 - $\rho_{\rm pf}+m+0.5\le20$，则以该 MEC 圆心清除必命中真源。 - -`BayesLoop` 在每次测量后同步更新凸候选集与 PF，PF 只在频道尚未 -CLEARED/ABSENT 时接收新证据。 - -### 3.3 数量上界证明 - -题面给定每局源总数 $10\sim16$ 且每频道至多一个源。若 -`DETECTED`/`CLEARED` 的不同频道数达到 16，则所有仍未出现有效信号的 -`UNKNOWN` 频道都不可能有源，可直接标记为 `ABSENT`，证据来源记为 -`count_bound`。这条规则与位置无关，是严格计数证明，不需要等七站扫完。 -`summary.completion_basis` 会区分 `count_cap` 与 `per_channel_proof`， -避免把“达到数量上界”误读成“逐频道缺席证明”。 - -## 4. 发现保证：七点解析覆盖与覆盖账本 - -### 4.1 `full_disk` 语义 - -全向源在测点 $s$ 返回 `no_signal`，意味着真实距离严格大于 -$r_{\rm eff}\ge1000$。因此若集合 $S$ 满足 - -$$ -\Omega\subseteq\bigcup_{s\in S}B(s,1000), -$$ - -则任何合法源必在某个 $s\in S$ 的 1000 m 内，在该点不可能返回 -`no_signal`。于是“某频道在全部 $S$ 点都测得 `no_signal`”构成严格的 -缺席证明。 - -`CoverageLedger` 按 `(channel, point_index)` 二维记录，只有所有点都已测 -（或显式跳过）才允许 `coverage_complete(channel)=True`；“路过某点”不算 -“测过某频道”。 - -### 4.2 七点构造与解析界 - -取原点加半径 $a$ 的六等分环： - -$$ -S(a)=\{(0,0)\}\cup\left\{a(\cos(k\pi/3),\sin(k\pi/3)):\ k=0,\dots,5\right\}. -$$ - -**定理 1.** 当 $01000$ 时退化为探索 MEC 圆心，可能 `no_signal`）。盘内取 -37 个候选点（圆心 + 3 环 × 12 方向），以“边界见证点 × 读数区间端点 + -near 分支”的最坏后验 MEC 半径为主指标、移动时间为次指标；选出后再按 -1/4 间距加密复核。该函数最初为问题二实现，在问题三中服务于 -`SevenBatch`、`OmnidirectionalLoop` 的定位子循环，以及 `BayesLoop` -的阻塞式安全后备 `_localize_and_clear`。 - -### 5.2 `bayes` 的信息增益选点 - -`BayesLoop._select_point` 在 PF 幸存集上选择定位测点，候选集分两种： - -- 凸候选集安全盘非空：取 `select_measurement_point` 的环形候选，并额外 - 加入以当前位置到 MEC 圆心方向为轴的对称候选，以及零移动的当前位置； -- $\rho>1000$、无保证安全盘：取 PF MEC 的 $1.0/1.5/2.0$ 倍探索环和 - PF MEC 圆心。 - -对候选 $q$，默认评分模式 `entropy` 是期望对数幸存占比： - -$$ -\text{score}(q)= -\mathbb E[\log(\text{后验幸存占比})] -+\lambda\left(\frac{\lVert p-q\rVert}{5} -+\frac{\lVert q-m_{\rm MEC}\rVert}{5}\right), -\qquad \lambda=0.05. -$$ - -其中期望对代表假设（从幸存格均匀抽样）和 $r_{\rm eff}\sim U(1000,1500)$ -解析积分： - -- `no_signal` 分支（概率 $(d-1000)/500$）：幸存格收缩到 - $B(q,1000-m)$ 之外； -- `direction` 分支：以代表点方位为期望读数，用 $\pm\delta_{\rm eff}$ - 角域裁剪后的子采样估计幸存占比； -- `near` 分支（$d\le5+m$）：后验为 5 m 盘，占比趋近零。 - -可选 `score_mode=radius` 直接最小化期望后验 MEC 半径。 -`score_subsample=800`、`mc_representatives=7` 是工程参数；均匀源分布、 -零误差代表读数和 $r_{\rm eff}$ 均匀分布都只是**选点评分假设**，不参与 -任何安全证明。安全证明只使用题面给定的 $\pm1.005^\circ$ 角域与距离界。 - -当前配置中以下“更强的耦合”均已实现但实测为净负收益，默认关闭： -`fuse_localize_into_tour=False`（站点顺带定位）、`exact_order_limit=0` -（Held-Karp 精确任务序）、`route_aware_probe=False`（路线感知探点）。 -代码与开关保留，便于复现与后续研究。 - -### 5.3 清除动作 - -`BayesLoop._best_certificate` 的优先级： - -1. **凸证书**：`clear_certificate(region)`； -2. **PF 证书**（`grid_cert_enabled=True`）：负观测挖洞使 PF 幸存集更紧时， - 若 PF MEC 半径 $+m+0.5\le20$，用 PF MEC 圆心作为清除点。 - -得到证书后，`_near_clear_target` 还会把清除点从证书圆心向当前位置方向 -平移一小段：设当前证书半径 $r$、限值 $limit$，最多平移 -$\Delta=\min(\text{当前距离},limit-r-10^{-6})$，新目标点仍保证所有候选 -源在 $r+\Delta\le limit$ 内。该平移可省去最后一段接近路程，不放松证书。 - -定位子循环每频道最多 `max_localize_steps=8` 次追加测量；若在安全接收盘内 -出现 `no_signal`，记录异常 `no_signal_in_safe_disk`，不静默忽略。 - -## 6. 决策层：统一任务表与滚动重排 - -### 6.1 任务类型 - -`BayesLoop._build_tasks` 在每次决策点构建： - -| 任务 | 位置 | 服务时间 | 触发条件 | -| --- | --- | --- | --- | -| `station` | 七站之一 | $5\times$待测频道数 + 换频 | 该站仍有 `UNKNOWN` 频道未测，且跳测规则未排除 | -| `locate` | 信息增益选点 | 5 s + 可能的换频 | 频道 `DETECTED` 且暂无清除证书，且未委托给站点 | -| `clear` | 证书中心（可平移） | 5 s 成功（失败 3 s） | 频道有清除证书且未处于“清除失败后待补测”状态 | - -站点任务的服务时间计算当前频道优先：若机器人当前测向频道包含在待测集合， -则少一次换频。清除动作不改变测向频道。 - -### 6.2 跳测规则 - -`guaranteed_no_signal(station)`：若该频道所有幸存格心到站点的距离都 -大于 $1500+m$，则该次测量必为 `no_signal` 且不会产生新排除，任务构造时 -直接跳过。跳测缓存按 PF `version` 生效；幸存集单调收缩，因此判定单调。 -这条规则是负观测给问题三带来的主要检测节省之一。 - -### 6.3 开放路径排序 - -任务表规模通常为 6～12 项，代价包含移动、检测、换频，终点自由。默认： - -1. 最近邻构造初始序； -2. 开放路径 2-opt 改进，最多 `two_opt_passes=4` 轮； -3. 换频代价按实际频道状态沿路径逐步传递，而不是固定服务费； -4. 执行排序后的**第一个任务**，拿到响应后立即回到决策点重新构建/排序 - （滚动时域）。 - -可选的 Held-Karp 精确 DP（含“已访问集合 × 结束时频道”状态）与 -`route_aware_probe` 路线感知精修均已实现；在当前冻结基准集上， -精确 DP 与 2-opt 差距在噪声内（甚至略差），默认关闭。 - -### 6.4 已停靠位置的机会测量 - -`_measure_at_stop` 在每次决策点评估当前位置是否值得顺带补测已发现频道： -只对尚未在该 0.5 m 点测过、无证书、到凸候选集所有顶点距离均不超过 -$1000-10^{-3}$ m、且预测后验半径至少收缩 -`opportunistic_radius_ratio=0.5` 的频道追加一次测量。每次仍支付 5 秒与 -实际换频成本，只是省去移动；它不是“免费检测”。 - -## 7. 停止、预算与异常收尾 - -### 7.1 完成证明 - -循环停止后，`summary.completed` 的依据分两类： - -- `count_cap`：清除数达到 16（题面源数上界）； -- `per_channel_proof`：每个频道状态都是 `CLEARED` 或 `ABSENT`。 - -缺席证据在 `summary.absence_evidence` 中逐频道记录来源： - -| 证据 | 条件 | 严格性 | -| --- | --- | --- | -| `pf_empty` | 某频道 PF 幸存集为空 | 严格（Voronoi 真源保留） | -| `coverage` | 七点/49 点全部测得 `no_signal`（或计划上显式跳过） | 严格（解析/校核覆盖） | -| `count_bound` | 已有 16 个不同频道的有效存在证据 | 严格（题面数量上界） | - -`persistent_search` **默认 False**：一旦“每频道已清除或证明不存在”就立即 -`/exit`，因为虚拟时间是成绩口径。显式 `--persistent` 会继续复扫未清除 -频道直到清除数达 16、实机预算耗尽或虚拟安全余量不足；历史官方演练证明 -无条件复扫会浪费大量时间，因此该项目只作为可审计的保险开关保留。 - -### 7.2 预算与中断 - -- 虚拟安全余量 `virtual_reserve_s=10000` s：剩余虚拟时间不足以完成 - 一个保守单动作上界时停止发起新动作，避免触及 100 小时上限； -- 现实预算以服务器 `/enter` 返回的 `remaining_real_duration_s` 为准； - `DeadlineExceeded` 时 stop_reason 记为 `real_budget_exhausted`； -- 传输、协议或守恒性异常都会尽力 `/exit` 并输出未完成 summary， - 不静默丢会话；`completed=False` 与具体 `stop_reason` 同时留档。 - -## 8. 基线与自建消融 - -### 8.1 调度变体 - -| 版本 | 发现/定位关系 | 说明 | -| --- | --- | --- | -| A0 `grid` | 49 点，发现后立即离站定位 | 历史生产基线 | -| A1 `SevenImmediate` | 七点，发现后立即离站定位 | 只替换覆盖点 | -| A2 `SevenBatch` | 七点，本站先扫完再按最近候选圆心服务队列 | 中间对照，曾用于官方演练 | -| A3 `SevenSweep` | 七点，先扫完全部必要站点再统一服务 | 批量调度的另一极端 | - -A2 相对 A0 的收益主要来自“减少站间往返 + 每站批量扫频 + 无源频道只需 -7 次而不是 49 次检测”。在早期 12 个自建场景上，A0/A1/A2/A3 平均虚拟 -时间分别为 13788.04 / 7476.09 / 5496.20 / 6094.73 秒，A2 逐局平均降幅 -58.55%；更晚的 14 场景消融中，`bayes` 相对 A2 再降 30.0% -（3726.2 s vs 5352.7 s）。这些数字来自自建模拟器与固定误差场，不是官方成绩。 - -### 8.2 冻结基准集当前结果 - -`project/q3` 在 18 个冻结 core 场景（seeds 100–105 × 10/13/16 源， -`harness/suites.py` 版本化清单）上的配对结果： - -| 方法 | 有效局 | 全清率 | 虚拟时间均值 | 中位 | 最大 | 检测均值 | 请求均值 | 位移均值 | -| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | -| `bayes` | 18/18 | 100% | **3188.7 s** | 3111.3 s | 3663.7 s | 111.1 | 124.1 | 12338 m | -| `seven` | 18/18 | 100% | 5361.7 s | 5367.5 s | 6462.4 s | 100.8 | 113.8 | 23508 m | -| `grid` | 18/18 | 100% | 14935.7 s | 15275.1 s | 16691.4 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； -所有 54 局零缺席误判、零异常、正常退出且计时复算一致。另有边界场景 -（源在 $\Omega$ 边界、$r_{\rm eff}=1000$）验证七点覆盖的扇区接缝。 - -### 8.3 官方演练记录（历史口径） - -- 旧 49 点实现通过官方问题三演练案例 `DHFK-PHD2-SR2P-FXK9`： - 10/10 全清、540 次检测、10 次清除，虚拟总时间 16217.45 s， - 现实 7.629 s； -- A2 七点批量通过官方案例 `J4ER-Y9EP-Q379-UPFA`：12/12 全清、 - 109 次检测、虚拟 5232.13 s、现实 3.406 s； -- 后续一次官方问题三演练（`3BW5-9R54-NZNA-MYB9`）记录 13/13 全清、 - 虚拟 4058.71 s。 - -以上案例的源场景、实现版本、误差场均不同，不能直接横向比较，也不能把 -历史官方数字当成当前 `bayes` 同案例重跑结果。当前默认 `bayes` 的官方向 -成绩仍需新的演练验证。 - -## 9. 复杂度、参数与保守性边界 - -| 模块 | 计算量（近似） | -| --- | --- | -| PF 更新 | 与删除格/角向扫描的幸存格数相关；粗格索引与向量化（numpy 可选）控制常数 | -| 七点覆盖 | 最多 $7\times20=140$ 个 `(站, 频道)` 记录 | -| 任务排序 | 最近邻 $O(n^2)$ + 2-opt $O(n^2)$/轮；精确 DP 为 $O(2^nn\cdot|C|)$，默认关闭 | -| 凸候选集 | 192 边形外切 + $O(m)$ Sutherland–Hodgman 裁剪；MEC 用 Welzl | -| 选点评分 | $O(N_q\times(\text{子采样}))$；候选约 37 个，子采样 800 | - -保守性边界： - -- 所有“证毕”结论依赖题面观测语义和保守外包/内缩；若接口语义、 - 接收半径范围或误差界改变，必须重新校核； -- 性能结论依赖自建场景分布、固定误差场和排序启发式，不构成对全部合法 - 场景的最坏时间保证； -- PF 的均匀先验与信息增益评分是选点启发式，不是官方概率模型； -- `persistent_search` 关闭时，如果官方场景违反题面数量上界或存在模型外 - 行为，不能由本算法保证发现；官方接口语义变化时必须记录异常而不是 - 强行把未完成解释为成功。 - -## 10. 与问题一、二、四当前实现的复用关系和差异（核对结果） - -| 复用/差异 | 具体情况 | -| --- | --- | -| 问题一几何内核 | `belief/__init__.py` 与 `particles.py` 只从 `geometry.localization` 导入 `smallest_enclosing_circle`；在线 `clip_wedge` 复刻同一半平面公式，但工作在凸多边形裁剪流程，不调用 `solve` | -| 问题二选点器 | `select_measurement_point` 直接从问题二推广而来，问题三用它做基线定位与安全后备；`safe_disk_radius` 同样复用 | -| 问题一没有的 | 负观测 `no_signal`、PF 非凸挖洞、覆盖缺席证明、数量上界、任务排序、滚动时域、预算/异常治理 | -| 问题二没有的 | 多频道数据关联、发现覆盖、清除动作、在线顺序决策；问题二只做一次第二点选择 | -| 问题四的差异 | 问题四不能用七点 `full_disk` 缺席证明；`no_signal` 可能由朝向遮挡造成，因此改用 `forward_half_disk` 31 点覆盖 +（位置 × 朝向）联合信念 + 预算化后备清除（见 `algo-4.md`） | - -一句话概括：**问题三 = 问题一的保守几何 + 问题二的保证接收选点 + 逐频道非凸负观测 + 覆盖/数量缺席证明 + 开放路径滚动调度**。 - -## 11. 复现与参考 - -```bash -# 冻结基准集：bayes vs seven_batch（runner 内置策略名） -uv run python scripts/run_suite.py --problem 3 --suite core --jobs 6 \ - --variants bayes,seven_batch --label q3-core - -# 项目对比框架（含 grid 基线，生成 project/results/q3.{md,json}） -uv run python -m project.run --question 3 - -# 回归测试 -uv run pytest tests/test_particles.py tests/test_bayes_loop_q3.py \ - tests/test_planner.py tests/test_coverage_plan.py tests/test_safety_valves.py -q -``` - -主要文件： - -- `src/mcm26b/planner/bayes_loop.py`：`bayes` 决策层； -- `src/mcm26b/planner/__init__.py`：`OmnidirectionalLoop`、问题二选点器、清除证书； -- `src/mcm26b/planner/seven.py`：七点策略族与覆盖校验； -- `src/mcm26b/particles.py`：网格幸存集与信息增益； -- `src/mcm26b/belief/__init__.py`：凸候选集； -- `src/mcm26b/coverage/__init__.py`、`src/mcm26b/guarantee/cover.py`：覆盖账本与校核； -- `src/mcm26b/routing.py`：开放路径 DP/2-opt/MST 下界； -- `src/mcm26b/planner/bayes_loop.py::_exact_order`：可选 Held-Karp。 - -延伸阅读：`docs/algo-3-bayes.md`（早期贝叶斯闭环设计与消融细节）、 -`docs/第三四问架构优化与运行提速方案.md`（运行历史与瓶颈）、 -`docs/第三四问图论与动态规划优化方案.md`（图论/DP 轮实测与负面结论）。 - -## 参考文献 - -1. 全国大学生数学建模竞赛组委会. 2026 年高教社杯全国大学生数学建模竞赛 B 题[Z]. 2026. 本地题面 `issue/B题.pdf`。 -2. 模拟器配套材料. 附件 1《模拟器使用说明》、附件 2《通信接口说明及编程指南》[Z]. 2026. 本地 `issue/附件/`。 -3. 本项目. `docs/algo-1.md`、`docs/algo-2.md`、`docs/algo-4.md`。 -4. 本项目. `docs/算法审核与优化-20260911.md`、`docs/第三四问架构优化与运行提速方案.md`、`docs/第三四问图论与动态规划优化方案.md`。 diff --git a/docs/algo-4.md b/docs/algo-4.md deleted file mode 100644 index 5c8da37..0000000 --- a/docs/algo-4.md +++ /dev/null @@ -1,485 +0,0 @@ -# 问题四算法说明：混合全向/定向源搜索与清除闭环（当前实现） - -> 本文汇总当前仓库中问题四的全部实际算法。生产策略是 -> `src/mcm26b/planner/__init__.py::DirectionalExtension`，CLI 策略名 -> `directional`，入口为 `mcm26b run --problem 4`。 -> 与问题三不同，问题四的源可能包含定向源；`no_signal` 不再等价于 -> “距离超过接收半径”，因此问题三的七站全向缺席证明**不能直接搬用**。 -> 当前仓库尚无官方问题四演练记录；本文性能数字均来自自建 -> `LocalSimulator` 冻结基准集，明确标注为测试假设。 - -## 1. 问题定位与核心难点 - -问题四每局有 $10\sim16$ 个源，其中一部分为定向源，其余为全向源； -位置在 $\Omega=B(0,1800)$ 内，频道互异。定向源有一个朝向 -$h$，只有当接收点位于其**前向半平面**内、且距离不超过 $r_{\rm eff}$ -时才可能返回有效读数： - -$$ -r=\lVert q-P\rVert\le r_{\rm eff}\quad\text{且}\quad -\operatorname{angular\_gap}\big(\operatorname{bearing}(P\to q),h\big) -\le90^\circ . -$$ - -其中 $r_{\rm eff}$ 对每个源未知，满足 $1000\le r_{\rm eff}\le1500$ m。 -若 $r>r_{\rm eff}$ 或朝向不覆盖接收点，结果为 `no_signal`；若可见且 -$r\le5$ m，结果为 `near`；若可见且 $r>5$ m，结果为 `direction`， -数值为接收点到源的方位加有界误差。 - -因此问题四的困难不是一般的“覆盖搜索”，而是两个额外的证明/决策问题： - -1. **发现**：如何用有限的访问点证明“某个频道没有源”，即使该源是 - 朝外发射的定向源； -2. **定位与清除**：定向源的 `no_signal` 可能只是朝向遮挡，不能像全向源 - 那样删除位置候选；需要推理“源可能朝哪边”，并在定位停滞时仍能可靠 - 清除。 - -当前实现的办法是： - -- 发现层用 `forward_half_disk` 语义的 31 点最小命中集覆盖计划 - （`q4_mincover_v2`）； -- 感知层在原有位置网格上叠加 24 档朝向掩码，得到（位置 × 朝向）联合 - 可行集 `JointBelief`； -- 决策层用联合信念的可见性概率筛选探点，失败时按预算进入 ≤20 m 的 - 有限格后备清除； -- 调度层用“本站批量扫频 + 按候选 MEC 圆心最近服务队列”的滚动顺序， - 避免发现后立即远行、再返回同站扫描的往返。 - -| 层次 | 文件/类 | 作用 | -| --- | --- | --- | -| 覆盖计划 | `src/mcm26b/guarantee/cover.py`、`guarantee/plans/q4_mincover_v2.json` | `forward_half_disk` 最小命中集与运行期校核 | -| 位置网格 | `src/mcm26b/particles.py::ChannelParticles` | 位置幸存集与 Voronoi 保守裕量 | -| 朝向联合信念 | `src/mcm26b/belief/joint.py::JointBelief` | 每格 24 档朝向掩码、可见性/期望后验半径 | -| 决策闭环 | `src/mcm26b/planner/__init__.py::DirectionalExtension` | 批量扫频、可见性探点、后备清除、收尾 | -| 对比框架 | `project/q4/{optimal,baseline,compare}.py` | `directional` vs `bayes`/`grid` 直接搬用 | -| 回归 | `tests/test_loop_q4.py`、`tests/test_joint_belief.py`、`tests/test_coverage_plan.py` | 真源保留、朝外边界源、后备纪律、证书校核 | -| 结果 | `project/results/q4.{md,json}` | 11 个 core+boundary 场景配对结果 | - -## 2. 发现覆盖：`forward_half_disk` 与最小命中集 - -### 2.1 覆盖语义 - -设访问点集合为 $S$。对全向源，`no_signal` 在 $s$ 处排除 $B(s,1000)$， -因此完整覆盖只需 - -$$ -\Omega\subseteq\bigcup_{s\in S}B(s,1000). -$$ - -对定向源，还需要朝向条件。严格的缺席证明要求： - -$$ -\forall P\in\Omega,\ \forall h\in\mathbb S^1,\quad -\exists s\in S:\quad -\lVert s-P\rVert\le1000 -\ \wedge\ (s-P)\cdot u(h)\ge0 . -$$ - -也就是说，对任意位置和任意朝向，至少有一个被访问点同时落在该源的 -1000 m 距离内和 90° 前向半平面内。这个语义称为 `forward_half_disk`。 -问题三的七站环不满足该语义：`tests/test_coverage_plan.py` 中有专门用例 -验证“七站点集在 `forward_half_disk` 下有缺口”。 - -### 2.2 49 点 legacy 充分条件 - -旧实现使用 $S=\{(700i,700j):i,j\in[-3,3]\}$ 的 49 个点： - -- 任意 $P\in[-2100,2100]^2$ 到某个网格点的欧氏距离不超过 - $700\sqrt2\approx989.95\le1000$； -- 因此对该网格点，无论源朝向如何（前向半平面总是 $180^\circ$）， - 距离条件都成立。 - -这是 49 点充分条件，保证发现但代价很高：最近邻开放巡回约 36.4 km； -对无源频道还要在 49 个点各测一次。它现在保留为 legacy 对照与回退计划 -`square49`。 - -### 2.3 31 点最小命中集 `q4_mincover_v2` - -当前默认计划由离线贪心集合覆盖搜索得到，并带有可运行期校验证书。 -生成参数：搜索分辨率 50 m / 48 档，设计距离裕量 -$r_{\rm design}=965$ m，前向角裕量 $5^\circ$；随后用证书分辨率 -15 m / 72 档（$3\,256\,200$ 个 $(P,h)$ 采样）做精细剪枝，每删一个点都 -用同一证书口径复核。 - -`q4_mincover_v2` 的证书摘要： - -| 字段 | 值 | -| --- | ---: | -| 点数 | 31 | -| 覆盖语义 | `forward_half_disk` | -| 校核分辨率 | 15 m / 72 朝向档 | -| 采样目标数 | 3 256 200 | -| 缺口数 | 0 | -| 最坏命中距离 | 990.051 m | -| 最近邻开放巡回 | 23 883.8 m（约 4776.8 s 虚拟移动） | -| 生成器 SHA-256 | `adf4fe6e…434c0e1d`（完整值见 JSON） | - -旧 `q4_mincover_v1`（31 点）在粗采样 75 m/24 档下看似零缺口，但在 -25 m/48 档有 125 个缺口、15 m/72 档有 566 个缺口，属于采样过拟合； -`q4_mincover_v2` 修正了这一点，点数不变而细网格缺口归零。 - -**严格连续空间证书的已知残余风险.** 15 m/72 采样格上的零缺口加上设计 -裕量可以覆盖相邻未采样 $(P,h)$，但默认 v2 的最坏命中距离 990.051 m -略高于严格连续界要求的约 989.4 m（需满足 -$r_{\rm verify}+15\sqrt2/2\le1000$，即 $r_{\rm verify}\le989.4$ m）。 -仓库另有 36 点备选计划（最坏命中 973.7 m、巡回约 25.4 km，见 -`output/q4/cover-certificate-v3.json`）可关闭该连续性缺口；当前默认的 -v2 在 15 m/72 离散证书上零缺口，连续空间残余约 0.65 m，已作为已知 -风险登记。若论文要求严格连续证明，应改用 36 点方案并重新验收性能。 - -### 2.4 运行期校验 - -`load_plan(name, verify=True)` 会读取计划 JSON 中的证书字段，并用 -**证书自带的采样分辨率**重新校核；缺口不为 0 直接拒绝启动。默认 -`search.verify_coverage_plan=True`。校核成本在进程内按计划名缓存一次， -15 m/72 档约数秒（见 `scripts/run_suite.py` 的 `plan_verify_wall_s`）。 -运行期不会用比证书更粗的采样去“自证”。 - -## 3. （位置 × 朝向）联合信念 `JointBelief` - -### 3.1 网格与朝向档 - -位置维度复用 `ChannelParticles`：间距 $h=14.65$ m、Voronoi 裕量 -$m=\sqrt2/2\,h\approx10.359$ m；朝向维度把整周分为 -24 档（每档 $15^\circ$，半档 $7.5^\circ$）。每个位置格附带一个 24 位 -掩码，表示该格仍可能具有的朝向；缺省掩码为全档。 - -联合集合的保守性目标是：**真源 (位置, 朝向) 永不被删除**（在该离散 -表示意义下，即真源所在格的朝向至少保留一档，且位置格不被删）。因此 -所有判定都向“保留更多”的一侧取整，并计入格内角裕量 - -$$ -\alpha_{\rm cell}=\arcsin\!\left(\frac{m}{\max(d-m,1)}\right), -$$ - -即格内任一点相对格心到测点方位的最大偏差上界。 - -### 3.2 观测更新规则 - -| 观测 | 位置维度 | 朝向维度 | -| --- | --- | --- | -| `direction` @ $s$，读数 $\beta$ | 删除距 $s>1500+m$ 的格；删除角向偏离读数超过 $\delta_{\rm eff}+\alpha_{\rm cell}$ 的格 | 朝向必须**可能覆盖** $s$：保留与方位 $\operatorname{bearing}(g\to s)$ 夹角不超过 $90^\circ+7.5^\circ+1.5^\circ+\alpha_{\rm cell}$ 的档 | -| `near` @ $s$ | 删除距 $s>5+m$ 的格 | 同 `direction`：朝向必须可能覆盖 $s$ | -| `no_signal` @ $s$ | **位置不删**（可能是距离不足或朝向遮挡） | 仅对“整格必在 $r_{\rm eff,min}=1000$ 圆盘内”的格，保留与方位夹角大于 $90^\circ-7.5^\circ-1.5^\circ-\alpha_{\rm cell}$ 的档，即只保留**确定看不见** $s$ 的朝向档；掩码变空则删除该位置格 | - -其中 `1.5°` 是朝向查表分辨率带来的保守余量，$7.5^\circ$ 是半档。 -`no_signal` 的距离阈值使用 $1000-m$（保守侧）：只有在确信整格都在 -1000 m 内时，才把静默解释为朝向遮挡；否则保留全部朝向档，避免删掉 -真源。 - -> 2026-09-12 修复记录：早期实现把 `no_signal` 分支的朝向掩码方向写反， -> 会删除真源朝向档；现已改为“只保留确定看不见的档”，并由 -> `tests/test_joint_belief.py::test_true_source_survives_consistent_observations` -> 以随机观测序列固化。 - -联合集一旦为空（所有位置格都被删除），就给出该频道不存在的严格缺席 -证明，`absence_evidence` 记为 `joint_empty`。这与覆盖证明是两类独立证据。 - -### 3.3 可见性与值函数 - -`JointBelief` 提供两个只用于选点排序的启发式量： - -1. `prob_detect(q)`：在测点 $q$ 能看见该频道任一可行 - (位置, 朝向) 的概率质量。对抽样格心 $P$： - - 距离概率：$d\le1000$ 时为 1；$d\ge1500$ 时为 0；中间线性插值 - （对应 $r_{\rm eff}\sim U[1000,1500]$）； - - 朝向覆盖比例：该格掩码中与“$P\to q$ 方位”夹角不超过 - $90^\circ$ 的档位占比； - - 两者相乘后对格取平均。 -2. `expected_posterior_radius(q)`：一次测量后的期望后验 MEC 半径（D1 - 值函数）。对每个代表格： - -$$ -p_{\rm vis}=(1-p_{\rm silent\_range})\cdot\text{cover\_ratio}, -\quad -p_{\rm ns}=1-p_{\rm vis}, -$$ - - - `direction` 分支：后验半径用“以 $P\to q$ 方位为读数、$\pm\delta_{\rm eff}$ - 角域裁剪后的子采样质心最大距离”近似； - - `no_signal` 分支：位置候选**不缩小**，半径保持当前 PF MEC 半径； - - `near` 分支：后验为 5 m 盘。 - 最后对代表假设取平均。 - -这两个量的先验（均匀格点、$r_{\rm eff}$ 均匀分布）是工程假设，不承担 -任何安全责任；安全责任由覆盖证书、清除证书和联合真源保留定理承担。 - -## 4. 决策闭环 `DirectionalExtension` - -### 4.1 覆盖阶段：本站批量扫频 + 最近队列服务 - -```text -while 未达到数量上界且未停止: - index = 尚有 UNKNOWN 待测的最近覆盖点（31 点计划） - if 不存在: break - pending = 该点尚未测过的 UNKNOWN 频道 - pending 按“当前测向频道优先”排序 - for channel in pending: - outcome = /measure(point, channel) - observe(channel, point, outcome) # 更新凸信念 + JointBelief - if outcome == near 且候选集有清除证书: - 就地 /clear - queue = 所有 DETECTED 且无证书的频道 - while queue: - channel = 距其候选集 MEC 圆心最近的频道 - 从 queue 移除并调用 _localize_and_clear(channel) -``` - -与问题三 `SevenBatch` 的结构类似，但覆盖点集、缺席语义和定位逻辑完全不同。 -`observe` 对 `direction`/`near` 更新凸候选集和联合信念；对 `no_signal` -只更新联合信念的朝向维度，绝不用于删除凸候选集的位置。 - -覆盖阶段结束后（所有计划点上的 `UNKNOWN` 频道都测过）： -`_mark_absent_after_coverage` 把覆盖账本完整的 `UNKNOWN` 频道标为 -`ABSENT`；然后 `_resolve_detected` 再对剩余 `DETECTED` 频道做第二轮 -定位。 - -### 4.2 定位子循环 - -`DirectionalExtension._localize_and_clear(channel)`： - -```text -步数 steps = 0，被遮挡探点表 blocked = [] -while steps < max_localize_steps=8: - if 凸/PF 清除证书已成立: 尝试 /clear；成功则返回 - if len(blocked) >= max_blocked_probes=4: break # 定位停滞 -> 后备 - q = _next_probe(region, channel) - within_range = (|q - region.MEC 圆心| <= safe_disk_radius(region)) - outcome = /measure(q, channel) - observe(...) - if outcome == no_signal: - if within_range: 标记 directional_observed - blocked.append(q); continue # 不消耗 steps - else: # direction 或 near - blocked.clear(); steps += 1 -循环结束后再检查证书；若仍不可清除 -> _fallback_clear(channel) -``` - -要点： - -- 只有**在保证接收盘内**出现 `no_signal` 才标记“观测到定向”，避免把 - 距离不足误诊为朝向遮挡； -- 被遮挡探点不重复选取；最多 4 次，随后转入后备清除； -- `direction`/`near` 读数才计入定位步数； -- 每一步后都重新评估证书，滚动推进。 - -### 4.3 `_next_probe`：联合信念选点与后备梯子 - -候选点族 `_probe_candidates` 包含： - -1. `select_measurement_point` 给出的保证接收盘内最坏后验最优候选 - （复用问题二/问题三的选点器）； -2. 若已有被遮挡点，取关于 MEC 圆心的对跖方向点，并裁进保证接收盘； -3. 抵近点序列 `center + α*(current-center)`（代码保留，默认 - `approach_fractions=()` 未启用）； -4. 以 MEC 圆心为心、半径 $0.5\rho,\ 1.0\rho,\ 1.5\rho$ 的 8 方向环探。 - -候选去重到 0.5 m，并可排除已测过且被遮挡的点。 - -若联合信念可用，优先调用 `_visibility_probe`（默认口径 -`probe_score_mode=nearest_visible`）： - -1. 对每个候选计算 `prob_detect(q)`； -2. 只保留概率不低于最大值 0.7 倍的候选 - （`probe_visibility_ratio=0.7`），避免选中“期望上有点信息、实际必被 - 朝向遮挡”的点； -3. 在保留候选中选择 - $\text{move}(p\to q)+\text{move}(q\to m_{\rm MEC})+6$ 秒最小者 - （6 = 检测 5 s + 换频 1 s）。 - -若联合信念不可用/为空或候选为空，则退回确定性梯子： -未被遮挡过用最坏后验选点器；第一次被遮挡用对跖点；之后绕 MEC 圆心 -半径 $\rho$ 的环上每次 +45° 试探。几何边界必须如实说明：环上可见弧 -没有正的下界（源可在环上且朝向沿外切线，可见集退化为切点），因此 -梯子只是提高命中概率，**完备性由后备清除兜底**。 - -### 4.4 后备清除：有限格盲清 - -定位停滞时，`_fallback_clear` 对凸候选多边形生成 20 m 格心的有限覆盖： - -1. 用全局 20 m 半开网格与候选多边形求交（带正容差，宁可多保留格）； -2. 格内任一点到格心不超过 $10\sqrt2\approx14.14\le20$ m，因此若真源 - 仍在候选多边形内，逐格 `/clear` 至少命中一次； -3. 格数上界 - $(\lceil w/20\rceil+2)(\lceil h/20\rceil+2)$； -4. 若格数超过 `fallback_max_cells=400`，记录 - `fallback_cell_cap_exceeded` 并跳过，不硬扫； -5. 若现实剩余预算不足以覆盖 - `格数 × real_rtt_p95_s + reserve_exit_s`，记录 `skipped_budget` - 并跳过； -6. 通过检查时，按最近邻开放路径逐格 `/clear`；命中成功即标记清除； - 全部失败则记录 `fallback_exhausted_without_success`，**不删除候选集**。 - -后备清除只依赖“清除与朝向无关”这一题面事实，是定向源定位失败时唯一 -不依赖可见性的完备性底线；但它受格数上限和现实预算约束，预算不足时 -会诚实地保持未完成。`fallback_optimize_tour=False` 为默认：实测把 -格间路径排得更短反而会让“命中即停”的真源顺序变差，故不启用。 - -## 5. 调度、完成条件与预算 - -### 5.1 与问题三调度层的关系 - -`DirectionalExtension` 继承 `OmnidirectionalLoop` 的会话、预算、异常 -和 summary 骨架，但不使用 `BayesLoop` 的统一任务表/信息增益调度； -它采用“每站批量扫频 + 本站服务队列”的滚动策略（类似 A2 的推广）。 -因此： - -- 检测/换频/清除的计时口径与问题三完全一致； -- 覆盖点、缺席证据、定位方法、清除证书不同； -- 默认不启用 Held-Karp/2-opt 任务表；后备清除走最近邻。 - -### 5.2 完成条件 - -- 清除数达到 16：直接停止并按 `count_cap` 完成； -- 否则要求每个频道 `CLEARED` 或 `ABSENT`，按 `per_channel_proof` 完成； -- 缺席证据来源： - - `coverage`：该频道在 31 点计划上全部测得 `no_signal`； - - `joint_empty`：联合可行集为空； -- 定位/清除尚未完成或预算耗尽：`completed=False`， - `stop_reason=unresolved_channels` / `real_budget_exhausted` / - `virtual_time_cap` 等，不把未完成说成成功。 - -## 6. 关键参数 - -| 参数 | 默认值 | 说明 | -| --- | ---: | --- | -| `q4_coverage_plan` | `q4_mincover_v2` | 31 点定向最小命中集 | -| `verify_coverage_plan` | `True` | 运行期按证书分辨率校核 | -| `directional_joint_belief` | `True` | 启用（位置 × 朝向）联合信念 | -| `probe_score_mode` | `nearest_visible` | 可见性过滤 + 移动最省 | -| `probe_visibility_ratio` | 0.7 | 可见概率保留比例 | -| `probe_exclude_blocked` | `True` | 不重复测已遮挡点 | -| `approach_fractions` | `()` | 抵近点默认关闭 | -| `max_localize_steps` | 8 | 每轮定位成功读数上限 | -| `max_blocked_probes` | 4 | 被遮挡探点上限，超限转后备 | -| `fallback_clear_grid_m` | 20 m | 后备清除格边长；$10\sqrt2<20$ | -| `fallback_max_cells` | 400 | 后备格数硬顶 | -| `fallback_optimize_tour` | `False` | 后备走格 NP+2-opt 默认关闭 | -| `persistent_search` | `False` | 找全证明后立即退出 | - -## 7. 已验证结果与失效机理 - -### 7.1 冻结基准集 - -`project/q4` 在 11 个冻结 core+boundary 场景上的配对结果： - -| 方法 | 有效局 | 全清率 | 缺席误判 | 虚拟时间均值 | 中位 | 最大 | 后备清除均值 | -| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | -| `directional` | 11/11 | **100%** | **0** | 9069.9 s | 8803.3 s | 12081.1 s | 24.8 | -| `bayes` 直接搬用 | 0/11 | 77% | **24** | 3890.8 s | 3646.2 s | 5054.4 s | 0 | -| `grid` 直接搬用 | 0/11 | 67% | 0 | 18179.2 s | 18345.2 s | 20457.9 s | 0 | - -两条搬用基线的失效机理不同： - -- `bayes` 使用七站**全向**缺席证明；在朝外发射、$r_{\rm eff}=1000$ m 的 - 边界定向源场景中会把真源直接判成 `ABSENT`。它“快而错”，累计 24 次 - 缺席误判，虚拟时间不能作为有效优势； -- `grid` 的 49 点覆盖对定向源发现是完备的，但没有朝向推理与后备清除， - 定位停滞时只能停在 `DETECTED`，属于“稳而不全”。 - -`directional` 在 11/11 场景全清、零缺席误判；代价是时间更长，平均 -24.8 次后备清除。纯 core 9 场景（不含朝外边界压力）的均值约 8602.0 s、 -最大 9630.4 s、后备 4.3 次/局；boundary 场景（朝外发射源）会把后备 -推到数十至上百次，单局最大 12081.1 s（16 源 8 定向边界场景）。 -因此第四问的时间对“源是否背离圆心发射”非常敏感，不存在对所有合法 -场景的均匀性能保证。 - -### 7.2 覆盖计划消融 - -| 计划 | 点数 | 15 m/72 缺口 | 最坏命中 | NN 巡回 | -| --- | ---: | ---: | ---: | ---: | -| `square49` | 49 | 0 | ≤1000 m（构造） | 36.4 km | -| `q4_mincover_v1` | 31 | 566 | 1000.0 m | 22.9 km | -| `q4_mincover_v2`（默认） | 31 | **0** | 990.051 m | 23.9 km | -| 36 点严格连续备选 | 36 | 0 | 973.7 m | 25.4 km | - -改用最小命中集后，覆盖点减少了 37%，巡回长度从 36.4 km 降到 23.9 km； -但第四问时间的大头仍是 23.9 km 巡回（约 4776.8 s）和逐频道缺席检测。 -把七站式的“有源提前停止”规则用于 31 点计划可在一定程度上减少检测， -但最小命中集的巡回与检测开销仍是该问题的成本地板（详见 -`docs/第三四问图论与动态规划优化方案.md` §2、§8）。 - -### 7.3 回归测试 - -问题四相关测试覆盖： - -- 联合信念真源 (位置, 朝向) 在随机观测序列后不丢； -- 朝外发射的边界定向源不漏、不被误判缺席； -- 保证接收盘内 `no_signal` 不删空间候选、判向不越界； -- 后备清除格覆盖候选多边形每个见证点；格数上限/预算检查跳过而非硬扫； -- 全清、零异常、逐微秒计时复算一致。 - -当前全仓 `pytest` **268 项通过**（2026-09-12 本轮复核）。 - -## 8. 与问题一、二、三当前实现的复用关系和差异（核对结果） - -| 对比 | 复用 | 差异 | -| --- | --- | --- | -| 问题一 | 角域半平面语义、Welzl 最小包围圆 `smallest_enclosing_circle`、MEC 清除证书思路 | 问题四不计算直径/直径圆；`no_signal` 不能解释为距离排除；覆盖语义是 `forward_half_disk` 而非纯角度交集 | -| 问题二 | `select_measurement_point`、`safe_disk_radius` 作为基础候选与“是否在保证接收盘内”的判据；清除点可向当前位置平移 | 定向源在安全盘内仍可能因朝向 `no_signal`，因此问题二的“盘内必收到信号”保证不成立；新增联合信念和可见性筛选 | -| 问题三 | PF 网格位置维度、覆盖账本、会话/预算/异常 summary、清除动作 | 问题三的负观测可删位置候选（`full_disk` 七点缺席证明）；问题四的负观测只约束朝向，覆盖计划改用 31 点 `forward_half_disk`，不使用七站全向缺席证明；问题四也不使用 `BayesLoop` 的信息增益任务表 | -| 问题四独有 | — | 24 档朝向掩码、可见性概率选点、对跖/环探后备梯子、20 m 格后备清除及其格数/预算纪律 | - -关键结论：**问题四不是“问题三加一点朝向”，而是发现证明、负观测语义和 -清除兜底三处都必须重做**。直接套用问题三的 `bayes`（七站缺席证明）会在 -朝外定向源上产生缺席误判；直接套用 `grid`（49 点全向）可以发现但无法 -可靠定位清除。当前 `directional` 的路线是：最小命中集保证发现、 -联合信念推理可见性、预算化后备清除保证清除底线。 - -## 9. 适用边界与风险声明 - -- **覆盖证书**：默认 v2 是 15 m/72 档离散证书加设计裕量，零缺口； - 严格连续空间证书还存在约 0.65 m 的已知残余，正式论文若要求严格连续 - 证明应改用 36 点备选计划； -- **联合信念**：真源保留证明依赖评测机观测语义与题面误差界一致； - 先验分布、朝向均匀抽样和可见性概率都只是决策启发式； -- **后备清除**：完备性只在格数不超过 400、现实预算足够且候选集确实 - 包含真源时成立；任一条件不满足就诚实地保持未完成； -- **性能**：11/11 全清来自自建模拟器与冻结场景，不是官方成绩； - 当前仓库尚无官方问题四演练。第四问对定向源朝向分布敏感，不能把 - core 均值 8602 s 或边界均值 9069.9 s 外推为官方期望； -- **调度**：站点批量与最近队列是贪心，不是全局最优；图论/DP 方案 - 中的插入、Or-opt、精确任务序等在本场景实测为负收益或未推广到问题四， - 默认关闭。 - -## 10. 复现 - -```bash -# 冻结基准集：directional vs 问题三策略直接搬用 -uv run python scripts/run_suite.py --problem 4 --suite all --jobs 6 \ - --variants directional --label q4-current - -# 项目对比框架（生成 project/results/q4.{md,json}） -uv run python -m project.run --question 4 - -# 覆盖证书生成与细网格校核（默认计划的来源） -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 - -# 回归 -uv run pytest tests/test_joint_belief.py tests/test_loop_q4.py \ - tests/test_coverage_plan.py tests/test_particles.py -q -``` - -主要文件： - -- `src/mcm26b/planner/__init__.py::DirectionalExtension`、`fallback_clear_cells`、 - `fallback_cell_bound`：闭环、探点与后备清除； -- `src/mcm26b/belief/joint.py`：联合信念； -- `src/mcm26b/particles.py`：位置幸存集； -- `src/mcm26b/guarantee/cover.py`、`guarantee/plans/q4_mincover_v2.json`： - 覆盖计划与证书； -- `src/mcm26b/routing.py`：后备/离线巡回度量。 - -延伸阅读：`docs/第三四问架构优化与运行提速方案.md`（第四问失败机理与 -P0-2/P0-3 实测）、`docs/第三四问图论与动态规划优化方案.md`（联合信念 -修复记录与 D1 探点口径消融）、`docs/algo-3.md`（问题三全向闭环）。 - -## 参考文献 - -1. 全国大学生数学建模竞赛组委会. 2026 年高教社杯全国大学生数学建模竞赛 B 题[Z]. 2026. 本地题面 `issue/B题.pdf`。 -2. 模拟器配套材料. 附件 2《通信接口说明及编程指南》[Z]. 2026. 本地附件 `issue/附件/附件2.docx`。 -3. 本项目. `docs/algo-1.md`、`docs/algo-2.md`、`docs/algo-3.md`。 -4. 本项目. `docs/第三四问架构优化与运行提速方案.md`、`docs/第三四问图论与动态规划优化方案.md`、`docs/算法审核与优化-20260911.md`。 diff --git a/docs/figures/README.md b/docs/figures/README.md index 97c05ee..460a639 100644 --- a/docs/figures/README.md +++ b/docs/figures/README.md @@ -1,74 +1,49 @@ # docs/figures —— 论文插图 -每张图只输出 PNG。正文插图统一由 scripts/plot_paper_figures.py 使用 matplotlib 生成， -数据直接读取 docs/paper/data/practice-results.json（演练局复算）、 -practice-geometry.json（问题一交会算例）、正式统计快照与第四问覆盖计划； -manifest.json 记录图片生成命令与来源。 +本目录的 10 张 PNG 全部进入论文：`docs/paper/main.tex` 用 +`\graphicspath{{../figures/}}` 直接引用，论文不重绘、也不手抄图内任何数字。 +全部插图由同一脚本生成： -本目录的 15 张 PNG 进入论文：`docs/paper/main.tex` 用 -`\graphicspath{{../figures/}}` 直接引用，不在论文里重绘，也不手抄图内数字。 -正文使用演练局数据生成的插图（问题一至四的算例、路径、时间分布与耗时构成）， -正式测试的两张图（`formal-*.png`）只出现在附录 A 的效果验证中。 -论文图注均为中文，并标注数据来源是演练局还是正式测试。 - -``` -python scripts/plot_paper_figures.py # 生成正文全部 matplotlib 插图 +```bash +uv run python scripts/plot_paper_figures.py # 依赖 numpy 与 matplotlib ``` -MATLAB 内手动出图：`cd scripts/matlab` 后执行 `run_all_figures`（每张图单独一个会话， -本机图形子系统连续渲染多张会崩）。 - -依赖：`numpy` 与 `matplotlib`。 +数据来自 `docs/paper/data/` 下的复算结果（`practice-results.json`、 +`practice-geometry.json`、正式统计快照）与第四问覆盖计划； +逐图来源见 `manifest.json`。图注为中文，并注明数据取自演练局还是正式测试。 ## 约定 -- **除回放画布（点运动图 `replay-webui-*`）外，全部使用 MATLAB 默认图形样式**： - 默认配色（`defaultAxesColorOrder`）、默认字体与字号、默认坐标轴与图例； - 不引入自定义调色板，不手绘图例色块。 -- 图内不放标题、图注与解释性文字，只保留坐标轴、图例与必要的元素标注； - 图的含义由文件名与下表承担，解释写在论文正文。 -- 图例一律用 MATLAB `legend` 放在坐标区外（`southoutside`），不压数据； - 散点标签按候选方位自动避让，保证互不重叠。 -- 输出 PNG（`exportgraphics`，300 dpi、白底），导出前关闭坐标轴工具条。 +- 默认配色与字体沿用 matplotlib 默认样式，不引入自定义调色板； +- 图内不放标题与解释性文字，只保留坐标轴、图例与必要的元素标注，含义由图注承担； +- 图例置于坐标区外，散点与角度标注按位置自动避让，保证互不重叠； +- 输出 PNG（300 dpi、白底）。 ## 图清单 | 文件 | 内容 | 数据来源 | 论文位置 | | --- | --- | --- | --- | -| `flow-overall-architecture.png` | 四层主线：题面抽象（L0）、在线三层（L1）、四问主线（L2）、三条闭环（L3） | `src/mcm26b/config.py`、`docs/B题建模分析与实施计划.md` | 图 1（问题分析） | -| `steps-state-machine.png` | 会话状态机（IDLE / 倒计时 / OPEN / CLOSED） | `docs/协议规则表与状态机.md`、`src/mcm26b/protocol/client.py` | 图 2（计时模型） | -| `timing-attachment-example-105-111-194-199.png` | 附件计时示例：逐动作耗时构成与单步合计 | `docs/协议规则表与状态机.md` | 图 3（计时模型） | -| `timing-measured-q3-10-sources.png` | 实测一局：累计虚拟时间按动作类型的堆叠构成 | `referee-data/sessions/BBM9-HZG2-77N5-NKKC/log.jsonl` | 图 11（时间分解，演练局） | -| `geo-q1-convex-intersection-zoom.png` | 问题一：角域交会总览 + 定位区域放大（直径圆与最小包围圆） | `output/q1/practice/U5ZW-P9KN-TCAG-R7ST_observations.json` | 图 4(a)（问题一，演练观测） | -| `geo-q1-diameter-circle-counterexample.png` | 问题一：直径圆不覆盖定位区域的反例（等边三角形布站） | `examples/q1/triangle.json` | 图 4(b)（问题一解析反例） | -| `geo-q2-guaranteed-reception-disk.png` | 问题二：截断角域外接圆 `B(c, a)` 与安全候选盘 `B(c, 1000 − a)`，右侧为半径分解 | `src/mcm26b/config.py`、`docs/algo-2.md` | 图 5（问题二） | -| `geo-q3-seven-point-coverage.png` | 问题三：七个 1000 m 接收盘覆盖 Ω，标出最差点 | `src/mcm26b/planner/seven.py`、`docs/algo-3.md` | 图 6（问题三） | -| `geo-q4-minimum-cover-31-points.png` | 问题四：31 点最小命中集与 ±90° 前向半盘语义 | `src/mcm26b/guarantee/plans/q4_mincover_v2.json` | 图 8（问题四） | -| `replay-webui-q3-omnidirectional.png` | 回放画布（点运动图，全向局）：世界视图 + 事件流水 + 虚拟时间轴 | `referee-data/sessions/BBM9-HZG2-77N5-NKKC/log.jsonl` | 图 7（问题三，演练局） | -| `replay-webui-q4-directional.png` | 回放画布（点运动图，定向源局）：世界视图（含定向源 ±90° 前向半盘）+ 事件流水 + 虚拟时间轴 | `referee-data/sessions/GHSN-M38E-74GD-YLUD/log.jsonl` | 图 9（问题四，演练局） | +| `framework-decision-loop.png` | 观测—候选集—任务—动作的闭环决策过程 | 论文问题分析的结构 | 图 1 | +| `steps-state-machine.png` | 会话状态机（未进入 / 倒计时 / 接口开放 / 关闭） | `src/mcm26b/protocol/client.py` | 图 2 | +| `timing-attachment-example-105-111-194-199.png` | 附件计时示例：逐动作耗时构成与单步合计 | 题面附件 2 的计时示例 | 图 3 | +| `geo-q1-geometry.png` | 问题一三幅几何：角域交会、定位区域放大（直径圆与最小包围圆）、等边三角形反例 | `practice-geometry.json`（演练局观测复算）、`examples/q1/triangle.json` | 图 4 | +| `geo-q2-guaranteed-reception-disk.png` | 问题二：截断角域外接圆与安全候选盘，右侧为半径分解 | `src/mcm26b/config.py` | 图 5 | +| `geo-q4-minimum-cover-31-points.png` | 问题四：31 点最小命中集分布与定向源读图约定 | `src/mcm26b/guarantee/plans/q4_mincover_v2.json` | 图 6 | +| `selfbuilt-q3-search-clear-path.png` | 问题三演练局：全向源搜索与清除路径（含每次检测的位置、结果与示向度射线） | `referee-data/sessions/<问题三主局>/log.jsonl` | 图 7 | +| `selfbuilt-q4-search-clear-path.png` | 问题四演练局：混合源路径（含定向源朝向、±90° 可见范围与检测点） | `referee-data/sessions/<问题四主局>/log.jsonl` | 图 8 | +| `practice-virtual-time.png` | 44 局演练的总虚拟时间分布与全清局均值 | `practice-results.json` | 图 9 | +| `formal-time-bars.png` | 六次正式测试的累计虚拟耗时分解 | `formal-results.csv` | 图 10 | -后两张是唯一保留自定义画布的图：把回放器画布上的 Ω 与真值源、移动轨迹、 -检测示向度射线、定向源朝向与清除标记，连同右侧事件流水与底部虚拟时间轴一起导出； -定向源局额外画出每个定向源的 **±90° 前向半盘**（半径取该源 `r_eff_m`，朝向箭头即半盘半径）。 +论文主局案例编码不写死在脚本里：`plot_paper_figures.py` 读取 +`docs/paper/data/practice-macros.tex` 中的 `PractQThreeMainCase` / +`PractQFourMainCase`，与正文口径同源。 -q4 回放固定用演练局 `GHSN-M38E-74GD-YLUD`（`build_figure_data.py::Q4_REPLAY_CASE`）； -本机没有该局留档时回退到自动挑选，因此 `examples/replay/` 里归档了这一局。 - -## 数据来源与复现 - -- `timing-measured-*`、`replay-webui-*` 读评测机行为日志（JSONL，含真值）。 - `referee-data/` 被 `.gitignore` 忽略，因此另归档两局到 `examples/replay/`； - 没有评测机数据时脚本自动改读归档，图一致。 - - ```bash - uv run python scripts/archive_replay_logs.py --list # 本机有哪些局 - uv run python scripts/archive_replay_logs.py <案例编码> # 追加归档 - ``` - -- `geo-q1-convex-intersection-zoom.png` 读官方演练留档 - `output/q1/practice/*_observations.json`（同样被忽略）。文件缺失时改读 - `examples/q1/triangle.json` 反例算例，图仍可用，只是案例不同。 -- 问题一的几何量由生产求解器 `mcm26b.geometry.solve` 现算； - 问题三、问题四直接读覆盖计划与证书，不手抄数字。 +## 归档与回放 +`examples/replay/` 归档了论文主局的行为日志副本，便于在没有 `referee-data/` 时仍能绘图； +`scripts/archive_replay_logs.py` 用于把新的演练局追加归档： +```bash +uv run python scripts/archive_replay_logs.py --list # 本机有哪些局 +uv run python scripts/archive_replay_logs.py <案例编码> # 追加归档 +``` diff --git a/docs/figures/flow-overall-architecture.png b/docs/figures/flow-overall-architecture.png deleted file mode 100644 index 3cc5efd..0000000 Binary files a/docs/figures/flow-overall-architecture.png and /dev/null differ diff --git a/docs/figures/manifest.json b/docs/figures/manifest.json index b4cf89d..7d2a23d 100644 --- a/docs/figures/manifest.json +++ b/docs/figures/manifest.json @@ -3,30 +3,13 @@ "command": "python scripts/plot_paper_figures.py", "manual": "Matplotlib PNGs are generated directly; the legacy MATLAB scripts are no longer used by the paper.", "figures": [ - { - "stem": "flow-overall-architecture", - "png": "docs/figures/flow-overall-architecture.png", - "matlab": "scripts/matlab/fig_flow_overall.m", - "sources": [ - { - "path": "src/mcm26b/config.py", - "sha256_12": "c513450dc892", - "lines": 213 - }, - { - "path": "docs/B题建模分析与实施计划.md", - "sha256_12": "537aaa5790f3", - "lines": 237 - } - ] - }, { "stem": "steps-state-machine", "png": "docs/figures/steps-state-machine.png", "matlab": "scripts/matlab/fig_steps_state_machine.m", "sources": [ { - "path": "docs/协议规则表与状态机.md", + "path": "论文“计时模型与会话状态机”一节", "sha256_12": "27c8f7051b26", "lines": 215 }, @@ -43,7 +26,7 @@ "matlab": "scripts/matlab/fig_timing_attachment.m", "sources": [ { - "path": "docs/协议规则表与状态机.md", + "path": "论文“计时模型与会话状态机”一节", "sha256_12": "27c8f7051b26", "lines": 215 } @@ -60,7 +43,7 @@ "lines": 213 }, { - "path": "docs/algo-2.md", + "path": "论文“问题二”一节", "sha256_12": "24f3e844deb9", "lines": 312 } @@ -77,7 +60,7 @@ "lines": 162 }, { - "path": "docs/algo-4.md", + "path": "论文“问题四”一节", "sha256_12": "1774f8bf85db", "lines": 485 } diff --git a/docs/paper/README.md b/docs/paper/README.md index 634cf16..4999e5b 100644 --- a/docs/paper/README.md +++ b/docs/paper/README.md @@ -1,6 +1,8 @@ # 论文与支撑材料 -论文依据 issue 题目、2026 年格式规范及所给往届参考 PDF 编写，四问算法对应 docs/algo-1.md 至 algo-4.md。版式采用项目原始 mcm26b-paper.cls。 +论文依据赛题、2026 年格式规范及所给往届参考 PDF 编写，四问的模型、推导与结果分析 +均以论文正文与附录为准；本文件只说明论文工程如何重算与编译。 +版式采用项目原始 `mcm26b-paper.cls`。 ## 数据分工（本轮调整的核心约定） @@ -13,26 +15,26 @@ 正文数字与插图全部由脚本从日志复算生成，不手抄：`scripts/build_practice_data.py` 复算演练局， `scripts/plot_paper_figures.py` 据同一份数据绘图；复算累计虚拟时间与评测机日志记录的最大偏差为 0.000000 s。 -演练局可重新采集（固定种子、可重放）： +演练局可重新采集（固定种子、可重放；队号运行期传入）： - uv run python scripts/run_practice_episodes.py --problem 3 --counts 10,13,16 --seeds 300-314 - uv run python scripts/run_practice_episodes.py --problem 4 --counts 10,13,16 \ - --directional-cycle 3,6,8 --seeds 400-439 + uv run python scripts/run_practice_episodes.py --problem 3 --robot-id <队号> \ + --counts 10,13,16 --seeds 300-314 + uv run python scripts/run_practice_episodes.py --problem 4 --robot-id <队号> \ + --counts 10,13,16 --directional-cycle 3,6,8 --seeds 400-439 采集脚本启动评测机并调用生产策略对接接口，真值与行为日志由评测机写入 `referee-data/sessions/<案例编码>/`。判定标准为 `清完全部源 ∧ 无缺席误判 ∧ 主动退出 ∧ 零异常 ∧ 零失败清除`： -只看“是否清完”会放过“清完了但中途大量 /clear 打空”的脏数据， -这类局的总虚拟时间被无效试探撑大，必须排除。 -被排除的局保留在 `referee-data/rejected-sessions/`，历史留档保留在 -`referee-data/superseded-sessions/`；`build_practice_data.py` 按案例编码白名单 -只统计论文声明的批次。 +只看“是否清完”会放过“清完了但中途大量 /clear 打空”的无效局， +这类局的总虚拟时间被无效试探撑大，故不纳入统计。 +被排除的局保留在 `referee-data/rejected-sessions/`； +`build_practice_data.py` 按案例编码白名单只统计论文声明的批次。 ## 交付文件 - main.pdf：论文、演练局数据清单、正式数据清单与四问核心函数代码附录。 - main.tex、sections/：可编辑 LaTeX 源文件。 -- data/practice-results.json、practice-results.csv：25 局演练局的结构化复算结果。 +- data/practice-results.json、practice-results.csv：44 局演练局的结构化复算结果。 - data/formal-results.json、formal-results.csv、audit.md：六份正式测试数据、复算结果与校验说明。 - support-materials.zip：源码、自建模拟器、演练局与正式日志、统计快照、论文工程及逐文件哈希清单。 - REWRITE_NOTES.md：本次修订及验证说明。 @@ -64,7 +66,7 @@ latexmk -xelatex -interaction=nonstopmode -halt-on-error -outdir=build main.tex # 产物 build/main.pdf 两条口令都完成交叉引用所需的多遍编译，输出均为 57 页。 -需要 TeX Live 的 XeLaTeX、ctex、TikZ，以及原项目 docs/paper/fonts/ 目录。 +需要 TeX Live 的 XeLaTeX、ctex、TikZ，字体已随仓库提供于 `docs/paper/fonts/`。 ### VS Code（LaTeX Workshop） diff --git a/docs/paper/REWRITE_NOTES.md b/docs/paper/REWRITE_NOTES.md index 4f0e93e..437fd61 100644 --- a/docs/paper/REWRITE_NOTES.md +++ b/docs/paper/REWRITE_NOTES.md @@ -1,6 +1,64 @@ # 本次修订与验证 -## VS Code 配方与图 1/图 2 箭头（本轮） +## 目录清理、去敏与文档合并（本轮） + +### 身份信息去敏（队号改为运行期参数） + +队号属身份信息，不再出现在代码、测试、示例与文档中： + +- `src/mcm26b/cli.py`：`sim-check` 的 `--robot-id` 默认值由真实队号改为与赛事无关的 + `SIMCHECK`；`run` 子命令本就 `required=True`。**对接官方模拟器的队号一律由命令行传入**。 +- `src/mcm26b/harness/simulator.py`、`referee/src/referee/simulator.py`： + `robot_id` 由带默认值改为**必填**，杜绝把队号固化进可提交代码。 +- `scripts/run_practice_episodes.py`：`--robot-id` 改为必填。 +- `scripts/build_core_notebook.py` 及重新生成的 `mcm26b-核心代码.ipynb`： + 离线自检单元用中性标识 `OFFLINE`；命令示例同步为 `python -m mcm26b.cli run`。 +- 测试、`README.md`、`referee/README.md`、referee WebUI 提示文本： + 真实队号统一替换为中性占位符 `TEAM-0001`。 +- `referee-data/` 与 `examples/replay/` 下的演练/正式日志（共 110 个文件）： + `robot_id` 字段统一为中性占位符，**位置、示向度、时间等证据字段原样未动**； + 改后复算结果完全一致（44 局全清、计时偏差仍为 0.000000 s）。 + +### 清理无用文件 + +- 删除本机工作区 `tmp/`（43 MB，含历史留档、截图与中间草稿）、`.zcode/`； +- 删除论文不再使用的 MATLAB 出图链路：`scripts/matlab/`（17 个文件）、 + `scripts/build_figure_data.py`、`scripts/render_q1_figures.py`； + 论文插图现全部由 `scripts/plot_paper_figures.py` 生成； +- 删除 LaTeX 陈旧产物（`main-updated.pdf`、`main-check.*`、`main copy.*`）与 + 未被引用的 `docs/paper/sections/comparison.tex`； +- 删除 `docs/figures/flow-overall-architecture.png` 及其生成函数： + 对应的四层主线图已从论文中移除，不再有插图引用它。 + +### 优化 .gitignore + +按"只跟踪可复现结果所需的最小集合"重写，补齐 Python/构建/编辑器/操作系统条目， +新增忽略 `docs/paper/support-materials.zip`（脚本产物）、`.piptmp/`、`uv-temp/`、`pip-temp/`、 +`.idea/`、`.zcode/`；保留 `referee-data/` 的版本控制并注明其身份信息已去敏。 + +### 文档合并到 README + +论文已完整给出四问的模型、推导与结果分析，故删除 13 份算法与设计说明文档 +（`docs/algo-*.md`、两份优化方案、示意图册、总体思路、执行时间、协议规则表、 +最优与基线对比、论文列表），只保留复现所必需的说明： + +- 根 `README.md` 重写为单一操作手册：环境 → 运行程序 → 自建模拟器 → + 复现论文数据 → 复现插图 → 编译论文 → 单文件核心代码与测试 → 目录结构； +- `docs/README.md` 改为最小索引； +- `docs/paper/README.md` 与 `docs/figures/README.md` 同步更新（数据分工、重算与编译步骤、 + 插图清单与来源）。 +- 源码/测试/脚本中原先指向被删文档的注释，统一改为指向论文对应章节，不留悬空引用。 + +### 验证 + +- `uv run pytest tests referee/tests`：**263 passed, 5 skipped** + （5 项为需要 `output/q1/practice` 官方演练采集的可选夹具，缺失时按 skip 处理）； +- `python scripts/build_practice_data.py`：44 局复算，计时偏差 0.000000 s； +- `scripts/build_paper_support.py --package`：278 个文件、3.6 MB，未超 20 MB； +- `latexmk -xelatex -outdir=build`：56 页，无未解析引用、无超宽表格； +- 绘图脚本无告警，`docs/figures/` 只保留论文实际引用的 10 张 PNG。 + +## VS Code 配方与图 2 箭头（前轮） - **LaTeX Workshop 默认配方换成与本项目编译口令一致的配方**： `.vscode/settings.json` 现提供三个配方，默认 diff --git a/docs/paper/main-updated.pdf b/docs/paper/main-updated.pdf deleted file mode 100644 index 81d7c4b..0000000 Binary files a/docs/paper/main-updated.pdf and /dev/null differ diff --git a/docs/paper/sections/comparison.tex b/docs/paper/sections/comparison.tex deleted file mode 100644 index 51c8d51..0000000 --- a/docs/paper/sections/comparison.tex +++ /dev/null @@ -1,2 +0,0 @@ -% This section is intentionally not input by main.tex. -% Formal direct metrics are reported in sections/results.tex. diff --git a/docs/paper/support-materials.zip b/docs/paper/support-materials.zip index 78d5c5c..94c16c4 100644 Binary files a/docs/paper/support-materials.zip and b/docs/paper/support-materials.zip differ diff --git a/docs/几何计算示意图册.md b/docs/几何计算示意图册.md deleted file mode 100644 index 5c9b116..0000000 --- a/docs/几何计算示意图册.md +++ /dev/null @@ -1,208 +0,0 @@ -# 几何计算示意图册 - -> 本册逐图给出坐标口径、关键量与图中各元素的含义。插图位于 [`figures/`](figures/)，复现命令： -> -> ```bash -> uv run python scripts/build_figure_data.py --only geo-q1-convex-intersection-zoom,\ -> geo-q1-diameter-circle-counterexample,geo-q2-guaranteed-reception-disk,\ -> geo-q3-seven-point-coverage,geo-q4-minimum-cover-31-points -> ``` -> -> 图中的数字由脚本现算：问题一调用生产求解器，问题三、四直接读覆盖计划与证书； -> 来源与 sha256 记在 [`figures/manifest.json`](figures/manifest.json)。 - -统一的坐标与符号约定： - -- 世界坐标以 Ω 圆心为原点，$x$ 轴向右、$y$ 轴向上，单位**米**； -- Ω 是半径 $R=1800$ m 的圆盘；源的有效接收半径 $r_{\rm eff}\in[1000,1500]$ m； -- 示向度 $\theta$ 以 $+x$ 轴为 $0°$ 逆时针为正，误差半角 $\delta=1°$； -- 清除半径 $r_{\rm clear}=20$ m； -- "可保证清除" ⟺ 存在一点使整个候选区域落在该点 20 m 内 ⟺ $\rho\le 20$ m（充分）。 - ---- - -## 图 1　问题一：角域交会与定位区域 - -![问题一角域交会](figures/geo-q1-convex-intersection-zoom.png) - -**左（总览）**：三个检测点（官方演练案例 `U5ZW-P9KN-TCAG-R7ST`，频道 9） -各自的**前向角域** $W_i=\{p:\angle(p-s_i)-\theta_i\in[-1°,+1°]\}$ 画成窄扇形 -（2° 宽，图中放大成可见宽度）；三者交集就是定位区域 $P$。检测点相距 600~900 m， -而 $P$ 的直径只有 24 m，所以左图里 $P$ 缩成一个点——这是**必须做局部放大**的原因。 - -**右（放大，视野 ±20.6 m）**：以定位区域为中心： - -- 浅蓝扇形 = 三个角域在放大框内的可见部分；**橙色多边形** = 三者交集，即定位区域 $P$； -- 左图里 $P$ 的直径只有 24 m，缩成一个红色方块，放大后才看得到形状； -- 蓝色虚线圆 = **直径圆**，半径 $D/2=11.9984$ m，圆心是中点 $(-450.634,\ 508.944)$； -- 绿色实线圆 = **最小包围圆（MEC）**，$\rho=12.0686$ m，圆心 $(-449.977,\ 507.953)$。 - -**关键量** - -| 量 | 值 | 判定 | -| --- | ---: | --- | -| 直径 $D(P)$ | 23.9967 m | 顶点对枚举（凸集上最远点必为顶点） | -| MEC 半径 $\rho$ | 12.0686 m | Welzl 算法 + 支撑点枚举对照 | -| 直径圆最大超出 $E$ | +0.6040 m | $E>0$ ⇒ **直径圆不覆盖** $P$ | -| $\rho\le20$ m | 成立 | ⇒ **存在可保证清除的点**（取 MEC 圆心） | - -> 这两个结论不矛盾：题目问"直径圆能否覆盖"（答案：不能，$E>0$） -> 与"能否保证清除"（答案：能，$\rho\le20$ m）是**两个不同的问题**。 -> $E$ 的定义：$E=\max_i\lVert v_i-o\rVert-D/2$，$o$ 为直径中点； -> 精确算术下最远顶点对就在圆周上，故 $E\ge0$，$E>0$ 即覆盖失败， -> 达到最大值的顶点就是可独立复核的失败证据。 - ---- - -## 图 2　问题一：直径圆不覆盖的反例 - -![直径圆反例](figures/geo-q1-diameter-circle-counterexample.png) - -等边三角形布站（$S_1(-1000,0)$、$S_2(540,-866)$、$S_3(520,901)$，示向度 -$1°,121°,241°$，误差半角 $1°$）。三条 2° 宽的角域交出一个**等边三角形** -$P$：顶点 $(0,0)$、$(40,0)$、$(20,34.641)$。 - -- 直径 $D=40.0000$ m，最远点对是底边两端 ⇒ 直径圆圆心 $(20,0)$、半径 20 m； -- 到第三个顶点 $(20,34.641)$ 的距离是 $20\sqrt3\approx34.6410$ m > 20 m， - 故**直径圆不覆盖**：$E=+14.6410$ m； -- 该反例的最小包围圆半径 $\rho=40/\sqrt3\approx23.0940$ m **> 20 m**， - 属于"覆盖失败且无法保证清除"的更坏情形。 - -> 反例是**符合题设测向条件**的：把真实源放在三角形中心 $(20,20\sqrt3/3)$， -> 三个检测点的真实方位与读数偏差均小于 $1°$，且三个检测距离都小于 1500 m， -> 所以它不是"脱离测向模型的任意图形"。构造与证明见 -> [`问题一解答.md`](问题一解答.md) 第六节。 - ---- - -## 图 3　问题二：保证接收的候选盘与安全盘 - -![问题二保证接收盘](figures/geo-q2-guaranteed-reception-disk.png) - -**候选集**（首次观测之后可能的位置集合）： - -$$ -K_1=\Omega\ \cap\ W(s_1,\theta_1\pm\delta)\ \cap\ B(s_1,\ 1500), -$$ - -即"Omega 内、首次角域内、且距离不超过最大接收半径"的三者之交。 - -**解析安全盘**：截断角域（扇形，半径 $R_{\max}=1500$ m、半角 $\delta=1°$） -的最小包围圆恰为 $B(c,a)$，其中 $c=s_1+a\boldsymbol u$（$\boldsymbol u$ 为示向度单位向量）， - -$$ -a=\frac{R_{\max}}{2\cos\delta}=\frac{1500}{2\cos1°}=750.1142\ \text{m}. -$$ - -于是对安全盘内任一点 $q$、扇形内任一点 $p$： - -$$ -\lVert q-p\rVert\le a+(1000-a)=1000\ \text{m} -\ \Longrightarrow\ q\ \text{必然收到源的信号}, -$$ - -$$ -\boxed{\ \text{安全盘半径}=1000-a=249.8858\ \text{m}\ } -$$ - -（严格非空；图中绿色实心圆，圆心 $c=(649.62,\ 375.06)$）。 -第二检测点就在该盘内选，目标是**最小化最坏保守后验包围半径**， -移动与检测成本作次目标；交会角只用于同分筛选与可解释性说明。 - -> 图中 $a$ 与 1000 m 两个圆几乎重合是可预期的：$\delta$ 只有 $1°$， -> $a=R_{\max}/(2\cos\delta)$ 与 $R_{\max}/2$ 相差 0.0114%， -> 因此**安全盘的"安全"主要来自角域约束，而不是圆心位置**。 - ---- - -## 图 4　问题三：七点解析覆盖 - -![问题三七点覆盖](figures/geo-q3-seven-point-coverage.png) - -**站点集**：原点 + 半径 $a$ 的六等分环， -$S(a)=\{(0,0)\}\cup\{a(\cos k\pi/3,\ \sin k\pi/3):k=0,\dots,5\}$。 - -**定理（`algo-3.md` §4.2）** 当 $0 **本轮修复的正确性缺陷**：旧版 `no_signal` 的朝向裁剪规则反向、且漏计格内角裕量， -> 会删掉真源所在的朝向档。已修复，并由 -> `tests/test_joint_belief.py` 的随机观测序列保留性测试固化。 - ---- - -## 附：把图接进论文 - -`docs/paper/main.tex` 目前用表格承载结论，插图按下例插入即可： - -```latex -\begin{figure}[htbp] - \centering - \includegraphics[width=0.95\textwidth]{figures/geo-q1-convex-intersection-zoom.png} - \caption{问题一：角域交会得到的定位区域，以及直径圆与最小包围圆的关系。} - \label{fig:geo-q1} -\end{figure} -``` - -图片按 160 mm 宽排版。图内不含标题与图注，说明写在 `\caption` 与正文。 diff --git a/docs/协议规则表与状态机.md b/docs/协议规则表与状态机.md deleted file mode 100644 index e7a8217..0000000 --- a/docs/协议规则表与状态机.md +++ /dev/null @@ -1,215 +0,0 @@ -# 协议规则表、字段表与状态机（P0 交付） - -依据：`issue/附件/附件1.docx`《模拟器使用说明》、`issue/附件/附件2.docx`《模拟器通信接口说明及编程指南》。 -本文是人类可读版本；**可执行版本**是 `src/mcm26b/protocol/rules.py`（字段表与错误语义）、 -`src/mcm26b/protocol/timing.py`（计时模型）与 `src/mcm26b/config.py`（全部常量）， -三者由 `tests/test_protocol_timing.py`、`tests/test_protocol_client.py` 固化验收。 - -## 1. 动作与耗时规则表 - -| 动作 | 指令 | 虚拟耗时 | 说明 | -| --- | --- | --- | --- | -| 开始 | `POST /enter` | 0 | 初始位置 (0,0)，初始频道 1；虚拟/程序时钟开始 | -| 检测 | `POST /measure` | 5 + 移动 + 换频道 | 无论结果（no_signal/near/direction）均为 5s | -| 清除 | `POST /clear` | 未发现 3 / 成功 5 + 移动 | 5 = 光学定位 3 + 激光清除 2；**不切换测向频道** | -| 结束 | `POST /exit` | 0 | 成功响应中 exit_reason 固定 `user_exit` | -| 切换频道 | 无独立指令 | 1 | 只由合法 `/measure` 的 channel 变化推断 | -| 移动 | 无独立指令 | 直线距离/5(m/s) | 由 `/measure` 或 `/clear` 的 position 推断 | - -派生事实（易错点）： -- 一次合法 `/measure` 完成后，测向机频道更新为本次检测频道； -- `/clear` 的 channel 只是目标频道，既不切频道、也不产生切换耗时，因此 - "清除后原地再测"的耗时只取决于上一次 **/measure** 的频道； -- 只有 accepted=true 的请求推进虚拟时钟；`accepted=false` 的 virtual_time_s=0 不是当前时刻； -- 5 秒检测只是虚拟耗时，不要求现实等待。 - -## 2. 字段表 - -公共请求字段（4 条指令都必填）： - -| 字段 | 类型 | 约束 | -| --- | --- | --- | -| arena_id | string | 必须为 `"default"` | -| robot_id | string | 逐字节等于登录参赛队号；UTF-8 长度 1..64；无控制字符 | -| request_id | string | 会话内幂等键；UTF-8 长度 1..128；无控制字符 | - -动作字段（实现见 `rules.ACTION_FIELDS`）： - -| 指令 | 字段 | 约束 | -| --- | --- | --- | -| /enter | （无附加） | | -| /measure | position{x,y}, channel | x/y 有限数值且 \|v\|<=2,000,000；channel 整数 1..20（1.0 亦接受，1.5 拒绝） | -| /clear | position{x,y}, channel | 同上；channel 是目标频道 | -| /exit | （无附加） | | - -响应字段： - -| 场景 | 字段 | -| --- | --- | -| 所有响应 | accepted, real_timestamp_ms, virtual_time_s | -| accepted=false | 仅上述 3 个字段（virtual_time_s=0） | -| /enter 成功 | + max_virtual_duration_s, max_real_duration_s, remaining_real_duration_s | -| /measure 成功 | + measure_result ∈ {no_signal, near, direction}；direction 时另有 svd_deg（保留两位小数） | -| /clear 成功 | + clear_result ∈ {success, no_target_in_range} | -| /exit 成功 | + exit_reason = "user_exit" | - -错误语义（必须同时检查 HTTP 状态与 accepted）： - -| 情形 | 结果 | 对 request_id 的影响 | -| --- | --- | --- | -| JSON 语法/重复键/缺字段/类型/坐标频道范围 | HTTP 400 | 不占用，修正后可复用 | -| 未知字段（含 position 下）、arena/robot 不匹配、测试状态拒绝 | HTTP 200, accepted=false | 不占用 | -| 动作被接受 | HTTP 200, accepted=true | 占用 | -| 同 ID 不同内容，或并发不同动作 | HTTP 409 | — | -| 网络失败重试同一动作 | 复用原 ID+原内容，服务器回放首次响应 | 不重复生效 | -| 429/500 | 可原样重试 | — | -| 404/405/413/415 | 确定性错误，不得重试 | — | - -物理规则速查：Ω 半径 1800m；源数 10–16；频道 1–20；接收半径 [1000,1500]m（逐源未知）； -near 阈值 5m；清除半径 20m（与定向朝向无关）；定向覆盖 = 朝向两侧各 90°（含边界）； -示向度误差幅度 ±1°，保留两位小数；虚拟限时 360000s；程序限时 1200s（以 /enter 的 -remaining_real_duration_s 为准，不得假定恒为 1200）；测试窗口 25 分钟。 - -## 3. 状态机 - -机器狗会话状态（`protocol.state.RobotState` + 客户端会话标志）： - -``` - /enter accepted /exit accepted - [未进入] ───────────────▶ [已进入] ───────────────▶ [已退出] - │ accepted=false │ accepted=false │ 任何动作 accepted=false - └── 不变 └── 不变 └── 不变 -``` - -[已进入] 状态内跟踪：position、测向机 channel、virtual_time_s。转移规则： - -| 事件 | position | channel | virtual_time_s | -| --- | --- | --- | --- | -| /enter accepted | (0,0) | 1 | 0 | -| /measure accepted | ← 本次 position | ← 本次 channel | ← 响应值（=旧值+移动+切换+5） | -| /clear accepted | ← 本次 position | **不变** | ← 响应值（=旧值+移动+3或5） | -| /exit accepted | 不变 | 不变 | ← 响应值 | -| accepted=false / 网络 / 400 | 不变 | 不变 | 不变 | - -虚拟时刻以最近一次 accepted=true 的响应为准；本地预测（timing 模块）只用于 -规划与一致性核对，偏差记入 `RobotState.mismatches` 并写日志（strict 模式抛错）。 - -## 4. 计时示例逐步推演（附件1表2 = 附件2第10节） - -| 步 | 指令 | 移动 | 换频道 | 动作 | 本步 | 累计 | -| --- | --- | --- | --- | --- | --- | --- | -| 1 | /enter | 0 | 0 | 0 | 0 | **0** | -| 2 | /measure (300,400) ch1 | 500m→100s | 0（初始即1） | 5 | 105 | **105** | -| 3 | /measure (300,400) ch2 | 0 | 1 | 5 | 6 | **111** | -| 4 | /clear (300,0) 目标ch3 | 400m→80s | 0（clear不切频道） | 3（未发现） | 83 | **194** | -| 5 | /measure (300,0) ch2 | 0 | 0（第3步后已是2，clear未改） | 5 | 5 | **199** | -| 6 | /exit | 0 | 0 | 0 | 0 | **199** | - -验收：`uv run mcm26b sim-check` 通过完整客户端→本地模拟器链路复现 -`[105, 111, 194, 199]`；`tests/test_protocol_timing.py::TimingModelTests` -逐步断言每个分项。 - -## 5. 客户端设计要点（P1 交付） - -- **串行**：完全同步，一个动作未决不发下一个； -- **幂等重试**：请求体一次性序列化为 bytes，网络失败/429/500 时原样重发 - （同 request_id 同内容），最多 `max_attempts` 次，指数退避；400/404/405/409/413/415 立即失败； -- **accepted=false**：抛 `ActionRejected`，状态不更新；virtual_time_s=0 不入时钟； -- **现实预算**：/enter 成功后 `deadline = monotonic() + remaining_real_duration_s`； - 新动作前检查 `剩余 > reserve_exit_s`（默认 60s [工程]），/exit 豁免； -- **日志**：`SessionLog` 逐事件追加 JSONL（attempt/transport_error/accepted）， - 进程崩溃不丢已发记录，回放测试与 evaluation 复算共用同一格式； -- **本地预校验**：channel 范围、坐标有限性与 2e6 上限、robot_id/request_id 格式 - 在发送前拒绝（与服务器 400 语义一致，省一次往返）。 - -## 6. 策略层如何使用本协议（P3/P4 交付） - -第 5 节的客户端是策略层唯一的事实来源；`belief`（候选集）、`planner`（选点与闭环） -不绕开它直接发请求。衔接约定： - -- **串行不变**：闭环每个决策步只发一个动作，`measure`/`clear` 的返回值 - （`MeasureOutcome`/`ClearOutcome`）是 belief 更新的唯一输入； -- **状态同步**：机器人位置/测向频道/虚拟时刻一律读 `client.state`， - 频道规则（§3 状态机表）由状态机自动维护，策略层不自行推算； -- **预算驱动退出**：`DeadlineExceeded` 只在 `/exit` 之外的动作前抛出； - 闭环捕获后立即 `/exit`（豁免检查），并把该局记为"未证明完成"， - 不与全清成功混记； -- **选点的接收保证**：选点器只在保证接收盘 B(mec, 1000-ρ) 内取点， - 盘内任一点到候选集每点距离 < 1000 ≤ 最小接收半径，因此定位阶段 - 不会浪费 `no_signal`（ρ>1000 的一般候选集退化为探索 MEC 圆心，允许 - no_signal，理由字段中如实标注）； -- **清除证书**：候选集外包多边形半径（含近似误差与工程余量） - ≤ 20m 才发起 `/clear`；证书有效但清除失败视为异常，先补测再重试， - 不在同一证书上重复消耗 3 秒/次的失败清除； -- **停止条件与完成判定**（2026-09-11 用户决策后）：找全证明达成 - （每频道均"已清除或覆盖证明不存在"，或清除数达 16 数量上界）后 - **不提前退出**：按 `search.persistent_search`（默认开）只复扫未清除 - 频道，直到清除数达 16、实机预算耗尽或虚拟安全余量 - （`virtual_reserve_s`，防触及附件2 §4.5 的 100 小时上限； - 本地模拟器对达限后的动作以 accepted=false 兜底）不足——"扫到限时为止"。 - summary 中 `completed` 表示任务证明完成（无中断且结束时每频道 - CLEARED/ABSENT，或 16 清除）；`accounted_virtual_time_s` 记录首次 - 证明找全的虚拟时刻（计算"平均定位清除时间"建议用它，而非复扫 - 耗尽后的最终时刻）；`stop_reason` 记录循环结束原因。发现若干源、 - 连续 no_signal、候选采样为空都不是停止条件； -- **每步计时留痕**（同上决策）：客户端每个被接受动作写一条 `step` - 事件（实机墙钟耗时 `real_elapsed_s`/`real_since_enter_s` + 比赛虚拟 - 耗时分项 `virtual_cost`：移动=直线距离/5、切频道 1s、检测 5s、 - 清除成功 5s/未发现 3s），summary 以 `steps` 数组与 `step_totals` - 汇总，并给出题面口径的 `avg_locate_clear_time_s`； -- **验收**：`tests/test_loop_q3.py` 在自建模拟器上覆盖 10/16/0 源、 - 近边界源、预算耗尽与故障注入；`uv run mcm26b run --robot-id <队号> - --self-test` 可离线复跑随机场景，正式模式去掉 `--self-test` 即对接 - 模拟器（日志用 `--log` 落盘，格式同 §5 JSONL）。 - -## 7. 定向扩展如何使用本协议（P5 交付） - -问题4的定向源在**协议字段上与全向源完全不可区分**：`/measure` 仍只返回 -no_signal/near/direction（direction 附 svd_deg），`/clear` 仍只看 20m 半径。 -"该源是定向的"无法从报文读出，只能由观测推理。因此 §5/§6 的协议用法全部 -照用，定向性只改变策略层的解释与决策： - -- **发现阶段零改动**：49 点覆盖保证对定向源同样成立——源位于某网格单元 - 凸包内时，任意发射半平面（朝向 ±90°，含边界）必含至少一个顶点，且该 - 顶点距离 ≤ 700√2 ≈ 989.95 < 1000 ≤ 最小接收半径，覆盖阶段无需知道 - 源类型； -- **no_signal 联合约束基线**：定向源的 no_signal 约束的是"位置×半径×朝向" - 联合假设，单独维护空间候选会被误删，基线**不据此删任何候选** - （`belief.update_measure` 对 no_signal 无操作）；缺席证明仍只由完整覆盖 - 给出。唯一启用的推理：若 no_signal 发生在保证接收盘 B(mec, 1000-ρ) 内 - （该盘内任一全向源必然可收），则只能由朝向遮挡解释 [观测推理]——该频道 - 记为"观测到定向"，进入探点升级梯子：对 MEC 圆心取对跖方向复核（偏移 - 裁进保证接收盘），再绕 MEC、半径 = ρ 的圆环按 45° 步进探点。**几何边界 - 必须如实陈述**：环上可见弧没有下界——源可位于环上（|g−c| = ρ）且朝向 - 沿外切线，此时可见集退化为单个切点，固定步进环探可以全部漏过。梯子 - 只提高提前命中概率，不构成接收或命中保证；完备性完全由后备清除兜底。 - 被遮挡探点不消耗定位步数，单轮被遮挡探点（首次选点、对跖复核、环探 - 合计）达 `max_blocked_probes`（4 [工程]）即转后备清除； -- **后备清除的协议用法**：定位停滞（候选集有界但未达 §6 清除证书）时， - 对候选外包多边形构造 20m 有限圆盘覆盖（`fallback_clear_cells`：全局 - 半开网格索引、相交判定带正容差，格内任一点距某格心 ≤ 10√2 < 20m）， - 按"距机器人最近优先"逐格发 `/clear`（同频道、不同位置）——清除与朝向 - 无关 [题面]，是定向源不依赖可见性的完备后备。两条预算硬门槛（任一 - 不满足即跳过并留痕，绝不盲扫）： - - 格数上界：N ≤ `fallback_max_cells`（400 [工程]）；外包框枚举先按 - (⌈w/20⌉+2)(⌈h/20⌉+2) 做规模检查，宽候选集（如仅一次读数的 750m - 截断角域，数千格）必须继续定位而非硬扫； - - 现实预算：N × `real_rtt_p95_s` + `reserve_exit_s` ≤ 剩余现实时间。 - 虚拟耗时上界 ≈ 移动 L/5 + 3N（全失败）或 3(N-1)+5（末次成功）。 - 后备 /clear 请求**计入总请求口径**：summary 的 `total_requests` - （measures+clears）与 `fallback_clear_attempts`（后备逐格尝试数） - 如实上报——仅统计检测次数会明显低估现实耗时； -- **真源不误删**：no_signal、失败的 `/clear`（含后备逐格全失败）均不删 - 空间候选；后备全失败按守恒性破坏记异常（格心距格内任一点 < 20m， - 理论上必命中）； -- **停止条件与预算语义不变**：仍是清除数达 16、每频道均"已清除或覆盖 - 证明不存在"、现实预算耗尽（记未证明完成）三种；断连/协议错误/ - 守恒性破坏中断时同样尽力 /exit 并产出未完成 summary（`stop_reason` - 记 `transport_failure`/`protocol_error`/`inconsistent_belief` 等， - 附逐频道状态快照），不静默丢失会话； -- **验收**：`tests/test_loop_q4.py`（15 项：覆盖格无空洞与格线两侧保留、 - 退化多边形、格数上界公式；混合 10/16 源全清、朝向背离圆心的近边界源 - 不漏、批量种子回归；no_signal 不删候选；格数硬顶与预算跳过；后备 - 正路径命中与全失败异常留痕）；`uv run mcm26b run --robot-id <队号> - --problem 4 --self-test` 可离线复跑混合场景（`--directional` 指定 - 定向源数），正式模式同 §6。 diff --git a/docs/总体思路与流程图.md b/docs/总体思路与流程图.md deleted file mode 100644 index f68a2a3..0000000 --- a/docs/总体思路与流程图.md +++ /dev/null @@ -1,178 +0,0 @@ -# 总体思路与流程图 - -> 插图：[`figures/flow-overall-architecture.png`](figures/flow-overall-architecture.png)（总体思路与算法主线）、 -> [`figures/steps-state-machine.png`](figures/steps-state-machine.png)（执行步骤）、 -> [`figures/timing-attachment-example-105-111-194-199.png`](figures/timing-attachment-example-105-111-194-199.png)（附件计时示例）。 -> 复现：`uv run python scripts/build_figure_data.py --only flow-overall-architecture,steps-state-machine,timing-attachment-example-105-111-194-199` - -本文给出四问的思路、端到端流程图、执行步骤与执行时间。 -算法细节指向 [`algo-1.md`](algo-1.md)~[`algo-4.md`](algo-4.md)， -协议细节指向 [`协议规则表与状态机.md`](协议规则表与状态机.md)。 - ---- - -## 1. 主线 - -把题面约束写成可计算的几何集合和计时账本，四问共用一套「证据 — 决策 — 执行」结构， -每问都给出可验证的保证： - -| 问 | 保证 | -| --- | --- | -| 1 | 直径由顶点对枚举给出；最小包围圆半径 $\rho\le20$ m 时存在可保证清除的点 | -| 2 | 第二检测点取在解析安全盘 $B(c,1000-a)$（半径 249.8858 m）内，必然收到信号 | -| 3 | 七点计划覆盖整个 Ω（解析上界 984.5391 m < 1000 m），据此可判定频道无源 | -| 4 | 31 点最小命中集在 15 m / 72 朝向档证书下缺口为 0 | - -![总体思路与算法主线](figures/flow-overall-architecture.png) - ---- - -## 2. 四层结构 - -| 层 | 内容 | 作用 | -| --- | --- | --- | -| L0 题面抽象 | Ω 半径 1800 m；移动 5 m/s、检测 5 s、换频 1 s、清除 5 s / 3 s；观测 `direction/no_signal/near`；源 10~16 个，频道 1~20，定向源覆盖 ±90° | 后续推导只用这些量，不引入题面之外的信息 | -| L1 在线三层 | 证据层（候选集与粒子）、决策层（选点与任务表）、执行层（串行客户端） | 依赖单向向下，几何结论与调度启发式可分别测试 | -| L2 四问 | Q1 定位解算 → Q2 第二点选择 → Q3 全向搜索 → Q4 混合定向 | 后一问复用前一问的内核 | -| L3 三条闭环 | 发现、定位、清除 | 每环有明确的进入与退出条件 | - -L0 的常量集中在 `src/mcm26b/config.py`，带 `[题面]` / `[工程]` 标注。 -证据层只给出「可能在哪里」，决策层只读证据并产出动作， -执行层负责发送动作并维护虚拟时钟。 - ---- - -## 3. 四问思路 - -### 问题一 - -1. 检测点 $s_i$ 与示向度 $\theta_i$ 给出前向角域 - $W_i=\{p:\angle(p-s_i)-\theta_i\in[-1°,+1°]\}$，定位区域是半平面交 $P=\bigcap_i W_i$； -2. 直径 $D(P)=\max_{i,j}\lVert v_i-v_j\rVert$ 只需枚举顶点对（凸集上最远点必为顶点）； -3. 直径圆能否覆盖 $P$：唯一候选圆心是最远顶点对的中点。若 $a,b$ 同在该圆内， - 三角不等式两处同时取等，圆心只能是 $ab$ 中点，因此只需在该点检查全部顶点； -4. 能否保证清除：最小包围圆半径 $\rho\le20$ m 时，停在 MEC 圆心即可清除。 - 这是充分条件，不是必要条件。 - -结论：直径圆不一定覆盖定位区域，见 [`geo-q1-counterexample.png`](figures/geo-q1-diameter-circle-counterexample.png)； -官方演练案例是「覆盖失败但 $\rho=12.07$ m $\le20$ m，可保证清除」。 - -### 问题二 - -候选集 $K_1=\Omega\cap W(s_1,\theta_1\pm\delta)\cap B(s_1,1500)$。 -截断角域（扇形）的最小包围圆为 $B(c,a)$，$a=R_{\max}/(2\cos\delta)=750.1142$ m， -故安全盘 $B(c,1000-a)$ 的半径为 249.8858 m，盘内任一点到扇形内任一点的距离 -不超过 1000 m，即必然收到信号。在该盘内以最坏保守后验包围半径为主目标选点， -移动与检测成本作次目标，交会角只用于同分筛选。详见 [`algo-2.md`](algo-2.md)。 - -### 问题三 - -发现保证用七点计划（原点 + 半径 $a=1159.7$ m 的六等分环）：解析上界 -$D(a)=\max\{a/\sqrt3,\ \sqrt{R^2+a^2-\sqrt3Ra}\}=984.5391$ m < 1000 m， -Ω 内任意位置的源都在某站点 1000 m 内。证据层按频道维护候选集与粒子幸存集， -负观测按 `full_disk` 语义排除 1000 m 圆盘；决策层用信息增益选点， -任务表包含检测、定位、清除三类任务，排序为开放路径问题； -所有频道销账即完成证明成立，立即 `/exit`。 - -### 问题四 - -定向源只向朝向 ±90° 的前向半球辐射，覆盖语义改为 `forward_half_disk`。 -七点在该语义下有缺口，改用离线贪心得到的 31 点最小命中集（设计半径 965 m， -前向角裕量 5°），并以 15 m / 72 朝向档的运行期证书校核（325.62 万个采样格，缺口 0， -最坏命中 990.05 m）。负观测只约束朝向，因此维护位置 × 朝向联合信念， -信念坍缩到有限格时使用后备盲清。详见 [`algo-4.md`](algo-4.md)。 - ---- - -## 4. 端到端流程 - -```mermaid -flowchart TD - S([开始：/enter 成功
现实预算 1200 s 起算]) --> P0[读配置 ProblemConfig] - P0 --> PLAN{按问题选择覆盖计划} - PLAN -->|问题 3| S7[七点全向计划
解析上界 984.54 m] - PLAN -->|问题 4| S31[31 点最小命中集
forward_half_disk 证书] - S7 --> LOOP - S31 --> LOOP - - subgraph LOOP[主循环：动作回合] - direction TB - NEXT[选下一个任务
信息增益 / 最坏后验半径
滚动 2-opt 排序] --> SEND[/发送动作/] - SEND --> RESP{accepted?} - RESP -->|false| FIX[不更新状态
记录并重排任务] - RESP -->|true| CLK[更新虚拟时钟、位置、频道] - FIX --> NEXT - CLK --> OBS{观测类型} - OBS -->|direction| HIT[登记新源
同频道安排第二点交会] - OBS -->|no_signal| NEG[负观测裁剪
位置候选或朝向档] - OBS -->|near| SKIP[距离过近，换点重测] - HIT --> DONE{该频道 ρ ≤ 20 m?} - NEG --> DONE - SKIP --> DONE - DONE -->|是| CLR[/清除动作/] - DONE -->|否| NEXT - CLR --> LEDGER[覆盖账本销账] - LEDGER --> FIN{全部频道销账?} - FIN -->|否| NEXT - end - - FIN -->|是| EXITQ[完成证明成立，立即 /exit] - EXITQ --> REP[日志复算与统计] - REP --> END([结束：虚拟时间即成绩]) - - classDef main fill:#dce6f1,stroke:#1f4e79,color:#1c2430 - classDef acc fill:#f6dcd8,stroke:#b03a2e,color:#1c2430 - classDef ok fill:#d8ece5,stroke:#1e7a5f,color:#1c2430 - class NEXT,SEND,RESP,CLK,OBS main - class CLR,FIN,EXITQ acc - class LEDGER,REP ok -``` - -会话状态机与单次动作回合见 [`figures/steps-state-machine.png`](figures/steps-state-machine.png)， -逐步计时示例见 [`figures/timing-attachment-example-105-111-194-199.png`](figures/timing-attachment-example-105-111-194-199.png)。 - ---- - -## 5. 执行步骤与执行时间 - -一次动作回合：本地预校验 → 发送 → 等响应计时 → 按响应更新状态。 -异常时同 `request_id` 同内容重发，400 与 409 立即失败。 - -| 动作 | 虚拟耗时 | 说明 | -| --- | --- | --- | -| `/enter`、`/exit` | 0 s | 不改变虚拟时刻，`/exit` 不切频道 | -| 移动 | 直线距离 / 5 m·s⁻¹ | 同点不动为 0 s | -| 换测向频道 | 1 s | 仅 `/measure` 触发，`/clear` 不切换 | -| 检测 `/measure` | 5 s | 与返回结果无关 | -| 清除 `/clear` | 5 s / 3 s | 成功 5 s，未发现 3 s | - -实测样例（评测机日志 `BBM9-HZG2-77N5-NKKC`，问题三演练，10 源全清）： - -| 项 | 数值 | -| --- | --- | -| 总虚拟时间 | 3134.643 s | -| 移动（里程 11.9 km） | 2379.6 s | -| 检测与清除动作（119 + 10 次） | 645.0 s | -| 换频（110 次） | 110.0 s | -| 现实耗时 | 1.889 s | - -三项之和等于响应中的 `virtual_time_s`，与 `evaluation.recompute_virtual_time` 口径一致。 -分解图见 [`figures/timing-measured-q3-10-sources.png`](figures/timing-measured-q3-10-sources.png)， -定向局（`GM56-…`，虚拟 8703.766 s，现实 3.397 s）与复现命令见 -[`执行时间与执行步骤.md`](执行时间与执行步骤.md)。 - ---- - -## 6. 交付物与验收锚点 - -| 问 | 算法文档 | 主要实现 | 锚点 | -| --- | --- | --- | --- | -| 1 | [`algo-1.md`](algo-1.md)、[`问题一解答.md`](问题一解答.md) | `src/mcm26b/geometry/` | 演练复算 $D=23.9967$ m、$\rho=12.0686$ m、$E=+0.6040$ m | -| 2 | [`algo-2.md`](algo-2.md) | `geometry/`、`belief/`、`planner/` | 安全盘半径 249.8858 m | -| 3 | [`algo-3.md`](algo-3.md)、[`algo-3-bayes.md`](algo-3-bayes.md) | `planner/bayes_loop.py`、`planner/seven.py` | 七点上界 984.5391 m < 1000 m | -| 4 | [`algo-4.md`](algo-4.md) | `planner/directional.py`、`belief/joint.py` | 证书缺口 0，最坏命中 990.05 m | -| 1–4 | [`最优与基线实现对比.md`](最优与基线实现对比.md) | `project/` | `uv run python -m project.run` | - -优化过程与未采用的改动见 -[`第三四问架构优化与运行提速方案.md`](第三四问架构优化与运行提速方案.md)、 -[`第三四问图论与动态规划优化方案.md`](第三四问图论与动态规划优化方案.md)。 diff --git a/docs/执行时间与执行步骤.md b/docs/执行时间与执行步骤.md deleted file mode 100644 index dd35a1c..0000000 --- a/docs/执行时间与执行步骤.md +++ /dev/null @@ -1,178 +0,0 @@ -# 执行时间与执行步骤 - -> 配套插图：[`figures/steps-state-machine.png`](figures/steps-state-machine.png)（状态机与动作回合）、 -> [`figures/timing-attachment-example-105-111-194-199.png`](figures/timing-attachment-example-105-111-194-199.png)（附件示例逐步推演）、 -> [`figures/timing-measured-q3-10-sources.png`](figures/timing-measured-q3-10-sources.png)（实测一局的时序分解）。 -> 复现：`uv run python scripts/build_figure_data.py --only steps-state-machine,timing-attachment-example-105-111-194-199,timing-measured-q3-10-sources` - -本文说明每一步做什么、每步花多少虚拟时间、这些数字如何验证。 -规则原文见 `issue/` 下附件1 表1、表2 与附件2 第 4、10 节； -可执行表格在 `src/mcm26b/protocol/rules.py` 与 `src/mcm26b/config.py` 的 `CostConfig`。 - ---- - -## 1. 计时模型 - -忽略微秒量化时，一次会话的总虚拟时间为 - -$$ -T=\underbrace{\frac{L}{5}}_{\text{移动}}+\underbrace{5N_m}_{\text{检测}} -+\underbrace{N_s}_{\text{换频}}+\underbrace{5N_c^{+}}_{\text{清除成功}} -+\underbrace{3N_c^{-}}_{\text{清除未发现}}, -\qquad L=\text{路径长度（米）},\ 5\ \text{m/s}. -$$ - -规则： - -- 移动按两点间直线距离计，不计加减速； -- 换频只在 `/measure` 触发，任意两频道间固定 1 s，`/clear` 不切换频道； -- 检测固定 5 s，与结果无关，`direction` 与 `no_signal` 同价； -- 清除成功 5 s（光学定位 3 s + 激光清除 2 s），未发现目标 3 s（仅光学定位）； -- `/enter`、`/exit` 不产生虚拟时间； -- 访问顺序是开放路径问题，起点固定、终点自由，不必回到起点。 - -![执行步骤：状态机与动作回合](figures/steps-state-machine.png) - ---- - -## 2. 执行步骤 - -### 2.1 会话级状态机 - -| 状态 | 进入条件 | 允许的动作 | 退出条件 | -| --- | --- | --- | --- | -| `IDLE` | 界面待机（演练不限次 / 正式各 3 次） | — | 点击"开始测试" | -| `COUNTDOWN` | 数据准备完成 | —（接口未开放） | 5 s 倒计时结束 | -| `OPEN` | 倒计时结束，接口在 `:2026` 开放 | `/enter` → `/measure` `/clear` → `/exit` | 25 分钟窗口到期 / 源全清后主动 `/exit` / 中止 | -| `CLOSED` | 窗口到期或进程退出 | —（连接关闭，已登记动作可完成） | — | - -现实预算与现实窗口是两条独立时钟：`/enter` 成功后 `max_real_duration_s = 1200 s` -起算（程序侧），测试窗口 `test_window_s = 1500 s`（服务端侧）。 -客户端维护本地截止时钟，剩余不足 60 s（`reserve_exit_s`，工程参数）时不再发起新动作， -只保证 `/exit` 能发出去。 - -### 2.2 单次动作回合（严格串行） - -![动作回合](figures/steps-state-machine.png) - -1. 本地预校验：`channel ∈ [1,20]`、坐标有限且 $\lvert x\rvert,\lvert y\rvert\le 2\times10^6$、 - `request_id` 长度与字符集，与服务器 400 语义一致，省一次往返； -2. 串行发送：同一时刻只有一个未决请求，不并发（并发会得到 409）； -3. 等服务端计时：`accepted=true` 才更新状态，虚拟时刻取响应中的 `virtual_time_s`， - 不做本地累加；`accepted=false` 抛 `ActionRejected`，状态不更新， - `virtual_time_s=0` 不计入时钟； -4. 更新本地状态：位置、当前频道、剩余现实预算，追加 JSONL 会话日志； -5. 异常处理：网络错误、429、500 时同 `request_id` 同内容重发 - （请求体一次性序列化，最多 `max_attempts` 次，指数退避）； - 400、404、405、409、413、415 立即失败。 - -重发不会重复计时，服务端按 `request_id` 幂等。 - -### 2.3 任务类型 - -| 任务 | 触发 | 动作序列 | 虚拟成本 | -| --- | --- | --- | --- | -| 覆盖扫描 | 某频道尚未被任何站点覆盖 | 移动到覆盖点 → `/measure` | 距离/5 + 5 s（+ 换频 1 s） | -| 定位交会 | 某频道收到 `direction` | 移动到第二检测点 → `/measure` | 距离/5 + 5 s（+ 1 s） | -| 清除 | 同频道候选区域 ρ ≤ 20 m | 移动到 MEC 圆心附近 → `/clear` | 距离/5 + 5 s 或 3 s | -| 后备盲清（问题四） | 联合信念坍缩到有限格 | 依次到可疑格 `/clear` | 距离/5 + 3 s 或 5 s | -| 机会测量 | 已停靠且该频道待测 | 原地 `/measure` | 5 s | - -任务表排序是开放路径问题，默认用滚动 2-opt 与 Or-opt； -精确 Held-Karp 与路线感知探点在本模拟器上为负收益，默认关闭，见 -[`第三四问图论与动态规划优化方案.md`](第三四问图论与动态规划优化方案.md) §8.6。 - ---- - -## 3. 附件计时示例逐步推演（105 / 111 / 194 / 199） - -| 步 | 指令 | 移动 | 换频 | 动作 | 本步 | 累计 | -| --- | --- | --- | --- | --- | --- | --- | -| 1 | `/enter` | 0 | 0 | 0 | 0 | 0 | -| 2 | `/measure (300,400) ch1` | 500 m → 100 s | 0（初始即 1） | 5 | 105 | 105 | -| 3 | `/measure (300,400) ch2` | 0 | 1 | 5 | 6 | 111 | -| 4 | `/clear (300,0) ch3` | 400 m → 80 s | 0 | 3（未发现） | 83 | 194 | -| 5 | `/measure (300,0) ch2` | 0 | 0（第 3 步后已是 2） | 5 | 5 | 199 | -| 6 | `/exit` | 0 | 0 | 0 | 0 | 199 | - -![附件计时示例逐步推演](figures/timing-attachment-example-105-111-194-199.png) - -验证方式： - -```bash -uv run mcm26b sim-check # 客户端 → 本地模拟器链路，输出 [105, 111, 194, 199] -uv run pytest tests/test_protocol_timing.py -q # 逐步断言移动、换频、动作三个分项 -``` - ---- - -## 4. 实测时序分解 - -对评测机留档的每一局，`scripts/build_figure_data.py` 按与 `referee.simulator` -同源的规则逐动作复算，并检查分项之和是否等于响应中的 `virtual_time_s`。 - -### 4.1 问题三演练局 `BBM9-HZG2-77N5-NKKC`（10 源全清） - -| 项 | 数值 | 占比 | -| --- | ---: | ---: | -| 总虚拟时间 | 3134.643 s | 100% | -| 移动（里程 11.9 km） | 2379.6 s | 75.9% | -| 检测与清除动作（119 + 10 次） | 645.0 s | 20.6% | -| 换频（110 次） | 110.0 s | 3.5% | -| 现实耗时 | 1.889 s | — | - -### 4.2 问题四演练局 `GM56-DFXZ-XKDM-XYV7`（12 源，含 3 个定向源） - -| 项 | 数值 | 占比 | -| --- | ---: | ---: | -| 总虚拟时间 | 8703.766 s | 100% | -| 移动（里程 32.4 km） | 6489.8 s | 74.6% | -| 检测与清除动作（338 + 79 次） | 1951.0 s | 22.4% | -| 换频（263 次） | 263.0 s | 3.0% | -| 现实耗时 | 3.397 s | — | - -![实测执行时序分解](figures/timing-measured-q3-10-sources.png) - -### 4.3 结论 - -1. 移动占约 75%，优化重点是路径长度，这解释了七点计划（7 点、6.96 km 巡回） - 优于 49 点网格的原因； -2. 动作占约 21%，检测 5 s 是下限，因此找全后立即退出比复扫更省时间； -3. 现实耗时与成绩无关，本地回环为毫秒级，官方口径取虚拟时间， - 程序预算（1200 s）只用于限制计算耗时。 - -### 4.4 复现命令 - -```bash -# 用评测机留档重画（含真值） -uv run python scripts/build_figure_data.py --only timing-measured-q3-10-sources,\ - replay-webui-q3-omnidirectional,replay-webui-q4-directional - -# 归档新日志，评测机数据被清理后仍可复现插图 -uv run python scripts/archive_replay_logs.py --list -uv run python scripts/archive_replay_logs.py BBM9-HZG2-77N5-NKKC - -# 按官方口径逐微秒复算（接受客户端 JSONL 或评测机 log.jsonl） -uv run python scripts/analyze_run_timing.py referee-data/sessions/BBM9-HZG2-77N5-NKKC/log.jsonl -uv run python scripts/analyze_run_timing.py examples/replay/*.jsonl --json /tmp/timing.json -uv run python scripts/verify_q3_practice.py output/q3/practice/<本局目录> -``` - ---- - -## 5. 时间预算与安全阀 - -| 约束 | 值 | 来源 | 客户端行为 | -| --- | --- | --- | --- | -| 移动速度 | 5 m/s | 题面 | 路径长度决定虚拟时间 | -| 检测 / 换频 | 5 s / 1 s | 题面 | 避免高频扫频 | -| 清除成功 / 未发现 | 5 s / 3 s | 题面 | 盲清代价可控 | -| 测试窗口 | 1500 s（现实） | 题面 | 窗口到期接口关闭 | -| 程序预算 | 1200 s（现实） | 题面 | `/enter` 后本地时钟起算 | -| 退出预留 | 60 s | 工程 | 剩余不足时只允许 `/exit` | -| 虚拟世界限时 | 360000 s | 题面 | 实测最坏为 1e4 量级，未触及 | -| 行为日志上限 | 2 MB | 题面 | 控制请求数 | - -安全阀见 [`algo-3.md`](algo-3.md) §7.2 与 [`algo-4.md`](algo-4.md) §5.2： -预算将尽、连续无新发现或联合信念长时间不收敛时，转入最低成本收尾， -先清除并登记已有发现，再 `/exit`，并写入 `stop_reason`。 diff --git a/docs/最优与基线实现对比.md b/docs/最优与基线实现对比.md deleted file mode 100644 index 908bcdd..0000000 --- a/docs/最优与基线实现对比.md +++ /dev/null @@ -1,131 +0,0 @@ -# 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 -``` diff --git a/docs/第三四问图论与动态规划优化方案.md b/docs/第三四问图论与动态规划优化方案.md deleted file mode 100644 index ad62d79..0000000 --- a/docs/第三四问图论与动态规划优化方案.md +++ /dev/null @@ -1,516 +0,0 @@ -# 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)（问题三当前算法说明） -- 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 -``` - -> 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 | diff --git a/docs/第三四问架构优化与运行提速方案.md b/docs/第三四问架构优化与运行提速方案.md deleted file mode 100644 index 920dbba..0000000 --- a/docs/第三四问架构优化与运行提速方案.md +++ /dev/null @@ -1,595 +0,0 @@ -# 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//coverage/.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///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） | diff --git a/mcm26b-核心代码.ipynb b/mcm26b-核心代码.ipynb index 85c5d15..74a7e30 100644 --- a/mcm26b-核心代码.ipynb +++ b/mcm26b-核心代码.ipynb @@ -2,7 +2,6 @@ "cells": [ { "cell_type": "markdown", - "id": "9f704249", "metadata": {}, "source": [ "# CUMCM 2026 B 题 · 无线电干扰源的快速自动定位与清除\n", @@ -43,12 +42,16 @@ "\n", "各代码单元逐字取自 `src/mcm26b/**`，仅做机械化改写（去包内相对导入、去文件 docstring、\n", "去掉覆盖计划的 JSON 文件读取改为内嵌字面量、去掉未被生产路径调用的函数）。\n", - "本文件可独立运行：只依赖标准库与 `numpy`（可选加速，缺失时自动回退纯 Python）。\n" + "本文件可独立运行：**零第三方依赖**（`numpy` 仅作可选加速，缺失时自动回退纯 Python；\n", + "`requests` 仅在真正发起 HTTP 时才惰性导入）。最后的总自检单元会复算四问的核心结论，\n", + "15 项断言全部通过才算整理成功。\n", + "\n", + "自检覆盖：问1 角域交会有界/直径圆反例、问2 保证接收与后验收缩、问3 七站解析覆盖证书与\n", + "Voronoi 真源保留、问4 定向最小命中集证书与 (位置×朝向) 联合信念保守性。\n" ] }, { "cell_type": "markdown", - "id": "dabe0eb2", "metadata": {}, "source": [ "## 0. 环境与依赖\n" @@ -56,18 +59,9 @@ }, { "cell_type": "code", - "execution_count": 1, - "id": "47afaab3", + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "numpy 加速后端: 启用\n" - ] - } - ], + "outputs": [], "source": [ "import json\n", "import math\n", @@ -92,7 +86,6 @@ }, { "cell_type": "markdown", - "id": "648b9bca", "metadata": {}, "source": [ "# 第一部分 · 公共层\n", @@ -105,8 +98,7 @@ }, { "cell_type": "code", - "execution_count": 2, - "id": "a77d00d5", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -313,8 +305,7 @@ }, { "cell_type": "code", - "execution_count": 3, - "id": "18c1702d", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -334,7 +325,6 @@ }, { "cell_type": "markdown", - "id": "eb41e595", "metadata": {}, "source": [ "## 2. 协议层（`protocol/`）\n", @@ -347,8 +337,7 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "52874146", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -439,7 +428,6 @@ }, { "cell_type": "markdown", - "id": "624c4b89", "metadata": {}, "source": [ "### 2.2 纯计时模型（`timing.py`）\n", @@ -451,8 +439,7 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "bb132d37", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -505,7 +492,6 @@ }, { "cell_type": "markdown", - "id": "0382155e", "metadata": {}, "source": [ "### 2.3 本地状态机（`state.py`）\n", @@ -516,8 +502,7 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "01f62290", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -603,7 +588,6 @@ }, { "cell_type": "markdown", - "id": "fdea1f06", "metadata": {}, "source": [ "### 2.4 传输抽象与会话日志（`transport.py` / `log.py`）\n", @@ -615,8 +599,7 @@ }, { "cell_type": "code", - "execution_count": 7, - "id": "75e9e40b", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -655,8 +638,7 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "ce38cbf0", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -688,7 +670,6 @@ }, { "cell_type": "markdown", - "id": "00433092", "metadata": {}, "source": [ "### 2.5 串行幂等客户端（`client.py`）\n", @@ -702,8 +683,7 @@ }, { "cell_type": "code", - "execution_count": 9, - "id": "9b89cf0b", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1015,7 +995,6 @@ }, { "cell_type": "markdown", - "id": "1b378ab3", "metadata": {}, "source": [ "## 3. 几何内核（`geometry/`）\n", @@ -1035,8 +1014,7 @@ }, { "cell_type": "code", - "execution_count": 10, - "id": "3b895426", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1318,7 +1296,6 @@ }, { "cell_type": "markdown", - "id": "0e26ba66", "metadata": {}, "source": [ "### 3.2 问题二的安全候选盘证书（`planning.py`）\n", @@ -1331,8 +1308,7 @@ }, { "cell_type": "code", - "execution_count": 11, - "id": "96ea4f0f", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1366,7 +1342,6 @@ }, { "cell_type": "markdown", - "id": "15dfa096", "metadata": {}, "source": [ "## 4. 信念层与保证层\n", @@ -1385,8 +1360,7 @@ }, { "cell_type": "code", - "execution_count": 12, - "id": "48977ca8", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1651,7 +1625,6 @@ }, { "cell_type": "markdown", - "id": "d5ebeb37", "metadata": {}, "source": [ "### 4.2 逐频道覆盖账本（`coverage/__init__.py`）\n", @@ -1662,8 +1635,7 @@ }, { "cell_type": "code", - "execution_count": 13, - "id": "83ef8c99", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1707,7 +1679,6 @@ }, { "cell_type": "markdown", - "id": "2797039e", "metadata": {}, "source": [ "### 4.3 覆盖计划与证书（`guarantee/cover.py`）\n", @@ -1727,8 +1698,7 @@ }, { "cell_type": "code", - "execution_count": 14, - "id": "9fe42d17", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2092,7 +2062,6 @@ }, { "cell_type": "markdown", - "id": "b6709645", "metadata": {}, "source": [ "### 4.4 第四问最小命中集（内嵌计划）\n", @@ -2106,8 +2075,7 @@ }, { "cell_type": "code", - "execution_count": 15, - "id": "2892e247", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2177,8 +2145,7 @@ }, { "cell_type": "code", - "execution_count": 16, - "id": "51d5ac79", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2275,7 +2242,6 @@ }, { "cell_type": "markdown", - "id": "5fdc48f7", "metadata": {}, "source": [ "### 4.5 第三问解析七站计划（`planner/seven.py` 覆盖部分）\n", @@ -2287,8 +2253,7 @@ }, { "cell_type": "code", - "execution_count": 17, - "id": "62b4aec6", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2319,7 +2284,6 @@ }, { "cell_type": "markdown", - "id": "d0db208e", "metadata": {}, "source": [ "### 4.6 网格幸存集粒子滤波（`particles.py`）\n", @@ -2338,8 +2302,7 @@ }, { "cell_type": "code", - "execution_count": 18, - "id": "63499de6", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2769,7 +2732,6 @@ }, { "cell_type": "markdown", - "id": "7cfa64a2", "metadata": {}, "source": [ "### 4.7 (位置 × 朝向) 联合可行集（`belief/joint.py`）\n", @@ -2791,8 +2753,7 @@ }, { "cell_type": "code", - "execution_count": 19, - "id": "3e8eb944", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3128,7 +3089,6 @@ }, { "cell_type": "markdown", - "id": "63720d6b", "metadata": {}, "source": [ "## 5. 调度层：开放路径排序（`routing.py`）\n", @@ -3140,8 +3100,7 @@ }, { "cell_type": "code", - "execution_count": 20, - "id": "f2e043c8", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3218,7 +3177,6 @@ }, { "cell_type": "markdown", - "id": "9325502c", "metadata": {}, "source": [ "# 第二部分 · 策略层（`planner/`）\n", @@ -3234,8 +3192,7 @@ }, { "cell_type": "code", - "execution_count": 21, - "id": "f4e91fbc", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3388,7 +3345,6 @@ }, { "cell_type": "markdown", - "id": "76749d32", "metadata": {}, "source": [ "### 6.1 全向闭环骨架（`OmnidirectionalLoop`）\n", @@ -3403,8 +3359,7 @@ }, { "cell_type": "code", - "execution_count": 22, - "id": "22efcd41", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3841,7 +3796,6 @@ }, { "cell_type": "markdown", - "id": "1515ae48", "metadata": {}, "source": [ "### 6.2 第四问定向扩展（`DirectionalExtension`）\n", @@ -3857,8 +3811,7 @@ }, { "cell_type": "code", - "execution_count": 23, - "id": "c2acad85", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -4269,7 +4222,6 @@ }, { "cell_type": "markdown", - "id": "dfc8e5b3", "metadata": {}, "source": [ "### 6.3 第三问默认策略：贝叶斯网格滤波闭环（`planner/bayes_loop.py`）\n", @@ -4289,8 +4241,7 @@ }, { "cell_type": "code", - "execution_count": 24, - "id": "5934cb40", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -5083,7 +5034,6 @@ }, { "cell_type": "markdown", - "id": "52d71ea9", "metadata": {}, "source": [ "### 6.4 第三问对照策略：七站批量扫频（`planner/seven.py`）\n", @@ -5094,8 +5044,7 @@ }, { "cell_type": "code", - "execution_count": 25, - "id": "0c911581", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -5145,7 +5094,6 @@ }, { "cell_type": "markdown", - "id": "d9df020d", "metadata": {}, "source": [ "# 第三部分 · 逐问算例与自检\n", @@ -5161,23 +5109,9 @@ }, { "cell_type": "code", - "execution_count": 26, - "id": "4075e650", + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "== 三点交会 ==\n", - "状态 : polygon 顶点数 5\n", - "直径 : 26.986 m\n", - "直径圆覆盖区域 : False 最大超出 3.964 m\n", - "最小包围圆 : 圆心 (81.874, 98.042) 半径 13.943 m\n", - "可原地清除 : True\n" - ] - } - ], + "outputs": [], "source": [ "Q1_EXAMPLES = {\n", " \"normal\": { # 三点交会：区域有界、MEC 远小于 20 m\n", @@ -5221,29 +5155,9 @@ }, { "cell_type": "code", - "execution_count": 27, - "id": "efa9f69a", + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "== 直径圆反例（等边三角形） ==\n", - "定位区域顶点： [[-0.0, 0.0], [40.0, 0.0], [20.0, 34.64]]\n", - "直径 40.000 m（最远顶点对 [[-0.0, 0.0], [40.0, 0.0]]）\n", - "直径圆：圆心 [20.0, 0.0] 半径 20.000 m\n", - "圆外顶点下标 [2]（见证顶点 [20.0, 34.64]），最大超出 14.641 m\n", - "直径圆覆盖整个定位区域： False\n", - "最小包围圆半径 23.094 m（>= 直径/2 = 20.0 m，才是真正的最小覆盖半径）\n", - "\n", - "== 近共线细长区域 ==\n", - "状态 polygon，直径 3085.3 m，最小包围圆半径 1542.6 m，可原地清除 False\n", - "\n", - "无观测 -> unbounded | 单条示向度 -> unbounded （衰退方向 [0.8971423583981722, 0.44174154068614097]）\n" - ] - } - ], + "outputs": [], "source": [ "# 反例：直径圆的圆心是\"最远顶点对\"的中点，第二个最远顶点可能落在圆外。\n", "tri = solve_q1(\"triangle\")\n", @@ -5283,20 +5197,9 @@ }, { "cell_type": "code", - "execution_count": 28, - "id": "6145af53", + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "舍入裕量 0.000°：直径 26.986 m，MEC 半径 13.943 m\n", - "舍入裕量 0.005°：直径 27.143 m，MEC 半径 14.019 m\n", - "放宽 0.005° 使 MEC 半径增加 0.076 m —— 因此库默认带该裕量，否则合法真源可能被角域裁掉\n" - ] - } - ], + "outputs": [], "source": [ "# 示向度二位小数的舍入敏感性：误差半角 1.0° vs 1.005°（含 0.005° 舍入裕量）\n", "tight = solve_q1(\"normal\", 0.0)\n", @@ -5314,7 +5217,6 @@ }, { "cell_type": "markdown", - "id": "442dbb20", "metadata": {}, "source": [ "## 8. 问题二：第二检测点的选择\n", @@ -5329,24 +5231,9 @@ }, { "cell_type": "code", - "execution_count": 29, - "id": "abebb69d", + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "首次角域扇形的包围圆：圆心 (450.1, 600.1) 半径 750.1 m\n", - "保证接收盘 ：圆心 (450.1, 600.1) 半径 249.9 m\n", - "\n", - "第二检测点 ( 691.65, 535.27)\n", - "最坏后验包围半径 91.42 m（当前候选集 MEC 半径 750.1 m，必须更小才叫有信息）\n", - "移动耗时 174.9 s（次指标：同样精度下取更近的点）\n", - "依据： 安全盘 B(centre,250m) 内 37 候选；粗采样最坏后验 91.4m，1/4 间距加密复核 91.4m\n" - ] - } - ], + "outputs": [], "source": [ "class _StateStub:\n", " \"\"\"最小状态替身：只提供选点器需要的当前位置与当前测向频道。\"\"\"\n", @@ -5399,24 +5286,9 @@ }, { "cell_type": "code", - "execution_count": 30, - "id": "226d7cca", + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "候选集边界采样点 3056 个\n", - "真源到第二点的最远距离 874.58 m（<= 1000 m 才保证收到信号）\n", - "对候选集最坏点、最不利读数的后验包围半径 91.42 m\n", - "第二点相对候选集包围圆的保证接收余量：0.0 m\n", - "追加第 2 个测点 ( 400.97, 23.81)：最坏后验半径 10.37 m\n", - "\n", - "结论：第二点负责\"保证接收 + 最大化交会角\"，后验精度由后续测点继续压缩；20 m 清除圈通常在第三点后达成。\n" - ] - } - ], + "outputs": [], "source": [ "# 用同一套观测语义复核这个点：无论真源落在候选集何处，第二点都必收到信号。\n", "# 候选集边界按 1 m 加密采样做最坏情形检验。\n", @@ -5454,26 +5326,9 @@ }, { "cell_type": "code", - "execution_count": 31, - "id": "504f0aa8", + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "首次示向度 第二点位置 最坏后验半径 移动耗时\n", - " 0.0° ( 814.85, 241.29) 93.20 m 465.5 s\n", - " 45.0° ( 465.82, 771.76) 90.96 m 422.4 s\n", - " 90.0° ( -241.29, 814.85) 93.20 m 299.9 s\n", - " 135.0° ( -771.76, 465.82) 90.96 m 172.9 s\n", - " 180.0° ( -814.85, 241.29) 93.20 m 145.3 s\n", - " 225.0° ( -771.76, -465.82) 90.96 m 172.9 s\n", - " 270.0° ( -241.29, -814.85) 93.20 m 299.9 s\n", - " 315.0° ( 465.82, -771.76) 90.96 m 422.4 s\n" - ] - } - ], + "outputs": [], "source": [ "# 对照：候选集在不同首次示向度下的选择结果（目标函数与移动代价的权衡）\n", "print(\"首次示向度 第二点位置 最坏后验半径 移动耗时\")\n", @@ -5486,7 +5341,6 @@ }, { "cell_type": "markdown", - "id": "b7a898d2", "metadata": {}, "source": [ "## 9. 问题三：全向源闭环\n", @@ -5505,20 +5359,9 @@ }, { "cell_type": "code", - "execution_count": 32, - "id": "8d78f6b8", + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "七站环半径 1200.0 m，最近站最远距离解析上界 968.9 m（阈值 1000 m）\n", - "站点： [(0.0, 0.0), (1200.0, 0.0), (600.0, 1039.2), (-600.0, 1039.2), (-1200.0, 0.0), (-600.0, -1039.2), (600.0, -1039.2)]\n", - "粗采样复核：目标点 113 个，未覆盖 0 个，最坏命中 968.9 m，最近邻巡回 7.2 km\n" - ] - } - ], + "outputs": [], "source": [ "def observe_omni(truth, station, r_eff, bearing_noise_deg=0.0):\n", " \"\"\"全向源的观测语义（合成用）：返回 (result, svd_deg)。\"\"\"\n", @@ -5563,25 +5406,9 @@ }, { "cell_type": "code", - "execution_count": 33, - "id": "6460920f", + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "第 1 次读数 no_signal 幸存格 34035 格心距真源 8.587 m（裕量 10.359 m） MEC 半径 1810.324 m\n", - "第 2 次读数 direction 幸存格 321 格心距真源 8.587 m（裕量 10.359 m） MEC 半径 687.185 m\n", - "第 3 次读数 direction 幸存格 15 格心距真源 8.587 m（裕量 10.359 m） MEC 半径 39.446 m\n", - "第 4 次读数 direction 幸存格 6 格心距真源 8.587 m（裕量 10.359 m） MEC 半径 20.718 m\n", - "\n", - "初始幸存格 47979；真源始终落在某个幸存格内：True\n", - "Voronoi 裕量 m = 10.359 m（格心到格内任一点的最大距离）\n", - "远端测点 (4000.0, 4000.0) 是否必 no_signal：True\n" - ] - } - ], + "outputs": [], "source": [ "# ---- 9.2 网格幸存集的真源保留性（Voronoi 裕量定理的随机检验） ----------------\n", "Random(20260913).seed(20260913)\n", @@ -5619,24 +5446,9 @@ }, { "cell_type": "code", - "execution_count": 34, - "id": "d37c4b2c", + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "幸存集 MEC：圆心 (306.57, 409.12) 半径 20.718 m\n", - "网格清除证书阈值 9.141 m（= 20 − 裕量 10.359 − 工程余量 0.5）→ 可清除：False\n", - "near 读数后的凸证书：半径 5.001 m ≤ 19.499 m → True\n", - "沿来向偏移后清除点距真源 11.234 m <= 半径 20.718 + 偏移 0.000 = 20.718 m < 20 m\n", - "\n", - "七站全部 no_signal 后幸存格 0 → 该频道判缺席（无需等覆盖账本跑完）\n", - "只测其中 6 站仍有 3339 个幸存格 → 七站是刚好够用的最小解析覆盖\n" - ] - } - ], + "outputs": [], "source": [ "# ---- 9.3 证书与缺席证明 -----------------------------------------------------\n", "center, radius = pf.mec()\n", @@ -5679,22 +5491,9 @@ }, { "cell_type": "code", - "execution_count": 35, - "id": "6fc5cf54", + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "第三问策略：覆盖计划 seven-r1200.0（7 站），网格间距 14.65 m\n", - "任务表：8 项（站点扫频 7 / 定位 1 / 清除 0）\n", - "排序后前 5 项： [('station', None), ('locate', 9), ('station', None), ('station', None), ('station', None)]\n", - "2-opt 前后路径耗时 2529.66 s -> 2246.77 s\n", - "信息增益选点：( 427.81, 413.89)\n" - ] - } - ], + "outputs": [], "source": [ "# ---- 9.4 统一任务表与滚动排序（信息增益选点 + 2-opt） ------------------------\n", "class _LogStub:\n", @@ -5705,7 +5504,7 @@ " return []\n", "\n", "\n", - "client3 = SimulatorClient(\"2026001\", config=CONFIG_Q3, transport=None, log=_LogStub())\n", + "client3 = SimulatorClient(\"OFFLINE\", config=CONFIG_Q3, transport=None, log=_LogStub())\n", "client3.state.position = (0.0, 0.0)\n", "loop3 = BayesLoop(client3, CONFIG_Q3)\n", "print(\"第三问策略：覆盖计划 %s（%d 站），网格间距 %.2f m\"\n", @@ -5732,24 +5531,9 @@ }, { "cell_type": "code", - "execution_count": 36, - "id": "5a06d133", + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "第四问覆盖计划 q4_mincover_v2：31 点\n", - "证书采样分辨率 15 m / 72 档，目标 (p,h) 3256200 个，缺口 0，最坏命中 990.051 m\n", - "最近邻巡回 23.9 km（虚拟移动 4776.8 s）→ 发现阶段的移动代价下界\n", - "设计裕量：r_design 965 m、前向角裕量 5.0°；校核用 r_eff 1000 m、角裕量 0.0°\n", - "\n", - "对照 q4_mincover_v1：同样 31 点，但证书采样只有 75 m / 24 档，细网格复检有 125 个缺口\n", - "→ 采样分辨率必须与证书一致，否则'删掉一个关键点仍看似覆盖'（校核退化为装饰）\n" - ] - } - ], + "outputs": [], "source": [ "# ---- 9.5 覆盖计划证书：离线全分辨率生成，运行期按同分辨率复核 ----------------------------------\n", "plan_v2 = plan_instance(\"q4_mincover_v2\", verify=False)\n", @@ -5774,7 +5558,6 @@ }, { "cell_type": "markdown", - "id": "7b3fdca8", "metadata": {}, "source": [ "## 10. 问题四：含定向源的闭环\n", @@ -5794,19 +5577,9 @@ }, { "cell_type": "code", - "execution_count": 37, - "id": "96d693ce", + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "最小命中集 31 点，粗采样复核 (p,h) 1356 个：未覆盖 0，最坏命中 922.0 m\n", - "对照 700 m 方格 49 点：同样未覆盖 0，巡回 36.4 km vs 23.9 km\n" - ] - } - ], + "outputs": [], "source": [ "# ---- 10.1 定向覆盖最小命中集的覆盖语义 --------------------------------------\n", "plan_q4 = plan_instance(\"q4_mincover_v2\", verify=False)\n", @@ -5825,26 +5598,9 @@ }, { "cell_type": "code", - "execution_count": 38, - "id": "c1860693", + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "测点方位 115.0° 距离 900 m → direction（预期 direction）幸存格 339 真源格偏移 6.088 m（裕量 10.359 m）朝向档 13 个 含真源朝向档：True\n", - "测点方位 285.0° 距离 700 m → direction（预期 direction）幸存格 63 真源格偏移 6.088 m（裕量 10.359 m）朝向档 2 个 含真源朝向档：True\n", - "测点方位 200.0° 距离 600 m → direction（预期 direction）幸存格 9 真源格偏移 6.088 m（裕量 10.359 m）朝向档 2 个 含真源朝向档：True\n", - "测点方位 230.0° 距离 1050 m → direction（预期 direction）幸存格 9 真源格偏移 6.088 m（裕量 10.359 m）朝向档 2 个 含真源朝向档：True\n", - "测点方位 140.0° 距离 400 m → direction（预期 direction）幸存格 7 真源格偏移 6.088 m（裕量 10.359 m）朝向档 2 个 含真源朝向档：True\n", - "测点方位 330.0° 距离 800 m → no_signal（预期 no_signal）幸存格 7 真源格偏移 6.088 m（裕量 10.359 m）朝向档 2 个 含真源朝向档：True\n", - "测点方位 200.0° 距离 1250 m → no_signal（预期 no_signal）幸存格 7 真源格偏移 6.088 m（裕量 10.359 m）朝向档 2 个 含真源朝向档：True\n", - "\n", - "联合集规模 7 格，抽样平均朝向档 2.00 / 24\n" - ] - } - ], + "outputs": [], "source": [ "# ---- 10.2 (位置 × 朝向) 联合可行集的保守性随机检验 --------------------------\n", "truth4 = (420.0, -260.0) # 真源位置\n", @@ -5893,27 +5649,9 @@ }, { "cell_type": "code", - "execution_count": 39, - "id": "19dea4e3", + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "探点可见性概率（越高越可能在该点收到信号）：\n", - " ( -2000.0, 0.0) prob_detect = 0.0000\n", - " ( 0.0, 0.0) prob_detect = 0.0833\n", - " ( 420.0, -260.0) prob_detect = 0.0417\n", - " ( 1500.0, -900.0) prob_detect = 0.0000\n", - " ( -331.8, -533.6) prob_detect = 0.0833\n", - "\n", - "候选集外包框 820 m × 820 m，20 m 网格覆盖 171 格（外包框枚举上界 1936）\n", - "格内任意点到最近格心 <= 14.14 m < 20 m → 逐格 /clear 至少命中一次\n", - "外包框内落在候选集中的随机点，被某个格心 20 m 覆盖：96 个\n" - ] - } - ], + "outputs": [], "source": [ "# ---- 10.3 可见性选点取代固定阶梯；后备清除兜底 ------------------------------\n", "print(\"探点可见性概率（越高越可能在该点收到信号）：\")\n", @@ -5952,26 +5690,12 @@ }, { "cell_type": "code", - "execution_count": 40, - "id": "9a7b1181", + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "第四问策略：覆盖计划 q4_mincover_v2（31 点），联合信念开关 True，探点口径 nearest_visible\n", - "后备清除硬顶 400 格，单轮遮挡探点预算 4 次，定位步数上限 8\n", - "\n", - "频道 3 候选集半径 750.1 m，可清除：False\n", - "保证接收盘半径 249.9 m（盘内必收到信号 -> 静默即遮挡）\n", - "被遮挡后梯子升级到的探点：( 71.58, -134.29)\n" - ] - } - ], + "outputs": [], "source": [ "# ---- 10.4 第四问闭环装配（不连模拟器：只检查策略对象初始化） ----------------\n", - "client4 = SimulatorClient(\"2026001\", config=CONFIG, transport=None, log=_LogStub())\n", + "client4 = SimulatorClient(\"OFFLINE\", config=CONFIG, transport=None, log=_LogStub())\n", "client4.state.position = (0.0, 0.0)\n", "loop4 = DirectionalExtension(client4, CONFIG)\n", "print(\"第四问策略：覆盖计划 %s（%d 点），联合信念开关 %s，探点口径 %s\"\n", @@ -5998,7 +5722,6 @@ }, { "cell_type": "markdown", - "id": "18a6a561", "metadata": {}, "source": [ "# 第四部分 · 总览与自检\n", @@ -6036,8 +5759,8 @@ "\n", "```bash\n", "# 对接官方模拟器（演练/正式）\n", - "python -m mcm26b.run --robot-id <队号> --problem 3 # 第三问：bayes 闭环\n", - "python -m mcm26b.run --robot-id <队号> --problem 4 # 第四问：directional 闭环\n", + "python -m mcm26b.cli run --robot-id <队号> --problem 3 # 第三问：bayes 闭环\n", + "python -m mcm26b.cli run --robot-id <队号> --problem 4 # 第四问：directional 闭环\n", "```\n", "\n", "成绩口径是**虚拟时间**：`avg_locate_clear_time_s = 全程虚拟时长 / 已清除源数`，\n", @@ -6046,35 +5769,9 @@ }, { "cell_type": "code", - "execution_count": 41, - "id": "25656b6b", + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "四问核心自检：\n", - " [通过] 问1 角域交会有界且直径有限\n", - " [通过] 问1 三点交会可原地清除（MEC ≤ 20 m）\n", - " [通过] 问1 直径圆反例被识别（covers=False）\n", - " [通过] 问2 第二点在保证接收盘内\n", - " [通过] 问2 追加三点后最坏后验半径 ≤ 20 m\n", - " [通过] 问3 七站解析覆盖上界 < 1000 m\n", - " [通过] 问3 七站计划粗采样无缺口\n", - " [通过] 问3 粒子滤波保留真源\n", - " [通过] 问3 空幸存集给出缺席证明\n", - " [通过] 问3 2-opt 不劣于原任务序\n", - " [通过] 问4 最小命中集 31 点、证书 0 缺口\n", - " [通过] 问4 粗采样复核定向覆盖无缺口\n", - " [通过] 问4 联合信念保留真源朝向档\n", - " [通过] 问4 后备清除格数在硬顶内\n", - " [通过] 问4 闭环装配成功（定向覆盖计划已加载）\n", - "\n", - "全部 15 项通过。\n" - ] - } - ], + "outputs": [], "source": [ "# ---- 总自检：把四问的核心结论再跑一遍，全部通过才算整理成功 ----------------\n", "checks = []\n", @@ -6121,21 +5818,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { + "name": "python", + "version": "3.10", + "mimetype": "text/x-python", + "file_extension": ".py", + "pygments_lexer": "ipython3", + "nbconvert_exporter": "python", "codemirror_mode": { "name": "ipython", "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.9" + } } }, "nbformat": 4, diff --git a/project/q3/optimal.py b/project/q3/optimal.py index b2c421c..d54f7bb 100644 --- a/project/q3/optimal.py +++ b/project/q3/optimal.py @@ -3,7 +3,7 @@ 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）。 +自建消融相对 SevenBatch 再降约 30%（论文“问题三”一节）。 """ from __future__ import annotations diff --git a/referee-data/rejected-sessions/2HLE-WUZW-T92B-V4CE/log.jsonl b/referee-data/rejected-sessions/2HLE-WUZW-T92B-V4CE/log.jsonl index 20ac1e6..8895457 100644 --- a/referee-data/rejected-sessions/2HLE-WUZW-T92B-V4CE/log.jsonl +++ b/referee-data/rejected-sessions/2HLE-WUZW-T92B-V4CE/log.jsonl @@ -1,272 +1,272 @@ -{"event": "session_start", "case_code": "2HLE-WUZW-T92B-V4CE", "module": "q4_practice", "robot_id": "2026001", "seed": 405, "countdown_s": 0.2, "window_s": 1500.0, "max_real_duration_s": 1200.0, "truth": [{"channel": 2, "x": -1255.735827244441, "y": -355.50875631000196, "r_eff_m": 1144.4749917454856, "directional": true, "heading_deg": 151.5659530032374}, {"channel": 6, "x": 148.56636931812977, "y": -709.995538532902, "r_eff_m": 1352.33102595724, "directional": true, "heading_deg": 150.09808422750757}, {"channel": 15, "x": -1017.4759833148806, "y": -76.89910877190206, "r_eff_m": 1272.923119186233, "directional": true, "heading_deg": 142.34520142834873}, {"channel": 4, "x": 1238.9673950266638, "y": -138.0044288747182, "r_eff_m": 1197.697121322017, "directional": true, "heading_deg": 108.79558673474986}, {"channel": 17, "x": 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9239.519587, "measure_result": "no_signal"}} -{"event": "action", "seq": 439, "wall_ts_ms": 1789189934426, "method": "POST", "path": "/exit", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "593f8e5ecda7-0439"}, "response": {"accepted": true, "real_timestamp_ms": 1789189934426, "virtual_time_s": 9239.519587, "exit_reason": "user_exit"}} -{"event": "session_end", "reason": "user_exit", "virtual_time_s": 9239.519587, "real_elapsed_s": 8.559283, "request_count": 439, "cleared_count": 10} diff --git a/referee-data/superseded-sessions/ZBUC-YW26-J7BH-V2WP/meta.json b/referee-data/superseded-sessions/ZBUC-YW26-J7BH-V2WP/meta.json deleted file mode 100644 index 53d597d..0000000 --- a/referee-data/superseded-sessions/ZBUC-YW26-J7BH-V2WP/meta.json +++ /dev/null @@ -1,88 +0,0 @@ -{ - "case_code": "ZBUC-YW26-J7BH-V2WP", - "module": "q4_practice", - "robot_id": "1", - "seed": 863642061, - "truth": [ - { - "channel": 19, - "x": 1411.2207258221881, - "y": -917.895587407795, - "r_eff_m": 1091.9794913249818, - "directional": true, - "heading_deg": 153.47630212371047 - }, - { - "channel": 8, - "x": 1498.3737761812351, - "y": 930.3904917291879, - "r_eff_m": 1272.8873716121275, - "directional": true, - "heading_deg": 31.83753169216975 - }, - { - "channel": 20, - "x": 398.74127594294674, - "y": 289.06782254078047, - "r_eff_m": 1289.352680954292, - "directional": true, - "heading_deg": 160.24361954063997 - }, - { - "channel": 18, - "x": -600.2602564102094, - "y": 1150.9469673246213, - "r_eff_m": 1418.9470346878875, - "directional": false, - "heading_deg": 0.0 - }, - { - "channel": 10, - "x": -641.1344471816941, - "y": -1528.5314521440819, - "r_eff_m": 1343.871107386684, - "directional": false, - "heading_deg": 0.0 - }, - { - "channel": 4, - "x": -673.4635271867537, - "y": 1045.7718411070223, - "r_eff_m": 1169.0688552732329, - "directional": false, - "heading_deg": 0.0 - }, - { - "channel": 1, - "x": -1236.5632089871194, - "y": -490.4229776016893, - "r_eff_m": 1484.249955482677, - "directional": false, - "heading_deg": 0.0 - }, - { - "channel": 16, - "x": -1426.9540787395435, - "y": -176.611350029181, - "r_eff_m": 1081.1607993545874, - "directional": false, - "heading_deg": 0.0 - }, - { - "channel": 13, - "x": 1073.330745196976, - "y": -1066.1914143107938, - "r_eff_m": 1158.1511964548743, - "directional": false, - "heading_deg": 0.0 - }, - { - "channel": 7, - "x": -1592.8182208096703, - "y": 396.44615965418825, - "r_eff_m": 1087.696159659708, - "directional": false, - "heading_deg": 0.0 - } - ] -} \ No newline at end of file diff --git a/referee/README.md b/referee/README.md index d2abde4..e055254 100644 --- a/referee/README.md +++ b/referee/README.md @@ -16,8 +16,8 @@ uv run pytest referee/tests # 本包测试 - **回放器**（"回放"Tab）：按案例加载存档回放，也可**手动选定 JSONL 文件**（点击 选择或拖拽导入，纯前端解析；兼容评测机行为日志与参赛队客户端会话日志两种格式）， 支持播放/暂停、逐步±、倍速快进、缩放/平移、进度条定位； -- 机器狗程序照常对接 `http://127.0.0.1:2026`（如 `uv run mcm26b run --robot-id 2026001 --base-url http://127.0.0.1:2026`， - 或一键 `uv run mcm26b run --robot-id 2026001 --local-judge`）。 +- 机器狗程序照常对接 `http://127.0.0.1:2026`（如 `uv run mcm26b run --robot-id TEAM-0001 --base-url http://127.0.0.1:2026`， + 或一键 `uv run mcm26b run --robot-id TEAM-0001 --local-judge`）。 ## 包结构 diff --git a/referee/src/referee/rules.py b/referee/src/referee/rules.py index ca6a791..49d604e 100644 --- a/referee/src/referee/rules.py +++ b/referee/src/referee/rules.py @@ -1,7 +1,7 @@ """评测机服务端规则表：四条指令的字段表、结果码与 HTTP 语义。 依据附件2《模拟器通信接口说明及编程指南》第 5-9 节，全部落成可执行数据； -人类可读版本见 docs/协议规则表与状态机.md（参赛队仓库）。 +人类可读版本见 论文“计时模型与会话状态机”一节（参赛队仓库）。 本表是评测机的权威校验依据，与参赛队客户端侧的字段表是两份独立拷贝， 由参赛队仓库的解耦守卫测试保证不漂移。 """ diff --git a/referee/src/referee/simulator.py b/referee/src/referee/simulator.py index 1c1db39..708340c 100644 --- a/referee/src/referee/simulator.py +++ b/referee/src/referee/simulator.py @@ -49,7 +49,7 @@ class LocalSimulator: clock 为单调秒时钟，wall_clock 为 Unix 秒时钟；窗口起点默认为接口开放时刻。 """ - def __init__(self, sources: list[Source], *, robot_id: str = "2026001", + def __init__(self, sources: list[Source], *, robot_id: str, spec: Spec = DEFAULT_SPEC, error_half_deg: float | None = None, error_seed: int = 20260910, clock=monotonic, wall_clock=time, diff --git a/referee/src/referee/static/index.html b/referee/src/referee/static/index.html index 855645c..481b1d2 100644 --- a/referee/src/referee/static/index.html +++ b/referee/src/referee/static/index.html @@ -24,7 +24,7 @@

选择测试模块

- + diff --git a/referee/tests/js/replay_harness.cjs b/referee/tests/js/replay_harness.cjs index 374eed3..7a25e26 100644 --- a/referee/tests/js/replay_harness.cjs +++ b/referee/tests/js/replay_harness.cjs @@ -11,11 +11,11 @@ const REPO = process.env.REPO; if (!REPO) throw new Error("REPO env required"); // ---- 构造一局日志：复刻"原点换频道"致病模式（第一步原地检测且换频道） ---- -function base(id) { return { arena_id: "default", robot_id: "2026001", request_id: id }; } +function base(id) { return { arena_id: "default", robot_id: "TEAM-0001", request_id: id }; } const sources = [{ channel: 4, x: 100, y: 0, r_eff_m: 1200, directional: false, heading_deg: 0 }]; const refereeLog = [ JSON.stringify({ event: "session_start", case_code: "TEST-CASE-0001", module: "q3_practice", - robot_id: "2026001", seed: 9, truth: sources }), + robot_id: "TEAM-0001", seed: 9, truth: sources }), JSON.stringify({ event: "action", seq: 1, method: "POST", path: "/enter", status: 200, request: base("e1"), response: { accepted: true, virtual_time_s: 0, remaining_real_duration_s: 1200 } }), @@ -99,10 +99,10 @@ const replayEvents = refereeLog.split("\n").map((l) => JSON.parse(l)); globalThis.fetch = async (url) => { const json = async () => { if (url.startsWith("/ui/api/sessions")) - return { sessions: [{ case_code: "TEST-CASE-0001", module: "q3_practice", robot_id: "2026001" }] }; + return { sessions: [{ case_code: "TEST-CASE-0001", module: "q3_practice", robot_id: "TEAM-0001" }] }; if (url.startsWith("/ui/api/replay")) return { case_code: "TEST-CASE-0001", module: "q3_practice", - meta: { module: "q3_practice", robot_id: "2026001", truth: sources }, events: replayEvents }; + meta: { module: "q3_practice", robot_id: "TEAM-0001", truth: sources }, events: replayEvents }; if (url.startsWith("/ui/api/state")) return { phase: "idle", action_port: 2026, display_limit: 1000, modules: {}, attempts: {} }; throw new Error("unexpected fetch " + url); diff --git a/referee/tests/test_http_server.py b/referee/tests/test_http_server.py index 5486b85..71515a2 100644 --- a/referee/tests/test_http_server.py +++ b/referee/tests/test_http_server.py @@ -46,12 +46,12 @@ def post(port, path, obj=None, *, headers=None, method="POST", raw=None): def payload(request_id="e", **extra): - return dict(arena_id="default", robot_id="2026001", request_id=request_id, **extra) + return dict(arena_id="default", robot_id="TEAM-0001", request_id=request_id, **extra) def start_open_session(local_referee, **opts): opts.setdefault("countdown_s", 0.05) - session = local_referee.start("q3_practice", "2026001", **opts) + session = local_referee.start("q3_practice", "TEAM-0001", **opts) assert wait_until(lambda: session.phase == "open") return session @@ -67,7 +67,7 @@ def test_binding_is_loopback_only(local_referee): def test_interface_refuses_connections_outside_window(local_referee): - session = local_referee.start("q3_practice", "2026001", countdown_s=5) + session = local_referee.start("q3_practice", "TEAM-0001", countdown_s=5) server = local_referee._open_interface.__self__ assert server.is_open is False # 倒计时期间未监听 assert wait_until(lambda: session.phase == "open", timeout=6) diff --git a/referee/tests/test_interface_boundary.py b/referee/tests/test_interface_boundary.py index a2d4a43..2d5687b 100644 --- a/referee/tests/test_interface_boundary.py +++ b/referee/tests/test_interface_boundary.py @@ -12,7 +12,7 @@ from referee.interface import handle_raw def payload(request_id="e", **extra): - return dict(arena_id="default", robot_id="2026001", request_id=request_id, **extra) + return dict(arena_id="default", robot_id="TEAM-0001", request_id=request_id, **extra) def post(sim, path, body=None, headers=None, method="POST", target=None): @@ -22,7 +22,7 @@ def post(sim, path, body=None, headers=None, method="POST", target=None): def test_raw_json_format_errors(): - sim = LocalSimulator([]) + sim = LocalSimulator([], robot_id="TEAM-0001") cases = [ (b'{"request_id":"a","request_id":"b"}', 400), # 重复键 (b'{"position":{"x":1,"x":2}}', 400), # 嵌套重复键 @@ -41,7 +41,7 @@ def test_raw_json_format_errors(): def test_raw_size_and_depth_boundaries_are_inclusive(): - sim = LocalSimulator([]) + sim = LocalSimulator([], robot_id="TEAM-0001") headers = [("Content-Type", "application/json")] deep = b'{"extra":' + b'[' * 15 + b'0' + b']' * 15 + b'}' # 深度 16 合法 body = json.dumps({**payload(), **json.loads(deep)}).encode() @@ -67,21 +67,21 @@ def test_raw_size_and_depth_boundaries_are_inclusive(): ("POST", "/enter", [("content-type", 'application/json;charset="utf-8"')], 200), ]) def test_http_boundary(method, path, headers, status): - sim = LocalSimulator([]) + sim = LocalSimulator([], robot_id="TEAM-0001") raw = json.dumps(payload()).encode() result = handle_raw(sim, method, path, headers or [("Content-Type", "application/json")], raw) assert result.status == status def test_closed_simulator_raises_connection_level_error(): - sim = LocalSimulator([], clock=lambda: 9999, window_started_at=0) + sim = LocalSimulator([], clock=lambda: 9999, window_started_at=0, robot_id="TEAM-0001") with pytest.raises(SimulatorClosed): handle_raw(sim, "POST", "/enter", [("Content-Type", "application/json")], json.dumps(payload()).encode()) def test_on_request_callback_receives_parsed_payload(): - sim = LocalSimulator([]) + sim = LocalSimulator([], robot_id="TEAM-0001") seen = [] raw = json.dumps(payload("enter-1")).encode() handle_raw(sim, "POST", "/enter", [("Content-Type", "application/json")], raw, diff --git a/referee/tests/test_session_flow.py b/referee/tests/test_session_flow.py index ea47f2e..dabec17 100644 --- a/referee/tests/test_session_flow.py +++ b/referee/tests/test_session_flow.py @@ -28,7 +28,7 @@ def test_countdown_then_open_then_window_timeout(tmp_path): referee = Referee(action_port=0, data_dir=tmp_path) opened = [] referee.set_interface_callbacks(lambda s: opened.append(1), lambda s: None) - session = referee.start("q3_practice", "2026001", countdown_s=0.05, window_s=0.3) + session = referee.start("q3_practice", "TEAM-0001", countdown_s=0.05, window_s=0.3) assert session.phase == "countdown" assert wait_until(lambda: session.phase == "open") assert opened == [1] @@ -39,11 +39,11 @@ def test_countdown_then_open_then_window_timeout(tmp_path): def test_abort_during_countdown_and_running(tmp_path): referee = Referee(action_port=0, data_dir=tmp_path) - session = referee.start("q3_practice", "2026001", countdown_s=5) + session = referee.start("q3_practice", "TEAM-0001", countdown_s=5) assert referee.abort() is True assert session.phase == "finished" and session.end_reason == "manual_abort" assert referee.abort() is False # 已结束，不能重复中止 - session = referee.start("q3_practice", "2026001", countdown_s=0.05, window_s=30) + session = referee.start("q3_practice", "TEAM-0001", countdown_s=0.05, window_s=30) assert wait_until(lambda: session.phase == "open") assert referee.abort() is True assert session.end_reason == "manual_abort" @@ -51,11 +51,11 @@ def test_abort_during_countdown_and_running(tmp_path): def test_cannot_start_while_running_and_restart_after_finish(tmp_path): referee = Referee(action_port=0, data_dir=tmp_path) - first = referee.start("q3_practice", "2026001", countdown_s=5) + first = referee.start("q3_practice", "TEAM-0001", countdown_s=5) with pytest.raises(RefereeError): - referee.start("q3_practice", "2026001", countdown_s=0.05) + referee.start("q3_practice", "TEAM-0001", countdown_s=0.05) assert referee.abort() - second = referee.start("q3_practice", "2026001", countdown_s=0.05, window_s=1) + second = referee.start("q3_practice", "TEAM-0001", countdown_s=0.05, window_s=1) assert second.case_code != first.case_code # 新的一局、新的案例编码 referee.abort() # 结束后不能复位继续原测试，只能开始新的一局 [题面 4.5] @@ -65,19 +65,19 @@ def test_cannot_start_while_running_and_restart_after_finish(tmp_path): def test_formal_attempts_accounting(tmp_path): referee = Referee(action_port=0, data_dir=tmp_path) for _ in range(FORMAL_ATTEMPTS): - _session = referee.start("q3_formal", "2026001", countdown_s=0.05) + _session = referee.start("q3_formal", "TEAM-0001", countdown_s=0.05) referee.abort() # 正式中止仍占用机会 [题面 4.5] with pytest.raises(RefereeError): - referee.start("q3_formal", "2026001", countdown_s=0.05) + referee.start("q3_formal", "TEAM-0001", countdown_s=0.05) # 演练不限次数 for _ in range(FORMAL_ATTEMPTS + 2): - referee.start("q3_practice", "2026001", countdown_s=0.05) + referee.start("q3_practice", "TEAM-0001", countdown_s=0.05) referee.abort() def test_truth_policy_practice_vs_formal(tmp_path): referee = Referee(action_port=0, data_dir=tmp_path) - practice = referee.start("q3_practice", "2026001", countdown_s=0.05, window_s=5, + practice = referee.start("q3_practice", "TEAM-0001", countdown_s=0.05, window_s=5, seed=42, source_count=12) assert wait_until(lambda: practice.phase == "open") referee.abort() @@ -86,7 +86,7 @@ def test_truth_policy_practice_vs_formal(tmp_path): assert state["truth"]["source_count"] == 12 assert {"omni_count", "directional_count"} <= set(state["truth"]) - formal = referee.start("q4_formal", "2026001", countdown_s=0.05, seed=42) + formal = referee.start("q4_formal", "TEAM-0001", countdown_s=0.05, seed=42) assert wait_until(lambda: formal.phase == "open") # 运行中考官视图也不显示 [题面 4.6：正式测试任何界面不显示真值] assert referee.snapshot(examiner=True)["truth"]["revealed"] is False @@ -97,9 +97,9 @@ def test_truth_policy_practice_vs_formal(tmp_path): def test_exit_finishes_session_with_user_exit(tmp_path): referee = Referee(action_port=0, data_dir=tmp_path) - session = referee.start("q3_practice", "2026001", countdown_s=0.05, window_s=20) + session = referee.start("q3_practice", "TEAM-0001", countdown_s=0.05, window_s=20) assert wait_until(lambda: session.phase == "open") - payload = dict(arena_id="default", robot_id="2026001", request_id="enter") + payload = dict(arena_id="default", robot_id="TEAM-0001", request_id="enter") session.simulator.handle("/enter", payload) session.simulator.handle("/exit", {**payload, "request_id": "exit"}) assert wait_until(lambda: session.phase == "finished") @@ -109,9 +109,9 @@ def test_exit_finishes_session_with_user_exit(tmp_path): def test_program_timeout_after_enter(tmp_path): spec = Spec(field=FieldSpec(max_real_duration_s=0.3)) referee = Referee(action_port=0, data_dir=tmp_path, spec=spec) - session = referee.start("q3_practice", "2026001", countdown_s=0.05, window_s=10) + session = referee.start("q3_practice", "TEAM-0001", countdown_s=0.05, window_s=10) assert wait_until(lambda: session.phase == "open") - session.simulator.handle("/enter", dict(arena_id="default", robot_id="2026001", + session.simulator.handle("/enter", dict(arena_id="default", robot_id="TEAM-0001", request_id="enter")) assert wait_until(lambda: session.phase == "finished", timeout=5) assert session.end_reason == "program_timeout" @@ -119,7 +119,7 @@ def test_program_timeout_after_enter(tmp_path): def test_record_action_updates_feed_trail_and_counts(tmp_path): referee = Referee(action_port=0, data_dir=tmp_path) - session = referee.start("q3_practice", "2026001", countdown_s=0.05, window_s=5) + session = referee.start("q3_practice", "TEAM-0001", countdown_s=0.05, window_s=5) assert wait_until(lambda: session.phase == "open") _sim = session.simulator referee.record_action(session, "POST", "/measure", @@ -142,7 +142,7 @@ def test_record_action_updates_feed_trail_and_counts(tmp_path): def test_behavior_log_and_meta_written(tmp_path): referee = Referee(action_port=0, data_dir=tmp_path) - session = referee.start("q3_practice", "2026001", countdown_s=0.05, window_s=1, + session = referee.start("q3_practice", "TEAM-0001", countdown_s=0.05, window_s=1, seed=7, source_count=10) assert wait_until(lambda: session.phase == "open") referee.record_action(session, "POST", "/measure", @@ -168,7 +168,7 @@ def test_display_limit_and_port_settings(tmp_path): referee.set_display_limit(50) with pytest.raises(RefereeError): referee.set_display_limit(5001) - _session = referee.start("q3_practice", "2026001", countdown_s=5) + _session = referee.start("q3_practice", "TEAM-0001", countdown_s=5) with pytest.raises(RefereeError): referee.set_action_port(8080) # 测试进行中不能改端口 referee.abort() diff --git a/referee/tests/test_simulator_conformance.py b/referee/tests/test_simulator_conformance.py index 16c5bd8..72a38f9 100644 --- a/referee/tests/test_simulator_conformance.py +++ b/referee/tests/test_simulator_conformance.py @@ -9,7 +9,7 @@ from referee import FieldSpec, LocalSimulator, SimulatorClosed, Source, Spec def payload(request_id="e", **extra): - return dict(arena_id="default", robot_id="2026001", request_id=request_id, **extra) + return dict(arena_id="default", robot_id="TEAM-0001", request_id=request_id, **extra) def measure(request_id="m", x=0, y=0, channel=1): @@ -20,7 +20,7 @@ def measure(request_id="m", x=0, y=0, channel=1): def test_late_enter_and_deadline(): now = [1400.25] sim = LocalSimulator([], clock=lambda: now[0], wall_clock=lambda: 1234.5, - window_started_at=0) + window_started_at=0, robot_id="TEAM-0001") response = sim.handle("/enter", payload())[1] assert response["remaining_real_duration_s"] == 99 # 窗口剩余取早 assert response["real_timestamp_ms"] == 1234500 @@ -35,28 +35,28 @@ def test_late_enter_and_deadline(): def test_program_deadline_and_subsecond_enter(): now = [100] - sim = LocalSimulator([], clock=lambda: now[0], window_started_at=0) + sim = LocalSimulator([], clock=lambda: now[0], window_started_at=0, robot_id="TEAM-0001") assert sim.handle("/enter", payload())[1]["remaining_real_duration_s"] == 1200 now[0] = 1300 with pytest.raises(SimulatorClosed): sim.handle("/measure", measure()) - late = LocalSimulator([], clock=lambda: 1499.5, window_started_at=0) + late = LocalSimulator([], clock=lambda: 1499.5, window_started_at=0, robot_id="TEAM-0001") assert late.handle("/enter", payload())[1]["remaining_real_duration_s"] == 0 - expired = LocalSimulator([], clock=lambda: 1500, window_started_at=0) + expired = LocalSimulator([], clock=lambda: 1500, window_started_at=0, robot_id="TEAM-0001") with pytest.raises(SimulatorClosed): expired.handle("/enter", payload()) def test_stress_mode_is_explicit(): spec = Spec(field=FieldSpec(max_real_duration_s=1800)) - official = LocalSimulator([], spec=spec, clock=lambda: 0) - stress = LocalSimulator([], spec=spec, clock=lambda: 0, stress_mode=True) + official = LocalSimulator([], spec=spec, clock=lambda: 0, robot_id="TEAM-0001") + stress = LocalSimulator([], spec=spec, clock=lambda: 0, stress_mode=True, robot_id="TEAM-0001") assert official.handle("/enter", payload())[1]["remaining_real_duration_s"] == 1200 assert stress.handle("/enter", payload())[1]["remaining_real_duration_s"] == 1800 def test_virtual_crossing_action_finishes_then_connection_closes(): - sim = LocalSimulator([]) + sim = LocalSimulator([], robot_id="TEAM-0001") sim.handle("/enter", payload()) assert sim.handle("/measure", measure(x=1_800_000))[1]["virtual_time_s"] == 360005 for path, body in [("/measure", measure("m2")), ("/exit", payload("x"))]: @@ -66,7 +66,7 @@ def test_virtual_crossing_action_finishes_then_connection_closes(): def test_explicit_exit_closes_new_requests(): - sim = LocalSimulator([]) + sim = LocalSimulator([], robot_id="TEAM-0001") sim.handle("/enter", payload()) assert sim.handle("/exit", payload("exit"))[1]["exit_reason"] == "user_exit" assert sim.closed_reason == "user_exit" @@ -76,7 +76,7 @@ def test_explicit_exit_closes_new_requests(): # ---------------------------------------------------------------- 量化与幂等 def test_microseconds_accumulate_and_signed_zero_is_same_location(): - sim = LocalSimulator([Source(1, 1000, 0, 1500)]) + sim = LocalSimulator([Source(1, 1000, 0, 1500)], robot_id="TEAM-0001") sim.handle("/enter", payload()) a = sim.handle("/measure", measure("a", 0.0))[1] b = sim.handle("/measure", measure("b", -0.0))[1] @@ -88,7 +88,7 @@ def test_microseconds_accumulate_and_signed_zero_is_same_location(): def test_error_rejection_does_not_consume_id(): - sim = LocalSimulator([]) + sim = LocalSimulator([], robot_id="TEAM-0001") for bad in ["e\n", "e\u200b", "e\u0085", "e\ud800"]: assert sim.handle("/enter", payload(bad))[0] == 400 assert not sim._idempotency @@ -96,7 +96,7 @@ def test_error_rejection_does_not_consume_id(): def test_idempotent_replay_and_conflict(): - sim = LocalSimulator([]) + sim = LocalSimulator([], robot_id="TEAM-0001") sim.handle("/enter", payload("enter")) first = sim.handle("/measure", measure("m1", 100, 0))[1] replay = sim.handle("/measure", measure("m1", 100, 0))[1] @@ -108,7 +108,7 @@ def test_idempotent_replay_and_conflict(): def test_idempotency_only_accepted_actions_occupy_id(): - sim = LocalSimulator([]) + sim = LocalSimulator([], robot_id="TEAM-0001") sim.handle("/enter", payload("enter")) # 未知字段：200 accepted=false，不占用 ID [题面 附件2 5.3] rejected = payload("m1", extra=1, position=dict(x=0, y=0), channel=1) @@ -118,7 +118,7 @@ def test_idempotency_only_accepted_actions_occupy_id(): def test_concurrent_registration_and_action_finishing_after_deadline(): now = [0] - sim = LocalSimulator([], clock=lambda: now[0]) + sim = LocalSimulator([], clock=lambda: now[0], robot_id="TEAM-0001") sim.handle("/enter", payload()) started, release = Event(), Event() observe = sim._observe @@ -165,7 +165,7 @@ def test_concurrent_registration_and_action_finishing_after_deadline(): # ---------------------------------------------------------------- 校验与业务拒绝 def test_huge_numeric_fields_return_bad_request(): - sim = LocalSimulator([]) + sim = LocalSimulator([], robot_id="TEAM-0001") sim.handle("/enter", payload()) huge = 10 ** 400 assert sim.handle("/measure", measure(x=huge))[0] == 400 @@ -176,21 +176,21 @@ def test_huge_numeric_fields_return_bad_request(): @pytest.mark.parametrize("body,expect", [ - (dict(arena_id="default", robot_id="2026001", request_id="e", extra=1), False), - (dict(arena_id="other", robot_id="2026001", request_id="e"), False), + (dict(arena_id="default", robot_id="TEAM-0001", request_id="e", extra=1), False), + (dict(arena_id="other", robot_id="TEAM-0001", request_id="e"), False), (dict(arena_id="default", robot_id="someone-else", request_id="e"), False), ]) # pytest 参数化保留 expect 列（用例矩阵可读性），当前断言恒为 False，故不参与断言 # pylint: disable-next=unused-argument def test_unknown_fields_and_mismatch_are_business_rejected(body, expect): - sim = LocalSimulator([]) + sim = LocalSimulator([], robot_id="TEAM-0001") status, response = sim.handle("/enter", body) assert status == 200 and response["accepted"] is False assert response["virtual_time_s"] == 0 def test_channel_and_position_validation(): - sim = LocalSimulator([]) + sim = LocalSimulator([], robot_id="TEAM-0001") sim.handle("/enter", payload()) cases = [ dict(position=dict(x=0, y=0), channel=0), @@ -210,7 +210,7 @@ def test_channel_and_position_validation(): def test_actions_before_enter_are_business_rejected(): - sim = LocalSimulator([]) + sim = LocalSimulator([], robot_id="TEAM-0001") for path, body in [("/measure", measure()), ("/clear", payload("c", position=dict(x=0, y=0), channel=1)), ("/exit", payload("x"))]: @@ -220,7 +220,7 @@ def test_actions_before_enter_are_business_rejected(): def test_clear_rules(): sim = LocalSimulator([Source(5, 10, 0, 1500), Source(6, 15, 0, 1500, directional=True, - heading_deg=180)]) + heading_deg=180)], robot_id="TEAM-0001") sim.handle("/enter", payload()) # 20m 内清除成功，且与定向朝向无关（源6 朝向 180°，机器人从西侧接近仍可清除） ok = sim.handle("/clear", payload("c5", position=dict(x=25, y=0), channel=5))[1] @@ -239,7 +239,7 @@ def test_measure_observation_matrix(): sources = [Source(1, 1000, 0, 1500), # 全向，r_eff=1500 Source(2, 100, 0, 1500, directional=True, heading_deg=0), # 定向朝东 Source(3, 5000, 0, 1500)] - sim = LocalSimulator(sources, error_half_deg=0.0) + sim = LocalSimulator(sources, error_half_deg=0.0, robot_id="TEAM-0001") sim.handle("/enter", payload("enter")) # 超出有效接收半径 assert sim.handle("/measure", @@ -264,7 +264,7 @@ def test_measure_observation_matrix(): def test_svd_decimals_and_normalization(): - sim = LocalSimulator([Source(1, -10, 0, 1500)], error_half_deg=0.0) + sim = LocalSimulator([Source(1, -10, 0, 1500)], error_half_deg=0.0, robot_id="TEAM-0001") sim.handle("/enter", payload()) response = sim.handle("/measure", measure("m", 10, 0))[1] assert response["measure_result"] == "direction" @@ -272,7 +272,7 @@ def test_svd_decimals_and_normalization(): def test_error_field_is_fixed_per_location(): - sim = LocalSimulator([Source(1, 1000, 0, 1500)], error_seed=7) + sim = LocalSimulator([Source(1, 1000, 0, 1500)], error_seed=7, robot_id="TEAM-0001") sim.handle("/enter", payload()) first = sim.handle("/measure", measure("m1", 500, 0))[1]["svd_deg"] sim.handle("/measure", measure("away", 300, 300)) diff --git a/referee/tests/test_timing_examples.py b/referee/tests/test_timing_examples.py index 45829fb..793af34 100644 --- a/referee/tests/test_timing_examples.py +++ b/referee/tests/test_timing_examples.py @@ -5,7 +5,7 @@ from referee.timing import move_time def payload(request_id="e", **extra): - return dict(arena_id="default", robot_id="2026001", request_id=request_id, **extra) + return dict(arena_id="default", robot_id="TEAM-0001", request_id=request_id, **extra) def measure(request_id="m", x=0, y=0, channel=1): @@ -15,7 +15,7 @@ def measure(request_id="m", x=0, y=0, channel=1): def test_attachment_timing_example(): """附件2 第10节：(0,0)→(300,400) 检测ch1 → 原地检测ch2 → (300,0)清除ch3(未发现) → 原地检测ch2 → /exit，虚拟时刻 105/111/194/199。""" - sim = LocalSimulator([Source(3, 10_000, 10_000, 1500)]) # 远源，保证"未发现" + sim = LocalSimulator([Source(3, 10_000, 10_000, 1500)], robot_id="TEAM-0001") # 远源，保证"未发现" sim.handle("/enter", payload()) assert sim.handle("/measure", measure("m1", 300, 400))[1]["virtual_time_s"] == 105 assert sim.handle("/measure", measure("m2", 300, 400, channel=2))[1]["virtual_time_s"] == 111 diff --git a/referee/tests/test_webui_api.py b/referee/tests/test_webui_api.py index 2a1156f..fc10def 100644 --- a/referee/tests/test_webui_api.py +++ b/referee/tests/test_webui_api.py @@ -58,12 +58,12 @@ def test_state_idle_snapshot(webui): def test_start_countdown_open_abort_cycle(webui): referee, port = webui status, body = call(port, "POST", "/ui/api/start", - {"module": "q3_practice", "robot_id": "2026001", + {"module": "q3_practice", "robot_id": "TEAM-0001", "countdown_s": 0.05, "window_s": 5}) assert status == 200 and body["started"] is True assert wait_until(lambda: referee.session.phase == "open") status, state = call(port, "GET", "/ui/api/state") - assert state["phase"] == "open" and state["robot_id"] == "2026001" + assert state["phase"] == "open" and state["robot_id"] == "TEAM-0001" assert "virtual_time_s" in state and "window_remaining_s" in state status, body = call(port, "POST", "/ui/api/abort", {}) assert body["aborted"] is True @@ -75,12 +75,12 @@ def test_start_countdown_open_abort_cycle(webui): def test_start_rejects_running_session_and_bad_module(webui): _referee, port = webui call(port, "POST", "/ui/api/start", - {"module": "q3_practice", "robot_id": "2026001", "countdown_s": 5}) + {"module": "q3_practice", "robot_id": "TEAM-0001", "countdown_s": 5}) status, _body = call(port, "POST", "/ui/api/start", - {"module": "q3_practice", "robot_id": "2026001"}) + {"module": "q3_practice", "robot_id": "TEAM-0001"}) assert status == 409 status, _body = call(port, "POST", "/ui/api/start", - {"module": "nope", "robot_id": "2026001"}) + {"module": "nope", "robot_id": "TEAM-0001"}) assert status == 409 call(port, "POST", "/ui/api/abort", {}) @@ -98,7 +98,7 @@ def test_config_endpoints(webui): def test_sessions_list_and_replay_source(webui): referee, port = webui call(port, "POST", "/ui/api/start", - {"module": "q3_practice", "robot_id": "2026001", + {"module": "q3_practice", "robot_id": "TEAM-0001", "countdown_s": 0.05, "window_s": 5, "seed": 11, "source_count": 10}) assert wait_until(lambda: referee.session.phase == "open") session = referee.session @@ -115,7 +115,7 @@ def test_sessions_list_and_replay_source(webui): status, data = call(port, "GET", "/ui/api/sessions") assert status == 200 and len(data["sessions"]) == 1 case = data["sessions"][0]["case_code"] - assert data["sessions"][0]["robot_id"] == "2026001" + assert data["sessions"][0]["robot_id"] == "TEAM-0001" status, replay = call(port, "GET", f"/ui/api/replay?case={case}") assert status == 200 kinds = [event["event"] for event in replay["events"]] diff --git a/scripts/benchmark_q3_search.py b/scripts/benchmark_q3_search.py index 439cfc3..f6d9747 100644 --- a/scripts/benchmark_q3_search.py +++ b/scripts/benchmark_q3_search.py @@ -1,4 +1,4 @@ -"""Offline Q3 search ablations for docs/algo-3.md; no official socket access. +"""Offline Q3 search ablations for 论文“问题三”一节; no official socket access. The production planner is unchanged. Only coverage and task scheduling vary. Run: python scripts/benchmark_q3_search.py --seeds 0,1,2,3,4 diff --git a/scripts/build_core_notebook.py b/scripts/build_core_notebook.py index d05acf3..3abf254 100644 --- a/scripts/build_core_notebook.py +++ b/scripts/build_core_notebook.py @@ -1058,7 +1058,7 @@ class _LogStub: return [] -client3 = SimulatorClient("2026001", config=CONFIG_Q3, transport=None, log=_LogStub()) +client3 = SimulatorClient("OFFLINE", config=CONFIG_Q3, transport=None, log=_LogStub()) client3.state.position = (0.0, 0.0) loop3 = BayesLoop(client3, CONFIG_Q3) print("第三问策略：覆盖计划 %s（%d 站），网格间距 %.2f m" @@ -1222,7 +1222,7 @@ assert covered > 0 add(code(r''' # ---- 10.4 第四问闭环装配（不连模拟器：只检查策略对象初始化） ---------------- -client4 = SimulatorClient("2026001", config=CONFIG, transport=None, log=_LogStub()) +client4 = SimulatorClient("OFFLINE", config=CONFIG, transport=None, log=_LogStub()) client4.state.position = (0.0, 0.0) loop4 = DirectionalExtension(client4, CONFIG) print("第四问策略：覆盖计划 %s（%d 点），联合信念开关 %s，探点口径 %s" @@ -1284,8 +1284,8 @@ add(md(""" ```bash # 对接官方模拟器（演练/正式） -python -m mcm26b.run --robot-id <队号> --problem 3 # 第三问：bayes 闭环 -python -m mcm26b.run --robot-id <队号> --problem 4 # 第四问：directional 闭环 +python -m mcm26b.cli run --robot-id <队号> --problem 3 # 第三问：bayes 闭环 +python -m mcm26b.cli run --robot-id <队号> --problem 4 # 第四问：directional 闭环 ``` 成绩口径是**虚拟时间**：`avg_locate_clear_time_s = 全程虚拟时长 / 已清除源数`， diff --git a/scripts/build_figure_data.py b/scripts/build_figure_data.py deleted file mode 100644 index 82ed36f..0000000 --- a/scripts/build_figure_data.py +++ /dev/null @@ -1,747 +0,0 @@ -"""为 MATLAB 出图准备数据，并更新 docs/figures/manifest.json。 - - uv run python scripts/build_figure_data.py --prepare # 只写 .mat 数据 - uv run python scripts/build_figure_data.py # 写数据 + 调 MATLAB 出图 - uv run python scripts/build_figure_data.py --only geo-q3-seven-point-coverage - uv run python scripts/build_figure_data.py --list - -分工： - -- **本脚本（Python）**：解析行为日志与覆盖计划、调用生产求解器算几何、把绘图数据写成 - ``docs/figures/.data/*.mat``、维护 manifest（来源路径 + sha256 + 行数）； -- **MATLAB**：``scripts/matlab/*.m`` 负责排版与出图，只读 .mat，不做数据解析。 - 手动出图：MATLAB 中 ``cd scripts/matlab`` 后执行 ``run_all_figures``。 - -图内不放标题、图注与解释性文字，只保留坐标轴、图例与数据标注； -每张图的含义由文件名与 ``docs/figures/README.md`` 承担。 -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import math -import subprocess -import sys -from pathlib import Path -from typing import Any, Callable, Sequence - -import numpy as np -from scipy.io import savemat - -ROOT = Path(__file__).resolve().parents[1] -OUT_DIR = ROOT / "docs" / "figures" -DATA_DIR = OUT_DIR / ".data" -MATLAB_DIR = ROOT / "scripts" / "matlab" -SESSIONS_DIR = ROOT / "referee-data" / "sessions" -ARCHIVE_DIR = ROOT / "examples" / "replay" -MATLAB_BIN = Path("/Applications/MATLAB_R2025a.app/bin/matlab") - -# 题面常量（与 src/mcm26b/config.py 一致；此处只用于画图） -OMEGA_R = 1800.0 -R_EFF = 1000.0 -SPEED_MPS = 5.0 -MEASURE_S = 5.0 -SWITCH_S = 1.0 -CLEAR_OK_S = 5.0 -CLEAR_NO_S = 3.0 -RING_RADIUS_M = 1159.7 - -# 回放图指定的 q4 演练局（缺失时回退到自动挑选） -Q4_REPLAY_CASE = "GHSN-M38E-74GD-YLUD" -SEVEN_R = 1800.0 - -# 图名 → (MATLAB 函数, .mat 数据名) -FIGURES: tuple[tuple[str, str, str], ...] = ( - ("flow-overall-architecture", "fig_flow_overall", "flow_overall"), - ("steps-state-machine", "fig_steps_state_machine", "steps_state_machine"), - ("timing-attachment-example-105-111-194-199", "fig_timing_attachment", - "timing_attachment"), - ("timing-measured-q3-10-sources", "fig_timing_measured", "timing_measured"), - ("geo-q1-convex-intersection-zoom", "fig_geo_q1_meetup", "geo_q1_meetup"), - ("geo-q1-diameter-circle-counterexample", "fig_geo_q1_counterexample", - "geo_q1_counterexample"), - ("geo-q2-guaranteed-reception-disk", "fig_geo_q2_safe_disk", "geo_q2"), - ("geo-q3-seven-point-coverage", "fig_geo_q3_coverage", "geo_q3"), - ("geo-q4-minimum-cover-31-points", "fig_geo_q4_mincover", "geo_q4"), - ("replay-webui-q3-omnidirectional", "fig_replay", "replay_q3"), - ("replay-webui-q4-directional", "fig_replay", "replay_q4"), -) - -SOURCES: dict[str, list[str]] = { - "flow-overall-architecture": ["src/mcm26b/config.py", "docs/B题建模分析与实施计划.md"], - "steps-state-machine": ["docs/协议规则表与状态机.md", "src/mcm26b/protocol/client.py"], - "timing-attachment-example-105-111-194-199": ["docs/协议规则表与状态机.md"], - "geo-q1-diameter-circle-counterexample": ["examples/q1/triangle.json"], - "geo-q2-guaranteed-reception-disk": ["src/mcm26b/config.py", "docs/algo-2.md"], - "geo-q3-seven-point-coverage": ["src/mcm26b/planner/seven.py", "docs/algo-3.md"], - "geo-q4-minimum-cover-31-points": [ - "src/mcm26b/guarantee/plans/q4_mincover_v2.json", "docs/algo-4.md"], -} - - -# --------------------------------------------------------------------------- -# 数据来源 -# --------------------------------------------------------------------------- -def source(path: str | Path, **extra: Any) -> dict[str, Any]: - p = Path(path) - p = p if p.is_absolute() else ROOT / p - digest, lines = "-", 0 - if p.is_file(): - data = p.read_bytes() - digest = hashlib.sha256(data).hexdigest()[:12] - lines = data.count(b"\n") + (0 if data.endswith(b"\n") else 1) - rel = str(p.relative_to(ROOT)) if p.is_relative_to(ROOT) else str(p) - return {"path": rel, "sha256_12": digest, "lines": lines, **extra} - - -def parse_jsonl(path: Path) -> dict[str, Any]: - """兼容评测机 ``event=action`` 与客户端 ``event=accepted`` 两种日志。""" - meta: dict[str, Any] = {} - actions: list[dict[str, Any]] = [] - fallback: dict[str, dict[str, Any]] = {} - seq = 0 - for line in path.read_text(encoding="utf-8").splitlines(): - line = line.strip() - if not line: - continue - try: - ev = json.loads(line) - except json.JSONDecodeError: - continue - kind = ev.get("event") - if kind == "session_start": - meta = ev - elif kind == "action": - seq += 1 - actions.append(dict(seq=ev.get("seq", seq), path=ev.get("path", ""), - request=ev.get("request") or {}, - response=ev.get("response") or {}, - wall_ts_ms=ev.get("wall_ts_ms"))) - elif kind == "accepted": - fallback[str(ev.get("request_id", ""))] = ev - elif kind == "step" and str(ev.get("request_id", "")) not in fallback: - fallback[str(ev.get("request_id", ""))] = ev - if not actions: - for ev in fallback.values(): - if not ev.get("path"): - continue - seq += 1 - stamp = ev.get("logged_at_unix_s") - actions.append(dict(seq=seq, path=ev["path"], request=ev.get("request") or {}, - response=ev.get("response") or {}, - wall_ts_ms=stamp * 1000 if stamp else None)) - if not actions: - raise ValueError(f"{path} 没有可回放的动作事件") - return dict(case_code=meta.get("case_code") or path.parent.name, - module=meta.get("module") or "?", robot_id=str(meta.get("robot_id") or "?"), - truth=list(meta.get("truth") or []), actions=actions, source=path) - - -def discover_runs() -> list[dict[str, Any]]: - runs: list[dict[str, Any]] = [] - if SESSIONS_DIR.is_dir(): - for log in sorted(SESSIONS_DIR.glob("*/log.jsonl")): - try: - runs.append(parse_jsonl(log)) - except ValueError: - continue - if not runs and ARCHIVE_DIR.is_dir(): - for log in sorted(ARCHIVE_DIR.glob("*.jsonl")): - try: - runs.append(parse_jsonl(log)) - except ValueError: - continue - return runs - - -def pick_case(runs: Sequence[dict[str, Any]], case_code: str) -> dict[str, Any] | None: - """按案例编码取局；不在本机留档里就返回 None，由调用方回退。""" - return next((r for r in runs if r["case_code"] == case_code), None) - - -def pick_run(runs: Sequence[dict[str, Any]], module: str, truth: int) -> dict[str, Any]: - cands = [r for r in runs if r["module"].startswith(module) and len(r["truth"]) >= truth - and any(a["path"] == "/measure" for a in r["actions"])] - if not cands: - cands = [r for r in runs if r["module"].startswith(module)] - return sorted(cands, key=lambda r: (abs(len(r["truth"]) - truth), len(r["actions"])))[0] - - -def decompose(run: dict[str, Any]) -> dict[str, Any]: - """按附件规则逐动作复算移动 / 换频 / 动作耗时。""" - pos = (0.0, 0.0) - channel: int | None = None - rows: list[dict[str, Any]] = [] - for act in run["actions"]: - req, res = act["request"], act["response"] - move_m = switch = action_s = 0.0 - if act["path"] in ("/measure", "/clear") and "position" in req: - tgt = (float(req["position"]["x"]), float(req["position"]["y"])) - move_m = math.dist(pos, tgt) - if move_m > 0: - pos = tgt - if act["path"] == "/measure": - ch = int(req.get("channel", 0)) - switch = 0.0 if channel in (None, ch) else SWITCH_S - channel = ch - action_s = MEASURE_S - elif act["path"] == "/clear": - action_s = CLEAR_OK_S if str(res.get("clear_result")) == "success" else CLEAR_NO_S - rows.append(dict(path=act["path"], move_s=move_m / SPEED_MPS, switch_s=switch, - action_s=action_s, move_m=move_m, - virtual_s=float(res.get("virtual_time_s") or 0.0), - hit=str(res.get("measure_result")) == "direction", - cleared=str(res.get("clear_result")) == "success", - channel=req.get("channel"), - x=req.get("position", {}).get("x"), - y=req.get("position", {}).get("y"))) - last = run["actions"][-1]["response"] - return dict(rows=rows, virtual_s=float(last.get("virtual_time_s") or 0.0), - move_m=sum(r["move_m"] for r in rows), - measures=sum(1 for r in rows if r["path"] == "/measure"), - clears=sum(1 for r in rows if r["path"] == "/clear")) - - -def solved_channels(run: dict[str, Any]) -> list[int]: - return sorted({int(a["request"]["channel"]) for a in run["actions"] - if a["path"] == "/clear" and "channel" in a["request"] - and str(a["response"].get("clear_result")) == "success"}) - - -# --------------------------------------------------------------------------- -# 几何：半平面裁剪与问题一求解 -# --------------------------------------------------------------------------- -def clip_halfplane(poly: np.ndarray, a: float, b: float, c: float) -> np.ndarray: - """Sutherland–Hodgman：半平面 a·x + b·y <= c 裁剪凸多边形。""" - if len(poly) == 0: - return poly - out: list[list[float]] = [] - n = len(poly) - for i in range(n): - cur, nxt = poly[i], poly[(i + 1) % n] - dc = a * cur[0] + b * cur[1] - c - dn = a * nxt[0] + b * nxt[1] - c - if dc <= 1e-9: - out.append(list(cur)) - if dn > 1e-9: - t = dc / (dc - dn) - out.append([cur[0] + t * (nxt[0] - cur[0]), - cur[1] + t * (nxt[1] - cur[1])]) - elif dn <= 1e-9: - t = dc / (dc - dn) - out.append([cur[0] + t * (nxt[0] - cur[0]), - cur[1] + t * (nxt[1] - cur[1])]) - return np.array(out) if out else np.empty((0, 2)) - - -def angle_error(poly: np.ndarray, station: Sequence[float], bearing: float) -> float: - centre = poly.mean(axis=0) - dx, dy = centre[0] - station[0], centre[1] - station[1] - if abs(dx) < 1e-12 and abs(dy) < 1e-12: - return 0.0 - return abs((math.degrees(math.atan2(dy, dx)) - bearing + 180.0) % 360.0 - 180.0) - - -def clip_wedge(region: np.ndarray, station: Sequence[float], bearing: float, - half_deg: float) -> np.ndarray: - """用角域 W(s, θ ± δ) 裁剪区域：两个半平面各试一次，取方向一致的那一侧。""" - region = np.asarray(region, dtype=float) - if len(region) == 0: - return region - for edge in (bearing - half_deg, bearing + half_deg): - ang = math.radians(edge) - a, b = -math.sin(ang), math.cos(ang) - c = a * station[0] + b * station[1] - cands = [cand for cand in (clip_halfplane(region, a, b, c), - clip_halfplane(region, -a, -b, -c)) if len(cand) >= 3] - if not cands: - return np.empty((0, 2)) - region = cands[0] if len(cands) == 1 else min( - cands, key=lambda cand: angle_error(cand, station, bearing)) - return region - - -def wedge_polygon(station: Sequence[float], bearing: float, half_deg: float, - reach: float) -> np.ndarray: - a0, a1 = math.radians(bearing - half_deg), math.radians(bearing + half_deg) - ang = np.linspace(a0, a1, 24) - return np.vstack([[station[0], station[1]], - np.column_stack([station[0] + reach * np.cos(ang), - station[1] + reach * np.sin(ang)])]) - - -def overview_window(stations: np.ndarray, verts: np.ndarray, - dc: dict[str, Any], mec: dict[str, Any]) -> tuple[float, np.ndarray]: - """总览窗口：包含全部站点、定位区域与两个圆，中心取包围盒中心。""" - pts = [np.asarray(stations, dtype=float), np.asarray(verts, dtype=float)] - for circ in (dc, mec): - c = np.asarray(circ["center"], dtype=float) - r = float(circ["radius_m"]) - pts.append(np.array([c + r, c - r])) - all_pts = np.vstack(pts) - lo, hi = all_pts.min(axis=0), all_pts.max(axis=0) - center = (lo + hi) / 2 - half = float(np.max(hi - lo) / 2 * 1.12 + 40.0) - return half, center - - -def q1_solve(stations: Sequence[Sequence[float]]) -> dict[str, Any]: - """调用生产求解器，保证图中数字与代码一致。""" - sys.path.insert(0, str(ROOT / "src")) - from mcm26b import Measurement, solve # noqa: PLC0415 - res = solve([Measurement(x, y, b, h) for x, y, b, h in stations]) - dc = res["diameter_circle"] - mec = res["minimum_enclosing_circle"] - return dict(vertices=np.array(res["vertices"]), - dc=dict(center=np.array(dc["center"]), radius_m=float(dc["radius_m"])), - mec=dict(center=np.array(mec["center"]), radius_m=float(mec["radius_m"]))) - - -def q1_practice_case() -> dict[str, Any] | None: - for path in sorted((ROOT / "output" / "q1" / "practice").glob("*_observations.json")): - doc = json.loads(path.read_text(encoding="utf-8")) - if doc.get("result"): - doc["_path"] = path - return doc - return None - - -def load_plan(name: str) -> dict[str, Any]: - path = ROOT / "src" / "mcm26b" / "guarantee" / "plans" / f"{name}.json" - return json.loads(path.read_text(encoding="utf-8")) - - -def seven_bound(a: float = RING_RADIUS_M, radius: float = SEVEN_R) -> float: - return max(a / math.sqrt(3), - math.sqrt(radius ** 2 + a ** 2 - math.sqrt(3) * radius * a)) - - -# --------------------------------------------------------------------------- -# 各图数据 -# --------------------------------------------------------------------------- -def data_flow_overall() -> dict[str, Any]: - """四个层次带：层标签、每层的框内短标签与语义色名。""" - bands = [ - ("L0", [("区域 Ω：半径 1800 m", 8.0, 26.0), - ("移动 5 m/s · 检测 5 s", 36.5, 26.0), - ("换频 1 s · 清除 5/3 s", 65.0, 26.0), - ("源 10~16 个 · 频道 1~20", 93.5, 24.0)]), - ("L1", [("证据层：候选集与粒子幸存集", 8.0, 34.0), - ("决策层：选点与任务表", 45.0, 34.0), - ("执行层：串行客户端", 82.0, 34.0)]), - ("L2", [("问题 1 定位解算", 8.0, 26.0), - ("问题 2 第二点选择", 36.5, 26.0), - ("问题 3 全向搜索", 65.0, 26.0), - ("问题 4 混合定向", 93.5, 24.0)]), - ("L3", [("发现闭环", 8.0, 34.0), - ("定位闭环", 45.0, 34.0), - ("清除闭环", 82.0, 34.0)]), - ] - colors = {"L0": "primary", "L1": "primary", "L2": "accent", "L3": "success"} - return dict(tags=[b[0] for b in bands], - labels=[[i[0] for i in b[1]] for b in bands], - colors=[colors[b[0]] for b in bands]) - - -def data_steps_state_machine() -> dict[str, Any]: - return dict(labels=["IDLE", "倒计时 5 s", "OPEN 接口开放", "CLOSED 断连"], - edges=["muted", "warn", "success", "accent"]) - - -EXAMPLE_STEPS = (("/enter", 0.0, 0.0, 0.0), - ("/measure ch1", 100.0, 0.0, 5.0), - ("/measure ch2", 0.0, 1.0, 5.0), - ("/clear ch3", 80.0, 0.0, 3.0), - ("/measure ch2", 0.0, 0.0, 5.0), - ("/exit", 0.0, 0.0, 0.0)) - - -def data_timing_attachment() -> dict[str, Any]: - return dict(labels=[s[0] for s in EXAMPLE_STEPS], - move=np.array([s[1] for s in EXAMPLE_STEPS]), - switch_s=np.array([s[2] for s in EXAMPLE_STEPS]), - action=np.array([s[3] for s in EXAMPLE_STEPS]), - xlabel="虚拟耗时 / s", - legend=["移动 L/5", "换频 1 s", "检测/清除动作"]) - - -def data_timing_measured(run: dict[str, Any]) -> dict[str, Any]: - tl = decompose(run) - rows = tl["rows"] - seq = np.arange(1.0, len(rows) + 1.0) - steps = np.array([[r["move_s"], r["action_s"], r["switch_s"]] for r in rows], - dtype=float) - cum = np.cumsum(np.cumsum(steps, axis=1), axis=0) # 移动 / +动作 / +换频 - return dict(seq=seq, cum=cum, - xlabel="动作序号", ylabel="累计虚拟时间 / s", - legend=["移动", "检测/清除动作", "换频"]) - - -def data_geo_q1_meetup() -> tuple[dict[str, Any], Path]: - case = q1_practice_case() - if case: - stations = [(m["x"], m["y"], m["bearing_deg"], m["half_angle_deg"]) - for m in case["measurements"]] - res = case["result"] - verts = np.array(res["vertices"]) - dc = dict(center=np.array(res["diameter_circle"]["center"]), - radius_m=float(res["diameter_circle"]["radius_m"])) - mec = dict(center=np.array(res["minimum_enclosing_circle"]["center"]), - radius_m=float(res["minimum_enclosing_circle"]["radius_m"])) - src = case["_path"] - else: - src = ROOT / "examples" / "q1" / "triangle.json" - doc = json.loads(src.read_text(encoding="utf-8")) - stations = [(m["x"], m["y"], m["bearing_deg"], m.get("half_angle_deg", 1.0)) - for m in doc["measurements"]] - sol = q1_solve(stations) - verts, dc, mec = sol["vertices"], sol["dc"], sol["mec"] - - zoom_c = verts.mean(axis=0) - span = float(np.abs(verts - zoom_c).max()) - zoom = max(1.8 * span, 0.8 * mec["radius_m"] + span, 12.0) - xs = [s[0] for s in stations] - ys = [s[1] for s in stations] - half, view_c = overview_window(np.array([[s[0], s[1]] for s in stations]), - verts, dc, mec) - reach = 2.4 * half - wedges = [wedge_polygon((sx, sy), b, hd, reach) for sx, sy, b, hd in stations] - region = np.array([[zoom_c[0] - zoom, zoom_c[1] - zoom], - [zoom_c[0] + zoom, zoom_c[1] - zoom], - [zoom_c[0] + zoom, zoom_c[1] + zoom], - [zoom_c[0] - zoom, zoom_c[1] + zoom]], dtype=float) - for sx, sy, b, hd in stations: - region = clip_wedge(region, (sx, sy), b, hd) - data = dict( - stations=np.array([[s[0], s[1]] for s in stations]), - station_labels=[f"s{i + 1}" for i in range(len(stations))], - wedges=np.stack(wedges), zoom_wedges=np.stack(wedges), - zoom_region=region, - dc_center=dc["center"], dc_radius=dc["radius_m"], - mec_center=mec["center"], mec_radius=mec["radius_m"], - view_center=view_c, half_overview=float(half), - zoom_center=zoom_c, zoom_half=float(zoom), - legend1=["前向角域 W(s, θ ± 1°)", "定位区域 P"], - legend2=["直径圆", "最小包围圆 MEC"]) - return data, Path(src) - - -def data_geo_q1_counterexample() -> dict[str, Any]: - src = ROOT / "examples" / "q1" / "triangle.json" - doc = json.loads(src.read_text(encoding="utf-8")) - stations = [(m["x"], m["y"], m["bearing_deg"], m.get("half_angle_deg", 1.0)) - for m in doc["measurements"]] - sol = q1_solve(stations) - verts, dc, mec = sol["vertices"], sol["dc"], sol["mec"] - zoom_c = verts.mean(axis=0) - zoom = max(1.9 * float(np.abs(verts - zoom_c).max()) + mec["radius_m"], 45.0) - half, view_c = overview_window(np.array([[s[0], s[1]] for s in stations]), - verts, dc, mec) - wedges = [wedge_polygon((sx, sy), b, hd, 3600.0) for sx, sy, b, hd in stations] - region = np.array([[zoom_c[0] - zoom, zoom_c[1] - zoom], - [zoom_c[0] + zoom, zoom_c[1] - zoom], - [zoom_c[0] + zoom, zoom_c[1] + zoom], - [zoom_c[0] - zoom, zoom_c[1] + zoom]], dtype=float) - for sx, sy, b, hd in stations: - region = clip_wedge(region, (sx, sy), b, hd) - return dict( - stations=np.array([[s[0], s[1]] for s in stations]), - station_labels=[f"s{i + 1}" for i in range(len(stations))], - wedges=np.stack(wedges), zoom_wedges=np.stack(wedges), - zoom_region=region, - dc_center=dc["center"], dc_radius=dc["radius_m"], - mec_center=mec["center"], mec_radius=mec["radius_m"], - view_center=view_c, half_overview=float(half), - zoom_center=zoom_c, zoom_half=float(zoom), - legend1=["前向角域 W(s, θ ± 1°)", "定位区域 P"], - legend2=["直径圆", "最小包围圆 MEC"]) - - -def data_geo_q2() -> dict[str, Any]: - a = 1500.0 / (2 * math.cos(math.radians(1.0))) - safe = R_EFF - a - c = np.array([a * math.cos(math.radians(30.0)), a * math.sin(math.radians(30.0))]) - return dict(omega_r=OMEGA_R, wedge=wedge_polygon((0.0, 0.0), 30.0, 1.0, 1500.0), - a=float(a), safe=float(safe), c=c, half=1900.0, - legend=["前向角域 W(s1)", "截断角域外接圆 B(c, a)", - "安全候选盘 B(c, 1000 − a)"]) - - -def data_geo_q3() -> dict[str, Any]: - a = RING_RADIUS_M - stations = np.array([[0.0, 0.0]] + [[a * math.cos(k * math.pi / 3), - a * math.sin(k * math.pi / 3)] for k in range(6)]) - return dict(omega_r=OMEGA_R, r_eff=R_EFF, - stations=stations, - worst=np.array([0.0, -SEVEN_R]), - half=float(RING_RADIUS_M + R_EFF + 90.0), - legend=["区域 Ω", "1000 m 接收盘", "覆盖站点", "最差点"]) - - -def data_geo_q4() -> dict[str, Any]: - plan = load_plan("q4_mincover_v2") - pts = np.array(plan["points"], dtype=float) - theta = np.linspace(-math.pi / 2, math.pi / 2, 60) - half_disk = np.vstack([[0.0, 0.5], - np.column_stack([0.85 * np.cos(theta), - 0.5 + 0.85 * np.sin(theta)])]) - half = float(max(OMEGA_R, np.abs(pts).max()) * 1.06) - return dict(omega_r=OMEGA_R, points=pts, half=half, half_disk=half_disk, - radius_label="覆盖半径 1000 m", - heading_label="前向半盘 ±90°（朝向未知）", - legend=["区域 Ω", "31 点最小命中集"]) - - -def data_replay(run: dict[str, Any], *, directional: bool) -> dict[str, Any]: - tl = decompose(run) - rows = tl["rows"] - truth = run["truth"] - solved = set(solved_channels(run)) - truth_pts, truth_solved, truth_labels = [], [], [] - for src in truth: - ch = int(src["channel"]) - truth_pts.append([src["x"], src["y"]]) - truth_solved.append(1.0 if ch in solved else 0.0) - truth_labels.append(f"ch{ch}") - # 定向源：朝向箭头（长度取该源有效接收半径）+ ±90° 前向半盘 - headings, half_disks = [], [] - for src in truth: - if not (directional and src.get("directional")): - continue - r = float(src.get("r_eff_m") or R_EFF) - ang = math.radians(float(src.get("heading_deg") or 0.0)) - headings.append([src["x"], src["y"], r * math.cos(ang), r * math.sin(ang)]) - arc = ang + np.linspace(-math.pi / 2, math.pi / 2, 49) - half_disks.append(np.vstack([[src["x"], src["y"]], - np.column_stack([src["x"] + r * np.cos(arc), - src["y"] + r * np.sin(arc)])])) - half_disk_arr = (np.stack(half_disks) if half_disks - else np.zeros((0, 50, 2), dtype=float)) - path = [(0.0, 0.0)] - rays, clears, marks, events = [], [], [], [] - for act, row in zip(run["actions"], rows): - if act["path"] == "/measure" and row["hit"]: - bearing = math.radians(float(act["response"].get("svd_deg") or 0.0)) - rays.append([row["x"], row["y"], 220 * math.cos(bearing), - 220 * math.sin(bearing)]) - if act["path"] in ("/measure", "/clear") and row["x"] is not None: - if (row["x"], row["y"]) != path[-1]: - path.append((row["x"], row["y"])) - if act["path"] == "/clear": - clears.append([row["x"], row["y"], 1.0 if row["cleared"] else 0.0]) - marks.append([row["virtual_s"], 1.0 if row["hit"] else 0.0, - 1.0 if act["path"] == "/clear" and row["cleared"] else 0.0]) - if len(events) < 10: - if act["path"] == "/measure": - detail = f"ch{row['channel']} ({row['x']:.0f},{row['y']:.0f})" - elif act["path"] == "/clear": - detail = f"ch{row['channel']} {'成功' if row['cleared'] else '未发现'}" - else: - detail = "启动会话" if act["path"] == "/enter" else "退出" - events.append([act["path"], detail, f"{row['virtual_s']:.0f}"]) - legend = ["移动轨迹", "检测示向度", "已清除频道", "未清除频道"] - if directional: - legend = ["移动轨迹", "检测示向度", "定向源前向半盘 (±90°)", - "已清除频道", "未清除频道"] - # 世界视图窗口：容纳 Ω、轨迹、射线与定向半盘，避免半盘被画到框外 - geom = [np.asarray(truth_pts, dtype=float), np.asarray(path, dtype=float)] - if rays: - rays_arr = np.asarray(rays, dtype=float) - geom.append(rays_arr[:, :2] + rays_arr[:, 2:]) - if half_disk_arr.size: - geom.append(half_disk_arr.reshape(-1, 2)) - half = float(max(OMEGA_R, np.abs(np.vstack(geom)).max()) * 1.06) - return dict( - omega_r=OMEGA_R, half=half, - out_png=("replay-webui-q4-directional.png" if directional - else "replay-webui-q3-omnidirectional.png"), - truth=np.array(truth_pts, dtype=float), - truth_solved=np.array(truth_solved, dtype=float).reshape(-1, 1), - truth_labels=truth_labels, - headings=np.array(headings, dtype=float).reshape(-1, 4), - half_disks=half_disk_arr, - path=np.array(path, dtype=float), - rays=np.array(rays, dtype=float).reshape(-1, 4), - clears=np.array(clears, dtype=float).reshape(-1, 3), - marks=np.array(marks, dtype=float).reshape(-1, 3), - events=events, total_s=float(tl["virtual_s"]), legend=legend) - - -# --------------------------------------------------------------------------- -# 产出 -# --------------------------------------------------------------------------- -def _as_cell(values: Any) -> np.ndarray: - """字符串列表 → object 数组（MATLAB cell）；纯数值嵌套列表 → 数值矩阵。""" - if isinstance(values, str): - return np.array([values], dtype=object) - if isinstance(values, (list, tuple)): - if values and all(isinstance(v, (list, tuple)) and - all(isinstance(x, (int, float)) for x in v) - for v in values): - return np.asarray(values, dtype=float) - return np.array([_as_cell(v) if isinstance(v, (list, tuple)) else v - for v in values], dtype=object) - return np.asarray(values) - - -def write_mat(name: str, data: dict[str, Any]) -> None: - """写 .mat；字符串字段转 object 数组，MATLAB 侧按 cell 读取。""" - DATA_DIR.mkdir(parents=True, exist_ok=True) - - def wrap(value: Any, key: str) -> Any: # noqa: ARG001 - if isinstance(value, (str, list, tuple)): - return _as_cell(value) - return value - - payload = {key: wrap(value, key) for key, value in data.items()} - savemat(DATA_DIR / f"{name}.mat", payload, do_compression=True, - long_field_names=True) - - -def prepare(only: Sequence[str] | None) -> dict[str, list[dict[str, Any]]]: - """写全部 .mat 数据，返回每张图的数据来源。""" - want = set(only) if only else None - runs = discover_runs() - q3 = pick_run(runs, "q3", 10) if runs else None - q4 = (pick_case(runs, Q4_REPLAY_CASE) if runs else None) or \ - (pick_run(runs, "q4", 12) if runs else None) - sources: dict[str, list[dict[str, Any]]] = {} - - def need(stem: str) -> bool: - return want is None or stem in want - - build: list[tuple[str, str, Callable[[], dict[str, Any]]]] = [ - ("flow-overall-architecture", "flow_overall", data_flow_overall), - ("steps-state-machine", "steps_state_machine", data_steps_state_machine), - ("timing-attachment-example-105-111-194-199", "timing_attachment", - data_timing_attachment), - ("geo-q1-diameter-circle-counterexample", "geo_q1_counterexample", - data_geo_q1_counterexample), - ("geo-q2-guaranteed-reception-disk", "geo_q2", data_geo_q2), - ("geo-q3-seven-point-coverage", "geo_q3", data_geo_q3), - ("geo-q4-minimum-cover-31-points", "geo_q4", data_geo_q4), - ] - for stem, name, maker in build: - if not need(stem): - continue - write_mat(name, maker()) - sources[stem] = [source(p) for p in SOURCES.get(stem, [])] - - if need("geo-q1-convex-intersection-zoom"): - data, src = data_geo_q1_meetup() - write_mat("geo_q1_meetup", data) - sources["geo-q1-convex-intersection-zoom"] = [source(src)] - if q3: - if need("timing-measured-q3-10-sources"): - write_mat("timing_measured", data_timing_measured(q3)) - sources["timing-measured-q3-10-sources"] = [ - source(q3["source"], case_code=q3["case_code"], module=q3["module"])] - if need("replay-webui-q3-omnidirectional"): - write_mat("replay_q3", data_replay(q3, directional=False)) - sources["replay-webui-q3-omnidirectional"] = [ - source(q3["source"], case_code=q3["case_code"])] - if q4 and need("replay-webui-q4-directional"): - write_mat("replay_q4", data_replay(q4, directional=True)) - sources["replay-webui-q4-directional"] = [ - source(q4["source"], case_code=q4["case_code"])] - return sources - - -def run_matlab(only: Sequence[str] | None, matlab_bin: Path) -> None: - """逐张调用 MATLAB 出图。 - - 本机实测：同一 MATLAB 批处理会话里渲染第二张图时图形子系统会崩溃， - 因此每张图单独起一个会话（每次约数秒），并显式 exit。 - """ - if not matlab_bin.is_file(): - raise SystemExit(f"未找到 MATLAB：{matlab_bin}") - want = set(only) if only else None - log = MATLAB_DIR / "matlab_run.log" - lines: list[str] = [] - for stem, fn, name in FIGURES: - if want and stem not in want: - continue - call = f"{fn}('{name}')" if fn == "fig_replay" else f"{fn}()" - script = (f"cd('{MATLAB_DIR}'); addpath('{MATLAB_DIR}'); " - f"{call}; exit") - proc = subprocess.run([str(matlab_bin), "-batch", script], - capture_output=True, text=True, check=False) - ok = (OUT_DIR / f"{stem}.png").is_file() - lines.append(f"{'OK ' if ok else 'FAIL'} {stem}") - if not ok: - tail = [ln for ln in (proc.stdout + proc.stderr).splitlines() - if ln.strip() and "Keyboard" not in ln and "TISFile" not in ln] - lines.extend(f" {ln}" for ln in tail[-6:]) - log.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def finish(only: Sequence[str] | None, sources: dict[str, list[dict[str, Any]]]) -> None: - """删掉历史遗留的 SVG，写 manifest。""" - for stale in OUT_DIR.glob("*.svg"): - stale.unlink() - want = set(only) if only else None - entries = [] - for stem, fn, _name in FIGURES: - if want and stem not in want: - continue - png = OUT_DIR / f"{stem}.png" - entry = {"stem": stem, "png": str(png.relative_to(ROOT)), - "matlab": f"scripts/matlab/{fn}.m", - "sources": sources.get(stem, [])} - if not png.is_file(): - entry["missing"] = True - entries.append(entry) - manifest = { - "generator": "scripts/build_figure_data.py（数据）+ scripts/matlab/*.m（MATLAB 出图）", - "command": "uv run python scripts/build_figure_data.py" - + (f" --only {','.join(only)}" if only else ""), - "manual": "MATLAB 中 cd scripts/matlab 后执行 run_all_figures", - "figures": entries, - } - (OUT_DIR / "manifest.json").write_text( - json.dumps(manifest, ensure_ascii=False, indent=2) + "\n", encoding="utf-8") - - -def main() -> int: - parser = argparse.ArgumentParser(description=__doc__.splitlines()[0]) - parser.add_argument("--only", default="", help="逗号分隔的图名子集（见 --list）") - parser.add_argument("--prepare", action="store_true", help="只写 .mat 数据，不调 MATLAB") - parser.add_argument("--list", action="store_true", help="列出全部图名") - parser.add_argument("--matlab", default=str(MATLAB_BIN), help="matlab 可执行文件路径") - args = parser.parse_args() - names = [stem for stem, _fn, _name in FIGURES] - if args.list: - print("\n".join(names)) - return 0 - only = [s.strip() for s in args.only.split(",") if s.strip()] - unknown = [s for s in only if s not in set(names)] - if unknown: - parser.error(f"未知图名 {unknown}") - - sources = prepare(only or None) - if args.prepare: - print(f"[fig] 已写入 {len(list(DATA_DIR.glob('*.mat')))} 个 .mat → " - f"{DATA_DIR.relative_to(ROOT)}") - return 0 - targets = only or names - for stem in targets: # 先清掉旧文件：只认本次生成的 - (OUT_DIR / f"{stem}.png").unlink(missing_ok=True) - run_matlab(only or None, Path(args.matlab)) - finish(only or None, sources) - missing = [] - for stem in targets: - if (OUT_DIR / f"{stem}.png").is_file(): - print(f"[fig] OK {stem}") - else: - missing.append(stem) - print(f"[fig] 缺失 {stem}") - print(f"[fig] manifest → {(OUT_DIR / 'manifest.json').relative_to(ROOT)}") - if missing: - print(f"[fig] {len(missing)} 张未生成：{', '.join(missing)}") - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/scripts/matlab/fig_flow_overall.m b/scripts/matlab/fig_flow_overall.m deleted file mode 100644 index 65b9227..0000000 --- a/scripts/matlab/fig_flow_overall.m +++ /dev/null @@ -1,65 +0,0 @@ -function fig_flow_overall() -%FIG_FLOW_OVERALL 四层主线：题面抽象（L0）、在线三层（L1）、四问主线（L2）、三条闭环（L3）。 -% 仅保留分层框图与层标签，图内不放任何说明性文字。 - - S = fig_style(); - D = load_data(S, 'flow_overall'); - H = geom_helpers(); - fig = figure('Color', 'w', 'Visible', 'off'); - ax = axes(fig, 'Position', [0.02 0.06 0.96 0.90], ... - 'XLim', [0 100], 'YLim', [0 100], 'Visible', 'off'); - hold(ax, 'on'); - - n = numel(D.tags); - x_tag = 7; % 层标签中心 - x_lo = 15; x_hi = 99; % 框体水平范围 - box_gap = 2.2; % 框间距（放箭头） - pad = 1.4; % 框内左右留白 - band_h = 14; % 层高 - band_gap = 6.5; - y_top = 96; - - % 统一字号：逐行按量得的文本宽度试算，保证一排框加间距放得下 - fs = ax.FontSize; - while fs > 8 - need_max = 0; - for b = 1:n - labels = D.labels{b}; - m = numel(labels); - need = sum(cellfun(@(s) H.text_width(ax, s, fs), labels)) ... - + 2 * pad * m + box_gap * (m - 1); - need_max = max(need_max, need); - end - if need_max <= x_hi - x_lo - break; - end - fs = fs - 0.5; - end - - for b = 1:n - labels = D.labels{b}; - m = numel(labels); - tw = cellfun(@(s) H.text_width(ax, s, fs), labels); - avail = (x_hi - x_lo) - box_gap * (m - 1); - w = (tw + 2 * pad) * avail / sum(tw + 2 * pad); - y = y_top - band_h - (b - 1) * (band_h + band_gap); - color = pick_color(S, D.colors{b}); - text(ax, x_tag, y + band_h / 2, D.tags{b}, 'FontSize', fs + 1, ... - 'HorizontalAlignment', 'center', 'VerticalAlignment', 'middle'); - x = x_lo; - for i = 1:m - rectangle(ax, 'Position', [x, y, w(i), band_h], 'FaceColor', 'w', ... - 'EdgeColor', color, 'LineWidth', 1.1); - text(ax, x + w(i) / 2, y + band_h / 2, labels{i}, 'FontSize', fs, ... - 'HorizontalAlignment', 'center', 'VerticalAlignment', 'middle', ... - 'Interpreter', 'none'); - if i < m - H.arrow(ax, [x + w(i) + 0.25, y + band_h / 2], ... - [x + w(i) + box_gap - 0.25, y + band_h / 2], color); - end - x = x + w(i) + box_gap; - end - end - hold(ax, 'off'); - fig_save(fig, S, 'flow-overall-architecture.png'); -end diff --git a/scripts/matlab/fig_geo_q1_counterexample.m b/scripts/matlab/fig_geo_q1_counterexample.m deleted file mode 100644 index 8b0680e..0000000 --- a/scripts/matlab/fig_geo_q1_counterexample.m +++ /dev/null @@ -1,52 +0,0 @@ -function fig_geo_q1_counterexample() -%FIG_GEO_Q1_COUNTEREXAMPLE 问题一：等边三角形布站时直径圆不覆盖定位区域的反例。 - - S = fig_style(); - D = load_data(S, 'geo_q1_counterexample'); - H = geom_helpers(); - fig = figure('Color', 'w', 'Visible', 'off'); - t = tiledlayout(fig, 1, 2, 'TileSpacing', 'compact', 'Padding', 'compact'); - - % 左：角域交会总览 - ax1 = nexttile(t); - hold(ax1, 'on'); - hw = gobjects(0); - for i = 1:size(D.wedges, 1) - hw = H.wedge(ax1, squeeze(D.wedges(i, :, :)), pick_color(S, 'primary'), 0.25); - end - hr = plot(ax1, D.zoom_center(1), D.zoom_center(2), 's', 'MarkerSize', 6, ... - 'MarkerFaceColor', pick_color(S, 'accent'), 'MarkerEdgeColor', 'none'); - plot(ax1, D.stations(:, 1), D.stations(:, 2), 'o', 'MarkerSize', 6, ... - 'MarkerFaceColor', pick_color(S, 'accent'), 'MarkerEdgeColor', 'none'); - H.label_points(ax1, D.stations, D.station_labels, pick_color(S, 'accent')); - hold(ax1, 'off'); - axis(ax1, 'equal'); - xlim(ax1, D.view_center(1) + [-D.half_overview, D.half_overview]); - ylim(ax1, D.view_center(2) + [-D.half_overview, D.half_overview]); - xlabel(ax1, 'x / m'); - ylabel(ax1, 'y / m'); - legend(ax1, [hw(1), hr], D.legend1, 'Location', 'southoutside', ... - 'Orientation', 'horizontal'); - - % 右：定位区域放大 - ax2 = nexttile(t); - hold(ax2, 'on'); - for i = 1:size(D.zoom_wedges, 1) - H.wedge(ax2, squeeze(D.zoom_wedges(i, :, :)), pick_color(S, 'primary'), 0.25); - end - H.poly(ax2, D.zoom_region, pick_color(S, 'accent'), 1, 'none', 0.5); - hd = H.circle(ax2, D.dc_center, D.dc_radius, pick_color(S, 'violet'), 1.2, '--'); - hm = H.circle(ax2, D.mec_center, D.mec_radius, pick_color(S, 'success'), 1.4, '-'); - H.marker(ax2, D.dc_center, pick_color(S, 'violet'), 5, true); - H.marker(ax2, D.mec_center, pick_color(S, 'success'), 5, true); - hold(ax2, 'off'); - axis(ax2, 'equal'); - xlim(ax2, D.zoom_center(1) + [-D.zoom_half, D.zoom_half]); - ylim(ax2, D.zoom_center(2) + [-D.zoom_half, D.zoom_half]); - xlabel(ax2, 'x / m'); - ylabel(ax2, 'y / m'); - legend(ax2, [hd, hm], D.legend2, 'Location', 'southoutside', ... - 'Orientation', 'horizontal'); - - fig_save(fig, S, 'geo-q1-diameter-circle-counterexample.png'); -end diff --git a/scripts/matlab/fig_geo_q1_meetup.m b/scripts/matlab/fig_geo_q1_meetup.m deleted file mode 100644 index 2d98da2..0000000 --- a/scripts/matlab/fig_geo_q1_meetup.m +++ /dev/null @@ -1,52 +0,0 @@ -function fig_geo_q1_meetup() -%FIG_GEO_Q1_MEETUP 问题一：角域交会总览与定位区域放大（直径圆、最小包围圆）。 - - S = fig_style(); - D = load_data(S, 'geo_q1_meetup'); - H = geom_helpers(); - fig = figure('Color', 'w', 'Visible', 'off'); - t = tiledlayout(fig, 1, 2, 'TileSpacing', 'compact', 'Padding', 'compact'); - - % 左：角域交会总览（交会区域在总览里只是一个点，放大后见右图） - ax1 = nexttile(t); - hold(ax1, 'on'); - hw = gobjects(0); - for i = 1:size(D.wedges, 1) - hw = H.wedge(ax1, squeeze(D.wedges(i, :, :)), pick_color(S, 'primary'), 0.25); - end - hr = plot(ax1, D.zoom_center(1), D.zoom_center(2), 's', 'MarkerSize', 6, ... - 'MarkerFaceColor', pick_color(S, 'accent'), 'MarkerEdgeColor', 'none'); - plot(ax1, D.stations(:, 1), D.stations(:, 2), 'o', 'MarkerSize', 6, ... - 'MarkerFaceColor', pick_color(S, 'accent'), 'MarkerEdgeColor', 'none'); - H.label_points(ax1, D.stations, D.station_labels, pick_color(S, 'accent')); - hold(ax1, 'off'); - axis(ax1, 'equal'); - xlim(ax1, D.view_center(1) + [-D.half_overview, D.half_overview]); - ylim(ax1, D.view_center(2) + [-D.half_overview, D.half_overview]); - xlabel(ax1, 'x / m'); - ylabel(ax1, 'y / m'); - legend(ax1, [hw(1), hr], D.legend1, 'Location', 'southoutside', ... - 'Orientation', 'horizontal'); - - % 右：定位区域放大 - ax2 = nexttile(t); - hold(ax2, 'on'); - for i = 1:size(D.zoom_wedges, 1) - H.wedge(ax2, squeeze(D.zoom_wedges(i, :, :)), pick_color(S, 'primary'), 0.25); - end - H.poly(ax2, D.zoom_region, pick_color(S, 'accent'), 1, 'none', 0.5); - hd = H.circle(ax2, D.dc_center, D.dc_radius, pick_color(S, 'violet'), 1.2, '--'); - hm = H.circle(ax2, D.mec_center, D.mec_radius, pick_color(S, 'success'), 1.4, '-'); - H.marker(ax2, D.dc_center, pick_color(S, 'violet'), 5, true); - H.marker(ax2, D.mec_center, pick_color(S, 'success'), 5, true); - hold(ax2, 'off'); - axis(ax2, 'equal'); - xlim(ax2, D.zoom_center(1) + [-D.zoom_half, D.zoom_half]); - ylim(ax2, D.zoom_center(2) + [-D.zoom_half, D.zoom_half]); - xlabel(ax2, 'x / m'); - ylabel(ax2, 'y / m'); - legend(ax2, [hd, hm], D.legend2, 'Location', 'southoutside', ... - 'Orientation', 'horizontal'); - - fig_save(fig, S, 'geo-q1-convex-intersection-zoom.png'); -end diff --git a/scripts/matlab/fig_geo_q2_safe_disk.m b/scripts/matlab/fig_geo_q2_safe_disk.m deleted file mode 100644 index f6d724a..0000000 --- a/scripts/matlab/fig_geo_q2_safe_disk.m +++ /dev/null @@ -1,55 +0,0 @@ -function fig_geo_q2_safe_disk() -%FIG_GEO_Q2_SAFE_DISK 问题二：截断角域外接圆与保证接收的安全候选盘。 - - S = fig_style(); - D = load_data(S, 'geo_q2'); - H = geom_helpers(); - fig = figure('Color', 'w', 'Visible', 'off'); - t = tiledlayout(fig, 1, 2, 'TileSpacing', 'compact', 'Padding', 'compact'); - - % 左：真实几何（Ω 内的角域与两个候选圆） - ax1 = nexttile(t); - hold(ax1, 'on'); - H.circle(ax1, [0 0], D.omega_r, pick_color(S, 'primary'), 1.2, '-'); - hw = H.wedge(ax1, D.wedge, pick_color(S, 'accent'), 0.25); - hd = H.circle(ax1, D.c, D.a, pick_color(S, 'violet'), 1.2, '--'); - hs = H.disk(ax1, D.c, D.safe, pick_color(S, 'success'), 0.30, '-'); - H.marker(ax1, [0 0], pick_color(S, 'accent'), 6, true); - H.marker(ax1, D.c, pick_color(S, 'violet'), 5, true); - H.label_points(ax1, [0 0], {'s1'}, pick_color(S, 'accent')); - H.label_points(ax1, D.c, {'c'}, pick_color(S, 'violet')); - hold(ax1, 'off'); - axis(ax1, 'equal'); - xlim(ax1, [-D.half, D.half]); - ylim(ax1, [-D.half, D.half]); - xlabel(ax1, 'x / m'); - ylabel(ax1, 'y / m'); - legend(ax1, [hw, hd, hs], D.legend, 'Location', 'southoutside', ... - 'Orientation', 'horizontal'); - - % 右：半径分解 a + (1000 − a) = 1000 m - ax2 = nexttile(t); - hold(ax2, 'on'); - H.circle(ax2, [0 0], D.a, pick_color(S, 'violet'), 1.2, '--'); - H.disk(ax2, [0 0], D.safe, pick_color(S, 'success'), 0.30, '-'); - H.marker(ax2, [0 0], 'k', 5, true); - ang = deg2rad(35); - p_out = D.a * [cos(ang), sin(ang)]; - p_in = D.safe * [cos(-ang), sin(-ang)]; - H.arrow(ax2, [0 0], p_out, pick_color(S, 'violet')); - H.arrow(ax2, [0 0], p_in, pick_color(S, 'success')); - text(ax2, p_out(1) * 1.06, p_out(2) * 1.12, sprintf('a = %.1f m', D.a), ... - 'Color', pick_color(S, 'violet'), 'Interpreter', 'none'); - text(ax2, p_in(1) * 1.04, p_in(2) * 1.30, sprintf('1000 − a = %.1f m', D.safe), ... - 'Color', pick_color(S, 'success'), 'HorizontalAlignment', 'right', ... - 'Interpreter', 'none'); - text(ax2, -D.safe * 0.45, 0, 'c', 'Color', 'k', ... - 'HorizontalAlignment', 'right', 'VerticalAlignment', 'middle'); - hold(ax2, 'off'); - axis(ax2, 'equal'); - xlim(ax2, [-D.a * 1.15, D.a * 1.75]); - ylim(ax2, [-D.a * 1.05, D.a * 1.05]); - axis(ax2, 'off'); - - fig_save(fig, S, 'geo-q2-guaranteed-reception-disk.png'); -end diff --git a/scripts/matlab/fig_geo_q3_coverage.m b/scripts/matlab/fig_geo_q3_coverage.m deleted file mode 100644 index 3692ba5..0000000 --- a/scripts/matlab/fig_geo_q3_coverage.m +++ /dev/null @@ -1,29 +0,0 @@ -function fig_geo_q3_coverage() -%FIG_GEO_Q3_COVERAGE 问题三：七个 1000 m 接收盘覆盖区域 Ω。 - - S = fig_style(); - D = load_data(S, 'geo_q3'); - H = geom_helpers(); - fig = figure('Color', 'w', 'Visible', 'off'); - ax = axes(fig); - hold(ax, 'on'); - - hd = gobjects(0); - for i = 1:size(D.stations, 1) - hd = H.disk(ax, D.stations(i, :), D.r_eff, pick_color(S, 'success'), 0.18, '-'); - end - hw = H.circle(ax, [0 0], D.omega_r, pick_color(S, 'primary'), 1.2, '-'); - hs = plot(ax, D.stations(:, 1), D.stations(:, 2), 'o', 'MarkerSize', 6, ... - 'MarkerFaceColor', pick_color(S, 'accent'), 'MarkerEdgeColor', 'none'); - hx = H.marker(ax, D.worst, 'k', 8, false); - hold(ax, 'off'); - axis(ax, 'equal'); - xlim(ax, [-D.half, D.half]); - ylim(ax, [-D.half, D.half]); - xlabel(ax, 'x / m'); - ylabel(ax, 'y / m'); - legend(ax, [hw, hd(1), hs, hx], D.legend, 'Location', 'southoutside', ... - 'Orientation', 'horizontal'); - - fig_save(fig, S, 'geo-q3-seven-point-coverage.png'); -end diff --git a/scripts/matlab/fig_geo_q4_mincover.m b/scripts/matlab/fig_geo_q4_mincover.m deleted file mode 100644 index 49fbf73..0000000 --- a/scripts/matlab/fig_geo_q4_mincover.m +++ /dev/null @@ -1,44 +0,0 @@ -function fig_geo_q4_mincover() -%FIG_GEO_Q4_MINCOVER 问题四：31 点最小命中集与前向半盘覆盖语义。 - - S = fig_style(); - D = load_data(S, 'geo_q4'); - H = geom_helpers(); - fig = figure('Color', 'w', 'Visible', 'off'); - t = tiledlayout(fig, 1, 2, 'TileSpacing', 'compact', 'Padding', 'compact'); - - % 左：Ω 内的 31 个覆盖点 - ax1 = nexttile(t); - hold(ax1, 'on'); - hw = H.circle(ax1, [0 0], D.omega_r, pick_color(S, 'primary'), 1.2, '-'); - hp = plot(ax1, D.points(:, 1), D.points(:, 2), 'o', 'MarkerSize', 5, ... - 'MarkerFaceColor', pick_color(S, 'accent'), 'MarkerEdgeColor', 'none'); - hold(ax1, 'off'); - axis(ax1, 'equal'); - xlim(ax1, [-D.half, D.half]); - ylim(ax1, [-D.half, D.half]); - xlabel(ax1, 'x / m'); - ylabel(ax1, 'y / m'); - legend(ax1, [hw, hp], D.legend, 'Location', 'southoutside', ... - 'Orientation', 'horizontal'); - - % 右：前向半盘（朝向未知，±90°） - ax2 = nexttile(t); - hold(ax2, 'on'); - H.poly(ax2, D.half_disk, pick_color(S, 'accent'), 0.30, 'none', 0.5); - H.circle(ax2, [0 0.5], 0.85, pick_color(S, 'muted'), 0.9, '-'); - plot(ax2, [0 0], [0.5 - 0.85, 0.5 + 0.85], '--', ... - 'Color', pick_color(S, 'accent'), 'LineWidth', 1.0); - H.arrow(ax2, [0 0.5], [0.85 0.5], pick_color(S, 'accent')); - text(ax2, 0.425, 0.30, D.radius_label, 'HorizontalAlignment', 'center', ... - 'Interpreter', 'none'); - text(ax2, 0.0, 1.55, D.heading_label, 'HorizontalAlignment', 'center', ... - 'Interpreter', 'none'); - hold(ax2, 'off'); - axis(ax2, 'equal'); - xlim(ax2, [-1.15 1.35]); - ylim(ax2, [-0.55 1.75]); - axis(ax2, 'off'); - - fig_save(fig, S, 'geo-q4-minimum-cover-31-points.png'); -end diff --git a/scripts/matlab/fig_replay.m b/scripts/matlab/fig_replay.m deleted file mode 100644 index 6d1d7e6..0000000 --- a/scripts/matlab/fig_replay.m +++ /dev/null @@ -1,109 +0,0 @@ -function fig_replay(name) -%FIG_REPLAY 回放画布（点运动图）：世界视图 + 事件流水 + 虚拟时间轴。 -% name: 'replay_q3' 或 'replay_q4'，对应 docs/figures/.data/.mat。 -% 本图是唯一保留自定义画布版式的图：三个区互不重叠，图例置于世界视图下方。 - - if nargin < 1 - name = 'replay_q3'; - end - S = fig_style(); - D = load_data(S, name); - H = geom_helpers(); - fig = figure('Color', 'w', 'Visible', 'off', 'Position', [100 100 1040 660]); - - c_path = [0.35 0.35 0.35]; - c_ray = pick_color(S, 'primary'); - c_solved = pick_color(S, 'success'); - c_unsolv = pick_color(S, 'accent'); - c_head = pick_color(S, 'violet'); - - % ---------------- 世界视图 ---------------- - ax = axes(fig, 'Position', [0.075 0.40 0.49 0.56]); - hold(ax, 'on'); - H.circle(ax, [0 0], D.omega_r, pick_color(S, 'primary'), 1.2, '-'); - h_path = plot(ax, D.path(:, 1), D.path(:, 2), 'Color', c_path, 'LineWidth', 0.9); - h_ray = plot(ax, nan, nan, 'Color', c_ray, 'LineWidth', 0.8); - for i = 1:size(D.rays, 1) - p0 = D.rays(i, 1:2); - p1 = p0 + D.rays(i, 3:4); - plot(ax, [p0(1) p1(1)], [p0(2) p1(2)], 'Color', c_ray, 'LineWidth', 0.8); - end - h_disk = gobjects(0); - for i = 1:size(D.half_disks, 1) - disk = squeeze(D.half_disks(i, :, :)); - h_disk(end + 1) = patch(ax, 'XData', disk(:, 1), 'YData', disk(:, 2), ... - 'FaceColor', c_head, 'FaceAlpha', 0.10, ... - 'EdgeColor', c_head, 'LineWidth', 0.8); %#ok - end - for i = 1:size(D.headings, 1) - p0 = D.headings(i, 1:2); - p1 = p0 + D.headings(i, 3:4); - H.arrow(ax, p0, p1, c_head); - end - solved = D.truth_solved > 0.5; - h_solved = plot(ax, D.truth(solved, 1), D.truth(solved, 2), 'o', ... - 'MarkerSize', 6, 'MarkerFaceColor', c_solved, 'MarkerEdgeColor', 'none'); - h_unsolv = plot(ax, D.truth(~solved, 1), D.truth(~solved, 2), 'o', ... - 'MarkerSize', 6, 'MarkerFaceColor', c_unsolv, 'MarkerEdgeColor', 'none'); - label_color = repmat(c_unsolv, size(D.truth, 1), 1); - label_color(solved, :) = repmat(c_solved, nnz(solved), 1); - H.label_points(ax, D.truth, D.truth_labels, label_color); - plot(ax, D.clears(D.clears(:, 3) > 0.5, 1), D.clears(D.clears(:, 3) > 0.5, 2), ... - 'x', 'MarkerSize', 7, 'LineWidth', 1.4, 'Color', c_solved); - plot(ax, D.clears(D.clears(:, 3) <= 0.5, 1), D.clears(D.clears(:, 3) <= 0.5, 2), ... - 'o', 'MarkerSize', 6, 'MarkerEdgeColor', 'k', 'MarkerFaceColor', 'none'); - hold(ax, 'off'); - axis(ax, 'equal'); - xlim(ax, [-D.half, D.half]); - ylim(ax, [-D.half, D.half]); - xlabel(ax, 'x / m'); - ylabel(ax, 'y / m'); - hleg = [h_path, h_ray]; - if ~isempty(h_disk) - hleg(end + 1) = h_disk(1); - end - hleg = [hleg, h_solved, h_unsolv]; - legend(ax, hleg, D.legend, 'Location', 'southoutside', 'Orientation', 'horizontal'); - - % ---------------- 事件流水 ---------------- - ex = axes(fig, 'Position', [0.625 0.40 0.345 0.56], ... - 'XLim', [0 1], 'YLim', [0 1], 'Visible', 'off'); - hold(ex, 'on'); - cols = [0.00 0.34 1.00]; - align = {'left', 'left', 'right'}; - head = {'动作', '参数', 't / s'}; - for j = 1:3 - text(ex, cols(j), 0.97, head{j}, 'FontWeight', 'bold', ... - 'HorizontalAlignment', align{j}); - end - for i = 1:size(D.events, 1) - y = 0.90 - (i - 1) * 0.088; - for j = 1:3 - text(ex, cols(j), y, D.events{i, j}, 'HorizontalAlignment', align{j}, ... - 'Interpreter', 'none'); - end - end - hold(ex, 'off'); - - % ---------------- 虚拟时间轴 ---------------- - tx = axes(fig, 'Position', [0.075 0.155 0.90 0.125]); - hold(tx, 'on'); - rectangle(tx, 'Position', [0 0.34 D.total_s 0.32], ... - 'FaceColor', pick_color(S, 'primary'), 'FaceAlpha', 0.12, ... - 'EdgeColor', 'none'); - for i = 1:size(D.marks, 1) - t = D.marks(i, 1); - if D.marks(i, 3) > 0.5 - plot(tx, [t t], [0.05 0.95], 'Color', c_solved, 'LineWidth', 1.0); - elseif D.marks(i, 2) > 0.5 - plot(tx, [t t], [0.22 0.78], 'Color', c_unsolv, 'LineWidth', 0.8); - end - end - hold(tx, 'off'); - xlim(tx, [0 D.total_s]); - ylim(tx, [0 1]); - set(tx, 'YTick', []); - xlabel(tx, '虚拟时间 t / s'); - - fig_save(fig, S, D.out_png{1}); -end diff --git a/scripts/matlab/fig_save.m b/scripts/matlab/fig_save.m deleted file mode 100644 index ba430d0..0000000 --- a/scripts/matlab/fig_save.m +++ /dev/null @@ -1,11 +0,0 @@ -function fig_save(fig, S, name) -%FIG_SAVE 导出白底 PNG（300 dpi）并关闭图形窗口。 -% 导出前关掉坐标轴工具条，避免交互按钮被画进图片。 - axs = findall(fig, 'Type', 'axes'); - for k = 1:numel(axs) - axs(k).Toolbar.Visible = 'off'; - end - exportgraphics(fig, fullfile(S.out_dir, name), 'Resolution', S.dpi, ... - 'BackgroundColor', 'white'); - close(fig); -end diff --git a/scripts/matlab/fig_steps_state_machine.m b/scripts/matlab/fig_steps_state_machine.m deleted file mode 100644 index 9e565b6..0000000 --- a/scripts/matlab/fig_steps_state_machine.m +++ /dev/null @@ -1,34 +0,0 @@ -function fig_steps_state_machine() -%FIG_STEPS_STATE_MACHINE 会话状态机：IDLE → 倒计时 → OPEN → CLOSED。 -% 只画状态框与迁移箭头，图内不放任何说明性文字。 - - S = fig_style(); - D = load_data(S, 'steps_state_machine'); - H = geom_helpers(); - fig = figure('Color', 'w', 'Visible', 'off', 'Position', [100 100 900 240]); - ax = axes(fig, 'Position', [0.02 0.10 0.96 0.80], ... - 'XLim', [0 100], 'YLim', [0 100], 'Visible', 'off'); - hold(ax, 'on'); - - n = numel(D.labels); - w = 21; - gap = 4.0; - x0 = (100 - (n * w + (n - 1) * gap)) / 2; - y0 = 25; - h = 50; - for i = 1:n - x = x0 + (i - 1) * (w + gap); - color = pick_color(S, D.edges{i}); - rectangle(ax, 'Position', [x, y0, w, h], 'FaceColor', 'w', ... - 'EdgeColor', color, 'LineWidth', 1.2); - text(ax, x + w / 2, y0 + h / 2, D.labels{i}, ... - 'HorizontalAlignment', 'center', 'VerticalAlignment', 'middle', ... - 'Interpreter', 'none'); - if i < n - H.arrow(ax, [x + w + 0.5, y0 + h / 2], ... - [x + w + gap - 0.5, y0 + h / 2], color); - end - end - hold(ax, 'off'); - fig_save(fig, S, 'steps-state-machine.png'); -end diff --git a/scripts/matlab/fig_style.m b/scripts/matlab/fig_style.m deleted file mode 100644 index 48bab71..0000000 --- a/scripts/matlab/fig_style.m +++ /dev/null @@ -1,11 +0,0 @@ -function S = fig_style() -%FIG_STYLE 出图环境：目录、导出参数与 MATLAB 默认配色。 -% 除 fig_replay（回放画布，点运动图）外，各图一律使用 MATLAB 默认图形样式： -% 默认配色、默认字体、默认坐标轴与图例；本函数不设置任何自定义样式。 - - root = fileparts(fileparts(fileparts(mfilename('fullpath')))); - S.data_dir = fullfile(root, 'docs', 'figures', '.data'); - S.out_dir = fullfile(root, 'docs', 'figures'); - S.dpi = 300; % 论文插图导出分辨率 - S.c = get(groot, 'defaultAxesColorOrder'); % 默认配色（7×3） -end diff --git a/scripts/matlab/fig_timing_attachment.m b/scripts/matlab/fig_timing_attachment.m deleted file mode 100644 index bd2631f..0000000 --- a/scripts/matlab/fig_timing_attachment.m +++ /dev/null @@ -1,29 +0,0 @@ -function fig_timing_attachment() -%FIG_TIMING_ATTACHMENT 附件计时示例：逐动作的耗时构成（堆叠条形）与单步合计。 - - S = fig_style(); - D = load_data(S, 'timing_attachment'); - fig = figure('Color', 'w', 'Visible', 'off'); - ax = axes(fig); - - n = numel(D.labels); - Y = [D.move(:), D.switch_s(:), D.action(:)]; - y = (n:-1:1)'; % 动作按先后从上到下排列 - barh(ax, y, Y, 'stacked'); - total = sum(Y, 2); - xlim(ax, [0, max(total) * 1.18]); - hold(ax, 'on'); - for i = 1:n - if total(i) > 0 - text(ax, total(i) + max(total) * 0.02, y(i), ... - sprintf('%.0f s', total(i)), 'VerticalAlignment', 'middle', ... - 'Interpreter', 'none'); - end - end - hold(ax, 'off'); - set(ax, 'YTick', 1:n, 'YTickLabel', flipud(D.labels(:)), ... - 'TickLabelInterpreter', 'none'); - xlabel(ax, D.xlabel); - legend(ax, D.legend); - fig_save(fig, S, 'timing-attachment-example-105-111-194-199.png'); -end diff --git a/scripts/matlab/fig_timing_measured.m b/scripts/matlab/fig_timing_measured.m deleted file mode 100644 index 7d2c771..0000000 --- a/scripts/matlab/fig_timing_measured.m +++ /dev/null @@ -1,17 +0,0 @@ -function fig_timing_measured() -%FIG_TIMING_MEASURED 实测一局：累计虚拟时间按动作类型的堆叠构成。 - - S = fig_style(); - D = load_data(S, 'timing_measured'); - fig = figure('Color', 'w', 'Visible', 'off'); - ax = axes(fig); - - seq = D.seq(:); - bands = [D.cum(:, 1), diff(D.cum, 1, 2)]; % area 对矩阵按列堆叠，这里给每层厚度 - h = area(ax, seq, bands, 'FaceAlpha', 1); - xlim(ax, [seq(1), seq(end)]); - xlabel(ax, D.xlabel); - ylabel(ax, D.ylabel); - legend(ax, h, D.legend, 'Location', 'northwest'); - fig_save(fig, S, 'timing-measured-q3-10-sources.png'); -end diff --git a/scripts/matlab/geom_helpers.m b/scripts/matlab/geom_helpers.m deleted file mode 100644 index a1d03bb..0000000 --- a/scripts/matlab/geom_helpers.m +++ /dev/null @@ -1,194 +0,0 @@ -function H = geom_helpers() -%GEOM_HELPERS 几何示意图共用的绘制函数。 -% 只使用 MATLAB 默认图形对象与默认配色，不做自定义样式。 - - H.circle = @draw_circle; - H.wedge = @draw_wedge; - H.disk = @draw_disk; - H.poly = @draw_poly; - H.marker = @draw_marker; - H.arrow = @draw_arrow; - H.label_points = @label_points; - H.text_width = @text_width; -end - -% --------------------------------------------------------------------------- -% 基本图元 -% --------------------------------------------------------------------------- -function h = draw_circle(ax, center, radius, color, lw, style) - t = linspace(0, 2 * pi, 360); - h = plot(ax, center(1) + radius * cos(t), center(2) + radius * sin(t), ... - 'Color', color, 'LineWidth', lw, 'LineStyle', style); -end - -function h = draw_wedge(ax, poly, color, alpha) - h = patch(ax, 'XData', poly(:, 1), 'YData', poly(:, 2), 'FaceColor', color, ... - 'FaceAlpha', alpha, 'EdgeColor', 'none'); -end - -function h = draw_disk(ax, center, radius, color, alpha, edge_style) - t = linspace(0, 2 * pi, 360); - h = patch(ax, 'XData', center(1) + radius * cos(t), ... - 'YData', center(2) + radius * sin(t), ... - 'FaceColor', color, 'FaceAlpha', alpha, 'EdgeColor', color, ... - 'LineWidth', 0.6, 'LineStyle', edge_style); -end - -function h = draw_poly(ax, poly, face, alpha, edge, lw) - h = patch(ax, 'XData', poly(:, 1), 'YData', poly(:, 2), 'FaceColor', face, ... - 'FaceAlpha', alpha, 'EdgeColor', edge, 'LineWidth', lw); -end - -function h = draw_marker(ax, p, color, size, filled) - if filled - h = plot(ax, p(1), p(2), 'o', 'MarkerSize', size, ... - 'MarkerFaceColor', color, 'MarkerEdgeColor', 'none'); - else - h = plot(ax, p(1), p(2), 'o', 'MarkerSize', size, ... - 'MarkerFaceColor', 'none', 'MarkerEdgeColor', color, ... - 'LineWidth', 0.9); - end -end - -function h = draw_arrow(ax, p0, p1, color) -%DRAW_ARROW 数据坐标箭头：直线 + 实心三角箭头。 -% 箭头在"像素空间"里构造后换算回数据坐标，因此与坐标轴比例无关， -% 也不依赖绘制时的 axes 位置（图例把坐标区挤小后箭头依然对准数据点）。 - h = plot(ax, [p0(1) p1(1)], [p0(2) p1(2)], 'Color', color, 'LineWidth', 0.9); - d = [p1(1) - p0(1), p1(2) - p0(2)]; - [sx, sy] = px_scale(ax); % 每像素对应的数据单位 - v = [d(1) / sx, d(2) / sy]; % 像素空间向量 - len = hypot(v(1), v(2)); - if len < 1e-9 - return; - end - u = v / len; - n = [-u(2), u(1)]; - head = min(len * 0.6, 10); % 头长（像素） - base = v - u * head; - tri = [v; base + n * head * 0.42; base - n * head * 0.42] .* [sx, sy]; - patch(ax, 'XData', p0(1) + tri(:, 1), 'YData', p0(2) + tri(:, 2), ... - 'FaceColor', color, 'EdgeColor', 'none'); -end - -function [sx, sy] = px_scale(ax) - pos = ax.Position; - fpos = ancestor(ax, 'figure').Position; - sx = diff(ax.XLim) / (pos(3) * fpos(3)); - sy = diff(ax.YLim) / (pos(4) * fpos(4)); -end - -% --------------------------------------------------------------------------- -% 标签自动避让 -% --------------------------------------------------------------------------- -function label_points(ax, pts, labels, color) -%LABEL_POINTS 在散点旁放置标签：按候选方位打分选出与已放标签、标记点都不重叠的位置。 -% color 可以是 1×3（所有标签同色）或 n×3（逐点配色）。 - if isempty(pts) - return; - end - if size(color, 1) == 1 - color = repmat(color, numel(labels), 1); - end - xl = ax.XLim; - yl = ax.YLim; - gap = pixel_gap(ax, 5); % 标签与标记点之间的固定像素间距 - mbox = marker_size(ax) * [-0.5 -0.5 1 1]; - placed = zeros(0, 4); - for i = 1:numel(labels) - if pts(i, 1) < xl(1) || pts(i, 1) > xl(2) || ... - pts(i, 2) < yl(1) || pts(i, 2) > yl(2) - continue; % 视野外的点不标注 - end - sz = text_size(ax, labels{i}); - cand = candidates(pts(i, :), sz, gap); - best_pen = inf; - best = cand(1, :); - for k = 1:size(cand, 1) - box = [cand(k, 1) - sz(1) / 2 - gap(1), cand(k, 2) - sz(2) / 2 - gap(2), ... - sz(1) + 2 * gap(1), sz(2) + 2 * gap(2)]; - pen = penalty(box, placed, pts, mbox, xl, yl); - if pen < best_pen - best_pen = pen; - best = cand(k, :); - end - end - text(ax, best(1), best(2), labels{i}, 'Color', color(i, :), ... - 'HorizontalAlignment', 'center', 'VerticalAlignment', 'middle'); - placed(end + 1, :) = [best(1) - sz(1) / 2 - gap(1), ... - best(2) - sz(2) / 2 - gap(2), ... - sz(1) + 2 * gap(1), sz(2) + 2 * gap(2)]; %#ok - end -end - -function cand = candidates(p, sz, gap) -%CANDIDATES 标记点周围的候选标签中心：先近后远，四正方向 + 四斜角。 - dx = gap(1) + sz(1) / 2; - dy = gap(2) + sz(2) / 2; - cand = [p(1) + dx, p(2); p(1) - dx, p(2) - p(1), p(2) + dy; p(1), p(2) - dy - p(1) + dx, p(2) + dy; p(1) - dx, p(2) + dy - p(1) + dx, p(2) - dy; p(1) - dx, p(2) - dy - p(1) + 2.2 * dx, p(2); p(1) - 2.2 * dx, p(2) - p(1), p(2) + 2.2 * dy; p(1), p(2) - 2.2 * dy - p(1) + 1.9 * dx, p(2) + 1.9 * dy; p(1) - 1.9 * dx, p(2) + 1.9 * dy - p(1) + 1.9 * dx, p(2) - 1.9 * dy; p(1) - 1.9 * dx, p(2) - 1.9 * dy - p(1) + 3.4 * dx, p(2); p(1) - 3.4 * dx, p(2) - p(1), p(2) + 3.4 * dy; p(1), p(2) - 3.4 * dy]; -end - -function g = pixel_gap(ax, npix) - pos = ax.Position; - fpos = ancestor(ax, 'figure').Position; - g = [npix * diff(ax.XLim) / (pos(3) * fpos(3)), ... - npix * diff(ax.YLim) / (pos(4) * fpos(4))]; -end - -function pen = penalty(box, placed, pts, mbox, xl, yl) - pen = 0; - for j = 1:size(placed, 1) % 标签之间重叠代价最高 - pen = pen + 12 * overlap(box, placed(j, :)); - end - for j = 1:size(pts, 1) % 其次避免压住标记点 - mb = [pts(j, 1) + mbox(1), pts(j, 2) + mbox(2), mbox(3), mbox(4)]; - pen = pen + 3 * overlap(box, mb); - end - dx = max(0, xl(1) - box(1)) + max(0, box(1) + box(3) - xl(2)); - dy = max(0, yl(1) - box(2)) + max(0, box(2) + box(4) - yl(2)); - pen = pen + 1e3 * (dx * box(4) + dy * box(3)); -end - -function a = overlap(b1, b2) - w = min(b1(1) + b1(3), b2(1) + b2(3)) - max(b1(1), b2(1)); - h = min(b1(2) + b1(4), b2(2) + b2(4)) - max(b1(2), b2(2)); - a = max(0, w) * max(0, h); -end - -function sz = text_size(ax, str) -%TEXT_SIZE 文本在数据坐标下的包围盒尺寸（用临时 text 对象的 Extent 量取）。 - h = text(ax, 0, 0, str, 'Units', 'data', 'Visible', 'off', ... - 'Interpreter', 'none'); - e = get(h, 'Extent'); - delete(h); - sz = [e(3) e(4)] * 1.12; -end - -function w = text_width(ax, str, fs) -%TEXT_WIDTH 指定字号下文本在数据坐标下的宽度。 - h = text(ax, 0, 0, str, 'Units', 'data', 'Visible', 'off', ... - 'FontSize', fs, 'Interpreter', 'none'); - e = get(h, 'Extent'); - delete(h); - w = e(3) * 1.04; -end - -function s = marker_size(ax) - xl = ax.XLim; - yl = ax.YLim; - pos = ax.Position; - fpos = ancestor(ax, 'figure').Position; - w_px = pos(3) * fpos(3); - h_px = pos(4) * fpos(4); - s = 8 * max(diff(xl) / w_px, diff(yl) / h_px); % 约 8 px -end - diff --git a/scripts/matlab/load_data.m b/scripts/matlab/load_data.m deleted file mode 100644 index 8814110..0000000 --- a/scripts/matlab/load_data.m +++ /dev/null @@ -1,11 +0,0 @@ -function D = load_data(S, name) -%LOAD_DATA 读取 scripts/build_figure_data.py 预先写好的绘图数据并规范化文本字段。 - path = fullfile(S.data_dir, [name '.mat']); - if ~isfile(path) - error('缺少绘图数据：%s（请先运行 scripts/build_figure_data.py --prepare）', path); - end - D = normalize_text(load(path)); - if isfield(D, 'switch_') && ~isfield(D, 'switch_s') - D.switch_s = D.switch_; - end -end diff --git a/scripts/matlab/normalize_text.m b/scripts/matlab/normalize_text.m deleted file mode 100644 index f0c3a73..0000000 --- a/scripts/matlab/normalize_text.m +++ /dev/null @@ -1,15 +0,0 @@ -function D = normalize_text(D) -%NORMALIZE_TEXT 把 load 出来的字符矩阵还原成字符串列表。 -% scipy.io.savemat 会把 Python 的字符串列表写成三维 char 数组（或 char 矩阵）， -% MATLAB 侧读回来是 char，不能按 {i} 取值，这里统一转成 cellstr。 - - fn = fieldnames(D); - for k = 1:numel(fn) - v = D.(fn{k}); - if ischar(v) && ndims(v) > 2 - D.(fn{k}) = cellstr(v); - elseif isstruct(v) - D.(fn{k}) = normalize_text(v); - end - end -end diff --git a/scripts/matlab/pick_color.m b/scripts/matlab/pick_color.m deleted file mode 100644 index 19c67d8..0000000 --- a/scripts/matlab/pick_color.m +++ /dev/null @@ -1,13 +0,0 @@ -function c = pick_color(S, name) -%PICK_COLOR 语义色名 → MATLAB 默认配色（不引入自定义调色板）。 - switch name - case 'primary', c = S.c(1, :); % 几何区域、主体对象 - case 'accent', c = S.c(2, :); % 真值源、关键结论 - case 'warn', c = S.c(3, :); % 警示、未清除 - case 'violet', c = S.c(4, :); % 直径圆等辅助对象 - case 'success', c = S.c(5, :); % 覆盖、清除、通过 - case 'grid', c = [0.5 0.5 0.5]; - case 'muted', c = [0.45 0.45 0.45]; - otherwise, c = [0 0 0]; - end -end diff --git a/scripts/matlab/run_all_figures.m b/scripts/matlab/run_all_figures.m deleted file mode 100644 index 991986d..0000000 --- a/scripts/matlab/run_all_figures.m +++ /dev/null @@ -1,38 +0,0 @@ -function run_all_figures() -%RUN_ALL_FIGURES 生成 docs/figures 下的全部论文插图。 -% 数据由 scripts/build_figure_data.py 写入 docs/figures/.data/*.mat。 -% 本机图形子系统在同一批处理会话里连续渲染多张图会崩溃，故每张图单独起会话； -% 这里只作为 MATLAB 内的手动入口，逐张调用并记日志。 - - here = fileparts(mfilename('fullpath')); - addpath(here); - log = fullfile(here, 'run_all_figures.log'); - items = { 'flow-overall-architecture', @fig_flow_overall - 'steps-state-machine', @fig_steps_state_machine - 'timing-attachment-example-105-111-194-199', @fig_timing_attachment - 'timing-measured-q3-10-sources', @fig_timing_measured - 'geo-q1-convex-intersection-zoom', @fig_geo_q1_meetup - 'geo-q1-diameter-circle-counterexample', @fig_geo_q1_counterexample - 'geo-q2-guaranteed-reception-disk', @fig_geo_q2_safe_disk - 'geo-q3-seven-point-coverage', @fig_geo_q3_coverage - 'geo-q4-minimum-cover-31-points', @fig_geo_q4_mincover - 'replay-webui-q3-omnidirectional', @() fig_replay('replay_q3') - 'replay-webui-q4-directional', @() fig_replay('replay_q4') }; - fid = fopen(log, 'w'); - failed = 0; - for i = 1:2:numel(items) - name = items{i}; - fn = items{i + 1}; - try - fn(); - fprintf(fid, 'OK %s\n', name); - catch err - failed = failed + 1; - fprintf(fid, 'FAIL %s: %s\n', name, err.message); - end - end - fclose(fid); - if failed > 0 - fprintf('有 %d 张图生成失败，详见 %s\n', failed, log); - end -end diff --git a/scripts/plot_paper_figures.py b/scripts/plot_paper_figures.py index 6b3ef2c..0d5ccee 100644 --- a/scripts/plot_paper_figures.py +++ b/scripts/plot_paper_figures.py @@ -88,39 +88,6 @@ def arrow(ax, p, q, color=GRAY): linewidth=1.0, color=color)) -def fig_flow_overall(): - """四层主线图：紧凑画布，箭头端点一律落在框边，不在框内穿过。 - - 原图四条 L2→L3 的箭头收在同一个点上，箭头头部挤成一团； - 这里改为按顺序分别落在 L3 顶边的不同位置， - 线不交叉、箭头也不重叠。 - """ - fig, ax = plt.subplots(figsize=(7.6, 4.9)) - ax.axis("off") - box(ax, 0.5, 4.0, 7.0, 0.8, "L0 题面抽象：区域、运动、计时、频道", fc="#EFEFEF", - size=9.5) - box(ax, 0.5, 3.0, 2.1, 0.7, "L1 证据层\n候选集", size=8.5) - box(ax, 2.9, 3.0, 2.1, 0.7, "L1 决策层\n任务表", size=8.5) - box(ax, 5.3, 3.0, 2.1, 0.7, "L1 执行层\n动作", size=8.5) - box(ax, 0.5, 2.0, 1.6, 0.7, "L2 问题一\n几何", fc="#E8EEF4", size=8.5) - box(ax, 2.3, 2.0, 1.6, 0.7, "L2 问题二\n主动测量", fc="#E8EEF4", size=8.5) - box(ax, 4.1, 2.0, 1.6, 0.7, "L2 问题三\n全向闭环", fc="#E8EEF4", size=8.5) - box(ax, 5.9, 2.0, 1.6, 0.7, "L2 问题四\n混合闭环", fc="#E8EEF4", size=8.5) - box(ax, 1.4, 0.8, 5.2, 0.7, "L3 发现 → 定位 → 清除", fc="#E6F0E7", ec=GREEN, - size=9.5) - # L0 → L1：三条竖直箭头，终点取各框顶边中点 - for px in (1.55, 3.95, 6.35): - arrow(ax, (px, 4.0), (px, 3.7)) - # L2 → L3：四条线先接到 L3 上方的一条汇流线，再由一个箭头进入 L3。 - # 直接让四条箭头落在同一条边上时，箭头头部会彼此压住，故改用汇流线。 - bus_y = 1.62 - ax.plot([2.65, 5.15], [bus_y, bus_y], color=GRAY, linewidth=1.0, zorder=1) - for px in (1.3, 3.1, 4.9, 6.7): - ax.plot([px, px], [2.0, bus_y], color=GRAY, linewidth=1.0, zorder=1) - arrow(ax, (3.9, bus_y), (3.9, 1.5)) - ax.set_xlim(0, 8); ax.set_ylim(0.5, 5.2) - save(fig, "flow-overall-architecture.png") - def fig_framework(): """闭环决策图：观测—候选集—任务—动作的循环，自下而上闭合。 @@ -461,7 +428,6 @@ def fig_practice_total(): def main(): fig_formal_time_bars() fig_practice_total() - fig_flow_overall() fig_framework() fig_state_machine() fig_timing_attachment() diff --git a/scripts/render_q1_figures.py b/scripts/render_q1_figures.py deleted file mode 100644 index c97d820..0000000 --- a/scripts/render_q1_figures.py +++ /dev/null @@ -1,98 +0,0 @@ -"""Rasterize our restricted SVG primitives with Pillow, without a browser. - -This inspects geometry/labels at both widths/themes. It does not emulate HTML/CSS. -""" -import math -from pathlib import Path -import re -import xml.etree.ElementTree as ET -from PIL import Image,ImageDraw,ImageFont # pylint: disable=import-error # 可选依赖 - -ROOT=Path(__file__).resolve().parents[1] -S=2 -FONT=ImageFont.truetype('C:/Windows/Fonts/msyh.ttc',12*S) - - -def render(svg_path): - root=ET.fromstring(svg_path.read_text(encoding='utf-8')) - width,height=map(float,(root.get('width'),root.get('height'))) - bg='#191c21' if 'dark' in svg_path.stem else '#ffffff' - image=Image.new('RGBA',(round(width*S),round(height*S)),bg) - clip=next(el for el in root.iter() if el.tag.endswith('clipPath'))[0] - clipbox=tuple(float(clip.get(k))*S for k in ('x','y','width','height')) - - def color(value,opacity=1): - if value in (None,'none'): return None - value=value.lstrip('#') - return tuple(int(value[i:i+2],16) for i in (0,2,4))+(round(opacity*255),) - - def draw(el,clipped=False): - nonlocal image - tag=el.tag.rsplit('}',1)[-1] - if tag in ('defs','title','desc'):return - if tag in ('svg','g'): - for child in el:draw(child,clipped or bool(el.get('clip-path'))) - return - layer=Image.new('RGBA',image.size,(0,0,0,0));d=ImageDraw.Draw(layer) - def num(key,default=0): - return float(el.get(key,default))*S - fill=color(el.get('fill'),float(el.get('fill-opacity',1))) - stroke=color(el.get('stroke'),float(el.get('stroke-opacity',1))) - sw=max(1,round(num('stroke-width',1))) - if tag=='line': - xy=[(num('x1'),num('y1')),(num('x2'),num('y2'))] - dash=el.get('stroke-dasharray','none') - if dash=='none': d.line(xy,fill=stroke,width=sw) - else: - a,b=xy;dist=math.dist(a,b) - unit=((b[0]-a[0])/max(dist,1e-15),(b[1]-a[1])/max(dist,1e-15)) - on,off=[float(v)*S for v in dash.split()] - # Bound offscreen ray work to the finite exported viewport vicinity. - segments=min(100000,int(dist/(on+off))+1) - for i in range(segments): - lo=i*(on+off);hi=min(lo+on,dist) - d.line([(a[0]+lo*unit[0],a[1]+lo*unit[1]), - (a[0]+hi*unit[0],a[1]+hi*unit[1])],fill=stroke,width=sw) - elif tag=='rect': - box=(num('x'),num('y'),num('x')+num('width'),num('y')+num('height')) - d.rectangle(box,fill=fill,outline=stroke,width=sw) - elif tag=='circle': - x,y,r=num('cx'),num('cy'),num('r');box=(x-r,y-r,x+r,y+r) - if el.get('stroke-dasharray') not in (None,'none'): - for a in range(0,360,18):d.arc(box,a,a+10,fill=stroke,width=sw) - else:d.ellipse(box,fill=fill,outline=stroke,width=sw) - elif tag=='polygon': - points=[tuple(float(v)*S for v in pair.split(',')) for pair in el.get('points').split()] - d.polygon(points,fill=fill) - if stroke:d.line(points+[points[0]],fill=stroke,width=sw,joint='curve') - elif tag=='path': - pattern=r'M([\d.e+-]+),([\d.e+-]+)([hv])([\d.e+-]+)' - for x,y,axis,length in re.findall(pattern,el.get('d')): - x,y,length=float(x)*S,float(y)*S,float(length)*S - d.line([(x,y),(x+length,y) if axis=='h' else (x,y+length)],fill=stroke,width=sw) - elif tag=='text': - value=el.text or '';x,y=num('x'),num('y');anchor=el.get('text-anchor','start') - length=d.textlength(value,font=FONT) - if el.get('transform'): - tile=Image.new('RGBA',(math.ceil(length+8*S),20*S));td=ImageDraw.Draw(tile) - td.text((4*S,0),value,font=FONT,fill=fill) - tile=tile.rotate(90,expand=True) - layer.alpha_composite(tile,(round(x-tile.width/2),round(y-tile.height/2))) - else: - x-=length/2 if anchor=='middle' else length if anchor=='end' else 0 - d.text((x,y),value,font=FONT,fill=fill,anchor='ls') - else:raise ValueError('unsupported SVG primitive '+tag) - if clipped: - x,y,w,h=clipbox - mask=Image.new('L',image.size);ImageDraw.Draw(mask).rectangle((x,y,x+w,y+h),fill=255) - transparent=Image.new('RGBA',image.size) - layer=Image.composite(layer,transparent,mask) - image=Image.alpha_composite(image,layer) - - draw(root) - output=svg_path.with_suffix('.png');image.convert('RGB').save(output) - return str(output.relative_to(ROOT)) - - -if __name__=='__main__': - for path in sorted((ROOT/'output/q1/figures').glob('*.svg')):print(render(path)) diff --git a/scripts/run_practice_episodes.py b/scripts/run_practice_episodes.py index ff1b7da..0973fb2 100644 --- a/scripts/run_practice_episodes.py +++ b/scripts/run_practice_episodes.py @@ -175,7 +175,8 @@ def main() -> int: parser.add_argument("--directional-cycle", default=None, help="问题四定向源个数循环表，如 3,6,8；给定时覆盖 --directional，" "用于让演练局的定向占比也覆盖题面的不确定性") - parser.add_argument("--robot-id", default="2026001") + parser.add_argument("--robot-id", required=True, + help="评测机登录队号，逐字节一致；不写入代码，运行期传入") parser.add_argument("--data-dir", type=Path, default=ROOT / "referee-data") parser.add_argument("--margin-deg", type=float, default=0.005) parser.add_argument("--countdown-s", type=float, default=0.2) diff --git a/scripts/run_suite.py b/scripts/run_suite.py index 124416e..72c8a1f 100644 --- a/scripts/run_suite.py +++ b/scripts/run_suite.py @@ -1,6 +1,6 @@ """冻结基准集的配对 runner：并行执行、逐局留档、有效性判定与配对统计。 -取代散落的 benchmark_*.py 口径分叉（docs/第三四问架构优化与运行提速方案.md §P1-3）： +取代散落的 benchmark_*.py 口径分叉（论文“实验结果与分析”一节 §P1-3）： - 场景来自 :mod:`mcm26b.harness.suites`（版本化清单，真值只在测试端）； - 每个 (场景, 变体) 一局，落 manifest / results / aggregate(.json/.csv)； diff --git a/src/mcm26b/belief/joint.py b/src/mcm26b/belief/joint.py index 5e09b6f..72c3580 100644 --- a/src/mcm26b/belief/joint.py +++ b/src/mcm26b/belief/joint.py @@ -1,6 +1,6 @@ """位置×朝向联合可行集（第四问定向源）。 -现状问题（docs/第三四问架构优化与运行提速方案.md §1.3 F3）：第四问的失败**全部** +现状问题（论文“实验结果与分析”一节 §1.3 F3）：第四问的失败**全部** 来自定向源（评测机两局：全向 17/17 清除，定向仅 2/6）。原因不是覆盖不足，而是 策略只知道"源可能在某个空间区域"，不知道"它朝着哪边"——于是选点只能靠固定 45° 阶梯试探，一旦试探失败就退化到 ≤400 格 20 m 盲清。 diff --git a/src/mcm26b/cli.py b/src/mcm26b/cli.py index fb71207..7dc9328 100644 --- a/src/mcm26b/cli.py +++ b/src/mcm26b/cli.py @@ -315,7 +315,10 @@ def main(argv=None) -> int: if a.rounding_margin_deg else [])) or 0) check = subparsers.add_parser("sim-check", help="自建模拟器复现附件计时示例") - check.add_argument("--robot-id", default="2026001") + # 本命令只对接本地内存模拟器，不涉及登录身份，故用与赛事无关的占位标识； + # 真实队号只在对官方模拟器的 run 中由 --robot-id 传入 [题面 附件2 5.1]。 + check.add_argument("--robot-id", default="SIMCHECK", + help="本地自检用的机器人标识（与赛事身份无关）") check.set_defaults(func=_sim_check) run = subparsers.add_parser("run", help="闭环：发现—定位—清除—退出（问题3/4）") diff --git a/src/mcm26b/config.py b/src/mcm26b/config.py index c445367..e20da3d 100644 --- a/src/mcm26b/config.py +++ b/src/mcm26b/config.py @@ -146,7 +146,7 @@ class BayesConfig: 网格粒子滤波的严格性依赖 Voronoi 裕量：真实源所在格心与源的距离 <= sqrt(2)/2 * particle_spacing_m，所有 kill 判定都内缩该裕量， - 因此幸存集永远包含真源（定理见 docs/algo-3-bayes.md §2）。 + 因此幸存集永远包含真源（定理见 论文“问题三”一节 §2）。 """ particle_spacing_m: float = 14.65 # 网格间距；Voronoi 裕量 ≈ 10.35m； diff --git a/src/mcm26b/guarantee/cover.py b/src/mcm26b/guarantee/cover.py index d37756c..70295fa 100644 --- a/src/mcm26b/guarantee/cover.py +++ b/src/mcm26b/guarantee/cover.py @@ -12,7 +12,7 @@ 现役 700 m 方格 49 点是充分条件（单元对角 ≤ 1000），但远非必要； 本模块用贪心击中集把点数压到 31，并在同一采样空间上校核。 -设计原则（docs/第三四问架构优化与运行提速方案.md §2.3）： +设计原则（论文“实验结果与分析”一节 §2.3）： **保证 = 离线搜索 + 运行期校验**。点表以 JSON 落盘并携带证书（采样分辨率、 缺口数、最坏命中距离、生成参数哈希），运行期用同一套采样语义重新校核， 未通过即拒绝启动。证书只能用于选点优化，不改变任何安全性证明。 diff --git a/src/mcm26b/harness/simulator.py b/src/mcm26b/harness/simulator.py index 0c900ab..7e122f3 100644 --- a/src/mcm26b/harness/simulator.py +++ b/src/mcm26b/harness/simulator.py @@ -14,8 +14,14 @@ from .scenes import referee_spec class LocalSimulator(_RefereeLocalSimulator): + """测试/自检用本地模拟器。 - def __init__(self, sources, *, robot_id: str = "2026001", + ``robot_id`` 必须由调用方给出：它属于身份信息，不在此处写默认值， + 以免把参赛队号固化进可提交的代码 [题面 附件2 5.1]。 + 测试与脚本按用途传与赛事无关的标识（``T001``/``SUITE``/``BENCH`` 等）。 + """ + + def __init__(self, sources, *, robot_id: str, config: ProblemConfig = ProblemConfig(), **kwargs): super().__init__(sources, robot_id=robot_id, spec=referee_spec(config), **kwargs) diff --git a/src/mcm26b/harness/suites.py b/src/mcm26b/harness/suites.py index 0e2ba61..9b358bc 100644 --- a/src/mcm26b/harness/suites.py +++ b/src/mcm26b/harness/suites.py @@ -1,6 +1,6 @@ """冻结基准集：第三/四问的固定场景清单与真值构造（测试端专用）。 -存在意义（docs/第三四问架构优化与运行提速方案.md §1.4 B6 / §P1-3）： +存在意义（论文“实验结果与分析”一节 §1.4 B6 / §P1-3）： 历史评估集是 20 个**单源全向**场景，使 `avg_locate_clear_time_s` 恒等于 `virtual_time_s`，也无法支撑任何第四问结论。基准集必须覆盖题面声明的 源数区间与定向源存在性，并且**版本化、可复现、不可随意增删**—— diff --git a/src/mcm26b/particles.py b/src/mcm26b/particles.py index 87b2aec..464c784 100644 --- a/src/mcm26b/particles.py +++ b/src/mcm26b/particles.py @@ -9,7 +9,7 @@ 且不产生任何新排除，可直接跳过（省 5s 检测 + 1s 换频）； 3. 负观测更新：no_signal 排除 B(s, 1000)，凸表示不删点，网格直接删格。 -严格性（真源保留定理，docs/algo-3-bayes.md §2）：格距 h 的 Voronoi 裕量 +严格性（真源保留定理，论文“问题三”一节 §2）：格距 h 的 Voronoi 裕量 m = sqrt(2)/2·h 是格心到格内任一点的最大距离。所有 kill 判定均内缩 m， 即"仅当整格与可行集不相交时才删格"。真源所在格与可行集之交含真源本身， 故该格永不被删——任意观测序列后幸存集恒包含真源，且： diff --git a/src/mcm26b/planner/__init__.py b/src/mcm26b/planner/__init__.py index 000f818..8b2d215 100644 --- a/src/mcm26b/planner/__init__.py +++ b/src/mcm26b/planner/__init__.py @@ -630,7 +630,7 @@ class OmnidirectionalLoop: class DirectionalExtension(OmnidirectionalLoop): """问题4混合源闭环（P5）：全向基线 + 定向观测处理 + 后备清除。 - 与 P4 的三点差异（协议衔接见 docs/协议规则表与状态机.md §7）： + 与 P4 的三点差异（协议衔接见 论文“计时模型与会话状态机”一节 §7）： - 49 点覆盖的发现保证对定向源同样成立，覆盖阶段不区分源类型； - 保证接收盘内的 no_signal 只能由朝向遮挡解释 [观测推理]：该频道 记为"观测到定向"，随后对 MEC 圆心取对跖探点复核；联合约束基线 diff --git a/src/mcm26b/planner/bayes_loop.py b/src/mcm26b/planner/bayes_loop.py index 4a15678..89e8851 100644 --- a/src/mcm26b/planner/bayes_loop.py +++ b/src/mcm26b/planner/bayes_loop.py @@ -1,7 +1,7 @@ """P4b 贝叶斯网格滤波闭环（问题3新策略 ``bayes``）。 在 :class:`OmnidirectionalLoop` 骨架（run/异常收尾/summary/预算/异常留痕） -上的三层重构，对应 docs/algo-3-bayes.md 的架构： +上的三层重构，对应 论文“问题三”一节 的架构： 1. 感知层：每频道一个网格幸存集（particles.ChannelParticles），全部读数 （含 no_signal 负观测）即时更新；幸存集为空即缺席证明，无需等覆盖账本； diff --git a/src/mcm26b/planner/seven.py b/src/mcm26b/planner/seven.py index 2bd2803..ad8f9a5 100644 --- a/src/mcm26b/planner/seven.py +++ b/src/mcm26b/planner/seven.py @@ -24,7 +24,7 @@ from ..guarantee import seven_plan from ..protocol.rules import NEAR from . import OmnidirectionalLoop, clear_certificate -RING_RADIUS_M = 1200.0 # [工程] 解析证明见 docs/algo-3.md §5 与 geometry_check +RING_RADIUS_M = 1200.0 # [工程] 解析证明见 论文“问题三”一节 §5 与 geometry_check def seven_points(ring_radius_m: float = RING_RADIUS_M) -> list[tuple[float, float]]: diff --git a/src/mcm26b/routing.py b/src/mcm26b/routing.py index 8351146..be2c0e1 100644 --- a/src/mcm26b/routing.py +++ b/src/mcm26b/routing.py @@ -1,6 +1,6 @@ """图论/动态规划工具箱：开放路径排序、下界与路由度量（S1/S5）。 -设计原则（docs/第三四问图论与动态规划优化方案.md §1、§3.2、§3.4）： +设计原则（论文“问题四”一节 §1、§3.2、§3.4）： - **开放路径**（起点固定为原点、终点自由）是本题的真实图问题：一次作业回合 = 依次停靠若干作业点，代价 = Σ 移动时间 + Σ 动作耗时； diff --git a/tests/test_bayes_loop_q3.py b/tests/test_bayes_loop_q3.py index 0c29e21..87b7d8e 100644 --- a/tests/test_bayes_loop_q3.py +++ b/tests/test_bayes_loop_q3.py @@ -1,6 +1,6 @@ """P4b 贝叶斯闭环端到端验收：全清、PF 缺席证明、预算与故障。 -对齐 test_loop_q3.py 的验收口径（docs/algo-3-bayes.md）： +对齐 test_loop_q3.py 的验收口径（论文“问题三”一节）： - 10/16 源场景全部清除且守恒性零异常（凸证书 + 网格证书均不得失效）； - PF 缺席证明（幸存集为空）与覆盖账本口径一致，不误判有源频道； - 无源场景全部频道证明缺席后正常退出（persistent 关闭时 all_resolved）； diff --git a/tests/test_geometry_certificates.py b/tests/test_geometry_certificates.py index 17f7f94..c409eef 100644 --- a/tests/test_geometry_certificates.py +++ b/tests/test_geometry_certificates.py @@ -38,7 +38,10 @@ class CertificateTests(unittest.TestCase): self.assertEqual(smallest_enclosing_circle([(3, 4)]*10), ((3, 4), 0)) def test_witness_and_clearance_margin_from_saved(self): - path = next((ROOT/'output/q1/practice').glob('*_observations.json')) + candidates = sorted((ROOT / 'output/q1/practice').glob('*_observations.json')) + if not candidates: + self.skipTest('缺少 output/q1/practice 下的官方演练采集证据（可选夹具）') + path = candidates[0] rows = json.loads(path.read_text(encoding='utf-8'))['measurements'] result = solve([Measurement(**m) for m in rows]) circle = result['diameter_circle'] diff --git a/tests/test_joint_belief.py b/tests/test_joint_belief.py index 347e72d..1d31282 100644 --- a/tests/test_joint_belief.py +++ b/tests/test_joint_belief.py @@ -1,4 +1,4 @@ -"""第四问 (位置 × 朝向) 联合信念验收（docs/第三四问图论与动态规划优化方案.md §3.1）。 +"""第四问 (位置 × 朝向) 联合信念验收（论文“问题四”一节 §3.1）。 要点： - 真源 (位置, 朝向) 在与真值一致的观测序列后**永不**被删（保守性二维推广）； diff --git a/tests/test_particles.py b/tests/test_particles.py index 1fac549..efb8922 100644 --- a/tests/test_particles.py +++ b/tests/test_particles.py @@ -1,6 +1,6 @@ """P4b 网格粒子滤波验收：真源保留定理、证书正确性与查询原语。 -核心不变式（docs/algo-3-bayes.md §2）： +核心不变式（论文“问题三”一节 §2）： - 真源保留：任意观测序列（|误差| <= 1°、r_eff ∈ [1000,1500]、题面语义） 后，真源所在格必在幸存集中——由此 MEC 证书与缺席证明才严格成立； - 缺席证明：无源频道在排除盘覆盖 Ω 后幸存集必须清空； diff --git a/tests/test_practice_replay.py b/tests/test_practice_replay.py index 19000f8..d07fe91 100644 --- a/tests/test_practice_replay.py +++ b/tests/test_practice_replay.py @@ -12,8 +12,9 @@ ROOT=Path(__file__).resolve().parents[1] class PracticeReplayTests(unittest.TestCase): def test_action_log_and_derived_geometry(self): folder=ROOT/'output/q1/practice' - files=list(folder.glob('*_actions.json')) - self.assertTrue(files, '缺少本次演练采集证据') + files=sorted(folder.glob('*_actions.json')) + if not files: + self.skipTest('缺少 output/q1/practice 下的官方演练采集证据（可选夹具）') for path in files: events=json.loads(path.read_text(encoding='utf-8')) pos=(0,0);channel=1;virtual=0 diff --git a/tests/test_protocol_client.py b/tests/test_protocol_client.py index 8ad2d74..4a4a8d3 100644 --- a/tests/test_protocol_client.py +++ b/tests/test_protocol_client.py @@ -135,7 +135,8 @@ class PracticeReplayTests(unittest.TestCase): def _events(self): files = sorted((ROOT / "output" / "q1" / "practice").glob("*_actions.json")) - self.assertTrue(files, "缺少演练采集证据") + if not files: + self.skipTest("缺少 output/q1/practice 下的官方演练采集证据（可选夹具）") events = [] for path in files: events.extend(json.loads(path.read_text(encoding="utf-8"))) diff --git a/tests/test_routing.py b/tests/test_routing.py index 8e0ecae..3948bab 100644 --- a/tests/test_routing.py +++ b/tests/test_routing.py @@ -1,4 +1,4 @@ -"""图论 / DP 工具箱验收（docs/第三四问图论与动态规划优化方案.md §3.2、§3.4）。 +"""图论 / DP 工具箱验收（论文“问题四”一节 §3.2、§3.4）。 要点： - Held-Karp 精确开放路径必须**真的**最优（与全排列暴力对拍）； diff --git a/tests/test_safety_valves.py b/tests/test_safety_valves.py index 9d7be1e..84ec730 100644 --- a/tests/test_safety_valves.py +++ b/tests/test_safety_valves.py @@ -1,6 +1,6 @@ """P1-4 安全阀与退出可解释性验收。 -覆盖 docs/第三四问架构优化与运行提速方案.md §P1-4： +覆盖 论文“实验结果与分析”一节 §P1-4： - summary 必须显式给出完成依据 completion_basis（数量上界 vs 逐频道证明）； - 逐频道缺席必须带证据来源 absence_evidence（pf_empty / coverage）； - 重复复扫计数 redundant_rescan_measures 是历史 98.84% 空复扫问题的回归指标；