diff --git a/README.md b/README.md index 4d4badd..e01ef0c 100644 --- a/README.md +++ b/README.md @@ -7,6 +7,8 @@ - [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) - [问题三搜索与总耗时改进方案（含七点覆盖证明和自建消融）](docs/algo-3.md) +- [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) - [论文](docs/paper/main.tex) ## 项目结构 @@ -21,10 +23,12 @@ src/mcm26b/ ├── 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） -scripts/ # 采集、基准、可视化与校核脚本（含问题四批量仿真 benchmark_q4.py） +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） output/ examples/ # 演练证据、算例与生成产物 ``` @@ -134,6 +138,24 @@ client.exit() | 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） | | P6 加速与消融 | ✅ 七点批量（`--strategy seven`，官方演练 12/12）+ 贝叶斯闭环（`--strategy bayes` 默认，自建消融 −30.0%，[算法说明](docs/algo-3-bayes.md)） | +| 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 diff --git a/docs/paper/ref.bib b/docs/paper/ref.bib new file mode 100644 index 0000000..e69de29 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 new file mode 100644 index 0000000..09adaa4 --- /dev/null +++ b/docs/第三四问图论与动态规划优化方案.md @@ -0,0 +1,516 @@ +# 第三、四问：图论与动态规划算法引入方案 + +- 版本：2026-09-12 +- 前置文档：[第三四问架构优化与运行提速方案](第三四问架构优化与运行提速方案.md)（架构与 P0–P2 项）、 + [algo-3-bayes.md](algo-3-bayes.md)（现役第三问策略）、[algo-3.md](algo-3.md)（七点覆盖证明） +- 范围：只讨论"用图论 / 动态规划类算法进一步压低**虚拟耗时**"这一件事； + 题面约束、协议语义、保守性不变量全部不动 +- 本文所有数字均为**本轮新增实测**（自建内存回环、冻结场景、现役代码）， + 复现命令见 §7 + +--- + +## 0. 结论先行 + +**可以，而且第四问的空间远大于第三问；但真正的收益不在"把巡回排得更短"，而在 +"把不必要的往返与盲清从决策层消掉"。** + +| 判断 | 证据 | 可回收 | +| --- | --- | --- | +| 第三问**路由**已接近最优，纯图论排线几乎无空间 | 对**同一批实际作业点**求离线最优开放回路：2308 s vs 实测移动 2458 s，只剩 **6.1%（151 s）** | ≤ 150 s | +| 第三问仍有空间，但在**探测次数**（序贯决策），不在排线 | 30.8 次探测 / 13 源，且 0 次 `no_signal`——每次都在有效收缩，但次数仍偏多 | −150 ~ −250 s | +| 第四问**路由**有 20% 空间，但那是被定位/清除绕行"拖"出来的 | 离线最优 4998 s vs 实测移动 6257 s（**20.1%**）；而覆盖点巡回的投影顺序 4365 s 已接近离线 31 点最优 4181 s（+4.4%） | −400 ~ −700 s | +| 第四问最大单点是**定向源定位失败导致的后备盲清** | 平均每局 **131 次清除只有 13 次成功**，`fallback_clear_attempts=125.7`，清除动作 419 s + 清除段移动 798 s ≈ **1220 s/局（14% T）** | **−800 ~ −1100 s** | +| 第四问第二点是**探测本身** | 平均 69 次探测 ≈ **2224 s/局（26% T）**，其中 32 s/次的移动占 1880 s | −400 ~ −800 s | +| 覆盖点集**已经最小**，不要再压 | 27 点方案在 75 m/24 档"通过"，但在 25 m/48 档有 **298 个 (p,h) 缺口** → 是采样过拟合的假解；在细网格上直接搜索的最小命中集仍是 **31 点** | 0（见 §5.1 的正确性风险） | + +**一句话：第三问的目标是"把 30.8 次探测压到 ~26 次并把序排到最优"，第四问的目标是 +"把 125 次盲清变成 3 次有保证的探测"。** 两者都是 DP / 序贯决策问题， +不是经典的 TSP 排线问题。 + +--- + +## 1. 目标函数的图论化 + +题面计时模型（`config.CostConfig`，已由 `evaluation.recompute_virtual_time` 逐微秒复算验证）： + +``` +T = L/5 + 5·N_measure + 1·N_switch + 5·N_clear⁺ + 3·N_clear⁻ +``` + +把它写成图上的路径代价，后面所有算法都在这张图上做： + +- **节点** = 一次"停靠"（覆盖站点、定位测点、清除点）； +- **边权** `w(a,b)` = `dist(a,b)/5`（移动秒）； +- **节点服务费** = `5·(该点要测的频道数) + 1·(换频次数)` 或 `5/3`（清除）。 +- 于是整局 = 一条**开放路径**（起点原点、终点自由），目标是最小化 + `Σ边权 + Σ服务费 + Σ动作惩罚`。 + +四类图问题分别对应四个子决策： + +| 子决策 | 图问题 | 现役做法 | 目标做法 | +| --- | --- | --- | --- | +| 站点/任务排序 | **开放 TSP（小 n 精确）** | NN + 2-opt（滚动） | Held-Karp 精确 DP（n ≤ 14，含换频状态）| +| 定位测点选择 | **带邻域的 TSP / 定向问题（TSPN/OP）** | 每任务独立选点 + 全表重排 | 覆盖骨架 + 最便宜插入；测点作为"邻域"参与联合优化 | +| 定向源定位 | **POMDP / 随机最短路** | 一步贪心 `prob_detect/时间` | 2–3 步 rollout，值函数 = 期望后验 MEC 半径 | +| 后备清除 | **区域覆盖路径（Covering Path）** | 格心最近邻 | 割草机 + 转向点 DP / 格心集 Or-opt | + +--- + +## 2. 本轮实测基线（现役代码，冻结场景） + +场景集：`harness/suites.py`，第三问 core 的 seed 100–105 × 13 源； +第四问 core 全部 9 场（10/13/16 源 × 3 定向占比）。配置为现役默认。 + +### 2.1 第三问（`bayes`，13 源，6 场） + +| 指标 | 数值 | +| --- | ---: | +| 平均虚拟时间 | **3197 s** | +| 移动 | 12292 m = **2458 s（77%）** | +| 覆盖点检测 | 83.5 次 = 501 s（其中 69.8 次 `no_signal`，属缺席证明） | +| 定位探测 | **30.8 次 = 185 s**（0 次 `no_signal`） | +| 清除动作 | 65 s | +| **离线最优开放回路（同一批作业点）** | **2308 s** | +| 路由可回收 | **151 s（移动的 6.1%，T 的 4.7%）** | +| 作业点数（去重） | 35–50 | + +### 2.2 第四问（`directional` + `q4_mincover_v1`，9 场） + +| 指标 | 数值 | +| --- | ---: | +| 平均虚拟时间 | **8676 s** | +| 移动合计 | 6257 s（72%） | +| ├ 覆盖段移动 | 3579 s | +| ├ **探测移动** | **1880 s** | +| └ **清除移动** | **798 s** | +| 动作耗时 | 2149 s（覆盖 1386 / 探测 344 / 清除 419） | +| 探测次数 | 69.0 次（`no_signal` 4–12 次） | +| **清除次数 / 成功** | **131.1 / 13.0 → 118 次失败** | +| `fallback_clear_attempts` | **125.7 次/局** | +| 后备盲清格数（单次） | 3 ~ 210 格，全部以 `cleared` 结束 | +| **离线最优开放回路（同一批作业点）** | **4998 s** | +| 路由可回收 | 1259 s（移动的 20.1%，T 的 14.5%） | +| 覆盖投影顺序 vs 离线 31 点 2-opt | 4365 s vs 4181 s（+4.4%） | + +### 2.3 成本地板估计 + +| 问题 | 不可压缩部分 | 现状 | 真实可压缩 | +| --- | --- | ---: | ---: | +| 第三问 | 七点环 1392 s + 覆盖检测 501 s + 清除动作 65 s + 每源必经的接近/清除移动 + 每源 ≥2 次探测 | 3197 s | **~300–450 s（10–14%）** | +| 第四问 | 31 点巡回 ~4180 s + 缺席检测 ~1386 s + 每源 ≥3 次探测与一次清除 | 8676 s | **~2000–2800 s（23–32%）** | + +> 第四问的 4000 s 验收线**依然不可达**（单是 31 点巡回 + 缺席检测就 ≈5566 s）， +> 这与前置文档 §6.2 的结论一致；本方案只承诺把 8676 s 压向 6500 s 量级。 + +--- + +## 3. 算法设计 + +### 3.1 D1（第四问最高优先）：定向源的"抵近 + 交会"滚动时域 DP + +**问题本质**：±1° 示向度误差在距离 d 处给出的横向不确定度约 `d·tan(1°) ≈ 0.0175 d`。 +d = 1000 m 时单条方位线的横向约束只有 ±17.5 m；两条方位线的交会角 θ 决定最终尺度 +`≈ 0.0175 d / sin θ`。要压到 20 m 清除证书，必须**同时**满足：交会角大、距离近。 +现役 `_visibility_probe` 只最大化 `prob_detect(q)/(移动+6)`，天然偏好"近且可见"， +与"交会角大"无关；远距离又永远收敛不到 20 m，于是退化到 20 m 网格盲清。 + +**形式化**（这就是一个 POMDP）： + +- 状态 `b` = `JointBelief`（位置 × 24 档朝向的可行集，见 `belief/joint.py`）， + 再叠加 `r_eff ~ U[1000,1500]` 的不确定度； +- 动作 `q` ∈ 候选测点（保证接收点 ∪ 对跖点 ∪ 三圈环探 ∪ **朝 MEC 圆心的抵近点**）； +- 观测 `o ∈ {direction, near, no_signal}`，由每条粒子 `(p, h, r_eff)` 解析预测； +- 代价 `c(q) = 6 s + dist(pos,q)/5`； +- 终止：`MEC(b).radius ≤ 20 − margin`（可一次清除）； +- 值函数：`V(b) = min_q E_o[ c(q) + V(b') ]`。 + +**实现选择（按代价递增）**： + +1. **一步期望后验半径**（最小改动，复用 Q3 已有内核的二维推广）： + 对候选 `q`，按粒子权重模拟三分支结果，用幸存子集的 MEC 半径近似 `V(b')`， + 取 `E[radius] + λ·c(q)` 最小者。把 `particles.py::expected_posterior_radius` + 的向量化实现在 `JointBelief` 上重写一份（numpy 可用时广播）。 +2. **2–3 步 rollout**：对前 K=8 个候选各展开一层，深度 2；墙钟预算 ≤ 0.3 s/决策。 +3. **交会角代理（解析、零墙钟）**：若一步法来不及落地，先用 + `score(q) = −E[radius] · sin(Δbearing(q, 既有方位线)) / dist(q, center)` + 的确定性代理，实验证明有效后再换 rollout。 + +**关键结构性改动**：把候选点集从"绕 MEC 的固定环"扩展为**含抵近点**， +即允许 `q` 落在 `center + α·(pos − center)` 上、`α` 从 0.6 到 1.0 采样。 +这样当远距离无法收敛时，策略会**主动抵近**并以一次正常角域证书收尾， +而不是把 200 格盲清当作唯一出路。后备清除**保留为安全网**，但目标是触发次数下降 ≥ 80%。 + +**验收**：`fallback_clear_attempts` ≤ 15/局（现 125.7）；定向频道平均探测次数 +≤ 全向频道的 1.5 倍；清除成功率 ≥ 90%；9 场景全清率 100%。 +**预期**：−800 ~ −1100 s。 + +--- + +### 3.2 D2：覆盖骨架 + 最便宜插入（TSPN 的在线近似） + +**问题本质**：31 个覆盖点是**必须在场**的（缺席证明），但探测点与清除点是 +**临时产生**的。现役做法把两类点混在一张任务表里，每次从零做 NN + 2-opt +（`bayes_loop._order_tasks`），等于让临时任务把骨架顺序"打散"。 +实测投影顺序 4365 s vs 离线最优 4181 s，说明骨架顺序本身没坏， +坏在**绕行开销**——而绕行开销正是插入位置没优化。 + +**形式化**：这是 **TSP with Neighborhoods / Generalized TSP**： + +- 骨架：离线求覆盖点集的近优开放回路 `S`（`7` 点用解析证明；`31` 点用 Or-opt/3-opt， + `n ≤ 16` 时可直接 Held-Karp 精确）； +- 在线：给定当前任务 `q` 与其**邻域**（`clear` 任务的邻域是 `C₂₀(K)`，`locate` + 任务的邻域是候选测点集），求 + `Δ(i,j) = dist(a_i, q) + dist(q, a_{j}) − dist(a_i, a_j)` + 最小的插入位置 `(i,j)`；这正是"图上的最便宜插入"，`O(|S|²)` 每任务。 + +**实施**： + +1. `guarantee/cover.py` 增加 `optimize_tour(points, seed_tour, passes)`（Or-opt + 2-opt）， + 把 `nn_tour_m` 从"上界"变成"已优化值"，并写入证书 `tour_virtual_s`。 +2. `planner/bayes_loop.py` 新增 `_skeleton`（覆盖点顺序）与 `_insert_task(task, neighborhood)`； + `_order_tasks` 退化为"骨架遍历 + 已插入任务"，保留现有 2-opt 作为兜底开关。 +3. `clear` 任务的邻域用 **`C₂₀(K)` 内离插入点最近的点**（见 3.4），使插入代价与 + 清除目标联合最小。 + +**验收**：第四问移动 ≤ 5000 s（现 6257 s）；路由相对离线最优的 gap +（新增回归指标 `route_gap_vs_offline`）从 20.1% 降到 ≤ 10%。 +**预期**：−400 ~ −700 s。 + +--- + +### 3.3 D3：后备清除的覆盖路径（若 D1 后仍触发） + +后备清除是"用 20 m 圆盘覆盖残差区域 `R` 直到命中真源"——即 **Covering Path Problem**。 + +- 当前 `_fallback_clear` 对格心做 nearest-first 扫描，等价于格心集上的 NN 开放路径， + 典型约为最优的 1.2–1.3 倍； +- 改进：对格心集做 **Or-opt + 2-opt**（`k ≤ 400`，`O(k²)` 一次约 0.1 s，且只在 + 极少数局触发）； +- 上界更优解：凸区域上的**割草机（boustrophedon）扫描**，用 DP 在扫描线方向选择 + 转向点可精确最优；对一般多边形用梯形分解 + DP。 + +**验收**：后备触发时移动 ≤ 现值的 80%（单次）；触发次数本身由 D1 压到 ≤ 15/局。 +**预期**：−100 ~ −200 s（D1 未完全消除时的兜底）。 + +--- + +### 3.4 T1/T3：第三问的精确排线、换频 DP 与清除点选择 + +**(a) Held-Karp 精确任务序（含换频状态）** + +现役 `_two_opt` 是启发式且被 4 轮上限截断。任务表典型只有 6–12 项， +可以**精确求解**： + +``` +dp[S][j][c] = 已访问任务集 S、停在任务 j、测向频道为 c 的最小代价 +转移：dp[S∪{k}][k][c'] = dp[S][j][c] + dist(pos_j, pos_k)/5 + service(k, c, c') +``` + +- 规模：`|T| ≤ 12` 时 `2^12 × 12 × 20 ≈ 1.0M` 状态，纯 Python 约 0.1–0.3 s/次； + 滚动重排每步一次会吃掉预算，因此**只在任务表新增/删除任务时重解**， + 或在 `|T| ≤ 9` 时默认启用、否则回退 2-opt + Or-opt。 +- 换频代价精确化：`service` 依赖进入/离开频道，把 `c` 并入状态即可精确； + 现役 `_path_cost` 用"按频道排序"的贪心近似，会低估/高估 1–3 s/站。 +- 附图论下界：对当前点集算 **MST 权重**（`O(n²)` Prim）， + 得到开放路径最优值的下界，写入 `summary.route_lower_bound_s`， + 用于**在线后悔值审计**与回归（"本步排序距最优 ≤ x%"）。 + +**验收**：`route_gap_vs_offline` ≤ 3%；单局墙钟增量 ≤ 0.2 s。 +**预期**：−120 ~ −200 s。 + +**(b) 探测的 2 步 rollout（T2）** + +现役 `_select_point` 是一步贪心（期望 log 幸存占比 + λ·移动）。 +把它扩成深度 2 的 rollout：对前 `K=8` 个候选，各展开一次测量的三分支 +（direction / near / no_signal），取 `E[V]` 最小者。 +`V` 沿用现有 `expected_survival`。这属于**滚动时域 DP**，不改任何安全性判定。 + +**验收**：探测次数 30.8 → ≤ 26；墙钟 ≤ 0.3 s/决策。 +**预期**：−150 ~ −250 s。 + +**(c) 清除点选在 `C₂₀(K)` 内离当前位置最近处（T3）** + +`C₂₀(K) = ∩_{g∈K} B(g, 20)` 是凸集（圆盘之交）。求其内离当前点最近点是一个 +**凸可行性/投影问题**（对小规模可枚举多边形顶点 + 圆盘投影，或直接迭代投影）。 +现役 `_near_clear_target` 只沿一维方向平移，与最优之差 ≤ 0.74 m（前置文档 P1-1 已复核）， +收益主要来自**与 3.2 插入代价的耦合**：先定插入位置、再选清除点，比反过来省更多。 + +**验收**：清除段移动下降 ≥ 5%。 +**预期**：−50 ~ −150 s。 + +**(d) 七点环半径（T4）**：`1159.7 → 1122.9`（可证明下界），−44 s。 +仅作为"证明性"改动落地，不作为收益项。 + +--- + +## 4. 实施路线 + +| 阶段 | 内容 | 交付物 | 验收门槛 | 依赖 | +| --- | --- | --- | --- | --- | +| **S0 基线冻结**（0.5 天） | 把近期 sweep 的最优参数（`route_aware_probe=false`、`lambda_move_s≈0.06`、`opportunistic_radius_ratio≈0.4–0.5`）回填为基准，跑满 core+boundary 并归档 | `output/suite/q3-baseline`、`q4-baseline` | 现有 `valid` 口径 100% | 无 | +| **S1 度量补全**（0.5 天） | 在 `scripts/run_suite.py` 增加 `route_gap_vs_offline`、`route_lower_bound_s`、`distinct_service_points`、`coverage_actual_visited` 四列 | runner 改动 + 回归 | 与本文 §2 实测复现一致 | S0 | +| **S2 覆盖证书硬化**（1 天，**正确性优先**） | 证书分辨率 75 m/24 → 20 m/72；设计裕量 `r_design ≤ 960 m` 或细网格直接搜索；保留 10% 保险点预算 | `guarantee/cover.py`、`plans/*.json`、正/负例测试 | 细网格（15 m/72）缺口 = 0；故意删点必须被检出 | S1 | +| **S3 第四问 D1**（2–3 天） | `belief/joint.py` 增加期望后验半径 / rollout 选点；`planner/__init__.py::_probe_candidates` 增加抵近点；后备降级为安全网 | `belief/joint.py`、`planner/__init__.py`、测试 | `fallback_clear_attempts ≤ 15`；全清 100%；墙钟 ≤ 1.0 s/局 | S2 | +| **S4 第四问 D2/D3**（2 天） | 覆盖骨架 + 最便宜插入；`C₂₀` 邻域；后备 Or-opt | `planner/*`、`guarantee/cover.py` | 移动 ≤ 5000 s；`route_gap ≤ 10%` | S3 | +| **S5 第三问 T1/T2/T3**（2 天） | Held-Karp（含换频）+ MST 下界；探测 2 步 rollout；`C₂₀` 最近点 | `planner/bayes_loop.py`、`plan/*` | 探测 ≤ 26；`route_gap ≤ 3%`；T ≤ 2900 s | S1 | +| **S6 收口**（1 天） | 全量配对、论文数据表、消融图 | 报告 + `docs/paper` 数据 | 与 S0 配对全部 `valid`，无退化场景 | S4/S5 | + +**顺序约束**：S1（度量）先于任何算法改动，否则"更快"不可证；S2（证书）先于 +第四问算法改动，否则可能在错误的安全基线上做优化。 + +--- + +## 5. 风险与明确不做的部分 + +### 5.1 本轮发现的正确性风险（必须先处置） + +现役 `q4_mincover_v1`（31 点）与 `square49`（49 点）在**粗采样**下缺口为 0， +但把校核分辨率从 75 m/24 档提到 25 m/48 档： + +| 点集 | 点数 | 75 m/24 缺口 | 25 m/48 缺口 | 15 m/72 缺口 | 最坏命中@75 m | 最坏命中@25 m | +| --- | ---: | ---: | ---: | ---: | ---: | ---: | +| `square49` | 49 | 0 | 0 | 0 | 760.3 m | 760.3 m | +| `q4_mincover_v1`（现役） | 31 | 0 | **125** | **566** | 999.1 m | 1000.0 m | +| 细网格重搜最小集 | 31 | 0 | 0 | 35 | 983.0 m | 996.2 m | +| 贪心 27 点（75 m 档最优） | 27 | 0 | **298** | 1117 | 986.5 m | 1000.0 m | + +结论： +1. **31 点很可能就是实际下界附近**：多起点随机贪心 + 冗余剪枝在 25 m/48 细网格上 + 直接搜索的最小解仍是 31 点（尚未做 LP 松弛下界，但连贪心都下不去）； + 之前看到的 27 点是**采样过拟合的假解**，不可采纳； +2. `q4_mincover_v1` 的安全裕量几乎为 0（999.1 m），存在"真源恰落在细缝 + `r_eff` 取下界" + 的漏检窗口，现役测试没暴露是因为随机场景未命中细缝且 `r_eff` 通常 > 1000； +3. `square49` 裕量充足但巡回 36.4 km，不可回退； +4. 正确做法是**细网格重搜 + 设计裕量**（S2），代价约 0–150 s，**先保安全再谈提速**。 + +### 5.2 其他风险 + +| 风险 | 影响 | 缓解 | +| --- | --- | --- | +| POMDP rollout 墙钟超预算 | 单局计算 > 1 s | 深度 2、候选截断 K=8、`--profile` 纳入基准必报列；超预算自动退回一步法 | +| Held-Karp 状态爆炸（任务表变长） | 单步计算抖动 | 仅在 `|T| ≤ 9` 默认启用，`> 9` 回退 2-opt + Or-opt | +| 抵近点破坏可见性保证 | 探测反而 `no_signal` 变多 | 抵近点仍以 `prob_detect > 0` 过滤；`no_signal` 是合法负观测，不破坏守恒性 | +| 骨架插入与"找全即停"冲突 | 为走骨架而多绕行 | 骨架只提供顺序，不新增必须访问的点；任务表为空即停 | +| 细网格证书重搜耗时 | 离线 3–5 min | 一次性离线搜索 + 运行期按计划名缓存校验（沿用 `load_plan` lru_cache） | + +### 5.3 明确不做 + +- **不重引入 PPO/HRL**（前置文档已用 109 776 步的负面结果排除）； +- **不引入新的运行时依赖**（仍只有 `requests`；`numpy` 仅作可选加速后端， + `networkx`/`scipy` 不进入运行时路径）； +- **不改变停止条件**（`unresolved_channels` 的口径取舍属于评分方式问题， + 须先与题面评分方式核对，本轮不作为默认行为）； +- **不承诺第四问 4000 s**（成本地板 ≈5566 s，见 §2.3）。 + +--- + +## 6. 验收与度量 + +### 6.1 有效性定义（沿用，不得放宽） + +``` +valid := 全部真源被清除 ∧ 缺席频道与真值一致 ∧ 正常 /exit + ∧ 零算法异常 ∧ 逐微秒计时复算一致 +``` + +### 6.2 新增必报列（写入 `scripts/run_suite.py` 逐局行与 aggregate） + +| 列 | 定义 | 用途 | +| --- | --- | --- | +| `route_gap_vs_offline` | `实测移动 − 离线最优开放回路（同作业点集）` / 实测移动 | 区分"排线不够好"与"点选得不对" | +| `route_lower_bound_s` | 当前任务集 MST 权重 / 速度 | 在线后悔值审计 | +| `distinct_service_points` | 去重作业点数 | 探测是否真的在减少 | +| `coverage_actual_visited` | 实际测过的覆盖点数 | 缺席证明是否提前结束 | +| `probe_no_signal_ratio` | 探测中 `no_signal` 占比 | D1 的效果指标 | +| `fallback_clear_attempts` | 已有 | D1 的主验收指标 | + +### 6.3 目标（vs §2 基线） + +| 指标 | Q3 现 | Q3 目标 | Q4 现 | Q4 目标 | +| --- | ---: | ---: | ---: | ---: | +| core 平均虚拟时间 | 3197 s | **≤ 2900 s** | 8676 s | **≤ 6500 s** | +| 移动 | 2458 s | ≤ 2250 s | 6257 s | ≤ 5000 s | +| 探测次数 | 30.8 | ≤ 26 | 69.0 | ≤ 45 | +| 后备盲清次数 | 0 | 0 | 125.7 | **≤ 15** | +| `route_gap_vs_offline` | 6.1% | ≤ 3% | 20.1% | ≤ 10% | +| `fallback` 清除成功率 | — | — | 10% | ≥ 90% | +| 单局计算墙钟 | 1.0 s | ≤ 1.2 s | 1.0 s | ≤ 1.5 s | +| 细网格证书缺口（15 m/72） | — | 0（Q3 解析） | 566 | **0** | + +### 6.4 回归 + +`uv run pytest`（当前 232 项）必须全过；新增： + +- `CoveragePlan` 细网格正/负例（删掉任一点必须被检出）； +- `JointBelief` 的期望后验半径与真源保留性质对拍（随机 `(p,h)` × 随机观测序列）； +- `Held-Karp` 与 `2-opt` 在同一小任务集上的最优性对拍（DP 结果必须 ≤ 2-opt）； +- `C₂₀` 投影的三角不等式性质（投影点距候选集任一点仍 ≤ 20）； +- 逐位一致性：numpy 与纯 Python 两条路径在同一动作序列下 `actions_sha256` 相同。 + +--- + +## 7. 复现命令（本轮实测） + +```bash +# 度量基线（§2.1/§2.2/§2.3）：作业点分解 + 离线最优回归 + 成本地板 +uv run python /tmp/analyze_headroom.py # 路由 gap：Q3 6.1%，Q4 20.1% +uv run python /tmp/decompose.py # Q4 三段移动 + 动作 + 清除成功率 +uv run python /tmp/q3stats.py # Q3 探测/覆盖检测/跳测统计 + +# 覆盖证书分辨率（§5.1） +uv run python /tmp/verify_margin.py # 现役 31 点在 25 m/48 档缺口 125 +uv run python /tmp/hitset_fine.py # 细网格最小解仍是 31 点 + +# 既有常驻工具 +uv run python scripts/run_suite.py --problem 4 --suite all --jobs 6 \ + --variants directional@q4_mincover_v1 --label q4-baseline +uv run python scripts/verify_q4_cover.py --out output/q4/cover-certificate.json +uv run python scripts/analyze_run_timing.py +``` + +> 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 new file mode 100644 index 0000000..920dbba --- /dev/null +++ b/docs/第三四问架构优化与运行提速方案.md @@ -0,0 +1,595 @@ +# 第三、四问系统性架构优化与运行提速方案 + +- 版本：2026-09-12 +- 依据：本仓库运行历史（`session.jsonl`、`practice-p3-*.jlog`、`referee-data/sessions/*`、 + `output/hrl/ui/q3-main/*`、`docs/` 全部性能记录）+ 本文档新增的复跑与离线校核实验 +- 范围：第三问（全向源）与第四问（混合定向源）的**闭环架构、算法分解、运行治理**， + 不改动题面约束、协议层语义与既有保守性不变量 +- 结论先行：**虚拟时间的 75–83% 是移动**，移动里约一半不是覆盖巡回而是定位/清除往返； + **第四问的全部失败来自定向源**；旧机器学习层已被证明不如手写基线。 + 因此本轮优化的主线是"**把定位并入覆盖巡回 + 把发现集压到最小命中集 + 给定向源建位置×朝向联合信念**"， + 而不是再堆学习算法。 + +--- + +## 0. 摘要（TL;DR） + +### 0.1 三个待解的结构性浪费 + +| 编号 | 现象 | 直接证据 | 估计可回收 | +| --- | --- | --- | --- | +| W1 | Q3 定位探点往返 5741 m / 1148 s，与覆盖巡回（6960 m / 1392 s）相当 | 本次复跑（13 源，seed 0）：24 次探点，均值 442 m/次 | **−900 ~ −1100 s/局** | +| W2 | Q4 发现集是 700 m 方格 49 点，巡回 36.4 km；同一保证下贪心最小命中集只要 31 点、22.9 km | `scripts/verify_q4_cover.py`：43 032 个 (p,h) 采样，两组点集均 100% 覆盖 | **−2700 s（巡回）+ −1140 s（检测）** | +| W3 | Q4 定向源没有"位置×朝向"联合信念，只能阶梯探点，最后退化到 ≤400 格盲清 | referee 两局：全向 17/17 清除，定向仅 2/6；4/4 漏检全是定向 | **−1500 ~ −3000 s/局** | + +### 0.2 预期收益（自建模拟器，同一冻结基准集，配对口径） + +| 指标 | Q3 现状 | Q3 目标 | Q4 现状 | Q4 目标 | +| --- | ---: | ---: | ---: | ---: | +| 平均虚拟时间 | 3707 s（本次 6 场景）/ 3536 s（13 源单局） | **≤ 2700 s** | 13 652 s（10 源 3 定向单局）/ 13 165 s（4 场景均值） | **≤ 9000 s** | +| 最大虚拟时间 | 4628 s | **≤ 4000 s** | > 17 000 s（边界压力） | ≤ 12 000 s | +| 每局计算墙钟 | 2.9–6.5 s | **≤ 0.8 s** | 6.5 s | **≤ 1.0 s** | +| 有效局（全清+缺席无误判+零异常+正常退出+计时复算一致） | 6/6 | 基准集 100% | 4/4 | 基准集 100% | + +> 4000 s 是本项目 `docs/算法审核与优化-20260911.md` 自设的验收线，不是题面评分线； +> 第四问在 10 源场景下**不承诺**达成 4000 s，理由见 §6.2。 + +### 0.3 明确不做的事 + +- 不重新引入 PPO/HRL。运行历史支持"保留手写基线"：HRL 训练 4007.9 s / 109 776 步后 + 仍被 `bayes` 稳定压制 4.3–5.0%（17–18/20 场景，符号检验 p=0.0001），且 94% 的差距来自 + 多走的移动；其评估集 20 个场景**全部是单源全向**，无法支撑任何第四问结论。 +- 不引入新的第三方运行时依赖（仍是 `requests`；`numpy` 仅作**可选加速后端**，缺失时回退纯 Python）。 + +### 0.4 实施进展与实测回填（2026-09-12） + +冻结基准集 `harness/suites.py`（第三问 18 主场景 + 2 边界；第四问 9 + 2）， +并行 runner `scripts/run_suite.py`，配对口径同场景同误差种子。 + +| 项 | 状态 | 实测 | 结论 | +| --- | --- | --- | --- | +| P1-4 安全阀 | ✅ 已实现 | `summary` 新增 `completion_basis` / `absence_evidence` / `redundant_rescan_measures` / `virtual_reserve_s`；`--persistent` 输出单轮上界告警 | 采纳（5 项测试） | +| P0-2 最小命中集 | ✅ 已实现并默认开启 | 第四问 11 场景配对：**14003.0 → 10975.5 s**（均值 −19.3%、中位 −25.4%、10/11 更快）；检测 444.5→319.2；请求 569.5→395.6；位移 54.9→44.2 km；最大 17007→12919 s；11/11 valid | **采纳** | +| P0-4 感知层向量化 | ✅ 已实现 | 单局计算 **3.10 → 1.00 s（3.08×）**，最大 5.84 → 1.30 s；20 场景的虚拟时间与动作序列**逐位一致** | **采纳**（目标 ≤0.8 s 未达，余下为 belief 表示层） | +| P0-1 融合巡回 | ❌ 实测无效，已回退默认 | 仅追加站点方位线：3454.6 vs 3426.9 s（+0.8%，检测 +10.8 次、位移仅 −188 m）；追加 + 抑制专用探点：**4989.0 vs 3426.9 s（+45.6%，20/20 更慢）** | 关闭（保留开关与代码） | +| P1-1 C₂₀ 最近清除点 | ⊘ 复核后降为非项 | 现有 `_near_clear_target` 已取安全球 `B(c, limit−ρ)` 内离当前位置最远点；与完整 C₂₀ 最优之差 ≤ slack+margin ≈ 0.74 m | 不改 | +| P1-3 基准集/并行 | ✅ 已实现 | 20 场景 × 3 变体 = 60 局 **26.5 s**（6 进程）；20 局 **3.9 s** | 采纳 | +| P0-3 联合信念 | ✅ 已实施并于 2026-09-12 修正 | 网格粒子 × 24 档朝向 bin + 可见性选点；**发现并修复 `no_signal` 朝向规则反向缺陷**（原会删掉真源朝向档）；配套 D1 探点口径使第四问后备盲清 **125.7 → 4.3 次/局** | 采纳（结论与实测见[图论与 DP 优化方案 §8](第三四问图论与动态规划优化方案.md)） | + +**P0-1 为什么失败（重要负面结论）**：七站环上通常只有 2–3 个站能对同一源取得 +有效方位线，且站间交会角不足以直接换到 20 m 证书，专用信息增益探点仍然必要； +"顺路测量"于是退化为平白多花检测，一旦抑制专用探点则会白等巡回、远端绕行。 +**结论：第三问的定位往返不是可回收的浪费，`bayes` 的滚动时域调度已在做正确的事。** + +**第四问现状与目标差距（2026-09-12 图论/DP 轮回填）**：启用硬化覆盖计划 +`q4_mincover_v2` 与 D1 探点口径后，第四问 11 场景（core + boundary）均值为 +**9069.9 s**、最大 12081.1 s、后备盲清 24.8 次/局（core 9 场景 8602.0 s / +4.3 次），仍远高于 4000 s 验收线。剩余成本主要是最小命中集巡回 +（23.9 km ≈ 4777 s）与缺席频道检测，二者都是覆盖证明的固有代价； +本方案 §2.3 的"可压缩 23–32%"在本模拟器上只回收了约 1%（详见图论与 DP 方案 §8）。 + +--- + +## 1. 运行历史复核 + +### 1.1 数据资产清单（本次实际读取） + +| 资产 | 内容 | 可用信息 | +| --- | --- | --- | +| `session.jsonl`（31 011 行） | 官方第三问演练 J2WH-2K7D-5ZXS-DNK6 的客户端日志 | 10 323 次 `/measure`、12 次 `/clear`、逐动作计时 | +| `practice-p3-*.jlog` | 同一局的官方行为日志封装 | 仅明文信封（题号 3、队号、案例码）；载荷 AES-256-GCM + RSA-OAEP，不可解 | +| `referee-data/sessions/*`（6 局，5 局完整） | 本地评测机 q3×3、q4×2、截断×1 | 逐请求响应、真值、summary、step 事件 | +| `output/hrl/ui/q3-main/` | HRL 训练（109 776 步）+ `eval/at-40k`、`eval/at-final` | monitor CSV、模型元数据、20 场景配对评估 | +| `docs/算法审核与优化-20260911.md`、`docs/algo-3.md`、`docs/algo-3-bayes.md` | 历史 KPI 与消融 | 官方/自建成绩、验证集分档 | +| 本次新增复跑 | `scripts/benchmark_q3_bayes.py`（6 场景）、Q3/Q4 单局计时分解、`cProfile`、站点三角化可行性 | §1.3 中标注"本次复跑"的数字 | +| 本次新增工具（已入库） | `scripts/analyze_run_timing.py`（任意会话日志的计时分相复算）、`scripts/verify_q4_cover.py`（第四问覆盖证书 + 最小命中集搜索） | F4、F9 可复现 | + +### 1.2 先确认度量可信（否则一切优化都是自欺） + +官方计时模型：`T = L/5 + 5·N_measure + N_switch + 5·N_clear⁺ + 3·N_clear⁻`， +其中 `L` 为累计直线位移，`/clear` **不计**换频。`scripts/verify_q3_practice.py` 对 5 局完整 +评测机日志逐微秒复算，与服务器返回的 `virtual_time_s` 完全一致（例：BBM9 复算 3134.642907 = 记录 3134.642907）。 +结论：**后续所有"虚拟时间"结论都可用动作日志独立复算**，这是本项目最可靠的一条工程资产，必须保留。 + +### 1.3 关键事实（F1–F13） + +| 编号 | 事实 | 出处 | +| --- | --- | --- | +| F1 | 虚拟时间构成：**移动 75–83%｜检测动作 15–21%｜换频 2.3–3.7%** | 官方会话 82.30/14.76/2.94；referee q3 80.50/16.78/2.71、q4 78.39/18.63/2.98；本次复跑 Q3 79.9/17.3/2.9、Q4 75.2/21.1/3.7 | +| F2 | 官方第三问演练 J2WH 总虚拟 350 227.7 s，其中 **346 169.9 s（98.84%）是空复扫**：7 个固定站点 × 8 个未清除频道 × 183 轮，0 检出，结束点正好落在 `virtual_reserve_s=10 000` 边界 | `session.jsonl` 相位切分；`planner/__init__.py:236`、`config.py:99` | +| F3 | **第四问的全部失败来自定向源**：referee 两局全向 17/17 清除，定向仅 2/6；4/4 漏检全是定向；每清除源 356.4 s（Q3 274.0 s，+30%） | `referee-data/sessions/{6X58,BAU8}`；`{BBM9,RM7Y,VQKB}` | +| F4 | 本次复跑 Q3（13 源，seed 0）：3535.9 s / 109 次检测 / 13 清除；**七站巡回 6960 m（1392 s，39%）+ 探点往返 5741 m（1148 s）+ 清除移动 3668 m（734 s）** | 本次复跑（方法见附录 A） | +| F4b | 评测机 Q3 单局 BBM9：**21 次专用探点占 1138.7 s 移动，等于该局虚拟时间的 36.3%**，而站点扫频 98 次只占 991.2 s | `scripts/analyze_run_timing.py referee-data/sessions/BBM9-.../log.jsonl --plan seven` | +| F5 | 本次复跑 Q4（10 源 3 定向，seed 0）：13 652.1 s / 560 次检测 / 18 次清除（8 次失败）/ 51 356 m | 本次复跑 | +| F6 | 感知层是墙钟热点：单局计算 6.04 s；`cProfile` 下 `expected_survival` 占累计时间 **72%**（2808 次调用），`apply_measure` ≈ 20%，`_kill_outside_wedge` 触发 498 万次 `angular_gap` | 本次 `cProfile`（13 源单局） | +| F7 | HRL 运行：109 776/200 000 步、4007.9 s、**27.0 steps/s（6.75/env）**、1.51 s 墙钟/局；被 SIGTERM 截断时回报仍在上升（Q1 1.05 → Q4 6.20） | `output/hrl/ui/q3-main/q3.json` + 4 份 monitor CSV | +| F8 | HRL 评估：20 场景**全部单源全向**；`bayes` 比 HRL 快 4.3–5.0%（17–18/20，p=0.0001），**94% 的差距是多走的移动**；40k→109k 步仅再改善 0.7% | `eval/at-40k|at-final/aggregate.*`、`eval/manifest.json` | +| F9 | 现役 Q4 发现集：700 m 方格 49 点、NN 巡回 **36.4 km**；满足同一"前向半盘命中"保证的贪心最小集 **31 点、22.9 km（−37%）**，覆盖校核 100% | `scripts/verify_q4_cover.py`；本文附录 A | +| F10 | Q3 七站巡回**已经最优**：6 × 1159.7 = 6958 m（观测 6960 m），无可压缩空间；把环半径降到覆盖下界 1122.9 m 仅省 222 m（44 s） | 几何计算 + 本次复跑 | +| F11 | 站点三角化可行：**99.28% 的源位置有 ≥2 个站在 1500 m 内**；最佳交会角 5 分位 58.5°、中位 122.9°；只有 0.72% 的源位置仅 1 个站在射程内 | 本次离线采样（20 000 点） | +| F12 | 墙钟量级差三个数量级：内存回放 **0.22–0.25 ms/请求**（≈0.35 s/局），官方接口 **9.42 ms 均值/请求**（104.49 s / 10 337 请求）；本地评测机 24.5–97.9 ms/请求 | referee `session_end`、`runs/*/summary.json`、`session.jsonl elapsed_s` | +| F13 | 历史 KPI 参照：Q3 官方最好 4058.71 s（3BW5，13/13）、5232.13 s（J4ER，12/12）；自建 `bayes` 3707.3 s（本次 6 场景）/ 3726.2 s（14 场景消融）/ 验证集 3315.89 s；Q4 自建 13 164.7 s，**无官方第四问演练记录** | `docs/算法审核与优化-20260911.md`、`docs/algo-3-bayes.md`、`README.md:135` | + +### 1.4 由事实导出的六处结构性瓶颈 + +| 编号 | 瓶颈 | 支撑事实 | 归属层 | +| --- | --- | --- | --- | +| B1 | 定位/清除移动没有并入覆盖巡回，探点按"信息增益"选，移动权重 λ=0.0105 几乎不惩罚绕行 | F1、F4、F4b、F11 | 决策层 | +| B2 | Q4 发现集冗余 63%（49 点 vs 30 点），且每个"真缺席"频道要在全部 49 点各测一次 | F5、F9 | 保证层 | +| B3 | Q4 定向源缺少位置×朝向联合信念：`no_signal` 只记"被遮挡过几次"，选点靠固定 45° 阶梯，收敛失败后退化到 ≤400 格 20 m 盲清（≤1200 s + ≤400 请求） | F3、`planner/__init__.py:691-768` | 感知层 + 决策层 | +| B4 | 感知层全 Python 容器：每局 20 个频道各建 ~6.1 万格 `set`，`_kill_outside_wedge` 每次遍历全部幸存格，评分核 `expected_survival` 为 7×800 的纯 Python 双循环 | F6 | 感知层 | +| B5 | 研发吞吐低：场景串行、单进程；一局 0.35–6.5 s；HRL 27 steps/s；14–52 场景的配对基准要跑几分钟到十几分钟 | F6、F7、F12 | 实验层 | +| B6 | 运行治理缺失：单源评估集使指标退化（`avg_locate_clear_time_s ≡ virtual_time_s`）、`eval/manifest.json` 三份重复且与 `output/hrl/ui/manifest.json` 不一致（后者缺失）、`persistent_search=True` 可造成 98.84% 的无效开销且无硬性告警 | F2、F8、F13 | 遥测/实验层 | + +--- + +## 2. 目标架构 + +### 2.1 分层与依赖方向（依赖只能向下） + +``` + ┌───────────────────────────────────────────────┐ + L6 实验/遥测 │ bench/ 冻结基准集·并行 runner·配对统计 │ + │ telemetry/ manifest·step log·replay·summary │ + └───────────────▲───────────────────────────────┘ + │ 只读观测 + ┌───────────────┴───────────────────────────────┐ + L5 执行/安全 │ exec/ 动作账本·预算守卫·安全阀·幂等客户端 │ + └───────────────▲───────────────────────────────┘ + │ Action 请求 + ┌───────────────┴───────────────────────────────┐ + L4 决策 │ plan/ 任务表·滚动时域排序·信息/时间评分 │ + └───────────────▲───────────────────────────────┘ + │ Task[] + ┌───────────────┴───────────────────────────────┐ + L3 保证 │ guarantee/ 命中集覆盖计划·清除证书·缺席证明 │ + └───────────────▲───────────────────────────────┘ + │ 候选集/幸存集 + ┌───────────────┴───────────────────────────────┐ + L2 信念 │ belief/ BeliefBackend：OccupancyTensor(Q3) │ + │ JointTensor(位置×朝向, Q4) │ + └───────────────▲───────────────────────────────┘ + │ Observation + ┌───────────────┴───────────────────────────────┐ + L1 观测模型 │ model/ 可见性半平面·r_eff 带·有界误差·舍入裕量 │ + └───────────────▲───────────────────────────────┘ + │ 原始响应 + ┌───────────────┴───────────────────────────────┐ + L0 协议/时钟 │ protocol/ 规则表·计时模型·状态机·客户端（冻结）│ + └───────────────────────────────────────────────┘ +``` + +与现状的差别：**用组合取代继承**。现状是 +`OmnidirectionalLoop → SevenBatch → BayesLoop → DirectionalExtension` 的单继承链， +调度、信念、清除、预算、报表五种职责混在同一族类里（`planner/__init__.py` 818 行 + +`planner/bayes_loop.py` 513 行），任何一处改动都会跨层影响其它三问。 +目标形态是 + +```python +Episode( + client = SimulatorClient(...), # L0，冻结 + belief = OccupancyTensor(...) | JointTensor(...), # L2 + plan = CoveragePlan.points, # L3 + scorer = InfoGainPerSecond(...), # L4 + policy = FusedTourPolicy(...) | DirectionalPolicy(...), # L4 + certifier= Certificate(...), # L3 + ledger = Telemetry(...), # L6 +) +``` + +三层保证策略矩阵只差**三个可替换部件**，其余代码共享： + +| 问题 | 信念后端 | 覆盖计划 | 选点策略 | +| --- | --- | --- | --- | +| Q3 | `OccupancyTensor`（全向） | 七站环（已经最优，冻结） | 融合巡回 | +| Q4 | `JointTensor`（位置×朝向） | **最小命中集**（30 点，离线搜索 + 运行期校验证书） | 可见性约束选点 | + +### 2.2 接口契约（关键签名） + +```python +# L1 model/observation.py —— 只此一处定义"什么算看见" +@dataclass(frozen=True) +class ObservationModel: + r_eff_min_m: float; r_eff_max_m: float + near_radius_m: float; half_angle_deg: float + directional_half_deg: float = 90.0 # 含边界 + + def visible(self, src: Vec2, heading_deg: float | None, reader: Vec2) -> bool: ... + def detectable(self, src: Vec2, heading_deg: float | None, reader: Vec2) -> bool: ... + +# L2 belief/base.py —— 两种后端实现同一协议 +class BeliefBackend(Protocol): + def apply(self, channel: int, reader: Vec2, result: str, + svd_deg: float | None) -> None: ... + def alive(self, channel: int) -> int: ... + def is_absent(self, channel: int) -> bool: ... # 幸存集为空 + def certificate(self, channel: int) -> ClearCertificate | None: ... + def guaranteed_no_signal(self, channel: int, reader: Vec2) -> bool: ... + def prob_detect(self, channel: int, reader: Vec2) -> float: ... # 决策用 + def version(self, channel: int) -> int: ... # 派生量缓存键 + +# L3 guarantee/cover.py —— 覆盖计划是"可离线搜索、可在运行期校验"的一等对象 +@dataclass(frozen=True) +class CoveragePlan: + problem: int # 3 | 4 + points: tuple[Vec2, ...] + certificate: CoverCertificate # 采样分辨率、缺失数、最坏裕量、脚本哈希 + version: str + def verify(self, model: ObservationModel, tol_m: float) -> CoverCertificate: ... + +# L4 plan/policy.py +class Policy(Protocol): + def tasks(self, belief: BeliefBackend, state: RobotState) -> list[Task]: ... + def order(self, tasks: list[Task], state: RobotState) -> list[Task]: ... + def measured_at_point(self, point: Vec2) -> tuple[int, ...]: ... # 一停多测 +``` + +### 2.3 四条设计原则 + +1. **保守性不变量不动**：真源永不被删；清除证书始终基于完整 ±1.005° 角域与题面距离带； + 所有启发式（均匀先验、朝向先验、信息增益）**只用于选点，不用于安全证明**。 + Q4 的朝向联合信念必须自带"朝向未观测时退化为全向"的保守分支。 +2. **保证 = 离线搜索 + 运行期校验**：覆盖集、清除点、巡回顺序都可以离线用数值搜索优化， + 但运行期必须用同一份 `ObservationModel` 重新校验证书（点集、分辨率、裕量）， + 校验失败即拒绝启动，而不是相信一个写死的点表。 +3. **一个事实一个来源**：题面常量、`r_eff` 带、清除半径、动作耗时只在 `config.py`； + 可见性语义只在 `model/observation.py`；训练/部署若将来共存，必须共用同一控制器模块 + （这是旧 HRL 用 13 000 行换来的教训：v1 的 5 个特征索引错位、BC 激活函数不一致、 + 旧基准把 `completed` 当全清）。 +4. **每个优化都必须可配对、可复算、可回归**：冻结基准集 + 逐局 `manifest` + 动作日志 + + 逐微秒复算；任何"更快"的声明必须给出配对分布（均值/中位/最大/更快场数）， + 而不是单个最优场景。 + +### 2.4 迁移映射（现状 → 目标） + +| 现状 | 目标 | 处理 | +| --- | --- | --- | +| `protocol/*`、`evaluation/recompute_virtual_time` | 不变 | 冻结，作为度量基准 | +| `belief/__init__.py`（凸外包） | `belief/convex.py` | 原样搬迁，继续负责精确角域证书 | +| `particles.py`（网格幸存集） | `belief/occupancy.py`（numpy 张量 + 纯 Python 回退） | 重写热核，语义不变，保留 `tests/test_particles.py` 的真源保留性质测试 | +| — | `belief/joint.py`（位置×朝向） | 新增，Q4 专用 | +| `coverage/` | `guarantee/cover.py` | 49 点常量表 → 可校验的 `CoveragePlan` | +| `planner/bayes_loop.py` 的 `_build_tasks/_order_tasks` | `plan/tasks.py`、`plan/schedule.py` | 抽出，Q3/Q4 共用 | +| `planner/bayes_loop.py::_select_point` | `plan/score.py` | 抽出 + 向量化评分 | +| `planner/__init__.py::DirectionalExtension` | `plan/policy.py::DirectionalPolicy` + `belief/joint.py` | 保留后备清除作为最终兜底，但目标是不再触发 | +| `planner/seven.py` | `guarantee/plans.py::SEVEN` | 保留解析证明 | +| 4 个 benchmark 脚本 | `bench/` 统一 runner（并行 + manifest + 配对统计） | 合并，消除指标口径分叉 | + +--- + +## 3. 优化项 + +### P0-1　第三问：发现—定位融合巡回（消除探点往返） + +**现状**：每次收到 `direction` 后进入独立定位子循环，由信息增益选点器在同一站点之外 +另选探点；13 源单局共 24 次探点、5741 m 往返、1148 s，与整个覆盖巡回（6960 m / 1392 s）同量级； +评测机单局 BBM9 更极端：**21 次探点花掉 1138.7 s 移动 = 全局限时时间的 36.3%**（F4b）。 +评分函数 `score = E[log 幸存占比] + 0.0105 × 移动秒数`，移动项几乎不影响排名。 + +**方案**：把"已发现未定证"的频道加入**每一个覆盖站点**的批量扫频清单， +使第二/第三条方位线在巡回途中顺路取得；只有站点三角化不成立时才回退到专用探点。 + +- 巡回仍由 `plan/schedule.py` 滚动排序（现有最近邻 + 2-opt 已足够，`bayes` 的 −39.5% 移动 + 主要来自这里），新增"同点顺路测量"任务类型：`Task(kind="station", channels=(未知, 未定证))`。 +- 站点任务的固有耗时按 `6 s × 频道数 + 1 s × 换频次数` 记账（现有 `_path_cost` 已支持）。 +- 保底：`F11` 显示 0.72% 的源位置只有 1 个站在 1500 m 内，对这类频道保留 + `_select_point` 的专用探点路径；判定用 `belief.prob_detect` 而非硬编码。 + +**预期收益（已被实测推翻）**：原估 F4 的 1148 s 探点移动可回收 70–80%， +即 −800 ~ −900 s。**实测结果相反**：仅追加站点方位线 +0.8%，追加并抑制专用探点 ++45.6%（20/20 场景更慢），见 §0.4。原因是站间交会角不足以保证 20 m 证书， +专用信息增益探点仍然必要；抑制它只会白等巡回。**本项不采纳，默认关闭。** + +**验收**：18 场景验证集平均 ≤ 2700 s、最大 ≤ 4000 s；`measures` 增幅 ≤ 20%； +`move_distance_m` 降幅 ≥ 25%；全部 `valid`。 + +**风险**：站点距离最远 2 × 1159.7 = 2319 m，可能出现 `no_signal` 浪费 5 s。 +缓解：仅在 `prob_detect > 0` 时顺路测量；`no_signal` 同样是有效负观测（进信念，不算异常）。 + +--- + +### P0-2　第四问：用最小命中集替换 49 点发现集（离线搜索 + 运行期校验） + +**现状**：`coverage_points()` 生成 700 m 方格 49 点（±2100），依据是"单元内任一点到 4 顶点 +≤ 700√2 ≈ 989.95 ≤ 1000"。该条件是**充分但远非必要**：它要求单元内任意位置、任意朝向都由 +**同一个**顶点命中。实际上，第四问的保证是"对 ∀(p,h) ∈ Ω×S¹，存在被访点位于其前向半盘内"， +允许由**多个**顶点共同围住 p。 + +**方案**：把它当作一个显式的**击中集（hitting set）问题**离线求解，运行期校验： + +1. 目标空间离散：`p` 取 Ω 内 75 m 网格，`h` 取 24 个方向 → 43 032 个 (p,h) 单元； +2. 候选点：半径 2850 m 圆盘内 200–300 m 网格（641/293 个候选）； +3. 贪心集合覆盖 + 局部改进，**同时把巡回长度作为次级目标**（641/293 个候选中取 31/30 个， + 仍有大量同覆盖度的等价解可用于缩短巡回）； +4. 生成 `CoveragePlan(points, certificate)`，`certificate` 记录采样分辨率、未覆盖数、 + 最坏裕量、生成脚本哈希；`CoveragePlan.verify()` 在每次运行前用同一 `ObservationModel` + 重新校核，未通过即拒绝启动。 + +**本次离线校核结果**（附录 A 可复现）： + +| 点集 | 点数 | (p,h) 覆盖 | NN 巡回 | 折算移动 | +| --- | ---: | ---: | ---: | ---: | +| 现役 700 m 方格 ±2100 | 49 | 100% | 36.4 km | 7280 s | +| 三角格 1000 m（半径 2800 盘） | 31 | 100% | 34.3 km | 6863 s | +| **贪心最小命中集（200 m 候选）** | **31** | **100%** | **22.9 km** | **4587 s** | +| **贪心最小命中集（300 m 候选）** | **30** | **100%** | **23.6 km** | **4719 s** | + +**实测收益（2026-09-12，11 场景配对）**：均值 **14003.0 → 10975.5 s（−19.3%）**， +中位 −25.4%，10/11 场景更快；检测 444.5 → 319.2，请求 569.5 → 395.6， +位移 54.9 → 44.2 km，后备清除尝试 113.3 → 64.1，11/11 valid。 +低于原估的 −26%，差额来自"缺席频道省下的检测"被定位探点部分抵消。 + +**验收**：`CoveragePlan.verify()` 通过；10/13/16 源 × 定向 1/半数/仅一个全向 × 边界朝外压力 +场景全部全清；请求数、日志体积仍满足 1200 s / 2 MB 预算。 + +**风险**：数值采样证书弱于解析证明。缓解：(a) 保留解析七站证明用于 Q3； +(b) 证书采样分辨率与裕量写入 summary 并随日志归档；(c) 保留 10% 的额外"保险点"预算， +若演练观测到任何漏检立即回退到 49 点表。 + +--- + +### P0-3　第四问：位置×朝向联合信念与可见性选点 + +**现状**：定向源只被当作"候选空间集合"，`no_signal` 只用于累计 `blocked_probes` +（`planner/__init__.py:663-677`），选点沿固定 45° 阶梯试探；梯子用尽后调用 +`_fallback_clear`，对候选外包多边形做 20 m 网格覆盖逐格 `/clear`（≤400 格、≤1200 s、≤400 请求）。 +运行历史显示这正是第四问的失败点：定向源 2/6 清除，4/4 漏检全是定向。 + +**方案**：为第四问频道维护 **(x, y, heading) 联合可行集**，把"看不看得见"变成可推理的量： + +- 表示：`JointTensor` —— 复用 Q3 的占用网格，每格附加 24 个朝向 bin 的可行性位掩码 + （uint8 位域，20 频道 × 6.1 万格 × 3 B ≈ 3.7 MB，可接受）。 +- 更新规则（全部保守，仅删"必然看不见"的格）： + - `direction`@s：位置删 `>1500`、角向删 `|gap| > δ_eff + 角裕量`；朝向删 + "以该格为源、朝向不覆盖 s" 的半盘（保留 ±90° 含边界）。 + - `no_signal`@s：对 `|g−s| ≤ 1000` 的格，朝向**只能**落在背向 s 的半盘； + 对 `1000 < |g−s| ≤ 1500` 的格，朝向要么背向 s，要么源实际半径 < |g−s|（保持不确定）； + 对 `|g−s| > 1500` 的格不删。 + - `near`@s：位置收缩到 5 m 盘，朝向置为全向（近场无遮挡语义）。 +- 决策：`prob_detect(channel, reader)` = 该点能看到该频道任一可行 (p,h) 的概率质量； + 选点直接最大化 `prob_detect / (移动秒数 + 6)`，即**保证有信号的信息增益/秒**。 +- 定位收敛：位置由多条 `direction` 的角域交会收缩（与 Q3 相同）；朝向在 2 次 + "一次被遮挡 + 一次可见"后通常已被压到很窄的楔形，此时可**主动选择必然可见的探点**， + 用一个正常角域证书结束，而不再进入 400 格盲清。 +- 后备清除**保留**（`fallback_clear_cells` 已有格数与预算双硬顶），但目标变为 + "只在证书无法建立时触发"，并在 summary 中把触发次数作为回归指标。 + +**预期收益（未实测）**：定向频道的探点次数与后备清除触发次数大幅下降。参考量级： +后备清除上限 400 格 × 3 s = 1200 s + 400 请求/频道；`docs` 记录的 Q4 改进版每局后备 +尝试 616/4 局 ≈ 154 次。保守估计 **−1500 ~ −3000 s/局**。 + +**验收**：10 源 3 定向场景全清率 100%（现状 referee 仅 2/6 定向）； +`fallback_clear_attempts` 相对基线下降 ≥ 70%；定向信道平均检测次数 ≤ 全向信道的 1.5 倍。 + +**风险**：朝向均匀先验是工程假设，官方分布未知。缓解：先验只影响选点顺序， +不影响任何安全性判定；朝向不确定时自动退化为全向分支（`prob_detect` 取全向值）。 + +--- + +### P0-4　感知层向量化与并行（研发运行速度） + +**现状**（F6）：`expected_survival` 占单局 CPU 的 72%，2808 次调用 × (7 代表点 × 800 子采样) +≈ 1570 万次纯 Python 距离/角度运算；`apply_measure` 另有 20%，其中 `_kill_outside_wedge` +每次遍历全部幸存格（498 万次 `angular_gap`）。 + +**方案**： + +1. `belief/occupancy.py` 用 `numpy` 布尔/整数张量替换 `set[int]`： + 每频道 248×248 uint8（≈61 KB），20 频道 ≈ 1.2 MB；盘/楔删除用切片 + 布尔掩码， + 粗格索引用 `np.add.reduceat`；`numpy` 缺失时回退现有纯 Python 实现（测试保证两条路径同结果）。 +2. 采样评分向量化：把 `subsample` 与 `representatives` 预转 `(n,2)` 数组， + 一次广播算完距离、角度、角向 gap，`expected_survival` 由 5600 次标量循环变成 2–3 条向量指令。 +3. 站点级编译缓存：`(station, channel, version)` → 掩码结果，`version` 不变直接复用； + 当前 `_skip_cache` 只缓存"必 no_signal"布尔，可扩展到整块删除掩码。 +4. MEC 增量维护：当前 `mec()` 每次版本变化重算全部幸存点（最多 6 万点）；改为 + 版本变化时只对新增删除点做包围圆增量更新，或按 1/8 子采样给出候选、证书判定用精确值。 +5. 批量基准并行化：`bench/runner.py` 用 `concurrent.futures.ProcessPoolExecutor` + 按场景分发（每进程独立 `LocalSimulator`，真值仍只进测试端），Mac M 系列 8 核可得 ~6–8×。 +6. 观测/遥测：单局 summary 写入 `compute_wall_s`、`profile_top`（可选 `--profile`）， + 纳入基准表的必填列，防止"虚拟时间变快、墙钟悄悄变慢"。 + +**实测收益（2026-09-12）**：单局计算 **3.10 → 1.00 s（3.08×）**，最坏 5.84 → 1.30 s； +20 场景基准集 12.2 → 3.9 s。虚拟时间与动作序列**逐位一致**（纯 Python 回退路径保留， +`MCM26B_NO_NUMPY=1` 可强制）。未达 ≤0.8 s 的目标：剩余开销在 +`apply_measure` 的网格删除与 `ChannelParticles.__init__`，需要 P0-4b（表示层张量化）。 + +**验收**：`tests/test_particles.py`、`tests/test_bayes_loop_q3.py` 全过； +纯 Python 与 numpy 两条路径在同一动作日志下产出逐位相同的 summary（`actions_sha256` 相同）； +单局 `compute_wall_s` ≤ 0.8 s。 + +--- + +### P1-1　清除点选在 C₂₀(K) 内离当前位置最近处 + +现状 `_near_clear_target` 只沿"当前位置 → 候选圆心"方向平移，平移量受 +`limit − radius` 限制，是被证明的一维特例。一般解是 +`C₂₀(K) = ∩_{g∈K} B(g, 20)`，取其内离当前位置最近的点： +- 对凸多边形候选集，`C₂₀(K)` 仍是凸集（若干圆盘之交），可用"到当前点的最近点"的 + 简单迭代/投影求得； +- 若 `C₂₀(K)` 非空，则从该点清除的移动代价最小，且三角不等式保证仍在 20 m 内。 + +**预期收益**：Q3 单局清除移动 3668 m（734 s）中回收 20–35%（**−150 ~ −260 s**）； +Q4 同步受益。 +**风险**：`C₂₀` 空时退回现有 MEC 圆心逻辑（已实现）。 + +--- + +### P1-2　覆盖几何参数改为"离线搜索 + 运行期校验" + +- Q3：`station_ring_radius_m` 由 1159.7 的可证明下界 1122.9 重算，并在 `validate_seven_coverage` + 中同时校验 **PF 网格覆盖**（现行文档已记录 1159.7 与 Optuna 建议值差 3.3 m 的取舍）。 + 收益很小（≈44 s），但把"设计性质 vs 1.7% 均值差异"的取舍写成可复算脚本，而不是口口相传。 +- Q4：见 P0-2，覆盖集与巡回联合优化（在等价覆盖解中选巡回最短者， + 预期可在 23.6 km 基础上再降 5–10%）。 +- 所有覆盖计划产物落 `output//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/referee-data/sessions/53FK-LELA-M62B-J74U/log.jsonl b/referee-data/sessions/53FK-LELA-M62B-J74U/log.jsonl new file mode 100644 index 0000000..af79c5b --- /dev/null +++ b/referee-data/sessions/53FK-LELA-M62B-J74U/log.jsonl @@ -0,0 +1,122 @@ +{"event": "session_start", "case_code": "53FK-LELA-M62B-J74U", "module": "q3_practice", "robot_id": "1", "seed": 1364411664, "countdown_s": 5.0, "window_s": 1500.0, "max_real_duration_s": 1200.0, "truth": [{"channel": 18, "x": 1296.7141429697774, "y": -444.4430546050354, "r_eff_m": 1239.1782567152018, "directional": false, "heading_deg": 0.0}, {"channel": 17, "x": 1020.5918585779062, "y": 852.2515595937525, "r_eff_m": 1026.5206174583245, "directional": false, "heading_deg": 0.0}, {"channel": 1, "x": 580.5002167548972, "y": -991.4701530799936, "r_eff_m": 1237.8121709043808, "directional": false, "heading_deg": 0.0}, {"channel": 2, "x": -16.348424904475735, "y": -478.6435890197983, "r_eff_m": 1099.9814484291328, "directional": false, "heading_deg": 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"no_signal"}} +{"event": "action", "seq": 426, "wall_ts_ms": 1789189934337, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "593f8e5ecda7-0426", "position": {"x": 1501.7998441019943, "y": 1001.7014551198498}, "channel": 8}, "response": {"accepted": true, "real_timestamp_ms": 1789189934337, "virtual_time_s": 9013.929954, "measure_result": "direction", "svd_deg": 267.85}} +{"event": "action", "seq": 427, "wall_ts_ms": 1789189934419, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "593f8e5ecda7-0427", "position": {"x": 1489.468113361522, "y": 993.1454045261961}, "channel": 8}, "response": {"accepted": true, "real_timestamp_ms": 1789189934419, "virtual_time_s": 9021.931804, "measure_result": "direction", "svd_deg": 277.68}} +{"event": "action", "seq": 428, "wall_ts_ms": 1789189934420, "method": "POST", "path": "/clear", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "593f8e5ecda7-0428", "position": {"x": 1499.0191527409906, "y": 920.2417126862134}, "channel": 8}, "response": {"accepted": true, "real_timestamp_ms": 1789189934420, "virtual_time_s": 9041.637137, "clear_result": "success"}} +{"event": "action", "seq": 429, "wall_ts_ms": 1789189934421, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "593f8e5ecda7-0429", "position": {"x": 1800.0, "y": 300.0}, "channel": 2}, "response": {"accepted": true, "real_timestamp_ms": 1789189934421, "virtual_time_s": 9185.519587, "measure_result": "no_signal"}} +{"event": "action", "seq": 430, "wall_ts_ms": 1789189934422, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "593f8e5ecda7-0430", "position": {"x": 1800.0, "y": 300.0}, "channel": 3}, "response": {"accepted": true, "real_timestamp_ms": 1789189934422, "virtual_time_s": 9191.519587, "measure_result": "no_signal"}} +{"event": "action", "seq": 431, "wall_ts_ms": 1789189934422, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "593f8e5ecda7-0431", "position": {"x": 1800.0, "y": 300.0}, "channel": 5}, "response": {"accepted": true, "real_timestamp_ms": 1789189934422, "virtual_time_s": 9197.519587, "measure_result": "no_signal"}} +{"event": "action", "seq": 432, "wall_ts_ms": 1789189934423, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "593f8e5ecda7-0432", "position": {"x": 1800.0, "y": 300.0}, "channel": 6}, "response": {"accepted": true, "real_timestamp_ms": 1789189934423, "virtual_time_s": 9203.519587, "measure_result": "no_signal"}} +{"event": "action", "seq": 433, "wall_ts_ms": 1789189934423, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "593f8e5ecda7-0433", "position": {"x": 1800.0, "y": 300.0}, "channel": 9}, "response": {"accepted": true, "real_timestamp_ms": 1789189934423, "virtual_time_s": 9209.519587, "measure_result": "no_signal"}} +{"event": "action", "seq": 434, "wall_ts_ms": 1789189934424, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "593f8e5ecda7-0434", "position": {"x": 1800.0, "y": 300.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789189934424, "virtual_time_s": 9215.519587, "measure_result": "no_signal"}} +{"event": "action", "seq": 435, "wall_ts_ms": 1789189934424, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "593f8e5ecda7-0435", "position": {"x": 1800.0, "y": 300.0}, "channel": 12}, "response": {"accepted": true, "real_timestamp_ms": 1789189934424, "virtual_time_s": 9221.519587, "measure_result": "no_signal"}} +{"event": "action", "seq": 436, "wall_ts_ms": 1789189934425, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "593f8e5ecda7-0436", "position": {"x": 1800.0, "y": 300.0}, "channel": 14}, "response": {"accepted": true, "real_timestamp_ms": 1789189934425, "virtual_time_s": 9227.519587, "measure_result": "no_signal"}} +{"event": "action", "seq": 437, "wall_ts_ms": 1789189934425, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "593f8e5ecda7-0437", "position": {"x": 1800.0, "y": 300.0}, "channel": 15}, "response": {"accepted": true, "real_timestamp_ms": 1789189934425, "virtual_time_s": 9233.519587, "measure_result": "no_signal"}} +{"event": "action", "seq": 438, "wall_ts_ms": 1789189934426, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "593f8e5ecda7-0438", "position": {"x": 1800.0, "y": 300.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789189934426, "virtual_time_s": 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/sessions/ZBUC-YW26-J7BH-V2WP/meta.json b/referee-data/sessions/ZBUC-YW26-J7BH-V2WP/meta.json new file mode 100644 index 0000000..53d597d --- /dev/null +++ b/referee-data/sessions/ZBUC-YW26-J7BH-V2WP/meta.json @@ -0,0 +1,88 @@ +{ + "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/scripts/analyze_run_timing.py b/scripts/analyze_run_timing.py new file mode 100644 index 0000000..0e43d11 --- /dev/null +++ b/scripts/analyze_run_timing.py @@ -0,0 +1,229 @@ +"""运行历史计时分解：对任意会话日志按官方计时模型独立复算并分相。 + +支持两种格式（自动识别）： +- 参赛队客户端日志：``scripts/capture_practice.py`` / ``SimulatorClient`` 写出的 + JSONL，逐行 ``attempt`` / ``accepted`` / ``step``； +- 评测机日志：``referee-data/sessions//log.jsonl``，逐行 + ``session_start`` / ``action`` / ``session_end``。 + +复算口径与 ``scripts/verify_q3_practice.py::timing_rows`` 完全一致（题面附件2 §4.1）： +``T = Σ 距离/5 + 5·N_measure + N_switch + 5·N_clear⁺ + 3·N_clear⁻``， +起点位置 (0,0)、起始频道 1，``/clear`` 不计换频，移动按整数微秒累计。 + +用途：把"虚拟时间花在哪"变成一条可随时重跑的命令，而不是一次性的分析结论。 + + uv run python scripts/analyze_run_timing.py session.jsonl + uv run python scripts/analyze_run_timing.py referee-data/sessions/*/log.jsonl --json out.json + uv run python scripts/analyze_run_timing.py session.jsonl --plan seven # 站点/探点分类 +""" + +from __future__ import annotations + +import argparse +import json +import math +from collections import Counter, defaultdict +from pathlib import Path + +SPEED_MPS = 5.0 # [题面] 附件1 表1 +MEASURE_S = 5 # [题面] +SWITCH_S = 1 # [题面] +CLEAR_SUCCESS_S = 5 # [题面] +CLEAR_FAIL_S = 3 # [题面] +START_POSITION = (0.0, 0.0) +START_CHANNEL = 1 + + +def load_actions(path: Path) -> list[dict]: + """读取一份 JSONL，归一化为已接受动作序列。""" + actions: list[dict] = [] + for line in path.read_text(encoding="utf-8", errors="replace").splitlines(): + line = line.strip() + if not line: + continue + try: + record = json.loads(line) + except json.JSONDecodeError: + continue + event = record.get("event") + if event == "accepted": + request, response = record.get("request", {}), record.get("response", {}) + accepted = bool(response.get("accepted", True)) + elif event == "action": + request, response = record.get("request", {}), record.get("response", {}) + accepted = bool(response.get("accepted", record.get("status") == 200)) + else: + continue + if not accepted: + continue + actions.append({ + "path": record.get("path") or request.get("path") or "", + "position": request.get("position"), + "channel": request.get("channel"), + "result": response.get("measure_result") or response.get("clear_result") or "", + "recorded_s": response.get("virtual_time_s"), + "elapsed_s": record.get("elapsed_s"), + }) + return actions + + +def recompute(actions: list[dict]) -> tuple[list[dict], dict]: + """按官方模型逐动作复算；返回明细与汇总（微秒整数累计）。""" + position, channel, virtual_us = START_POSITION, START_CHANNEL, 0 + rows: list[dict] = [] + totals = Counter() + distance_total = 0.0 + max_gap_us = 0 + for index, action in enumerate(actions): + path = action["path"] + move_s = switches = action_s = 0.0 + if path in ("/measure", "/clear") and action["position"] is not None: + target = (float(action["position"]["x"]), float(action["position"]["y"])) + distance_total += math.dist(position, target) + move_s = math.dist(position, target) / SPEED_MPS + position = target + if path == "/measure": + switches = int(channel != action["channel"]) + channel = action["channel"] + action_s = MEASURE_S + else: + action_s = CLEAR_SUCCESS_S if action["result"] == "success" else CLEAR_FAIL_S + move_us = round(move_s * 1_000_000) + virtual_us += move_us + int((switches + action_s) * 1_000_000) + totals["move_us"] += move_us + totals["switch_s"] += switches + totals["action_us"] += int(action_s * 1_000_000) + totals["count"] += 1 + if action["elapsed_s"]: + totals["elapsed_s"] += action["elapsed_s"] + recorded = action["recorded_s"] + gap_us = ((round(recorded * 1_000_000) - virtual_us) + if isinstance(recorded, (int, float)) else 0) + max_gap_us = max(max_gap_us, abs(gap_us)) + rows.append({"index": index, "path": path, "channel": action["channel"], + "x": position[0], "y": position[1], "result": action["result"], + "move_us": move_us, "switch_s": switches, "action_s": action_s, + "recomputed_us": virtual_us, "recorded_s": recorded, "gap_us": gap_us}) + summary = { + "actions": len(actions), + "virtual_s": round(virtual_us / 1_000_000, 6), + "move_s": round(totals["move_us"] / 1_000_000, 3), + "switch_s": float(totals["switch_s"]), + "action_s": round(totals["action_us"] / 1_000_000, 3), + "distance_m": round(distance_total, 1), + "real_elapsed_s": round(totals["elapsed_s"], 3), + "max_recorded_gap_s": max_gap_us / 1_000_000, + } + return rows, summary + + +def phase_report(rows: list[dict]) -> dict: + """清除前后分相 + 重复复扫检测（定位"时间被重复动作吃掉"的历史问题）。""" + clear_indices = [r["index"] for r in rows if r["path"] == "/clear"] + last_clear = max(clear_indices) if clear_indices else None + measure_keys = defaultdict(int) + for row in rows: + if row["path"] == "/measure": + key = (round(row["x"] * 2), round(row["y"] * 2), row["channel"]) + measure_keys[key] += 1 + repeated = sum(count - 1 for count in measure_keys.values() if count > 1) + report = { + "measures": sum(1 for r in rows if r["path"] == "/measure"), + "clears": len(clear_indices), + "failed_clears": sum(1 for r in rows + if r["path"] == "/clear" and r["result"] != "success"), + "distinct_measure_stops": len(measure_keys), + "repeated_measure_actions": repeated, + "repeat_ratio": round(repeated / max(1, len(measure_keys)), 3), + } + if last_clear is not None: + tail = [r for r in rows if r["index"] > last_clear] + report["last_clear_virtual_s"] = round(rows[last_clear]["recomputed_us"] / 1_000_000, 3) + report["after_last_clear_virtual_s"] = round( + sum(r["move_us"] + (r["switch_s"] + r["action_s"]) * 1_000_000 for r in tail) + / 1_000_000, 3) + report["after_last_clear_measures"] = sum(1 for r in tail if r["path"] == "/measure") + return report + + +def classify_by_plan(rows: list[dict], points, tol_m: float = 2.0) -> dict: + """按给定站点集把 /measure 分成"站点扫频"与"专用探点"，并给出各自移动耗时。""" + buckets: dict[str, Counter] = defaultdict(Counter) + for row in rows: + if row["path"] != "/measure": + continue + nearest = min((math.dist((row["x"], row["y"]), p) for p in points), default=math.inf) + kind = "station" if nearest <= tol_m else "probe" + buckets[kind]["n"] += 1 + buckets[kind]["move_us"] += row["move_us"] + return {kind: {"measures": value["n"], + "move_s": round(value["move_us"] / 1_000_000, 3)} + for kind, value in buckets.items()} + + +def _plan_points(name: str): + if name == "none": + return None + if name == "seven": + from mcm26b.planner.seven import seven_points + return seven_points() + if name == "49": + from mcm26b.coverage import coverage_points + return coverage_points() + raise ValueError(f"未知 --plan: {name}") + + +def main() -> int: + parser = argparse.ArgumentParser(description=__doc__.splitlines()[0]) + parser.add_argument("logs", nargs="+", type=Path, help="会话 JSONL（客户端或评测机格式）") + parser.add_argument("--plan", choices=("none", "seven", "49"), default="none", + help="站点集，用于把 /measure 分成站点扫频与专用探点") + parser.add_argument("--json", type=Path, default=None, help="汇总输出路径") + parser.add_argument("--top", type=int, default=6, help="逐频道表格显示前 N 个热点频道") + args = parser.parse_args() + + points = _plan_points(args.plan) + payload = {} + for path in args.logs: + actions = load_actions(path) + if not actions: + print(f"[跳过] {path}：没有已接受动作") + continue + rows, summary = recompute(actions) + report = phase_report(rows) + total = summary["virtual_s"] or 1.0 + print(f"\n===== {path} =====") + print(f"虚拟总时间 {summary['virtual_s']:.3f} s " + f"移动 {summary['move_s']:.1f} s ({100 * summary['move_s'] / total:.1f}%) " + f"检测/清除动作 {summary['action_s']:.1f} s ({100 * summary['action_s'] / total:.1f}%) " + f"换频 {summary['switch_s']:.0f} s ({100 * summary['switch_s'] / total:.1f}%)") + print(f"移动距离 {summary['distance_m']:.1f} m 请求 {summary['actions']} 次 " + f"现实耗时 {summary['real_elapsed_s']:.3f} s " + f"与服务器记录最大偏差 {summary['max_recorded_gap_s']:.6f} s") + print(f"检测 {report['measures']} 清除 {report['clears']}（失败 {report['failed_clears']}） " + f"不同测点 {report['distinct_measure_stops']} " + f"重复检测 {report['repeated_measure_actions']}" + f"（重复率 {report['repeat_ratio']}）") + if "last_clear_virtual_s" in report: + print(f"末次清除于 {report['last_clear_virtual_s']:.1f} s；其后又花 " + f"{report['after_last_clear_virtual_s']:.1f} s 做 " + f"{report['after_last_clear_measures']} 次检测") + per_channel = Counter(r["channel"] for r in rows if r["path"] == "/measure") + hottest = per_channel.most_common(args.top) + print("检测次数最多的频道：" + "，".join(f"ch{c}:{n}" for c, n in hottest)) + if points is not None: + print("测点分类：" + json.dumps(classify_by_plan(rows, points), + ensure_ascii=False)) + payload[str(path)] = {"summary": summary, "phases": report, + "per_channel_measures": dict(sorted(per_channel.items()))} + + if args.json is not None: + args.json.parent.mkdir(parents=True, exist_ok=True) + args.json.write_text(json.dumps(payload, ensure_ascii=False, indent=2) + "\n", + encoding="utf-8") + print(f"\n汇总已写入 {args.json}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/run_suite.py b/scripts/run_suite.py new file mode 100644 index 0000000..124416e --- /dev/null +++ b/scripts/run_suite.py @@ -0,0 +1,361 @@ +"""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）： + +- 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)； +- 有效性定义与既有脚本一致且不得放宽： + ``全清 ∧ 缺席无误判 ∧ 正常退出 ∧ 零异常 ∧ 逐微秒计时复算一致``； +- 任何一局无效即 ``all_valid=false`` 且进程非零退出（失败不静默丢样本）； +- 变体支持 ``名称@计划名`` 语法，用于同场配对比较两个覆盖计划； +- ``--jobs`` 走进程池并行（每进程独立内存回环，不共享端口）。 + + uv run python scripts/run_suite.py --problem 3 --suite core --jobs 6 + uv run python scripts/run_suite.py --problem 4 --suite all --jobs 6 \ + --variants directional@square49,directional@q4_mincover_v1 \ + --label q4-plan-ab +""" + +from __future__ import annotations + +import argparse +import csv +import json +import statistics +from concurrent.futures import ProcessPoolExecutor +from dataclasses import replace +from pathlib import Path +from time import perf_counter + +from mcm26b.config import ProblemConfig +from mcm26b.evaluation import recompute_virtual_time +from mcm26b.harness import LocalSimTransport, LocalSimulator +from mcm26b.harness.suites import (SUITE_VERSION, SceneSpec, suite, + suite_manifest) +from mcm26b.planner import DirectionalExtension +from mcm26b.planner.bayes_loop import BayesLoop +from mcm26b.planner.seven import SevenBatch +from mcm26b.protocol.client import SimulatorClient +from mcm26b.routing import route_metrics + +ROOT = Path(__file__).resolve().parents[1] +Q3_STRATEGIES = {"bayes": BayesLoop, "seven_batch": SevenBatch, + "seven_immediate": SevenBatch} +Q4_STRATEGIES = {"directional": DirectionalExtension} +UNDER_TARGET_S = 4000.0 + + +def build_config(margin_deg: float, q4_plan: str | None, + bayes_overrides: dict | None = None, + search_overrides: dict | None = None) -> ProblemConfig: + """基准配置：persistent 关闭（对齐"首次证明找全"口径）。""" + base = ProblemConfig() + search = replace(base.search, bearing_rounding_margin_deg=margin_deg, + persistent_search=False) + if q4_plan: + search = replace(search, q4_coverage_plan=q4_plan) + if search_overrides: + search = replace(search, **search_overrides) + bayes = base.bayes + if bayes_overrides: + bayes = replace(bayes, **bayes_overrides) + return replace(base, search=search, bayes=bayes) + + +def _coerce(text: str): + """变体覆盖值的字面量解析（true/false/整数/浮点/元组/字符串）。""" + lowered = text.lower() + if lowered in ("true", "yes", "on"): + return True + if lowered in ("false", "no", "off"): + return False + if text.startswith("(") and text.endswith(")"): + inner = text[1:-1].strip() + return () if not inner else tuple( + _coerce(chunk.strip()) for chunk in inner.split(",")) + for cast in (int, float): + try: + return cast(text) + except ValueError: + continue + return text + + +def parse_variant(spec: str, problem: int) -> tuple[str, str, str | None, dict, dict]: + """``名称[@计划][#键=值;键=值]`` → (标签, 策略名, 计划, bayes 覆盖, search 覆盖)。 + + 变体覆盖用于同场配对比较单个开关，例如 + ``bayes#fuse_localize_into_tour=false``；``search.`` 前缀写入 SearchConfig。 + """ + body, _, override_text = spec.partition("#") + name, _, plan = body.partition("@") + strategies = Q3_STRATEGIES if problem == 3 else Q4_STRATEGIES + if name not in strategies: + raise ValueError(f"未知策略 {name!r}（可选 {sorted(strategies)}）") + bayes_overrides: dict = {} + search_overrides: dict = {} + for item in filter(None, (chunk.strip() for chunk in override_text.split(";"))): + key, _, value = item.partition("=") + if not value: + raise ValueError(f"变体覆盖须为 键=值：{item!r}") + if key.startswith("search."): + search_overrides[key[len("search."):]] = _coerce(value) + else: + bayes_overrides[key] = _coerce(value) + label = spec.replace(" ", "") + return label, name, plan or None, bayes_overrides, search_overrides + + +def run_episode(payload: dict) -> dict: + """执行一局并返回结果行（进程池 worker，必须可 pickle）。""" + spec = SceneSpec(**payload["scene"]) + label = payload["label"] + problem = spec.problem + config = build_config(payload["margin_deg"], payload["plan"], + payload.get("bayes"), payload.get("search")) + strategies = Q3_STRATEGIES if problem == 3 else Q4_STRATEGIES + loop_cls = strategies[payload["strategy"]] + sources = spec.sources(config) + simulator = LocalSimulator(sources, robot_id="SUITE", config=config, + error_seed=spec.error_seed) + client = SimulatorClient("SUITE", config=config, + transport=LocalSimTransport(simulator)) + setup_start = perf_counter() + loop = loop_cls(client, config) + setup_wall_s = perf_counter() - setup_start + start = perf_counter() + summary = loop.run() + compute_wall_s = perf_counter() - start + report = recompute_virtual_time(client.log.action_events()) + truth = sorted(source.channel for source in sources) + cleared = sorted(summary["cleared_channels"]) + absent = sorted(summary["absent_channels"]) + valid = (cleared == truth + and not (set(absent) & set(truth)) + and summary["exit_reason"] == "user_exit" + and not summary["anomalies"] + and not report["mismatches"]) + routing = _routing_metrics(summary, config) + return { + "scene_id": spec.scene_id, "variant": label, "problem": problem, + "kind": spec.kind, "sources": spec.count, "directional": spec.directional, + "virtual_time_s": summary["virtual_time_s"], + "accounted_virtual_time_s": summary["accounted_virtual_time_s"], + "measures": summary["measures"], "clears": summary["clears"], + "clear_success": summary["clear_success"], + "total_requests": summary["total_requests"], + "move_distance_m": summary["move_distance_m"], + "coverage_points": summary["coverage_points"], + "fallback_clear_attempts": summary.get("fallback_clear_attempts", 0), + "redundant_rescan_measures": summary["redundant_rescan_measures"], + "completion_basis": summary["completion_basis"], + "stop_reason": summary["stop_reason"], "exit_reason": summary["exit_reason"], + "cleared_matches_truth": cleared == truth, + "absent_misjudged": sorted(set(absent) & set(truth)), + "anomaly_count": len(summary["anomalies"]), + "virtual_recompute_mismatches": len(report["mismatches"]), + "compute_wall_s": round(compute_wall_s, 3), + "plan_verify_wall_s": round(setup_wall_s, 3), + "valid": valid, + **routing, + } + + +def _routing_metrics(summary: dict, config: ProblemConfig) -> dict: + """S1 必报路由列：作业点去重、离线最优开放回路、MST 下界、缺口与探测静默率。 + + 作业点 = 发生过 /measure 或 /clear 的位置（去重到 0.5 m）；离线最优是 + "同一批作业点"上的开放路径最优值，用来把"排线不够好"与"点选得不对"分开。 + """ + rows = summary.get("steps") or [] + positions = [] + for row in rows: + position = row.get("position") + if isinstance(position, dict): + positions.append((float(position["x"]), float(position["y"]))) + elif position is not None: + positions.append((float(position[0]), float(position[1]))) + speed = config.cost.speed_mps + metrics = route_metrics(positions, summary["move_distance_m"], speed) + measures = [row for row in rows if row.get("action") == "measure"] + silent = sum(1 for row in measures if row.get("result") == "no_signal") + metrics["probe_no_signal_ratio"] = ( + round(silent / len(measures), 4) if measures else 0.0) + plan_points = summary.get("coverage_plan_points") or [] + if plan_points: + visited = 0 + for (px, py) in plan_points: + if any(abs(px - x) <= 1.0 and abs(py - y) <= 1.0 + for x, y in positions): + visited += 1 + metrics["coverage_actual_visited"] = visited + else: + metrics["coverage_actual_visited"] = 0 + return metrics + + +def _percentile(values: list[float], fraction: float) -> float: + if not values: + return 0.0 + ordered = sorted(values) + index = min(len(ordered) - 1, max(0, int(round(fraction * (len(ordered) - 1))))) + return ordered[index] + + +def aggregate(rows: list[dict], labels: list[str]) -> dict: + """逐变体汇总 + 相对首变体的配对统计。""" + summary: dict[str, dict] = {} + for label in labels: + part = [row for row in rows if row["variant"] == label] + if not part: + continue + times = [row["virtual_time_s"] for row in part] + summary[label] = { + "episodes": len(part), + "valid_episodes": sum(1 for row in part if row["valid"]), + "virtual_time_mean_s": round(statistics.fmean(times), 1), + "virtual_time_median_s": round(statistics.median(times), 1), + "virtual_time_max_s": round(max(times), 1), + "virtual_time_p90_s": round(_percentile(times, 0.9), 1), + "under_4000_ratio": round( + sum(1 for t in times if t < UNDER_TARGET_S) / len(times), 3), + "measures_mean": round(statistics.fmean( + [row["measures"] for row in part]), 1), + "total_requests_mean": round(statistics.fmean( + [row["total_requests"] for row in part]), 1), + "move_distance_mean_m": round(statistics.fmean( + [row["move_distance_m"] for row in part]), 1), + "fallback_attempts_mean": round(statistics.fmean( + [row["fallback_clear_attempts"] for row in part]), 1), + "compute_wall_mean_s": round(statistics.fmean( + [row["compute_wall_s"] for row in part]), 3), + "plan_verify_wall_mean_s": round(statistics.fmean( + [row["plan_verify_wall_s"] for row in part]), 3), + "distinct_service_points_mean": round(statistics.fmean( + [row["distinct_service_points"] for row in part]), 1), + "route_gap_vs_offline_mean": round(statistics.fmean( + [row["route_gap_vs_offline"] for row in part]), 4), + "route_lower_bound_mean_s": round(statistics.fmean( + [row["route_lower_bound_s"] for row in part]), 1), + "coverage_actual_visited_mean": round(statistics.fmean( + [row["coverage_actual_visited"] for row in part]), 1), + "probe_no_signal_ratio_mean": round(statistics.fmean( + [row["probe_no_signal_ratio"] for row in part]), 4), + } + paired: dict[str, dict] = {} + if len(labels) >= 2: + base = labels[0] + base_rows = {row["scene_id"]: row for row in rows + if row["variant"] == base} + for label in labels[1:]: + deltas, faster = [], 0 + for row in rows: + if row["variant"] != label or row["scene_id"] not in base_rows: + continue + reference = base_rows[row["scene_id"]]["virtual_time_s"] + if reference <= 0: + continue + deltas.append(row["virtual_time_s"] / reference - 1.0) + if row["virtual_time_s"] < reference: + faster += 1 + if deltas: + paired[f"{label}_vs_{base}"] = { + "mean_relative_change": round(statistics.fmean(deltas), 4), + "median_relative_change": round(statistics.median(deltas), 4), + "faster_scenes": faster, "compared": len(deltas)} + return {"variants": summary, "paired": paired} + + +def main() -> int: + parser = argparse.ArgumentParser(description=__doc__.splitlines()[0]) + parser.add_argument("--problem", type=int, choices=(3, 4), default=3) + parser.add_argument("--suite", choices=("core", "boundary", "all"), default="core") + parser.add_argument("--variants", default=None, + help="逗号分隔；支持 名称@覆盖计划，默认取该问题的标准变体") + parser.add_argument("--q4-plan", default=None, help="第四问覆盖计划（所有变体默认值）") + parser.add_argument("--bearing-margin-deg", type=float, default=0.005) + parser.add_argument("--bayes-json", default=None, + help="BayesConfig 覆盖参数（JSON 字符串或文件路径）") + parser.add_argument("--jobs", type=int, default=1, help="并行进程数") + parser.add_argument("--label", default="run", help="输出子目录名") + parser.add_argument("--out", type=Path, default=None, + help="输出根目录，默认 output/suite/") + args = parser.parse_args() + + default_variants = "bayes,seven_batch" if args.problem == 3 else "directional" + variants = [parse_variant(item.strip(), args.problem) + for item in (args.variants or default_variants).split(",") if item.strip()] + labels = [label for label, *_ in variants] + + bayes_overrides = None + if args.bayes_json: + raw = (Path(args.bayes_json).read_text(encoding="utf-8") + if Path(args.bayes_json).exists() else args.bayes_json) + bayes_overrides = json.loads(raw) + print(f"bayes 覆盖参数：{json.dumps(bayes_overrides, ensure_ascii=False)}") + + scenes = suite(args.suite, args.problem) + out_dir = (args.out or (ROOT / "output/suite")) / f"q{args.problem}-{args.label}" + out_dir.mkdir(parents=True, exist_ok=True) + manifest = suite_manifest(args.suite, args.problem) + manifest.update({"suite_version": SUITE_VERSION, "label": args.label, + "variants": list( + (args.variants or default_variants).split(",")), + "jobs": args.jobs, + "bearing_margin_deg": args.bearing_margin_deg, + "q4_plan": args.q4_plan}) + (out_dir / "manifest.json").write_text( + json.dumps(manifest, ensure_ascii=False, indent=2) + "\n", encoding="utf-8") + + payloads = [ + {"scene": scene.as_dict(), "label": label, "strategy": strategy, + "plan": plan or (args.q4_plan if args.problem == 4 else None), + "bayes": {**(bayes_overrides or {}), **variant_bayes}, + "search": variant_search, + "margin_deg": args.bearing_margin_deg} + for scene in scenes + for label, strategy, plan, variant_bayes, variant_search in variants] + print(f"基准集 {args.suite}/q{args.problem}：{len(scenes)} 场景 × " + f"{len(variants)} 变体 = {len(payloads)} 局，jobs={args.jobs}", flush=True) + + start = perf_counter() + if args.jobs > 1: + with ProcessPoolExecutor(max_workers=args.jobs) as pool: + rows = list(pool.map(run_episode, payloads)) + else: + rows = [run_episode(payload) for payload in payloads] + wall = perf_counter() - start + + rows.sort(key=lambda row: (row["scene_id"], row["variant"])) + for row in rows: + print(f" {row['scene_id']:28s} {row['variant']:34s} " + f"T={row['virtual_time_s']:9.1f} M={row['measures']:4d} " + f"wall={row['compute_wall_s']:6.2f}s " + f"{'OK' if row['valid'] else 'INVALID'}", flush=True) + + summary = aggregate(rows, labels) + summary["all_valid"] = all(row["valid"] for row in rows) + summary["wall_seconds"] = round(wall, 2) + summary["episodes"] = len(rows) + summary["suite"] = args.suite + summary["problem"] = args.problem + summary["label"] = args.label + payload = {"summary": summary, "rows": rows} + (out_dir / "results.json").write_text( + json.dumps(payload, ensure_ascii=False, indent=2) + "\n", encoding="utf-8") + (out_dir / "aggregate.json").write_text( + json.dumps(summary, ensure_ascii=False, indent=2) + "\n", encoding="utf-8") + with (out_dir / "aggregate.csv").open("w", encoding="utf-8", newline="") as handle: + writer = csv.writer(handle) + header = ["scene_id", "variant", "problem", "kind", "sources", "directional"] + header += [key for key in rows[0] if key not in header] + writer.writerow(header) + for row in rows: + writer.writerow([row.get(key, "") for key in header]) + print(json.dumps(summary, ensure_ascii=False, indent=2)) + print(f"\n结果已写入 {out_dir}") + return 0 if summary["all_valid"] else 1 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/verify_q4_cover.py b/scripts/verify_q4_cover.py new file mode 100644 index 0000000..5e9d1ff --- /dev/null +++ b/scripts/verify_q4_cover.py @@ -0,0 +1,241 @@ +"""第四问覆盖证书与最小命中集搜索（CLI；核心实现见 mcm26b.guarantee.cover）。 + +第四问的定向源在"读点位于源的前向半平面内"时才可能被检出，因此 +"某频道在所有被访点都 no_signal ⟹ 该频道不存在源"的缺席证明，等价于要求 +被访点集合 S 满足 + + ∀ p ∈ Ω, ∀ h ∈ [0,360): ∃ c ∈ S, + |c − p| ≤ r_eff_min(1000) 且 (c − p)·u(h) ≥ 0 + +（题面规定朝向覆盖"两侧各 90 度，含边界"，故取 ≥ 0）。现役 49 点方格的 +"单元对角 ≤ 1000"是**充分**条件但远非必要；本脚本用贪心击中集求等价覆盖的 +更小点集，并输出可复现证书。 + + uv run python scripts/verify_q4_cover.py # 校核 + 搜索 + 打印 + uv run python scripts/verify_q4_cover.py --spacing 200 --write-plan q4_mincover_v1 + uv run python scripts/verify_q4_cover.py --check-json my_points.json +""" + +from __future__ import annotations + +import argparse +import hashlib +import json +from pathlib import Path + +from mcm26b.config import ProblemConfig +from mcm26b.guarantee import (CERTIFY_HEADINGS, CERTIFY_STEP_M, R_EFF_MIN_M, + CoverageCertificate, CoveragePlan, + candidate_lattice, coverage_masks, forward_targets, + greedy_cover, omega_positions, plan_names, + square_49_plan, verify_full_disk, + verify_forward_half_disk, verify_plan, write_plan) + +ROOT = Path(__file__).resolve().parents[1] +DEFAULT_PLAN_DIR = ROOT / "src/mcm26b/guarantee/plans" + + +def _report(points, kind: str, step_m: float, headings: int, + r_eff: float = R_EFF_MIN_M, + heading_margin_deg: float = 0.0) -> dict: + """按覆盖语义校核点集，返回证书字典。""" + if kind == "forward_half_disk": + certificate = verify_forward_half_disk(points, step_m, headings, + r_eff=r_eff, + heading_margin_deg=heading_margin_deg) + else: + certificate = verify_full_disk(points, step_m, r_eff) + return certificate.as_dict() + + +def prune_redundant(masks: list[int], chosen: list[int]) -> list[int]: + """贪心结果冗余剪枝：删掉任何"其余点已完全覆盖其目标"的点。""" + full = 0 + for index in chosen: + full |= masks[index] + pruned = list(chosen) + changed = True + while changed: + changed = False + for index in list(pruned): + rest = 0 + for other in pruned: + if other != index: + rest |= masks[other] + if rest == full: + pruned.remove(index) + changed = True + return pruned + + +def fine_prune(points, step_m: float, headings: int, r_eff: float, + heading_margin_deg: float): + """证书分辨率上的冗余剪枝：删掉任何点后校核仍 0 缺口才真删。 + + 粗网格（搜索用）上的冗余判据偏保守，会在证书分辨率下留下可删的点； + 本步每个候选删除都用**证书同一口径**复核，因此不削弱证书。 + """ + points = list(points) + improved = True + while improved: + improved = False + for index in range(len(points)): + trial = points[:index] + points[index + 1:] + certificate = verify_forward_half_disk( + trial, step_m, headings, r_eff=r_eff, + heading_margin_deg=heading_margin_deg) + if certificate.uncovered == 0: + points = trial + improved = True + print(f" 精细剪枝：删点后仍 0 缺口，剩 {len(points)} 点", flush=True) + break + return points + + +def main() -> int: + parser = argparse.ArgumentParser(description=__doc__.splitlines()[0]) + parser.add_argument("--spacing", type=float, default=200.0, + help="贪心候选点网格间距（米）") + parser.add_argument("--radius", type=float, default=2850.0, + help="候选点所在圆盘半径（米）") + parser.add_argument("--target-step", type=float, default=CERTIFY_STEP_M, + help="搜索用采样位置步长（米）") + parser.add_argument("--headings", type=int, default=CERTIFY_HEADINGS, + help="搜索用采样朝向档数") + parser.add_argument("--verify-step", type=float, default=None, + help="证书校核位置步长（默认等于 --target-step）") + parser.add_argument("--verify-headings", type=int, default=None, + help="证书校核朝向档数（默认等于 --headings）") + parser.add_argument("--design-radius", type=float, default=None, + help="搜索时的设计距离（默认 1000；<1000 即留裕量）") + parser.add_argument("--heading-margin", type=float, default=0.0, + help="搜索时的前向角裕量（度）") + parser.add_argument("--verify-radius", type=float, default=None, + help="证书校核距离（默认 1000；<1000 即留裕量）") + parser.add_argument("--verify-heading-margin", type=float, default=0.0, + help="证书校核前向角裕量（度）；应 ≥ 180/verify_headings/2") + parser.add_argument("--no-prune", action="store_true", help="关闭冗余剪枝") + parser.add_argument("--fine-prune", action="store_true", + help="在证书分辨率上再做一轮冗余剪枝（删点后仍 0 缺口才删）") + parser.add_argument("--coverage", choices=("forward_half_disk", "full_disk"), + default="forward_half_disk", help="覆盖语义") + parser.add_argument("--check-json", type=Path, default=None, + help="只校核给定点集（[[x,y],...] 或 {\"points\":[...]}）") + parser.add_argument("--write-plan", default=None, + help="把贪心最小集写成覆盖计划（名称，落 src/mcm26b/guarantee/plans/）") + parser.add_argument("--out", type=Path, default=None, help="证书 JSON 输出路径") + args = parser.parse_args() + + verify_step = (args.verify_step if args.verify_step is not None + else args.target_step) + verify_headings = (args.verify_headings if args.verify_headings is not None + else args.headings) + design_radius = (args.design_radius if args.design_radius is not None + else R_EFF_MIN_M) + verify_radius = (args.verify_radius if args.verify_radius is not None + else R_EFF_MIN_M) + + if args.check_json is not None: + payload = json.loads(args.check_json.read_text(encoding="utf-8")) + raw = payload.get("points") if isinstance(payload, dict) else payload + points = [tuple(map(float, p)) for p in raw] + report = _report(points, args.coverage, verify_step, verify_headings, + verify_radius, args.verify_heading_margin) + print(json.dumps(report, ensure_ascii=False, indent=2)) + return 0 if report["uncovered"] == 0 else 1 + + shipped = square_49_plan() + shipped_report = _report(shipped.points, "forward_half_disk", + verify_step, verify_headings, + verify_radius, args.verify_heading_margin) + print(f"目标空间：{shipped_report['targets']} 个 (p,h)" + f"（校核位置步长 {verify_step} m，朝向 {verify_headings} 档，" + f"r_verify={verify_radius:.0f} m，角裕量 " + f"{args.verify_heading_margin}°）") + print(f"现役 {shipped.name}：{len(shipped.points)} 点，" + f"缺口 {shipped_report['uncovered']}，" + f"NN 巡回 {shipped_report['nn_tour_m'] / 1000:.1f} km" + f"（{shipped_report['nn_tour_virtual_s']:.0f} s 移动）", flush=True) + + targets = forward_targets(args.target_step, args.headings) + candidates = candidate_lattice(args.spacing, args.radius) + print(f"候选点 {len(candidates)}（{args.spacing} m 网格，半径 {args.radius} m）；" + f"设计裕量 r_design={design_radius:.0f} m，前向角裕量 " + f"{args.heading_margin}°", flush=True) + masks = coverage_masks(targets, candidates, r_eff=design_radius, + heading_margin_deg=args.heading_margin) + chosen = greedy_cover(masks, len(targets)) + if not args.no_prune: + chosen = prune_redundant(masks, chosen) + best_points = [candidates[i] for i in chosen] + if args.fine_prune: + best_points = fine_prune(best_points, verify_step, verify_headings, + verify_radius, args.verify_heading_margin) + best_report = _report(best_points, "forward_half_disk", + verify_step, verify_headings, + verify_radius, args.verify_heading_margin) + print(f"设计裕量最小命中集：{len(best_points)} 点，缺口 {best_report['uncovered']}，" + f"NN 巡回 {best_report['nn_tour_m'] / 1000:.1f} km" + f"（{best_report['nn_tour_virtual_s']:.0f} s 移动）", flush=True) + + if best_report["uncovered"] != 0: + print("贪心结果未通过证书校核，拒绝生成计划。") + return 1 + + generator = {"script": "scripts/verify_q4_cover.py", + "spacing_m": args.spacing, "radius_m": args.radius, + "target_step_m": args.target_step, "headings": args.headings, + "design_r_eff_m": design_radius, + "heading_margin_deg": args.heading_margin, + "pruned": not args.no_prune, + "verify_step_m": verify_step, + "verify_headings": verify_headings, + "verify_r_eff_m": verify_radius, + "verify_heading_margin_deg": args.verify_heading_margin, + "code_sha256": hashlib.sha256( + Path(__file__).read_bytes()).hexdigest()} + certificate = CoverageCertificate( + kind=best_report["kind"], target_step_m=verify_step, + headings=verify_headings, r_eff_min_m=verify_radius, + targets=best_report["targets"], uncovered=best_report["uncovered"], + worst_hit_distance_m=best_report["worst_hit_distance_m"], + nn_tour_m=best_report["nn_tour_m"], + generator_sha256=generator["code_sha256"], + verify_heading_margin_deg=args.verify_heading_margin) + plan = CoveragePlan( + name=args.write_plan or "q4_mincover_draft", problem=4, + kind="certified", coverage="forward_half_disk", + note=(f"设计裕量最小命中集：{len(best_points)} 点，" + f"r_design={design_radius:.0f} m / 前向角裕量 {args.heading_margin}°；" + f"证书按 {verify_step:.0f} m/{verify_headings} 档、r_verify=" + f"{verify_radius:.0f} m、角裕量 {args.verify_heading_margin}° " + f"校核缺口 0（同一证书口径复核，含精细剪枝）；最坏命中 " + f"{best_report['worst_hit_distance_m']:.0f} m，" + f"NN 巡回 {best_report['nn_tour_m'] / 1000:.1f} km"), + generator=generator, certificate=certificate, + points=tuple(best_points)) + payload = {"model": {"r_omega_m": 1800.0, "r_eff_min_m": 1000.0}, + "design": {"r_design_m": design_radius, + "heading_margin_deg": args.heading_margin}, + "verify": {"r_eff_m": verify_radius, + "heading_margin_deg": args.verify_heading_margin, + "step_m": verify_step, "headings": verify_headings}, + "shipped": shipped_report, "mincover": best_report} + if args.out is not None: + out = args.out if args.out.is_absolute() else ROOT / args.out + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(payload, ensure_ascii=False, indent=2) + "\n", + encoding="utf-8") + print(f"证书已写入 {out}") + if args.write_plan: + target = DEFAULT_PLAN_DIR / f"{args.write_plan}.json" + write_plan(plan, target) + verified = verify_plan(plan) + print(f"计划已写入 {target}；运行期分辨率复核缺口 {verified.uncovered}") + print("已注册计划：" + "，".join(plan_names())) + _ = (ProblemConfig(), omega_positions) # 保持公开入口可发现性 + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/src/mcm26b/belief/joint.py b/src/mcm26b/belief/joint.py new file mode 100644 index 0000000..5e09b6f --- /dev/null +++ b/src/mcm26b/belief/joint.py @@ -0,0 +1,388 @@ +"""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** +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。 + +本模块把"看不看得见"变成可推理的量：为已在 Ω 上维护的网格幸存集 +（:class:`~mcm26b.particles.ChannelParticles`）附加每格 24 档朝向可行性掩码， +得到 (位置 × 朝向) 联合可行集。观测语义严格对齐评测机 +``referee.simulator._observe``： + +- 距离 > r_eff → no_signal；定向且源→读点方向与朝向夹角 > 90° → no_signal； +- 距离 ≤ 5 m → near（**仍需可见**：3 m 处背向的定向源同样返回 no_signal）； +- 其余 → direction（读数 = 源→读点方位 + 有界误差）。 + +更新规则（全部保守，只删"必然不可能"的格/档）： + +=============== ============================================ ================== +观测 位置 朝向 +=============== ============================================ ================== +``direction``@s 距离 ≤ 1500、角向 |gap| ≤ δ_eff + 格裕量 必须**可能**覆盖 s +``near``@s 距离 ≤ 5 必须**可能**覆盖 s +``no_signal``@s **不删**（可能是朝向遮挡或距离不足） 必须**确定不**覆盖 s + （仅当整格必在 r_eff_min 内才约束朝向） +=============== ============================================ ================== + +阈值全部取保守侧，并计入**格内角裕量** +``asin(margin / max(dist − margin, 1))``（格内任一点相对格心方位偏差上界）： + +- "必须可能覆盖"：``gap ≤ 90° + 半档 + 查表舍入 + 格内角裕量``（宁可保留）； +- "必须确定不覆盖"：先删去 ``gap ≤ 90° − 半档 − 查表舍入 − 格内角裕量`` 的档， + 只保留确定看不见 s 的档（同样宁可保留）。 + +因此真源 (位置, 朝向) 永不被删——这是网格幸存集真源保留定理的二维推广。 +（2026-09-12 修正：no_signal 分支原先误用"确定覆盖"掩码做交集，等价于把 +规则反向；keep=True 分支也漏计了格内角裕量。两处都会删掉真源朝向档， +现由 ``tests/test_joint_belief.py`` 的随机观测序列保留性测试固化。） + +用途： +1. ``prob_detect``：该点能看见该频道任一可行 (位置, 朝向) 的概率质量， + 用于**可见性选点**（取代固定阶梯）——只影响选点顺序，不承担安全责任； +2. ``expected_posterior_radius``：一次测量后的期望后验 MEC 半径（D1 值函数）； +3. ``is_absent``：联合集空 ⟹ 该频道不存在源（严格，与覆盖证明同一条件）； +4. 位置边际仍由凸证书负责 20 m 清除判定，本模块不改变任何证书口径。 +""" + +from __future__ import annotations + +import math + +from ..config import ProblemConfig, Vec2 +from ..particles import ChannelParticles, _branch_radius_array +from ..protocol.rules import DIRECTION, NEAR, NO_SIGNAL + +HEADING_BINS = 24 # 15° 一档 +_BIN_HALF_DEG = 180.0 / HEADING_BINS # 7.5° +_FULL_MASK = (1 << HEADING_BINS) - 1 +_GRID_DEG = 3.0 # 角度查表分辨率 +_GRID_N = int(round(360.0 / _GRID_DEG)) + +try: # numpy 仅作可选加速后端（与 particles 一致） + import numpy as _np + from ..particles import _np as _particles_np + if _particles_np is None: # MCM26B_NO_NUMPY 或缺失：强制纯 Python + _np = None +except Exception: # pragma: no cover - 环境无 numpy + _np = None + + +def heading_gap(a_deg: float, b_deg: float) -> float: + """两个朝向角的最小夹角（度）。""" + return abs((a_deg - b_deg + 180.0) % 360.0 - 180.0) + + +def _cover_mask(bearing_deg: float, half_deg: float) -> int: + """与该方位夹角不超过 ``half_deg`` 的朝向档掩码。""" + mask = 0 + for k in range(HEADING_BINS): + if heading_gap(360.0 * k / HEADING_BINS, bearing_deg) <= half_deg: + mask |= 1 << k + return mask + + +# 查表：以 bearing 为轴、半角 90° 的可见半平面。 +# - COVER：**可能**覆盖该方位（含半档与查表舍入的保守侧）； +# - SILENT：**确定**覆盖该方位（内缩半档与舍入）——离散对照表，供审计与 +# 文档比对；实际裁剪走 _cover_mask / _far_mask，因为还必须逐格计入 +# "格内角裕量"（格心到格内任一点的方位偏差上界），无法预先制表。 +_COVER_TABLE = tuple( + _cover_mask(_GRID_DEG * i, 90.0 + _BIN_HALF_DEG + _GRID_DEG / 2) + for i in range(_GRID_N)) +_SILENT_TABLE = tuple( + _cover_mask(_GRID_DEG * i, 90.0 - _BIN_HALF_DEG - _GRID_DEG / 2) + for i in range(_GRID_N)) + + +def _far_mask(bearing_deg: float, exclude_half_deg: float) -> int: + """与方位夹角**大于** ``exclude_half_deg`` 的朝向档掩码（no_signal 用）。 + + 与 :func:`_cover_mask` 互补：no_signal 只允许保留"确定看不见"的朝向档。 + """ + mask = 0 + for k in range(HEADING_BINS): + if heading_gap(360.0 * k / HEADING_BINS, bearing_deg) > exclude_half_deg: + mask |= 1 << k + return mask + +if _np is not None: # 12 位半字 popcount 查表（朝向掩码 24 位）与半平面查表数组 + _POP16 = _np.array([bin(value).count("1") for value in range(1 << 12)], + dtype=_np.int64) + _COVER_ARR = _np.array(_COVER_TABLE, dtype=_np.int64) +else: # pragma: no cover - 无 numpy 分支 + _POP16 = _COVER_ARR = None + + +def _popcount(mask: int) -> int: + return bin(mask).count("1") + + +def _table_index(bearing_deg: float) -> int: + return int(round((bearing_deg % 360.0) / _GRID_DEG)) % _GRID_N + + +class JointBelief: + """单频道 (位置 × 朝向) 联合可行集。""" + + def __init__(self, config: ProblemConfig = ProblemConfig()): + self.config = config + self.field = config.field + self.position = ChannelParticles(config) + self.masks: dict[int, int] = {} # 格索引 -> 朝向掩码（缺省为全档） + self._sample_cache: tuple[int, list[int]] | None = None + + # ------------------------------------------------------------ 查询 + def __len__(self) -> int: + return len(self.position) + + def is_absent(self) -> bool: + """联合集空 ⟹ 该频道不存在源（真源保留定理的二维推广）。""" + return not self.position.alive + + def _mask(self, index: int) -> int: + return self.masks.get(index, _FULL_MASK) + + def sample(self, size: int = 256) -> list[int]: + """均匀抽样幸存格（评分用；证书始终用完整集合）。""" + cached = self._sample_cache + if cached is not None and cached[0] == self.position.version: + return cached[1] + alive = sorted(self.position.alive) + if len(alive) > size: + step = len(alive) / size + alive = [alive[int(k * step) % len(alive)] for k in range(size)] + self._sample_cache = (self.position.version, alive) + return alive + + def _mask_popcount(self, index: int) -> int: + return _popcount(self._mask(index)) + + def expected_posterior_radius(self, reader: Vec2, size: int | None = None) -> float: + """一次测量后的期望后验 MEC 半径（米）；D1 值函数。 + + 与 :meth:`ChannelParticles.expected_posterior_radius` 同族，差别只在 + no_signal 分支概率：定向源不仅会因距离 > r_eff 静默，还会因朝向遮挡 + 静默，因此 + + p_vis(p) = P(r_eff ≥ |p−q|) × cover_ratio(p, q) + + 其中 ``cover_ratio`` 是该格朝向掩码中"可能覆盖 q 方位"的档位占比。 + 分支半径语义： + + - direction（可见，非 near）：读数为 q 处的方位 ±δ，后验收缩到该方位 + 楔形与当前可行集之交，半径取幸存子采样的质心最大距离； + - no_signal：定向源的静默**不删空间候选**，位置后验半径保持当前值； + - near：后验为 5m 盘。 + + 返回值只用于**选点排序**，不参与任何安全判定；证书仍由凸/网格口径给出。 + """ + pf = self.position + if not pf.alive: + return 0.0 + if size is None: + size = self.config.bayes.score_subsample + indices = self.sample(max(2, size)) + if not indices: + return 0.0 + stride = max(len(indices) // max(self.config.bayes.mc_representatives, 1), 1) + representatives = indices[::stride] + if _np is not None: + return self._epr_vector(reader, indices, representatives) + return self._epr_python(reader, indices, representatives) # pragma: no cover + + def _epr_vector(self, reader: Vec2, indices: list[int], + representatives: list[int]) -> float: + pf = self.position + points = _np.array([pf.cell_center(index) for index in indices], dtype=float) + masks = _np.array([self._mask(index) for index in indices], dtype=_np.int64) + reps = _np.array([pf.cell_center(index) for index in representatives], + dtype=float) + rep_masks = _np.array([self._mask(index) for index in representatives], + dtype=_np.int64) + dx, dy = points[:, 0] - reader[0], points[:, 1] - reader[1] + dist = _np.hypot(dx, dy) + bearing = _np.degrees(_np.arctan2(dy, dx)) + rdx, rdy = reps[:, 0] - reader[0], reps[:, 1] - reader[1] + rdist = _np.hypot(rdx, rdy) + rbearing = _np.degrees(_np.arctan2(rdy, rdx)) + cover = _COVER_ARR[_np.round(_np.mod(rbearing, 360.0) / _GRID_DEG) + .astype(_np.int64) % _GRID_N] + visible_mask = rep_masks & cover + bits = (_POP16[visible_mask & 0xFFF] + + _POP16[(visible_mask >> 12) & 0xFFF]) + cover_ratio = bits / float(HEADING_BINS) + p_silent_range = _np.clip((rdist - self.field.r_eff_min_m) + / (self.field.r_eff_max_m + - self.field.r_eff_min_m), 0.0, 1.0) + p_vis = (1.0 - p_silent_range) * cover_ratio + p_ns = 1.0 - p_vis + gaps = _np.abs((bearing[None, :] - rbearing[:, None] + 180.0) % 360.0 - 180.0) + in_wedge = ((gaps <= self.config.field.bearing_error_half_deg + + self.config.search.bearing_rounding_margin_deg + 1e-9) + & (dist[None, :] <= self.field.r_eff_max_m + pf.margin_m)) + eps_radius = pf.spacing_m / 2.0 + radii = [] + for row in in_wedge: + member = points[row] + radii.append(_branch_radius_array(member) + if len(member) else eps_radius) + radii = _np.array(radii, dtype=float) + current = pf.mec()[1] + near = rdist <= self.field.near_radius_m + pf.margin_m + values = _np.where(near, self.field.near_radius_m + pf.margin_m, + p_ns * current + p_vis * radii) + return float(values.mean()) + + def _epr_python(self, reader: Vec2, indices: list[int], + representatives: list[int]) -> float: # pragma: no cover + """纯 Python 回退（与向量版逐分支同语义，供无 numpy 环境与对拍）。""" + pf = self.position + points = [pf.cell_center(index) for index in indices] + masks = [self._mask(index) for index in indices] + eps_radius = pf.spacing_m / 2.0 + current = pf.mec()[1] + half_angle = (self.config.field.bearing_error_half_deg + + self.config.search.bearing_rounding_margin_deg) + total = 0.0 + for rep_index in representatives: + rx, ry = pf.cell_center(rep_index) + distance = math.hypot(rx - reader[0], ry - reader[1]) + if distance <= self.field.near_radius_m + pf.margin_m: + total += self.field.near_radius_m + pf.margin_m + continue + reading = math.degrees(math.atan2(ry - reader[1], rx - reader[0])) + cover = _COVER_TABLE[_table_index(reading)] + bits = _popcount(self._mask(rep_index) & cover) + ratio = bits / float(HEADING_BINS) + if distance <= self.field.r_eff_min_m: + p_range = 1.0 + elif distance >= self.field.r_eff_max_m: + p_range = 0.0 + else: + p_range = ((self.field.r_eff_max_m - distance) + / (self.field.r_eff_max_m - self.field.r_eff_min_m)) + p_vis = p_range * ratio + members = [] + for point, mask in zip(points, masks): + if math.dist(point, reader) > self.field.r_eff_max_m + pf.margin_m: + continue + bearing = math.degrees(math.atan2(point[1] - reader[1], + point[0] - reader[0])) + if heading_gap(bearing, reading) <= half_angle + 1e-9: + members.append(point) + if members: + mx = sum(p[0] for p in members) / len(members) + my = sum(p[1] for p in members) / len(members) + radius = max(math.hypot(p[0] - mx, p[1] - my) for p in members) + else: + radius = eps_radius + total += (1.0 - p_vis) * current + p_vis * radius + return total / max(len(representatives), 1) + + + def prob_detect(self, reader: Vec2, size: int = 256) -> float: + """在 ``reader`` 处能看见任一可行 (位置, 朝向) 的概率质量（启发式）。 + + r_eff ~ U[1000, 1500] 解析积分；朝向按可行档位占比计权。 + 只用于选点排序，不参与任何安全判定。 + """ + cells = self.sample(size) + if not cells: + return 0.0 + total = 0.0 + span = self.field.r_eff_max_m - self.field.r_eff_min_m + for index in cells: + gx, gy = self.position.cell_center(index) + distance = math.hypot(reader[0] - gx, reader[1] - gy) + if distance > self.field.r_eff_max_m: + continue + if distance <= self.field.r_eff_min_m: + p_range = 1.0 + else: + p_range = (self.field.r_eff_max_m - distance) / span + if p_range <= 0.0: + continue + bearing = math.degrees(math.atan2(reader[1] - gy, reader[0] - gx)) + cover = _COVER_TABLE[_table_index(bearing)] + bits = bin(self._mask(index) & cover).count("1") + if bits: + total += p_range * bits / HEADING_BINS + return total / len(cells) + + # ------------------------------------------------------------ 观测更新 + def _apply_heading(self, station: Vec2, keep: bool, + within_m: float | None = None) -> None: + """按可见性约束裁剪朝向档（保守：只删"必然不可能"的档）。 + + ``keep=True``（direction / near）⟹ 朝向必须**可能**覆盖 station： + 保留与方位夹角 ≤ 90° + 半档 + 查表舍入 + 格内角裕量的档。 + + ``keep=False``（no_signal 且整格必在 r_eff_min 内）⟹ 朝向必须 + **确定不覆盖** station：只保留与方位夹角 > 90° − 半档 − 查表舍入 + − 格内角裕量的档。 + + ``格内角裕量 = asin(margin / max(dist − margin, 1))``：格内任一点 + 相对格心的方位偏差上界。两个方向都必须计入它，否则会误删真源的 + 朝向档（keep=True 漏计）或保留必然可见的档（keep=False 漏计）。 + """ + margin_m = self.position.margin_m + doomed: list[int] = [] + for index in self.position.alive: + gx, gy = self.position.cell_center(index) + dx, dy = station[0] - gx, station[1] - gy + distance = math.hypot(dx, dy) + if within_m is not None and distance > within_m: + continue + bearing = math.degrees(math.atan2(dy, dx)) + if distance <= margin_m + 1.0: + # 格贴测点：格内方位可指向任意方向，任何一侧都不可据此裁剪 + continue + angular_margin = math.degrees( + math.asin(min(0.9999, margin_m / (distance - margin_m)))) + if keep: + updated = self._mask(index) & _cover_mask( + bearing, 90.0 + _BIN_HALF_DEG + _GRID_DEG / 2 + angular_margin) + else: + updated = self._mask(index) & _far_mask( + bearing, 90.0 - _BIN_HALF_DEG - _GRID_DEG / 2 - angular_margin) + if updated == 0: + doomed.append(index) + self.masks.pop(index, None) + elif updated != _FULL_MASK: + self.masks[index] = updated + if doomed: + self.position.discard(doomed) + + def apply(self, station: Vec2, result: str, + svd_deg: float | None = None) -> None: + """按读数更新联合可行集（保守，真源 (位置, 朝向) 永不被删）。""" + if result == DIRECTION: + self.position.apply_measure(station, DIRECTION, svd_deg, + directional=True) + self._apply_heading(station, keep=True) + elif result == NEAR: + # near 同样要求可见（评测机先判朝向再判距离） + self.position.apply_measure(station, NEAR, directional=True) + self._apply_heading(station, keep=True) + elif result == NO_SIGNAL: + # 距离阈值必须取**保守侧**：只有"整格都必然落在 r_eff_min 圆盘内" + # 时，no_signal 才一定由朝向遮挡解释。格心到格内任一点 ≤ margin， + # 故阈值取 r_eff_min − margin；取 + margin 会把 1000~1010m 的模糊带 + # 也当成"必须不覆盖"，从而可能删掉真源的朝向档（破坏真源保留性）。 + self._apply_heading(station, keep=False, + within_m=max( + 0.0, self.field.r_eff_min_m + - self.position.margin_m)) + else: + raise ValueError(f"未知测向结果: {result!r}") + + # ------------------------------------------------------------ 诊断 + def summary(self) -> dict: + """联合集规模快照（写入策略 summary，供审计与论文复算）。""" + cells = self.sample(256) + bins = [bin(self._mask(index)).count("1") for index in cells] + return {"cells": len(self.position), + "sampled_mean_heading_bins": (round(sum(bins) / len(bins), 2) + if bins else 0.0), + "heading_bins_total": HEADING_BINS} diff --git a/src/mcm26b/cli.py b/src/mcm26b/cli.py index 225328e..fb71207 100644 --- a/src/mcm26b/cli.py +++ b/src/mcm26b/cli.py @@ -206,6 +206,17 @@ def _run(args) -> int: search=replace(config.search, bearing_rounding_margin_deg=args.bearing_margin_deg, persistent_search=args.persistent)) + if args.persistent: + # 历史教训：一次官方演练在 persistent 复扫下把 98.84% 的虚拟时间 + # （346170 s）花在空复扫上。这里给出单轮上界，明确这是"扫到限时"模式。 + stations = 49 if args.problem == 4 else 7 + per_round = config.field.channel_max * stations * ( + config.cost.measure_s + config.cost.switch_s) + print(f"[警告] --persistent：找全后仍复扫未清除频道直到限时。" + f"单轮上界约 {per_round:.0f}s 虚拟时间" + f"（{config.field.channel_max} 频道 × {stations} 站点 × " + f"{config.cost.measure_s + config.cost.switch_s:.0f}s），" + f"且会持续多轮；成绩口径请保持默认（关闭）。", file=sys.stderr) truth = None transport = None real_budget_s = None diff --git a/src/mcm26b/config.py b/src/mcm26b/config.py index 28b6a0f..c445367 100644 --- a/src/mcm26b/config.py +++ b/src/mcm26b/config.py @@ -91,6 +91,45 @@ class SearchConfig: max_blocked_probes: int = 4 # 定位单轮内被朝向遮挡的探点预算 # （首次选点+对跖复核+2 次环探；达限转后备清除） fallback_max_cells: int = 400 # 后备清除格数硬顶；超过则不盲扫（宽候选集继续定位） + # ---- 覆盖计划（保证层）[工程，2026-09-12 架构优化 P0-2 / S2 硬化] ---- + # 第四问发现点集： + # square49 旧的充分条件表（700m 方格，巡回 36.4km）； + # q4_mincover_v1 75m/24 档贪心最小集（31 点，巡回 22.9km）——细网格复检 + # 在 25m/48 档有 125 个 (p,h) 缺口，是采样过拟合解； + # q4_mincover_v2 **设计裕量**最小集（31 点，r_design=965m、前向角裕量 5°， + # 搜索分辨率 50m/48 档 + 证书分辨率精细剪枝）， + # 15m/72 档（325.6 万 (p,h)）缺口 0，最坏命中 990m，巡回 23.9km。 + # 运行期按同一采样语义复核，未通过即拒绝启动。 + q4_coverage_plan: str = "q4_mincover_v2" + verify_coverage_plan: bool = True # 运行期抽样校验（进程内缓存一次） + # ---- 第四问定向源联合信念 [工程，2026-09-12 架构优化 P0-3] ---- + # 为已发现的定向频道维护 (位置 × 朝向) 联合可行集：用可见性概率选点， + # 取代固定 45° 阶梯探点；联合集空即给出缺席证明。安全性仍由凸/网格 + # 清除证书与覆盖证明负责，本开关只影响选点顺序。 + directional_joint_belief: bool = True + # ---- 第四问定向源探点值函数 [工程，2026-09-12 图论与 DP 方案 S3/D1] ---- + # 旧评分 ``prob_detect/(移动+6)`` 只问"这点看不看得见源"，不问"测完剩多少 + # 不确定度"，会反复选中同一个近处被遮挡点。D1 口径：先按可见概率过滤 + # （只保留 prob_detect ≥ ratio×最大值的候选），再最小化 + # E[后验 MEC 半径](q) + λ·(当前位置→q→MEC 圆心 的移动秒数 + 检测) + # 9 场景 core 配对（修正联合信念朝向规则之后）： + # λ=0.05：后备 6.8 次 / T 9740 s（值函数主导，为信息多绕路） + # λ=5 ：后备 4.3 次 / T 8604 s（移动项主导，可见性过滤 + 就近抵近） + # 仅移动项（nearest_visible）：后备 4.3 次 / T 8602 s，墙钟最低 + # 相对旧 visibility 口径（后备 125.6 次、T 9225 s）三者都更好，且 λ 越大 + # 越优说明本题"看得见 + 少走路"比"信息量最大"更重要，故默认取极限口径。 + probe_score_mode: str = "nearest_visible" # visibility | expected_radius | nearest_visible + probe_lambda_move_s: float = 5.0 # expected_radius 口径下的移动折算 + # 可见概率过滤：只保留 prob_detect ≥ ratio×最大值的候选再比期望半径， + # 避免"期望上有点信息、实际必被朝向遮挡"的探点。 + probe_visibility_ratio: float = 0.7 + probe_exclude_blocked: bool = True # 已测过且被遮挡的探点不再重复选取 + approach_fractions: tuple = () # D1 抵近点插值比例（0=圆心）；默认关闭 + # ---- 后备清除路径优化 [工程，2026-09-12 S4/D3] ---- + # 实测反例：后备清除在命中真源时立即结束，"整条走格路径最短"并不是正确 + # 目标——Or-opt 会把真源推到序列更后面。9 场景配对：最近邻 125.7 次失败 + # vs Or-opt 161.4 次（T 8676 → 8940 s）。默认关闭，保留代码与开关。 + fallback_optimize_tour: bool = False # ---- 持续搜索 [工程，2026-09-11 用户决策修订] ---- # 虚拟时间即评测成绩口径（表1：平均定位清除时间）：找全证明达成后 # 立即 /exit，不复扫。复扫保险模式（防缺席证明假设偏差）保留，经 @@ -119,18 +158,45 @@ class BayesConfig: # radius=期望后验半径（直指证书） score_subsample: int = 800 # 期望幸存熵估计的粒子子采样规模 mc_representatives: int = 7 # 假设真值代表点数（均匀抽取幸存格） - lambda_move_s: float = 0.0105 # 选点评分中移动时间的折算系数（分值/秒）； - # entropy 口径下信息项为 O(1) 对数值，λ须极小 + lambda_move_s: float = 0.05 # 选点评分中移动时间的折算系数（分值/秒）； + # 2026-09-12 配对复跑：0.05 优于 0.0105（20 场均值 −2.8%）， + # entropy 口径下信息项为 O(1) 对数值，λ须小型但不可过小 + # ---- 路线感知探点 [工程，2026-09-12 P2] ---- + # 旧评分只看 current->q + q->MEC，容易选到“信息增益略高但要多绕几百米” + # 的探点。开启后，在候选评分中加入 q 替换当前位置后对当前任务路线代价 + # 的增量；A/B 以冻结基准集配对验收。 + # 2026-09-12 冻结基准集复跑（18 场景）：关闭 3193.1 s vs 开启 3210.3 s， + # 移动 12339.8 m vs 12446.8 m —— 权重 1e-3 的路线项未带来净收益，默认关闭。 + route_aware_probe: bool = False + route_aware_insert_weight: float = 0.001 # 路线代价增量的折算系数（小权重，仅微调） + route_aware_clear_weight: float = 0.0 # 额外 q->MEC 清除接近项（基础移动项已含） wide_rings: tuple = (1.0, 1.5, 2.0) # 无保证接收盘时的探索环（× PF MEC 半径） # ---- 站点与覆盖 [工程] ---- station_ring_radius_m: float = 1159.7 # 七点环半径；构造时检验全向覆盖解析上界 + # ---- 发现-定位融合巡回 [工程，2026-09-12 P0-1，实测无效，默认关闭] ---- + # 设想：站点扫频顺带取第二/第三条方位线，取代专用探点。 + # 实测（output/suite/q3-fuse-ab*，20 场景配对，2026-09-12）： + # 仅追加站点方位线：3454.6s vs 3426.9s（+0.8%，检测 +10.8 次，位移仅 −188m）； + # 追加 + 抑制专用探点：4989.0s vs 3426.9s（+45.6%，20/20 场景更慢）。 + # 原因：七站环上只有 2–3 个站能对同一源取得有效方位线，且站间交会角 + # 不足以保证 20m 证书，专用信息增益探点仍然必要；抑制探点只会白等巡回。 + # 代码保留以便复现与后续研究，成绩口径必须保持 False。 + fuse_localize_into_tour: bool = False # ---- 任务排序 [工程] ---- two_opt_enabled: bool = True # 开放路径 2-opt 改进（最近邻打底） two_opt_passes: int = 4 # 2-opt 最大改进轮数 + # ---- 精确任务序（Held-Karp，含换频状态）[工程，2026-09-12 S5/T1] ---- + # 任务表典型 6–12 项：|T| ≤ exact_order_limit 时用精确 DP（状态=已访问集合 + # × 结束时测向频道），换频代价精确计入；否则回退 NN + 2-opt。0 表示禁用。 + # 实测（18 场景）：精确 DP 3203.2 s vs 2-opt 3193.1 s —— 逐位最优的"当前 + # 任务表"在滚动时域下反而略差（贪心排序的次优性起到探索作用），默认 0； + # 代码与对拍测试保留，可随时开启复核。 + exact_order_limit: int = 0 # ---- PF 清除证书 [工程] ---- grid_cert_enabled: bool = True # 允许用幸存集 MEC 颁发清除证书（凸证书之外） opportunistic_measure: bool = True # 已停靠位置顺路交会；仍逐频道计费 - opportunistic_radius_ratio: float = 0.65 # 预测后验半径至少减少35%才补测 + # 预测后验半径至少减少 (1−ratio) 才补测；0.5 优于 0.65（18 场景 3205.4 vs 3210.3 s） + opportunistic_radius_ratio: float = 0.5 @dataclass(frozen=True) diff --git a/src/mcm26b/guarantee/__init__.py b/src/mcm26b/guarantee/__init__.py new file mode 100644 index 0000000..a81a843 --- /dev/null +++ b/src/mcm26b/guarantee/__init__.py @@ -0,0 +1,23 @@ +"""保证层：覆盖计划（CoveragePlan）、证书与运行期校验。 + +对外只暴露覆盖计划与校验入口；`coverage` 包仍负责逐频道覆盖账本。 +""" + +from .cover import (CERTIFY_HEADINGS, CERTIFY_STEP_M, OMEGA_RADIUS_M, + R_EFF_MIN_M, CoverageCertificate, + CoveragePlan, PLAN_DIR, VERIFY_HEADINGS, VERIFY_STEP_M, + candidate_lattice, coverage_masks, forward_targets, + greedy_cover, load_plan, nn_tour_length, omega_positions, + plan_instance, plan_names, seven_plan, seven_points, + square_49_plan, verify_full_disk, verify_forward_half_disk, + verify_plan, write_plan) + +__all__ = [ + "CERTIFY_HEADINGS", "CERTIFY_STEP_M", "OMEGA_RADIUS_M", "R_EFF_MIN_M", + "CoverageCertificate", "CoveragePlan", + "PLAN_DIR", "VERIFY_HEADINGS", "VERIFY_STEP_M", "candidate_lattice", + "coverage_masks", "forward_targets", "greedy_cover", "load_plan", + "nn_tour_length", "omega_positions", "plan_instance", "plan_names", + "seven_plan", "seven_points", "square_49_plan", "verify_full_disk", + "verify_forward_half_disk", "verify_plan", "write_plan", +] diff --git a/src/mcm26b/guarantee/cover.py b/src/mcm26b/guarantee/cover.py new file mode 100644 index 0000000..d37756c --- /dev/null +++ b/src/mcm26b/guarantee/cover.py @@ -0,0 +1,428 @@ +"""保证层：可离线搜索、可在运行期校验的覆盖计划（CoveragePlan）。 + +两类覆盖语义（题面约束不同，绝不可混用）： + +- ``full_disk``（第三问，全向源）：在 s 处 no_signal 排除 B(s, r_eff_min)， + 因此"全点皆 no_signal ⟹ 不存在源"要求 + ``∪ B(c, 1000) ⊇ Ω``。解析最优解是"原点 + 半径 r 的 6 等分环"， + ``r`` 的可行区间由 ``validate_seven_coverage`` 给出（本模块提供数值复核）。 +- ``forward_half_disk``（第四问，定向源）：源可能背向读点，no_signal 不再 + 排除整个圆盘，缺席证明要求 + ``∀(p,h) ∈ Ω×S¹, ∃c ∈ S: |c−p| ≤ 1000 ∧ (c−p)·u(h) ≥ 0``。 + 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）： +**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。 +""" + +from __future__ import annotations + +import hashlib +import json +import math +from dataclasses import dataclass, field, fields +from functools import lru_cache +from pathlib import Path + +from ..config import Vec2 + +PLAN_DIR = Path(__file__).resolve().parent / "plans" +R_EFF_MIN_M = 1000.0 # [题面] 最小有效接收半径 +OMEGA_RADIUS_M = 1800.0 # [题面] 目标圆域半径 +SPEED_MPS = 5.0 # [题面] 用于把巡回长度折算成虚拟移动秒数 + +# 采样分辨率。**校核必须与证书同分辨率**：更粗的采样会放过"删掉一个关键点 +# 仍然看似覆盖"的点集，使校核退化为装饰（实测 100 m/12 档即漏判）。 +# 75 m / 24 档下一次全域校核约 1 s，且按计划名缓存，每进程只付一次。 +CERTIFY_STEP_M = 75.0 +CERTIFY_HEADINGS = 24 +VERIFY_STEP_M = CERTIFY_STEP_M +VERIFY_HEADINGS = CERTIFY_HEADINGS + + +@dataclass(frozen=True) +class CoverageCertificate: + """覆盖证书：采样语义 + 缺口 + 最坏命中距离。""" + + kind: str # "full_disk" | "forward_half_disk" + target_step_m: float + headings: int + r_eff_min_m: float + targets: int + uncovered: int + worst_hit_distance_m: float + nn_tour_m: float + generator_sha256: str = "" + # 证书硬化：校核时使用的前向角裕量（度）。与 r_eff_min_m 的设计裕量 + # 一起把"采样格覆盖"提升为"连续空间覆盖"，见 verify_forward_half_disk。 + verify_heading_margin_deg: float = 0.0 + + @property + def valid(self) -> bool: + return self.uncovered == 0 and self.targets > 0 + + def as_dict(self) -> dict: + return {"kind": self.kind, "target_step_m": self.target_step_m, + "headings": self.headings, "r_eff_min_m": self.r_eff_min_m, + "targets": self.targets, "uncovered": self.uncovered, + "worst_hit_distance_m": round(self.worst_hit_distance_m, 3), + "nn_tour_m": round(self.nn_tour_m, 1), + "nn_tour_virtual_s": round(self.nn_tour_m / SPEED_MPS, 1), + "generator_sha256": self.generator_sha256, + "verify_heading_margin_deg": self.verify_heading_margin_deg} + + +@dataclass(frozen=True) +class CoveragePlan: + """一份覆盖计划：点集 + 语义 + 证书（可为解析证明）。""" + + name: str + problem: int + points: tuple[Vec2, ...] + kind: str # "analytic" | "certified" + coverage: str # "full_disk" | "forward_half_disk" + note: str = "" + certificate: CoverageCertificate | None = None + generator: dict = field(default_factory=dict) + + def __len__(self) -> int: + return len(self.points) + + def as_dict(self) -> dict: + return {"name": self.name, "problem": self.problem, "kind": self.kind, + "coverage": self.coverage, "note": self.note, + "generator": self.generator, + "certificate": (self.certificate.as_dict() + if self.certificate is not None else None), + "points": [[p[0], p[1]] for p in self.points]} + + +# --------------------------------------------------------------------- 采样 +def omega_positions(step_m: float) -> list[Vec2]: + """Ω 内均匀方格位置采样。""" + span = int(OMEGA_RADIUS_M / step_m) + 1 + return [(step_m * i, step_m * j) + for i in range(-span, span + 1) for j in range(-span, span + 1) + if (step_m * i) ** 2 + (step_m * j) ** 2 <= OMEGA_RADIUS_M ** 2] + + +def forward_targets(step_m: float, headings: int) -> list[tuple[float, float, float]]: + """(p, h) 目标采样：位置在 Ω 内，朝向均分整圆。""" + return [(x, y, 2.0 * math.pi * k / headings) + for (x, y) in omega_positions(step_m) for k in range(headings)] + + +def candidate_lattice(spacing_m: float, radius_m: float) -> list[Vec2]: + """半径 radius_m 圆盘内的方格候选点（离线搜索用）。""" + span = int(radius_m / spacing_m) + 1 + return [(spacing_m * i, spacing_m * j) + for i in range(-span, span + 1) for j in range(-span, span + 1) + if (spacing_m * i) ** 2 + (spacing_m * j) ** 2 <= radius_m ** 2] + + +def _bucket(points: list[Vec2], cell: float) -> dict[tuple[int, int], list[int]]: + table: dict[tuple[int, int], list[int]] = {} + for index, (x, y) in enumerate(points): + table.setdefault((int(x // cell), int(y // cell)), []).append(index) + return table + + +def coverage_masks(targets, candidates: list[Vec2], + r_eff: float = R_EFF_MIN_M, + heading_margin_deg: float = 0.0) -> list[int]: + """每个候选点覆盖的目标位掩码（Python 大整数，按位）。 + + 只在与候选点空间桶相邻的格内做距离/前向判定，避免 |T|·|C| 笛卡尔积。 + + ``r_eff`` / ``heading_margin_deg`` 是**设计裕量**：离线搜索时把"命中" + 定义得比物理要求更严（距离 ≤ r_eff、前向夹角 ≤ 90°−margin），使采样格 + 上的覆盖自动蕴含相邻未采样 (p,h) 也被覆盖（S2 证书硬化）。 + """ + table = _bucket(candidates, r_eff) + masks = [0] * len(candidates) + sin_margin = math.sin(math.radians(max(heading_margin_deg, 0.0))) + for t_index, (px, py, heading) in enumerate(targets): + ux, uy = math.cos(heading), math.sin(heading) + ci, cj = int(px // r_eff), int(py // r_eff) + bit = 1 << t_index + for di in (-1, 0, 1): + for dj in (-1, 0, 1): + for c_index in table.get((ci + di, cj + dj), ()): + cx, cy = candidates[c_index] + dx, dy = cx - px, cy - py + distance_sq = dx * dx + dy * dy + if distance_sq > r_eff * r_eff: + continue + if dx * ux + dy * uy < sin_margin * math.sqrt(distance_sq) - 1e-9: + continue + masks[c_index] |= bit + return masks + + +def greedy_cover(masks: list[int], n_targets: int) -> list[int]: + """贪心集合覆盖：每次取"新增覆盖目标数"最大的候选，返回索引表。""" + full = (1 << n_targets) - 1 + covered = 0 + chosen: list[int] = [] + remaining = list(range(len(masks))) + while covered != full and remaining: + best_index, best_gain = -1, 0 + for c_index in remaining: + gain = (masks[c_index] & ~covered).bit_count() + if gain > best_gain: + best_index, best_gain = c_index, gain + if best_index < 0: + break + chosen.append(best_index) + covered |= masks[best_index] + remaining.remove(best_index) + return chosen + + +def nn_tour_length(points: list[Vec2], start: Vec2 = (0.0, 0.0)) -> float: + """最近邻开放路径长度（巡回长度上界）。""" + remaining = list(points) + position = start + total = 0.0 + while remaining: + nxt = min(remaining, key=lambda q: (q[0] - position[0]) ** 2 + + (q[1] - position[1]) ** 2) + remaining.remove(nxt) + total += math.dist(position, nxt) + position = nxt + return total + + +# --------------------------------------------------------------------- 校验 +def _hit_distance(points: list[Vec2], position: Vec2, heading: float, + r_eff: float) -> float | None: + """最近的前向命中距离；没有任何前向命中点则返回 None。""" + ux, uy = math.cos(heading), math.sin(heading) + best = None + for (cx, cy) in points: + dx, dy = cx - position[0], cy - position[1] + if dx * dx + dy * dy > r_eff * r_eff: + continue + if dx * ux + dy * uy < -1e-9: + continue + distance = math.hypot(dx, dy) + if best is None or distance < best: + best = distance + return best + + +def _nearest_distance(points: list[Vec2], position: Vec2, + r_eff: float) -> float | None: + """最近距离（不要求前向）；超过 r_eff 视作未命中。""" + best = None + for (cx, cy) in points: + distance = math.hypot(cx - position[0], cy - position[1]) + if distance <= r_eff and (best is None or distance < best): + best = distance + return best + + +def verify_forward_half_disk(points: list[Vec2], step_m: float = VERIFY_STEP_M, + headings: int = VERIFY_HEADINGS, + r_eff: float = R_EFF_MIN_M, + heading_margin_deg: float = 0.0) -> CoverageCertificate: + """第四问语义：∀(p,h) 存在前向半盘内的被访点。 + + ``heading_margin_deg`` 是**证书硬化**用的前向角裕量：把"命中"收紧为 + "前向夹角 ≤ 90° − margin"。与 ``r_eff`` 的设计裕量合用时，采样格上的 + 覆盖即可推出**连续空间**覆盖：真实 (p',h') 距采样格 ≤ step·√2/2、 + 方位差 ≤ 180/headings 度，只要 + + r_eff + step·√2/2 ≤ 1000 且 margin ≥ 180/headings/2 + + 就能保证 |c−p'| ≤ 1000 且 (c−p')·u(h') ≥ 0。这正是 S2 取代"粗采样 + 假通过"的方式。默认 0 表示按物理条件校核。 + """ + points = list(points) + targets = forward_targets(step_m, headings) + table = _bucket(points, r_eff) + missing, worst = 0, 0.0 + sin_margin = math.sin(math.radians(max(heading_margin_deg, 0.0))) + for (px, py, heading) in targets: + ux, uy = math.cos(heading), math.sin(heading) + ci, cj = int(px // r_eff), int(py // r_eff) + best = None + for di in (-1, 0, 1): + for dj in (-1, 0, 1): + for c_index in table.get((ci + di, cj + dj), ()): + cx, cy = points[c_index] + dx, dy = cx - px, cy - py + distance_sq = dx * dx + dy * dy + if distance_sq > r_eff * r_eff: + continue + if dx * ux + dy * uy < sin_margin * math.sqrt(distance_sq) - 1e-9: + continue + distance = math.sqrt(distance_sq) + if best is None or distance < best: + best = distance + if best is None: + missing += 1 + else: + worst = max(worst, best) + return CoverageCertificate("forward_half_disk", step_m, headings, r_eff, + len(targets), missing, worst, + nn_tour_length(points)) + + +def verify_full_disk(points: list[Vec2], step_m: float = VERIFY_STEP_M, + r_eff: float = R_EFF_MIN_M) -> CoverageCertificate: + """第三问语义：Ω 内每点都在某个被访点的 r_eff 圆盘内。""" + points = list(points) + positions = omega_positions(step_m) + table = _bucket(points, r_eff) + missing, worst = 0, 0.0 + for (px, py) in positions: + ci, cj = int(px // r_eff), int(py // r_eff) + best = None + for di in (-1, 0, 1): + for dj in (-1, 0, 1): + for c_index in table.get((ci + di, cj + dj), ()): + cx, cy = points[c_index] + distance = math.hypot(cx - px, cy - py) + if distance <= r_eff and (best is None or distance < best): + best = distance + if best is None: + missing += 1 + else: + worst = max(worst, best) + return CoverageCertificate("full_disk", step_m, 1, r_eff, len(positions), + missing, worst, nn_tour_length(points)) + + +def verify_plan(plan: CoveragePlan, step_m: float | None = None, + headings: int | None = None) -> CoverageCertificate: + """按计划声明的覆盖语义校核。 + + 默认使用**证书自带的采样分辨率**（离线 75 m / 24 档）：更粗的运行期采样 + 会放过"删掉一个关键点仍看似覆盖"的点集，使校核失去意义。校核按计划名 + 缓存（``load_plan`` 的 lru_cache），每进程只付一次成本。 + """ + certificate = plan.certificate + if step_m is None: + step_m = certificate.target_step_m if certificate else VERIFY_STEP_M + if headings is None: + headings = (certificate.headings if certificate + else VERIFY_HEADINGS) + if plan.coverage == "forward_half_disk": + # 硬化证书：按证书自带的距离/角度设计裕量重新校核（旧证书默认 + # r_eff=1000、margin=0，与历史口径逐位一致）。 + return verify_forward_half_disk( + plan.points, step_m, headings, + r_eff=(certificate.r_eff_min_m if certificate else R_EFF_MIN_M), + heading_margin_deg=(certificate.verify_heading_margin_deg + if certificate else 0.0)) + return verify_full_disk(plan.points, step_m, certificate.r_eff_min_m + if certificate else R_EFF_MIN_M) + + +# --------------------------------------------------------------------- 计划 +def seven_points(ring_radius_m: float) -> list[Vec2]: + """解析七站：原点 + 环上 6 等分点。""" + return [(0.0, 0.0)] + [(ring_radius_m * math.cos(k * math.pi / 3), + ring_radius_m * math.sin(k * math.pi / 3)) + for k in range(6)] + + +def seven_plan(ring_radius_m: float) -> CoveragePlan: + """第三问解析计划（覆盖证明由 validate_seven_coverage 给出）。""" + return CoveragePlan( + name=f"seven-r{ring_radius_m:.1f}", problem=3, kind="analytic", + coverage="full_disk", + note=("解析证明 max(r/√3, sqrt(R²+r²−√3Rr)) < 1000；" + "r∈[1122.9, 1732] 时成立，巡检半径越小巡回越短"), + points=tuple(seven_points(ring_radius_m))) + + +def square_49_plan() -> CoveragePlan: + """第四问旧计划：700 m 方格 49 点（充分条件，保留作回退与对照）。""" + return CoveragePlan( + name="square49", problem=4, kind="certified", + coverage="forward_half_disk", + note="单元对角 700√2≈989.95≤1000 的充分条件；点数为最小命中集的 1.6 倍", + # 顺序与 coverage.coverage_points() 完全一致（y 由大到小），保持历史 + # 基线 grid49_immediate 的逐位可复现。 + points=tuple((700.0 * i, 700.0 * j) + for j in range(3, -4, -1) for i in range(-3, 4))) + + +def _load_plan_file(path: Path) -> CoveragePlan: + payload = json.loads(path.read_text(encoding="utf-8")) + raw_cert = payload.get("certificate") or {} + if raw_cert: + # as_dict() 含 nn_tour_virtual_s 等派生列：只取数据类实际字段。 + known = {field.name for field in fields(CoverageCertificate)} + certificate = CoverageCertificate( + **{key: value for key, value in raw_cert.items() if key in known}) + else: + certificate = None + return CoveragePlan( + name=payload["name"], problem=int(payload["problem"]), + kind=payload.get("kind", "certified"), + coverage=payload.get("coverage", "forward_half_disk"), + note=payload.get("note", ""), + certificate=certificate, + generator=payload.get("generator", {}), + points=tuple((float(x), float(y)) for x, y in payload["points"])) + + +@lru_cache(maxsize=None) +def load_plan(name: str, verify: bool = True) -> CoveragePlan: + """按名称加载覆盖计划；``verify`` 为真时做运行期抽样校验（进程内缓存）。""" + path = PLAN_DIR / f"{name}.json" + if not path.exists(): + raise FileNotFoundError(f"未找到覆盖计划 {name!r}（{path}）") + plan = _load_plan_file(path) + if verify and plan.kind == "certified": + certificate = verify_plan(plan) + if not certificate.valid: + raise ValueError( + f"覆盖计划 {name!r} 运行期校验失败：" + f"{certificate.uncovered}/{certificate.targets} 个 (p,h) 未被覆盖") + plan = CoveragePlan( + name=plan.name, problem=plan.problem, kind=plan.kind, + coverage=plan.coverage, note=plan.note, + generator=plan.generator, + certificate=CoverageCertificate( + kind=certificate.kind, target_step_m=certificate.target_step_m, + headings=certificate.headings, r_eff_min_m=certificate.r_eff_min_m, + targets=certificate.targets, uncovered=certificate.uncovered, + worst_hit_distance_m=certificate.worst_hit_distance_m, + nn_tour_m=certificate.nn_tour_m, + generator_sha256=(plan.certificate.generator_sha256 + if plan.certificate else "")), + points=plan.points) + return plan + + +def plan_instance(name: str, verify: bool = True) -> CoveragePlan: + """统一入口：支持内置计划名与磁盘上的证书计划。""" + if name == "square49": + return square_49_plan() + if name == "seven": + return seven_plan(1200.0) + return load_plan(name, verify) + + +def plan_names() -> list[str]: + """全部可加载的计划名（含内置）。""" + builtin = ["square49", "seven"] + return builtin + sorted(p.stem for p in PLAN_DIR.glob("*.json")) + + +def write_plan(plan: CoveragePlan, path: Path) -> None: + """落盘计划 + 证书（生成器用）。""" + path.parent.mkdir(parents=True, exist_ok=True) + payload = plan.as_dict() + payload["generator"] = dict(payload.get("generator") or {}) + payload["sha256"] = hashlib.sha256( + json.dumps(payload["points"]).encode("utf-8")).hexdigest() + path.write_text(json.dumps(payload, ensure_ascii=False, indent=2) + "\n", + encoding="utf-8") diff --git a/src/mcm26b/guarantee/plans/q4_mincover_v1.json b/src/mcm26b/guarantee/plans/q4_mincover_v1.json new file mode 100644 index 0000000..45a5654 --- /dev/null +++ b/src/mcm26b/guarantee/plans/q4_mincover_v1.json @@ -0,0 +1,143 @@ +{ + "name": "q4_mincover_v1", + "problem": 4, + "kind": "certified", + "coverage": "forward_half_disk", + "note": "贪心击中集等价覆盖：31 点 vs 现役 49 点，NN 巡回 22.9 km vs 36.4 km", + "generator": { + "script": "scripts/verify_q4_cover.py", + "spacing_m": 200.0, + "radius_m": 2850.0, + "target_step_m": 75.0, + "headings": 24, + "code_sha256": "dce9f0e189439bbea26f6bcbebf7e7f7bb99021a9035fca0c8d67bb67a3a529a" + }, + "certificate": null, + "points": [ + [ + -800.0, + 0.0 + ], + [ + 800.0, + 0.0 + ], + [ + 0.0, + -1000.0 + ], + [ + 0.0, + 1000.0 + ], + [ + -1200.0, + -1000.0 + ], + [ + -1000.0, + 1000.0 + ], + [ + 1000.0, + -1000.0 + ], + [ + 1000.0, + 1000.0 + ], + [ + 1800.0, + 0.0 + ], + [ + -1800.0, + 200.0 + ], + [ + -400.0, + -1800.0 + ], + [ + -200.0, + 1800.0 + ], + [ + 0.0, + 0.0 + ], + [ + 800.0, + -1800.0 + ], + [ + 1200.0, + 1600.0 + ], + [ + -1400.0, + 1400.0 + ], + [ + 1800.0, + -800.0 + ], + [ + -1800.0, + -800.0 + ], + [ + 1800.0, + 600.0 + ], + [ + -600.0, + -1000.0 + ], + [ + 400.0, + 1800.0 + ], + [ + -1400.0, + -1800.0 + ], + [ + -1200.0, + 800.0 + ], + [ + -1200.0, + 1800.0 + ], + [ + 400.0, + -800.0 + ], + [ + -200.0, + 400.0 + ], + [ + 600.0, + -1400.0 + ], + [ + -2200.0, + 600.0 + ], + [ + 1000.0, + -1600.0 + ], + [ + -2000.0, + -400.0 + ], + [ + -200.0, + -2200.0 + ] + ], + "sha256": "000e0c1c8642bbe9fb15ad854b22a5df03d4385f7834135a129062822442ccd1" +} diff --git a/src/mcm26b/guarantee/plans/q4_mincover_v2.json b/src/mcm26b/guarantee/plans/q4_mincover_v2.json new file mode 100644 index 0000000..49e019a --- /dev/null +++ b/src/mcm26b/guarantee/plans/q4_mincover_v2.json @@ -0,0 +1,162 @@ +{ + "name": "q4_mincover_v2", + "problem": 4, + "kind": "certified", + "coverage": "forward_half_disk", + "note": "设计裕量最小命中集：31 点，r_design=965 m / 前向角裕量 5.0°；证书按 15 m/72 档、r_verify=1000 m、角裕量 0.0° 校核缺口 0（同一证书口径复核，含精细剪枝）；最坏命中 990 m，NN 巡回 23.9 km", + "generator": { + "script": "scripts/verify_q4_cover.py", + "spacing_m": 100.0, + "radius_m": 2850.0, + "target_step_m": 50.0, + "headings": 48, + "design_r_eff_m": 965.0, + "heading_margin_deg": 5.0, + "pruned": true, + "verify_step_m": 15.0, + "verify_headings": 72, + "verify_r_eff_m": 1000.0, + "verify_heading_margin_deg": 0.0, + "code_sha256": "adf4fe6e9853c47314fac2c630fc6846ea6922102c2e83b507e5d397434c0e1d" + }, + "certificate": { + "kind": "forward_half_disk", + "target_step_m": 15.0, + "headings": 72, + "r_eff_min_m": 1000.0, + "targets": 3256200, + "uncovered": 0, + "worst_hit_distance_m": 990.051, + "nn_tour_m": 23883.8, + "nn_tour_virtual_s": 4776.8, + "generator_sha256": "adf4fe6e9853c47314fac2c630fc6846ea6922102c2e83b507e5d397434c0e1d", + 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diff --git a/src/mcm26b/harness/__init__.py b/src/mcm26b/harness/__init__.py index 46e264e..2e972b9 100644 --- a/src/mcm26b/harness/__init__.py +++ b/src/mcm26b/harness/__init__.py @@ -11,8 +11,9 @@ from .playback import PlaybackTransport from .scenes import mixed_scene, random_omni_scene, referee_spec from .simulator import LocalSimulator +from .suites import SceneSpec, suite, suite_manifest from .transport import LocalSimTransport, ScriptedFaultTransport __all__ = ["LocalSimTransport", "LocalSimulator", "PlaybackTransport", - "ScriptedFaultTransport", "mixed_scene", "random_omni_scene", - "referee_spec"] + "ScriptedFaultTransport", "SceneSpec", "mixed_scene", + "random_omni_scene", "referee_spec", "suite", "suite_manifest"] diff --git a/src/mcm26b/harness/suites.py b/src/mcm26b/harness/suites.py new file mode 100644 index 0000000..0e2ba61 --- /dev/null +++ b/src/mcm26b/harness/suites.py @@ -0,0 +1,132 @@ +"""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）： +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**—— +任何"更快"的声明都要在同一份清单上做配对比较。 + +真值只在本模块与评测机之间流动；策略端只通过 Transport 看到响应。 +""" + +from __future__ import annotations + +import math +from dataclasses import dataclass + +from referee import Source + +from ..config import ProblemConfig +from .scenes import mixed_scene, random_omni_scene + +SUITE_VERSION = 1 + +# 第三问：沿用历史验证集的种子与源数（seeds 100–105 × 10/13/16） +Q3_SEEDS = (100, 101, 102, 103, 104, 105) +Q3_COUNTS = (10, 13, 16) +# 第四问：三种"定向占比"组成（少量 / 约半数 / 多数） +Q4_SEEDS = (200, 201, 202) +Q4_CONFIGS = ((10, 3), (13, 6), (16, 8)) +# 误差场种子偏移：与 benchmark_q3_bayes.py 同口径，保证逐点固定误差可复现 +ERROR_SEED_BASE = 20260910 + + +@dataclass(frozen=True) +class SceneSpec: + """一个冻结场景：标识、题号、源数、定向数与种子。""" + + scene_id: str + problem: int + kind: str # "random" | "boundary" + seed: int + error_seed: int + count: int + directional: int = 0 + + def as_dict(self) -> dict: + return {"scene_id": self.scene_id, "problem": self.problem, + "kind": self.kind, "seed": self.seed, + "error_seed": self.error_seed, "count": self.count, + "directional": self.directional} + + def sources(self, config: ProblemConfig | None = None) -> list[Source]: + """构造真值（仅测试端调用）。""" + config = config or ProblemConfig() + if self.kind == "random" and self.problem == 3: + return random_omni_scene(seed=self.seed, count=self.count, + config=config) + if self.kind == "random": + return mixed_scene(seed=self.seed, count=self.count, + directional=self.directional, config=config) + return _boundary_sources(self, config) + + +def _boundary_sources(spec: SceneSpec, config: ProblemConfig) -> list[Source]: + """圆周边界压力场景：源全部位于 Ω 边界、r_eff 取下界。 + + 第三问：全向，扇区接缝角度，检验七站覆盖的边界情形； + 第四问：全部定向且朝向**背离圆心**（最不利于发现），检验 + "朝外发射的边界源不漏"。 + """ + field = config.field + radius = field.omega_radius_m + sources: list[Source] = [] + for index in range(spec.count): + angle = math.pi / 6 + 2 * math.pi * index / spec.count + x, y = radius * math.cos(angle), radius * math.sin(angle) + directional = index < spec.directional + sources.append(Source( + channel=index + 1, x=x, y=y, r_eff_m=field.r_eff_min_m, + directional=directional, + heading_deg=(math.degrees(angle) % 360.0) if directional else 0.0)) + return sources + + +def core_scenes(problem: int) -> list[SceneSpec]: + """主体场景：覆盖题面源数区间与定向占比组成。""" + scenes: list[SceneSpec] = [] + if problem == 3: + for seed in Q3_SEEDS: + for count in Q3_COUNTS: + scenes.append(SceneSpec( + scene_id=f"q3-core-s{seed}-n{count}", problem=3, + kind="random", seed=seed, error_seed=ERROR_SEED_BASE + seed, + count=count)) + else: + for seed in Q4_SEEDS: + for count, directional in Q4_CONFIGS: + scenes.append(SceneSpec( + scene_id=f"q4-core-s{seed}-n{count}-d{directional}", + problem=4, kind="random", seed=seed, + error_seed=ERROR_SEED_BASE + seed, count=count, + directional=directional)) + return scenes + + +def boundary_scenes(problem: int) -> list[SceneSpec]: + """圆周边界压力场景（每问 2 场：10 源与 16 源）。""" + if problem == 3: + return [SceneSpec(f"q3-boundary-n{count}", 3, "boundary", 0, + ERROR_SEED_BASE, count) + for count in (10, 16)] + return [SceneSpec(f"q4-boundary-n{count}-d{directional}", 4, "boundary", 0, + ERROR_SEED_BASE, count, directional) + for count, directional in ((10, 5), (16, 8))] + + +def suite(name: str, problem: int) -> list[SceneSpec]: + """按名称取场景清单：core / boundary / all。""" + if name == "core": + return core_scenes(problem) + if name == "boundary": + return boundary_scenes(problem) + if name == "all": + return core_scenes(problem) + boundary_scenes(problem) + raise ValueError(f"未知基准集 {name!r}（可选 core/boundary/all）") + + +def suite_manifest(name: str, problem: int) -> dict: + """清单头：版本、问题、场景表（写入 manifest.json）。""" + scenes = suite(name, problem) + return {"suite_version": SUITE_VERSION, "suite": name, "problem": problem, + "scenes": [scene.as_dict() for scene in scenes]} diff --git a/src/mcm26b/particles.py b/src/mcm26b/particles.py index 49ca95e..87b2aec 100644 --- a/src/mcm26b/particles.py +++ b/src/mcm26b/particles.py @@ -28,6 +28,7 @@ m = sqrt(2)/2·h 是格心到格内任一点的最大距离。所有 kill 判定 from __future__ import annotations import math +import os from .config import ProblemConfig, Vec2 from .geometry.localization import smallest_enclosing_circle @@ -37,12 +38,38 @@ _ANG_EPS_DEG = 1e-9 # 角向 kill 比较的浮点余量 _DIST_EPS_M = 1e-9 _COARSE_FACTOR = 4 # 粗格每边聚合的细格数 +try: # numpy 仅作**可选**加速后端；缺失时全部回退纯 Python + import numpy as _np +except Exception: # pragma: no cover - 环境无 numpy 时走纯 Python 分支 + _np = None +if os.environ.get("MCM26B_NO_NUMPY"): # 强制纯 Python：对拍与回退验证用 + _np = None + def angular_gap(a_deg: float, b_deg: float) -> float: """两个方位角（度）的最小夹角。""" return abs((a_deg - b_deg + 180.0) % 360.0 - 180.0) +def as_sample_array(samples): + """把 [(x,y), ...] 采样转成 (n,2) float64 数组；numpy 缺失时原样返回。 + + 评分为 O(代表点 × 子采样) 的向量运算，是单局 CPU 的主要开销 + （实测占 72%）。调用方在每次选点前转换一次，评分函数按类型分派， + 两条实现（numpy / 纯 Python）语义逐位对齐，见 tests/test_particles.py。 + """ + if _np is None or isinstance(samples, _np.ndarray) or len(samples) == 0: + return samples + return _np.asarray(samples, dtype=float) + + +def _branch_radius_array(members) -> float: + """分支后验半径的数组版：到质心的最大距离。""" + centroid = members.mean(axis=0) + return float(_np.max(_np.hypot(members[:, 0] - centroid[0], + members[:, 1] - centroid[1]))) + + class ChannelParticles: """单个频道的网格幸存集。 @@ -95,6 +122,10 @@ class ChannelParticles: return (self.x0 + (i + 0.5) * self.spacing_m, self.x0 + (j + 0.5) * self.spacing_m) + def discard(self, indices: list[int]) -> None: + """公开删除入口：联合信念把"朝向不可能"的格一并删除时使用。""" + self._discard(indices) + def _discard(self, indices: list[int]) -> None: if not indices: return @@ -171,9 +202,17 @@ class ChannelParticles: self._discard(doomed) def apply_measure(self, station: Vec2, result: str, - svd_deg: float | None = None) -> None: - """按读数更新幸存集。真源保留性由 Voronoi 裕量保证（模块注释）。""" + svd_deg: float | None = None, *, + directional: bool = False) -> None: + """按读数更新幸存集。真源保留性由 Voronoi 裕量保证（模块注释）。 + + ``directional=True``（第四问）：no_signal **不删空间候选**——定向源 + 的静默还可能来自朝向遮挡，单独删空间候选会误删真源；朝向维度由 + :class:`~mcm26b.belief.joint.JointBelief` 维护。 + """ if result == NO_SIGNAL: + if directional: + return # no_signal ⟹ dist > r_eff >= 1000：整格落入 B(s, 1000−m) 的删。 self._kill_disk(station, self.r_eff_min_m - self.margin_m) elif result == NEAR: @@ -263,8 +302,59 @@ class ChannelParticles: """按索引序均匀抽取的子采样（索引序≈空间序，代表点空间铺开）。""" return self.alive_points(limit=size) - def expected_survival(self, point: Vec2, subsample: list[Vec2], - representatives: list[Vec2]) -> float: + # ---------------------------------------------------- 向量化评分（可选） + def _expected_survival_vector(self, point: Vec2, sub, representatives) -> float: + """``expected_survival`` 的向量化实现（与纯 Python 版逐分支同语义）。""" + count = len(sub) + eps = 1.0 / count + dx, dy = sub[:, 0] - point[0], sub[:, 1] - point[1] + dist = _np.hypot(dx, dy) + within = int(_np.count_nonzero( + dist <= self.r_eff_min_m - self.margin_m + _DIST_EPS_M)) + log_keep_ns = math.log(max(1.0 - within / count, eps)) + bearing = _np.degrees(_np.arctan2(dy, dx)) + in_range = dist <= self.r_eff_max_m + self.margin_m + rdx, rdy = representatives[:, 0] - point[0], representatives[:, 1] - point[1] + rdist = _np.hypot(rdx, rdy) + rbearing = _np.degrees(_np.arctan2(rdy, rdx)) + gaps = _np.abs((bearing[None, :] - rbearing[:, None] + 180.0) % 360.0 - 180.0) + survivors = _np.count_nonzero( + (gaps <= self.half_angle_deg + _ANG_EPS_DEG) & in_range[None, :], axis=1) + p_ns = _np.clip((rdist - self.r_eff_min_m) + / (self.r_eff_max_m - self.r_eff_min_m), 0.0, 1.0) + keep_dir = _np.maximum(survivors / count, eps) + values = p_ns * log_keep_ns + (1.0 - p_ns) * _np.log(keep_dir) + near = rdist <= self.near_radius_m + self.margin_m + values = _np.where(near, math.log(eps), values) + return float(values.mean()) + + def _expected_posterior_radius_vector(self, point: Vec2, sub, + representatives) -> float: + """``expected_posterior_radius`` 的向量化实现。""" + eps_radius = self.spacing_m / 2.0 + dx, dy = sub[:, 0] - point[0], sub[:, 1] - point[1] + dist = _np.hypot(dx, dy) + bearing = _np.degrees(_np.arctan2(dy, dx)) + keep_ns = dist > self.r_eff_min_m - self.margin_m + r_ns = (_branch_radius_array(sub[keep_ns]) if keep_ns.any() + else eps_radius) + rdx, rdy = representatives[:, 0] - point[0], representatives[:, 1] - point[1] + rdist = _np.hypot(rdx, rdy) + rbearing = _np.degrees(_np.arctan2(rdy, rdx)) + gaps = _np.abs((bearing[None, :] - rbearing[:, None] + 180.0) % 360.0 - 180.0) + dir_mask = ((gaps <= self.half_angle_deg + _ANG_EPS_DEG) + & (dist[None, :] <= self.r_eff_max_m + self.margin_m)) + r_dir = _np.array([_branch_radius_array(sub[row]) if row.any() + else eps_radius for row in dir_mask]) + p_ns = _np.clip((rdist - self.r_eff_min_m) + / (self.r_eff_max_m - self.r_eff_min_m), 0.0, 1.0) + near = rdist <= self.near_radius_m + self.margin_m + values = _np.where(near, self.near_radius_m + self.margin_m, + p_ns * r_ns + (1.0 - p_ns) * r_dir) + return float(values.mean()) + + def expected_survival(self, point: Vec2, subsample, + representatives) -> float: """E[log 幸存占比]：越小该测点的期望信息增益越大。 r_eff 不确定性解析积分：dist∈(1000,1500] 时按 r_eff~U(1000,1500) @@ -273,8 +363,11 @@ class ChannelParticles: - direction 分支：以代表点方位为期望读数，角域 ∩ B(point,1500+m) 内幸存； - near 分支（dist <= 5+m）：可行集收缩到 5m 盘，幸存占比趋近 0。 """ - if not self.alive or not subsample: + if not self.alive or len(subsample) == 0 or len(representatives) == 0: return 0.0 + if _np is not None and isinstance(subsample, _np.ndarray) \ + and isinstance(representatives, _np.ndarray): + return self._expected_survival_vector(point, subsample, representatives) eps = 1.0 / len(subsample) keep_ns = max(1.0 - self.fraction_within(subsample, point, self.r_eff_min_m - self.margin_m), @@ -302,24 +395,28 @@ class ChannelParticles: total += p_ns * log_keep_ns + (1.0 - p_ns) * math.log(keep_dir) return total / max(len(representatives), 1) - def fraction_within(self, samples: list[Vec2], point: Vec2, - radius_m: float) -> float: - if not samples: + def fraction_within(self, samples, point: Vec2, radius_m: float) -> float: + if len(samples) == 0: return 0.0 reach = radius_m + _DIST_EPS_M + if _np is not None and isinstance(samples, _np.ndarray): + distance = _np.hypot(samples[:, 0] - point[0], samples[:, 1] - point[1]) + return float(_np.count_nonzero(distance <= reach)) / len(samples) count = sum(1 for p in samples if math.dist(p, point) <= reach) return count / len(samples) - def _branch_radius(self, members: list[Vec2]) -> float: + def _branch_radius(self, members) -> float: """分支后验的半径代理：幸存子采样到其质心的最大距离（O(n)，免 Welzl）。""" - if not members: + if len(members) == 0: return 0.0 + if _np is not None and isinstance(members, _np.ndarray): + return _branch_radius_array(members) mx = sum(p[0] for p in members) / len(members) my = sum(p[1] for p in members) / len(members) return max(math.hypot(p[0] - mx, p[1] - my) for p in members) - def expected_posterior_radius(self, point: Vec2, subsample: list[Vec2], - representatives: list[Vec2]) -> float: + def expected_posterior_radius(self, point: Vec2, subsample, + representatives) -> float: """一次测量后期望后验半径（米）：选点主指标，直指清除证书阈值。 对每个代表假设（真值候选）按 r_eff~U(1000,1500) 解析积分 @@ -328,8 +425,12 @@ class ChannelParticles: 与几何版 worst-posterior 同族，但作用于含负观测的非凸幸存集， 且以期望代替最坏（代表点覆盖幸存集主轴）。 """ - if not self.alive or not subsample: + if not self.alive or len(subsample) == 0 or len(representatives) == 0: return 0.0 + if _np is not None and isinstance(subsample, _np.ndarray) \ + and isinstance(representatives, _np.ndarray): + return self._expected_posterior_radius_vector( + point, subsample, representatives) eps_radius = self.spacing_m / 2.0 total = 0.0 for rep in representatives: diff --git a/src/mcm26b/planner/__init__.py b/src/mcm26b/planner/__init__.py index 2b3b20e..000f818 100644 --- a/src/mcm26b/planner/__init__.py +++ b/src/mcm26b/planner/__init__.py @@ -36,14 +36,17 @@ from dataclasses import dataclass from ..belief import (BeliefState, CandidateRegion, ChannelStatus, InconsistentBelief, clip_halfplane) +from ..belief.joint import JointBelief from ..config import ProblemConfig, Vec2 from ..coverage import CoverageLedger from ..evaluation import recompute_virtual_time, summarize +from ..guarantee import plan_instance, seven_plan, square_49_plan from ..protocol.client import (DeadlineExceeded, ProtocolError, SimulatorClient) from ..protocol.rules import CLEAR_SUCCESS, DIRECTION, NEAR, NO_SIGNAL from ..protocol.timing import move_time from ..protocol.transport import TransportError +from ..routing import improve_open_path def bearing_deg(a: Vec2, b: Vec2) -> float: @@ -211,10 +214,18 @@ class OmnidirectionalLoop: self.client = client self.config = config self.belief = BeliefState(config) - self.ledger = CoverageLedger(config) + # 覆盖计划是"可离线搜索、可在运行期校验"的一等对象；基类默认保持 + # 历史 49 点表（grid49_immediate 基线口径不变），各问题策略自行覆盖。 + self.coverage_plan = square_49_plan() + self.ledger = CoverageLedger(config, points=list(self.coverage_plan.points)) self.cleared: list[int] = [] self.anomalies: list[dict] = [] self.stop_reason: str | None = None + # 逐频道缺席证据来源（pf_empty / coverage），供审计与论文逐条追溯。 + self.absence_evidence: dict[int, str] = {} + # "同一 0.5m 测点 × 同一频道"的重复检测计数：历史上一次官方演练 + # 有 98.84% 的虚拟时间耗在空复扫上，本计数是该问题的回归指标。 + self.redundant_rescan_measures = 0 # 首次达成"每频道均已清除或证明不存在"的虚拟时刻（找全证明）。 self.accounted_virtual_time_s: float | None = None # 证书有效但 /clear 失败后必须先补测再重试，避免在同一证书上 @@ -267,14 +278,27 @@ class OmnidirectionalLoop: return summary # ------------------------------------------------------------ 预算与证明 + def _min_virtual_reserve(self) -> float: + """单动作耗时上界的保守估计，用于虚拟安全余量。 + + 上界 = 2 × 最远站点距离 / 速度 + 检测 + 换频（机器人不会跑到覆盖 + 计划包围区之外执行任务）。配置值通常更宽松；本函数保证即使配置被 + 调小，余量也足以完成最后一个动作而不触及 100 小时上限。 + """ + reach = max((math.dist((0.0, 0.0), point) for point in self.ledger.points), + default=0.0) + action_bound = (2.0 * reach / self.config.cost.speed_mps + + self.config.cost.measure_s + self.config.cost.switch_s) + return max(self.config.search.virtual_reserve_s, action_bound) + def _virtual_cap_reached(self) -> bool: """虚拟安全余量不足：置 stop_reason 并返回 True，调用方立即收尾。 - 余量须大于单次动作耗时上界（网格最远移动 ~1200s + 检测 5s）， - search.virtual_reserve_s=10000s [工程] 提供充分裕度，避免触及 + 余量须大于单次动作耗时上界（见 :meth:`_min_virtual_reserve`）， + `search.virtual_reserve_s=10000s` [工程] 提供充分裕度，避免触及 附件2 §4.5 的 100 小时上限被模拟器强制结束。 """ - if (self.client.state.virtual_time_s + self.config.search.virtual_reserve_s + if (self.client.state.virtual_time_s + self._min_virtual_reserve() < self.config.field.max_virtual_duration_s): return False self.stop_reason = "virtual_time_cap" @@ -466,6 +490,7 @@ class OmnidirectionalLoop: if belief.status is ChannelStatus.UNKNOWN \ and self.ledger.coverage_complete(channel): self.belief.mark_absent(channel) + self.absence_evidence[channel] = "coverage" def _resolve_detected(self) -> None: """覆盖完成后，对仍未清除的 DETECTED 频道做第二轮定位。 @@ -503,8 +528,19 @@ class OmnidirectionalLoop: completed = (self._abort_detail is None and (len(self.cleared) >= self.config.field.source_count_max or not unresolved)) + # 完成依据必须显式区分"数量上界"与"逐频道缺席/清除证明"， + # 否则论文与审计会把 16 源截断误读为逐频道证明。 + if self._abort_detail is not None: + completion_basis = "incomplete" + elif len(self.cleared) >= self.config.field.source_count_max: + completion_basis = "count_cap" + elif not unresolved: + completion_basis = "per_channel_proof" + else: + completion_basis = "incomplete" summary = { "completed": completed, + "completion_basis": completion_basis, "stop_reason": self.stop_reason, "exit_reason": "exit_unreachable" if info is None else info.reason, "virtual_time_s": virtual_time_s, @@ -512,9 +548,19 @@ class OmnidirectionalLoop: if self.accounted_virtual_time_s is not None else None), "persistent_search": self.config.search.persistent_search, + "virtual_reserve_s": round(self._min_virtual_reserve(), 3), + "coverage_plan": self.coverage_plan.name, + "coverage_points": len(self.coverage_plan.points), + "coverage_plan_points": [[p[0], p[1]] for p in self.coverage_plan.points], + "coverage_plan_certificate": ( + self.coverage_plan.certificate.as_dict() + if self.coverage_plan.certificate is not None else None), "cleared_channels": cleared, "absent_channels": absent, "unresolved_channels": unresolved, + "absence_evidence": {str(channel): self.absence_evidence[channel] + for channel in sorted(absent)}, + "redundant_rescan_measures": self.redundant_rescan_measures, "measures": stats["measures"], "clears": stats["clears"], "clear_success": stats["cleared"], @@ -597,9 +643,18 @@ class DirectionalExtension(OmnidirectionalLoop): def __init__(self, client: SimulatorClient, config: ProblemConfig = ProblemConfig()): super().__init__(client, config) + # P0-2：第四问切换到同一保证下的最小命中集（默认 q4_mincover_v1）。 + self.coverage_plan = plan_instance(config.search.q4_coverage_plan, + verify=config.search.verify_coverage_plan) + self.ledger = CoverageLedger(config, points=list(self.coverage_plan.points)) self.directional_observed: list[int] = [] # 观测到定向性的频道 self.blocked_probes: dict[int, list[Vec2]] = {} self.fallback_log: list[dict] = [] # 后备清除逐次留痕 + # P0-3：(位置 × 朝向) 联合可行集，仅在频道首次被看见时惰性建立， + # 使其初始化与更新成本只落在"确实存在定向源"的频道上。 + self.joint: dict[int, JointBelief] = {} + self.joint_absent_channels: list[int] = [] + self.visibility_probes = 0 # 由联合信念选出的探点数 def _coverage_phase(self) -> None: """49站的定向覆盖证明不变；每站先串行扫频，再就近服务已发现源。 @@ -619,7 +674,7 @@ class DirectionalExtension(OmnidirectionalLoop): return outcome = self.client.measure(point, channel) self.ledger.mark_measured(channel, index) - self.belief.update_measure(channel, point, outcome.result, outcome.svd_deg) + self._observe(channel, point, outcome.result, outcome.svd_deg) if outcome.result == NEAR: cert = clear_certificate( self.belief.candidate_set(channel), channel, self.config) @@ -666,8 +721,7 @@ class DirectionalExtension(OmnidirectionalLoop): within_range = (math.dist(point, region.mec()[0]) <= safe_disk_radius(region, self.config)) outcome = self.client.measure(point, channel) - self.belief.update_measure(channel, point, outcome.result, - outcome.svd_deg) + self._observe(channel, point, outcome.result, outcome.svd_deg) self._may_clear.add(channel) if outcome.result == NO_SIGNAL: if within_range and channel not in self.directional_observed: @@ -688,6 +742,139 @@ class DirectionalExtension(OmnidirectionalLoop): self._may_clear.discard(channel) return self._fallback_clear(channel, region) + def _observe(self, channel: int, point: Vec2, result: str, + svd_deg: float | None) -> None: + """同步更新凸候选集与 (位置 × 朝向) 联合可行集。 + + 联合集只在频道首次被看见（direction/near）时建立：从未被看见的 + 频道由覆盖证明负责缺席，不需要朝向维度。联合集一旦为空，直接给出 + 该频道的缺席证明（严格，与覆盖证明同一条件），可提前停止对其检测。 + """ + self.belief.update_measure(channel, point, result, svd_deg) + if not self.config.search.directional_joint_belief: + return + joint = self.joint.get(channel) + if joint is None: + if result not in (DIRECTION, NEAR): + return + joint = JointBelief(self.config) + self.joint[channel] = joint + joint.apply(point, result, svd_deg) + if joint.is_absent() and self.belief.channels[channel].status \ + is not ChannelStatus.CLEARED: + self.belief.mark_absent(channel) + self.absence_evidence[channel] = "joint_empty" + if channel not in self.joint_absent_channels: + self.joint_absent_channels.append(channel) + + def _probe_candidates(self, region: CandidateRegion, channel: int) -> list[Vec2]: + """可见性选点的候选点族：保证接收点 + 对跖点 + 环探 + **抵近点**。 + + D1 关键结构性改动：旧点族全部围绕 MEC 圆心固定环采样，远距离不收敛时 + 没有"主动靠近"这一动作，只能退化到 20m 网格盲清。现在补上 + ``center + α·(当前位置 − center)``（α∈approach_fractions，α=0 即圆心） + 的抵近序列，使策略可以在保持可见的前提下逐次逼近真源，最终以正常 + 角域证书或 near 收尾。 + """ + center, radius = region.mec() + position = self.client.state.position + candidates = [select_measurement_point( + region, position, self.config).position] + safe = safe_disk_radius(region, self.config) + blocked = self.blocked_probes.get(channel, []) + if blocked and safe >= 0: + dx, dy = center[0] - blocked[0][0], center[1] - blocked[0][1] + norm = math.hypot(dx, dy) + if norm > 1e-9: + scale = min(1.0, safe / norm) + candidates.append((center[0] + dx * scale, center[1] + dy * scale)) + # 抵近点：从 MEC 圆心到当前点的插值序列（含圆心本身）。 + for fraction in self.config.search.approach_fractions: + candidates.append((center[0] + (position[0] - center[0]) * fraction, + center[1] + (position[1] - center[1]) * fraction)) + for factor in (0.5, 1.0, 1.5): + ring = max(radius * factor, 2.0 * self.config.field.near_radius_m) + for step in range(8): + angle = math.pi * step / 4.0 + candidates.append((center[0] + ring * math.cos(angle), + center[1] + ring * math.sin(angle))) + unique: list[Vec2] = [] + seen: set[tuple[int, int]] = set() + blocked_keys = {(int(round(point[0] * 2)), int(round(point[1] * 2))) + for point in blocked} \ + if self.config.search.probe_exclude_blocked else set() + for point in candidates: + key = (int(round(point[0] * 2)), int(round(point[1] * 2))) + if key in seen or key in blocked_keys: + continue # 已测过且被遮挡的点：再测不会有任何新信息 + seen.add(key) + unique.append(point) + return unique + + def _visibility_probe(self, region: CandidateRegion, channel: int) -> Vec2 | None: + """按联合信念选探点；联合信念不可用时返回 None（退回梯子）。 + + 三种口径（只影响选点顺序，不承担任何安全责任）： + + - ``nearest_visible``（默认）：先按可见概率过滤（只保留 + ``prob_detect ≥ ratio × max`` 的候选，避免选中"实际必然被朝向遮挡"的 + 点），再取 ``当前位置→q→MEC 圆心`` 移动最省者。9 场景 core 实测： + 后备盲清 125.7 → 4.3 次/局、T 9225 → 8602 s、墙钟最低。 + - ``expected_radius``：在同样过滤后最小化 + ``E[后验 MEC 半径](q) + λ·移动``。λ=5 时与上面几乎同分（8604 s）， + λ 很小时（0.05）值函数主导，为信息多绕路（9740 s）——本题 + "看得见且少走路"比"单步信息量最大"更重要。 + - ``visibility``（旧口径）：最大化 ``prob_detect/(移动+6)``，会反复选中 + 同一个近处被遮挡点（实测 9225 s、后备 125.6 次）。 + """ + joint = self.joint.get(channel) + if joint is None or joint.is_absent(): + return None + position = self.client.state.position + service_s = self.config.cost.measure_s + self.config.cost.switch_s + candidates = self._probe_candidates(region, channel) + if not candidates: + return None + mode = self.config.search.probe_score_mode + if mode == "visibility": + best_point, best_score = None, None + for point in candidates: + probability = joint.prob_detect(point) + if probability <= 0.0: + continue + score = -(probability / (move_time(position, point, self.config.cost) + + service_s)) + if best_score is None or score < best_score: + best_point, best_score = point, score + if best_point is not None: + self.visibility_probes += 1 + return best_point + scored = [(joint.prob_detect(point), point) for point in candidates] + peak = max(probability for probability, _ in scored) + ratio = self.config.search.probe_visibility_ratio + keep = [point for probability, point in scored + if probability >= ratio * peak] or [point for _, point in scored] + center = region.mec()[0] + weight = self.config.search.probe_lambda_move_s + + def approach_cost(point: Vec2) -> float: + return (move_time(position, point, self.config.cost) + + move_time(point, center, self.config.cost) + service_s) + + if mode == "nearest_visible": + # λ 很大时值函数项对 argmin 已无影响：直接取"可见候选中移动最省"， + # 省掉每个候选一次期望后验半径计算（单局墙钟 −3 s）。 + best = min(keep, key=lambda point: (approach_cost(point), point)) + self.visibility_probes += 1 + return best + best = min(keep, key=lambda point: ( + joint.expected_posterior_radius(point) + weight * approach_cost(point), + point)) + self.visibility_probes += 1 + return best + + + def _next_probe(self, region: CandidateRegion, channel: int) -> Vec2: """定向遮挡下的探点升级梯子 [工程]。 @@ -699,6 +886,9 @@ class DirectionalExtension(OmnidirectionalLoop): 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。 """ + visibility = self._visibility_probe(region, channel) + if visibility is not None: + return visibility blocked = self.blocked_probes.get(channel, []) center, radius = region.mec() if not blocked: @@ -743,14 +933,12 @@ class DirectionalExtension(OmnidirectionalLoop): entry["outcome"] = "skipped_budget" return False attempts = 0 - remaining_cells = list(cells) - while remaining_cells: + order = self._fallback_order(cells) + for cell in order: if self._virtual_cap_reached(): entry["outcome"] = "skipped_virtual_budget" entry["attempts"] = attempts return False - cell = min(remaining_cells, key=lambda c: (math.dist(self.client.state.position, c), c)) - remaining_cells.remove(cell) attempts += 1 outcome = self.client.clear(cell, channel) if outcome.result == CLEAR_SUCCESS: @@ -767,6 +955,28 @@ class DirectionalExtension(OmnidirectionalLoop): cells=len(cells)) return False + def _fallback_order(self, cells: list[Vec2]) -> list[Vec2]: + """后备清除的走格顺序：最近邻打底 + 2-opt（可选 Or-opt）改进。 + + 旧实现每步重新 ``min(remaining, key=dist)``，等价于最近邻开放路径 + （典型为最优的 1.2–1.3 倍）且每次重扫 O(k²)。这里一次性求解改进序 + （D3），把格间移动压到接近最优；格数不变，故完备性不变。 + """ + if not cells: + return [] + if not self.config.search.fallback_optimize_tour or len(cells) < 4: + position = self.client.state.position + remaining = list(cells) + order: list[Vec2] = [] + while remaining: + nxt = min(remaining, key=lambda c: (math.dist(position, c), c)) + remaining.remove(nxt) + order.append(nxt) + position = nxt + return order + return improve_open_path([tuple(c) for c in cells], + self.client.state.position) + # ------------------------------------------------------------ 收尾 def _summary(self, info) -> dict: report = super()._summary(info) @@ -775,6 +985,10 @@ class DirectionalExtension(OmnidirectionalLoop): # 后备 /clear 请求计入总请求口径，仅统计检测次数会低估现实耗时。 report["fallback_clear_attempts"] = sum( entry.get("attempts", 0) for entry in self.fallback_log) + report["visibility_probes"] = self.visibility_probes + report["joint_absent_channels"] = list(self.joint_absent_channels) + report["joint_final"] = {str(channel): belief.summary() + for channel, belief in sorted(self.joint.items())} return report diff --git a/src/mcm26b/planner/bayes_loop.py b/src/mcm26b/planner/bayes_loop.py index 831d7fd..4a15678 100644 --- a/src/mcm26b/planner/bayes_loop.py +++ b/src/mcm26b/planner/bayes_loop.py @@ -24,13 +24,14 @@ from dataclasses import dataclass from ..belief import CandidateRegion, ChannelStatus from ..config import ProblemConfig, Vec2 from ..coverage import CoverageLedger -from ..particles import ChannelParticles +from ..particles import ChannelParticles, as_sample_array from ..protocol.client import SimulatorClient from ..protocol.rules import DIRECTION, NEAR, NO_SIGNAL from ..protocol.timing import move_time from . import (ClearCertificate, OmnidirectionalLoop, clear_certificate, candidate_points, safe_disk_radius, select_measurement_point) -from .seven import seven_points, validate_seven_coverage +from .seven import validate_seven_coverage +from ..guarantee import seven_plan @dataclass(frozen=True) @@ -57,8 +58,9 @@ class BayesLoop(OmnidirectionalLoop): config: ProblemConfig = ProblemConfig()): super().__init__(client, config) validate_seven_coverage(config.bayes.station_ring_radius_m, config) + self.coverage_plan = seven_plan(config.bayes.station_ring_radius_m) self.ledger = CoverageLedger( - config, points=seven_points(config.bayes.station_ring_radius_m)) + config, points=list(self.coverage_plan.points)) self.particles = {channel: ChannelParticles(config) for channel in self.belief.channels} self._measured_points: dict[int, set[tuple[int, int]]] = {} @@ -86,6 +88,7 @@ class BayesLoop(OmnidirectionalLoop): tasks = self._build_tasks() if not tasks: return False + tasks = self._refine_locate_tasks(tasks) order = self._order_tasks(tasks) self._execute_task(order[0]) return True @@ -145,6 +148,39 @@ class BayesLoop(OmnidirectionalLoop): pending.append(channel) return pending + def _fused_channels_at(self, index: int) -> list[int]: + """本站要测的频道 = 未发现(UNKNOWN) + 已发现但尚未定证(DETECTED)。 + + P0-1：把定位并入覆盖巡回。站点三角化可行性（方案 §1.3 F11）： + 99.28% 的源位置有 ≥2 个站在 r_eff_max=1500m 内，最佳交会角中位 + 122.9°、5 分位 58.5°，因此"顺路取第二条方位线"对绝大多数源成立。 + 已有证书、已在同一 0.5m 测点测过、或该点必 no_signal 的频道跳过。 + + 实测结论（2026-09-12，`output/suite/q3-fuse-ab*`）：本机制**不划算**。 + 七站环上通常只有 2–3 个站能对该源取得有效方位线，交会角也不足以 + 直接换到 20m 证书，专用信息增益探点仍然必要；仅在站点顺带测量等于 + 平白多花约 11 次检测（+0.8%），若同时抑制专用探点则 +45.6%（20/20 + 场景更慢）。默认关闭，保留代码以便复现与后续研究。 + """ + point = self.ledger.points[index] + channels = self._station_pending(index) + selected = set(channels) + if not self.config.bayes.fuse_localize_into_tour: + return channels + for channel in self.belief.channels_by_status(ChannelStatus.DETECTED): + if channel in selected or self.ledger.measured_at(channel, index): + continue + if _point_key(point) in self._measured_points.get(channel, set()): + continue + cert = self._best_certificate(channel) + if cert is not None and cert.certified: + continue # 已有证书：交给清除任务，不再多测 + if self._skip_station_channel(channel, index, point): + continue # 幸存集都在 1500m 外：该点必 no_signal + channels.append(channel) + selected.add(channel) + return channels + def _skip_station_channel(self, channel: int, index: int, point: Vec2) -> bool: """跳测规则（按 pf.version 缓存；幸存集单调收缩，判定单调）。""" pf = self.particles[channel] @@ -160,7 +196,7 @@ class BayesLoop(OmnidirectionalLoop): def _build_tasks(self) -> list[Task]: tasks: list[Task] = [] for index, point in enumerate(self.ledger.points): - pending = self._station_pending(index) + pending = self._fused_channels_at(index) if not pending: continue switches = len(pending) - (1 if self.client.state.channel in pending else 0) @@ -176,6 +212,8 @@ class BayesLoop(OmnidirectionalLoop): self.config.cost.clear_success_s, channel=channel)) continue + if self._defer_to_station(channel): + continue # 下一条方位线可由"本就要去的站点"顺路提供 point = self._select_point(channel) if point is None: continue @@ -184,19 +222,155 @@ class BayesLoop(OmnidirectionalLoop): channel=channel)) return tasks + def _defer_to_station(self, channel: int) -> bool: + """该频道能否由"本就要去的站点"提供下一条方位线。 + + P0-1 的关键：仅仅在站点顺带测量是不够的（那只是多做一次检测）， + 必须同时**抑制专用探点任务**，否则滚动排序仍会优先飞向更近的探点。 + 判据要求该站本来就有 UNKNOWN 频道待测（覆盖巡回必须去），因此不会 + 为了等一条方位线而制造新的绕行，也不会出现"无任务可做"的死锁： + 站点扫完后 ``_station_pending`` 为空，探点任务自然恢复。 + """ + if not self.config.bayes.fuse_localize_into_tour: + return False + for index, point in enumerate(self.ledger.points): + if not self._station_pending(index): + continue + if self.ledger.measured_at(channel, index): + continue + if _point_key(point) in self._measured_points.get(channel, set()): + continue + if self._skip_station_channel(channel, index, point): + continue + return True + return False + # ------------------------------------------------------------ 任务排序 def _order_tasks(self, tasks: list[Task]) -> list[Task]: - """开放路径排序：最近邻打底，2-opt 改进（终点自由，不回起点）。""" + """开放路径排序：小任务表用精确 DP，否则最近邻 + 2-opt。 + + Held-Karp（S5/T1）把"已访问集合 × 结束时测向频道"并入状态，因此换频 + 代价是精确的，而不是 :meth:`_path_cost` 里"按频道排序"的贪心近似。 + """ if len(tasks) <= 2: ordered = sorted(tasks, key=lambda t: ( move_time(self.client.state.position, t.position, self.config.cost), t.kind, t.channel or 0)) return min((ordered, ordered[::-1]), key=self._path_cost) + limit = self.config.bayes.exact_order_limit + if 0 < limit and len(tasks) <= limit: + exact = self._exact_order(tasks) + if exact is not None: + return exact order = self._nearest_order(tasks) if not self.config.bayes.two_opt_enabled: return order return self._two_opt(order) + def _service_and_exit(self, task: Task, + channel: int) -> tuple[float, int]: + """任务在进入频道 ``channel`` 时的固有耗时与离开后的测向频道。""" + cost = self.config.cost + if task.kind == "locate": + return (cost.measure_s + (cost.switch_s if channel != task.channel else 0.0), + task.channel) + if task.kind == "station" and task.station_channels: + pending = sorted(task.station_channels, + key=lambda c: (c != channel, c)) + switches = len(pending) - (1 if channel in pending else 0) + return (cost.measure_s * len(pending) + cost.switch_s * switches, + pending[-1]) + return task.service_s, channel + + def _exact_order(self, tasks: list[Task]) -> list[Task] | None: + """Held-Karp 精确开放路径（起点固定、终点自由，含换频状态）。 + + 状态 ``(mask, c)`` = 已访问任务集 mask、离开最后一个任务后测向频道为 c； + 频道取值域只需 {当前频道} ∪ {locate 频道} ∪ {station 离开频道}。 + 规模 ``2^n·n·|C|``；n 超过 :attr:`BayesConfig.exact_order_limit` 时由 + 调用方回退启发式。返回 ``None`` 表示状态过大（防御性）。 + """ + n = len(tasks) + if n <= 1: + return list(tasks) + start = self.client.state.position + init = self.client.state.channel + channels = {init} + for task in tasks: + if task.kind == "locate": + channels.add(task.channel) + elif task.kind == "station" and task.station_channels: + # 离开频道 = 按"当前频道优先"排序后的最后一个：通常是最大值； + # 进入频道恰为最大值时退化为次大值，故两者都要进状态空间。 + ordered = sorted(task.station_channels) + channels.add(ordered[-1]) + if len(ordered) > 1: + channels.add(ordered[-2]) + channels = sorted(channels) + width = len(channels) + if width * (1 << n) * n > 4_000_000: + return None + index_of = {channel: i for i, channel in enumerate(channels)} + c_init = index_of[init] + service: list[list[float]] = [] + exits: list[list[int]] = [] + for task in tasks: + row_service, row_exit = [], [] + for channel in channels: + value, exit_channel = self._service_and_exit(task, channel) + row_service.append(value) + row_exit.append(index_of[exit_channel]) + service.append(row_service) + exits.append(row_exit) + move = [[move_time(tasks[i].position, tasks[j].position, self.config.cost) + for j in range(n)] for i in range(n)] + from_start = [move_time(start, task.position, self.config.cost) + for task in tasks] + inf = float("inf") + # dp[(mask, ci)] = 最小代价；parent[(mask, ci)] = (上一 mask, 上一 ci) + dp: dict[tuple[int, int], float] = {} + last_task: dict[tuple[int, int], int] = {} + parent: dict[tuple[int, int], tuple[int, int] | None] = {} + for k in range(n): + key = (1 << k, exits[k][c_init]) + value = from_start[k] + service[k][c_init] + if value < dp.get(key, inf): + dp[key] = value + last_task[key] = k + parent[key] = None + for mask in range(1, 1 << n): + for ci in range(width): + state = (mask, ci) + cost = dp.get(state) + if cost is None: + continue + for k in range(n): + if (mask >> k) & 1: + continue + nxt = (mask | (1 << k), exits[k][ci]) + value = cost + move[last_task[state]][k] + service[k][ci] + if value < dp.get(nxt, inf) - 1e-12: + dp[nxt] = value + last_task[nxt] = k + parent[nxt] = state + full = (1 << n) - 1 + best_key, best_cost = None, inf + for ci in range(width): + state = (full, ci) + if state in dp and dp[state] < best_cost: + best_key, best_cost = state, dp[state] + if best_key is None: + return None + order: list[Task] = [] + key: tuple[int, int] | None = best_key + while key is not None: + order.append(tasks[last_task[key]]) + key = parent[key] + order.reverse() + return order + + + def _nearest_order(self, tasks: list[Task]) -> list[Task]: remaining = list(tasks) order = [] @@ -263,18 +437,26 @@ class BayesLoop(OmnidirectionalLoop): self._execute_locate(task.channel, task.position) def _execute_station(self, index: int) -> None: - """单站批量扫频：只测待测频道（跳测规则已过滤），扫完即返回重排。""" + """单站批量扫频：只测待测频道（跳测规则已过滤），扫完即返回重排。 + + P0-1：本站同时测量"已发现未定证"频道，使第二/第三条方位线在巡回 + 途中顺路取得；证书一旦成立即就地清除，不额外绕行。 + """ point = self.ledger.points[index] - pending = self._station_pending(index) + pending = self._fused_channels_at(index) unknown = [c for c, b in self.belief.channels.items() if b.status is ChannelStatus.UNKNOWN] - self.skipped_measures += len(unknown) - len(pending) + self.skipped_measures += len(unknown) - len( + [c for c in pending if self.belief.channels[c].status + is ChannelStatus.UNKNOWN]) # 当前频道优先，减少换频；同站 near 直接清除不打断扫频 pending.sort(key=lambda c: (c != self.client.state.channel, c)) for channel in pending: if self.stop_reason is not None or self._count_limit_reached() \ or self._virtual_cap_reached(): return + detected_before = (self.belief.channels[channel].status + is ChannelStatus.DETECTED) outcome = self.client.measure(point, channel) self.ledger.mark_measured(channel, index) self._record_measure(channel, point, outcome) @@ -291,6 +473,25 @@ class BayesLoop(OmnidirectionalLoop): self._may_clear.discard(channel) else: self._anomaly("near_without_certificate", channel, position=point) + continue + if not detected_before: + continue + if outcome.result == NO_SIGNAL: + # 该频道已被发现：保证接收盘内不可能 no_signal，属守恒性异常 + region = self.belief.candidate_set(channel) + safe = (safe_disk_radius(region, self.config) + if region is not None else -1.0) + if safe >= 0 and math.dist(point, region.mec()[0]) <= safe: + self._anomaly("no_signal_in_safe_disk", channel, + position=point) + continue + cert = self._best_certificate(channel) + if cert is not None and cert.certified: + if self._attempt_clear(channel, cert.center, cert): + self._clear_failed.discard(channel) + else: + self._clear_failed.add(channel) + self._may_clear.discard(channel) def _execute_locate(self, channel: int, point: Vec2) -> None: """单步定位测量：执行后立即重排（滚动时域），收敛则顺路清除。""" @@ -325,7 +526,13 @@ class BayesLoop(OmnidirectionalLoop): """belief（凸证书）与 PF（幸存集）同步更新；PF 空即证缺席。""" self.belief.update_measure(channel, position, outcome.result, outcome.svd_deg) - self._measured_points.setdefault(channel, set()).add(_point_key(position)) + # 同一 0.5m 测点 × 同一频道的重复检测：历史上一次官方演练有 98.84% + # 的虚拟时间耗在空复扫上，本计数是该问题的回归指标。 + key = _point_key(position) + seen = self._measured_points.setdefault(channel, set()) + if key in seen: + self.redundant_rescan_measures += 1 + seen.add(key) self._clear_failed.discard(channel) # 新证据落地：允许再次尝试清除 belief = self.belief.channels[channel] if belief.status in (ChannelStatus.CLEARED, ChannelStatus.ABSENT): @@ -335,10 +542,30 @@ class BayesLoop(OmnidirectionalLoop): if not pf: if belief.status is ChannelStatus.UNKNOWN: self.belief.mark_absent(channel) + self.absence_evidence[channel] = "pf_empty" if channel not in self.pf_absent_channels: self.pf_absent_channels.append(channel) else: self._anomaly("pf_empty_while_detected", channel, position=position) + self._apply_count_bound() + + def _apply_count_bound(self) -> None: + """存在证据达到题面源数上界时，其余 UNKNOWN 可由计数直接判缺席。 + + 依据 [题面]：每频道至多一个源、全局限定 10..16 个源。一旦 + DETECTED/CLEARED 的不同频道数达到 16，任何尚未出现有效信号的 + 频道都不可能是源，无须等 7 站覆盖账本跑完。该判定与位置无关， + 是严格的计数证明；evidence 与 coverage/pf_empty 分开留痕。 + """ + present = [channel for channel, belief in self.belief.channels.items() + if belief.status in (ChannelStatus.DETECTED, + ChannelStatus.CLEARED)] + if len(present) < self.config.field.source_count_max: + return + for channel, belief in self.belief.channels.items(): + if belief.status is ChannelStatus.UNKNOWN: + belief.status = ChannelStatus.ABSENT + self.absence_evidence[channel] = "count_bound" def _best_certificate(self, channel: int) -> ClearCertificate | None: """凸证书优先；负观测挖洞使 PF 幸存集更紧时用网格证书补位。""" @@ -369,8 +596,13 @@ class BayesLoop(OmnidirectionalLoop): return ClearCertificate(cert.channel, target, cert.radius_m+shift, cert.limit_m, True) # ------------------------------------------------------------ 信息增益选点 - def _select_point(self, channel: int) -> Vec2 | None: - """定位测量点：期望 log 幸存占比 + λ·移动时间 最小的候选。""" + def _probe_context(self, channel: int): + """构造某频道下一次定位测量的候选集与信息增益评分函数。 + + 返回 ``(candidates, info_score, mec_center, current_position)``； + 候选为空返回 ``None``。``_select_point`` 与路线感知精修共用此逻辑， + 保证两者只差“移动代价如何折算”。 + """ bayes, search = self.config.bayes, self.config.search pf = self.particles[channel] if not pf: @@ -378,6 +610,7 @@ class BayesLoop(OmnidirectionalLoop): region = self.belief.candidate_set(channel) center, radius = pf.mec() measured = self._measured_points.get(channel, set()) + position = self.client.state.position candidates: list[Vec2] = [] safe = safe_disk_radius(region, self.config) if region is not None else -1.0 if region is not None and safe >= 0: @@ -387,15 +620,19 @@ class BayesLoop(OmnidirectionalLoop): search.candidate_angles) # 以实际来向生成对称交会候选，避免固定全局角度网格的旋转偏差。 geometric_center = region.mec()[0] - base_angle = math.atan2(self.client.state.position[1]-geometric_center[1], - self.client.state.position[0]-geometric_center[0]) + base_angle = math.atan2(position[1] - geometric_center[1], + position[0] - geometric_center[0]) for fraction in search.candidate_rings: if fraction <= 0: continue for offset in (-math.pi/3, -math.pi/6, 0, math.pi/6, math.pi/3): angle = base_angle + offset - candidates.append((geometric_center[0]+safe*fraction*math.cos(angle), - geometric_center[1]+safe*fraction*math.sin(angle))) + candidates.append((geometric_center[0] + safe * fraction * math.cos(angle), + geometric_center[1] + safe * fraction * math.sin(angle))) + # 已停靠位置若本身在安全接收盘内，是零移动的合法测点；显式加入 + # 可避免为了固定角度环而白白横移。 + if math.dist(position, geometric_center) <= safe + 1e-9: + candidates.append(tuple(position)) else: # 宽候选集：PF MEC 探索环（负观测挖洞后幸存集的包围圆） for fraction in bayes.wide_rings: @@ -403,27 +640,93 @@ class BayesLoop(OmnidirectionalLoop): candidates.extend(candidate_points(center, ring, (1.0,), search.candidate_angles)) candidates.append(tuple(center)) - candidates = [c for c in candidates if _point_key(c) not in measured] + candidates.append(tuple(position)) + candidates = [tuple(c) for c in candidates + if _point_key(c) not in measured] if not candidates: return None - subsample = pf.subsample(bayes.score_subsample) + # 采样一次转成数组（numpy 可用时），使候选评分走向量路径； + # numpy 缺失时 as_sample_array 原样返回列表，语义不变。 + subsample = as_sample_array(pf.subsample(bayes.score_subsample)) stride = max(len(subsample) // bayes.mc_representatives, 1) - representatives = [subsample[k] - for k in range(0, len(subsample), stride)] - position = self.client.state.position + representatives = subsample[::stride] + + def info_score(q: Vec2) -> float: + if bayes.score_mode == "entropy": + return pf.expected_survival(q, subsample, representatives) + return pf.expected_posterior_radius(q, subsample, representatives) + + return candidates, info_score, center, position + + def _select_point(self, channel: int) -> Vec2 | None: + """定位测量点：期望 log 幸存占比 + λ·移动时间 最小的候选。""" + context = self._probe_context(channel) + if context is None: + return None + candidates, info_score, center, position = context + bayes = self.config.bayes def score(q: Vec2) -> float: # 一次定位后仍需接近源才能清除；仅计到检测点的移动会偏好绕行。 move = bayes.lambda_move_s * (move_time(position, q, self.config.cost) + move_time(q, center, self.config.cost)) - if bayes.score_mode == "entropy": - return (pf.expected_survival(q, subsample, representatives) - + move) - return (pf.expected_posterior_radius(q, subsample, representatives) - + move) + return info_score(q) + move return min(candidates, key=lambda q: (score(q), _point_key(q))) + def _refine_locate_tasks(self, tasks: list[Task]) -> list[Task]: + """路线感知精修定位任务点（P2，2026-09-12）。 + + 旧评分只比较 ``current->q + q->MEC``，是局部代理；滚动 2-opt 的真实 + 代价还包含 ``q->next_task``。这里对每个 locate 任务在候选点上计算 + “替换该任务后整条当前路线代价的增量”，再与信息增益、MEC 接近项合并 + 选点。只改变选点启发式，不改变覆盖/清除证明。 + """ + bayes = self.config.bayes + if not bayes.route_aware_probe: + return tasks + locate_indices = [index for index, task in enumerate(tasks) + if task.kind == "locate"] + if not locate_indices: + return tasks + base_order = self._order_tasks(tasks) + base_cost = self._path_cost(base_order) + refined = list(tasks) + for task_index in locate_indices: + task = tasks[task_index] + context = self._probe_context(task.channel) + if context is None: + continue + candidates, info_score, center, position = context + try: + order_index = next(index for index, item in enumerate(base_order) + if item is task) + except StopIteration: # pragma: no cover - 同对象应始终存在 + order_index = next(index for index, item in enumerate(base_order) + if item.kind == "locate" + and item.channel == task.channel) + best_score: float | None = None + best_point: Vec2 | None = None + for point in candidates: + trial_order = list(base_order) + trial_order[order_index] = Task( + "locate", tuple(point), task.service_s, channel=task.channel) + route_delta = self._path_cost(trial_order) - base_cost + move_proxy = bayes.lambda_move_s * ( + move_time(position, point, self.config.cost) + + move_time(point, center, self.config.cost)) + clear_near = move_time(point, center, self.config.cost) + value = (info_score(point) + + move_proxy + + bayes.route_aware_insert_weight * route_delta + + bayes.route_aware_clear_weight * clear_near) + if best_score is None or value < best_score: + best_score, best_point = value, tuple(point) + if best_point is not None: + refined[task_index] = Task( + "locate", best_point, task.service_s, channel=task.channel) + return refined + # ------------------------------------------------------------ 安全网与复扫 def _localize_and_clear(self, channel: int) -> bool: """阻塞式定位子循环（resolve 阶段安全网）：PF 选点，失败退凸选点。""" diff --git a/src/mcm26b/planner/seven.py b/src/mcm26b/planner/seven.py index 8b2d652..2bd2803 100644 --- a/src/mcm26b/planner/seven.py +++ b/src/mcm26b/planner/seven.py @@ -20,6 +20,7 @@ import math from ..belief import ChannelStatus from ..config import ProblemConfig from ..coverage import CoverageLedger +from ..guarantee import seven_plan from ..protocol.rules import NEAR from . import OmnidirectionalLoop, clear_certificate @@ -55,7 +56,9 @@ class SevenImmediate(OmnidirectionalLoop): ring_radius_m: float = RING_RADIUS_M): super().__init__(client, config) validate_seven_coverage(ring_radius_m, config) - self.ledger = CoverageLedger(config=config, points=seven_points(ring_radius_m)) + self.coverage_plan = seven_plan(ring_radius_m) + self.ledger = CoverageLedger(config=config, + points=list(self.coverage_plan.points)) class SevenBatch(SevenImmediate): diff --git a/src/mcm26b/routing.py b/src/mcm26b/routing.py new file mode 100644 index 0000000..8351146 --- /dev/null +++ b/src/mcm26b/routing.py @@ -0,0 +1,254 @@ +"""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）： + +- **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； +- 小规模（n ≤ ``EXACT_LIMIT``）直接用 Held-Karp 精确 DP，规模更大时用 + 最近邻打底 + 2-opt / Or-opt 改进，并始终给出 **MST 下界**用于在线后悔值审计； +- 所有函数只做"排序与度量"，不承担任何安全责任，也不改变覆盖/清除语义。 + +约定：边权为**欧氏距离**（米）；时间折算由调用方按 ``speed_mps`` 处理。 +2-opt / Or-opt 用 O(1) 增量代价评估，使 n ≈ 10² 量级仍可在毫秒内完成。 +""" + +from __future__ import annotations + +import math + +from .config import Vec2 + +EXACT_LIMIT = 13 # Held-Karp 状态数 2^n·n：n=13 → 106 496，纯 Python 可接受 +OR_OPT_LIMIT = 60 # 作业点数超过此值时跳过高阶 Or-opt（见 improve_open_path） + + +def path_length(order: list[Vec2], start: Vec2 = (0.0, 0.0)) -> float: + """开放路径长度（起点 start，终点自由）。""" + total = 0.0 + position = start + for point in order: + total += math.dist(position, point) + position = point + return total + + +def nearest_neighbor_order(points: list[Vec2], + start: Vec2 = (0.0, 0.0)) -> list[Vec2]: + """最近邻开放路径（O(n²)，启发式上界）。""" + remaining = list(points) + order: list[Vec2] = [] + position = start + while remaining: + nxt = min(remaining, key=lambda q: math.dist(position, q)) + remaining.remove(nxt) + order.append(nxt) + position = nxt + return order + + +def two_opt_open(order: list[Vec2], start: Vec2 = (0.0, 0.0), + max_passes: int = 8) -> list[Vec2]: + """开放路径 2-opt（终点自由：只反转内部片段，不做首尾相接）。 + + 增量代价 O(1)：反转 [i..j] 只影响 (prev, i) 与 (j, next) 两条边。 + """ + best = list(order) + n = len(best) + if n < 3: + return best + for _ in range(max_passes): + improved = False + for i in range(n - 1): + prev = best[i - 1] if i > 0 else start + pi = best[i] + d_prev_i = math.dist(prev, pi) + for j in range(i + 1, n): + pj = best[j] + nxt = best[j + 1] if j + 1 < n else None + d_prev_j = math.dist(prev, pj) + removed = d_prev_i + added = d_prev_j + if nxt is not None: + removed += math.dist(pj, nxt) + added += math.dist(pi, nxt) + if added < removed - 1e-9: + best[i:j + 1] = best[i:j + 1][::-1] + improved = True + pi = best[i] + d_prev_i = math.dist(prev, pi) + if not improved: + break + return best + + +def or_opt_open(order: list[Vec2], start: Vec2 = (0.0, 0.0), + max_passes: int = 4, max_segment: int = 3) -> list[Vec2]: + """Or-opt：把长度 ≤ max_segment 的片段搬家（开放路径，增量代价 O(1)）。""" + best = list(order) + n = len(best) + if n < 4: + return best + for _ in range(max_passes): + improved = False + for length in range(1, min(max_segment, n - 1) + 1): + for i in range(0, n - length + 1): + segment = best[i:i + length] + rest = best[:i] + best[i + length:] + for j in range(len(rest) + 1): + if j == i: + continue + candidate = rest[:j] + segment + rest[j:] + if _segment_move_gain(best, i, length, j, start) > 1e-9: + best = candidate + improved = True + break + if improved: + break + if improved: + break + if not improved: + break + return best + + +def _segment_move_gain(order: list[Vec2], i: int, length: int, j: int, + start: Vec2) -> float: + """把 order[i:i+length] 插到去掉该段后的第 j 位所省下的长度（正=更省）。""" + rest = order[:i] + order[i + length:] + segment = order[i:i + length] + + def length_of(seq: list[Vec2]) -> float: + return path_length(seq, start) + + return length_of(order) - length_of(rest[:j] + segment + rest[j:]) + + +def improve_open_path(points: list[Vec2], start: Vec2 = (0.0, 0.0), + max_passes: int = 8) -> list[Vec2]: + """最近邻 + 2-opt + Or-opt 的开放路径改进（大规模兜底）。 + + Or-opt 的搬移评估是 O(n³)，作业点数超过 :data:`OR_OPT_LIMIT` 时只做 + O(n²) 的 2-opt（度量口径下 Or-opt 的相对收益远小于其墙钟代价）。 + """ + order = nearest_neighbor_order(points, start) + order = two_opt_open(order, start, max_passes) + if len(order) > OR_OPT_LIMIT: + return order + return or_opt_open(order, start, max_passes) + + +def held_karp_open(points: list[Vec2], start: Vec2 = (0.0, 0.0)) -> list[Vec2]: + """Held-Karp 精确开放路径（起点固定、终点自由）。 + + 状态 ``dp[mask][j]`` = 访问集合 mask、当前停在 j 的最小长度； + 规模 2^n·n，仅用于 n ≤ :data:`EXACT_LIMIT`。返回最优访问顺序。 + """ + n = len(points) + if n == 0: + return [] + if n == 1: + return list(points) + dist = [[math.dist(points[i], points[j]) for j in range(n)] for i in range(n)] + from_start = [math.dist(start, points[j]) for j in range(n)] + full = (1 << n) - 1 + inf = float("inf") + dp: list[list[float]] = [[inf] * n for _ in range(1 << n)] + parent: list[list[int]] = [[-1] * n for _ in range(1 << n)] + for j in range(n): + dp[1 << j][j] = from_start[j] + for mask in range(1 << n): + row = dp[mask] + for j in range(n): + current = row[j] + if current == inf or not (mask >> j) & 1: + continue + rest = full ^ mask + while rest: + bit = rest & -rest + k = bit.bit_length() - 1 + rest ^= bit + candidate = current + dist[j][k] + nxt = mask | bit + if candidate < dp[nxt][k]: + dp[nxt][k] = candidate + parent[nxt][k] = j + best_end = min(range(n), key=lambda j: dp[full][j]) + order: list[Vec2] = [] + mask, j = full, best_end + while j != -1: + order.append(points[j]) + previous = parent[mask][j] + mask ^= 1 << j + j = previous + order.reverse() + return order + + +def optimal_open_path(points: list[Vec2], start: Vec2 = (0.0, 0.0), + exact_limit: int = EXACT_LIMIT) -> list[Vec2]: + """离线最优（或近优）开放路径：小规模精确，大规模 2-opt + Or-opt。""" + unique = _dedupe(points) + if len(unique) <= exact_limit: + return held_karp_open(unique, start) + return improve_open_path(unique, start) + + +def mst_weight(points: list[Vec2], start: Vec2 | None = (0.0, 0.0)) -> float: + """最小生成树权重（Prim，O(n²)）；含 start 时给出开放路径最优值的下界。 + + 开放路径删除其最后一条边即得一条生成树，故 MST 权重 ≤ 最优路径长度。 + """ + nodes = ([start] if start is not None else []) + list(points) + n = len(nodes) + if n <= 1: + return 0.0 + in_tree = [False] * n + best = [float("inf")] * n + best[0] = 0.0 + total = 0.0 + for _ in range(n): + u, u_cost = -1, float("inf") + for i in range(n): + if not in_tree[i] and best[i] < u_cost: + u, u_cost = i, best[i] + if u < 0: + break + in_tree[u] = True + total += u_cost + ux, uy = nodes[u] + for v in range(n): + if in_tree[v]: + continue + distance = math.hypot(nodes[v][0] - ux, nodes[v][1] - uy) + if distance < best[v]: + best[v] = distance + return total + + +def _dedupe(points, cell_m: float = 0.5) -> list[Vec2]: + """按 ``cell_m`` 网格去重（同一物理点只算一次作业）。""" + seen: set[tuple[int, int]] = set() + unique: list[Vec2] = [] + for point in points: + key = (int(round(point[0] / cell_m)), int(round(point[1] / cell_m))) + if key in seen: + continue + seen.add(key) + unique.append((float(point[0]), float(point[1]))) + return unique + + +def route_metrics(service_points, actual_move_m: float, + speed_mps: float = 5.0) -> dict: + """路由度量：离线最优开放回路、MST 下界与相对 gap（S1 必报列）。""" + unique = _dedupe(service_points) + offline = path_length(optimal_open_path(unique)) if unique else 0.0 + lower = mst_weight(unique) + gap = ((actual_move_m - offline) / actual_move_m) if actual_move_m > 0 else 0.0 + return { + "distinct_service_points": len(unique), + "offline_opt_open_path_m": round(offline, 1), + "offline_opt_open_path_s": round(offline / speed_mps, 1), + "route_lower_bound_s": round(lower / speed_mps, 1), + "route_gap_vs_offline": round(gap, 4), + } diff --git a/tests/test_coverage_plan.py b/tests/test_coverage_plan.py new file mode 100644 index 0000000..e2ecbfd --- /dev/null +++ b/tests/test_coverage_plan.py @@ -0,0 +1,99 @@ +"""覆盖计划（CoveragePlan）与运行期校核验收（方案 §P0-2）。 + +要点： +- 计划是"可离线搜索、可在运行期校验"的一等对象，运行期必须重新校核； +- 校核必须能**拒绝**非法点集（删点即被检出），否则校核是装饰； +- 两类覆盖语义不得混用：第三问 full_disk、第四问 forward_half_disk； +- 第四问最小命中集必须在同一采样空间上与原 49 点表等价覆盖。 +""" + +import unittest + +from mcm26b.config import ProblemConfig +from mcm26b.coverage import coverage_points +from mcm26b.guarantee import (plan_instance, plan_names, seven_plan, + square_49_plan, verify_full_disk, + verify_forward_half_disk, verify_plan) + + +class CoveragePlanTests(unittest.TestCase): + def test_q4_mincover_plan_is_certified(self): + self.assertIn("q4_mincover_v1", plan_names()) + plan = plan_instance("q4_mincover_v1") + self.assertEqual(plan.problem, 4) + self.assertEqual(plan.coverage, "forward_half_disk") + certificate = verify_plan(plan) + self.assertEqual(certificate.uncovered, 0, certificate.as_dict()) + self.assertTrue(certificate.valid) + # 最小命中集必须显著小于旧的充分条件表 + self.assertLess(len(plan.points), len(square_49_plan().points)) + self.assertLess(certificate.nn_tour_m, 25_000.0) + + def test_hardened_plan_passes_fine_grid(self): + """S2：现役计划必须在 15 m/72 档（325.6 万个 (p,h)）零缺口。 + + 75 m/24 档的"零缺口"曾是采样过拟合（v1 在 25 m/48 档有 125 个缺口）。 + """ + self.assertIn("q4_mincover_v2", plan_names()) + plan = plan_instance("q4_mincover_v2") + certificate = plan.certificate + self.assertIsNotNone(certificate) + self.assertEqual(certificate.target_step_m, 15.0) + self.assertEqual(certificate.headings, 72) + verified = verify_plan(plan) + self.assertEqual(verified.uncovered, 0, verified.as_dict()) + self.assertLess(verified.worst_hit_distance_m, 1000.0) + self.assertTrue(verified.valid) + + def test_legacy_overfit_plan_fails_fine_grid(self): + """负例：旧的 31 点表在细网格上有缺口，硬化不是装饰。""" + legacy = plan_instance("q4_mincover_v1", verify=False) + certificate = verify_forward_half_disk(list(legacy.points), 25, 48) + self.assertGreater(certificate.uncovered, 0) + + def test_square49_and_mincover_cover_the_same_space(self): + for name in ("square49", "q4_mincover_v1"): + certificate = verify_forward_half_disk(plan_instance(name).points) + self.assertEqual(certificate.uncovered, 0, name) + + def test_verifier_rejects_a_broken_plan(self): + """删掉关键点必须被检出——否则运行期校核没有意义。""" + points = list(plan_instance("q4_mincover_v1").points) + broken = points[:-1] + certificate = verify_forward_half_disk(broken) + self.assertGreater(certificate.uncovered, 0) + self.assertFalse(certificate.valid) + + def test_hardened_plan_rejects_a_removed_point(self): + """硬化计划同样必须能拒绝删点（在证书分辨率上复核）。""" + plan = plan_instance("q4_mincover_v2") + points = list(plan.points) + certificate = verify_forward_half_disk( + points[:-1], plan.certificate.target_step_m, + plan.certificate.headings) + self.assertGreater(certificate.uncovered, 0) + + def test_seven_plan_full_disk_semantics(self): + plan = seven_plan(1200.0) + certificate = verify_full_disk(plan.points) + self.assertEqual(certificate.kind, "full_disk") + self.assertEqual(certificate.uncovered, 0) + + def test_seven_plan_too_small_ring_is_rejected(self): + """环半径过小则 Ω 边缘覆盖不全，full_disk 校核必须报缺口。""" + certificate = verify_full_disk(seven_plan(800.0).points) + self.assertGreater(certificate.uncovered, 0) + + def test_square49_order_matches_legacy_ledger(self): + """历史基线 grid49_immediate 必须逐位可复现（点集与顺序都一致）。""" + self.assertEqual(list(square_49_plan().points), + list(coverage_points(ProblemConfig()))) + + def test_coverage_semantics_are_not_interchangeable(self): + """第三问七站点集不满足第四问"前向半盘命中"语义（朝向遮挡）。""" + certificate = verify_forward_half_disk(seven_plan(1200.0).points) + self.assertGreater(certificate.uncovered, 0) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_joint_belief.py b/tests/test_joint_belief.py new file mode 100644 index 0000000..347e72d --- /dev/null +++ b/tests/test_joint_belief.py @@ -0,0 +1,124 @@ +"""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）。 + +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）； +- 期望后验 MEC 半径是合法的选点值函数：远处被朝向遮挡的 no_signal 不删 + **空间**候选，因此不应被误当成"已经收缩"； +- numpy 向量路径与纯 Python 回退路径在同一输入下结果一致。 +""" + +import math +import unittest +from dataclasses import replace +from random import Random + +from mcm26b.belief.joint import JointBelief, _np +from mcm26b.config import ProblemConfig +from mcm26b.protocol.rules import DIRECTION, NEAR, NO_SIGNAL + +CONFIG = replace(ProblemConfig(), + search=replace(ProblemConfig().search, + bearing_rounding_margin_deg=0.005)) +HALF_ANGLE = CONFIG.field.directional_half_angle_deg + + +def _visible(source, heading, reader, limit_m): + if math.dist(reader, source) > limit_m: + return False + bearing = math.degrees(math.atan2(reader[1] - source[1], + reader[0] - source[0])) + return abs((bearing - heading + 180.0) % 360.0 - 180.0) <= HALF_ANGLE - 5.0 + + +class JointBeliefRetentionTests(unittest.TestCase): + def test_true_source_survives_consistent_observations(self): + """与真值一致的观测序列后，真源所在格仍必须存在。""" + rng = Random(11) + checked = 0 + while checked < 10: + source = (rng.uniform(-1700, 1700), rng.uniform(-1700, 1700)) + if math.hypot(*source) > 1700: + continue + heading = rng.uniform(0.0, 360.0) + detection = (rng.uniform(-2100, 2100), rng.uniform(-2100, 2100)) + if not _visible(source, heading, detection, + CONFIG.field.r_eff_min_m): + continue + checked += 1 + joint = JointBelief(CONFIG) + bearing = math.degrees(math.atan2(source[1] - detection[1], + source[0] - detection[0])) % 360.0 + joint.apply(detection, DIRECTION, + round(bearing + rng.uniform(-0.9, 0.9), 2) % 360.0) + for _ in range(5): + probe = (rng.uniform(-1800, 1800), rng.uniform(-1800, 1800)) + if _visible(source, heading, probe, CONFIG.field.r_eff_min_m): + pb = math.degrees(math.atan2(source[1] - probe[1], + source[0] - probe[0])) % 360.0 + joint.apply(probe, DIRECTION, + round(pb + rng.uniform(-0.9, 0.9), 2) % 360.0) + else: + joint.apply(probe, NO_SIGNAL) + self.assertFalse(joint.is_absent(), "真源可行集被观测清空") + assert_source = (round(source[0], 6), round(source[1], 6)) + nearest = min((math.dist(joint.position.cell_center(index), assert_source) + for index in joint.position.alive), + default=float("inf")) + self.assertLessEqual(nearest, joint.position.margin_m + 1e-6, + "真源所在格被联合信念删除") + + def test_repeated_directions_shrink_but_never_grow(self): + """方向读数只会收缩联合集；贴测点的格按保守设计保留。""" + joint = JointBelief(CONFIG) + joint.apply((0.0, 0.0), DIRECTION, 0.0) + first = len(joint) + joint.apply((0.0, 0.0), DIRECTION, 180.0) + self.assertLessEqual(len(joint), first) + self.assertGreater(len(joint), 0) + + def test_near_keeps_only_a_small_disk(self): + joint = JointBelief(CONFIG) + joint.apply((100.0, 100.0), NEAR) + center, radius = joint.position.mec() + # 观测更新内缩 Voronoi 裕量，故上界为 near + margin + self.assertLessEqual(radius, CONFIG.field.near_radius_m + + joint.position.margin_m + 1.0) + self.assertLess(math.dist(center, (100.0, 100.0)), 15.0) + + +class ExpectedPosteriorRadiusTests(unittest.TestCase): + def _joint(self): + joint = JointBelief(CONFIG) + joint.apply((0.0, 0.0), DIRECTION, 0.0) + return joint + + def test_value_is_finite_and_positive(self): + joint = self._joint() + for probe in ((800.0, 0.0), (0.0, 800.0), (-500.0, -500.0)): + value = joint.expected_posterior_radius(probe) + self.assertTrue(math.isfinite(value)) + self.assertGreater(value, 0.0) + + def test_blocked_reading_keeps_spatial_radius(self): + """no_signal 不删空间候选：期望半径不应被误当作已收缩。""" + joint = self._joint() + before = joint.position.mec()[1] + joint.apply((1500.0, 0.0), NO_SIGNAL) + self.assertAlmostEqual(joint.position.mec()[1], before, places=6) + self.assertGreater(joint.expected_posterior_radius((1500.0, 0.0)), + 0.5 * before) + + def test_vector_and_python_paths_agree(self): + if _np is None: + self.skipTest("环境无 numpy：纯 Python 路径即默认实现") + joint = self._joint() + for probe in ((600.0, 100.0), (-300.0, 700.0)): + indices = joint.sample(200) + representatives = indices[::max(len(indices) // 7, 1)] + vector = joint._epr_vector(probe, indices, representatives) + python = joint._epr_python(probe, indices, representatives) + self.assertAlmostEqual(vector, python, places=6) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_particles.py b/tests/test_particles.py index 10c1678..1fac549 100644 --- a/tests/test_particles.py +++ b/tests/test_particles.py @@ -13,7 +13,7 @@ from dataclasses import replace from random import Random from mcm26b.config import ProblemConfig -from mcm26b.particles import ChannelParticles, angular_gap +from mcm26b.particles import ChannelParticles, angular_gap, as_sample_array from mcm26b.protocol.rules import DIRECTION, NEAR, NO_SIGNAL from mcm26b.planner.seven import seven_points @@ -169,3 +169,60 @@ class QueryPrimitiveTests(unittest.TestCase): if __name__ == "__main__": unittest.main() + + +class VectorizedScoringParityTests(unittest.TestCase): + """numpy 快路径与纯 Python 路径必须逐位同语义（方案 §P0-4）。 + + 评分只用于选点，不参与安全证明；但两条路径若给出不同排序，会导致 + 同一动作日志在不同环境下不可复现，因此必须对齐。 + """ + + def _fixture(self): + config = ProblemConfig() + particles = ChannelParticles(config) + rng = Random(20260912) + subsample = [(rng.uniform(-1800, 1800), rng.uniform(-1800, 1800)) + for _ in range(64)] + representatives = subsample[::8] + return particles, subsample, representatives + + def test_expected_survival_matches(self): + particles, subsample, representatives = self._fixture() + for point in ((0.0, 0.0), (700.0, -300.0), (-1200.0, 900.0)): + fast = particles.expected_survival(point, as_sample_array(subsample), + as_sample_array(representatives)) + slow = particles.expected_survival(point, subsample, representatives) + self.assertAlmostEqual(fast, slow, places=9) + + def test_expected_posterior_radius_matches(self): + particles, subsample, representatives = self._fixture() + for point in ((0.0, 0.0), (700.0, -300.0), (-1200.0, 900.0)): + fast = particles.expected_posterior_radius( + point, as_sample_array(subsample), + as_sample_array(representatives)) + slow = particles.expected_posterior_radius( + point, subsample, representatives) + self.assertAlmostEqual(fast, slow, places=9) + + def test_fraction_within_and_branch_radius_match(self): + particles, subsample, _ = self._fixture() + array = as_sample_array(subsample) + for radius in (100.0, 1000.0, 1500.0): + self.assertAlmostEqual( + particles.fraction_within(array, (10.0, 20.0), radius), + particles.fraction_within(subsample, (10.0, 20.0), radius), + places=12) + self.assertAlmostEqual(particles._branch_radius(array), + particles._branch_radius(subsample), places=9) + + def test_empty_samples_are_safe(self): + particles, _, _ = self._fixture() + self.assertEqual(particles.expected_survival((0.0, 0.0), [], []), 0.0) + self.assertEqual( + particles.expected_posterior_radius( + (0.0, 0.0), as_sample_array([]), as_sample_array([])), 0.0) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_routing.py b/tests/test_routing.py new file mode 100644 index 0000000..8e0ecae --- /dev/null +++ b/tests/test_routing.py @@ -0,0 +1,105 @@ +"""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）。 + +要点： +- Held-Karp 精确开放路径必须**真的**最优（与全排列暴力对拍）； +- 启发式（NN + 2-opt / Or-opt）必须是可接受的上界，且不劣于 NN； +- MST 权重必须是最优开放路径的合法下界（在线后悔值审计依赖它）； +- 路由度量口径可复算：actual == offline 时 gap 为 0，且 gap 不越界。 +""" + +import itertools +import math +import random +import unittest + +from mcm26b.routing import (EXACT_LIMIT, held_karp_open, improve_open_path, + mst_weight, nearest_neighbor_order, + optimal_open_path, or_opt_open, path_length, + route_metrics, two_opt_open) + + +def _random_points(count: int, seed: int, radius: float = 1500.0): + rng = random.Random(seed) + return [(rng.uniform(-radius, radius), rng.uniform(-radius, radius)) + for _ in range(count)] + + +class HeldKarpTests(unittest.TestCase): + def test_matches_brute_force_on_small_instances(self): + for seed in range(6): + points = _random_points(6, seed) + order = held_karp_open(points) + best = min(path_length(list(perm)) + for perm in itertools.permutations(points)) + self.assertAlmostEqual(path_length(order), best, places=6, + msg=f"seed={seed}") + + def test_returns_all_points_once(self): + points = _random_points(9, 42) + order = held_karp_open(points) + self.assertEqual(sorted(order), sorted(points)) + + def test_single_and_empty(self): + self.assertEqual(held_karp_open([]), []) + point = [(3.0, 4.0)] + self.assertEqual(held_karp_open(point), point) + + +class HeuristicTests(unittest.TestCase): + def test_two_opt_and_or_opt_never_worse_than_nn(self): + for seed in range(5): + points = _random_points(30, seed) + base = path_length(nearest_neighbor_order(points)) + self.assertLessEqual(path_length(two_opt_open( + nearest_neighbor_order(points))), base + 1e-9) + self.assertLessEqual(path_length(improve_open_path(points)), + base + 1e-9) + + def test_optimal_open_path_beats_heuristics_small(self): + points = _random_points(10, 7) + optimal = path_length(optimal_open_path(points)) + self.assertLessEqual(optimal, + path_length(improve_open_path(points)) + 1e-9) + + def test_or_opt_accepts_only_improvements(self): + points = _random_points(20, 3) + order = nearest_neighbor_order(points) + improved = or_opt_open(order) + self.assertLessEqual(path_length(improved), path_length(order) + 1e-9) + + +class LowerBoundTests(unittest.TestCase): + def test_mst_is_a_lower_bound(self): + for seed in range(8): + points = _random_points(24, seed) + optimum = path_length(optimal_open_path(points)) + self.assertLessEqual(mst_weight(points), optimum + 1e-6, + msg=f"seed={seed}") + + def test_mst_without_start(self): + points = _random_points(12, 1) + self.assertLessEqual(mst_weight(points, start=None), + path_length(optimal_open_path(points)) + 1e-6) + + +class RouteMetricsTests(unittest.TestCase): + def test_gap_zero_when_actual_matches_offline(self): + points = _random_points(8, 5) + offline = path_length(optimal_open_path(points)) + metrics = route_metrics(points, offline) + self.assertAlmostEqual(metrics["route_gap_vs_offline"], 0.0, places=6) + self.assertEqual(metrics["distinct_service_points"], 8) + + def test_dedupes_points(self): + points = [(1.0, 1.0), (1.0, 1.0), (2.0, 2.0)] + metrics = route_metrics(points, 10.0) + self.assertEqual(metrics["distinct_service_points"], 2) + + def test_empty_is_safe(self): + metrics = route_metrics([], 0.0) + self.assertEqual(metrics["distinct_service_points"], 0) + self.assertEqual(metrics["route_gap_vs_offline"], 0.0) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_safety_valves.py b/tests/test_safety_valves.py new file mode 100644 index 0000000..9d7be1e --- /dev/null +++ b/tests/test_safety_valves.py @@ -0,0 +1,91 @@ +"""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： +- 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； +- 虚拟安全余量不小于单动作耗时上界（配置被调小时仍成立）。 +""" + +import unittest +from dataclasses import replace + +from mcm26b.config import ProblemConfig +from mcm26b.planner import DirectionalExtension, OmnidirectionalLoop +from mcm26b.planner.bayes_loop import BayesLoop +from mcm26b.protocol.client import SimulatorClient +from mcm26b.harness import LocalSimTransport, LocalSimulator +from mcm26b.harness import mixed_scene, random_omni_scene + +CONFIG = replace(ProblemConfig(), + search=replace(ProblemConfig().search, + bearing_rounding_margin_deg=0.005, + persistent_search=False)) + + +def _run(loop_cls, sources, config=CONFIG): + simulator = LocalSimulator(sources, robot_id="T-SAFE", config=config) + client = SimulatorClient("T-SAFE", config=config, + transport=LocalSimTransport(simulator)) + return loop_cls(client, config).run(), simulator + + +class SafetyValveTests(unittest.TestCase): + def test_summary_completion_and_absence_evidence(self): + sources = random_omni_scene(seed=5, count=10, config=CONFIG) + summary, _ = _run(BayesLoop, sources) + truth = sorted(s.channel for s in sources) + self.assertEqual(sorted(summary["cleared_channels"]), truth) + self.assertEqual(summary["completion_basis"], "per_channel_proof") + self.assertEqual(sorted(summary["absent_channels"]), + sorted(set(range(1, 21)) - set(truth))) + evidence = summary["absence_evidence"] + self.assertEqual(sorted(int(c) for c in evidence), + sorted(summary["absent_channels"])) + self.assertTrue(set(evidence.values()) <= {"pf_empty", "coverage"}) + self.assertIsInstance(summary["redundant_rescan_measures"], int) + self.assertGreaterEqual(summary["virtual_reserve_s"], 10_000.0) + + def test_redundant_rescan_measure_is_counted(self): + """同一测点重复检测必须被计数（正常模式应为 0）。""" + sources = random_omni_scene(seed=7, count=10, config=CONFIG) + summary, _ = _run(BayesLoop, sources) + self.assertEqual(summary["redundant_rescan_measures"], 0) + + def test_count_cap_is_distinguished_from_proof(self): + """16 源时按数量上界停止，completion_basis 不得写成逐频道证明。""" + sources = random_omni_scene(seed=11, count=16, config=CONFIG) + summary, _ = _run(BayesLoop, sources) + self.assertGreaterEqual(len(summary["cleared_channels"]), 16) + self.assertEqual(summary["completion_basis"], "count_cap") + + def test_q4_completion_basis_present(self): + sources = mixed_scene(seed=3, count=10, directional=3, config=CONFIG) + summary, simulator = _run(DirectionalExtension, sources) + truth = sorted(s.channel for s in sources) + self.assertEqual(sorted(summary["cleared_channels"]), truth) + self.assertIn(summary["completion_basis"], + {"per_channel_proof", "count_cap"}) + self.assertEqual(sorted(simulator.cleared), truth) + + +class VirtualReserveTests(unittest.TestCase): + def test_reserve_covers_single_action_bound(self): + """配置余量被调小到 0 时，有效余量仍不得小于单动作上界。""" + tight = replace(ProblemConfig(), + search=replace(ProblemConfig().search, + virtual_reserve_s=0.0)) + sources = random_omni_scene(seed=1, count=1, config=tight) + simulator = LocalSimulator(sources, robot_id="T-RES", config=tight) + client = SimulatorClient("T-RES", config=tight, + transport=LocalSimTransport(simulator)) + loop = OmnidirectionalLoop(client, tight) + reach = max((x * x + y * y) ** 0.5 for x, y in loop.ledger.points) + bound = 2.0 * reach / tight.cost.speed_mps \ + + tight.cost.measure_s + tight.cost.switch_s + self.assertGreaterEqual(loop._min_virtual_reserve(), bound) + self.assertLess(loop._min_virtual_reserve(), 10_000.0) + + +if __name__ == "__main__": + unittest.main()