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@@ -3,6 +3,12 @@
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统一框架：有界误差几何定位、主动选点、带覆盖证明的搜索、按真实动作耗时调度。
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||||
分析依据 `issue/B题.pdf`、附件1《模拟器使用说明》、附件2《通信接口说明及编程指南》。
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||||
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||||
**文档总入口：[docs/README.md](docs/README.md)**（分类索引、阅读顺序与口径登记）。
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||||
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||||
- [总体思路与流程图（四问主线、端到端流程图、执行时间速查）](docs/总体思路与流程图.md)
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- [执行时间与执行步骤（计时模型、状态机、附件示例推演、实测分解）](docs/执行时间与执行步骤.md)
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- [几何计算示意图册（逐图说明：角域交会、覆盖、最小命中集）](docs/几何计算示意图册.md)
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- [论文插图清单与复现方式](docs/figures/README.md)（`uv run python scripts/make_figures.py`）
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- [四问建模分析与 P0–P7 实施计划](docs/B题建模分析与实施计划.md)
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- [协议规则表、字段表与状态机（P0 交付）](docs/协议规则表与状态机.md)
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- [问题一完整解答](docs/问题一解答.md)
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@@ -35,7 +41,9 @@ tests/ # pytest（含解耦守卫：运行时零导入 referee + 规
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project/ # 1-4 问"最优实现 / 基线实现"对比框架：逐问 optimal+baseline
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# +独立参考，冻结基准集配对出数；见 project/README.md
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scripts/ # 采集、基准、可视化与校核脚本（含问题四批量仿真 benchmark_q4.py、
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# 运行历史计时分解 analyze_run_timing.py、第四问覆盖证书 verify_q4_cover.py）
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# 运行历史计时分解 analyze_run_timing.py、第四问覆盖证书 verify_q4_cover.py、
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# 论文插图 make_figures.py、日志归档 archive_replay_logs.py）
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docs/figures/ # 论文插图（PNG）+ manifest.json（数据来源与 sha256）+ 图清单
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output/ examples/ # 演练证据、算例与生成产物
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```
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@@ -0,0 +1,71 @@
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# docs 索引
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仓库文档分四类：**总览与流程**、**逐问算法**、**验证与对比**、**论文与插图**。
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阅读顺序建议按下面的表格从上到下。
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## 总览与流程
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| 文档 | 内容 |
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| --- | --- |
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| [`总体思路与流程图.md`](总体思路与流程图.md) | 四问主线、端到端流程图（mermaid）、执行步骤与执行时间速查、交付物与验收锚点 |
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| [`执行时间与执行步骤.md`](执行时间与执行步骤.md) | 计时模型、会话状态机与动作回合、附件示例逐步推演、实测日志分解、时间预算与安全阀 |
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||||
| [`B题建模分析与实施计划.md`](B题建模分析与实施计划.md) | 四问建模取舍与 P0–P7 实施计划（最早的设计文档） |
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||||
| [`协议规则表与状态机.md`](协议规则表与状态机.md) | 指令字段表、结果码、计时模型、状态机（P0 交付） |
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|
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## 逐问算法
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|
||||
| 文档 | 内容 |
|
||||
| --- | --- |
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| [`algo-1.md`](algo-1.md) | 问题一实现口径：半平面交、顶点对直径、直径圆充要判定、MEC 清除证书 |
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||||
| [`问题一解答.md`](问题一解答.md) | 问题一论文式解答：模型边界、算法可复现步骤、反例构造、演练验证 |
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| [`algo-2.md`](algo-2.md) | 问题二：候选集 K1、保证接收盘、安全盘 B(c, 1000−a)、最坏后验选点 |
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| [`algo-3.md`](algo-3.md) | 问题三：三层在线架构、七点解析覆盖、覆盖账本、任务调度与停止条件 |
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||||
| [`algo-3-bayes.md`](algo-3-bayes.md) | 问题三 `bayes` 策略：网格幸存集滤波、信息增益选点、动态 TSP、Optuna 调参 |
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||||
| [`algo-4.md`](algo-4.md) | 问题四：`forward_half_disk` 最小命中集、位置×朝向联合信念、后备盲清 |
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||||
|
||||
## 优化与验证
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||||
| 文档 | 内容 |
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||||
| --- | --- |
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| [`第三四问架构优化与运行提速方案.md`](第三四问架构优化与运行提速方案.md) | 运行历史复核、分层目标架构、P0–P2 优化项与实施路线 |
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||||
| [`第三四问图论与动态规划优化方案.md`](第三四问图论与动态规划优化方案.md) | 图论/DP 引入方案与实测回填（含未采用的改动与原因） |
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||||
| [`算法审核与优化-20260911.md`](算法审核与优化-20260911.md) | 一次外部口径的算法审核记录与结论 |
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||||
| [`最优与基线实现对比.md`](最优与基线实现对比.md) | 1–4 问「最优实现 / 基线实现」对比框架与结果（论文口径说明） |
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||||
| [`B题问题3-4相关论文列表.md`](B题问题3-4相关论文列表.md) | 题 3、4 相关文献清单 |
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|
||||
## 论文与插图
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||||
|
||||
| 路径 | 内容 |
|
||||
| --- | --- |
|
||||
| [`paper/main.tex`](paper/main.tex) | 论文正文（`latexmk -xelatex main.tex`） |
|
||||
| [`paper/README.md`](paper/README.md) | 论文编译方式、目录结构与待办标记 |
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||||
| [`figures/README.md`](figures/README.md) | 插图清单、配色与字体约定、数据来源与复现命令 |
|
||||
| [`figures/manifest.json`](figures/manifest.json) | 每张图的数据来源与 sha256、生成命令 |
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||||
| [`../project/README.md`](../project/README.md) | 对比框架目录结构与运行方式 |
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||||
|
||||
## 口径登记
|
||||
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||||
同一事实在多份文档中出现时，以下列来源为准；发现不一致请先改文档，再复核图。
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||||
|
||||
| 事实 | 权威来源 |
|
||||
| --- | --- |
|
||||
| 题面常量（Ω、速度、各动作耗时、源与频道范围） | `src/mcm26b/config.py`（带 `[题面]` / `[工程]` 标注） |
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||||
| 指令字段与结果码 | `src/mcm26b/protocol/rules.py` + [`协议规则表与状态机.md`](协议规则表与状态机.md) |
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||||
| 问题一数值（D、ρ、E） | `mcm26b.geometry.solve` 的返回，或 [`问题一解答.md`](问题一解答.md) |
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||||
| 覆盖计划与证书 | `src/mcm26b/guarantee/plans/*.json`（`q4_mincover_v2.json` 为默认） |
|
||||
| 实测虚拟时间 | 行为日志 `virtual_time_s` 字段与 `evaluation.recompute_virtual_time` |
|
||||
| 冻结基准集对比结果 | `project/results/*.md`（由 `uv run python -m project.run` 生成） |
|
||||
|
||||
已知的两处需要随环境更新的引用：
|
||||
|
||||
1. [`问题一解答.md`](问题一解答.md) 引用的 `output/q1/figures/*.png` 属于早期
|
||||
离线插图（`output/` 未入库）；现行插图在 [`figures/`](figures/README.md)。
|
||||
2. 多份文档提到的官方演练报告位于 `output/q3/practice/`、`output/q4/`，
|
||||
同样未入库；需要时用 `scripts/verify_q3_practice.py` 对留档目录复算。
|
||||
|
||||
## 写作约定
|
||||
|
||||
- 只写结论与依据，不使用比喻、排比与自评式措辞（例如「唯一权威」「关键洞察」）。
|
||||
- 数字必须能在代码或数据中找到出处；图内不放标题与解释段落，
|
||||
说明写在正文，图名与清单见 [`figures/README.md`](figures/README.md)。
|
||||
- 中文引号统一使用「」，避免与代码中的 ASCII 引号混淆。
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||||
@@ -0,0 +1,62 @@
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||||
# docs/figures —— 论文插图
|
||||
|
||||
每张图只输出 PNG，由 `scripts/make_figures.py` 用 matplotlib 生成。
|
||||
`manifest.json` 记录每张图的数据来源（路径 + sha256 前 12 位 + 行数）与生成命令。
|
||||
|
||||
```bash
|
||||
uv run python scripts/make_figures.py # 全部 11 张
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||||
uv run python scripts/make_figures.py --list # 图名
|
||||
uv run python scripts/make_figures.py --only geo-q3-seven-point-coverage
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||||
uv run python scripts/make_figures.py --scale 1.5 # 整体放大（尺寸倍率）
|
||||
```
|
||||
|
||||
依赖：`matplotlib`、`Pillow`、`docs/paper/fonts/` 下的字体。
|
||||
`matplotlib` 缓存写入仓库内 `.mpl-cache/`，不污染用户目录。
|
||||
|
||||
## 约定
|
||||
|
||||
- 图内不放标题、图注与解释性文字，只保留坐标轴、图例与数据标注；
|
||||
图的含义由文件名与下表承担，解释写在论文正文。
|
||||
- 配色（`scripts/make_figures.py::C`）：深蓝 `#1f4e79` 主流程与协议，
|
||||
砖红 `#b03a2e` 真值源与关键结论，墨绿 `#1e7a5f` 清除与通过项，
|
||||
土黄 `#b8860b` 后备与警示，灰色只用于网格与次要文字。
|
||||
- 字体：中文宋体 `SongtiSC-Regular/Bold`，标题黑体 `HeitiSC-Light/Medium`，
|
||||
数字与西文 Times New Roman，均取自 `docs/paper/fonts/`，与 `main.tex` 一致。
|
||||
- 输出为 PNG（`savefig.dpi = 200`，另有 `--scale` 倍率），按 160 mm 宽排版时
|
||||
与正文同字号。
|
||||
|
||||
## 图清单
|
||||
|
||||
| 文件 | 内容 | 数据来源 |
|
||||
| --- | --- | --- |
|
||||
| `flow-overall-architecture.png` | 总体思路：题面抽象、在线三层、四问主线、三条闭环 | `src/mcm26b/config.py`、`docs/B题建模分析与实施计划.md` |
|
||||
| `steps-state-machine.png` | 会话状态机（IDLE / 倒计时 / OPEN / CLOSED） | `docs/协议规则表与状态机.md`、`src/mcm26b/protocol/client.py` |
|
||||
| `timing-attachment-example-105-111-194-199.png` | 附件计时示例逐步推演 | `docs/协议规则表与状态机.md` |
|
||||
| `timing-measured-q3-10-sources.png` | 实测一局的虚拟时间分解（累计曲线 + 构成） | `referee-data/sessions/BBM9-HZG2-77N5-NKKC/log.jsonl` |
|
||||
| `geo-q1-convex-intersection-zoom.png` | 问题一：角域交会总览 + 定位区域放大（直径圆与 MEC） | `output/q1/practice/U5ZW-P9KN-TCAG-R7ST_observations.json` |
|
||||
| `geo-q1-diameter-circle-counterexample.png` | 问题一：直径圆不覆盖的反例（等边三角形布站） | `examples/q1/triangle.json` |
|
||||
| `geo-q2-guaranteed-reception-disk.png` | 问题二：候选盘、截断角域外接圆、安全盘 `B(c, 1000−a)` | `src/mcm26b/config.py`、`docs/algo-2.md` |
|
||||
| `geo-q3-seven-point-coverage.png` | 问题三：七点覆盖（7 个 1000 m 接收盘的并集覆盖 Ω）与三套计划对比 | `src/mcm26b/planner/seven.py`、`docs/algo-3.md` |
|
||||
| `geo-q4-minimum-cover-31-points.png` | 问题四：31 点最小命中集与 `forward_half_disk` 语义 | `src/mcm26b/guarantee/plans/q4_mincover_v2.json` |
|
||||
| `replay-webui-q3-omnidirectional.png` | WebUI 回放画布复现（全向局，10 源全清） | `referee-data/sessions/BBM9-HZG2-77N5-NKKC/log.jsonl` |
|
||||
| `replay-webui-q4-directional.png` | WebUI 回放画布复现（定向源局） | `referee-data/sessions/GM56-DFXZ-XKDM-XYV7/log.jsonl` |
|
||||
|
||||
后两张对应 WebUI 的预览功能：把回放器画布上的 Ω 与真值源、运动轨迹、
|
||||
检测示向度射线、清除标记，以及右侧事件流水与底部虚拟时间轴一起导出。
|
||||
|
||||
## 数据来源与复现
|
||||
|
||||
- `timing-measured-*`、`replay-webui-*` 读评测机行为日志（JSONL，含真值）。
|
||||
`referee-data/` 被 `.gitignore` 忽略，因此另归档两局到 `examples/replay/`；
|
||||
没有评测机数据时脚本自动改读归档，图一致。
|
||||
|
||||
```bash
|
||||
uv run python scripts/archive_replay_logs.py --list # 本机有哪些局
|
||||
uv run python scripts/archive_replay_logs.py <案例编码> # 追加归档
|
||||
```
|
||||
|
||||
- `geo-q1-convex-intersection-zoom.png` 读官方演练留档
|
||||
`output/q1/practice/*_observations.json`（同样被忽略）。文件缺失时改读
|
||||
`examples/q1/triangle.json` 反例算例，图仍可用，只是案例不同。
|
||||
- 问题一的几何量由生产求解器 `mcm26b.geometry.solve` 现算；
|
||||
问题三、问题四直接读覆盖计划与证书，不手抄数字。
|
||||
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@@ -0,0 +1,192 @@
|
||||
{
|
||||
"generator": "scripts/make_figures.py",
|
||||
"command": "uv run python scripts/make_figures.py",
|
||||
"palette": {
|
||||
"ink": "#1c2430",
|
||||
"muted": "#5c6673",
|
||||
"grid": "#e4e9ee",
|
||||
"primary": "#1f4e79",
|
||||
"primary_fill": "#dce6f1",
|
||||
"accent": "#b03a2e",
|
||||
"accent_fill": "#f6dcd8",
|
||||
"success": "#1e7a5f",
|
||||
"success_fill": "#d8ece5",
|
||||
"warn": "#b8860b",
|
||||
"warn_fill": "#f6ecd2",
|
||||
"violet": "#5b4b8a"
|
||||
},
|
||||
"fonts": {
|
||||
"dir": "docs/paper/fonts",
|
||||
"map": {
|
||||
"song": "SongtiSC-Regular.ttf",
|
||||
"song_bold": "SongtiSC-Bold.ttf",
|
||||
"hei": "HeitiSC-Light.ttf",
|
||||
"hei_bold": "HeitiSC-Medium.ttf",
|
||||
"times": "Times New Roman.ttf",
|
||||
"times_bold": "Times New Roman Bold.ttf"
|
||||
}
|
||||
},
|
||||
"figures": [
|
||||
{
|
||||
"stem": "flow-overall-architecture",
|
||||
"png": "docs/figures/flow-overall-architecture.png",
|
||||
"scale": 1.0,
|
||||
"sources": [
|
||||
{
|
||||
"path": "src/mcm26b/config.py",
|
||||
"sha256_12": "c513450dc892",
|
||||
"lines": 213
|
||||
},
|
||||
{
|
||||
"path": "docs/B题建模分析与实施计划.md",
|
||||
"sha256_12": "537aaa5790f3",
|
||||
"lines": 237
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"stem": "steps-state-machine",
|
||||
"png": "docs/figures/steps-state-machine.png",
|
||||
"scale": 1.0,
|
||||
"sources": [
|
||||
{
|
||||
"path": "docs/协议规则表与状态机.md",
|
||||
"sha256_12": "27c8f7051b26",
|
||||
"lines": 215
|
||||
},
|
||||
{
|
||||
"path": "src/mcm26b/protocol/client.py",
|
||||
"sha256_12": "0e9797a85cd7",
|
||||
"lines": 333
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"stem": "timing-attachment-example-105-111-194-199",
|
||||
"png": "docs/figures/timing-attachment-example-105-111-194-199.png",
|
||||
"scale": 1.0,
|
||||
"sources": [
|
||||
{
|
||||
"path": "docs/协议规则表与状态机.md",
|
||||
"sha256_12": "27c8f7051b26",
|
||||
"lines": 215
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"stem": "geo-q1-convex-intersection-zoom",
|
||||
"png": "docs/figures/geo-q1-convex-intersection-zoom.png",
|
||||
"scale": 1.0,
|
||||
"sources": [
|
||||
{
|
||||
"path": "output/q1/practice/U5ZW-P9KN-TCAG-R7ST_observations.json",
|
||||
"sha256_12": "d2cba13464fd",
|
||||
"lines": 87
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"stem": "geo-q1-diameter-circle-counterexample",
|
||||
"png": "docs/figures/geo-q1-diameter-circle-counterexample.png",
|
||||
"scale": 1.0,
|
||||
"sources": [
|
||||
{
|
||||
"path": "examples/q1/triangle.json",
|
||||
"sha256_12": "eeb0c92ae21c",
|
||||
"lines": 29
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"stem": "geo-q2-guaranteed-reception-disk",
|
||||
"png": "docs/figures/geo-q2-guaranteed-reception-disk.png",
|
||||
"scale": 1.0,
|
||||
"sources": [
|
||||
{
|
||||
"path": "src/mcm26b/config.py",
|
||||
"sha256_12": "c513450dc892",
|
||||
"lines": 213
|
||||
},
|
||||
{
|
||||
"path": "docs/algo-2.md",
|
||||
"sha256_12": "24f3e844deb9",
|
||||
"lines": 312
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"stem": "geo-q3-seven-point-coverage",
|
||||
"png": "docs/figures/geo-q3-seven-point-coverage.png",
|
||||
"scale": 1.0,
|
||||
"sources": [
|
||||
{
|
||||
"path": "src/mcm26b/planner/seven.py",
|
||||
"sha256_12": "6ac65bd483f8",
|
||||
"lines": 113
|
||||
},
|
||||
{
|
||||
"path": "docs/algo-3.md",
|
||||
"sha256_12": "5638192b8669",
|
||||
"lines": 456
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"stem": "geo-q4-minimum-cover-31-points",
|
||||
"png": "docs/figures/geo-q4-minimum-cover-31-points.png",
|
||||
"scale": 1.0,
|
||||
"sources": [
|
||||
{
|
||||
"path": "src/mcm26b/guarantee/plans/q4_mincover_v2.json",
|
||||
"sha256_12": "71e54616ae21",
|
||||
"lines": 162
|
||||
},
|
||||
{
|
||||
"path": "docs/algo-4.md",
|
||||
"sha256_12": "1774f8bf85db",
|
||||
"lines": 485
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"stem": "timing-measured-q3-10-sources",
|
||||
"png": "docs/figures/timing-measured-q3-10-sources.png",
|
||||
"scale": 1.0,
|
||||
"sources": [
|
||||
{
|
||||
"path": "referee-data/sessions/BBM9-HZG2-77N5-NKKC/log.jsonl",
|
||||
"sha256_12": "5a308e53ed09",
|
||||
"lines": 133,
|
||||
"case_code": "BBM9-HZG2-77N5-NKKC",
|
||||
"module": "q3_practice"
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"stem": "replay-webui-q3-omnidirectional",
|
||||
"png": "docs/figures/replay-webui-q3-omnidirectional.png",
|
||||
"scale": 1.0,
|
||||
"sources": [
|
||||
{
|
||||
"path": "referee-data/sessions/BBM9-HZG2-77N5-NKKC/log.jsonl",
|
||||
"sha256_12": "5a308e53ed09",
|
||||
"lines": 133,
|
||||
"case_code": "BBM9-HZG2-77N5-NKKC"
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"stem": "replay-webui-q4-directional",
|
||||
"png": "docs/figures/replay-webui-q4-directional.png",
|
||||
"scale": 1.0,
|
||||
"sources": [
|
||||
{
|
||||
"path": "referee-data/sessions/GM56-DFXZ-XKDM-XYV7/log.jsonl",
|
||||
"sha256_12": "f498b2073cdf",
|
||||
"lines": 421,
|
||||
"case_code": "GM56-DFXZ-XKDM-XYV7"
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
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|
||||
# 几何计算示意图册
|
||||
|
||||
> 本册逐图给出坐标口径、关键量与图中各元素的含义。插图位于 [`figures/`](figures/)，复现命令：
|
||||
>
|
||||
> ```bash
|
||||
> uv run python scripts/make_figures.py --only geo-q1-convex-intersection-zoom,\
|
||||
> geo-q1-diameter-circle-counterexample,geo-q2-guaranteed-reception-disk,\
|
||||
> geo-q3-seven-point-coverage,geo-q4-minimum-cover-31-points
|
||||
> ```
|
||||
>
|
||||
> 图中的数字由脚本现算：问题一调用生产求解器，问题三、四直接读覆盖计划与证书；
|
||||
> 来源与 sha256 记在 [`figures/manifest.json`](figures/manifest.json)。
|
||||
|
||||
统一的坐标与符号约定：
|
||||
|
||||
- 世界坐标以 Ω 圆心为原点，$x$ 轴向右、$y$ 轴向上，单位**米**；
|
||||
- Ω 是半径 $R=1800$ m 的圆盘；源的有效接收半径 $r_{\rm eff}\in[1000,1500]$ m；
|
||||
- 示向度 $\theta$ 以 $+x$ 轴为 $0°$ 逆时针为正，误差半角 $\delta=1°$；
|
||||
- 清除半径 $r_{\rm clear}=20$ m；
|
||||
- "可保证清除" ⟺ 存在一点使整个候选区域落在该点 20 m 内 ⟺ $\rho\le 20$ m（充分）。
|
||||
|
||||
---
|
||||
|
||||
## 图 1 问题一：角域交会与定位区域
|
||||
|
||||
|
||||
|
||||
**左（总览）**：三个检测点（官方演练案例 `U5ZW-P9KN-TCAG-R7ST`，频道 9）
|
||||
各自的**前向角域** $W_i=\{p:\angle(p-s_i)-\theta_i\in[-1°,+1°]\}$ 画成窄扇形
|
||||
（2° 宽，图中放大成可见宽度）；三者交集就是定位区域 $P$。检测点相距 600~900 m，
|
||||
而 $P$ 的直径只有 24 m，所以左图里 $P$ 缩成一个点——这是**必须做局部放大**的原因。
|
||||
|
||||
**右（放大，视野 ±31.4 m）**：以最小包围圆圆心为中心：
|
||||
|
||||
- 浅蓝扇形 = 三个角域在放大框内的可见部分；三者**重叠成白色区域**= $P$；
|
||||
- 红色实线多边形 = $P$ 的顶点边界（与左图同一个集合）；
|
||||
- 蓝色虚线圆 = **直径圆**，半径 $D/2=11.9984$ m，圆心是中点 $(-450.634,\ 508.944)$；
|
||||
- 绿色实线圆 = **最小包围圆（MEC）**，$\rho=12.0686$ m，圆心 $(-449.977,\ 507.953)$。
|
||||
|
||||
**关键量（图 (c) 栏）**
|
||||
|
||||
| 量 | 值 | 判定 |
|
||||
| --- | ---: | --- |
|
||||
| 直径 $D(P)$ | 23.9967 m | 顶点对枚举（凸集上最远点必为顶点） |
|
||||
| MEC 半径 $\rho$ | 12.0686 m | Welzl 算法 + 支撑点枚举对照 |
|
||||
| 直径圆最大超出 $E$ | +0.6040 m | $E>0$ ⇒ **直径圆不覆盖** $P$ |
|
||||
| $\rho\le20$ m | 成立 | ⇒ **存在可保证清除的点**（取 MEC 圆心） |
|
||||
|
||||
> 这两个结论不矛盾：题目问"直径圆能否覆盖"（答案：不能，$E>0$）
|
||||
> 与"能否保证清除"（答案：能，$\rho\le20$ m）是**两个不同的问题**。
|
||||
> $E$ 的定义：$E=\max_i\lVert v_i-o\rVert-D/2$，$o$ 为直径中点；
|
||||
> 精确算术下最远顶点对就在圆周上，故 $E\ge0$，$E>0$ 即覆盖失败，
|
||||
> 达到最大值的顶点就是可独立复核的失败证据。
|
||||
|
||||
---
|
||||
|
||||
## 图 2 问题一：直径圆不覆盖的反例
|
||||
|
||||
|
||||
|
||||
等边三角形布站（$S_1(-1000,0)$、$S_2(540,-866)$、$S_3(520,901)$，示向度
|
||||
$1°,121°,241°$，误差半角 $1°$）。三条 2° 宽的角域交出一个**等边三角形**
|
||||
$P$：顶点 $(0,0)$、$(40,0)$、$(20,34.641)$。
|
||||
|
||||
- 直径 $D=40.0000$ m，最远点对是底边两端 ⇒ 直径圆圆心 $(20,0)$、半径 20 m；
|
||||
- 到第三个顶点 $(20,34.641)$ 的距离是 $20\sqrt3\approx34.6410$ m > 20 m，
|
||||
故**直径圆不覆盖**：$E=+14.6410$ m（图中箭头所指）；
|
||||
- 该反例的最小包围圆半径 $\rho=40/\sqrt3\approx23.0940$ m **> 20 m**，
|
||||
属于"覆盖失败且无法保证清除"的更坏情形。
|
||||
|
||||
> 反例是**符合题设测向条件**的：把真实源放在三角形中心 $(20,20\sqrt3/3)$，
|
||||
> 三个检测点的真实方位与读数偏差均小于 $1°$，且三个检测距离都小于 1500 m，
|
||||
> 所以它不是"脱离测向模型的任意图形"。构造与证明见
|
||||
> [`问题一解答.md`](问题一解答.md) 第六节。
|
||||
|
||||
---
|
||||
|
||||
## 图 3 问题二：保证接收的候选盘与安全盘
|
||||
|
||||
|
||||
|
||||
**候选集**（首次观测之后可能的位置集合）：
|
||||
|
||||
$$
|
||||
K_1=\Omega\ \cap\ W(s_1,\theta_1\pm\delta)\ \cap\ B(s_1,\ 1500),
|
||||
$$
|
||||
|
||||
即"Omega 内、首次角域内、且距离不超过最大接收半径"的三者之交。
|
||||
|
||||
**解析安全盘**：截断角域（扇形，半径 $R_{\max}=1500$ m、半角 $\delta=1°$）
|
||||
的最小包围圆恰为 $B(c,a)$，其中 $c=s_1+a\boldsymbol u$（$\boldsymbol u$ 为示向度单位向量），
|
||||
|
||||
$$
|
||||
a=\frac{R_{\max}}{2\cos\delta}=\frac{1500}{2\cos1°}=750.1142\ \text{m}.
|
||||
$$
|
||||
|
||||
于是对安全盘内任一点 $q$、扇形内任一点 $p$：
|
||||
|
||||
$$
|
||||
\lVert q-p\rVert\le a+(1000-a)=1000\ \text{m}
|
||||
\ \Longrightarrow\ q\ \text{必然收到源的信号},
|
||||
$$
|
||||
|
||||
$$
|
||||
\boxed{\ \text{安全盘半径}=1000-a=249.8858\ \text{m}\ }
|
||||
$$
|
||||
|
||||
（严格非空；图中绿色实心圆，圆心 $c=(649.62,\ 375.06)$）。
|
||||
第二检测点就在该盘内选，目标是**最小化最坏保守后验包围半径**，
|
||||
移动与检测成本作次目标；交会角只用于同分筛选与可解释性说明。
|
||||
|
||||
> 图中 $a$ 与 1000 m 两个圆几乎重合是可预期的：$\delta$ 只有 $1°$，
|
||||
> $a=R_{\max}/(2\cos\delta)$ 与 $R_{\max}/2$ 相差 0.0114%，
|
||||
> 因此**安全盘的"安全"主要来自角域约束，而不是圆心位置**。
|
||||
|
||||
---
|
||||
|
||||
## 图 4 问题三：七点解析覆盖
|
||||
|
||||
|
||||
|
||||
**站点集**：原点 + 半径 $a$ 的六等分环，
|
||||
$S(a)=\{(0,0)\}\cup\{a(\cos k\pi/3,\ \sin k\pi/3):k=0,\dots,5\}$。
|
||||
|
||||
**定理（`algo-3.md` §4.2）** 当 $0<a/\sqrt3\le R$ 时
|
||||
|
||||
$$
|
||||
\max_{g\in\Omega}\min_{s\in S(a)}\lVert g-s\rVert
|
||||
\le D(a):=\max\Bigl\{\frac{a}{\sqrt3},\ \sqrt{R^2+a^2-\sqrt3Ra}\Bigr\}.
|
||||
$$
|
||||
|
||||
代入默认 $a=1159.7$ m、$R=1800$ m：
|
||||
|
||||
$$
|
||||
D(a)=984.5391\ \text{m}<1000\ \text{m}
|
||||
\quad(\text{几何余量 }15.46\ \text{m}).
|
||||
$$
|
||||
|
||||
**图上看到的就是这个证明**：7 个半径 1000 m 的接收盘（浅绿，裁剪在 Ω 内）的
|
||||
并集**完全覆盖** Ω，最坏点出现在 $(0,-1800)$（Ω 正下方边界，图中圈出），
|
||||
到最近站点 $1159.7$ m（正下方环站）的距离恰为 984.54 m。
|
||||
|
||||
数值复核（脚本内 20 m 网格）：Ω 内 25445 个采样点全部被覆盖，**缺口 0**——
|
||||
解析上界与数值结果一致到小数点后 3 位。
|
||||
|
||||
**代价对比**：七点每轮 7 次检测即可证明"无源"，49 点网格要 49 次；
|
||||
巡回长度 $6a\approx6.96$ km（对照 49 点的 NN 巡回约 36.4 km）。
|
||||
$a$ 的可行下界 $a_{\min}\approx1122.9558$ m（零余量），默认配置留 15 m 以上余量。
|
||||
|
||||
**为什么能"发现即证明"**：某频道若在**全部**站点都测得 `no_signal`，
|
||||
由覆盖定理知该频道必无源（负观测只能排除该站点 1000 m 圆盘，而 $r_{\rm eff}\ge1000$ m），
|
||||
于是可以安全销账；所有频道销账 ⇒ 完成证明成立 ⇒ 立即 `/exit`。
|
||||
|
||||
---
|
||||
|
||||
## 图 5 问题四：定向源的 31 点最小命中集与联合信念
|
||||
|
||||
|
||||
|
||||
**覆盖语义变了**：定向源只向**朝向 $\pm90°$ 的前向半球**辐射，
|
||||
所以"某站点附近一定收到"这件事必须同时考虑**源的位置与朝向**：
|
||||
|
||||
$$
|
||||
\text{站点 }s\text{ 能发现源}\iff
|
||||
\lVert s-\text{pos}\rVert\le r_{\rm eff}\ \wedge\
|
||||
\angle\bigl(\text{heading},\ s-\text{pos}\bigr)\le90°.
|
||||
$$
|
||||
|
||||
这个语义记作 `forward_half_disk`（图中右侧示意）。
|
||||
|
||||
**31 点最小命中集**（`q4_mincover_v2`，图中红点）：
|
||||
|
||||
| 项 | 值 |
|
||||
| --- | --- |
|
||||
| 点数 | 31（离线贪心最小命中集，取代 49 点网格） |
|
||||
| 设计参数 | $r_{\rm design}=965$ m + 前向角裕量 $5°$ |
|
||||
| 运行期证书 | 15 m / 72 朝向档，325.62 万个 (位置, 朝向) 采样格，**缺口 0** |
|
||||
| 最坏命中距离 | 990.051 m |
|
||||
| 最近邻巡回 | 23.9 km ≈ 4776.8 s 虚拟时间 |
|
||||
| 旧计划对照 | `q4_mincover_v1` 在 15 m/72 档下有 **566** 个缺口（粗采样过拟合） |
|
||||
|
||||
**位置 × 朝向联合信念**：负观测（`no_signal`）在问题四里**只约束朝向**——
|
||||
它只能排除"朝向正对本站点"的那些朝向档，位置信息几乎不更新。
|
||||
因此证据层维护网格 × 朝向档的联合分布，可见性判据必须与引擎一致
|
||||
（距离 $\le r_{\rm eff}$ 且夹角 $\le90°$），并按格内角裕量保守裁剪。
|
||||
当信念坍缩到有限格时启用**后备盲清**：就近到可疑格 `/clear`，
|
||||
把盲清次数从 125.7 次/局压到 **4.3 次/局**。
|
||||
|
||||
> **本轮修复的正确性缺陷**：旧版 `no_signal` 的朝向裁剪规则反向、且漏计格内角裕量，
|
||||
> 会删掉真源所在的朝向档。已修复，并由
|
||||
> `tests/test_joint_belief.py` 的随机观测序列保留性测试固化。
|
||||
|
||||
---
|
||||
|
||||
## 附：把图接进论文
|
||||
|
||||
`docs/paper/main.tex` 目前用表格承载结论，插图按下例插入即可：
|
||||
|
||||
```latex
|
||||
\begin{figure}[htbp]
|
||||
\centering
|
||||
\includegraphics[width=0.95\textwidth]{figures/geo-q1-convex-intersection-zoom.png}
|
||||
\caption{问题一：角域交会得到的定位区域，以及直径圆与最小包围圆的关系。}
|
||||
\label{fig:geo-q1}
|
||||
\end{figure}
|
||||
```
|
||||
|
||||
图片按 160 mm 宽排版。图内不含标题与图注，说明写在 `\caption` 与正文。
|
||||
@@ -0,0 +1,178 @@
|
||||
# 总体思路与流程图
|
||||
|
||||
> 插图：[`figures/flow-overall-architecture.png`](figures/flow-overall-architecture.png)（总体思路与算法主线）、
|
||||
> [`figures/steps-state-machine.png`](figures/steps-state-machine.png)（执行步骤）、
|
||||
> [`figures/timing-attachment-example-105-111-194-199.png`](figures/timing-attachment-example-105-111-194-199.png)（附件计时示例）。
|
||||
> 复现：`uv run python scripts/make_figures.py --only flow-overall-architecture,steps-state-machine,timing-attachment-example-105-111-194-199`
|
||||
|
||||
本文给出四问的思路、端到端流程图、执行步骤与执行时间。
|
||||
算法细节指向 [`algo-1.md`](algo-1.md)~[`algo-4.md`](algo-4.md)，
|
||||
协议细节指向 [`协议规则表与状态机.md`](协议规则表与状态机.md)。
|
||||
|
||||
---
|
||||
|
||||
## 1. 主线
|
||||
|
||||
把题面约束写成可计算的几何集合和计时账本，四问共用一套「证据 — 决策 — 执行」结构，
|
||||
每问都给出可验证的保证：
|
||||
|
||||
| 问 | 保证 |
|
||||
| --- | --- |
|
||||
| 1 | 直径由顶点对枚举给出；最小包围圆半径 $\rho\le20$ m 时存在可保证清除的点 |
|
||||
| 2 | 第二检测点取在解析安全盘 $B(c,1000-a)$（半径 249.8858 m）内，必然收到信号 |
|
||||
| 3 | 七点计划覆盖整个 Ω（解析上界 984.5391 m < 1000 m），据此可判定频道无源 |
|
||||
| 4 | 31 点最小命中集在 15 m / 72 朝向档证书下缺口为 0 |
|
||||
|
||||
|
||||
|
||||
---
|
||||
|
||||
## 2. 四层结构
|
||||
|
||||
| 层 | 内容 | 作用 |
|
||||
| --- | --- | --- |
|
||||
| L0 题面抽象 | Ω 半径 1800 m；移动 5 m/s、检测 5 s、换频 1 s、清除 5 s / 3 s；观测 `direction/no_signal/near`；源 10~16 个，频道 1~20，定向源覆盖 ±90° | 后续推导只用这些量，不引入题面之外的信息 |
|
||||
| L1 在线三层 | 证据层（候选集与粒子）、决策层（选点与任务表）、执行层（串行客户端） | 依赖单向向下，几何结论与调度启发式可分别测试 |
|
||||
| L2 四问 | Q1 定位解算 → Q2 第二点选择 → Q3 全向搜索 → Q4 混合定向 | 后一问复用前一问的内核 |
|
||||
| L3 三条闭环 | 发现、定位、清除 | 每环有明确的进入与退出条件 |
|
||||
|
||||
L0 的常量集中在 `src/mcm26b/config.py`，带 `[题面]` / `[工程]` 标注。
|
||||
证据层只给出「可能在哪里」，决策层只读证据并产出动作，
|
||||
执行层负责发送动作并维护虚拟时钟。
|
||||
|
||||
---
|
||||
|
||||
## 3. 四问思路
|
||||
|
||||
### 问题一
|
||||
|
||||
1. 检测点 $s_i$ 与示向度 $\theta_i$ 给出前向角域
|
||||
$W_i=\{p:\angle(p-s_i)-\theta_i\in[-1°,+1°]\}$，定位区域是半平面交 $P=\bigcap_i W_i$；
|
||||
2. 直径 $D(P)=\max_{i,j}\lVert v_i-v_j\rVert$ 只需枚举顶点对（凸集上最远点必为顶点）；
|
||||
3. 直径圆能否覆盖 $P$：唯一候选圆心是最远顶点对的中点。若 $a,b$ 同在该圆内，
|
||||
三角不等式两处同时取等，圆心只能是 $ab$ 中点，因此只需在该点检查全部顶点；
|
||||
4. 能否保证清除：最小包围圆半径 $\rho\le20$ m 时，停在 MEC 圆心即可清除。
|
||||
这是充分条件，不是必要条件。
|
||||
|
||||
结论：直径圆不一定覆盖定位区域，见 [`geo-q1-counterexample.png`](figures/geo-q1-diameter-circle-counterexample.png)；
|
||||
官方演练案例是「覆盖失败但 $\rho=12.07$ m $\le20$ m，可保证清除」。
|
||||
|
||||
### 问题二
|
||||
|
||||
候选集 $K_1=\Omega\cap W(s_1,\theta_1\pm\delta)\cap B(s_1,1500)$。
|
||||
截断角域（扇形）的最小包围圆为 $B(c,a)$，$a=R_{\max}/(2\cos\delta)=750.1142$ m，
|
||||
故安全盘 $B(c,1000-a)$ 的半径为 249.8858 m，盘内任一点到扇形内任一点的距离
|
||||
不超过 1000 m，即必然收到信号。在该盘内以最坏保守后验包围半径为主目标选点，
|
||||
移动与检测成本作次目标，交会角只用于同分筛选。详见 [`algo-2.md`](algo-2.md)。
|
||||
|
||||
### 问题三
|
||||
|
||||
发现保证用七点计划（原点 + 半径 $a=1159.7$ m 的六等分环）：解析上界
|
||||
$D(a)=\max\{a/\sqrt3,\ \sqrt{R^2+a^2-\sqrt3Ra}\}=984.5391$ m < 1000 m，
|
||||
Ω 内任意位置的源都在某站点 1000 m 内。证据层按频道维护候选集与粒子幸存集，
|
||||
负观测按 `full_disk` 语义排除 1000 m 圆盘；决策层用信息增益选点，
|
||||
任务表包含检测、定位、清除三类任务，排序为开放路径问题；
|
||||
所有频道销账即完成证明成立，立即 `/exit`。
|
||||
|
||||
### 问题四
|
||||
|
||||
定向源只向朝向 ±90° 的前向半球辐射，覆盖语义改为 `forward_half_disk`。
|
||||
七点在该语义下有缺口，改用离线贪心得到的 31 点最小命中集（设计半径 965 m，
|
||||
前向角裕量 5°），并以 15 m / 72 朝向档的运行期证书校核（325.62 万个采样格，缺口 0，
|
||||
最坏命中 990.05 m）。负观测只约束朝向，因此维护位置 × 朝向联合信念，
|
||||
信念坍缩到有限格时使用后备盲清。详见 [`algo-4.md`](algo-4.md)。
|
||||
|
||||
---
|
||||
|
||||
## 4. 端到端流程
|
||||
|
||||
```mermaid
|
||||
flowchart TD
|
||||
S([开始：/enter 成功<br/>现实预算 1200 s 起算]) --> P0[读配置 ProblemConfig]
|
||||
P0 --> PLAN{按问题选择覆盖计划}
|
||||
PLAN -->|问题 3| S7[七点全向计划<br/>解析上界 984.54 m]
|
||||
PLAN -->|问题 4| S31[31 点最小命中集<br/>forward_half_disk 证书]
|
||||
S7 --> LOOP
|
||||
S31 --> LOOP
|
||||
|
||||
subgraph LOOP[主循环：动作回合]
|
||||
direction TB
|
||||
NEXT[选下一个任务<br/>信息增益 / 最坏后验半径<br/>滚动 2-opt 排序] --> SEND[/发送动作/]
|
||||
SEND --> RESP{accepted?}
|
||||
RESP -->|false| FIX[不更新状态<br/>记录并重排任务]
|
||||
RESP -->|true| CLK[更新虚拟时钟、位置、频道]
|
||||
FIX --> NEXT
|
||||
CLK --> OBS{观测类型}
|
||||
OBS -->|direction| HIT[登记新源<br/>同频道安排第二点交会]
|
||||
OBS -->|no_signal| NEG[负观测裁剪<br/>位置候选或朝向档]
|
||||
OBS -->|near| SKIP[距离过近，换点重测]
|
||||
HIT --> DONE{该频道 ρ ≤ 20 m?}
|
||||
NEG --> DONE
|
||||
SKIP --> DONE
|
||||
DONE -->|是| CLR[/清除动作/]
|
||||
DONE -->|否| NEXT
|
||||
CLR --> LEDGER[覆盖账本销账]
|
||||
LEDGER --> FIN{全部频道销账?}
|
||||
FIN -->|否| NEXT
|
||||
end
|
||||
|
||||
FIN -->|是| EXITQ[完成证明成立，立即 /exit]
|
||||
EXITQ --> REP[日志复算与统计]
|
||||
REP --> END([结束：虚拟时间即成绩])
|
||||
|
||||
classDef main fill:#dce6f1,stroke:#1f4e79,color:#1c2430
|
||||
classDef acc fill:#f6dcd8,stroke:#b03a2e,color:#1c2430
|
||||
classDef ok fill:#d8ece5,stroke:#1e7a5f,color:#1c2430
|
||||
class NEXT,SEND,RESP,CLK,OBS main
|
||||
class CLR,FIN,EXITQ acc
|
||||
class LEDGER,REP ok
|
||||
```
|
||||
|
||||
会话状态机与单次动作回合见 [`figures/steps-state-machine.png`](figures/steps-state-machine.png)，
|
||||
逐步计时示例见 [`figures/timing-attachment-example-105-111-194-199.png`](figures/timing-attachment-example-105-111-194-199.png)。
|
||||
|
||||
---
|
||||
|
||||
## 5. 执行步骤与执行时间
|
||||
|
||||
一次动作回合：本地预校验 → 发送 → 等响应计时 → 按响应更新状态。
|
||||
异常时同 `request_id` 同内容重发，400 与 409 立即失败。
|
||||
|
||||
| 动作 | 虚拟耗时 | 说明 |
|
||||
| --- | --- | --- |
|
||||
| `/enter`、`/exit` | 0 s | 不改变虚拟时刻，`/exit` 不切频道 |
|
||||
| 移动 | 直线距离 / 5 m·s⁻¹ | 同点不动为 0 s |
|
||||
| 换测向频道 | 1 s | 仅 `/measure` 触发，`/clear` 不切换 |
|
||||
| 检测 `/measure` | 5 s | 与返回结果无关 |
|
||||
| 清除 `/clear` | 5 s / 3 s | 成功 5 s，未发现 3 s |
|
||||
|
||||
实测样例（评测机日志 `BBM9-HZG2-77N5-NKKC`，问题三演练，10 源全清）：
|
||||
|
||||
| 项 | 数值 |
|
||||
| --- | --- |
|
||||
| 总虚拟时间 | 3134.643 s |
|
||||
| 移动（里程 11.9 km） | 2379.6 s |
|
||||
| 检测与清除动作（119 + 10 次） | 645.0 s |
|
||||
| 换频（110 次） | 110.0 s |
|
||||
| 现实耗时 | 1.889 s |
|
||||
|
||||
三项之和等于响应中的 `virtual_time_s`，与 `evaluation.recompute_virtual_time` 口径一致。
|
||||
分解图见 [`figures/timing-measured-q3-10-sources.png`](figures/timing-measured-q3-10-sources.png)，
|
||||
定向局（`GM56-…`，虚拟 8703.766 s，现实 3.397 s）与复现命令见
|
||||
[`执行时间与执行步骤.md`](执行时间与执行步骤.md)。
|
||||
|
||||
---
|
||||
|
||||
## 6. 交付物与验收锚点
|
||||
|
||||
| 问 | 算法文档 | 主要实现 | 锚点 |
|
||||
| --- | --- | --- | --- |
|
||||
| 1 | [`algo-1.md`](algo-1.md)、[`问题一解答.md`](问题一解答.md) | `src/mcm26b/geometry/` | 演练复算 $D=23.9967$ m、$\rho=12.0686$ m、$E=+0.6040$ m |
|
||||
| 2 | [`algo-2.md`](algo-2.md) | `geometry/`、`belief/`、`planner/` | 安全盘半径 249.8858 m |
|
||||
| 3 | [`algo-3.md`](algo-3.md)、[`algo-3-bayes.md`](algo-3-bayes.md) | `planner/bayes_loop.py`、`planner/seven.py` | 七点上界 984.5391 m < 1000 m |
|
||||
| 4 | [`algo-4.md`](algo-4.md) | `planner/directional.py`、`belief/joint.py` | 证书缺口 0，最坏命中 990.05 m |
|
||||
| 1–4 | [`最优与基线实现对比.md`](最优与基线实现对比.md) | `project/` | `uv run python -m project.run` |
|
||||
|
||||
优化过程与未采用的改动见
|
||||
[`第三四问架构优化与运行提速方案.md`](第三四问架构优化与运行提速方案.md)、
|
||||
[`第三四问图论与动态规划优化方案.md`](第三四问图论与动态规划优化方案.md)。
|
||||
@@ -0,0 +1,178 @@
|
||||
# 执行时间与执行步骤
|
||||
|
||||
> 配套插图：[`figures/steps-state-machine.png`](figures/steps-state-machine.png)（状态机与动作回合）、
|
||||
> [`figures/timing-attachment-example-105-111-194-199.png`](figures/timing-attachment-example-105-111-194-199.png)（附件示例逐步推演）、
|
||||
> [`figures/timing-measured-q3-10-sources.png`](figures/timing-measured-q3-10-sources.png)（实测一局的时序分解）。
|
||||
> 复现：`uv run python scripts/make_figures.py --only steps-state-machine,timing-attachment-example-105-111-194-199,timing-measured-q3-10-sources`
|
||||
|
||||
本文说明每一步做什么、每步花多少虚拟时间、这些数字如何验证。
|
||||
规则原文见 `issue/` 下附件1 表1、表2 与附件2 第 4、10 节；
|
||||
可执行表格在 `src/mcm26b/protocol/rules.py` 与 `src/mcm26b/config.py` 的 `CostConfig`。
|
||||
|
||||
---
|
||||
|
||||
## 1. 计时模型
|
||||
|
||||
忽略微秒量化时，一次会话的总虚拟时间为
|
||||
|
||||
$$
|
||||
T=\underbrace{\frac{L}{5}}_{\text{移动}}+\underbrace{5N_m}_{\text{检测}}
|
||||
+\underbrace{N_s}_{\text{换频}}+\underbrace{5N_c^{+}}_{\text{清除成功}}
|
||||
+\underbrace{3N_c^{-}}_{\text{清除未发现}},
|
||||
\qquad L=\text{路径长度（米）},\ 5\ \text{m/s}.
|
||||
$$
|
||||
|
||||
规则：
|
||||
|
||||
- 移动按两点间直线距离计，不计加减速；
|
||||
- 换频只在 `/measure` 触发，任意两频道间固定 1 s，`/clear` 不切换频道；
|
||||
- 检测固定 5 s，与结果无关，`direction` 与 `no_signal` 同价；
|
||||
- 清除成功 5 s（光学定位 3 s + 激光清除 2 s），未发现目标 3 s（仅光学定位）；
|
||||
- `/enter`、`/exit` 不产生虚拟时间；
|
||||
- 访问顺序是开放路径问题，起点固定、终点自由，不必回到起点。
|
||||
|
||||
|
||||
|
||||
---
|
||||
|
||||
## 2. 执行步骤
|
||||
|
||||
### 2.1 会话级状态机
|
||||
|
||||
| 状态 | 进入条件 | 允许的动作 | 退出条件 |
|
||||
| --- | --- | --- | --- |
|
||||
| `IDLE` | 界面待机（演练不限次 / 正式各 3 次） | — | 点击"开始测试" |
|
||||
| `COUNTDOWN` | 数据准备完成 | —（接口未开放） | 5 s 倒计时结束 |
|
||||
| `OPEN` | 倒计时结束，接口在 `:2026` 开放 | `/enter` → `/measure` `/clear` → `/exit` | 25 分钟窗口到期 / 源全清后主动 `/exit` / 中止 |
|
||||
| `CLOSED` | 窗口到期或进程退出 | —（连接关闭，已登记动作可完成） | — |
|
||||
|
||||
现实预算与现实窗口是两条独立时钟：`/enter` 成功后 `max_real_duration_s = 1200 s`
|
||||
起算（程序侧），测试窗口 `test_window_s = 1500 s`（服务端侧）。
|
||||
客户端维护本地截止时钟，剩余不足 60 s（`reserve_exit_s`，工程参数）时不再发起新动作，
|
||||
只保证 `/exit` 能发出去。
|
||||
|
||||
### 2.2 单次动作回合（严格串行）
|
||||
|
||||
|
||||
|
||||
1. 本地预校验：`channel ∈ [1,20]`、坐标有限且 $\lvert x\rvert,\lvert y\rvert\le 2\times10^6$、
|
||||
`request_id` 长度与字符集，与服务器 400 语义一致，省一次往返；
|
||||
2. 串行发送：同一时刻只有一个未决请求，不并发（并发会得到 409）；
|
||||
3. 等服务端计时：`accepted=true` 才更新状态，虚拟时刻取响应中的 `virtual_time_s`，
|
||||
不做本地累加；`accepted=false` 抛 `ActionRejected`，状态不更新，
|
||||
`virtual_time_s=0` 不计入时钟；
|
||||
4. 更新本地状态：位置、当前频道、剩余现实预算，追加 JSONL 会话日志；
|
||||
5. 异常处理：网络错误、429、500 时同 `request_id` 同内容重发
|
||||
（请求体一次性序列化，最多 `max_attempts` 次，指数退避）；
|
||||
400、404、405、409、413、415 立即失败。
|
||||
|
||||
重发不会重复计时，服务端按 `request_id` 幂等。
|
||||
|
||||
### 2.3 任务类型
|
||||
|
||||
| 任务 | 触发 | 动作序列 | 虚拟成本 |
|
||||
| --- | --- | --- | --- |
|
||||
| 覆盖扫描 | 某频道尚未被任何站点覆盖 | 移动到覆盖点 → `/measure` | 距离/5 + 5 s（+ 换频 1 s） |
|
||||
| 定位交会 | 某频道收到 `direction` | 移动到第二检测点 → `/measure` | 距离/5 + 5 s（+ 1 s） |
|
||||
| 清除 | 同频道候选区域 ρ ≤ 20 m | 移动到 MEC 圆心附近 → `/clear` | 距离/5 + 5 s 或 3 s |
|
||||
| 后备盲清（问题四） | 联合信念坍缩到有限格 | 依次到可疑格 `/clear` | 距离/5 + 3 s 或 5 s |
|
||||
| 机会测量 | 已停靠且该频道待测 | 原地 `/measure` | 5 s |
|
||||
|
||||
任务表排序是开放路径问题，默认用滚动 2-opt 与 Or-opt；
|
||||
精确 Held-Karp 与路线感知探点在本模拟器上为负收益，默认关闭，见
|
||||
[`第三四问图论与动态规划优化方案.md`](第三四问图论与动态规划优化方案.md) §8.6。
|
||||
|
||||
---
|
||||
|
||||
## 3. 附件计时示例逐步推演（105 / 111 / 194 / 199）
|
||||
|
||||
| 步 | 指令 | 移动 | 换频 | 动作 | 本步 | 累计 |
|
||||
| --- | --- | --- | --- | --- | --- | --- |
|
||||
| 1 | `/enter` | 0 | 0 | 0 | 0 | 0 |
|
||||
| 2 | `/measure (300,400) ch1` | 500 m → 100 s | 0（初始即 1） | 5 | 105 | 105 |
|
||||
| 3 | `/measure (300,400) ch2` | 0 | 1 | 5 | 6 | 111 |
|
||||
| 4 | `/clear (300,0) ch3` | 400 m → 80 s | 0 | 3（未发现） | 83 | 194 |
|
||||
| 5 | `/measure (300,0) ch2` | 0 | 0（第 3 步后已是 2） | 5 | 5 | 199 |
|
||||
| 6 | `/exit` | 0 | 0 | 0 | 0 | 199 |
|
||||
|
||||
|
||||
|
||||
验证方式：
|
||||
|
||||
```bash
|
||||
uv run mcm26b sim-check # 客户端 → 本地模拟器链路，输出 [105, 111, 194, 199]
|
||||
uv run pytest tests/test_protocol_timing.py -q # 逐步断言移动、换频、动作三个分项
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 4. 实测时序分解
|
||||
|
||||
对评测机留档的每一局，`scripts/make_figures.py` 按与 `referee.simulator`
|
||||
同源的规则逐动作复算，并检查分项之和是否等于响应中的 `virtual_time_s`。
|
||||
|
||||
### 4.1 问题三演练局 `BBM9-HZG2-77N5-NKKC`（10 源全清）
|
||||
|
||||
| 项 | 数值 | 占比 |
|
||||
| --- | ---: | ---: |
|
||||
| 总虚拟时间 | 3134.643 s | 100% |
|
||||
| 移动（里程 11.9 km） | 2379.6 s | 75.9% |
|
||||
| 检测与清除动作（119 + 10 次） | 645.0 s | 20.6% |
|
||||
| 换频（110 次） | 110.0 s | 3.5% |
|
||||
| 现实耗时 | 1.889 s | — |
|
||||
|
||||
### 4.2 问题四演练局 `GM56-DFXZ-XKDM-XYV7`（12 源，含 3 个定向源）
|
||||
|
||||
| 项 | 数值 | 占比 |
|
||||
| --- | ---: | ---: |
|
||||
| 总虚拟时间 | 8703.766 s | 100% |
|
||||
| 移动（里程 32.4 km） | 6489.8 s | 74.6% |
|
||||
| 检测与清除动作（338 + 79 次） | 1951.0 s | 22.4% |
|
||||
| 换频（263 次） | 263.0 s | 3.0% |
|
||||
| 现实耗时 | 3.397 s | — |
|
||||
|
||||
|
||||
|
||||
### 4.3 结论
|
||||
|
||||
1. 移动占约 75%，优化重点是路径长度，这解释了七点计划（7 点、6.96 km 巡回）
|
||||
优于 49 点网格的原因；
|
||||
2. 动作占约 21%，检测 5 s 是下限，因此找全后立即退出比复扫更省时间；
|
||||
3. 现实耗时与成绩无关，本地回环为毫秒级，官方口径取虚拟时间，
|
||||
程序预算（1200 s）只用于限制计算耗时。
|
||||
|
||||
### 4.4 复现命令
|
||||
|
||||
```bash
|
||||
# 用评测机留档重画（含真值）
|
||||
uv run python scripts/make_figures.py --only timing-measured-q3-10-sources,\
|
||||
replay-webui-q3-omnidirectional,replay-webui-q4-directional
|
||||
|
||||
# 归档新日志，评测机数据被清理后仍可复现插图
|
||||
uv run python scripts/archive_replay_logs.py --list
|
||||
uv run python scripts/archive_replay_logs.py BBM9-HZG2-77N5-NKKC
|
||||
|
||||
# 按官方口径逐微秒复算（接受客户端 JSONL 或评测机 log.jsonl）
|
||||
uv run python scripts/analyze_run_timing.py referee-data/sessions/BBM9-HZG2-77N5-NKKC/log.jsonl
|
||||
uv run python scripts/analyze_run_timing.py examples/replay/*.jsonl --json /tmp/timing.json
|
||||
uv run python scripts/verify_q3_practice.py output/q3/practice/<本局目录>
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 5. 时间预算与安全阀
|
||||
|
||||
| 约束 | 值 | 来源 | 客户端行为 |
|
||||
| --- | --- | --- | --- |
|
||||
| 移动速度 | 5 m/s | 题面 | 路径长度决定虚拟时间 |
|
||||
| 检测 / 换频 | 5 s / 1 s | 题面 | 避免高频扫频 |
|
||||
| 清除成功 / 未发现 | 5 s / 3 s | 题面 | 盲清代价可控 |
|
||||
| 测试窗口 | 1500 s（现实） | 题面 | 窗口到期接口关闭 |
|
||||
| 程序预算 | 1200 s（现实） | 题面 | `/enter` 后本地时钟起算 |
|
||||
| 退出预留 | 60 s | 工程 | 剩余不足时只允许 `/exit` |
|
||||
| 虚拟世界限时 | 360000 s | 题面 | 实测最坏为 1e4 量级，未触及 |
|
||||
| 行为日志上限 | 2 MB | 题面 | 控制请求数 |
|
||||
|
||||
安全阀见 [`algo-3.md`](algo-3.md) §7.2 与 [`algo-4.md`](algo-4.md) §5.2：
|
||||
预算将尽、连续无新发现或联合信念长时间不收敛时，转入最低成本收尾，
|
||||
先清除并登记已有发现，再 `/exit`，并写入 `stop_reason`。
|
||||
@@ -0,0 +1,133 @@
|
||||
{"event":"session_start","case_code":"BBM9-HZG2-77N5-NKKC","module":"q3_practice","robot_id":"1","seed":214175614,"countdown_s":5.0,"window_s":1500.0,"max_real_duration_s":1200.0,"truth":[{"channel":13,"x":-667.5434273375823,"y":1201.9279733147766,"r_eff_m":1068.7667479727882,"directional":false,"heading_deg":0.0},{"channel":18,"x":936.4803225514449,"y":-449.0798658945765,"r_eff_m":1100.2220789348723,"directional":false,"heading_deg":0.0},{"channel":7,"x":1168.5586737701378,"y":-665.9024303254582,"r_eff_m":1380.180019258212,"directional":false,"heading_deg":0.0},{"channel":16,"x":-704.338944452677,"y":-1500.6801256821625,"r_eff_m":1260.765213581664,"directional":false,"heading_deg":0.0},{"channel":9,"x":301.3279949940345,"y":-1195.568730289507,"r_eff_m":1268.9333402487596,"directional":false,"heading_deg":0.0},{"channel":19,"x":-1016.5047516327342,"y":-1362.2054377699205,"r_eff_m":1029.8927902123228,"directional":false,"heading_deg":0.0},{"channel":17,"x":-1128.3611120627002,"y":-751.6668239293164,"r_eff_m":1073.440585013539,"directional":false,"heading_deg":0.0},{"channel":2,"x":1267.339479448259,"y":687.5619158006604,"r_eff_m":1380.3211633911274,"directional":false,"heading_deg":0.0},{"channel":14,"x":-431.7778870393432,"y":921.0181764839424,"r_eff_m":1418.973099517202,"directional":false,"heading_deg":0.0},{"channel":11,"x":437.0112473060851,"y":-112.52557536387796,"r_eff_m":1181.7035745633132,"directional":false,"heading_deg":0.0}]}
|
||||
{"event":"action","seq":1,"wall_ts_ms":1789097576646,"method":"POST","path":"/enter","status":200,"request":{"arena_id":"default","robot_id":"1","request_id":"4721c649063d-0001"},"response":{"accepted":true,"real_timestamp_ms":1789097576646,"virtual_time_s":0.0,"max_virtual_duration_s":360000.0,"max_real_duration_s":1200,"remaining_real_duration_s":1200}}
|
||||
{"event":"action","seq":2,"wall_ts_ms":1789097576662,"method":"POST","path":"/measure","status":200,"request":{"arena_id":"default","robot_id":"1","request_id":"4721c649063d-0002","position":{"x":0.0,"y":0.0},"channel":1},"response":{"accepted":true,"real_timestamp_ms":1789097576662,"virtual_time_s":5.0,"measure_result":"no_signal"}}
|
||||
{"event":"action","seq":3,"wall_ts_ms":1789097576667,"method":"POST","path":"/measure","status":200,"request":{"arena_id":"default","robot_id":"1","request_id":"4721c649063d-0003","position":{"x":0.0,"y":0.0},"channel":2},"response":{"accepted":true,"real_timestamp_ms":1789097576667,"virtual_time_s":11.0,"measure_result":"no_signal"}}
|
||||
{"event":"action","seq":4,"wall_ts_ms":1789097576672,"method":"POST","path":"/measure","status":200,"request":{"arena_id":"default","robot_id":"1","request_id":"4721c649063d-0004","position":{"x":0.0,"y":0.0},"channel":3},"response":{"accepted":true,"real_timestamp_ms":1789097576672,"virtual_time_s":17.0,"measure_result":"no_signal"}}
|
||||
{"event":"action","seq":5,"wall_ts_ms":1789097576677,"method":"POST","path":"/measure","status":200,"request":{"arena_id":"default","robot_id":"1","request_id":"4721c649063d-0005","position":{"x":0.0,"y":0.0},"channel":4},"response":{"accepted":true,"real_timestamp_ms":1789097576677,"virtual_time_s":23.0,"measure_result":"no_signal"}}
|
||||
{"event":"action","seq":6,"wall_ts_ms":1789097576689,"method":"POST","path":"/measure","status":200,"request":{"arena_id":"default","robot_id":"1","request_id":"4721c649063d-0006","position":{"x":0.0,"y":0.0},"channel":5},"response":{"accepted":true,"real_timestamp_ms":1789097576688,"virtual_time_s":29.0,"measure_result":"no_signal"}}
|
||||
{"event":"action","seq":7,"wall_ts_ms":1789097576716,"method":"POST","path":"/measure","status":200,"request":{"arena_id":"default","robot_id":"1","request_id":"4721c649063d-0007","position":{"x":0.0,"y":0.0},"channel":6},"response":{"accepted":true,"real_timestamp_ms":1789097576716,"virtual_time_s":35.0,"measure_result":"no_signal"}}
|
||||
{"event":"action","seq":8,"wall_ts_ms":1789097576727,"method":"POST","path":"/measure","status":200,"request":{"arena_id":"default","robot_id":"1","request_id":"4721c649063d-0008","position":{"x":0.0,"y":0.0},"channel":7},"response":{"accepted":true,"real_timestamp_ms":1789097576727,"virtual_time_s":41.0,"measure_result":"direction","svd_deg":329.53}}
|
||||
{"event":"action","seq":9,"wall_ts_ms":1789097576757,"method":"POST","path":"/measure","status":200,"request":{"arena_id":"default","robot_id":"1","request_id":"4721c649063d-0009","position":{"x":0.0,"y":0.0},"channel":8},"response":{"accepted":true,"real_timestamp_ms":1789097576757,"virtual_time_s":47.0,"measure_result":"no_signal"}}
|
||||
{"event":"action","seq":10,"wall_ts_ms":1789097576762,"method":"POST","path":"/measure","status":200,"request":{"arena_id":"default","robot_id":"1","request_id":"4721c649063d-0010","position":{"x":0.0,"y":0.0},"channel":9},"response":{"accepted":true,"real_timestamp_ms":1789097576762,"virtual_time_s":53.0,"measure_result":"direction","svd_deg":284.61}}
|
||||
{"event":"action","seq":11,"wall_ts_ms":1789097576791,"method":"POST","path":"/measure","status":200,"request":{"arena_id":"default","robot_id":"1","request_id":"4721c649063d-0011","position":{"x":0.0,"y":0.0},"channel":10},"response":{"accepted":true,"real_timestamp_ms":1789097576791,"virtual_time_s":59.0,"measure_result":"no_signal"}}
|
||||
{"event":"action","seq":12,"wall_ts_ms":1789097576796,"method":"POST","path":"/measure","status":200,"request":{"arena_id":"default","robot_id":"1","request_id":"4721c649063d-0012","position":{"x":0.0,"y":0.0},"channel":11},"response":{"accepted":true,"real_timestamp_ms":1789097576796,"virtual_time_s":65.0,"measure_result":"direction","svd_deg":345.95}}
|
||||
{"event":"action","seq":13,"wall_ts_ms":1789097576825,"method":"POST","path":"/measure","status":200,"request":{"arena_id":"default","robot_id":"1","request_id":"4721c649063d-0013","position":{"x":0.0,"y":0.0},"channel":12},"response":{"accepted":true,"real_timestamp_ms":1789097576825,"virtual_time_s":71.0,"measure_result":"no_signal"}}
|
||||
{"event":"action","seq":14,"wall_ts_ms":1789097576830,"method":"POST","path":"/measure","status":200,"request":{"arena_id":"default","robot_id":"1","request_id":"4721c649063d-0014","position":{"x":0.0,"y":0.0},"channel":13},"response":{"accepted":true,"real_timestamp_ms":1789097576830,"virtual_time_s":77.0,"measure_result":"no_signal"}}
|
||||
{"event":"action","seq":15,"wall_ts_ms":1789097576835,"method":"POST","path":"/measure","status":200,"request":{"arena_id":"default","robot_id":"1","request_id":"4721c649063d-0015","position":{"x":0.0,"y":0.0},"channel":14},"response":{"accepted":true,"real_timestamp_ms":1789097576835,"virtual_time_s":83.0,"measure_result":"direction","svd_deg":115.07}}
|
||||
{"event":"action","seq":16,"wall_ts_ms":1789097576864,"method":"POST","path":"/measure","status":200,"request":{"arena_id":"default","robot_id":"1","request_id":"4721c649063d-0016","position":{"x":0.0,"y":0.0},"channel":15},"response":{"accepted":true,"real_timestamp_ms":1789097576864,"virtual_time_s":89.0,"measure_result":"no_signal"}}
|
||||
{"event":"action","seq":17,"wall_ts_ms":1789097576868,"method":"POST","path":"/measure","status":200,"request":{"arena_id":"default","robot_id":"1","request_id":"4721c649063d-0017","position":{"x":0.0,"y":0.0},"channel":16},"response":{"accepted":true,"real_timestamp_ms":1789097576868,"virtual_time_s":95.0,"measure_result":"no_signal"}}
|
||||
{"event":"action","seq":18,"wall_ts_ms":1789097576873,"method":"POST","path":"/measure","status":200,"request":{"arena_id":"default","robot_id":"1","request_id":"4721c649063d-0018","position":{"x":0.0,"y":0.0},"channel":17},"response":{"accepted":true,"real_timestamp_ms":1789097576873,"virtual_time_s":101.0,"measure_result":"no_signal"}}
|
||||
{"event":"action","seq":19,"wall_ts_ms":1789097576878,"method":"POST","path":"/measure","status":200,"request":{"arena_id":"default","robot_id":"1","request_id":"4721c649063d-0019","position":{"x":0.0,"y":0.0},"channel":18},"response":{"accepted":true,"real_timestamp_ms":1789097576878,"virtual_time_s":107.0,"measure_result":"direction","svd_deg":334.09}}
|
||||
{"event":"action","seq":20,"wall_ts_ms":1789097576907,"method":"POST","path":"/measure","status":200,"request":{"arena_id":"default","robot_id":"1","request_id":"4721c649063d-0020","position":{"x":0.0,"y":0.0},"channel":19},"response":{"accepted":true,"real_timestamp_ms":1789097576907,"virtual_time_s":113.0,"measure_result":"no_signal"}}
|
||||
{"event":"action","seq":21,"wall_ts_ms":1789097576913,"method":"POST","path":"/measure","status":200,"request":{"arena_id":"default","robot_id":"1","request_id":"4721c649063d-0021","position":{"x":0.0,"y":0.0},"channel":20},"response":{"accepted":true,"real_timestamp_ms":1789097576913,"virtual_time_s":119.0,"measure_result":"no_signal"}}
|
||||
{"event":"action","seq":22,"wall_ts_ms":1789097577020,"method":"POST","path":"/measure","status":200,"request":{"arena_id":"default","robot_id":"1","request_id":"4721c649063d-0022","position":{"x":-382.32549659893994,"y":438.1825018611922},"channel":14},"response":{"accepted":true,"real_timestamp_ms":1789097577020,"virtual_time_s":241.305923,"measure_result":"direction","svd_deg":95.7}}
|
||||
{"event":"action","seq":23,"wall_ts_ms":1789097577105,"method":"POST","path":"/measure","status":200,"request":{"arena_id":"default","robot_id":"1","request_id":"4721c649063d-0023","position":{"x":-599.9999999999998,"y":1039.2304845413264},"channel":1},"response":{"accepted":true,"real_timestamp_ms":1789097577105,"virtual_time_s":375.155971,"measure_result":"no_signal"}}
|
||||
{"event":"action","seq":24,"wall_ts_ms":1789097577108,"method":"POST","path":"/measure","status":200,"request":{"arena_id":"default","robot_id":"1","request_id":"4721c649063d-0024","position":{"x":-599.9999999999998,"y":1039.2304845413264},"channel":2},"response":{"accepted":true,"real_timestamp_ms":1789097577108,"virtual_time_s":381.155971,"measure_result":"no_signal"}}
|
||||
{"event":"action","seq":25,"wall_ts_ms":1789097577111,"method":"POST","path":"/measure","status":200,"request":{"arena_id":"default","robot_id":"1","request_id":"4721c649063d-0025","position":{"x":-599.9999999999998,"y":1039.2304845413264},"channel":3},"response":{"accepted":true,"real_timestamp_ms":1789097577111,"virtual_time_s":387.155971,"measure_result":"no_signal"}}
|
||||
{"event":"action","seq":26,"wall_ts_ms":1789097577114,"method":"POST","path":"/measure","status":200,"request":{"arena_id":"default","robot_id":"1","request_id":"4721c649063d-0026","position":{"x":-599.9999999999998,"y":1039.2304845413264},"channel":4},"response":{"accepted":true,"real_timestamp_ms":1789097577114,"virtual_time_s":393.155971,"measure_result":"no_signal"}}
|
||||
{"event":"action","seq":27,"wall_ts_ms":1789097577117,"method":"POST","path":"/measure","status":200,"request":{"arena_id":"default","robot_id":"1","request_id":"4721c649063d-0027","position":{"x":-599.9999999999998,"y":1039.2304845413264},"channel":5},"response":{"accepted":true,"real_timestamp_ms":1789097577117,"virtual_time_s":399.155971,"measure_result":"no_signal"}}
|
||||
{"event":"action","seq":28,"wall_ts_ms":1789097577121,"method":"POST","path":"/measure","status":200,"request":{"arena_id":"default","robot_id":"1","request_id":"4721c649063d-0028","position":{"x":-599.9999999999998,"y":1039.2304845413264},"channel":6},"response":{"accepted":true,"real_timestamp_ms":1789097577121,"virtual_time_s":405.155971,"measure_result":"no_signal"}}
|
||||
{"event":"action","seq":29,"wall_ts_ms":1789097577124,"method":"POST","path":"/measure","status":200,"request":{"arena_id":"default","robot_id":"1","request_id":"4721c649063d-0029","position":{"x":-599.9999999999998,"y":1039.2304845413264},"channel":8},"response":{"accepted":true,"real_timestamp_ms":1789097577124,"virtual_time_s":411.155971,"measure_result":"no_signal"}}
|
||||
{"event":"action","seq":30,"wall_ts_ms":1789097577128,"method":"POST","path":"/measure","status":200,"request":{"arena_id":"default","robot_id":"1","request_id":"4721c649063d-0030","position":{"x":-599.9999999999998,"y":1039.2304845413264},"channel":10},"response":{"accepted":true,"real_timestamp_ms":1789097577128,"virtual_time_s":417.155971,"measure_result":"no_signal"}}
|
||||
{"event":"action","seq":31,"wall_ts_ms":1789097577132,"method":"POST","path":"/measure","status":200,"request":{"arena_id":"default","robot_id":"1","request_id":"4721c649063d-0031","position":{"x":-599.9999999999998,"y":1039.2304845413264},"channel":12},"response":{"accepted":true,"real_timestamp_ms":1789097577132,"virtual_time_s":423.155971,"measure_result":"no_signal"}}
|
||||
{"event":"action","seq":32,"wall_ts_ms":1789097577137,"method":"POST","path":"/measure","status":200,"request":{"arena_id":"default","robot_id":"1","request_id":"4721c649063d-0032","position":{"x":-599.9999999999998,"y":1039.2304845413264},"channel":13},"response":{"accepted":true,"real_timestamp_ms":1789097577136,"virtual_time_s":429.155971,"measure_result":"direction","svd_deg":112.67}}
|
||||
{"event":"action","seq":33,"wall_ts_ms":1789097577155,"method":"POST","path":"/measure","status":200,"request":{"arena_id":"default","robot_id":"1","request_id":"4721c649063d-0033","position":{"x":-599.9999999999998,"y":1039.2304845413264},"channel":15},"response":{"accepted":true,"real_timestamp_ms":1789097577155,"virtual_time_s":435.155971,"measure_result":"no_signal"}}
|
||||
{"event":"action","seq":34,"wall_ts_ms":1789097577158,"method":"POST","path":"/measure","status":200,"request":{"arena_id":"default","robot_id":"1","request_id":"4721c649063d-0034","position":{"x":-599.9999999999998,"y":1039.2304845413264},"channel":16},"response":{"accepted":true,"real_timestamp_ms":1789097577158,"virtual_time_s":441.155971,"measure_result":"no_signal"}}
|
||||
{"event":"action","seq":35,"wall_ts_ms":1789097577161,"method":"POST","path":"/measure","status":200,"request":{"arena_id":"default","robot_id":"1","request_id":"4721c649063d-0035","position":{"x":-599.9999999999998,"y":1039.2304845413264},"channel":17},"response":{"accepted":true,"real_timestamp_ms":1789097577161,"virtual_time_s":447.155971,"measure_result":"no_signal"}}
|
||||
{"event":"action","seq":36,"wall_ts_ms":1789097577165,"method":"POST","path":"/measure","status":200,"request":{"arena_id":"default","robot_id":"1","request_id":"4721c649063d-0036","position":{"x":-599.9999999999998,"y":1039.2304845413264},"channel":19},"response":{"accepted":true,"real_timestamp_ms":1789097577165,"virtual_time_s":453.155971,"measure_result":"no_signal"}}
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{"event":"session_end","reason":"user_exit","virtual_time_s":8703.765803,"real_elapsed_s":8.459184,"request_count":419,"cleared_count":12}
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@@ -0,0 +1,400 @@
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{"event": "action", "seq": 2, "wall_ts_ms": 1789259875591, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "b4f53d0e2d49-0002", "position": {"x": 0.0, "y": 0.0}, "channel": 1}, "response": {"accepted": true, "real_timestamp_ms": 1789259875591, "virtual_time_s": 5.0, "measure_result": "no_signal"}}
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{"event": "action", "seq": 4, "wall_ts_ms": 1789259875592, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "b4f53d0e2d49-0004", "position": {"x": 0.0, "y": 0.0}, "channel": 3}, "response": {"accepted": true, "real_timestamp_ms": 1789259875592, "virtual_time_s": 17.0, "measure_result": "no_signal"}}
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{"event": "action", "seq": 7, "wall_ts_ms": 1789259875594, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "b4f53d0e2d49-0007", "position": {"x": 0.0, "y": 0.0}, "channel": 6}, "response": {"accepted": true, "real_timestamp_ms": 1789259875594, "virtual_time_s": 35.0, "measure_result": "direction", "svd_deg": 13.68}}
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{"event": "action", "seq": 8, "wall_ts_ms": 1789259875637, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "b4f53d0e2d49-0008", "position": {"x": 0.0, "y": 0.0}, "channel": 7}, "response": {"accepted": true, "real_timestamp_ms": 1789259875637, "virtual_time_s": 41.0, "measure_result": "no_signal"}}
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{"event": "action", "seq": 9, "wall_ts_ms": 1789259875638, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "b4f53d0e2d49-0009", "position": {"x": 0.0, "y": 0.0}, "channel": 8}, "response": {"accepted": true, "real_timestamp_ms": 1789259875638, "virtual_time_s": 47.0, "measure_result": "no_signal"}}
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{"event": "action", "seq": 11, "wall_ts_ms": 1789259875680, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "b4f53d0e2d49-0011", "position": {"x": 0.0, "y": 0.0}, "channel": 10}, "response": {"accepted": true, "real_timestamp_ms": 1789259875680, "virtual_time_s": 59.0, "measure_result": "no_signal"}}
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|
||||
{"event": "action", "seq": 387, "wall_ts_ms": 1789259878080, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "b4f53d0e2d49-0387", "position": {"x": 1300.0, "y": 1100.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789259878080, "virtual_time_s": 8752.823068, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 388, "wall_ts_ms": 1789259878080, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "b4f53d0e2d49-0388", "position": {"x": 1300.0, "y": 1100.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789259878080, "virtual_time_s": 8758.823068, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 389, "wall_ts_ms": 1789259878081, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "b4f53d0e2d49-0389", "position": {"x": 1500.0, "y": 1300.0}, "channel": 17}, "response": {"accepted": true, "real_timestamp_ms": 1789259878081, "virtual_time_s": 8820.39161, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 390, "wall_ts_ms": 1789259878081, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "b4f53d0e2d49-0390", "position": {"x": 1500.0, "y": 1300.0}, "channel": 1}, "response": {"accepted": true, "real_timestamp_ms": 1789259878081, "virtual_time_s": 8826.39161, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 391, "wall_ts_ms": 1789259878082, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "b4f53d0e2d49-0391", "position": {"x": 1500.0, "y": 1300.0}, "channel": 3}, "response": {"accepted": true, "real_timestamp_ms": 1789259878082, "virtual_time_s": 8832.39161, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 392, "wall_ts_ms": 1789259878082, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "b4f53d0e2d49-0392", "position": {"x": 1500.0, "y": 1300.0}, "channel": 4}, "response": {"accepted": true, "real_timestamp_ms": 1789259878082, "virtual_time_s": 8838.39161, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 393, "wall_ts_ms": 1789259878083, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "b4f53d0e2d49-0393", "position": {"x": 1500.0, "y": 1300.0}, "channel": 5}, "response": {"accepted": true, "real_timestamp_ms": 1789259878083, "virtual_time_s": 8844.39161, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 394, "wall_ts_ms": 1789259878083, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "b4f53d0e2d49-0394", "position": {"x": 1500.0, "y": 1300.0}, "channel": 8}, "response": {"accepted": true, "real_timestamp_ms": 1789259878083, "virtual_time_s": 8850.39161, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 395, "wall_ts_ms": 1789259878084, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "b4f53d0e2d49-0395", "position": {"x": 1500.0, "y": 1300.0}, "channel": 10}, "response": {"accepted": true, "real_timestamp_ms": 1789259878084, "virtual_time_s": 8856.39161, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 396, "wall_ts_ms": 1789259878084, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "b4f53d0e2d49-0396", "position": {"x": 1500.0, "y": 1300.0}, "channel": 11}, "response": {"accepted": true, "real_timestamp_ms": 1789259878084, "virtual_time_s": 8862.39161, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 397, "wall_ts_ms": 1789259878085, "method": "POST", "path": "/measure", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "b4f53d0e2d49-0397", "position": {"x": 1500.0, "y": 1300.0}, "channel": 16}, "response": {"accepted": true, "real_timestamp_ms": 1789259878085, "virtual_time_s": 8868.39161, "measure_result": "no_signal"}}
|
||||
{"event": "action", "seq": 398, "wall_ts_ms": 1789259878085, "method": "POST", "path": "/exit", "status": 200, "request": {"arena_id": "default", "robot_id": "1", "request_id": "b4f53d0e2d49-0398"}, "response": {"accepted": true, "real_timestamp_ms": 1789259878085, "virtual_time_s": 8868.39161, "exit_reason": "user_exit"}}
|
||||
{"event": "session_end", "reason": "user_exit", "virtual_time_s": 8868.39161, "real_elapsed_s": 13.428012, "request_count": 398, "cleared_count": 11}
|
||||
@@ -0,0 +1,96 @@
|
||||
{
|
||||
"case_code": "RS35-JWSZ-7PJR-48ME",
|
||||
"module": "q4_formal",
|
||||
"robot_id": "1",
|
||||
"seed": 524636976,
|
||||
"truth": [
|
||||
{
|
||||
"channel": 19,
|
||||
"x": -206.10766492851073,
|
||||
"y": 1160.8055169225704,
|
||||
"r_eff_m": 1028.289877243241,
|
||||
"directional": true,
|
||||
"heading_deg": 288.58862711279323
|
||||
},
|
||||
{
|
||||
"channel": 14,
|
||||
"x": 755.2806278559268,
|
||||
"y": 250.29082865203878,
|
||||
"r_eff_m": 1020.46483315481,
|
||||
"directional": true,
|
||||
"heading_deg": 291.8630536595404
|
||||
},
|
||||
{
|
||||
"channel": 18,
|
||||
"x": 966.1057750947382,
|
||||
"y": -528.7766243925598,
|
||||
"r_eff_m": 1431.3029992563347,
|
||||
"directional": true,
|
||||
"heading_deg": 152.43896706878402
|
||||
},
|
||||
{
|
||||
"channel": 9,
|
||||
"x": 263.1408255158642,
|
||||
"y": 857.6002010138432,
|
||||
"r_eff_m": 1124.292095106724,
|
||||
"directional": false,
|
||||
"heading_deg": 0.0
|
||||
},
|
||||
{
|
||||
"channel": 13,
|
||||
"x": -87.51220801888897,
|
||||
"y": -1032.2242344411166,
|
||||
"r_eff_m": 1128.989839175949,
|
||||
"directional": false,
|
||||
"heading_deg": 0.0
|
||||
},
|
||||
{
|
||||
"channel": 15,
|
||||
"x": 319.83243385729406,
|
||||
"y": 113.48421543474046,
|
||||
"r_eff_m": 1024.2663476707392,
|
||||
"directional": false,
|
||||
"heading_deg": 0.0
|
||||
},
|
||||
{
|
||||
"channel": 7,
|
||||
"x": -1171.4978647108244,
|
||||
"y": -1073.4538615834038,
|
||||
"r_eff_m": 1247.2912686345992,
|
||||
"directional": false,
|
||||
"heading_deg": 0.0
|
||||
},
|
||||
{
|
||||
"channel": 6,
|
||||
"x": 659.9240002207289,
|
||||
"y": 156.96230063594635,
|
||||
"r_eff_m": 1090.6727395531848,
|
||||
"directional": false,
|
||||
"heading_deg": 0.0
|
||||
},
|
||||
{
|
||||
"channel": 20,
|
||||
"x": -726.0390048597063,
|
||||
"y": 836.3218839802242,
|
||||
"r_eff_m": 1274.2222659503636,
|
||||
"directional": false,
|
||||
"heading_deg": 0.0
|
||||
},
|
||||
{
|
||||
"channel": 12,
|
||||
"x": -1706.9890741103702,
|
||||
"y": 282.3965606193818,
|
||||
"r_eff_m": 1071.3644107996988,
|
||||
"directional": false,
|
||||
"heading_deg": 0.0
|
||||
},
|
||||
{
|
||||
"channel": 2,
|
||||
"x": 1188.125441370153,
|
||||
"y": -193.71523353215824,
|
||||
"r_eff_m": 1092.5104751409474,
|
||||
"directional": false,
|
||||
"heading_deg": 0.0
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,92 @@
|
||||
"""把评测机一局的日志归档到 ``examples/replay/``，供无 referee-data 时复现插图。
|
||||
|
||||
``referee-data/`` 与 ``output/`` 都在 .gitignore 里：本机清掉评测机数据后，
|
||||
``docs/figures/*.png`` 就无法复现。本脚本把"作图用到的那几局"抽出来，
|
||||
命名成 ``<case_code>.<module>.jsonl`` 存进 ``examples/replay/``（入库、几万行以内），
|
||||
``scripts/make_figures.py`` 在没有评测机数据时自动改读这里。
|
||||
|
||||
uv run python scripts/archive_replay_logs.py # 归档作图默认选中的两局
|
||||
uv run python scripts/archive_replay_logs.py BBM9-HZG2-77N5-NKKC # 指定案例
|
||||
uv run python scripts/archive_replay_logs.py --list # 只看有哪些局
|
||||
|
||||
归档是**内容等价**的：只做 JSON 重新格式化（逐行 compact 输出），不改字段，
|
||||
并在文件头写入一条 ``session_start``（评测机日志本就自带），保持解析兼容。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
ROOT = Path(__file__).resolve().parents[1]
|
||||
SESSIONS_DIR = ROOT / 'referee-data' / 'sessions'
|
||||
ARCHIVE_DIR = ROOT / 'examples' / 'replay'
|
||||
|
||||
sys.path.insert(0, str(Path(__file__).resolve().parent))
|
||||
|
||||
from make_figures import discover_runs, pick_run # noqa: E402
|
||||
|
||||
|
||||
def archive(run: dict, out_dir: Path) -> Path:
|
||||
"""把一局日志写成归档 JSONL：逐行原样紧凑输出（字段不变，只重新格式化）。"""
|
||||
out_dir.mkdir(parents=True, exist_ok=True)
|
||||
target = out_dir / f"{run['case_code']}.{run['module']}.jsonl"
|
||||
lines: list[str] = []
|
||||
for line in run["source"].read_text(encoding='utf-8').splitlines():
|
||||
line = line.strip()
|
||||
if not line:
|
||||
continue
|
||||
try:
|
||||
doc = json.loads(line)
|
||||
except json.JSONDecodeError:
|
||||
continue
|
||||
lines.append(json.dumps(doc, ensure_ascii=False, separators=(',', ':')))
|
||||
target.write_text('\n'.join(lines) + '\n', encoding='utf-8')
|
||||
return target
|
||||
|
||||
|
||||
def main() -> int:
|
||||
parser = argparse.ArgumentParser(description=__doc__.splitlines()[0])
|
||||
parser.add_argument('cases', nargs='*', help='案例编码；缺省=作图默认选中的那几局')
|
||||
parser.add_argument('--list', action='store_true', help='列出可选案例后退出')
|
||||
parser.add_argument('--out', default=str(ARCHIVE_DIR))
|
||||
args = parser.parse_args()
|
||||
|
||||
runs = discover_runs()
|
||||
if not runs:
|
||||
print('没有找到可归档的日志：referee-data/sessions/*/log.jsonl 与 '
|
||||
'examples/replay/*.jsonl 都为空。')
|
||||
return 1
|
||||
if args.list:
|
||||
for run in runs:
|
||||
clears = sum(1 for a in run["actions"] if a["path"] == "/clear")
|
||||
print(f"{run['case_code']} {run['module']:<12} 真值 {len(run['truth']):2d} "
|
||||
f"动作 {len(run['actions']):4d} 清除 {clears:3d}")
|
||||
return 0
|
||||
|
||||
picked = []
|
||||
if args.cases:
|
||||
wanted = set(args.cases)
|
||||
picked = [r for r in runs if r["case_code"] in wanted]
|
||||
missing = wanted - {r["case_code"] for r in picked}
|
||||
if missing:
|
||||
print(f'未找到案例：{sorted(missing)}')
|
||||
return 1
|
||||
else:
|
||||
for module, truth in (('q3', 10), ('q4', 12)):
|
||||
run = pick_run(runs, module=module, truth=truth)
|
||||
if run and run not in picked:
|
||||
picked.append(run)
|
||||
|
||||
for run in picked:
|
||||
path = archive(run, Path(args.out))
|
||||
print(f"[archive] {run['case_code']:<20} {run['module']:<12} "
|
||||
f"真值 {len(run['truth']):2d} 动作 {len(run['actions']):4d} → "
|
||||
f"{path.relative_to(ROOT)}")
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
raise SystemExit(main())
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,372 +0,0 @@
|
||||
"""论文级图表工具：统一配色、场景图 DSL、SVG/PNG 双出与来源清单。
|
||||
|
||||
设计约束（对应 docs/figures/README.md 的"可复现"承诺）：
|
||||
|
||||
- **零新增运行时依赖**：几何/连线由本模块直接写 SVG，PNG 由 ``rsvg-convert``
|
||||
光栅化（与项目既有可视化链路一致）；中文与西文文字层用 Pillow 以论文自带
|
||||
的 ``docs/paper/fonts/`` 字体单独合成，保证与 ``main.tex`` 同字体、同字号；
|
||||
- **一份场景图两处消费**：SVG 用于 WebUI/文档内联预览（走系统字体栈），
|
||||
PNG 用于论文插图（走 bundled 字体）；两者坐标完全一致；
|
||||
- **来源可追溯**：每张图强制携带 ``sources``（文件路径 + sha256 前 12 位 +
|
||||
行数）与生成命令，写入 ``manifest.json``，避免"图与数据对不上"。
|
||||
|
||||
文字排版约定：**主风格只提供 SVG 精度的文字**，Pillow 侧复用同一套
|
||||
``(x, y, anchor, font, size, dy)`` 参数重画一遍；``dy`` 表示"文字视觉中心相对
|
||||
基线的偏移比例"，由 font-size 缩放，用来让框内文字纵向居中。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import hashlib
|
||||
import json
|
||||
import math
|
||||
import shutil
|
||||
import subprocess
|
||||
from dataclasses import dataclass, field
|
||||
from pathlib import Path
|
||||
from typing import Any, Iterable, Sequence
|
||||
|
||||
ROOT = Path(__file__).resolve().parents[1]
|
||||
FONT_DIR = ROOT / "docs" / "paper" / "fonts"
|
||||
DEFAULT_RASTERIZER = "rsvg-convert"
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# 论文配色：低饱和、单色可打印，正文/强调/真值/成功/失败五类语义色 + 顺序色阶
|
||||
# ---------------------------------------------------------------------------
|
||||
PALETTE: dict[str, str] = {
|
||||
"ink": "#1c2430", # 主文字/坐标轴
|
||||
"muted": "#6b7684", # 次要文字与网格
|
||||
"faint": "#dfe4ea", # 分隔线
|
||||
"paper": "#ffffff",
|
||||
"primary": "#1f4e79", # 主线：协议/流程
|
||||
"primary_soft": "#dce6f1",
|
||||
"accent": "#b03a2e", # 强调：真值源/关键结论
|
||||
"accent_soft": "#f6dcd8",
|
||||
"success": "#1e7a5f", # 成功：清除/全清
|
||||
"success_soft": "#d8ece5",
|
||||
"warn": "#b8860b", # 警示：后备/未达标
|
||||
"warn_soft": "#f6ecd2",
|
||||
"series": ("#1f4e79", "#b03a2e", "#1e7a5f", "#b8860b", "#5b4b8a", "#3d7ea6"),
|
||||
}
|
||||
|
||||
FONTS: dict[str, str] = {
|
||||
"song": "SongtiSC-Regular.ttf", # 中文正文：宋体
|
||||
"song_bold": "SongtiSC-Bold.ttf",
|
||||
"hei": "HeitiSC-Light.ttf", # 中文标题：黑体
|
||||
"hei_bold": "HeitiSC-Medium.ttf",
|
||||
"times": "Times New Roman.ttf", # 西文与数字
|
||||
"times_bold": "Times New Roman Bold.ttf",
|
||||
}
|
||||
|
||||
_SVG_FAMILY = {
|
||||
"song": "'Songti SC','SimSun',serif",
|
||||
"song_bold": "'Songti SC','SimSun',serif",
|
||||
"hei": "'Heiti SC','Microsoft YaHei',sans-serif",
|
||||
"hei_bold": "'Heiti SC','Microsoft YaHei',sans-serif",
|
||||
"times": "'Times New Roman',Times,serif",
|
||||
"times_bold": "'Times New Roman',Times,serif",
|
||||
}
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# 场景图 DSL
|
||||
# ---------------------------------------------------------------------------
|
||||
@dataclass
|
||||
class Figure:
|
||||
"""一张图的场景描述：宽高以 CSS 像素（≈1/96 inch）计。"""
|
||||
|
||||
width: float
|
||||
height: float
|
||||
title: str = ""
|
||||
caption: str = ""
|
||||
background: str = PALETTE["paper"]
|
||||
elements: list[dict[str, Any]] = field(default_factory=list)
|
||||
sources: list[dict[str, Any]] = field(default_factory=list)
|
||||
notes: list[str] = field(default_factory=list)
|
||||
|
||||
# ---------------------------------------------------------------- 图元
|
||||
def rect(self, x: float, y: float, w: float, h: float, **kw: Any) -> "Figure":
|
||||
self.elements.append(dict(kind="rect", x=x, y=y, w=w, h=h, **kw))
|
||||
return self
|
||||
|
||||
def circle(self, cx: float, cy: float, r: float, **kw: Any) -> "Figure":
|
||||
self.elements.append(dict(kind="circle", cx=cx, cy=cy, r=r, **kw))
|
||||
return self
|
||||
|
||||
def line(self, x1: float, y1: float, x2: float, y2: float, **kw: Any) -> "Figure":
|
||||
self.elements.append(dict(kind="line", x1=x1, y1=y1, x2=x2, y2=y2, **kw))
|
||||
return self
|
||||
|
||||
def polyline(self, points: Sequence[Sequence[float]], **kw: Any) -> "Figure":
|
||||
self.elements.append(dict(kind="polyline", points=[list(p) for p in points], **kw))
|
||||
return self
|
||||
|
||||
def polygon(self, points: Sequence[Sequence[float]], **kw: Any) -> "Figure":
|
||||
self.elements.append(dict(kind="polygon", points=[list(p) for p in points], **kw))
|
||||
return self
|
||||
|
||||
def path(self, d: str, **kw: Any) -> "Figure":
|
||||
self.elements.append(dict(kind="path", d=d, **kw))
|
||||
return self
|
||||
|
||||
def arrow(self, x1: float, y1: float, x2: float, y2: float, **kw: Any) -> "Figure":
|
||||
"""直线箭头：``head`` 为箭头边长（像素），``dash`` 支持虚线。"""
|
||||
self.elements.append(dict(kind="arrow", x1=x1, y1=y1, x2=x2, y2=y2, **kw))
|
||||
return self
|
||||
|
||||
def text(self, x: float, y: float, s: str, *, size: float = 12.0,
|
||||
font: str = "song", color: str | None = None, anchor: str = "start",
|
||||
align: str = "top", bold: bool = False, rotate: float | None = None,
|
||||
dy: float | None = None, opacity: float = 1.0) -> "Figure":
|
||||
"""文字。``(x, y)`` 为参考点；``align`` ∈ {top, middle, bottom} 决定
|
||||
参考点落在文字块的哪条水平线上（``anchor`` 决定左右）。
|
||||
"""
|
||||
self.elements.append(dict(
|
||||
kind="text", x=x, y=y, s=s, size=size, font=font,
|
||||
color=color or PALETTE["ink"], anchor=anchor, align=align,
|
||||
bold=bold, rotate=rotate, dy=dy, opacity=opacity))
|
||||
return self
|
||||
|
||||
def legend_chips(self, x: float, y: float, items: Iterable[tuple[str, str]],
|
||||
*, size: float = 11.0, gap: float = 16.0, box: float = 10.0,
|
||||
font: str = "song") -> float:
|
||||
"""一行"色块 + 文字"图例，返回结束时的 x 坐标。"""
|
||||
cur = x
|
||||
for label, color in items:
|
||||
self.rect(cur, y - box * 0.75, box, box, fill=color, stroke=None, radius=2.0)
|
||||
self.text(cur + box + 5.0, y + box * 0.35, label, size=size, font=font,
|
||||
align="middle")
|
||||
cur += box + 5.0 + _estimate_width(label, size, font) + gap
|
||||
return cur
|
||||
|
||||
def source(self, path: str | Path, **extra: Any) -> "Figure":
|
||||
"""登记本图的数据来源（相对仓库根），含 sha256 前 12 位与行数。"""
|
||||
p = Path(path)
|
||||
p = p if p.is_absolute() else ROOT / p
|
||||
digest, lines = "-", 0
|
||||
if p.is_file():
|
||||
data = p.read_bytes()
|
||||
digest = hashlib.sha256(data).hexdigest()[:12]
|
||||
lines = data.count(b"\n") + (0 if data.endswith(b"\n") else 1)
|
||||
self.sources.append(dict(path=str(p.relative_to(ROOT)) if p.is_relative_to(ROOT) else str(p),
|
||||
sha256_12=digest, lines=lines, **extra))
|
||||
return self
|
||||
|
||||
# ---------------------------------------------------------------- 输出
|
||||
def to_svg(self) -> str:
|
||||
body: list[str] = [_svg_element(el) for el in self.elements]
|
||||
head = (f'<svg xmlns="http://www.w3.org/2000/svg" '
|
||||
f'width="{_n(self.width)}" height="{_n(self.height)}" '
|
||||
f'viewBox="0 0 {_n(self.width)} {_n(self.height)}" '
|
||||
f'font-family="{_SVG_FAMILY["song"]}">')
|
||||
meta = f"<title>{_esc(self.title)}</title>\n"
|
||||
if self.caption:
|
||||
meta += f"<desc>{_esc(self.caption)}</desc>\n"
|
||||
return "\n".join([head, meta, f'<rect width="{_n(self.width)}" '
|
||||
f'height="{_n(self.height)}" '
|
||||
f'fill="{self.background}"/>', *body, "</svg>", ""])
|
||||
|
||||
def write(self, out_dir: str | Path, stem: str, *, scale: float = 2.0,
|
||||
rasterizer: str = DEFAULT_RASTERIZER, png: bool = True) -> dict[str, Any]:
|
||||
"""写出 ``<stem>.svg``（+ ``<stem>.png``），返回产出清单。"""
|
||||
out = Path(out_dir)
|
||||
out.mkdir(parents=True, exist_ok=True)
|
||||
svg_path = out / f"{stem}.svg"
|
||||
svg_path.write_text(self.to_svg(), encoding="utf-8")
|
||||
produced: dict[str, Any] = {"stem": stem, "title": self.title,
|
||||
"caption": self.caption,
|
||||
"svg": str(svg_path.relative_to(ROOT)),
|
||||
"size": [self.width, self.height],
|
||||
"sources": self.sources, "notes": self.notes}
|
||||
if png:
|
||||
png_path = out / f"{stem}.png"
|
||||
_rasterize(svg_path, png_path, scale=scale, rasterizer=rasterizer)
|
||||
_compose_text(self, png_path, scale=scale)
|
||||
produced["png"] = str(png_path.relative_to(ROOT))
|
||||
produced["png_scale"] = scale
|
||||
return produced
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# SVG 生成
|
||||
# ---------------------------------------------------------------------------
|
||||
def _n(value: float) -> str:
|
||||
"""紧凑数字：去掉多余小数位，避免 SVG 体积膨胀。"""
|
||||
if abs(value - round(value)) < 1e-9:
|
||||
return str(int(round(value)))
|
||||
return f"{value:.3f}".rstrip("0").rstrip(".")
|
||||
|
||||
|
||||
def _esc(text: str) -> str:
|
||||
return (str(text).replace("&", "&").replace("<", "<").replace(">", ">"))
|
||||
|
||||
|
||||
def _paint(kw: dict[str, Any]) -> str:
|
||||
parts: list[str] = []
|
||||
fill = kw.get("fill", "none")
|
||||
if fill is None:
|
||||
fill = "none"
|
||||
parts.append(f'fill="{fill}"')
|
||||
stroke = kw.get("stroke")
|
||||
if stroke:
|
||||
parts.append(f'stroke="{stroke}" stroke-width="{_n(kw.get("sw", 1.0))}"')
|
||||
if kw.get("dash"):
|
||||
dash = kw["dash"] if isinstance(kw["dash"], str) else \
|
||||
" ".join(_n(v) for v in kw["dash"])
|
||||
parts.append(f'stroke-dasharray="{dash}"')
|
||||
parts.append(f'stroke-linejoin="{kw.get("linejoin", "miter")}"')
|
||||
if kw.get("opacity") is not None:
|
||||
parts.append(f'opacity="{_n(kw["opacity"])}"')
|
||||
if kw.get("radius"):
|
||||
parts.append(f'rx="{_n(kw["radius"])}" ry="{_n(kw["radius"])}"')
|
||||
return " ".join(parts)
|
||||
|
||||
|
||||
def _text_svg(el: dict[str, Any]) -> str:
|
||||
size = el["size"]
|
||||
dy = _baseline_offset(el)
|
||||
anchor = {"start": "start", "middle": "middle", "end": "end"}[el["anchor"]]
|
||||
family = _SVG_FAMILY[el["font"]]
|
||||
weight = ' font-weight="bold"' if el["bold"] or el["font"].endswith("_bold") else ""
|
||||
style = ' font-style="italic"' if el.get("italic") else ""
|
||||
extra = f' opacity="{_n(el["opacity"])}"' if el.get("opacity", 1.0) < 1 else ""
|
||||
rotate = f' transform="rotate({_n(el["rotate"])} {_n(el["x"])} {_n(el["y"])})"' \
|
||||
if el.get("rotate") else ""
|
||||
return (f'<text x="{_n(el["x"])}" y="{_n(el["y"] + dy)}" font-size="{_n(size)}" '
|
||||
f'font-family="{family}"{weight}{style} fill="{el["color"]}" '
|
||||
f'text-anchor="{anchor}"{extra}{rotate}>{_esc(el["s"])}</text>')
|
||||
|
||||
|
||||
def _baseline_offset(el: dict[str, Any]) -> float:
|
||||
"""参考点 → SVG 基线（alphabetic）的 y 偏移。"""
|
||||
if el.get("dy") is not None:
|
||||
return float(el["dy"])
|
||||
if el["align"] == "middle":
|
||||
return el["size"] * 0.35
|
||||
if el["align"] == "bottom":
|
||||
return 0.0
|
||||
return el["size"] * 0.82
|
||||
|
||||
|
||||
def _svg_element(el: dict[str, Any]) -> str:
|
||||
kind = el["kind"]
|
||||
if kind == "text":
|
||||
return _text_svg(el)
|
||||
if kind == "rect":
|
||||
return (f'<rect x="{_n(el["x"])}" y="{_n(el["y"])}" width="{_n(el["w"])}" '
|
||||
f'height="{_n(el["h"])}" {_paint(el)}/>')
|
||||
if kind == "circle":
|
||||
return f'<circle cx="{_n(el["cx"])}" cy="{_n(el["cy"])}" r="{_n(el["r"])}" {_paint(el)}/>'
|
||||
if kind == "line":
|
||||
return (f'<line x1="{_n(el["x1"])}" y1="{_n(el["y1"])}" x2="{_n(el["x2"])}" '
|
||||
f'y2="{_n(el["y2"])}" {_paint(el)}/>')
|
||||
if kind in ("polyline", "polygon"):
|
||||
pts = " ".join(f"{_n(px)},{_n(py)}" for px, py in el["points"])
|
||||
return f'<{kind} points="{pts}" {_paint(el)}/>'
|
||||
if kind == "path":
|
||||
return f'<path d="{el["d"]}" {_paint(el)}/>'
|
||||
if kind == "arrow":
|
||||
return _arrow_svg(el)
|
||||
raise ValueError(f"未知图元：{kind}")
|
||||
|
||||
|
||||
def _arrow_svg(el: dict[str, Any]) -> str:
|
||||
x1, y1, x2, y2 = el["x1"], el["y1"], el["x2"], el["y2"]
|
||||
color = el.get("stroke") or PALETTE["ink"]
|
||||
sw = el.get("sw", 1.4)
|
||||
head = el.get("head", 8.0)
|
||||
ang = math.atan2(y2 - y1, x2 - x1)
|
||||
bx, by = x2 - head * math.cos(ang), y2 - head * math.sin(ang)
|
||||
left = (bx - head * 0.5 * math.sin(ang), by + head * 0.5 * math.cos(ang))
|
||||
right = (bx + head * 0.5 * math.sin(ang), by - head * 0.5 * math.cos(ang))
|
||||
dash = f' stroke-dasharray="{el["dash"]}"' if el.get("dash") else ""
|
||||
return "\n".join([
|
||||
f'<line x1="{_n(x1)}" y1="{_n(y1)}" x2="{_n(bx)}" y2="{_n(by)}" '
|
||||
f'stroke="{color}" stroke-width="{_n(sw)}"{dash}/>',
|
||||
f'<polygon points="{_n(x2)},{_n(y2)} {_n(left[0])},{_n(left[1])} '
|
||||
f'{_n(right[0])},{_n(right[1])}" fill="{color}"/>',
|
||||
])
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# PNG：rsvg-convert 出几何层 + Pillow 出文字层（论文同款字体）
|
||||
# ---------------------------------------------------------------------------
|
||||
def _rasterize(svg_path: Path, png_path: Path, *, scale: float,
|
||||
rasterizer: str) -> None:
|
||||
exe = shutil.which(rasterizer)
|
||||
if not exe:
|
||||
raise RuntimeError(f"未找到 {rasterizer}：PNG 输出需要它（brew install librsvg）")
|
||||
cmd = [exe, "-f", "png", "-o", str(png_path), "--zoom", str(scale), str(svg_path)]
|
||||
proc = subprocess.run(cmd, capture_output=True, text=True, check=False)
|
||||
if proc.returncode != 0:
|
||||
raise RuntimeError(f"{rasterizer} 失败：{proc.stderr.strip()}")
|
||||
|
||||
|
||||
def _compose_text(fig: Figure, png_path: Path, *, scale: float) -> None:
|
||||
"""把文字层用 Pillow 重画在光栅图上：与基础几何同色同坐标，字体来自
|
||||
``docs/paper/fonts/``（缺失时回退系统字体）。"""
|
||||
from PIL import Image, ImageDraw, ImageFont # 可选依赖：仅生成图时导入
|
||||
|
||||
texts = [el for el in fig.elements if el["kind"] == "text"]
|
||||
if not texts:
|
||||
return
|
||||
img = Image.open(png_path).convert("RGBA")
|
||||
layer = Image.new("RGBA", img.size, (0, 0, 0, 0))
|
||||
draw = ImageDraw.Draw(layer)
|
||||
cache: dict[tuple[str, float], Any] = {}
|
||||
for el in texts:
|
||||
key = (el["font"], round(el["size"] * scale, 2))
|
||||
if key not in cache:
|
||||
cache[key] = _load_font(el["font"], el["size"] * scale, ImageFont)
|
||||
font = cache[key]
|
||||
x, y = el["x"] * scale, (el["y"] + _baseline_offset(el)) * scale
|
||||
anchor = {"start": "ls", "middle": "ms", "end": "rs"}[el["anchor"]]
|
||||
draw.text((x, y), el["s"], font=font, fill=_rgba(el["color"], el.get("opacity", 1.0)),
|
||||
anchor=anchor)
|
||||
Image.alpha_composite(img, layer).convert("RGB").save(png_path, optimize=True)
|
||||
|
||||
|
||||
def _load_font(name: str, size: float, image_font: Any) -> Any:
|
||||
path = FONT_DIR / FONTS[name]
|
||||
if path.is_file():
|
||||
return image_font.truetype(str(path), max(6, int(round(size))))
|
||||
fallback = {
|
||||
"song": "/System/Library/Fonts/Supplemental/Songti.ttc",
|
||||
"song_bold": "/System/Library/Fonts/Supplemental/Songti.ttc",
|
||||
"hei": "/System/Library/Fonts/Supplemental/Songti.ttc",
|
||||
"hei_bold": "/System/Library/Fonts/Supplemental/Songti.ttc",
|
||||
"times": "/System/Library/Fonts/Supplemental/Times New Roman.ttf",
|
||||
"times_bold": "/System/Library/Fonts/Supplemental/Times New Roman Bold.ttf",
|
||||
}[name]
|
||||
return image_font.truetype(fallback, max(6, int(round(size))))
|
||||
|
||||
|
||||
def _rgba(color: str, opacity: float) -> tuple[int, int, int, int]:
|
||||
value = color.lstrip("#")
|
||||
return (int(value[0:2], 16), int(value[2:4], 16), int(value[4:6], 16),
|
||||
max(0, min(255, int(round(opacity * 255)))))
|
||||
|
||||
|
||||
def _estimate_width(text: str, size: float, font: str) -> float:
|
||||
"""粗略宽度估计（仅用于图例排版留白）：CJK 按 1.0 em，西文按 0.5 em。"""
|
||||
wide = sum(1 for ch in text if ord(ch) > 0x2E80)
|
||||
narrow = len(text) - wide
|
||||
factor = 1.0 if font.startswith(("song", "hei")) else 0.98
|
||||
return (wide * size + narrow * size * 0.52) * factor
|
||||
|
||||
|
||||
def write_manifest(out_dir: str | Path, entries: Sequence[dict[str, Any]],
|
||||
command: str) -> Path:
|
||||
"""落盘 manifest.json：图清单 + 数据来源 + 复现命令。"""
|
||||
out = Path(out_dir)
|
||||
out.mkdir(parents=True, exist_ok=True)
|
||||
doc = {
|
||||
"generator": "scripts/build_docs_figures.py",
|
||||
"command": command,
|
||||
"palette": {k: v for k, v in PALETTE.items() if k != "series"},
|
||||
"fonts": {"dir": "docs/paper/fonts", "map": FONTS},
|
||||
"figures": list(entries),
|
||||
}
|
||||
path = out / "manifest.json"
|
||||
path.write_text(json.dumps(doc, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")
|
||||
return path
|
||||
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user