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Author SHA1 Message Date
Horldsence 7930eb65bf feat: 3 2026-09-11 03:33:36 +08:00
Horldsence fc70d6edfd feat: 3 2026-09-11 03:32:05 +08:00
14 changed files with 46032 additions and 65 deletions
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README.md
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@@ -37,6 +37,8 @@ uv run pytest # 全部测试
uv run mcm26b sim-check # P0 自检：复现附件计时示例 105/111/194/199
uv run mcm26b run --robot-id 2026001 --self-test # 问题3自检：随机全向场景闭环（不连官方）
uv run mcm26b run --robot-id 2026001 --self-test --problem 4 # 问题4自检：混合定向源场景
# 自检默认：源数按题面 10~16 随机；实机预算 30 分钟（--real-budget-s 可改，仅自检生效）；
# 找全后持续复扫未清除频道直到限时（--config 关闭 persistent_search 可回到"找全即退"）
uv run mcm26b run --robot-id <队号> --log output/run/session.jsonl # 对接模拟器正式/演练
uv run mcm26b q1 examples/q1/normal.json # 问题一求解（等价 python -m mcm26b.geometry ...）
uv run python main.py examples/q1/triangle.json --rounding-margin-deg 0.005
docs/paper/main.pdf
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docs/协议规则表与状态机.md
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@@ -141,9 +141,22 @@ remaining_real_duration_s 为准，不得假定恒为 1200）；测试窗口 25
- **清除证书**：候选集外包多边形半径（含近似误差与工程余量）
≤ 20m 才发起 `/clear`；证书有效但清除失败视为异常，先补测再重试，
不在同一证书上重复消耗 3 秒/次的失败清除；
- **停止条件**（全部显式实现）：清除数达 16（源数上界证明）、
每频道均"已清除或覆盖证明不存在"、现实预算耗尽。发现若干源、
- **停止条件与完成判定**（2026-09-11 用户决策后）：找全证明达成
（每频道均"已清除或覆盖证明不存在"，或清除数达 16 数量上界）后
**不提前退出**：按 `search.persistent_search`（默认开）只复扫未清除
频道，直到清除数达 16、实机预算耗尽或虚拟安全余量
（`virtual_reserve_s`，防触及附件2 §4.5 的 100 小时上限；
本地模拟器对达限后的动作以 accepted=false 兜底）不足——"扫到限时为止"。
summary 中 `completed` 表示任务证明完成（无中断且结束时每频道
CLEARED/ABSENT，或 16 清除）；`accounted_virtual_time_s` 记录首次
证明找全的虚拟时刻（计算"平均定位清除时间"建议用它，而非复扫
耗尽后的最终时刻）；`stop_reason` 记录循环结束原因。发现若干源、
连续 no_signal、候选采样为空都不是停止条件；
- **每步计时留痕**（同上决策）：客户端每个被接受动作写一条 `step`
事件（实机墙钟耗时 `real_elapsed_s`/`real_since_enter_s` + 比赛虚拟
耗时分项 `virtual_cost`：移动=直线距离/5、切频道 1s、检测 5s、
清除成功 5s/未发现 3s），summary 以 `steps` 数组与 `step_totals`
汇总，并给出题面口径的 `avg_locate_clear_time_s`；
- **验收**：`tests/test_loop_q3.py` 在自建模拟器上覆盖 10/16/0 源、
近边界源、预算耗尽与故障注入；`uv run mcm26b run --robot-id <队号>
--self-test` 可离线复跑随机场景，正式模式去掉 `--self-test` 即对接
output/run/selftest_q3_default.jsonl
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{"event": "step", "path": "/measure", "request_id": "92e98c35fbb0-0239", "real_elapsed_s": 0.000172, "virtual_time_s": 10806.429585351907, "virtual_cost": {"move_s": 0.0, "switch_s": 1.0, "action_s": 5.0, "total_s": 6.0}, "real_since_enter_s": 1.720688, "position": {"x": 700.0, "y": -700.0}, "channel": 18, "result": "no_signal", "logged_at_unix_s": 1789068492.303914}
{"event": "attempt", "path": "/measure", "request_id": "92e98c35fbb0-0240", "attempt": 1, "request": {"arena_id": "default", "robot_id": "2026001", "request_id": "92e98c35fbb0-0240", "position": {"x": 700.0, "y": 0.0}, "channel": 2}, "logged_at_unix_s": 1789068492.30409}
{"event": "accepted", "path": "/measure", "request_id": "92e98c35fbb0-0240", "request": {"arena_id": "default", "robot_id": "2026001", "request_id": "92e98c35fbb0-0240", "position": {"x": 700.0, "y": 0.0}, "channel": 2}, "response": {"accepted": true, "real_timestamp_ms": 1760000009640.0, "virtual_time_s": 10952.429585351907, "measure_result": "no_signal"}, "attempts": 1, "elapsed_s": 0.00011783300215029158, "logged_at_unix_s": 1789068492.3042078}
{"event": "step", "path": "/measure", "request_id": "92e98c35fbb0-0240", "real_elapsed_s": 0.000256, "virtual_time_s": 10952.429585351907, "virtual_cost": {"move_s": 140.0, "switch_s": 1.0, "action_s": 5.0, "total_s": 146.0}, "real_since_enter_s": 1.721114, "position": {"x": 700.0, "y": 0.0}, "channel": 2, "result": "no_signal", "logged_at_unix_s": 1789068492.304341}
{"event": "attempt", "path": "/measure", "request_id": "92e98c35fbb0-0241", "attempt": 1, "request": {"arena_id": "default", "robot_id": "2026001", "request_id": "92e98c35fbb0-0241", "position": {"x": 700.0, "y": 0.0}, "channel": 3}, "logged_at_unix_s": 1789068492.304533}
{"event": "accepted", "path": "/measure", "request_id": "92e98c35fbb0-0241", "request": {"arena_id": "default", "robot_id": "2026001", "request_id": "92e98c35fbb0-0241", "position": {"x": 700.0, "y": 0.0}, "channel": 3}, "response": {"accepted": true, "real_timestamp_ms": 1760000009680.0, "virtual_time_s": 10958.429585351907, "measure_result": "direction", "svd_deg": 83.13}, "attempts": 1, "elapsed_s": 0.00022708399774273857, "logged_at_unix_s": 1789068492.3047612}
{"event": "step", "path": "/measure", "request_id": "92e98c35fbb0-0241", "real_elapsed_s": 0.000361, "virtual_time_s": 10958.429585351907, "virtual_cost": {"move_s": 0.0, "switch_s": 1.0, "action_s": 5.0, "total_s": 6.0}, "real_since_enter_s": 1.721657, "position": {"x": 700.0, "y": 0.0}, "channel": 3, "result": "direction", "logged_at_unix_s": 1789068492.304885}
{"event": "attempt", "path": "/measure", "request_id": "92e98c35fbb0-0242", "attempt": 1, "request": {"arena_id": "default", "robot_id": "2026001", "request_id": "92e98c35fbb0-0242", "position": {"x": 1031.3543917051538, "y": 809.3802403625782}, "channel": 3}, "logged_at_unix_s": 1789068492.4264152}
{"event": "accepted", "path": "/measure", "request_id": "92e98c35fbb0-0242", "request": {"arena_id": "default", "robot_id": "2026001", "request_id": "92e98c35fbb0-0242", "position": {"x": 1031.3543917051538, "y": 809.3802403625782}, "channel": 3}, "response": {"accepted": true, "real_timestamp_ms": 1760000009720.0, "virtual_time_s": 11138.34580602817, "measure_result": "direction", "svd_deg": 106.37}, "attempts": 1, "elapsed_s": 0.00032262500099022873, "logged_at_unix_s": 1789068492.426736}
{"event": "step", "path": "/measure", "request_id": "92e98c35fbb0-0242", "real_elapsed_s": 0.001596, "virtual_time_s": 11138.34580602817, "virtual_cost": {"move_s": 174.916221, "switch_s": 0.0, "action_s": 5.0, "total_s": 179.916221}, "real_since_enter_s": 1.844747, "position": {"x": 1031.3543917051538, "y": 809.3802403625782}, "channel": 3, "result": "direction", "logged_at_unix_s": 1789068492.42798}
{"event": "attempt", "path": "/measure", "request_id": "92e98c35fbb0-0243", "attempt": 1, "request": {"arena_id": "default", "robot_id": "2026001", "request_id": "92e98c35fbb0-0243", "position": {"x": 1217.775209092359, "y": 1475.7930275010847}, "channel": 3}, "logged_at_unix_s": 1789068492.4490309}
{"event": "accepted", "path": "/measure", "request_id": "92e98c35fbb0-0243", "request": {"arena_id": "default", "robot_id": "2026001", "request_id": "92e98c35fbb0-0243", "position": {"x": 1217.775209092359, "y": 1475.7930275010847}, "channel": 3}, "response": {"accepted": true, "real_timestamp_ms": 1760000009760.0, "virtual_time_s": 11281.745043600784, "measure_result": "direction", "svd_deg": 198.85}, "attempts": 1, "elapsed_s": 0.0003187500005878974, "logged_at_unix_s": 1789068492.4493482}
{"event": "step", "path": "/measure", "request_id": "92e98c35fbb0-0243", "real_elapsed_s": 0.000692, "virtual_time_s": 11281.745043600784, "virtual_cost": {"move_s": 138.399238, "switch_s": 0.0, "action_s": 5.0, "total_s": 143.399238}, "real_since_enter_s": 1.866462, "position": {"x": 1217.775209092359, "y": 1475.7930275010847}, "channel": 3, "result": "direction", "logged_at_unix_s": 1789068492.4496908}
{"event": "attempt", "path": "/clear", "request_id": "92e98c35fbb0-0244", "attempt": 1, "request": {"arena_id": "default", "robot_id": "2026001", "request_id": "92e98c35fbb0-0244", "position": {"x": 870.5964611171546, "y": 1357.0746074506617}, "channel": 3}, "logged_at_unix_s": 1789068492.450069}
{"event": "accepted", "path": "/clear", "request_id": "92e98c35fbb0-0244", "request": {"arena_id": "default", "robot_id": "2026001", "request_id": "92e98c35fbb0-0244", "position": {"x": 870.5964611171546, "y": 1357.0746074506617}, "channel": 3}, "response": {"accepted": true, "real_timestamp_ms": 1760000009800.0, "virtual_time_s": 11360.1281877975, "clear_result": "success"}, "attempts": 1, "elapsed_s": 7.812499825377017e-05, "logged_at_unix_s": 1789068492.4501472}
{"event": "step", "path": "/clear", "request_id": "92e98c35fbb0-0244", "real_elapsed_s": 0.000227, "virtual_time_s": 11360.1281877975, "virtual_cost": {"move_s": 73.383144, "switch_s": 0.0, "action_s": 5.0, "total_s": 78.383144}, "real_since_enter_s": 1.867062, "position": {"x": 870.5964611171546, "y": 1357.0746074506617}, "channel": 3, "result": "success", "logged_at_unix_s": 1789068492.45029}
{"event": "attempt", "path": "/exit", "request_id": "92e98c35fbb0-0245", "attempt": 1, "request": {"arena_id": "default", "robot_id": "2026001", "request_id": "92e98c35fbb0-0245"}, "logged_at_unix_s": 1789068492.450457}
{"event": "accepted", "path": "/exit", "request_id": "92e98c35fbb0-0245", "request": {"arena_id": "default", "robot_id": "2026001", "request_id": "92e98c35fbb0-0245"}, "response": {"accepted": true, "real_timestamp_ms": 1760000009840.0, "virtual_time_s": 11360.1281877975, "exit_reason": "user_exit"}, "attempts": 1, "elapsed_s": 0.0001830000001064036, "logged_at_unix_s": 1789068492.45064}
{"event": "step", "path": "/exit", "request_id": "92e98c35fbb0-0245", "real_elapsed_s": 0.000254, "virtual_time_s": 11360.1281877975, "virtual_cost": {"move_s": 0.0, "switch_s": 0.0, "action_s": 0.0, "total_s": 0.0}, "real_since_enter_s": 1.867479, "logged_at_unix_s": 1789068492.450707}
output/run/selftest_q3_seed7.jsonl
+44808
View File
File diff suppressed because it is too large Load Diff
src/mcm26b/cli.py
+32 -4
View File
@@ -46,7 +46,13 @@ def _sim_check(args) -> int:
def _run(args) -> int:
"""问题3/4闭环。--self-test 时真值只存在于测试端入口，策略端仅见响应。"""
"""问题3/4闭环。--self-test 时真值只存在于测试端入口，策略端仅见响应。
实机预算：--self-test 时本地模拟器按 --real-budget-s（默认
session.self_test_real_budget_s = 1800s，即 30 分钟实机）返回
remaining_real_duration_s；正式对接以服务器 /enter 返回为准（题面
程序运行上限 20 分钟，不可延长）。
"""
config = ProblemConfig()
session = replace(config.session,
base_url=args.base_url or config.session.base_url)
@@ -56,23 +62,39 @@ def _run(args) -> int:
bearing_rounding_margin_deg=args.bearing_margin_deg))
truth = None
transport = None
real_budget_s = None
if args.self_test:
real_budget_s = (args.real_budget_s if args.real_budget_s is not None
else config.session.self_test_real_budget_s)
config = replace(config,
field=replace(config.field,
max_real_duration_s=real_budget_s))
if args.problem == 4:
sources = mixed_scene(seed=args.seed, count=args.sources,
directional=args.directional)
directional=args.directional, config=config)
else:
sources = random_omni_scene(seed=args.seed, count=args.sources)
sources = random_omni_scene(seed=args.seed, count=args.sources,
config=config)
truth = sorted(source.channel for source in sources)
transport = LocalSimTransport(LocalSimulator(sources, robot_id=args.robot_id,
config=config))
elif args.real_budget_s is not None:
print("--real-budget-s 仅在 --self-test 下生效；正式测试以服务器 "
"/enter 返回的 remaining_real_duration_s 为准。", file=sys.stderr)
log = SessionLog(path=Path(args.log)) if args.log else SessionLog()
client = SimulatorClient(args.robot_id, config=config, transport=transport, log=log)
loop_cls = DirectionalExtension if args.problem == 4 else OmnidirectionalLoop
summary = loop_cls(client, config).run()
summary["robot_id"] = args.robot_id
if real_budget_s is not None:
summary["self_test_real_budget_s"] = real_budget_s
if args.summary_steps is not None and len(summary.get("steps", [])) > args.summary_steps:
summary["steps"] = summary["steps"][:args.summary_steps]
summary["steps_truncated"] = True
if truth is not None:
# 仅测试端核对：清除集合是否与真值一致。
summary["truth"] = {"source_channels": truth,
"source_count": len(truth),
"all_cleared": sorted(summary["cleared_channels"]) == truth}
print(json.dumps(summary, ensure_ascii=False, indent=2))
return 0 if summary["completed"] and (truth is None or summary["truth"]["all_cleared"]) else 1
@@ -105,10 +127,16 @@ def main(argv=None) -> int:
help="问题3=全向源，问题4=混合定向源（默认 3）")
run.add_argument("--bearing-margin-deg", type=float, default=0.005,
help="示向度舍入裕量（服务器保留两位小数；默认 0.005°）")
run.add_argument("--real-budget-s", type=float, default=None,
help="自检实机预算秒数（默认 1800=30 分钟；仅 --self-test 生效，"
"正式测试以服务器 /enter 返回为准）")
run.add_argument("--summary-steps", type=int, default=None,
help="summary 中保留的逐步计时条数上限（默认全部；完整明细在日志）")
run.add_argument("--self-test", action="store_true",
help="走本地模拟器与随机场景（测试端自检，不连官方）")
run.add_argument("--seed", type=int, default=20260910, help="自检场景种子")
run.add_argument("--sources", type=int, default=10, help="自检场景源数量")
run.add_argument("--sources", type=int, default=None,
help="自检源数量（默认按题面在 10~16 中随机）")
run.add_argument("--directional", type=int, default=3,
help="问题4自检场景中定向源数量（--problem 4 生效）")
run.set_defaults(func=_run)
src/mcm26b/config.py
+10
View File
@@ -63,6 +63,10 @@ class SessionConfig:
retry_backoff_s: float = 0.25 # [工程] 重试退避基数，按 2^n 放大
reserve_exit_s: float = 60.0 # [工程] 预留退出与故障恢复的现实秒数
real_rtt_p95_s: float = 1.0 # [工程] 现实单请求高分位估计，预算核算用
# [工程] 自检/演练实机预算：题面正式测试程序运行上限 1200s 不可延长；
# 自检经 --real-budget-s 覆盖 LocalSimulator 返回的 remaining_real_duration_s，
# 默认 1800s（30 分钟实机），用于验证"限时内持续运行直到找全"的行为。
self_test_real_budget_s: float = 1800.0
@dataclass(frozen=True)
@@ -87,6 +91,12 @@ 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）
# ---- 持续搜索 [工程，2026-09-11 用户决策] ----
# 全部频道"已清除或证明不存在"后不提前退出：只复扫未清除频道，
# 直到清除数达 16、实机预算耗尽或虚拟安全余量不足（扫到限时为止）。
persistent_search: bool = True
virtual_reserve_s: float = 10_000.0 # 虚拟时长安全余量：剩余不足时停止发起动作，
# 避免触及 100 小时上限被模拟器强制结束
@dataclass(frozen=True)
src/mcm26b/planner/__init__.py
+195 -21
View File
@@ -9,11 +9,13 @@ P3 第二检测点（验收：保证收到信号；Q 空时退化；near 分支
最优解；选定后按 1/4 间距加密复核并如实上报数值；
- 返回 near 时：候选源在 5m 内，直接对当前频道尝试 /clear。
P4 全向闭环（验收：10/16 源正确退出；边界朝外不漏；停止条件正确）：
发现（coverage.CoverageLedger）→ 定位（belief 收敛）→ 清除 → 停止判定
（清除数达 16，或每频道均有清除成功/不存在证明）→ /exit。
发现 10 源、连续无信号、候选采样为空都不是停止条件；
现实预算耗尽退出记为未证明完成（completed=false），不与成功混记。
P4 全向闭环（验收：10/16/0 源场景、边界源、预算耗尽与故障注入）：
发现（coverage.CoverageLedger）→ 定位（belief 收敛）→ 清除 → 持续复扫
→ /exit。全部频道"已清除或证明不存在"后按 persistent_search（默认开
[用户决策 2026-09-11]）只复扫未清除频道，扫到限时为止（清除数达 16 /
实机预算耗尽 / 虚拟安全余量不足）。completed 表示任务证明完成（结束
时每频道均 CLEARED/ABSENT 且无中断）；accounted_virtual_time_s 记录
首次证明找全的虚拟时刻，与"复扫到限时才停"相互独立。
断连/协议错误/守恒性破坏中断时同样尽力 /exit 并产出未完成 summary
（stop_reason 记中断类别，附逐频道状态快照），不静默丢失会话。
@@ -192,12 +194,16 @@ def select_second_point(client: SimulatorClient, first_position: Vec2,
class OmnidirectionalLoop:
"""问题3全向"发现—定位—清除—停止"闭环（P4 基线）。
"""问题3全向"发现—定位—清除—持续复扫"闭环（P4 基线 + 持续搜索）。
串行驱动 SimulatorClient：覆盖阶段在 49 点网格上对每个 UNKNOWN 频道
检测；发现 direction 即进入该频道的定位子循环（选点→测量→收敛→
证书清除），near 直接清除。停止条件只有三种：清除数达 16（数量上界
证明）、每频道均 CLEARED/ABSENT、现实预算耗尽（记为未完成）。
证书清除），near 直接清除。全部频道"已清除或证明不存在"后按
``search.persistent_search``（默认开 [用户决策 2026-09-11]）只复扫
未清除频道，直到清除数达 16、实机预算耗尽或虚拟安全余量不足。
stop_reason 记录循环结束原因；``completed`` 表示任务证明完成
（结束时每频道均 CLEARED/ABSENT 且无中断），与"复扫到限时才停"
相互独立：accounted_virtual_time_s 记录首次证明找全的虚拟时刻。
"""
def __init__(self, client: SimulatorClient,
@@ -209,6 +215,8 @@ class OmnidirectionalLoop:
self.cleared: list[int] = []
self.anomalies: list[dict] = []
self.stop_reason: str | None = None
# 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
# 反复消耗 3 秒/次的失败清除。
self._may_clear: set[int] = set()
@@ -223,7 +231,17 @@ class OmnidirectionalLoop:
self._coverage_phase()
if self.stop_reason is None:
self._mark_absent_after_coverage()
self.stop_reason = self._resolve_detected()
self._resolve_detected()
self._record_accounted()
if self.config.search.persistent_search:
self._persistent_phase()
if self.stop_reason is None:
if len(self.cleared) >= self.config.field.source_count_max:
self.stop_reason = "sixteen_cleared"
elif self._all_accounted():
self.stop_reason = "all_resolved"
else:
self.stop_reason = "unresolved_channels"
except DeadlineExceeded:
self.stop_reason = "real_budget_exhausted"
except (TransportError, ProtocolError, InconsistentBelief) as exc:
@@ -248,6 +266,29 @@ class OmnidirectionalLoop:
summary["exit_error"] = _error_brief(exit_error)
return summary
# ------------------------------------------------------------ 预算与证明
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
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
< self.config.field.max_virtual_duration_s):
return False
self.stop_reason = "virtual_time_cap"
return True
def _all_accounted(self) -> bool:
return all(belief.status in (ChannelStatus.CLEARED, ChannelStatus.ABSENT)
for belief in self.belief.channels.values())
def _record_accounted(self) -> None:
"""首次"每频道均已清除或证明不存在"的时刻留档（找全证明）。"""
if self.accounted_virtual_time_s is None and self._all_accounted():
self.accounted_virtual_time_s = self.client.state.virtual_time_s
# ------------------------------------------------------------ 覆盖阶段
def _pending_channels_at(self, index: int) -> list[int]:
return [channel for channel, belief in self.belief.channels.items()
@@ -275,6 +316,8 @@ class OmnidirectionalLoop:
return
point = self.ledger.points[index]
for channel in self._pending_channels_at(index):
if self._virtual_cap_reached():
return
outcome = self.client.measure(point, channel)
self.ledger.mark_measured(channel, index)
self.belief.update_measure(channel, point, outcome.result,
@@ -289,9 +332,76 @@ class OmnidirectionalLoop:
else: # 理论上不发生（near 候选半径 5m）；保守记录
self._anomaly("near_without_certificate", channel,
position=point)
if self._count_limit_reached():
if self.stop_reason is not None or self._count_limit_reached():
return
# ------------------------------------------------------------ 持续复扫
def _persistent_phase(self) -> None:
"""全部频道已清除/证明不存在后的持续复扫 [用户决策 2026-09-11]。
只扫未清除频道（DETECTED 继续定位，ABSENT 全网格复测，防模拟器
或覆盖证明假设偏差），直到清除数达 16 或虚拟安全余量不足；
实机预算由客户端 DeadlineExceeded 兜底（扫到限时为止）。
"""
while self.stop_reason is None:
if self._count_limit_reached():
return
if self._virtual_cap_reached():
return
self._persistent_round()
def _persistent_round(self) -> None:
"""一轮全网格复扫：网格点最近邻序，逐点测量全部未清除频道。"""
measured: set[tuple[int, int]] = set()
while self.stop_reason is None:
index = self._next_resweep_point(measured)
if index is None:
return
point = self.ledger.points[index]
for channel in self._resweep_channels_at(index, measured):
if self._virtual_cap_reached():
return
measured.add((channel, index))
was_absent = (self.belief.channels[channel].status
is ChannelStatus.ABSENT)
outcome = self.client.measure(point, channel)
self.belief.update_measure(channel, point, outcome.result,
outcome.svd_deg)
if was_absent and outcome.result in (DIRECTION, NEAR):
# 复测推翻了覆盖缺席证明：守恒性留痕，按新证据定位清除。
self._anomaly("absent_detected_on_resweep", channel,
position=point, result=outcome.result)
if outcome.result == DIRECTION:
self._localize_and_clear(channel)
elif outcome.result == NEAR:
region = self.belief.candidate_set(channel)
cert = clear_certificate(region, channel, self.config)
if cert.certified:
self._attempt_clear(channel, cert.center, cert)
else:
self._anomaly("near_without_certificate", channel,
position=point)
if self.stop_reason is not None:
return
def _next_resweep_point(self, measured: set[tuple[int, int]]) -> int | None:
"""尚有未清除频道待复测的最近网格点（贪心最近邻 [工程]）。"""
position = self.client.state.position
best_index, best_distance = None, 0.0
for index, point in enumerate(self.ledger.points):
if not self._resweep_channels_at(index, measured):
continue
distance = math.dist(position, point)
if best_index is None or distance < best_distance:
best_index, best_distance = index, distance
return best_index
def _resweep_channels_at(self, index: int,
measured: set[tuple[int, int]]) -> list[int]:
return [channel for channel, belief in self.belief.channels.items()
if belief.status is not ChannelStatus.CLEARED
and (channel, index) not in measured]
def _count_limit_reached(self) -> bool:
if len(self.cleared) >= self.config.field.source_count_max:
self.stop_reason = "sixteen_cleared"
@@ -308,6 +418,8 @@ class OmnidirectionalLoop:
search = self.config.search
self._may_clear.add(channel)
for _ in range(search.max_localize_steps):
if self._virtual_cap_reached():
return False
region = self.belief.candidate_set(channel)
if region is None:
return False
@@ -355,33 +467,51 @@ class OmnidirectionalLoop:
and self.ledger.coverage_complete(channel):
self.belief.mark_absent(channel)
def _resolve_detected(self) -> str:
"""覆盖完成后，对仍未清除的 DETECTED 频道做第二轮定位。"""
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。
不再在此决定停止：持续搜索模式下由 _persistent_phase 接力，
非持续模式由 run() 依据 _all_accounted 判定 all_resolved。
"""
for channel in self.belief.channels_by_status(ChannelStatus.DETECTED):
if self._count_limit_reached() or self._virtual_cap_reached():
return
self._localize_and_clear(channel)
unresolved = [channel for channel, belief in self.belief.channels.items()
if belief.status not in (ChannelStatus.CLEARED,
ChannelStatus.ABSENT)]
return "all_resolved" if not unresolved else "unresolved_channels"
if self.stop_reason is not None:
return
def _summary(self, info) -> dict:
"""结果汇总。info 为 None 表示 /exit 未能完成（断连等），按未完成上报。"""
"""结果汇总。info 为 None 表示 /exit 未能完成（断连等），按未完成上报。
completed = 无中断且结束时每频道均 CLEARED/ABSENT（任务证明完成）；
stop_reason 另行记录循环结束原因（持续复扫模式下通常是
virtual_time_cap / real_budget_exhausted / sixteen_cleared）。
"""
events = self.client.log.action_events()
stats = summarize(events)
report = recompute_virtual_time(events)
steps = self._step_report()
cleared = self.belief.channels_by_status(ChannelStatus.CLEARED)
absent = self.belief.channels_by_status(ChannelStatus.ABSENT)
resolved = set(cleared) | set(absent)
unresolved = [channel for channel in sorted(self.belief.channels)
if channel not in resolved]
completed = (self.stop_reason == "sixteen_cleared"
or (self.stop_reason == "all_resolved" and not unresolved))
virtual_time_s = round((info.virtual_time_s if info is not None
else self.client.state.virtual_time_s), 3)
# 清除数达 16 即数量上界证明（其余频道无论状态如何都不可能有源）；
# 否则要求每频道均 CLEARED/ABSENT。
completed = (self._abort_detail is None
and (len(self.cleared) >= self.config.field.source_count_max
or not unresolved))
summary = {
"completed": completed,
"stop_reason": self.stop_reason,
"exit_reason": "exit_unreachable" if info is None else info.reason,
"virtual_time_s": round((info.virtual_time_s if info is not None
else self.client.state.virtual_time_s), 3),
"virtual_time_s": virtual_time_s,
"accounted_virtual_time_s": (round(self.accounted_virtual_time_s, 3)
if self.accounted_virtual_time_s is not None
else None),
"persistent_search": self.config.search.persistent_search,
"cleared_channels": cleared,
"absent_channels": absent,
"unresolved_channels": unresolved,
@@ -390,6 +520,11 @@ class OmnidirectionalLoop:
"clear_success": stats["cleared"],
"total_requests": stats["measures"] + stats["clears"],
"move_distance_m": round(stats["distance_m"], 1),
# 题面口径：定位清除总时间（全程虚拟时长）/ 被清除干扰源个数。
"avg_locate_clear_time_s": (round(virtual_time_s / len(cleared), 3)
if cleared else None),
"step_totals": steps["totals"],
"steps": steps["rows"],
"anomalies": self.anomalies,
"virtual_recompute_mismatches": len(report["mismatches"]),
"channel_status": self._channel_status_snapshot(),
@@ -398,6 +533,43 @@ class OmnidirectionalLoop:
summary["abort_detail"] = _error_brief(self._abort_detail)
return summary
def _step_report(self) -> dict:
"""每步计时汇总 [用户决策 2026-09-11]：实机墙钟 + 比赛虚拟耗时分项。"""
totals = {"count": 0, "real_elapsed_total_s": 0.0,
"real_since_enter_total_s": 0.0,
"virtual_move_s": 0.0, "virtual_switch_s": 0.0,
"virtual_measure_action_s": 0.0, "virtual_clear_action_s": 0.0,
"virtual_total_s": 0.0}
rows = []
for index, step in enumerate(self.client.log.step_events()):
cost = step["virtual_cost"]
totals["count"] += 1
totals["real_elapsed_total_s"] += step["real_elapsed_s"]
totals["real_since_enter_total_s"] = step.get(
"real_since_enter_s", totals["real_since_enter_total_s"])
totals["virtual_move_s"] += cost["move_s"]
totals["virtual_switch_s"] += cost["switch_s"]
if step["path"] == "/measure":
totals["virtual_measure_action_s"] += cost["action_s"]
elif step["path"] == "/clear":
totals["virtual_clear_action_s"] += cost["action_s"]
totals["virtual_total_s"] += cost["total_s"]
rows.append({"index": index, "action": step["path"].lstrip("/"),
"result": step.get("result"),
"channel": step.get("channel"),
"position": step.get("position"),
"real_elapsed_ms": round(step["real_elapsed_s"] * 1000.0, 3),
"real_since_enter_s": step.get("real_since_enter_s"),
"move_s": round(cost["move_s"], 3),
"switch_s": round(cost["switch_s"], 3),
"action_s": round(cost["action_s"], 3),
"step_virtual_s": round(cost["total_s"], 3),
"virtual_time_s": step["virtual_time_s"]})
for key, value in totals.items():
if isinstance(value, float):
totals[key] = round(value, 3)
return {"totals": totals, "rows": rows}
def _channel_status_snapshot(self) -> dict:
"""逐频道状态快照：中断收尾时的未完成状态留档（每频道一条）。"""
snapshot = {}
@@ -441,6 +613,8 @@ class DirectionalExtension(OmnidirectionalLoop):
self._may_clear.add(channel)
steps = 0
while steps < search.max_localize_steps:
if self._virtual_cap_reached():
return False
region = self.belief.candidate_set(channel)
if region is None:
return False
src/mcm26b/protocol/client.py
+46 -2
View File
@@ -129,10 +129,13 @@ class SimulatorClient:
self._seq = 0
self._deadline: float | None = None # 本地单调时钟截止
self._server_deadline_ms: float | None = None # 服务器时间戳交叉核对
self._enter_monotonic: float | None = None # 实机计时起点（/enter 成功）
# ------------------------------------------------------------ 公共动作 API
def enter(self) -> dict:
response = self._call("/enter", self._payload("/enter"))
started = self.clock()
payload = self._payload("/enter")
response = self._call("/enter", payload)
self.state.apply_enter(response)
remaining = response["remaining_real_duration_s"]
if not isinstance(remaining, (int, float)) or remaining <= 0:
@@ -140,9 +143,13 @@ class SimulatorClient:
self._deadline = self.clock() + remaining
self._server_deadline_ms = (response["real_timestamp_ms"]
+ remaining * 1000.0)
self._enter_monotonic = self.clock()
self._log_step("/enter", payload, response, started,
predicted=CostBreakdown(0.0, 0.0, 0.0))
return response
def measure(self, position: Vec2, channel: int) -> MeasureOutcome:
started = self.clock()
payload = self._payload("/measure", position=position, channel=channel)
response = self._call("/measure", payload)
predicted = self.state.apply_measure(payload, response,
@@ -154,10 +161,13 @@ class SimulatorClient:
if result == DIRECTION:
if not isinstance(svd, (int, float)) or isinstance(svd, bool):
raise ProtocolViolation("direction 响应缺少数值 svd_deg", body=response)
self._log_step("/measure", payload, response, started, result=result,
predicted=predicted, channel=payload["channel"])
return MeasureOutcome(result, float(svd) if svd is not None else None,
response["virtual_time_s"], predicted, response)
def clear(self, position: Vec2, channel: int) -> ClearOutcome:
started = self.clock()
payload = self._payload("/clear", position=position, channel=channel)
response = self._call("/clear", payload)
predicted = self.state.apply_clear(payload, response,
@@ -165,14 +175,20 @@ class SimulatorClient:
result = response.get("clear_result")
if result not in CLEAR_RESULTS:
raise ProtocolViolation(f"clear_result 非法: {result!r}", body=response)
self._log_step("/clear", payload, response, started, result=result,
predicted=predicted, channel=payload["channel"])
return ClearOutcome(result, response["virtual_time_s"], predicted, response)
def exit(self) -> ExitInfo:
response = self._call("/exit", self._payload("/exit"))
started = self.clock()
payload = self._payload("/exit")
response = self._call("/exit", payload)
self.state.apply_exit(response)
reason = response.get("exit_reason")
if reason != EXIT_USER:
raise ProtocolViolation(f"exit_reason 非法: {reason!r}", body=response)
self._log_step("/exit", payload, response, started,
predicted=CostBreakdown(0.0, 0.0, 0.0))
return ExitInfo(reason, response["virtual_time_s"], response)
# ---------------------------------------------------------------- 预算控制
@@ -181,6 +197,34 @@ class SimulatorClient:
"""按本地单调时钟估计的剩余现实秒数（未 enter 时为 None）。"""
return None if self._deadline is None else self._deadline - self.clock()
def _log_step(self, path: str, payload: dict, response: dict,
started: float, *, result: str | None = None,
predicted: CostBreakdown, channel: int | None = None) -> None:
"""每步计时留档 [用户决策 2026-09-11]：实机墙钟耗时 + 比赛虚拟耗时分项
（移动=直线距离/5、切频道 1s、检测 5s、清除成功 5s/未发现 3s）。
step 事件不含 response 字段，不会混入 action_events / 虚拟时刻复算。
"""
now = self.clock()
entry: dict = {"event": "step", "path": path,
"request_id": payload["request_id"],
"real_elapsed_s": round(now - started, 6),
"virtual_time_s": response["virtual_time_s"],
"virtual_cost": {"move_s": round(predicted.move_s, 6),
"switch_s": round(predicted.switch_s, 6),
"action_s": round(predicted.action_s, 6),
"total_s": round(predicted.total_s, 6)}}
if self._enter_monotonic is not None:
entry["real_since_enter_s"] = round(now - self._enter_monotonic, 6)
if "position" in payload:
entry["position"] = {"x": payload["position"]["x"],
"y": payload["position"]["y"]}
if channel is not None:
entry["channel"] = channel
if result is not None:
entry["result"] = result
self.log.append(entry)
def _check_budget(self, path: str) -> None:
if path == "/exit" or self._deadline is None:
return
src/mcm26b/protocol/log.py
+4
View File
@@ -32,3 +32,7 @@ class SessionLog:
"""仅最终被接受的动作事件（每个 request_id 一条，含重试次数）。"""
return [event for event in self.events if event.get("response") is not None
and event["response"].get("accepted") is True]
def step_events(self) -> list[dict]:
"""每步计时事件（客户端 _log_step 写入，按发生顺序）。"""
return [event for event in self.events if event.get("event") == "step"]
src/mcm26b/sim/scenes.py
+26 -21
View File
@@ -1,8 +1,9 @@
"""自检场景生成器（测试端专用）。
真值只进入测试与 CLI ``--self-test`` 入口，策略端仅见模拟器响应；
场景覆盖题面强调的边界情形：近圆域边界源、最小/最大接收半径两档，
以及问题4的定向源（含朝向背离圆心的朝外边界源）。
默认完全按题面生成：源总数 10~16 随机、位置在目标圆域内均匀分布、
频道互异（1..20 抽样）、有效接收半径 [1000,1500] 均匀。
边界情形（近圆域边界源、接收半径两档、定向朝外源）由专门测试显式构造。
"""
from __future__ import annotations
@@ -10,25 +11,29 @@ from __future__ import annotations
import math
from random import Random
from ..config import ProblemConfig
from .simulator import Source
def random_omni_scene(seed: int = 20260910, count: int = 10) -> list[Source]:
"""全向源场景：频道互异，含近圆域边界点，接收半径 1000/1500 交替。"""
if not 0 <= count <= 20:
raise ValueError("源数量须在 0..20")
def random_omni_scene(seed: int = 20260910, count: int | None = None,
config: ProblemConfig = ProblemConfig()) -> list[Source]:
"""全向源场景 [题面]：count=None 时源总数在 10..16 均匀随机。
位置在半径 omega_radius_m 圆域内按面积均匀（r = R*sqrt(u)），
有效接收半径在 [r_eff_min, r_eff_max] 均匀，频道从 1..20 无重复抽样。
"""
field = config.field
rng = Random(seed)
channels = rng.sample(range(1, 21), count)
if count is None:
count = rng.randint(field.source_count_min, field.source_count_max)
if not 0 <= count <= field.channel_max:
raise ValueError(f"源数量须在 0..{field.channel_max}")
channels = rng.sample(range(field.channel_min, field.channel_max + 1), count)
sources = []
for index, channel in enumerate(channels):
if index == 0:
radius = rng.uniform(1780.0, 1799.0) # 近圆域边界
elif index == 1 and count > 1:
radius = rng.uniform(1780.0, 1799.0)
else:
radius = rng.uniform(0.0, 1795.0)
for channel in channels:
radius = field.omega_radius_m * math.sqrt(rng.random())
angle = rng.uniform(0.0, 2 * math.pi)
r_eff = 1000.0 if index % 2 == 0 else 1500.0
r_eff = rng.uniform(field.r_eff_min_m, field.r_eff_max_m)
sources.append(Source(channel=channel,
x=radius * math.cos(angle),
y=radius * math.sin(angle),
@@ -36,21 +41,21 @@ def random_omni_scene(seed: int = 20260910, count: int = 10) -> list[Source]:
return sources
def mixed_scene(seed: int = 20260910, count: int = 10,
directional: int = 3) -> list[Source]:
def mixed_scene(seed: int = 20260910, count: int | None = None,
directional: int = 3,
config: ProblemConfig = ProblemConfig()) -> list[Source]:
"""问题4混合场景：count 个源中 directional 个为定向源。
改造 random_omni_scene 的前 directional 个源：近圆域边界的前两个定向源
朝向取"背离圆心"（发射半平面基本朝向域外，覆盖网格仍保证可发现，
即"朝外边界源不漏"验收情形），其余定向源朝向随机。
"""
if not 0 <= directional <= count:
raise ValueError("定向源数量须在 0..count")
sources = list(random_omni_scene(seed=seed, count=count))
sources = list(random_omni_scene(seed=seed, count=count, config=config))
directional = min(directional, len(sources))
rng = Random(seed + 1)
for index in range(directional):
source = sources[index]
if index < 2 and count > 1 and math.hypot(source.x, source.y) > 1700.0:
if index < 2 and len(sources) > 1 and math.hypot(source.x, source.y) > 1700.0:
heading = math.degrees(math.atan2(source.y, source.x)) % 360.0
else:
heading = rng.uniform(0.0, 360.0)
src/mcm26b/sim/simulator.py
+6
View File
@@ -127,6 +127,12 @@ class LocalSimulator:
})
if not self.entered or self.exited:
return 200, self._not_accepted()
if path in ("/measure", "/clear") \
and self.virtual_time_s >= self.field.max_virtual_duration_s:
# 虚拟世界限时 360000s 防死循环 [题面/附件2 §4.5：达限结束测试]。
# 官方在达限时关闭接口；本地以 accepted=false 模拟，作为计划层
# 虚拟安全余量（search.virtual_reserve_s）失效时的兜底。
return 200, self._not_accepted()
if path == "/exit":
self.exited = True
return 200, self._ok({"exit_reason": EXIT_USER})
tests/test_loop_q3.py
+138 -10
View File
@@ -1,11 +1,16 @@
"""P4 全向闭环端到端验收：10/16/0 源、边界源、预算耗尽与故障注入。
对应 docs/B题建模分析与实施计划.md P4 行验收门槛（自建部分）：
- 10/16 源场景正确退出，全清或数量上界证明；
- 10/16 源场景全部清除；16 源触发数量上界停止；
- 近圆域边界源不漏搜、可清除；
- 持续复扫 [用户决策 2026-09-11]：找全后只复扫未清除频道直到虚拟上限；
- 每步计时（实机 + 比赛虚拟耗时分项）逐步留档可汇总；
- 虚拟时刻逐步复算与服务器响应一致；
- 现实预算耗尽退出记为未完成，不与成功混记；
- 现实预算耗尽（找全前）退出记为未完成，不与成功混记；
- 网络故障重试（幂等回放）不污染闭环结果。
SMALL_CAP 把虚拟限时压到 60k 秒：持续复扫在数轮内触及安全余量，
避免测试扫满 360k 秒上限（等价行为，仅缩短复扫轮数）。
"""
import unittest
@@ -24,16 +29,18 @@ from mcm26b.sim.scenes import random_omni_scene
CONFIG = replace(ProblemConfig(),
search=replace(ProblemConfig().search,
bearing_rounding_margin_deg=0.005))
SMALL_CAP = replace(CONFIG,
field=replace(CONFIG.field, max_virtual_duration_s=60_000.0))
def _run_scene(sources, fault_script=None, clock=None):
simulator = LocalSimulator(sources, robot_id="T001", config=CONFIG)
def _run_scene(sources, fault_script=None, clock=None, config=SMALL_CAP):
simulator = LocalSimulator(sources, robot_id="T001", config=config)
transport = LocalSimTransport(simulator)
if fault_script is not None:
transport = ScriptedFaultTransport(transport, fault_script)
kwargs = {"clock": clock} if clock is not None else {}
client = SimulatorClient("T001", config=CONFIG, transport=transport, **kwargs)
loop = OmnidirectionalLoop(client, CONFIG)
client = SimulatorClient("T001", config=config, transport=transport, **kwargs)
loop = OmnidirectionalLoop(client, config)
summary = loop.run()
report = recompute_virtual_time(client.log.action_events())
return summary, report, simulator
@@ -45,10 +52,16 @@ class LoopAcceptanceTests(unittest.TestCase):
summary, report, simulator = _run_scene(sources)
truth = sorted(s.channel for s in sources)
self.assertTrue(summary["completed"])
self.assertEqual(summary["stop_reason"], "all_resolved")
self.assertEqual(summary["stop_reason"], "virtual_time_cap")
self.assertEqual(sorted(summary["cleared_channels"]), truth)
self.assertEqual(len(summary["absent_channels"]), 10)
self.assertEqual(summary["unresolved_channels"], [])
self.assertIsNotNone(summary["accounted_virtual_time_s"],
"找全证明时刻必须留档")
self.assertLessEqual(summary["accounted_virtual_time_s"],
summary["virtual_time_s"])
self.assertGreater(summary["measures"], 49 * 20,
"找全后应继续复扫未清除频道（扫到限时为止）")
self.assertEqual(summary["anomalies"], [], "守恒性或证书不应被打破")
self.assertEqual(report["mismatches"], [])
self.assertEqual(summary["virtual_recompute_mismatches"], 0)
@@ -70,10 +83,12 @@ class LoopAcceptanceTests(unittest.TestCase):
def test_empty_field_proves_absence_everywhere(self):
summary, report, _ = _run_scene([])
self.assertTrue(summary["completed"])
self.assertEqual(summary["stop_reason"], "all_resolved")
self.assertEqual(summary["stop_reason"], "virtual_time_cap")
self.assertEqual(summary["cleared_channels"], [])
self.assertEqual(summary["absent_channels"], list(range(1, 21)))
self.assertEqual(summary["measures"], 49 * 20, "缺席证明要求全网格逐频道检测")
self.assertGreaterEqual(summary["measures"], 49 * 20,
"缺席证明要求全网格逐频道检测")
self.assertIsNotNone(summary["accounted_virtual_time_s"])
self.assertEqual(report["mismatches"], [])
def test_boundary_source_close_to_domain_edge(self):
@@ -91,7 +106,7 @@ class LoopAcceptanceTests(unittest.TestCase):
self.assertEqual(sorted(summary["cleared_channels"]), [20])
def test_real_budget_exhaustion_exits_cleanly(self):
"""现实预算耗尽：立即退出并记为未证明完成。"""
"""现实预算在找全前耗尽：立即退出并记为未证明完成。"""
ticks = count(0.0, 200.0) # 每次查询时钟前进 200s，迅速耗尽 1200s 预算
sources = random_omni_scene(seed=8, count=4)
summary, _, _ = _run_scene(sources, clock=lambda: next(ticks))
@@ -112,6 +127,99 @@ class LoopAcceptanceTests(unittest.TestCase):
self.assertEqual(sorted(simulator.cleared), truth)
class StepTimingTests(unittest.TestCase):
"""每步计时 [用户决策 2026-09-11]：实机耗时 + 比赛虚拟耗时分项。"""
def test_steps_recorded_with_virtual_breakdown(self):
sources = random_omni_scene(seed=5, count=2)
summary, report, _ = _run_scene(sources)
steps = summary["steps"]
self.assertEqual(steps[0]["action"], "enter")
self.assertEqual(steps[-1]["action"], "exit")
self.assertTrue(all(step["real_elapsed_ms"] >= 0 for step in steps))
first_measure = next(step for step in steps if step["action"] == "measure")
self.assertEqual(first_measure["step_virtual_s"],
first_measure["move_s"] + first_measure["switch_s"]
+ first_measure["action_s"])
totals = summary["step_totals"]
self.assertEqual(totals["count"], len(steps))
# 虚拟分项之和 = 虚拟总时长（比赛口径：移动+切频道+检测+清除）
self.assertAlmostEqual(
totals["virtual_move_s"] + totals["virtual_switch_s"]
+ totals["virtual_measure_action_s"] + totals["virtual_clear_action_s"],
totals["virtual_total_s"], delta=0.5)
self.assertAlmostEqual(totals["virtual_total_s"],
summary["virtual_time_s"], delta=0.5)
self.assertGreaterEqual(totals["real_elapsed_total_s"], 0.0)
# 清除成功一步 5s、未发现 3s：步级记录可复核
clear_step = next(step for step in steps if step["action"] == "clear")
self.assertIn(clear_step["action_s"], (3.0, 5.0))
self.assertEqual(clear_step["switch_s"], 0.0, "/clear 不切频道")
def test_avg_locate_clear_time_metric(self):
"""题面口径：平均定位清除时间 = 虚拟总时长 / 被清除个数。"""
sources = random_omni_scene(seed=5, count=2)
summary, _, _ = _run_scene(sources)
cleared = len(summary["cleared_channels"])
self.assertEqual(summary["avg_locate_clear_time_s"],
round(summary["virtual_time_s"] / cleared, 3))
class PersistentSearchTests(unittest.TestCase):
def test_persistence_disabled_stops_when_resolved(self):
"""persistent_search=False：回到"证明找全即退出"的基线行为。"""
config = replace(CONFIG, search=replace(
CONFIG.search, persistent_search=False, bearing_rounding_margin_deg=0.005))
summary, _, _ = _run_scene(random_omni_scene(seed=5, count=2),
config=config)
self.assertTrue(summary["completed"])
self.assertEqual(summary["stop_reason"], "all_resolved")
self.assertLessEqual(summary["measures"], 49 * 20 + 60)
def test_persistence_resweeps_only_uncleared_channels(self):
"""复扫只测未清除频道：已清除频道不再消耗检测。"""
sources = random_omni_scene(seed=5, count=2)
summary, _, _ = _run_scene(sources)
cleared = set(summary["cleared_channels"])
resweep_channels = {step["channel"] for step in summary["steps"]
if step["index"] > 0
and step["virtual_time_s"]
> summary["accounted_virtual_time_s"]
and step["action"] == "measure"}
self.assertTrue(resweep_channels)
self.assertFalse(resweep_channels & cleared,
"已清除频道不得在找全后被复扫")
def test_virtual_budget_stops_before_simulator_cap(self):
"""计划层在虚拟安全余量处停下，不触发模拟器 accepted=false 兜底。"""
sources = random_omni_scene(seed=5, count=2)
summary, report, _ = _run_scene(sources)
self.assertEqual(summary["stop_reason"], "virtual_time_cap")
field = SMALL_CAP.field
self.assertLessEqual(summary["virtual_time_s"],
field.max_virtual_duration_s)
self.assertGreaterEqual(summary["virtual_time_s"],
field.max_virtual_duration_s
- SMALL_CAP.search.virtual_reserve_s - 5_000)
self.assertEqual(summary["anomalies"], [])
self.assertEqual(report["mismatches"], [])
class SimulatorVirtualCapTests(unittest.TestCase):
def test_simulator_rejects_actions_beyond_virtual_cap(self):
"""附件2 §4.5 兜底：虚拟时长达限后本地模拟器不再执行检测。"""
simulator = LocalSimulator([], robot_id="T001", config=CONFIG)
simulator.entered = True
simulator.virtual_time_s = CONFIG.field.max_virtual_duration_s
status, body = simulator.handle(
"/measure", {"arena_id": "default", "robot_id": "T001",
"request_id": "x1",
"position": {"x": 0.0, "y": 0.0}, "channel": 1})
self.assertEqual(status, 200)
self.assertFalse(body["accepted"])
self.assertEqual(body["virtual_time_s"], 0)
class AbortFinalizationTests(unittest.TestCase):
"""断连超过重试上限：仍须 /exit、产出未完成 summary，不得静默崩溃。"""
@@ -161,6 +269,26 @@ class CoverageLedgerIntegrationTests(unittest.TestCase):
self.assertLessEqual(worst, 700.0 * 2 ** 0.5 + 1e-9)
class SceneGenerationTests(unittest.TestCase):
"""场景生成 [题面]：默认 10~16 源、Ω 内均匀、频道互异、r_eff∈[1000,1500]。"""
def test_default_count_within_problem_range(self):
for seed in range(20):
sources = random_omni_scene(seed=seed)
self.assertTrue(10 <= len(sources) <= 16, f"seed={seed}")
def test_scene_respects_problem_constraints(self):
for seed in range(10):
sources = random_omni_scene(seed=seed)
channels = [s.channel for s in sources]
self.assertEqual(len(channels), len(set(channels)))
self.assertTrue(all(1 <= c <= 20 for c in channels))
for s in sources:
self.assertLessEqual((s.x ** 2 + s.y ** 2) ** 0.5, 1800.0)
self.assertGreaterEqual(s.r_eff_m, 1000.0)
self.assertLessEqual(s.r_eff_m, 1500.0)
def math_dist(a, b):
return ((a[0] - b[0]) ** 2 + (a[1] - b[1]) ** 2) ** 0.5
tests/test_loop_q4.py
+15 -5
View File
@@ -26,6 +26,9 @@ from mcm26b.sim.scenes import mixed_scene
CONFIG = replace(ProblemConfig(),
search=replace(ProblemConfig().search,
bearing_rounding_margin_deg=0.005))
# 持续复扫扫到虚拟上限为止 [用户决策]；SMALL_CAP 压到 60k 秒缩短复扫轮数。
SMALL_CAP = replace(CONFIG,
field=replace(CONFIG.field, max_virtual_duration_s=60_000.0))
def _run_scene(sources, config=CONFIG, clock=None):
@@ -124,9 +127,10 @@ class MixedLoopAcceptanceTests(unittest.TestCase):
summary, report, simulator, _, loop = _run_scene(sources)
truth = sorted(s.channel for s in sources)
self.assertTrue(summary["completed"])
self.assertEqual(summary["stop_reason"], "all_resolved")
self.assertEqual(summary["stop_reason"], "virtual_time_cap")
self.assertEqual(sorted(summary["cleared_channels"]), truth)
self.assertEqual(summary["unresolved_channels"], [])
self.assertIsNotNone(summary["accounted_virtual_time_s"])
self.assertEqual(summary["anomalies"], [], "守恒性或证书不应被打破")
self.assertEqual(report["mismatches"], [])
self.assertEqual(summary["virtual_recompute_mismatches"], 0)
@@ -152,7 +156,7 @@ class MixedLoopAcceptanceTests(unittest.TestCase):
"""朝向背离圆心、r_eff=1000 的近边界源：覆盖保证仍发现并清除。"""
source = Source(channel=7, x=1790.0, y=0.0, r_eff_m=1000.0,
directional=True, heading_deg=0.0)
summary, _, simulator, _, _ = _run_scene([source])
summary, _, simulator, _, _ = _run_scene([source], config=SMALL_CAP)
self.assertTrue(summary["completed"])
self.assertEqual(summary["cleared_channels"], [7])
self.assertEqual(sorted(simulator.cleared), [7])
@@ -162,7 +166,8 @@ class MixedLoopAcceptanceTests(unittest.TestCase):
"""固定批量种子回归：任何种子下不得全清失败或破坏守恒性。"""
for seed in range(5, 10):
sources = mixed_scene(seed=seed, count=10, directional=4)
summary, report, simulator, _, _ = _run_scene(sources)
summary, report, simulator, _, _ = _run_scene(sources,
config=SMALL_CAP)
truth = sorted(s.channel for s in sources)
self.assertTrue(summary["completed"], f"seed={seed}")
self.assertEqual(sorted(summary["cleared_channels"]), truth,
@@ -190,7 +195,7 @@ class JointConstraintBaselineTests(unittest.TestCase):
"""端到端保留性：定向源被遮挡若干次后仍被清除，不因 no_signal 误删。"""
source = Source(channel=3, x=600.0, y=800.0, r_eff_m=1500.0,
directional=True, heading_deg=200.0)
summary, _, simulator, _, _ = _run_scene([source])
summary, _, simulator, _, _ = _run_scene([source], config=SMALL_CAP)
self.assertTrue(summary["completed"])
self.assertEqual(summary["cleared_channels"], [3])
self.assertEqual(sorted(simulator.cleared), [3])
@@ -198,11 +203,16 @@ class JointConstraintBaselineTests(unittest.TestCase):
class FallbackDisciplineTests(unittest.TestCase):
def _stall_config(self, **search_updates):
"""迫使定位立即停滞进入后备清除的最小配置。"""
"""迫使定位立即停滞进入后备清除的最小配置。
persistent_search=False：本组验证的是后备清除纪律（格数/预算门槛），
与持续复扫无关，维持"证明找全即退出"的基线收尾便于断言。
"""
return replace(CONFIG,
search=replace(CONFIG.search,
max_localize_steps=1,
max_blocked_probes=0,
persistent_search=False,
**search_updates))
def test_cell_cap_skips_instead_of_blind_scan(self):