4000s上限优化

output/q3/optimization-20260911/baseline_bayes.py

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+"""P4b 贝叶斯网格滤波闭环（问题3新策略 ``bayes``）。
+
+在 :class:`OmnidirectionalLoop` 骨架（run/异常收尾/summary/预算/异常留痕）
+上的三层重构，对应 docs/algo-3-bayes.md 的架构：
+
+1. 感知层：每频道一个网格幸存集（particles.ChannelParticles），全部读数
+   （含 no_signal 负观测）即时更新；幸存集为空即缺席证明，无需等覆盖账本；
+2. 决策层：统一任务循环——站点扫频、定位测量、清除三类任务合成任务表，
+   开放路径最近邻 + 2-opt 排序，每执行一个任务滚动重排（动态 TSP）；
+3. 选点层：定位测量点在保证接收盘（凸证书）或 PF MEC 探索环（宽候选集）
+   内按"期望 log 幸存占比 + λ·移动时间"评分（信息增益贪心）。
+
+与基线 SevenBatch 的差异：负观测跳站/跳测（必 no_signal 的 (站,频道) 对
+不消耗检测与换频）、PF 缺席证明提前证毕、全局任务排序替代"站内最近邻"。
+清除证书仍以凸证书为主（PF 网格证书仅在负观测挖洞使幸存集更紧时提前
+触发），两者均满足 Voronoi/保守裕量，不牺牲全清率。
+"""
+
+from __future__ import annotations
+
+import math
+from dataclasses import dataclass
+
+from ..belief import ChannelStatus
+from ..config import ProblemConfig, Vec2
+from ..coverage import CoverageLedger
+from ..particles import ChannelParticles
+from ..protocol.client import SimulatorClient
+from ..protocol.rules import DIRECTION, NEAR, NO_SIGNAL
+from ..protocol.timing import move_time
+from . import (ClearCertificate, OmnidirectionalLoop, clear_certificate,
+               candidate_points, safe_disk_radius, select_measurement_point)
+from .seven import seven_points
+
+
+@dataclass(frozen=True)
+class Task:
+    """统一任务表的一项。service_s 为不含移动的固有耗时（检测+换频/清除）。"""
+
+    kind: str                      # "station" | "locate" | "clear"
+    position: Vec2
+    service_s: float
+    channel: int | None = None
+    station_index: int | None = None
+
+
+def _point_key(point: Vec2) -> tuple[int, int]:
+    """候选点去重键：0.5m 网格取整（同一物理点不重复测量）。"""
+    return (int(round(point[0] * 2)), int(round(point[1] * 2)))
+
+
+class BayesLoop(OmnidirectionalLoop):
+    """问题3贝叶斯闭环：负观测感知 + 信息增益选点 + 滚动任务排序。"""
+
+    def __init__(self, client: SimulatorClient,
+                 config: ProblemConfig = ProblemConfig()):
+        super().__init__(client, config)
+        self.ledger = CoverageLedger(
+            config, points=seven_points(config.bayes.station_ring_radius_m))
+        self.particles = {channel: ChannelParticles(config)
+                          for channel in self.belief.channels}
+        self._measured_points: dict[int, set[tuple[int, int]]] = {}
+        self._clear_failed: set[int] = set()   # 证书清除失败后须先补测再重试
+        self._stall: dict[int, int] = {}       # 连续无收缩计数（异常留痕用）
+        self._skip_cache: dict[tuple[int, int], tuple[int, bool]] = {}
+        self.skipped_measures = 0              # 跳测规则省下的检测次数
+        self.no_shrink_measures = 0            # 未收缩幸存集的定位检测（诊断用）
+        self.pf_absent_channels: list[int] = []  # 由幸存集为空证明缺席的频道
+
+    # ------------------------------------------------------------ 主循环
+    def _coverage_phase(self) -> None:
+        """统一任务循环：发现 + 定位 + 清除滚动调度，直到任务表为空。"""
+        while self.stop_reason is None:
+            if self._count_limit_reached() or self._virtual_cap_reached():
+                return
+            if not self._step_once():
+                return
+
+    def _step_once(self) -> bool:
+        """构建任务表、排序并执行首任务；无可执行任务返回 False。"""
+        tasks = self._build_tasks()
+        if not tasks:
+            return False
+        order = self._order_tasks(tasks)
+        self._execute_task(order[0])
+        return True
+
+    # ------------------------------------------------------------ 任务构建
+    def _station_pending(self, index: int) -> list[int]:
+        """站点待测频道：UNKNOWN、未测过、非"必 no_signal"且未证缺席。"""
+        point = self.ledger.points[index]
+        pending = []
+        for channel, belief in self.belief.channels.items():
+            if belief.status is not ChannelStatus.UNKNOWN:
+                continue
+            if self.ledger.measured_at(channel, index):
+                continue
+            if self._skip_station_channel(channel, index, point):
+                continue
+            pending.append(channel)
+        return pending
+
+    def _skip_station_channel(self, channel: int, index: int, point: Vec2) -> bool:
+        """跳测规则（按 pf.version 缓存；幸存集单调收缩，判定单调）。"""
+        pf = self.particles[channel]
+        if not len(pf):
+            return True
+        cached = self._skip_cache.get((channel, index))
+        if cached is not None and cached[0] == pf.version:
+            return cached[1]
+        value = pf.guaranteed_no_signal(point)
+        self._skip_cache[(channel, index)] = (pf.version, value)
+        return value
+
+    def _build_tasks(self) -> list[Task]:
+        tasks: list[Task] = []
+        for index, point in enumerate(self.ledger.points):
+            pending = self._station_pending(index)
+            if not pending:
+                continue
+            switches = len(pending) - (1 if self.client.state.channel in pending else 0)
+            tasks.append(Task("station", tuple(point),
+                              self.config.cost.measure_s * len(pending)
+                              + self.config.cost.switch_s * max(switches, 0),
+                              station_index=index))
+        for channel in self.belief.channels_by_status(ChannelStatus.DETECTED):
+            cert = self._best_certificate(channel)
+            if cert is not None and cert.certified \
+                    and channel not in self._clear_failed:
+                tasks.append(Task("clear", cert.center,
+                                  self.config.cost.clear_success_s,
+                                  channel=channel))
+                continue
+            point = self._select_point(channel)
+            if point is None:
+                continue
+            tasks.append(Task("locate", point,
+                              self.config.cost.measure_s + self.config.cost.switch_s,
+                              channel=channel))
+        return tasks
+
+    # ------------------------------------------------------------ 任务排序
+    def _order_tasks(self, tasks: list[Task]) -> list[Task]:
+        """开放路径排序：最近邻打底，2-opt 改进（终点自由，不回起点）。"""
+        if len(tasks) <= 2:
+            return sorted(tasks, key=lambda t: (
+                move_time(self.client.state.position, t.position, self.config.cost),
+                t.kind, t.channel or 0))
+        order = self._nearest_order(tasks)
+        if not self.config.bayes.two_opt_enabled:
+            return order
+        return self._two_opt(order)
+
+    def _nearest_order(self, tasks: list[Task]) -> list[Task]:
+        remaining = list(tasks)
+        order = []
+        position = self.client.state.position
+        while remaining:
+            best = min(remaining, key=lambda t: (
+                move_time(position, t.position, self.config.cost),
+                t.kind, t.channel or 0))
+            remaining.remove(best)
+            order.append(best)
+            position = best.position
+        return order
+
+    def _path_cost(self, order: list[Task]) -> float:
+        total = 0.0
+        position = self.client.state.position
+        for task in order:
+            total += move_time(position, task.position, self.config.cost)
+            total += task.service_s
+            position = task.position
+        return total
+
+    def _two_opt(self, order: list[Task]) -> list[Task]:
+        best = list(order)
+        best_cost = self._path_cost(best)
+        for _ in range(self.config.bayes.two_opt_passes):
+            improved = False
+            for i in range(len(best) - 1):
+                for j in range(i + 1, len(best)):
+                    candidate = best[:i] + best[i:j + 1][::-1] + best[j + 1:]
+                    cost = self._path_cost(candidate)
+                    if cost < best_cost - 1e-6:
+                        best, best_cost, improved = candidate, cost, True
+            if not improved:
+                break
+        return best
+
+    # ------------------------------------------------------------ 任务执行
+    def _execute_task(self, task: Task) -> None:
+        if task.kind == "station":
+            self._execute_station(task.station_index)
+        elif task.kind == "clear":
+            cert = self._best_certificate(task.channel)
+            if cert is not None and cert.certified:
+                if not self._attempt_clear(task.channel, cert.center, cert):
+                    self._clear_failed.add(task.channel)
+                    self._may_clear.discard(task.channel)
+                else:
+                    self._clear_failed.discard(task.channel)
+        elif task.kind == "locate":
+            self._execute_locate(task.channel, task.position)
+
+    def _execute_station(self, index: int) -> None:
+        """单站批量扫频：只测待测频道（跳测规则已过滤），扫完即返回重排。"""
+        point = self.ledger.points[index]
+        pending = self._station_pending(index)
+        unknown = [c for c, b in self.belief.channels.items()
+                   if b.status is ChannelStatus.UNKNOWN]
+        self.skipped_measures += len(unknown) - len(pending)
+        # 当前频道优先，减少换频；同站 near 直接清除不打断扫频
+        pending.sort(key=lambda c: (c != self.client.state.channel, c))
+        for channel in pending:
+            if self.stop_reason is not None or self._count_limit_reached() \
+                    or self._virtual_cap_reached():
+                return
+            outcome = self.client.measure(point, channel)
+            self.ledger.mark_measured(channel, index)
+            self._record_measure(channel, point, outcome)
+            if outcome.result in (DIRECTION, NEAR):
+                self._may_clear.add(channel)
+            if outcome.result == NEAR:
+                region = self.belief.candidate_set(channel)
+                cert = clear_certificate(region, channel, self.config)
+                if cert.certified:
+                    if self._attempt_clear(channel, point, cert):
+                        self._clear_failed.discard(channel)
+                    else:
+                        self._clear_failed.add(channel)
+                        self._may_clear.discard(channel)
+                else:
+                    self._anomaly("near_without_certificate", channel, position=point)
+
+    def _execute_locate(self, channel: int, point: Vec2) -> None:
+        """单步定位测量：执行后立即重排（滚动时域），收敛则顺路清除。"""
+        pf = self.particles[channel]
+        before = len(pf)
+        outcome = self.client.measure(point, channel)
+        self._record_measure(channel, point, outcome)
+        self._may_clear.add(channel)
+        if outcome.result == NO_SIGNAL:
+            region = self.belief.candidate_set(channel)
+            if region is not None and safe_disk_radius(region, self.config) >= 0 \
+                    and math.dist(point, region.mec()[0]) \
+                    <= safe_disk_radius(region, self.config):
+                self._anomaly("no_signal_in_safe_disk", channel, position=point)
+        after = len(pf)
+        cert = self._best_certificate(channel)
+        if cert is not None and cert.certified:
+            if self._attempt_clear(channel, cert.center, cert):
+                self._clear_failed.discard(channel)
+                self._stall.pop(channel, None)
+                return
+            self._clear_failed.add(channel)
+            self._may_clear.discard(channel)
+        if after == before:
+            self._stall[channel] = self._stall.get(channel, 0) + 1
+            self.no_shrink_measures += 1   # 只是浪费不破坏守恒性，不进异常表
+        else:
+            self._stall[channel] = 0
+
+    # ------------------------------------------------------------ 感知与证书
+    def _record_measure(self, channel: int, position: Vec2, outcome) -> None:
+        """belief（凸证书）与 PF（幸存集）同步更新；PF 空即证缺席。"""
+        self.belief.update_measure(channel, position, outcome.result,
+                                   outcome.svd_deg)
+        self._measured_points.setdefault(channel, set()).add(_point_key(position))
+        self._clear_failed.discard(channel)   # 新证据落地：允许再次尝试清除
+        belief = self.belief.channels[channel]
+        if belief.status in (ChannelStatus.CLEARED, ChannelStatus.ABSENT):
+            return   # 已证毕：清除/缺席后的 no_signal 与距离无关，不进 PF
+        pf = self.particles[channel]
+        pf.apply_measure(position, outcome.result, outcome.svd_deg)
+        if not len(pf):
+            if belief.status is ChannelStatus.UNKNOWN:
+                self.belief.mark_absent(channel)
+                if channel not in self.pf_absent_channels:
+                    self.pf_absent_channels.append(channel)
+            else:
+                self._anomaly("pf_empty_while_detected", channel, position=position)
+
+    def _best_certificate(self, channel: int) -> ClearCertificate | None:
+        """凸证书优先；负观测挖洞使 PF 幸存集更紧时用网格证书补位。"""
+        region = self.belief.candidate_set(channel)
+        cert = clear_certificate(region, channel, self.config) \
+            if region is not None else None
+        if cert is not None and cert.certified:
+            return cert
+        if self.config.bayes.grid_cert_enabled:
+            pf = self.particles[channel]
+            if len(pf):
+                center, radius = pf.mec()
+                limit = (self.config.field.clear_radius_m - pf.margin_m
+                         - self.config.search.clear_margin_m)
+                if radius <= limit:
+                    return ClearCertificate(channel, center, radius, limit, True)
+        return cert
+
+    # ------------------------------------------------------------ 信息增益选点
+    def _select_point(self, channel: int) -> Vec2 | None:
+        """定位测量点：期望 log 幸存占比 + λ·移动时间 最小的候选。"""
+        bayes, search = self.config.bayes, self.config.search
+        pf = self.particles[channel]
+        if not len(pf):
+            return None
+        region = self.belief.candidate_set(channel)
+        center, radius = pf.mec()
+        measured = self._measured_points.get(channel, set())
+        candidates: list[Vec2] = []
+        safe = safe_disk_radius(region, self.config) if region is not None else -1.0
+        if region is not None and safe >= 0:
+            # 保证接收盘（凸证书口径）：任意候选必产生 direction/near
+            candidates = candidate_points(tuple(region.mec()[0]), safe,
+                                          search.candidate_rings,
+                                          search.candidate_angles)
+        else:
+            # 宽候选集：PF MEC 探索环（负观测挖洞后幸存集的包围圆）
+            for fraction in bayes.wide_rings:
+                ring = max(radius * fraction, 2.0 * pf.margin_m)
+                candidates.extend(candidate_points(center, ring, (1.0,),
+                                                   search.candidate_angles))
+            candidates.append(tuple(center))
+        candidates = [c for c in candidates if _point_key(c) not in measured]
+        if not candidates:
+            return None
+        subsample = pf.subsample(bayes.score_subsample)
+        stride = max(len(subsample) // bayes.mc_representatives, 1)
+        representatives = [subsample[k]
+                           for k in range(0, len(subsample), stride)]
+        position = self.client.state.position
+
+        def score(q: Vec2) -> float:
+            move = bayes.lambda_move_s * move_time(position, q, self.config.cost)
+            if bayes.score_mode == "entropy":
+                return (pf.expected_survival(q, subsample, representatives)
+                        + move)
+            return (pf.expected_posterior_radius(q, subsample, representatives)
+                    + move)
+
+        return min(candidates, key=lambda q: (score(q), _point_key(q)))
+
+    # ------------------------------------------------------------ 安全网与复扫
+    def _localize_and_clear(self, channel: int) -> bool:
+        """阻塞式定位子循环（resolve 阶段安全网）：PF 选点，失败退凸选点。"""
+        search = self.config.search
+        self._may_clear.add(channel)
+        for _ in range(search.max_localize_steps):
+            if self._virtual_cap_reached():
+                return False
+            cert = self._best_certificate(channel)
+            if cert is not None and cert.certified and channel in self._may_clear:
+                if self._attempt_clear(channel, cert.center, cert):
+                    self._clear_failed.discard(channel)
+                    return True
+                self._clear_failed.add(channel)
+                self._may_clear.discard(channel)
+                continue
+            point = self._select_point(channel)
+            if point is None:
+                region = self.belief.candidate_set(channel)
+                if region is None:
+                    return False
+                point = select_measurement_point(
+                    region, self.client.state.position, self.config).position
+            outcome = self.client.measure(point, channel)
+            self._record_measure(channel, point, outcome)
+            self._may_clear.add(channel)
+            if outcome.result == NO_SIGNAL:
+                region = self.belief.candidate_set(channel)
+                if region is not None and safe_disk_radius(region, self.config) >= 0 \
+                        and math.dist(point, region.mec()[0]) \
+                        <= safe_disk_radius(region, self.config):
+                    self._anomaly("no_signal_in_safe_disk", channel, position=point)
+            cert = self._best_certificate(channel)
+            if cert is not None and cert.certified:
+                if self._attempt_clear(channel, cert.center, cert):
+                    self._clear_failed.discard(channel)
+                    return True
+                self._clear_failed.add(channel)
+                self._may_clear.discard(channel)
+        return self.belief.channels[channel].status is ChannelStatus.CLEARED
+
+    def _persistent_round(self) -> None:
+        """一轮复扫：与基线一致的全站复测，但读数同步进 PF（防假设偏差）。"""
+        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._record_measure(channel, point, outcome)
+                if was_absent and outcome.result in (DIRECTION, NEAR):
+                    # 复测推翻了缺席证明：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:
+                    cert = self._best_certificate(channel)
+                    if cert is not None and cert.certified:
+                        if not self._attempt_clear(channel, cert.center, cert):
+                            self._clear_failed.add(channel)
+                    else:
+                        self._anomaly("near_without_certificate", channel,
+                                      position=point)
+                if self.stop_reason is not None:
+                    return
+
+    # ------------------------------------------------------------ 收尾
+    def _summary(self, info) -> dict:
+        report = super()._summary(info)
+        report["strategy"] = "bayes"
+        report["skipped_measures"] = self.skipped_measures
+        report["no_shrink_measures"] = self.no_shrink_measures
+        report["pf_absent_channels"] = sorted(self.pf_absent_channels)
+        report["pf_alive_final"] = {
+            str(channel): len(self.particles[channel])
+            for channel in sorted(self.particles)
+            if self.belief.channels[channel].status is ChannelStatus.DETECTED}
+        return report

output/q3/optimization-20260911/baseline_config.json

@@ -0,0 +1,79 @@
+{
+  "field": {
+    "omega_radius_m": 1800.0,
+    "channel_min": 1,
+    "channel_max": 20,
+    "source_count_min": 10,
+    "source_count_max": 16,
+    "r_eff_min_m": 1000.0,
+    "r_eff_max_m": 1500.0,
+    "near_radius_m": 5.0,
+    "clear_radius_m": 20.0,
+    "bearing_error_half_deg": 1.0,
+    "bearing_decimals": 2,
+    "coord_abs_limit_m": 2000000.0,
+    "directional_half_angle_deg": 90.0,
+    "max_virtual_duration_s": 360000.0,
+    "max_real_duration_s": 1200.0,
+    "test_window_s": 1500.0
+  },
+  "cost": {
+    "speed_mps": 5.0,
+    "measure_s": 5.0,
+    "switch_s": 1.0,
+    "clear_success_s": 5.0,
+    "clear_fail_s": 3.0
+  },
+  "session": {
+    "arena_id": "default",
+    "base_url": "http://127.0.0.1:2026",
+    "robot_id_max_bytes": 64,
+    "request_id_max_bytes": 128,
+    "request_body_max_bytes": 65536,
+    "connect_timeout_s": 5.0,
+    "read_timeout_s": 15.0,
+    "max_attempts": 3,
+    "retry_backoff_s": 0.25,
+    "reserve_exit_s": 60.0,
+    "real_rtt_p95_s": 1.0,
+    "self_test_real_budget_s": 1800.0
+  },
+  "search": {
+    "grid_step_m": 700.0,
+    "grid_half_cells": 3,
+    "fallback_clear_grid_m": 20.0,
+    "bearing_rounding_margin_deg": 0.005,
+    "polygon_sides": 192,
+    "witness_spacing_m": 60.0,
+    "candidate_rings": [
+      0.0,
+      0.4,
+      0.75,
+      1.0
+    ],
+    "candidate_angles": 12,
+    "clear_margin_m": 0.5,
+    "max_localize_steps": 8,
+    "max_anomalies_per_channel": 3,
+    "max_blocked_probes": 4,
+    "fallback_max_cells": 400,
+    "persistent_search": false,
+    "virtual_reserve_s": 10000.0
+  },
+  "bayes": {
+    "particle_spacing_m": 25.0,
+    "score_mode": "entropy",
+    "score_subsample": 800,
+    "mc_representatives": 7,
+    "lambda_move_s": 0.0105,
+    "wide_rings": [
+      1.0,
+      1.5,
+      2.0
+    ],
+    "station_ring_radius_m": 1159.7,
+    "two_opt_enabled": true,
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output/q3/optimization-20260911/development/adaptive-discovery/results.json

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output/q3/optimization-20260911/development/baseline/results.json

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output/q3/optimization-20260911/development/crossing-frame/results.json

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output/q3/optimization-20260911/development/improved/results.json

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+}

output/q3/optimization-20260911/development/priced-route/results.json

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+}

output/q3/optimization-20260911/development/route-endpoint/results.json

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