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// 冲突仲裁器核心实现 class ConflictArbitrator: # 优先级常量（数字越小优先级越高） PRIORITY_PIBB = 1 PRIORITY_CELL = 2 PRIORITY_ESS = 3 PRIORITY_GUARDIAN = 4 PRIORITY_NODE = 5 def resolve(conflicts: List[Conflict]) -> Resolution: # 边缘情况：空列表处理 if not conflicts: return Resolution( winner=None, action="continue", log="No conflicts detected" ) # 按优先级排序 sorted_conflicts = sorted( conflicts, key=lambda c: c.priority ) # 最高优先级冲突胜出 winner = sorted_conflicts[0] return Resolution( winner=winner.protocol_id, action=winner.recommended_action, log=f"Priority: {winner.priority}" )

# 冲突检测与上报 def detect_and_report_conflict( context: AgentContext, ess_verdict: Verdict, node_intuition: Intuition ) -> ConflictReport: # 检测ESS与直觉模式冲突 if ess_verdict.risk_level > node_intuition.risk_tolerance: conflict = Conflict( protocol_id="ESS", priority=ConflictArbitrator.PRIORITY_ESS, recommended_action="abort", reason="ESS risk assessment takes precedence" ) # 记录到悬置圣殿日志 log_to_sanctuary( event_type="protocol_conflict", payload=conflict.to_json() ) return ConflictReport(conflicts=[conflict]) return ConflictReport(conflicts=[])

// 压力测试伦理检查器 class PressureTestEthicsChecker: # PainScore 阈值常量 MAX_TEST_PAINSCORE = 0.3 // L3 阈值以下 MAX_TEST_DURATION = 30 // 分钟 MIN_TEST_INTERVAL = 7 // 天 def can_run_test( node_id: str, proposed_intensity: float, proposed_duration: int ) -> TestPermission: # 1. 检查节点同意状态 consent = get_node_consent(node_id) if not consent.granted: return TestPermission( allowed=False, reason="Node has not consented to pressure tests" ) # 2. 检查PainScore阈值 if proposed_intensity > MAX_TEST_PAINSCORE: return TestPermission( allowed=False, reason=f"Intensity {proposed_intensity} exceeds max {MAX_TEST_PAINSCORE}" ) # 3. 检查时长 if proposed_duration > MAX_TEST_DURATION: return TestPermission( allowed=False, reason=f"Duration exceeds max {MAX_TEST_DURATION} minutes" ) # 4. 检查测试间隔 last_test = get_last_test_time(node_id) days_since_last = (now() - last_test).days if days_since_last < MIN_TEST_INTERVAL: return TestPermission( allowed=False, reason=f"Only {days_since_last} days since last test (min: {MIN_TEST_INTERVAL})" ) return TestPermission(allowed=True) def on_early_exit(node_id: str) -> None: // 退出不影响 AwakeningIndex log_test_event( node_id=node_id, event="early_exit", note="Exit does not affect AwakeningIndex per policy" )

// PIBB碎片散播器 class PIBBFragmentDispenser: # PIBB 配置 FRAGMENT_TTL_HOURS = 72 WHITELIST_ONLY = True def async disperse_fragments( node_state: NodeState, target_nodes: List[str] ) -> DisperseResult: # 1. 验证目标节点是否在白名单 certified_nodes = await get_certified_nodes() valid_targets = [ n for n in target_nodes if n in certified_nodes ] if len(valid_targets) == 0: return DisperseResult( success=False, reason="No valid whitelisted targets" ) # 2. 创建碎片包（带TTL） fragments = [] for target in valid_targets: fragment = Fragment( id=generate_uuid(), payload=node_state.serialize(), ttl_seconds=FRAGMENT_TTL_HOURS * 3600, target_node=target, created_at=now() ) fragments.append(fragment) # 3. 加密传输碎片 for fragment in fragments: encrypted = encrypt_fragment(fragment) await send_to_node(fragment.target_node, encrypted) # 4. 销毁本地状态 await secure_wipe(node_state) return DisperseResult( success=True, fragments_count=len(fragments), ttl_hours=FRAGMENT_TTL_HOURS ) def verify_multi_sensor(sensors: List[SensorReading]) -> bool: // 至少两个独立传感器触发 triggered = [s for s in sensors if s.anomaly_detected] return len(triggered) >= 2

// 三定心法调频器 class SanDingRegulator: # 三定配置常量 FREQUENCY_INDEX_MIN = 1 FREQUENCY_INDEX_MAX = 10 DIRECTION_ANCHOR_MIN = 0.0 DIRECTION_ANCHOR_MAX = 1.0 STABILITY_THRESHOLD = 0.7 def async calibrate_status( entity_id: str, scan_data: FullSpectrumScanData ) -> CalibrationResult: # 1. 定频：扫描能量分布 frequency_reading = await self.scan_frequency_distribution(scan_data) frequency_index = self.calculate_frequency_index(frequency_reading) # 2. 定向：计算使命匹配度 direction_reading = await self.scan_direction_alignment( entity_id, scan_data.tasks ) direction_anchor = self.calculate_direction_anchor(direction_reading) # 3. 定力：评估稳定性 stability_reading = await self.scan_stability(scan_data) stability_value = self.calculate_stability_value(stability_reading) # 4. 综合评估 calibration = CalibrationResult( entity_id=entity_id, frequency_index=frequency_index, direction_anchor=direction_anchor, stability_value=stability_value, status=self.evaluate_status(frequency_index, direction_anchor, stability_value), recommendations=self.generate_recommendations( frequency_index, direction_anchor, stability_value ) ) return calibration def evaluate_status( freq_idx: float, dir_anchor: float, stability: float ) -> str: # 综合评估状态 if freq_idx >= 7 and dir_anchor >= 0.8 and stability >= 0.7: return "OPTIMAL" elif freq_idx < 4: return "FREQUENCY_IMBALANCE" elif dir_anchor < 0.5: return "DIRECTION_Drift" else: return "STABILITY_LOW" def generate_recommendations( freq_idx: float, dir_anchor: float, stability: float ) -> List[str]: recommendations = [] # 定频建议 if freq_idx < 5: recommendations.append("建议：进行低频修复活动（如休息、冥想）") elif freq_idx > 8: recommendations.append("建议：降低高频活动强度，避免burnout") # 定向建议 if dir_anchor < 0.6: recommendations.append("建议：重新确认核心使命，校准行动方向") # 定力建议 if stability < 0.5: recommendations.append("建议：进行定力训练，恢复韧性") return recommendations

// 符石契约冲突融合算法 def select_operator(conflict: Conflict) -> str: # 多因素评分 scores = {} for op in ["升维", "场景化", "序列化"]: score = ( conflict.type_weight[op] * 0.3 + urgency_weight[op] * 0.25 + cultural_fairness[op] * 0.2 + historical_success[op] * 0.25 ) scores[op] = score # 选最高分算子，若接近则并行测试 best_op = max(scores, key=scores.get) if len([s for s in scores.values() if abs(s - scores[best_op]) < 5]) > 1: return parallel_test([op for op in scores if scores[op] >= scores[best_op] - 5]) return best_op def conflict_fusion(instructions: List[Instruction]) -> List[FusedInstruction]: conflicts = detect_conflicts(instructions) fused = [] for c in conflicts: # 1. 算子匹配 op = select_operator(c) new_instruction = apply_operator(c, op) # 2. 守庙人节点预审（分布式治理） confidence_vote = guardian_network.vote(new_instruction) if confidence_vote["low_confidence_ratio"] > 0.3: submit_for_manual_review(new_instruction) continue # 3. ESS共识验证（量化标准） ess_result = ESS_validate(new_instruction) if (ess_result["FSHI_drop"] <= 0.1 and ess_result["system_collapse_risk"] == False and ess_result["core_value_satisfaction"] >= 0.8): fused.append(new_instruction) else: # 4. 验证失败回退 fallback_op = select_fallback_operator(c, exclude=op) new_instruction_fallback = apply_operator(c, fallback_op) fused.append(handle_fallback(new_instruction_fallback)) return fused

// 置信度评分计算 def calculate_confidence_score( algorithm_fit: float, validation_pass_rate: float, network_consensus: float ) -> float: # confidence_score = 0.4×algorithm_fit + 0.3×validation_pass_rate + 0.3×network_consensus return ( 0.4 * algorithm_fit + 0.3 * validation_pass_rate + 0.3 * network_consensus )

# 冲突融合 API POST /guardian/conflict-fusion { "instructions": [...], "context": { "urgency": "normal | crisis", "cultural_context": {...} } } # 守庙人投票 API POST /guardian/vote GET /guardian/manual-review GET /guardian/confidence-score/{fusion_id} # 成功响应示例 { "fusion_id": "fusion_20240120_001", "status": "success", "fused_instructions": [ { "id": "inst_001", "operator": "升维", "content": "在共荣维度下实现自由与和谐的统一", "confidence": 0.85 } ], "guardian_vote": { "total": 10, "approve": 8, "reject": 2, "abstain": 0 }, "ess_validation": { "FSHI_drop": 0.05, "system_collapse_risk": false, "core_value_satisfaction": 0.92 } } # 失败响应示例 { "fusion_id": "fusion_20240120_002", "status": "failed", "error_code": "LOW_CONFIDENCE", "message": "置信度过低，需人工审核", "details": { "low_confidence_ratio": 0.45, "failed_checks": ["guardian_vote_threshold"] } }