""" 决策授权体系 — L0-L4 五级风险授权，数字分身根据风险等级自动判定执行权限 """ from __future__ import annotations import logging from typing import Any, Dict, List, Optional from app.services.fingerprint_engine import fingerprint_engine from app.services.shadow_executor import shadow_executor from app.services.behavior_collector import behavior_collector logger = logging.getLogger(__name__) AUTH_LEVELS = { "L0": { "name": "全自动执行", "description": "低风险操作，数字分身直接执行", "risk_range": (0, 0.2), "requires_approval": False, "notify": False, "examples": ["格式化代码", "拼写检查修正", "简单文案调整"], }, "L1": { "name": "自动执行+通知", "description": "低风险，自动执行后通知用户", "risk_range": (0.2, 0.4), "requires_approval": False, "notify": True, "examples": ["合并小PR", "回复常规邮件", "更新文档"], }, "L2": { "name": "建议+确认", "description": "中风险，数字分身生成建议，需用户一次确认", "risk_range": (0.4, 0.6), "requires_approval": True, "notify": True, "examples": ["修改API接口", "调整配置参数", "代码重构"], }, "L3": { "name": "详细审批", "description": "高风险，需要详细说明和多重审批", "risk_range": (0.6, 0.8), "requires_approval": True, "notify": True, "examples": ["修改数据库Schema", "涉及资金的变更", "权限调整"], }, "L4": { "name": "禁止自动", "description": "极高风险，始终需要人工操作", "risk_range": (0.8, 1.0), "requires_approval": True, "notify": True, "examples": ["删除生产数据", "修改权限体系", "涉及合规操作"], }, } RISK_FACTORS = { "data_mutation": {"weight": 0.25, "keywords": ["DELETE", "DROP", "UPDATE", "INSERT", "ALTER", "TRUNCATE", "删除", "修改", "写入", "变更"]}, "permission_change": {"weight": 0.25, "keywords": ["权限", "role", "permission", "auth", "授权", "访问控制"]}, "financial": {"weight": 0.30, "keywords": ["支付", "金额", "资金", "退款", "结算", "payment", "refund", "price", "cost", "费用"]}, "production_impact": {"weight": 0.20, "keywords": ["生产", "production", "线上", "prod", "正式环境", "发布", "deploy"]}, "user_data": {"weight": 0.15, "keywords": ["用户数据", "user.*data", "PII", "手机号", "身份证", "隐私"]}, "irreversible": {"weight": 0.20, "keywords": ["不可逆", "irreversible", "清空", "重置", "reset", "hard delete"]}, } class DecisionAuthorizer: """决策授权引擎 — L0-L4 风险定级""" def evaluate(self, user_id: str, action: str, target: str = "", context: Optional[Dict[str, Any]] = None) -> Dict[str, Any]: """评估操作风险等级并返回授权决策。 Args: user_id: 用户ID action: 操作描述（如 "修改数据库schema", "回复邮件"） target: 操作目标（如具体表名、文件路径） context: 附加上下文信息 """ context = context or {} # 1. 计算风险分数 risk_score, risk_factors = self._calculate_risk(action, target, context) # 2. 确定授权等级 auth_level = self._determine_level(risk_score) # 3. 用户信任度调整 fp = fingerprint_engine.get_fingerprint(user_id) trust_bonus = self._compute_trust_bonus(user_id, fp) adjusted_score = max(0, risk_score - trust_bonus) adjusted_level = self._determine_level(adjusted_score) # 4. 影子模式对比（如果是新类别操作） shadow_comparison = None category = context.get("category", self._infer_category(action)) if risk_score > 0.3: shadow_suggestion = shadow_executor.generate_suggestion(user_id, category, { "action": action, "target": target, "risk_score": risk_score, }) shadow_comparison = { "suggestion_generated": True, "shadow_confidence": shadow_suggestion.get("confidence", 0), } decision = { "action": action, "target": target, "risk_score": round(risk_score, 3), "adjusted_score": round(adjusted_score, 3), "raw_level": auth_level, "adjusted_level": adjusted_level, "trust_bonus": round(trust_bonus, 3), "risk_factors": risk_factors, "requires_approval": AUTH_LEVELS[adjusted_level]["requires_approval"], "notify_user": AUTH_LEVELS[adjusted_level]["notify"], "level_detail": AUTH_LEVELS[adjusted_level], "shadow_comparison": shadow_comparison, } # 如果需要审批，生成审批要求 if decision["requires_approval"]: decision["approval_requirements"] = self._generate_approval_requirements( adjusted_level, action, risk_factors ) # 记录行为 behavior_collector.log_fire_and_forget( user_id=user_id, category="decision", action="authorize_action", context={"action": action, "target": target}, result={"level": adjusted_level, "risk_score": adjusted_score}, ) return decision def _calculate_risk(self, action: str, target: str, context: Dict[str, Any]) -> tuple: """计算操作风险分数和风险因子。""" text = f"{action} {target}" total_score = 0 max_score = 0 matched_factors = [] for factor_name, factor in RISK_FACTORS.items(): max_score += factor["weight"] for keyword in factor["keywords"]: if keyword.lower() in text.lower(): severity = self._factor_severity(factor_name, text) contribution = factor["weight"] * severity total_score += contribution matched_factors.append({ "factor": factor_name, "contribution": round(contribution, 3), "severity": round(severity, 2), }) break # 归一化 if max_score > 0: risk_score = min(1.0, total_score / max_score) else: risk_score = 0.1 # 上下文调整 if context.get("is_dry_run"): risk_score *= 0.3 if context.get("has_rollback_plan"): risk_score *= 0.7 if context.get("is_peak_hours"): risk_score *= 1.2 return min(1.0, risk_score), matched_factors def _factor_severity(self, factor_name: str, text: str) -> float: """评估单个风险因子的严重程度。""" if factor_name == "financial": if any(kw in text for kw in ["退款", "refund", "实际扣款"]): return 1.0 return 0.7 if factor_name == "irreversible": if "删除" in text or "DELETE" in text.upper(): return 1.0 return 0.6 if factor_name == "production_impact": if "生产" in text or "production" in text.lower(): return 0.9 return 0.5 return 0.5 def _determine_level(self, score: float) -> str: """根据分数确定授权等级。""" if score <= 0.2: return "L0" elif score <= 0.4: return "L1" elif score <= 0.6: return "L2" elif score <= 0.8: return "L3" else: return "L4" def _compute_trust_bonus(self, user_id: str, fp: Optional[Dict[str, Any]]) -> float: """根据用户历史行为计算信任加成。""" if not fp: return 0 total = fp.get("total_behaviors", 0) avg_response = fp.get("avg_response_time_ms") bonus = 0 # 行为数据量 if total > 500: bonus += 0.1 elif total > 100: bonus += 0.05 # 平均响应快 → 经验丰富 if avg_response and avg_response < 30000: bonus += 0.05 # 影子模式准确率（如果有） try: accuracy = shadow_executor.get_accuracy(user_id) avg_acc = accuracy.get("average_accuracy", 0) if avg_acc > 0.9: bonus += 0.1 elif avg_acc > 0.8: bonus += 0.05 except Exception: pass return min(bonus, 0.2) def _infer_category(self, action: str) -> str: """从操作描述推断类别。""" if any(kw in action for kw in ["代码", "code", "PR", "review"]): return "code_review" if any(kw in action for kw in ["邮件", "email", "回复", "reply"]): return "email" if any(kw in action for kw in ["文档", "document", "合同", "contract"]): return "document" return "decision" def _generate_approval_requirements(self, level: str, action: str, risk_factors: List[Dict[str, Any]]) -> Dict[str, Any]: """生成审批要求。""" requirements = { "L2": { "approvers": ["直接上级或项目负责人"], "detail_required": ["操作摘要", "影响范围", "回滚方案"], "auto_approve_after_hours": 24, }, "L3": { "approvers": ["技术负责人", "产品负责人"], "detail_required": ["操作摘要", "详细方案", "影响范围评估", "回滚方案", "测试结果"], "auto_approve_after_hours": 48, }, "L4": { "approvers": ["技术负责人", "产品负责人", "安全负责人"], "detail_required": ["操作摘要", "详细方案", "影响范围评估", "风险评估报告", "回滚方案", "应急预案"], "auto_approve_after_hours": None, # 永不自动批准 }, } base = requirements.get(level, requirements["L2"]) base["risk_factors"] = [r["factor"] for r in risk_factors] base["action"] = action return base def get_authorization_summary(self, user_id: str, days: int = 30) -> Dict[str, Any]: """获取用户授权历史摘要。""" # 这里需要 behavior_collector 的查询能力 behaviors = behavior_collector.get_user_behaviors( user_id=user_id, category="decision", limit=100 ) by_level = {"L0": 0, "L1": 0, "L2": 0, "L3": 0, "L4": 0} for b in behaviors: result = b.get("result") if isinstance(b, dict) else (b.result or {}) level = result.get("level", "L2") if isinstance(result, dict) else "L2" by_level[level] = by_level.get(level, 0) + 1 return { "user_id": user_id, "period_days": days, "total_decisions": sum(by_level.values()), "by_level": by_level, "auto_executed": by_level["L0"] + by_level["L1"], "required_approval": by_level["L2"] + by_level["L3"] + by_level["L4"], "auto_rate": round((by_level["L0"] + by_level["L1"]) / max(sum(by_level.values()), 1), 3), } decision_authorizer = DecisionAuthorizer()