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