feat(backend): AI 服务层 — 千问流式 LLM + 月报解析 + 健康度计算 + 风险检测 + Copilot

- LLM 客户端：全部 SSE 流式输出，兼容 OpenAI 接口 - AI 月报解析：SSE 流式端点 POST /reports/{id}/parse - 健康度计算引擎：四维评分（财务/经营/AI商业化/AI成本） - 风险自动检测引擎：6 条规则自动检测指标越界 - AI Copilot：SSE 流式对话 POST /copilot/chat - 权限中间件：角色级 + 字段级权限控制 - 测试：21 个新测试（健康度 8 + 风险检测 8 + 权限 5），总计 64 passed
2026-07-18 22:16:40 +08:00
parent 94be6189e9
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+"""风险自动检测引擎。
+
+基于月报结构化数据，检测指标越界并自动生成风险事件。
+"""
+
+import logging
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+# 风险检测规则
+RULES = [
+    {
+        "name": "现金跑道不足",
+        "type": "financial",
+        "severity": "critical",
+        "condition": lambda d: _get_runway(d) < 3 and _get_runway(d) > 0,
+        "title": "现金跑道不足 3 个月",
+        "description": "当前现金跑道仅 {runway} 个月，需紧急融资",
+        "suggested_action": "立即启动融资对话，评估 bridge loan 可能性",
+    },
+    {
+        "name": "现金跑道预警",
+        "type": "financial",
+        "severity": "high",
+        "condition": lambda d: 3 <= _get_runway(d) < 6,
+        "title": "现金跑道低于 6 个月",
+        "description": "当前现金跑道 {runway} 个月，需加快融资进度",
+        "suggested_action": "与创始人沟通融资时间表，准备备选方案",
+    },
+    {
+        "name": "烧钱率上升",
+        "type": "financial",
+        "severity": "medium",
+        "condition": lambda d: _get_burn_trend(d) == "up",
+        "title": "烧钱率持续上升",
+        "description": "月度烧钱率呈上升趋势，需关注成本控制",
+        "suggested_action": "审查主要支出项，制定成本优化计划",
+    },
+    {
+        "name": "营收下滑",
+        "type": "financial",
+        "severity": "high",
+        "condition": lambda d: _get_yoy(revenue=d) < 0,
+        "title": "营收同比下滑",
+        "description": "营收同比下降 {yoy}%，需关注业务增长",
+        "suggested_action": "分析营收下滑原因，调整商业策略",
+    },
+    {
+        "name": "高人员流失",
+        "type": "org",
+        "severity": "medium",
+        "condition": lambda d: _get_departures(d) > 5,
+        "title": "人员流失率较高",
+        "description": "本月离职 {departures} 人，需关注团队稳定性",
+        "suggested_action": "了解离职原因，评估核心岗位风险",
+    },
+    {
+        "name": "团队净缩减",
+        "type": "org",
+        "severity": "high",
+        "condition": lambda d: _get_net_headcount(d) < -3,
+        "title": "团队规模显著缩减",
+        "description": "本月团队净减少 {net} 人，需关注组织健康",
+        "suggested_action": "与创始人沟通团队规划，评估关键岗位覆盖",
+    },
+]
+
+
+def _get_runway(data: dict[str, Any]) -> float:
+    """获取现金跑道月数。"""
+    cash = data.get("cash_balance", {})
+    try:
+        return float(cash.get("runway_months", 0) or 0)
+    except (ValueError, TypeError):
+        return 0.0
+
+
+def _get_burn_trend(data: dict[str, Any]) -> str:
+    """获取烧钱率趋势。"""
+    burn = data.get("burn_rate", {})
+    return burn.get("trend", "")
+
+
+def _get_yoy(revenue: dict[str, Any], d: dict[str, Any] = None) -> float:
+    """获取营收同比增长率。"""
+    if d is None:
+        d = revenue
+        revenue = d.get("revenue", {})
+    yoy = revenue.get("yoy_change", "")
+    try:
+        return float(str(yoy).replace("%", "").replace("+", "") or 0)
+    except (ValueError, TypeError):
+        return 0.0
+
+
+def _get_departures(data: dict[str, Any]) -> float:
+    """获取离职人数。"""
+    hc = data.get("headcount", {})
+    try:
+        return float(hc.get("departures", 0) or 0)
+    except (ValueError, TypeError):
+        return 0.0
+
+
+def _get_net_headcount(data: dict[str, Any]) -> float:
+    """获取团队净变化。"""
+    hc = data.get("headcount", {})
+    try:
+        new = float(hc.get("new_hires", 0) or 0)
+        dep = float(hc.get("departures", 0) or 0)
+        return new - dep
+    except (ValueError, TypeError):
+        return 0.0
+
+
+def detect_risks(
+    structured_data: dict[str, Any],
+    company_id: str,
+) -> list[dict[str, Any]]:
+    """从月报结构化数据中检测风险事件。
+
+    Args:
+        structured_data: 月报 AI 解析后的结构化数据
+        company_id: 企业 ID
+
+    Returns:
+        风险事件列表，每项包含 type/severity/title/description/suggested_action
+    """
+    if not structured_data:
+        return []
+
+    risks: list[dict[str, Any]] = []
+    runway = _get_runway(structured_data)
+    yoy = _get_yoy(structured_data)
+    departures = _get_departures(structured_data)
+    net_hc = _get_net_headcount(structured_data)
+
+    for rule in RULES:
+        try:
+            if rule["condition"](structured_data):
+                risk = {
+                    "company_id": company_id,
+                    "type": rule["type"],
+                    "severity": rule["severity"],
+                    "title": rule["title"],
+                    "description": rule["description"].format(
+                        runway=runway,
+                        yoy=abs(yoy),
+                        departures=departures,
+                        net=abs(net_hc),
+                    ),
+                    "suggested_action": rule["suggested_action"],
+                }
+                risks.append(risk)
+                logger.info("检测到风险: %s — %s", rule["name"], risk["title"])
+        except Exception as e:
+            logger.warning("风险检测规则 '%s' 执行异常: %s", rule["name"], e)
+
+    return risks