feat(alerts): 新增客户集中度预警规则 customer_concentration + 预测偏差规则启用/联动每日预警生成
- alert_rules: 新增 rule_type=customer_concentration（单一/前N客户收入占比超阈值触发，黄=超阈值/红=阈值x倍数） 数据源 zxt_masterbill 客户(往来单位)收入聚合，unit_id<=0 占位剔除；无客户数据可回退 KPI 口径 复用现有 KPIAlert 生成链路：check-all 内联分支 + 专用端点 POST /run-customer-concentration - alert_generator(每日9:00): 新增规则引擎步骤，复用上述检查函数跑 forecast_deviation/customer_concentration - AlertList.vue: 规则类型筛选/编辑项 + 参数表单 + 客户集中度检查按钮 - 测试: tests/test_customer_concentration_rule.py 17 条（规则注册/聚合/双数据源/黄红灯/去重/check-all） - 验证脚本: scripts/verify_forecast_deviation_rules_e2e.py / verify_customer_concentration_rule_e2e.py
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Hermes CI Fix
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#!/usr/bin/env python3
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"""任务A 交付验收脚本 — forecast_deviation 预测偏差规则（酣客entity1 / 博海entity2）
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做四件事（可复跑，幂等）：
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1. 经生产 API（管理员 JWT）为 entity1 / entity2 各建 1 条 forecast_deviation 规则
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2. 回查 API 列表 + 直查 DB 实证规则存在（含 rule_id / kpi / params）
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3. 触发测试：给 entity1 规则构造一期「预测偏差超阈值」场景（带标记的临时 KPIValue）
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→ 调 POST /api/cma/alert-rules/run-forecast-deviation → 断言 pending 预警生成
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4. 清理：删除临时 KPIValue 与该次生成的预警，回查归零（生产不留假数据）
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用法：cd /root/cma-management/backend && venv/bin/python3 scripts/verify_forecast_deviation_rules_e2e.py
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"""
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import json
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import sys
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import urllib.request
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import urllib.error
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BASE = "http://127.0.0.1:8010"
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ADMIN = {"username": "admin", "password": "admin123"}
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MARK = "【系统验证临时数据-验证后删除】"
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# 规则目标：酣客 → 应收账款周转天数(F_AR_DAYS, id=6，有 2026-09 预测日志)
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# 博海 → 营收(BH_REVENUE, id=421)
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TARGETS = [
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{"entity_id": 1, "kpi_id": 6, "kpi_label": "F_AR_DAYS"},
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{"entity_id": 2, "kpi_id": 421, "kpi_label": "BH_REVENUE"},
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]
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RULE_TYPE = "forecast_deviation"
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PARAMS = {"threshold_pct": 15, "red_ratio": 2} # >15% 黄，>30% 红（实现按 threshold×2 定红）
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def http(method, path, token=None, body=None, timeout=30):
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url = BASE + path
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data = json.dumps(body).encode() if body is not None else None
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req = urllib.request.Request(url, data=data, method=method)
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req.add_header("Content-Type", "application/json")
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if token:
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req.add_header("Authorization", f"Bearer {token}")
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try:
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with urllib.request.urlopen(req, timeout=timeout) as r:
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return r.status, json.loads(r.read().decode() or "{}")
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except urllib.error.HTTPError as e:
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return e.code, {"detail": e.read().decode()[:300]}
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def login(entity_id):
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st, r = http("POST", "/api/cma/auth/login", body={**ADMIN, "entity_id": entity_id})
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assert st == 200, f"登录 entity{entity_id} 失败: {st} {r}"
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return r["token"], r.get("entity_name")
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def main():
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ok = True
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# ── 1+2. 建规则 ────────────────────────────────────────────
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print("=== 1. 创建 forecast_deviation 规则 ===")
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created = {}
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for t in TARGETS:
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eid = t["entity_id"]
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token, ename = login(eid)
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st, rules = http("GET", f"/api/cma/alert-rules?rule_type={RULE_TYPE}", token=token)
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assert st == 200, f"列规则失败: {st} {rules}"
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exist = [r for r in rules.get("data", []) if r["kpi_id"] == t["kpi_id"] and r["rule_type"] == RULE_TYPE]
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if exist:
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rid = exist[0]["id"]
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print(f" entity{eid}({ename}) {t['kpi_label']}: 已存在 rule_id={rid}（幂等跳过）")
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else:
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st, r = http("POST", "/api/cma/alert-rules", token=token, body={
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"kpi_id": t["kpi_id"],
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"rule_type": RULE_TYPE,
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"trigger_on": "actual",
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"enabled": 1,
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"params": PARAMS,
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})
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assert st == 200, f"建规则失败 entity{eid}: {st} {r}"
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rid = r["data"]["id"]
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print(f" entity{eid}({ename}) {t['kpi_label']}: 新建 rule_id={rid} params={r['data']['params']}")
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created[eid] = rid
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# ── 3. DB 回查 ─────────────────────────────────────────────
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print("=== 2. DB 回查实证 ===")
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sys.path.insert(0, ".")
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from app.database import get_session_local
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from app.models import KPIDefinition, KPIValue, KPIAlert, KpiForecastLog
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from app.api.alert_rules import AlertRule
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db = get_session_local()()
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try:
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for eid, rid in created.items():
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row = db.query(AlertRule).filter(AlertRule.id == rid).first()
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kpi = db.query(KPIDefinition).filter(KPIDefinition.id == row.kpi_id).first()
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print(f" rule_id={row.id} entity_id={row.entity_id} kpi_id={row.kpi_id}"
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f"({kpi.kpi_code if kpi else '?'}) type={row.rule_type} enabled={row.enabled} params={row.params}")
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assert row.entity_id == eid and row.rule_type == RULE_TYPE
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assert row.enabled == 1
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# ── 4. 触发测试（entity1）─────────────────────────────
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print("=== 3. 触发测试（构造偏差场景, entity1）===")
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t1 = TARGETS[0]
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rid = created[1]
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rule = db.query(AlertRule).filter(AlertRule.id == rid).first()
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log = (db.query(KpiForecastLog)
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.filter(KpiForecastLog.entity_id == 1, KpiForecastLog.kpi_id == rule.kpi_id)
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.order_by(KpiForecastLog.created_at.desc()).first())
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forecast = float(log.forecast_value)
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# 构造：实际值 = 预测值的一半 → 偏差 100% > 15% → red（>30%）
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actual = round(forecast / 2, 2)
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temp = KPIValue(kpi_id=rule.kpi_id, entity_id=1, period=log.period,
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actual_value=actual, source_type="manual", data_status="pending", remark=MARK)
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db.add(temp)
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db.commit()
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db.refresh(temp)
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print(f" 临时KPIValue id={temp.id} period={log.period} 预测={forecast} 实际={actual}")
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token1, _ = login(1)
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st, r = http("POST", "/api/cma/alert-rules/run-forecast-deviation", token=token1, timeout=60)
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assert st == 200, f"触发失败: {st} {r}"
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print(f" run-forecast-deviation → {r}")
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db.expire_all()
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# MySQL REPEATABLE READ：API 在另一进程提交，本 session 需结束旧事务快照才能读到
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db.rollback()
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alerts = db.query(KPIAlert).filter(
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KPIAlert.entity_id == 1, KPIAlert.kpi_id == rule.kpi_id,
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KPIAlert.alert_type == "forecast", KPIAlert.status == "pending",
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).order_by(KPIAlert.id.desc()).all()
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assert alerts, "未生成预测偏差预警 → 触发测试 FAIL"
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a = alerts[0]
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alert_id = a.id
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print(f" ✅ 预警生成: id={a.id} level={a.alert_level} type={a.alert_type}")
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print(f" message={a.alert_message}")
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assert a.alert_level == "red", f"偏差100%应为红灯, 实际={a.alert_level}"
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# ── 5. 清理 ────────────────────────────────────────────
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print("=== 4. 清理临时数据（生产不留假数据）===")
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temp_id = temp.id
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db.rollback()
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a_row = db.query(KPIAlert).filter(KPIAlert.id == alert_id).first()
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temp_row = db.query(KPIValue).filter(KPIValue.id == temp_id).first()
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if a_row:
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db.delete(a_row)
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if temp_row:
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db.delete(temp_row)
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db.commit()
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left_v = db.query(KPIValue).filter(KPIValue.remark == MARK).count()
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left_a = db.query(KPIAlert).filter(KPIAlert.id == alert_id).count()
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print(f" 回查: 临时KPIValue剩余={left_v} / 本次预警剩余={left_a}")
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assert left_v == 0 and left_a == 0
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print("=== 5. 规则终态（交付物保留）===")
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for eid, rid in created.items():
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row = db.query(AlertRule).filter(AlertRule.id == rid).first()
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print(f" entity{eid}: rule_id={row.id} kpi_id={row.kpi_id} type={row.rule_type} enabled={row.enabled}")
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finally:
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db.close()
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print("\nALL PASS" if ok else "\nFAILED")
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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