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B 交付验收脚本 — customer_concentration 客户集中度预警（生产 E2E）
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做四件事（可复跑）：
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1. 为 entity1 配置常驻客户集中度规则（KPI=C_CUST_CONCENTRATION, source=auto → zxt 客户收入）
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2. 打印真实数据下的单一客户占比（zxt_masterbill 近12个月）作为基线
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3. 触发测试：建带标记的临时规则（source=kpi）+ 带标记的临时 KPIValue（占比 68%）
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→ 调 POST /api/cma/alert-rules/run-customer-concentration → 断言 pending 预警生成
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4. 清理临时规则/KPIValue/预警，回查归零
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用法：cd /root/cma-management/backend && venv/bin/python3 scripts/verify_customer_concentration_rule_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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TEMP_TAG = "e2e-verify-temp"
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RULE_TYPE = "customer_concentration"
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KPI_ID = 23 # C_CUST_CONCENTRATION 客户集中度 (entity1)
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def http(method, path, token=None, body=None, timeout=60):
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req = urllib.request.Request(
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BASE + path,
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data=json.dumps(body).encode() if body is not None else None,
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method=method,
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)
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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=1):
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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"登录失败: {st} {r}"
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return r["token"]
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def main():
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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 KPIValue, KPIAlert, KPIDefinition
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from app.api.alert_rules import AlertRule, _customer_revenue_shares
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token = login(1)
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db = get_session_local()()
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created_ids = []
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try:
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# ── 1+2. 常驻规则 + 真实基线 ──────────────────────────────
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print("=== 1. 常驻客户集中度规则（entity1）===")
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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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perm = [r for r in rules.get("data", []) if r["kpi_id"] == KPI_ID and r["entity_id"] == 1
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and not (r.get("params") or {}).get("_verify")]
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if perm:
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print(f" 已存在 rule_id={perm[0]['id']}（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": 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": {"threshold_pct": 50, "red_ratio": 1.5, "top_n": 1, "source": "auto"},
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})
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assert st == 200, f"建规则失败: {st} {r}"
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print(f" 新建 rule_id={r['data']['id']} params={r['data']['params']}")
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print("=== 2. 真实数据基线（zxt_masterbill 近12个月, entity1）===")
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db.rollback()
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shares, total, rng = _customer_revenue_shares(db, 1, lookback_months=12, top_n=5)
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assert total > 0, "entity1 无客户收入数据 → 基线取数失败"
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print(f" 客户收入合计={total:,.0f}元 ({rng[0]}~{rng[1]}), 客户数={len(shares)}")
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for uid, amt, pct in shares:
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print(f" 客户#{uid}: {amt:,.0f}元 / {pct}%")
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top1 = shares[0][2]
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print(f" → 单一客户占比 {top1}%（阈值50% → {'触发' if top1 > 50 else '未触发'}）")
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# ── 3. 触发测试（临时规则 + 临时KPI值）────────────────────
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print("=== 3. 触发测试（构造占比超阈值场景）===")
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st, r = http("POST", "/api/cma/alert-rules", token=token, body={
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"kpi_id": 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": {"threshold_pct": 50, "red_ratio": 1.5, "top_n": 1,
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"source": "kpi", "_verify": TEMP_TAG},
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})
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assert st == 200, f"建临时规则失败: {st} {r}"
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temp_rule_id = r["data"]["id"]
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created_ids.append(temp_rule_id)
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print(f" 临时规则 rule_id={temp_rule_id} (source=kpi, 阈值50%)")
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db.rollback()
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temp = KPIValue(kpi_id=KPI_ID, entity_id=1, period="2026-08", actual_value=68.0,
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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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temp_kv_id = temp.id
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print(f" 临时KPIValue id={temp_kv_id} 占比=68.0%")
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st, r = http("POST", "/api/cma/alert-rules/run-customer-concentration", token=token)
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assert st == 200, f"触发失败: {st} {r}"
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print(f" run-customer-concentration → {r}")
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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 == KPI_ID,
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KPIAlert.alert_type == "concentration", KPIAlert.status == "pending",
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).order_by(KPIAlert.id.desc()).all()
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assert alerts, "未生成客户集中度预警 → 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 == "yellow", f"68%应为黄灯(50%~75%), 实际={a.alert_level}"
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# ── 4. 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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db.rollback()
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for obj in (db.query(KPIAlert).filter(KPIAlert.id == alert_id).first(),
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db.query(KPIValue).filter(KPIValue.id == temp_kv_id).first(),
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db.query(AlertRule).filter(AlertRule.id == temp_rule_id).first()):
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if obj:
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db.delete(obj)
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db.commit()
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left = {
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"alert": db.query(KPIAlert).filter(KPIAlert.id == alert_id).count(),
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"kpi_value": db.query(KPIValue).filter(KPIValue.remark == MARK).count(),
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"temp_rule": db.query(AlertRule).filter(AlertRule.id == temp_rule_id).count(),
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}
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print(f" 回查: {left}")
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assert all(v == 0 for v in left.values())
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# ── 5. 终态 ────────────────────────────────────────────
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print("=== 5. 常驻规则终态 ===")
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st, rules = http("GET", f"/api/cma/alert-rules?rule_type={RULE_TYPE}", token=token)
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for r in rules.get("data", []):
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kpi = db.query(KPIDefinition).filter(KPIDefinition.id == r["kpi_id"]).first()
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print(f" rule_id={r['id']} entity={r['entity_id']} kpi={kpi.kpi_code if kpi else r['kpi_id']}"
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f" enabled={r['enabled']} params={r['params']}")
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finally:
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db.close()
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print("\nALL PASS")
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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