feat(budget): activate P1 auto-collect + attribution subject mapping (yanxue-20260914-budget-p13p14-activation)
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hermes
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#!/usr/bin/env python3
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"""预算偏差告警归因回填 —— yanxue-20260914-budget-p13p14-activation（P1-③ 激活）
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两类动作:
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1) attribution 为空的历史 open 告警 → 走现成接口 GET /budget/deviation-alerts/{id}/attribution
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（端点在响应前现场组装归因并落库，见 app/api/budget.py:1004-1012）
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2) 已有旧口径归因的告警 → 用修正后的科目口径重算覆盖（旧值见 /root/WB_WS/_backups/p13p14-20260914/）
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另: scenario_id 为空且 alert_type 命中 scenario_suggestions → 补 scenario_id（让告警"能解释、能关"）
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用法:
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/usr/bin/python3 scripts/kpi_alert_attribution_backfill.py # dry-run
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/usr/bin/python3 scripts/kpi_alert_attribution_backfill.py --apply
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"""
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import json
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import os
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import sys
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import urllib.error
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import urllib.request
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if __name__ == "__main__":
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from app.database import get_session_local
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from app.models import BudgetDeviationAlert, KPIDefinition, ScenarioSuggestion
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from app.utils.alert_attribution import build_attribution, match_scenario
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API = "http://127.0.0.1:8010"
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SECRET = "/root/.hermes/profiles/wecom-fullstack/.secrets/cma-admin.env"
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OUT = "/tmp/budget-p13p14/attribution-conclusions.json"
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def load_env(path):
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vals = {}
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with open(path) as fh:
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for line in fh:
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line = line.strip()
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if line and not line.startswith("#") and "=" in line:
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k, v = line.split("=", 1)
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vals[k.strip()] = v.strip()
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return vals
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def api(method, path, body=None, token=None):
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data = json.dumps(body).encode() if body is not None else None
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req = urllib.request.Request(API + path, 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=90) as r:
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return r.status, json.loads(r.read().decode())
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except urllib.error.HTTPError as e:
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return e.code, {"_error": e.read().decode()[:200]}
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def conclusion(alert, kpi, attribution, scenario):
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dims = attribution.get("dimensions") or []
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subs = attribution.get("subjects") or []
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trend = attribution.get("trend") or {}
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vtype = attribution.get("variance_type")
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hot = [s for s in subs if abs(s.get("amount_diff") or 0) > 0.01][:3]
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sub_txt = "; ".join(
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f"{s['subject_code']} {s.get('subject_name','')} {s['amount_diff']:+.2f}元(权重{s.get('weight')})"
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for s in hot) or "（科目明细为空：该KPI无可用取数映射，见报告§未接通清单）"
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dim_txt = "; ".join(
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f"{d.get('kpi_name')} 偏差{d.get('deviation_value')}" for d in dims
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if d.get("deviation_value") is not None and abs(d["deviation_value"]) > 0.01) or "无下级KPI层级"
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trend_txt = (f"{trend.get('type')}({trend.get('message')})" if trend.get("anomaly")
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else "无连续3期异常")
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return (
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f"[{alert.id}] {kpi.kpi_code if kpi else alert.kpi_id} {alert.period}: "
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f"实际{alert.actual_value} vs 预算{alert.budget_value}（偏差{alert.deviation_rate}%, {alert.alert_level}）| "
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f"场景={alert.alert_type or '未分类'}{'(建议:' + scenario['title'] + ')' if scenario else ''} | "
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f"量价={vtype} | 主科目={sub_txt} | 子KPI={dim_txt} | 趋势={trend_txt}"
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)
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def main():
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apply = "--apply" in sys.argv
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sec = load_env(SECRET)
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st, login = api("POST", "/api/cma/auth/login",
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{"username": sec["CMA_ADMIN_USER"], "password": sec["CMA_ADMIN_PASS"], "entity_id": 1})
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token = login.get("token")
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print(f"login: HTTP {st} entity={login.get('entity_short_name')}")
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db = get_session_local()()
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try:
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alerts = db.query(BudgetDeviationAlert).filter(
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BudgetDeviationAlert.entity_id == 1).order_by(BudgetDeviationAlert.id).all()
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print(f"== 告警: {len(alerts)} 条（open {sum(1 for a in alerts if a.status == 'open')}）")
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api_filled, recomputed, scenario_filled = 0, 0, 0
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lines = []
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for a in alerts:
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kpi = db.query(KPIDefinition).filter(KPIDefinition.id == a.kpi_id).first()
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had = a.attribution is not None
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if not had and apply:
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# 1) 现成接口路径：现场组装并落库
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st_a, resp = api("GET", f"/api/cma/budget/deviation-alerts/{a.id}/attribution", token=token)
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if st_a == 200:
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api_filled += 1
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db.expire(a) # 端点已 commit，重读
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a = db.query(BudgetDeviationAlert).filter(BudgetDeviationAlert.id == a.id).first()
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elif had and apply:
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# 2) 旧口径 → 按修正科目口径重算
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att, inferred = build_attribution(db, a.kpi_id, a.period, a.alert_type)
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a.attribution = att
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if a.alert_type is None:
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a.alert_type = inferred
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recomputed += 1
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scenario = match_scenario(db, a.alert_type)
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if scenario and a.scenario_id is None and apply:
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a.scenario_id = scenario["scenario_id"]
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scenario_filled += 1
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att_now = a.attribution or {}
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lines.append({
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"id": a.id, "kpi_id": a.kpi_id, "kpi_code": kpi.kpi_code if kpi else None,
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"period": a.period, "status": a.status, "alert_level": a.alert_level,
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"budget_value": a.budget_value, "actual_value": a.actual_value,
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"deviation_rate": a.deviation_rate, "alert_type": a.alert_type,
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"scenario_id": a.scenario_id, "has_attribution": bool(a.attribution),
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"attribution_keys": sorted(att_now.keys()),
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"view": conclusion(a, kpi, att_now, scenario),
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})
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if apply:
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db.commit()
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for row in lines:
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print(row["view"])
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with open(OUT, "w") as fh:
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json.dump(lines, fh, ensure_ascii=False, indent=1)
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print(f"\n== 输出结论: {OUT} ({len(lines)} 条)")
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n_attr = db.query(BudgetDeviationAlert).filter(
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BudgetDeviationAlert.entity_id == 1,
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BudgetDeviationAlert.attribution.isnot(None)).count()
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total = db.query(BudgetDeviationAlert).filter(BudgetDeviationAlert.entity_id == 1).count()
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n_open = db.query(BudgetDeviationAlert).filter(
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BudgetDeviationAlert.entity_id == 1, BudgetDeviationAlert.status == "open").count()
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n_scen = db.query(BudgetDeviationAlert).filter(
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BudgetDeviationAlert.entity_id == 1,
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BudgetDeviationAlert.scenario_id.isnot(None)).count()
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print(f"== 归因覆盖: {n_attr}/{total}（open {n_open}）| scenario_id 已补: {n_scen} | "
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f"接口落库 {api_filled} / 重算 {recomputed} / 场景补全 {scenario_filled}"
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f"{'' if apply else ' [dry-run 未写库]'}")
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finally:
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db.close()
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if __name__ == "__main__":
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main()
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