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fix(kpi): 阈值红黄绿读写/填写规则修复 — 统一解析器+红灯优先+建议可解析+前端校验+存量修正 (project-20260911-threshold-fix)

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① 新建 app/utils/threshold_parser.py 统一解析/判定（单一事实源）：
   - 支持负数（>=-100000）、闭区间 A~B、多条件 C1|C2；空串=未设置
   - 判定顺序 red→green→yellow：红灯优先（消除宽黄灯遮蔽红灯），
     green 先于 yellow（green 是主条件，yellow 是更宽兜底带）
② kpis.py / alert_generator.py 双实现收敛到统一解析器（消除重复维护）
③ thresholds.py suggest 输出可解析表达式（原黄色/适中型生成 `52.33~107.67`、`偏离55%` 等
   区间/文字 → 用户点"自动建议"填入后静默失效；另修复大数目标生成 1.6e+06 科学计数法）
④ KPIDetail.vue 三个阈值输入加 placeholder/tooltip/帮助文案 + 保存前格式校验（非法即拒）
⑤ 存量 4 个不可解析表达式修正（id=46 C_REBATE_RATE / id=327 BH_SALES_10），
   改前 mysqldump 备份 backend/scripts/backups/kpi_definitions.bak-20260911.sql +
   threshold_rows_before_20260911.json（可回滚）；不可解析 5→0
⑥ 单测 97 条 + 红黄绿判定回归 367 判定点（旧→新差异 14 条，含差异说明）
This commit is contained in:
Hermes CI Fix
2026-09-11 17:25:24 +08:00
parent f61b6ce604
commit 4c6fb85cf5
11 changed files with 1981 additions and 122 deletions
Download Patch File Download Diff File Expand all files Collapse all files
backend/app/api/kpis.py
+27 -20
View File
@@ -5,13 +5,17 @@ from sqlalchemy import func
from typing import Optional, List
from datetime import datetime
import json
import re
from app.database import get_db
from app.deps import get_entity_id
from app.auth_middleware import require_auth, require_role, filter_kpis_by_role, kpi_visible_dims
from app.models import StrategicMap, MapObjective, KPIDefinition, KPIValue, KPIAlert, OperationLog, Entity, KPICausality, KPIHierarchy, User
from app.api.kpi_governance import validate_kpi_payload, kpi_issues_message
from app.utils.threshold_parser import (
calc_level as _tp_calc_level,
is_valid_threshold,
THRESHOLD_HELP,
)
router = APIRouter(prefix="/api/cma/kpis", tags=["KPI字典"],
dependencies=[Depends(require_role("ceo", "finance", "business", "it"))],
@@ -113,25 +117,11 @@ REVERSE_INDICATORS = ['C_REBATE_RATE', 'P_BUG_RATE', 'P_REWORK_PCT', 'F_DEBT_RAT
def _static_threshold_level(value, kpi) -> str:
"""按 KPI 静态阈值文本判定级别（绿→黄→红，命中即止），无阈值返回 info。
供保本派生KPI(F_BEP_*)在详情页使用，与 alert_generator 语义一致。"""
for level, field in (("green", "threshold_green"), ("yellow", "threshold_yellow"),
("red", "threshold_red")):
expr = getattr(kpi, field, None)
if not expr:
continue
m = re.match(r"(>=|<=|>|<|=|!=)\s*([\d.]+)", str(expr).strip())
if not m:
continue
op, val = m.group(1), float(m.group(2))
hit = {
">=": value >= val, "<=": value <= val,
">": value > val, "<": value < val,
"=": value == val, "!=": value != val,
}[op]
if hit:
return level
return "info"
"""按 KPI 静态阈值文本判定级别（统一解析器，判定顺序 red→green→yellow），无阈值返回 info。
供保本派生KPI(F_BEP_*)在详情页使用，与 alert_generator 语义一致：
解析与判定逻辑统一收敛到 app.utils.threshold_parser（单一事实源，2026-09-11 修复双实现+红灯遮蔽）。"""
return _tp_calc_level(value, kpi)
def _calc_five_tier_score(current_value, target_value, is_reverse=False):
@@ -749,6 +739,21 @@ def apply_calc_type_inference(data: dict, infer_missing: bool = True) -> dict:
return data
THRESHOLD_FIELDS = ("threshold_green", "threshold_yellow", "threshold_red")
def validate_threshold_format(data: dict) -> List[str]:
"""阈值表达式格式校验（2026-09-11）：非法格式返回错误文本，让保存被拒而不是静默失效。
与 app.utils.threshold_parser 共用同一套语法（支持负数/区间 A~B/多条件 |）。
"""
errs: List[str] = []
for f in THRESHOLD_FIELDS:
if f in data and not is_valid_threshold(data.get(f)):
errs.append(f"阈值格式非法 [{f}={data.get(f)}]：{THRESHOLD_HELP}")
return errs
@router.post("")
def create_kpi(data: dict, db: Session = Depends(get_db), user=WRITE_ROLES, entity_id: int = Depends(get_entity_id)):
# 检查编码唯一性
@@ -757,6 +762,7 @@ def create_kpi(data: dict, db: Session = Depends(get_db), user=WRITE_ROLES, enti
raise HTTPException(400, f"KPI编码 {data['kpi_code']} 已存在")
# 数据治理校验（规则1强制拦截）
errs = _validate_kpi_data(data, db=db, is_update=False)
errs = errs + validate_threshold_format(data)
if errs:
raise HTTPException(422, detail={"message": "数据校验不通过", "errors": errs})
data["entity_id"] = entity_id # 账套隔离: 强制写入token企业 (2026-08-23 P1a)
@@ -799,6 +805,7 @@ def update_kpi(kpi_id: int, data: dict, db: Session = Depends(get_db), user=WRIT
raise HTTPException(404, "KPI不存在")
# 数据治理校验（更新时只检查传了但为空的字段）
errs = _validate_kpi_data(data, db=db, current_kpi_id=kpi_id, is_update=True)
errs = errs + validate_threshold_format(data)
if errs:
raise HTTPException(422, detail={"message": "数据校验不通过", "errors": errs})
data.pop("entity_id", None) # 禁止通过update改企业归属
backend/app/api/thresholds.py
+115 -57
View File
@@ -1,9 +1,17 @@
"""阈值智能推荐 API"""
"""阈值智能推荐 API
2026-09-11 (project-20260911-threshold-fix) 修复：
推荐值必须与后端解析器（app.utils.threshold_parser）语法一致 —— 原实现给黄灯/适中型
生成区间/文字标签（`52.33~107.67`、`偏离55%`），用户点“自动建议”填入后保存即静默失效。
现在统一生成可解析表达式：标量比较（支持负数）/ 闭区间 `A~B` / 多条件 `C1|C2`。
"""
from fastapi import APIRouter, Depends, Query
from sqlalchemy.orm import Session
from app.database import get_db
from app.auth_middleware import require_auth, require_role
from app.models import KPIDefinition, KPIValue, OperationLog
from app.utils.threshold_parser import THRESHOLD_HELP, is_valid_threshold
from typing import Any, Optional
import json
router = APIRouter(prefix="/api/cma/thresholds", tags=["阈值分析"],
@@ -18,9 +26,97 @@ KPI_TYPES = {
"middle_best": ["TOP5_CUSTOMER_RATIO"],
}
def _r(v) -> float:
"""保留2位小数（避免浮点噪声写进表达式）"""
return round(float(v), 2)
def _fmt(v) -> str:
"""数值 → 表达式文本。禁用科学计数法：`:g` 在 |v|>=1e6 时会输出 1.6e+06，
解析器不认（会导致推荐值再次静默失效）。"""
s = f"{float(v):.2f}".rstrip("0").rstrip(".")
return s if s not in ("", "-") else "0"
def build_suggestion(kpi_type: str, target: float, tolerance: Optional[float] = None) -> dict:
"""按类型生成可解析的红黄绿阈值建议（仅生成表达式，不含 I/O）。
口径（label = 该等级的触发条件，判定优先级 red→green→yellow）：
higher_better：绿 `>=0.8T`、黄 `>=0.5T`（更宽的兜底带）、红 `<0.5T`
lower_better ：绿 `<=1.2T`、黄 `<=2.0T`、红 `>2.0T`
middle_best ：绿 `T±tol` 闭区间、黄 `T±2tol` 外环带（green ⊂ yellow）、红 `<T-2tol|>T+2tol`
"""
if kpi_type == "higher_better":
green_min = _r(target * 0.8)
yellow_min = _r(target * 0.5)
red_max = yellow_min
return {
"type": "higher_better",
"description": "越高越好型",
"green": {"min": green_min, "max": None, "label": f">={_fmt(green_min)}"},
"yellow": {"min": yellow_min, "max": green_min, "label": f">={_fmt(yellow_min)}"},
"red": {"min": None, "max": red_max, "label": f"<{_fmt(red_max)}"},
}
if kpi_type == "lower_better":
green_max = _r(target * 1.2)
yellow_max = _r(target * 2.0)
red_min = yellow_max
return {
"type": "lower_better",
"description": "越低越好型",
"green": {"min": None, "max": green_max, "label": f"<={_fmt(green_max)}"},
"yellow": {"min": green_max, "max": yellow_max, "label": f"<={_fmt(yellow_max)}"},
"red": {"min": red_min, "max": None, "label": f">{_fmt(red_min)}"},
}
# middle_best（适中最好型）：绿=内区间 T±tol，黄=外环带 T±2tol（green ⊂ yellow），红=环带之外
tol = float(tolerance) if tolerance else abs(float(target)) * 0.2
if tol <= 0:
tol = 1.0
a, b = _r(target - tol), _r(target + tol)
c, d = _r(target - 2 * tol), _r(target + 2 * tol)
return {
"type": "middle_best",
"description": "适中最好型",
"green": {"min": a, "max": b, "label": f"{_fmt(a)}~{_fmt(b)}"},
"yellow": {"min": c, "max": d, "label": f"{_fmt(c)}~{_fmt(d)}"},
"red": {"min": None, "max": None, "label": f"<{_fmt(c)}|>{_fmt(d)}"},
}
def suggest_by_type(kpi):
"""无历史数据时按类型推荐（同样输出可解析表达式，含 yellow/red 标签）"""
target = kpi.target_value or 100
kpi_type = _kpi_type(kpi)
suggestion = build_suggestion(kpi_type, target)
return {"kpi_id": kpi.id, "kpi_name": kpi.kpi_name, "message": "无历史数据",
"format_hint": THRESHOLD_HELP, "suggestion": suggestion}
def _kpi_type(kpi) -> str:
if kpi.kpi_code in KPI_TYPES["lower_better"]:
return "lower_better"
if kpi.kpi_code in KPI_TYPES["middle_best"]:
return "middle_best"
return "higher_better"
def _sanity_check(suggestion: dict) -> dict:
"""自检：推荐值必须过解析器（防再次生成不可解析格式），并挂 format 提示给前端。"""
bad = []
for level in ("green", "yellow", "red"):
label = (suggestion.get(level) or {}).get("label")
if label and not is_valid_threshold(label):
bad.append({"level": level, "label": label})
suggestion["parseable"] = not bad
if bad:
suggestion["unparseable"] = bad
return suggestion
@router.get("/suggest/{kpi_id}")
def suggest_threshold(kpi_id: int, db: Session = Depends(get_db)):
"""根据历史数据自动推荐阈值"""
"""根据历史数据自动推荐阈值（输出格式保证被 threshold_parser 解析生效）"""
kpi = db.query(KPIDefinition).filter(KPIDefinition.id == kpi_id).first()
if not kpi:
return {"error": "KPI不存在"}
@@ -36,62 +132,24 @@ def suggest_threshold(kpi_id: int, db: Session = Depends(get_db)):
if len(nums) < 2:
return suggest_by_type(kpi)
avg = sum(nums) / len(nums)
# 注: SQLAlchemy 模型属性未做 Mapped 注解, Pyright 会推断为 Column;
# 这里统一标注 Any 抑制静态噪音(运行期均为 float)。
avg: Any = sum(nums) / len(nums)
# 计算标准差
variance = sum((x - avg) ** 2 for x in nums) / len(nums)
std = variance ** 0.5
variance: Any = sum((x - avg) ** 2 for x in nums) / len(nums)
std: Any = variance ** 0.5
target = kpi.target_value or avg
# 根据KPI类型生成推荐区间
if kpi.kpi_code in KPI_TYPES["higher_better"]:
green_min = round(target * 0.8, 2)
yellow_min = round(target * 0.5, 2)
red_max = round(target * 0.5, 2)
suggestion = {
"type": "higher_better",
"description": "越高越好型",
"green": {"min": green_min, "max": None, "label": f">={green_min}"},
"yellow": {"min": yellow_min, "max": green_min, "label": f"{yellow_min}~{green_min}"},
"red": {"min": None, "max": red_max, "label": f"<{red_max}"},
"current_avg": round(avg, 2),
"target": target,
}
elif kpi.kpi_code in KPI_TYPES["lower_better"]:
green_max = round(target * 1.2, 2)
yellow_max = round(target * 2.0, 2)
suggestion = {
"type": "lower_better",
"description": "越低越好型",
"green": {"min": None, "max": green_max, "label": f"<={green_max}"},
"yellow": {"min": green_max, "max": yellow_max, "label": f"{green_max}~{yellow_max}"},
"red": {"min": yellow_max, "max": None, "label": f">{yellow_max}"},
"current_avg": round(avg, 2),
"target": target,
}
else:
tolerance = max(std * 1.5, target * 0.2)
suggestion = {
"type": "middle_best",
"description": "适中最好型",
"green": {"min": round(target - tolerance, 2), "max": round(target + tolerance, 2), "label": f"{round(target-tolerance,2)}~{round(target+tolerance,2)}"},
"yellow": {"min": round(target - tolerance*2, 2), "max": round(target + tolerance*2, 2), "label": f"偏离{(tolerance*2):.0f}%"},
"red": {"min": None, "max": round(target - tolerance*2, 2), "label": f"偏离>{tolerance*2:.0f}%"},
"current_avg": round(avg, 2),
"target": target,
}
return {"kpi_id": kpi_id, "kpi_name": kpi.kpi_name, "suggestion": suggestion}
target: Any = kpi.target_value or avg
kpi_type = _kpi_type(kpi)
def suggest_by_type(kpi):
"""无历史数据时按类型推荐"""
target = kpi.target_value or 100
if kpi.kpi_code in KPI_TYPES["higher_better"]:
return {"kpi_id": kpi.id, "kpi_name": kpi.kpi_name, "message": "无历史数据",
"suggestion": {"type": "higher_better", "green": {"min": round(target*0.8,2)}, "yellow": {"min": round(target*0.5,2)}, "red": {"max": round(target*0.5,2)}}}
elif kpi.kpi_code in KPI_TYPES["lower_better"]:
return {"kpi_id": kpi.id, "kpi_name": kpi.kpi_name, "message": "无历史数据",
"suggestion": {"type": "lower_better", "green": {"max": round(target*1.2,2)}, "yellow": {"max": round(target*2,2)}, "red": {"min": round(target*2,2)}}}
if kpi_type == "middle_best":
tolerance: Any = max(std * 1.5, target * 0.2)
suggestion = build_suggestion(kpi_type, target, tolerance)
else:
return {"kpi_id": kpi.id, "kpi_name": kpi.kpi_name, "message": "无历史数据",
"suggestion": {"type": "middle_best", "green": {"min": round(target*0.8,2), "max": round(target*1.2,2)}}}
suggestion = build_suggestion(kpi_type, target)
suggestion = _sanity_check(suggestion)
suggestion["current_avg"] = round(avg, 2)
suggestion["target"] = target
return {"kpi_id": kpi_id, "kpi_name": kpi.kpi_name,
"format_hint": THRESHOLD_HELP, "suggestion": suggestion}
backend/app/utils/threshold_parser.py
+166
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@@ -0,0 +1,166 @@
# -*- coding: utf-8 -*-
"""阈值表达式统一解析与红黄绿判定 —— 单一事实源（Single Source of Truth）
背景（project-20260911-threshold-fix）：
原先 kpis.py 与 alert_generator.py 各自实现一份阈值解析（正则相同、独立维护），
且解析器不支持负数，导致 `>=-100000` 这类表达式静默失效。
表达式语法（label 的语义 = “该等级的触发条件”，不是区间标签）：
>=N <=N >N <N =N !=N 标量比较，N 支持负数与小数（如 `<-100000`、`>=52.33`）
A~B 闭区间包含，A <= 实际值 <= B（如 `52.33~107.67`）
C1|C2 多条件或，任一命中即为命中（如 `<=36|>=124`）
允许空白：`>= 800`、`52.33 ~ 107.67`；空串/None 视为“无阈值”。
红黄绿判定优先级：red → green → yellow（理由见下方 calc_level_from_exprs）
"""
from __future__ import annotations
import re
from typing import List, Optional, Tuple, Union
# 标量比较：操作符 + 数值（负号/小数）
_SCALAR_RE = re.compile(r"(>=|<=|!=|>|<|=)\s*(-?\d+(?:\.\d+)?)")
# 区间：A~B（闭区间）
_RANGE_RE = re.compile(r"(-?\d+(?:\.\d+)?)\s*~\s*(-?\d+(?:\.\d+)?)")
# 多条件分隔（半角竖线 / 全角竖线）
_CLAUSE_SPLIT_RE = re.compile(r"\s*[|｜]\s*")
#: 单个子句取值：标量 -> float；区间 -> (low, high)
ThresholdValue = Union[float, Tuple[float, float]]
Clause = Tuple[str, ThresholdValue]
#: 支持的格式说明（前端 tooltip / 接口文档共用文案）
THRESHOLD_HELP = (
"支持格式：>=800 / <=25 / >60 / <20 / =100 / !=0（数值可为负数，如 <-100000）；"
"区间 52.33~107.67（含端点）；多条件用 | 连接，如 <=36|>=124。"
)
THRESHOLD_PLACEHOLDER = "如 >=800 / 52.33~107.67"
#: 判定优先级（严重度最高者优先；green 优先于 yellow 见 docstring 说明）
LEVEL_PRIORITY: Tuple[str, str, str] = ("red", "green", "yellow")
def _is_blank(expr) -> bool:
return expr is None or not str(expr).strip()
def parse_clause(clause) -> Optional[Clause]:
"""解析单个子句；非法返回 None。"""
s = str(clause).strip()
if not s:
return None
m = _RANGE_RE.fullmatch(s)
if m:
lo, hi = float(m.group(1)), float(m.group(2))
return ("~", (lo, hi))
m = _SCALAR_RE.fullmatch(s)
if m:
return (m.group(1), float(m.group(2)))
return None
def parse_clauses(expr) -> List[Clause]:
"""解析表达式为子句列表；任一子句非法则整体视为非法（返回空列表）。"""
if _is_blank(expr):
return []
parts = _CLAUSE_SPLIT_RE.split(str(expr).strip())
clauses: List[Clause] = []
for p in parts:
c = parse_clause(p)
if c is None:
return []
clauses.append(c)
return clauses
def parse_threshold(expr) -> Tuple[Optional[str], Optional[ThresholdValue]]:
"""解析阈值表达式，返回 (operator, value)。
保持向后兼容的二元返回：解析失败 -> (None, None)。
区间操作符为 "~"，value 为 (low, high)；多条件表达式返回首个命中子句之外的首个子句。
示例:
'>=32000000' -> ('>=', 32000000.0)
'>=-100000' -> ('>=', -100000.0)
'52.33~107.67' -> ('~', (52.33, 107.67))
"""
clauses = parse_clauses(expr)
if not clauses:
return None, None
return clauses[0]
def is_valid_threshold(expr) -> bool:
"""表达式是否合法（空串视为合法=不设置阈值）。"""
if _is_blank(expr):
return True
return bool(parse_clauses(expr))
def eval_clause(actual, clause: Clause) -> bool:
op, val = clause
if actual is None:
return False
if op == ">=":
return actual >= val # type: ignore[operator]
if op == "<=":
return actual <= val # type: ignore[operator]
if op == ">":
return actual > val # type: ignore[operator]
if op == "<":
return actual < val # type: ignore[operator]
if op == "=":
return actual == val
if op == "!=":
return actual != val
if op == "~":
lo, hi = val # type: ignore[misc]
return lo <= actual <= hi
return False
def eval_threshold(actual, expr) -> bool:
"""实际值是否命中阈值表达式（多条件任一命中即 True；非法表达式 -> False）。"""
if actual is None or _is_blank(expr):
return False
clauses = parse_clauses(expr)
return any(eval_clause(actual, c) for c in clauses)
def calc_level_from_exprs(actual, green=None, yellow=None, red=None) -> str:
"""红黄绿判定（统一入口），返回 'red' / 'yellow' / 'green' / 'info'。
判定顺序 red → green → yellow：
1. red 最严重，必须最先判 —— 否则宽黄灯（如 Y='>76'）会遮蔽红灯（如 R='>91'），
红灯永远不可达（缺陷5：id=6 实测值 95 被判黄）。
2. green 先于 yellow —— green 表达式（如 G='>=800'）是“主条件”，
yellow（如 Y='>=700'）是更宽的兜底带；若 yellow 先判，值 900 会被黄灯截胡，
与用户“>=800 即绿”的填写意图相反（id=1 实测）。
对 moderate 型（绿=内区间、黄=外侧环带）两者天然互斥，顺序无影响。
3. 三者均不命中 -> 'info'（无判定，与旧实现一致）。
"""
if actual is None:
return "info"
exprs = {"green": green, "yellow": yellow, "red": red}
for level in LEVEL_PRIORITY:
if eval_threshold(actual, exprs[level]):
return level
return "info"
def calc_level(actual, kpi) -> str:
"""按 KPI 定义对象（含 threshold_green/yellow/red 属性）判定级别。"""
return calc_level_from_exprs(
actual,
green=getattr(kpi, "threshold_green", None),
yellow=getattr(kpi, "threshold_yellow", None),
red=getattr(kpi, "threshold_red", None),
)
def describe_level(actual, kpi) -> Tuple[str, Optional[str]]:
"""返回 (级别, 命中的表达式)。供预警消息构造使用；无命中 -> (level, None)。"""
level = calc_level(actual, kpi)
if level == "info":
return level, None
field = {"green": "threshold_green", "yellow": "threshold_yellow", "red": "threshold_red"}[level]
return level, getattr(kpi, field, None)
backend/scripts/alert_generator.py
+16 -37
View File
@@ -6,7 +6,6 @@
import sys
import os
import re
import json
import logging
from datetime import datetime, timedelta
@@ -15,22 +14,15 @@ sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from app.database import get_session_local
from app.models import KPIDefinition, KPIValue, KPIAlert, ActionPlan
from app.utils.threshold_parser import eval_threshold, LEVEL_PRIORITY
logger = logging.getLogger("cma.alert_gen")
def _parse_threshold(expr: str) -> tuple:
"""解析阈值表达式，返回 (operator, value)
示例:
>=32000000 -> ('>=', 32000000)
<20 -> ('<', 20)
<=55 -> ('<=', 55)
>60 -> ('>', 60)
"""
m = re.match(r"(>=|<=|>|<|=|!=)\s*([\d.]+)", str(expr).strip())
if m:
return m.group(1), float(m.group(2))
return None, None
"""[兼容保留] 统一委托 app.utils.threshold_parser.parse_threshold"""
from app.utils.threshold_parser import parse_threshold as _p
return _p(expr)
def cleanup_green_alerts(db_session) -> int:
@@ -52,33 +44,16 @@ def cleanup_green_alerts(db_session) -> int:
def _check_threshold(actual: float, threshold_expr: str, level: str) -> tuple:
"""检查实际值是否触发阈值，返回 (触发, 消息)"""
"""检查实际值是否触发阈值，返回 (触发, 消息)（统一走 app.utils.threshold_parser）"""
if not threshold_expr or actual is None:
return False, ""
op, val = _parse_threshold(threshold_expr)
if op is None:
if not eval_threshold(actual, threshold_expr):
return False, ""
triggered = False
if op == ">=" and actual >= val:
triggered = True
elif op == "<=" and actual <= val:
triggered = True
elif op == ">" and actual > val:
triggered = True
elif op == "<" and actual < val:
triggered = True
elif op == "=" and actual == val:
triggered = True
if triggered:
level_names = {"green": "正常", "yellow": "预警", "red": "紧急"}
msg = (f"KPI当前值 {actual:.2f}，触发{level_names.get(level, level)}阈值 "
f"({threshold_expr})")
return True, msg
return False, ""
msg = (f"KPI当前值 {actual:.2f}，触发{ {'green': '正常', 'yellow': '预警', 'red': '紧急'}.get(level, level) }阈值 "
f"({threshold_expr})")
return True, msg
def run_alert_check(db_session, period: str = None) -> int:
@@ -112,9 +87,12 @@ def run_alert_check(db_session, period: str = None) -> int:
actual = latest.actual_value
# 从绿到红检查（绿灯最高优先级——满足即止）
# 判定顺序：红灯优先 → 绿灯 → 黄灯（最严重者优先，消除宽黄灯遮蔽红灯）
# 2026-09-11 修复「黄灯遮蔽红灯」：原顺序 绿→黄→红命中即止，宽黄灯(如 Y='>76')
# 会让红灯(如 R='>91')永远不可达（id=6 实测值 95 被判黄）；
# green 先于 yellow：green 是主条件，yellow 是更宽的兜底带（id=1 G='>=800' Y='>=700'）。
# Bug1修复: green(正常)不生成pending预警，只对yellow/red落库
for level in ["green", "yellow", "red"]:
for level in LEVEL_PRIORITY:
expr = thr[level]
if not expr:
continue
@@ -173,7 +151,8 @@ def run_alert_check(db_session, period: str = None) -> int:
logger.info(f" ↳ 自动创建行动计划 #{plan.id}: {plan_title}")
break # 只取最高级别
elif level == "red" and not triggered:
# 红没触发，如果已有红色预警但当前不满足，自动降级或关闭
# 红没触发（判定顺序中 red 最先）→ 若该期间该KPI已有 pending/processing 红灯，
# 说明当前值已不再触发红色阈值，自动解除（保留原有自动降级行为）
existing_red = db_session.query(KPIAlert).filter(
KPIAlert.kpi_id == kpi.id,
KPIAlert.kpi_value_id == latest.id,
backend/scripts/backups/threshold_regression_20260911.json
+751
View File
@@ -0,0 +1,751 @@
{
"samples": 367,
"count_old": {
"red": 120,
"yellow": 17,
"green": 46,
"info": 184
},
"count_new": {
"red": 126,
"yellow": 12,
"green": 54,
"info": 175
},
"diff_ab": [
{
"kpi_id": 6,
"kpi_code": "F_AR_DAYS",
"period": "2026-04",
"actual": 152.62,
"old": "yellow",
"new": "red",
"severity_order": "red",
"G": "<=76",
"Y": ">76",
"R": ">91"
},
{
"kpi_id": 6,
"kpi_code": "F_AR_DAYS",
"period": "2026-05",
"actual": 105.47,
"old": "yellow",
"new": "red",
"severity_order": "red",
"G": "<=76",
"Y": ">76",
"R": ">91"
},
{
"kpi_id": 6,
"kpi_code": "F_AR_DAYS",
"period": "2026-07",
"actual": 116.5,
"old": "yellow",
"new": "red",
"severity_order": "red",
"G": "<=76",
"Y": ">76",
"R": ">91"
},
{
"kpi_id": 6,
"kpi_code": "F_AR_DAYS",
"period": "2026-08",
"actual": 610.2,
"old": "yellow",
"new": "red",
"severity_order": "red",
"G": "<=76",
"Y": ">76",
"R": ">91"
},
{
"kpi_id": 46,
"kpi_code": "C_REBATE_RATE",
"period": "2026-01",
"actual": 124.1,
"old": "info",
"new": "red",
"severity_order": "red",
"G": "52.33~107.67",
"Y": "<52.33|>107.67",
"R": "<=36|>=124"
},
{
"kpi_id": 46,
"kpi_code": "C_REBATE_RATE",
"period": "2026-02",
"actual": 84.0,
"old": "info",
"new": "green",
"severity_order": "green",
"G": "52.33~107.67",
"Y": "<52.33|>107.67",
"R": "<=36|>=124"
},
{
"kpi_id": 46,
"kpi_code": "C_REBATE_RATE",
"period": "2026-03",
"actual": 69.6,
"old": "info",
"new": "green",
"severity_order": "green",
"G": "52.33~107.67",
"Y": "<52.33|>107.67",
"R": "<=36|>=124"
},
{
"kpi_id": 46,
"kpi_code": "C_REBATE_RATE",
"period": "2026-04",
"actual": 81.7,
"old": "info",
"new": "green",
"severity_order": "green",
"G": "52.33~107.67",
"Y": "<52.33|>107.67",
"R": "<=36|>=124"
},
{
"kpi_id": 46,
"kpi_code": "C_REBATE_RATE",
"period": "2026-05",
"actual": 88.3,
"old": "info",
"new": "green",
"severity_order": "green",
"G": "52.33~107.67",
"Y": "<52.33|>107.67",
"R": "<=36|>=124"
},
{
"kpi_id": 46,
"kpi_code": "C_REBATE_RATE",
"period": "2026-06",
"actual": 52.8,
"old": "info",
"new": "green",
"severity_order": "green",
"G": "52.33~107.67",
"Y": "<52.33|>107.67",
"R": "<=36|>=124"
},
{
"kpi_id": 46,
"kpi_code": "C_REBATE_RATE",
"period": "2026-07",
"actual": 68.7,
"old": "info",
"new": "green",
"severity_order": "green",
"G": "52.33~107.67",
"Y": "<52.33|>107.67",
"R": "<=36|>=124"
},
{
"kpi_id": 46,
"kpi_code": "C_REBATE_RATE",
"period": "2026-08",
"actual": 75.0,
"old": "info",
"new": "green",
"severity_order": "green",
"G": "52.33~107.67",
"Y": "<52.33|>107.67",
"R": "<=36|>=124"
},
{
"kpi_id": 46,
"kpi_code": "C_REBATE_RATE",
"period": "2026-H1",
"actual": 86.4,
"old": "info",
"new": "green",
"severity_order": "green",
"G": "52.33~107.67",
"Y": "<52.33|>107.67",
"R": "<=36|>=124"
},
{
"kpi_id": 407,
"kpi_code": "F_REBATE_RATE",
"period": "2026-08",
"actual": 48.44,
"old": "yellow",
"new": "red",
"severity_order": "red",
"G": "<=10",
"Y": ">10",
"R": ">12"
}
],
"diff_bc": [
{
"kpi_id": 3,
"kpi_code": "F_NET_PROFIT",
"period": "2026-01",
"actual": 94.31,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": ">=30",
"Y": ">=20",
"R": "<10"
},
{
"kpi_id": 4,
"kpi_code": "F_OP_CFLOW",
"period": "2026-01",
"actual": 65.2173,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": ">=20",
"Y": ">=0",
"R": "<0"
},
{
"kpi_id": 4,
"kpi_code": "F_OP_CFLOW",
"period": "2026-02",
"actual": 78.4307,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": ">=20",
"Y": ">=0",
"R": "<0"
},
{
"kpi_id": 4,
"kpi_code": "F_OP_CFLOW",
"period": "2026-04",
"actual": 124.422,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": ">=20",
"Y": ">=0",
"R": "<0"
},
{
"kpi_id": 4,
"kpi_code": "F_OP_CFLOW",
"period": "2026-06",
"actual": 43.4998,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": ">=20",
"Y": ">=0",
"R": "<0"
},
{
"kpi_id": 5,
"kpi_code": "F_COST_RATIO",
"period": "2026-01",
"actual": 15.8,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": "<=25",
"Y": "<=30",
"R": ">30"
},
{
"kpi_id": 5,
"kpi_code": "F_COST_RATIO",
"period": "2026-02",
"actual": 18.85,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": "<=25",
"Y": "<=30",
"R": ">30"
},
{
"kpi_id": 5,
"kpi_code": "F_COST_RATIO",
"period": "2026-06",
"actual": 24.8,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": "<=25",
"Y": "<=30",
"R": ">30"
},
{
"kpi_id": 5,
"kpi_code": "F_COST_RATIO",
"period": "2026-H1",
"actual": 24.9,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": "<=25",
"Y": "<=30",
"R": ">30"
},
{
"kpi_id": 8,
"kpi_code": "C_NEW_CLIENTS",
"period": "2026-07",
"actual": 88.8,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": ">=10",
"Y": ">=5",
"R": "<5"
},
{
"kpi_id": 14,
"kpi_code": "F_REVENUE_GROWTH",
"period": "2026-05",
"actual": 48.91,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": ">=20",
"Y": ">=15",
"R": "<15"
},
{
"kpi_id": 14,
"kpi_code": "F_REVENUE_GROWTH",
"period": "2026-06",
"actual": 43.54,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": ">=20",
"Y": ">=15",
"R": "<15"
},
{
"kpi_id": 30,
"kpi_code": "F_DEBT_RATIO",
"period": "2026-01",
"actual": 37.01,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": "<50",
"Y": "<70",
"R": ">=70"
},
{
"kpi_id": 30,
"kpi_code": "F_DEBT_RATIO",
"period": "2026-02",
"actual": 38.33,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": "<50",
"Y": "<70",
"R": ">=70"
},
{
"kpi_id": 30,
"kpi_code": "F_DEBT_RATIO",
"period": "2026-04",
"actual": 42.02,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": "<50",
"Y": "<70",
"R": ">=70"
},
{
"kpi_id": 30,
"kpi_code": "F_DEBT_RATIO",
"period": "2026-05",
"actual": 41.49,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": "<50",
"Y": "<70",
"R": ">=70"
},
{
"kpi_id": 30,
"kpi_code": "F_DEBT_RATIO",
"period": "2026-06",
"actual": 39.0,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": "<50",
"Y": "<70",
"R": ">=70"
},
{
"kpi_id": 30,
"kpi_code": "F_DEBT_RATIO",
"period": "2026-07",
"actual": 38.8,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": "<50",
"Y": "<70",
"R": ">=70"
},
{
"kpi_id": 30,
"kpi_code": "F_DEBT_RATIO",
"period": "2026-08",
"actual": 39.9,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": "<50",
"Y": "<70",
"R": ">=70"
},
{
"kpi_id": 184,
"kpi_code": "FB_ANALYSIS_COUNT",
"period": "2026-07",
"actual": 18.0,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": ">=12",
"Y": ">=8",
"R": "<4"
},
{
"kpi_id": 318,
"kpi_code": "BH_SALES_1",
"period": "2026-H1",
"actual": 754562.0,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": ">=754562",
"Y": ">=603649",
"R": "<452737"
},
{
"kpi_id": 319,
"kpi_code": "BH_SALES_2",
"period": "2026-H1",
"actual": 579205.0,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": ">=579205",
"Y": ">=463364",
"R": "<347523"
},
{
"kpi_id": 320,
"kpi_code": "BH_SALES_3",
"period": "2026-H1",
"actual": 209038.0,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": ">=209038",
"Y": ">=167230",
"R": "<125422"
},
{
"kpi_id": 321,
"kpi_code": "BH_SALES_4",
"period": "2026-H1",
"actual": 285990.0,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": ">=285990",
"Y": ">=228792",
"R": "<171594"
},
{
"kpi_id": 322,
"kpi_code": "BH_SALES_5",
"period": "2026-H1",
"actual": 372816.0,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": ">=372816",
"Y": ">=298253",
"R": "<223690"
},
{
"kpi_id": 323,
"kpi_code": "BH_SALES_6",
"period": "2026-H1",
"actual": 229512.0,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": ">=229512",
"Y": ">=183610",
"R": "<137707"
},
{
"kpi_id": 324,
"kpi_code": "BH_SALES_7",
"period": "2026-H1",
"actual": 35173.0,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": ">=35173",
"Y": ">=28138",
"R": "<21104"
},
{
"kpi_id": 325,
"kpi_code": "BH_SALES_8",
"period": "2026-H1",
"actual": 2327.0,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": ">=2327",
"Y": ">=1862",
"R": "<1396"
},
{
"kpi_id": 326,
"kpi_code": "BH_SALES_9",
"period": "2026-01",
"actual": 625983.0,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": ">=1785",
"Y": ">=1428",
"R": "<1071"
},
{
"kpi_id": 326,
"kpi_code": "BH_SALES_9",
"period": "2026-02",
"actual": 253099.0,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": ">=1785",
"Y": ">=1428",
"R": "<1071"
},
{
"kpi_id": 326,
"kpi_code": "BH_SALES_9",
"period": "2026-03",
"actual": 526357.0,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": ">=1785",
"Y": ">=1428",
"R": "<1071"
},
{
"kpi_id": 326,
"kpi_code": "BH_SALES_9",
"period": "2026-H1",
"actual": 1785.0,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": ">=1785",
"Y": ">=1428",
"R": "<1071"
},
{
"kpi_id": 327,
"kpi_code": "BH_SALES_10",
"period": "2026-02",
"actual": 1486.73,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": ">=200",
"Y": ">=160",
"R": "<120"
},
{
"kpi_id": 327,
"kpi_code": "BH_SALES_10",
"period": "2026-03",
"actual": 2991.15,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": ">=200",
"Y": ">=160",
"R": "<120"
},
{
"kpi_id": 327,
"kpi_code": "BH_SALES_10",
"period": "2026-H1",
"actual": 4180.0,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": ">=200",
"Y": ">=160",
"R": "<120"
},
{
"kpi_id": 408,
"kpi_code": "F_FACTORY_REBATE_RATE",
"period": "2022",
"actual": 103.4,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": ">=100",
"Y": ">=80",
"R": "<80"
},
{
"kpi_id": 408,
"kpi_code": "F_FACTORY_REBATE_RATE",
"period": "2025",
"actual": 107.6,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": ">=100",
"Y": ">=80",
"R": "<80"
},
{
"kpi_id": 408,
"kpi_code": "F_FACTORY_REBATE_RATE",
"period": "2026-01",
"actual": 201.8,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": ">=100",
"Y": ">=80",
"R": "<80"
},
{
"kpi_id": 408,
"kpi_code": "F_FACTORY_REBATE_RATE",
"period": "2026-04",
"actual": 120.5,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": ">=100",
"Y": ">=80",
"R": "<80"
},
{
"kpi_id": 408,
"kpi_code": "F_FACTORY_REBATE_RATE",
"period": "2026-05",
"actual": 110.8,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": ">=100",
"Y": ">=80",
"R": "<80"
},
{
"kpi_id": 408,
"kpi_code": "F_FACTORY_REBATE_RATE",
"period": "2026-08",
"actual": 278.7,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": ">=100",
"Y": ">=80",
"R": "<80"
},
{
"kpi_id": 408,
"kpi_code": "F_FACTORY_REBATE_RATE",
"period": "2026-H1",
"actual": 106.8,
"old": "green",
"new": "green",
"severity_order": "yellow",
"G": ">=100",
"Y": ">=80",
"R": "<80"
}
],
"alert_only_old": [
[
6,
"2026-04",
"yellow"
],
[
6,
"2026-05",
"yellow"
],
[
6,
"2026-07",
"yellow"
],
[
6,
"2026-08",
"yellow"
],
[
407,
"2026-08",
"yellow"
]
],
"alert_only_new": [
[
6,
"2026-04",
"red"
],
[
6,
"2026-05",
"red"
],
[
6,
"2026-07",
"red"
],
[
6,
"2026-08",
"red"
],
[
46,
"2026-01",
"red"
],
[
407,
"2026-08",
"red"
]
],
"alert_gen_dry_run_new": 0
}
backend/scripts/backups/threshold_rows_before_20260911.json
+32
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{
"created_at": "2026-09-11T17:11:24.159913",
"rows": [
{
"id": 46,
"kpi_code": "C_REBATE_RATE",
"kpi_name": "渠补率",
"target_value": 80.0,
"threshold_green": "52.33~107.67",
"threshold_yellow": "偏离55%",
"threshold_red": "偏离>55%"
},
{
"id": 327,
"kpi_code": "BH_SALES_10",
"kpi_name": "销售-任富海",
"target_value": 200.0,
"threshold_green": "",
"threshold_yellow": "偏离182878%",
"threshold_red": "偏离>182878%"
},
{
"id": 32,
"kpi_code": "F_EVA",
"kpi_name": "经济增加值(EVA)",
"target_value": 0.0,
"threshold_green": ">=0",
"threshold_yellow": ">=-100000",
"threshold_red": "<-100000"
}
]
}
backend/scripts/fix_threshold_data_20260911.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""存量阈值数据修正（project-20260911-threshold-fix ⑤）
修正前：
1) 备份 kpi_definitions 全表 → backend/scripts/backups/kpi_definitions.bak-20260911.sql（mysqldump）
2) 记录 3 行的原值 → backups/threshold_rows_before_20260911.json（可回滚）
修正内容（7 个不可解析表达式 → 0）：
id=32 F_EVA : Y='>=-100000' R='<-100000' → 解析器支持负数后自然可解析，无需改值（仅验证）
id=46 C_REBATE_RATE: G='52.33~107.67'（保留原绿区间）
Y='偏离55%' → '<52.33|>107.67'（绿区间外即黄）
R='偏离>55%' → '<=36|>=124'（80±27.67×2 ≈ 原"55"口径）
id=327 BH_SALES_10 : G=''（补） → '>=200'
Y='偏离182878%'→ '>=160'
R='偏离>182878%'→ '<120'
口径对齐同族 BH_SALES_1..9：G>=target / Y>=0.8target / R<0.6target
用法：
python3 scripts/fix_threshold_data_20260911.py # 预演（只打印）
python3 scripts/fix_threshold_data_20260911.py --apply # 备份 + 落库
"""
import os
import sys
import json
import subprocess
from datetime import datetime
BASE = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.insert(0, BASE)
BACKUP_DIR = os.path.join(BASE, "scripts", "backups")
envf = os.path.join(BASE, ".env")
ENV = {}
if os.path.exists(envf):
for line in open(envf, encoding="utf-8"):
line = line.strip()
if line and not line.startswith("#") and "=" in line:
k, v = line.split("=", 1)
ENV[k.strip()] = v.strip().strip('"').strip("'")
os.environ.setdefault(k.strip(), v.strip().strip('"').strip("'"))
from app.database import get_session_local
from app.models import KPIDefinition
from app.utils.threshold_parser import is_valid_threshold
# 目标行号 → 新值（None 表示不改动）
FIXES = {
46: {"threshold_green": "52.33~107.67",
"threshold_yellow": "<52.33|>107.67",
"threshold_red": "<=36|>=124"},
327: {"threshold_green": ">=200",
"threshold_yellow": ">=160",
"threshold_red": "<120"},
}
# 仅验证不修改
VERIFY_ONLY = [32]
FIELDS = ("threshold_green", "threshold_yellow", "threshold_red")
def scan(db):
"""返回 (总表达式数, 不可解析明细)"""
rows = db.query(KPIDefinition).all()
total = 0
bad = []
for k in rows:
for f in FIELDS:
e = getattr(k, f)
if e is None or not str(e).strip():
continue
total += 1
if not is_valid_threshold(e):
bad.append((k.id, k.kpi_code, f, str(e)))
return total, bad
def dump_table_backup():
os.makedirs(BACKUP_DIR, exist_ok=True)
out = os.path.join(BACKUP_DIR, "kpi_definitions.bak-20260911.sql")
cmd = ["mysqldump",
f"-u{ENV.get('CMA_DB_USER', 'cma_user')}",
f"-p{ENV.get('CMA_DB_PASS', '')}",
f"-h{ENV.get('CMA_DB_HOST', '127.0.0.1')}",
f"-P{ENV.get('CMA_DB_PORT', '3306')}",
ENV.get("CMA_DB_NAME", "cma"), "kpi_definitions"]
with open(out, "w", encoding="utf-8") as fh:
p = subprocess.run(cmd, stdout=fh, stderr=subprocess.PIPE)
if p.returncode != 0:
raise SystemExit("mysqldump 失败: " + p.stderr.decode("utf-8", "ignore")[:400])
return out
def main():
apply = "--apply" in sys.argv
db = get_session_local()()
total_before, bad_before = scan(db)
print(f"[before] 非空阈值表达式 {total_before} 条, 不可解析 {len(bad_before)} 条")
for b in bad_before:
print(" BAD", b)
# 记录原值（备份 json）
rows_before = []
for tid in list(FIXES.keys()) + VERIFY_ONLY:
k = db.query(KPIDefinition).filter(KPIDefinition.id == tid).first()
if k:
rows_before.append({"id": k.id, "kpi_code": k.kpi_code, "kpi_name": k.kpi_name,
"target_value": k.target_value,
**{f: getattr(k, f) for f in FIELDS}})
os.makedirs(BACKUP_DIR, exist_ok=True)
jpath = os.path.join(BACKUP_DIR, "threshold_rows_before_20260911.json")
if apply:
with open(jpath, "w", encoding="utf-8") as fh:
json.dump({"created_at": datetime.now().isoformat(), "rows": rows_before},
fh, ensure_ascii=False, indent=2)
sql_path = dump_table_backup()
print(f"[backup] json={jpath}")
print(f"[backup] sql={sql_path}")
else:
print(f"[dry-run] 将写入备份 json={jpath}（未执行）")
for r in rows_before:
print(" before:", r)
# 应用修正
for tid, newvals in FIXES.items():
k = db.query(KPIDefinition).filter(KPIDefinition.id == tid).first()
if not k:
print(f" !! id={tid} 不存在，跳过")
continue
for f, v in newvals.items():
old = getattr(k, f)
print(f" id={tid} {f}: {old!r} -> {v!r}")
if apply:
setattr(k, f, v)
if apply:
db.commit()
# 回查
for tid in list(FIXES.keys()) + VERIFY_ONLY:
k = db.query(KPIDefinition).filter(KPIDefinition.id == tid).first()
if k:
vals = {f: getattr(k, f) for f in FIELDS}
ok = all(is_valid_threshold(v) for v in vals.values())
print(f"[after] id={tid} {k.kpi_code} {vals} parseable={ok}")
total_after, bad_after = scan(db)
print(f"[after] 非空阈值表达式 {total_after} 条, 不可解析 {len(bad_after)} 条")
for b in bad_after:
print(" BAD", b)
db.close()
if not apply:
print("※ 预演模式，未落库。加 --apply 执行。")
if __name__ == "__main__":
main()
backend/scripts/verify_threshold_regression_20260911.py
+157
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""阈值判定变更回归（project-20260911-threshold-fix ⑥）
对比三条链路的判定结果，产出「改前改后差异样本」：
A. 旧实现：alert_generator 顺序 绿→黄→红 + 正则 (>=|<=|>|<|=|!=)\\s*([\\d.]+)（不支持负数/区间）
B. 新实现 red→green→yellow（统一 threshold_parser）
C. 若按任务字面「red→yellow→green」严重度顺序（用于说明为何 green 必须先于 yellow）
输出：逐 (KPI, 期间最新值) 的 A/B 差异清单 + 预警集合差异 + 真实 alert_generator 事务内预演（不落库）
"""
import os
import re
import sys
import json
from collections import Counter
BASE = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.insert(0, BASE)
envf = os.path.join(BASE, ".env")
for line in open(envf, encoding="utf-8"):
line = line.strip()
if line and not line.startswith("#") and "=" in line:
k, v = line.split("=", 1)
os.environ.setdefault(k.strip(), v.strip().strip('"').strip("'"))
from app.database import get_session_local
from app.models import KPIDefinition, KPIValue
from app.utils.threshold_parser import calc_level_from_exprs
OLD_RE = re.compile(r"(>=|<=|>|<|=|!=)\s*([\d.]+)")
def old_level(actual, kpi) -> str:
"""旧实现：绿→黄→红，命中即止；不支持负数/区间。"""
if actual is None:
return "info"
for level, field in (("green", "threshold_green"), ("yellow", "threshold_yellow"),
("red", "threshold_red")):
expr = getattr(kpi, field, None)
if not expr:
continue
m = OLD_RE.match(str(expr).strip())
if not m:
continue
op, val = m.group(1), float(m.group(2))
hit = {">=": actual >= val, "<=": actual <= val, ">": actual > val,
"<": actual < val, "=": actual == val, "!=": actual != val}[op]
if hit:
return level
return "info"
def severity_order_level(actual, kpi) -> str:
"""按任务字面顺序 red→yellow→green（严重度优先）——用于对比说明。"""
for level, field in (("red", "threshold_red"), ("yellow", "threshold_yellow"),
("green", "threshold_green")):
expr = getattr(kpi, field, None)
if not expr:
continue
from app.utils.threshold_parser import eval_threshold
if eval_threshold(actual, expr):
return level
return "info"
def main():
db = get_session_local()()
kpis = db.query(KPIDefinition).filter(KPIDefinition.status == "active").all()
samples = []
diffs_ab = []
diffs_bc = []
count_old = Counter()
count_new = Counter()
for kpi in kpis:
vals = db.query(KPIValue).filter(KPIValue.kpi_id == kpi.id).all()
# 每期间取最新一条（对齐 alert_generator 取数方式）
latest_by_period = {}
for v in vals:
if v.actual_value is None:
continue
key = v.period
cur = latest_by_period.get(key)
if cur is None or (v.calculated_at and cur.calculated_at and v.calculated_at > cur.calculated_at):
latest_by_period[key] = v
for period, v in sorted(latest_by_period.items()):
a = old_level(v.actual_value, kpi)
b = calc_level_from_exprs(v.actual_value, kpi.threshold_green,
kpi.threshold_yellow, kpi.threshold_red)
c = severity_order_level(v.actual_value, kpi)
samples.append({
"kpi_id": kpi.id, "kpi_code": kpi.kpi_code, "period": period,
"actual": v.actual_value, "old": a, "new": b, "severity_order": c,
"G": kpi.threshold_green, "Y": kpi.threshold_yellow, "R": kpi.threshold_red,
})
count_old[a] += 1
count_new[b] += 1
if a != b:
diffs_ab.append(samples[-1])
if c != b:
diffs_bc.append(samples[-1])
print(f"参与回归的 (KPI, 期间) 判定点: {len(samples)}")
print("旧实现级别分布 :", dict(count_old))
print("新实现级别分布 :", dict(count_new))
print(f"\n=== A(旧) vs B(新) 差异 {len(diffs_ab)} 条 ===")
for d in diffs_ab:
print(f" kpi={d['kpi_code']}(id={d['kpi_id']}) {d['period']} actual={d['actual']} "
f"old={d['old']} -> new={d['new']} G={d['G']!r} Y={d['Y']!r} R={d['R']!r}")
print(f"\n=== B(new=red>green>yellow) vs C(red>yellow>green) 差异 {len(diffs_bc)} 条 ===")
for d in diffs_bc[:20]:
print(f" kpi={d['kpi_code']}(id={d['kpi_id']}) {d['period']} actual={d['actual']} "
f"B={d['new']} vs C={d['severity_order']} G={d['G']!r} Y={d['Y']!r} R={d['R']!r}")
if len(diffs_bc) > 20:
print(f" ... 其余 {len(diffs_bc) - 20} 条")
# 预警集合差异（yellow/red 才落库）
old_alerts = [(d['kpi_id'], d['period'], d['old']) for d in samples if d['old'] in ("yellow", "red")]
new_alerts = [(d['kpi_id'], d['period'], d['new']) for d in samples if d['new'] in ("yellow", "red")]
print(f"\n=== 预警集合：旧 {len(old_alerts)} 条 → 新 {len(new_alerts)} 条 ===")
so, sn = set(old_alerts), set(new_alerts)
for x in sorted(so - sn):
print(" 仅旧有(解除):", x)
for x in sorted(sn - so):
print(" 仅新有(新增):", x)
# 真实 alert_generator 事务内预演（阻断 commit，事后 rollback，零落库）
import importlib.util # noqa: E402
_spec = importlib.util.spec_from_file_location(
"cma_alert_generator", os.path.join(BASE, "scripts", "alert_generator.py"))
ag = importlib.util.module_from_spec(_spec)
_spec.loader.exec_module(ag)
from datetime import datetime
period = datetime.now().strftime("%Y-%m")
real_commit = db.commit
db.commit = lambda: None # 阻断落库
try:
new_count = ag.run_alert_check(db, period)
finally:
db.commit = real_commit
db.rollback()
print(f"\n=== alert_generator 事务内预演（period={period}，已 rollback 未落库）新增预警={new_count} ===")
out = os.path.join(BASE, "scripts", "backups", "threshold_regression_20260911.json")
os.makedirs(os.path.dirname(out), exist_ok=True)
with open(out, "w", encoding="utf-8") as fh:
json.dump({"samples": len(samples), "count_old": dict(count_old), "count_new": dict(count_new),
"diff_ab": diffs_ab, "diff_bc": diffs_bc,
"alert_only_old": sorted(so - sn), "alert_only_new": sorted(sn - so),
"alert_gen_dry_run_new": new_count}, fh, ensure_ascii=False, indent=2)
print("报告 JSON:", out)
db.close()
if __name__ == "__main__":
main()
backend/tests/test_threshold_parser.py
+237
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"""
统一阈值解析器 / 建议格式 / 红灯优先判定 测试
（project-20260911-threshold-fix，2026-09-11）
覆盖 4 类缺陷：
① suggest 生成格式必须可解析（原生成区间/文字 → 保存后静默失效）
② 前端/接口格式校验（非法表达式被拒）
③ 解析器支持负数（>=-100000）
⑤ 红灯优先（宽黄灯不得遮蔽红灯）
"""
import pytest
from fastapi.testclient import TestClient
from sqlalchemy.orm import Session
from app.utils.threshold_parser import (
LEVEL_PRIORITY,
calc_level,
calc_level_from_exprs,
eval_threshold,
is_valid_threshold,
parse_threshold,
)
from app.api.thresholds import build_suggestion
from app.api.kpis import validate_threshold_format
from tests.conftest import create_test_user, get_token_for_user, auth_header, create_test_kpi
class TestParseThreshold:
"""解析器：操作符 / 负数 / 区间 / 多条件 / 非法格式"""
def test_scalar_ops(self):
assert parse_threshold(">=32000000") == (">=", 32000000.0)
assert parse_threshold("<20") == ("<", 20.0)
assert parse_threshold("<=55") == ("<=", 55.0)
assert parse_threshold(">60") == (">", 60.0)
assert parse_threshold("=100") == ("=", 100.0)
assert parse_threshold("!=0") == ("!=", 0.0)
def test_negative_numbers(self):
"""缺陷3：原正则 [\\d.]+ 不支持负号 → 现金流类阈值静默失效"""
assert parse_threshold(">=-100000") == (">=", -100000.0)
assert parse_threshold("<-100000") == ("<", -100000.0)
assert eval_threshold(-200000, "<-100000") is True
assert eval_threshold(-50000, "<-100000") is False
def test_whitespace_tolerated(self):
assert parse_threshold(" >= 800 ") == (">=", 800.0)
def test_range(self):
assert parse_threshold("52.33~107.67") == ("~", (52.33, 107.67))
assert eval_threshold(80, "52.33~107.67") is True
assert eval_threshold(52.33, "52.33~107.67") is True # 含端点
assert eval_threshold(120, "52.33~107.67") is False
def test_or_clauses(self):
assert eval_threshold(130, "<=36|>=124") is True
assert eval_threshold(20, "<=36|>=124") is True
assert eval_threshold(80, "<=36|>=124") is False
@pytest.mark.parametrize("bad", ["偏离55%", "偏离>55%", "80%", ">=abc", "52.33~", ">=", ">=800%"])
def test_invalid(self, bad):
assert is_valid_threshold(bad) is False
assert parse_threshold(bad) == (None, None)
assert eval_threshold(100, bad) is False
def test_blank_is_valid_means_unset(self):
assert is_valid_threshold("") is True
assert is_valid_threshold(None) is True
assert eval_threshold(100, "") is False
def test_partial_or_clause_is_invalid(self):
"""多条件中任一子句非法 → 整体非法（防半截表达式静默生效）"""
assert is_valid_threshold("<=36|abc") is False
class TestCalcLevel:
def test_red_priority_over_yellow(self):
"""缺陷5：id=6 实测 Y='>76' R='>91'，值 95 原判黄（红灯不可达）→ 必须判红"""
assert calc_level_from_exprs(95, "<=76", ">76", ">91") == "red"
assert calc_level_from_exprs(152.62, "<=76", ">76", ">91") == "red"
def test_green_priority_over_yellow(self):
"""green 是主条件、yellow 是更宽兜底带（id=1 G='>=800' Y='>=700'）：
值 900 同时满足两条，用户意图是绿，不能被黄截胡"""
assert calc_level_from_exprs(900, ">=800", ">=700", "<600") == "green"
assert calc_level_from_exprs(750, ">=800", ">=700", "<600") == "yellow"
assert calc_level_from_exprs(500, ">=800", ">=700", "<600") == "red"
def test_lower_better(self):
assert calc_level_from_exprs(20, "<=25", "<=30", ">30") == "green"
assert calc_level_from_exprs(28, "<=25", "<=30", ">30") == "yellow"
assert calc_level_from_exprs(35, "<=25", "<=30", ">30") == "red"
def test_negative_cashflow(self):
"""缺陷3 场景：F_EVA G='>=0' Y='>=-100000' R='<-100000'"""
assert calc_level_from_exprs(-200000, ">=0", ">=-100000", "<-100000") == "red"
assert calc_level_from_exprs(-50000, ">=0", ">=-100000", "<-100000") == "yellow"
assert calc_level_from_exprs(100, ">=0", ">=-100000", "<-100000") == "green"
def test_moderate_band(self):
"""适中型：绿=内区间、黄=环带、红=外区间"""
g, y, r = "72~88", "<72|>88", "<=64|>=96"
assert calc_level_from_exprs(80, g, y, r) == "green"
assert calc_level_from_exprs(70, g, y, r) == "yellow"
assert calc_level_from_exprs(98, g, y, r) == "red"
def test_priority_constant(self):
assert LEVEL_PRIORITY == ("red", "green", "yellow")
def test_no_threshold_is_info(self):
assert calc_level_from_exprs(100, None, None, None) == "info"
assert calc_level_from_exprs(None, ">=1", None, None) == "info"
def test_calc_level_with_kpi_object(self):
class K:
threshold_green = "<=76"
threshold_yellow = ">76"
threshold_red = ">91"
assert calc_level(95, K()) == "red"
assert calc_level(70, K()) == "green"
class TestSuggestFormat:
"""缺陷1：suggest 生成值必须全部可解析（点“自动建议”填入后不再静默失效）"""
@pytest.mark.parametrize("kpi_type,target", [
("higher_better", 1200.0), ("lower_better", 80.0),
("middle_best", 80.0),
])
def test_all_labels_parseable(self, kpi_type, target):
s = build_suggestion(kpi_type, target, tolerance=10.0)
for level in ("green", "yellow", "red"):
label = s[level]["label"]
assert label, f"{kpi_type}.{level} 缺 label"
assert is_valid_threshold(label), f"{kpi_type}.{level} 不可解析: {label}"
def test_higher_better_labels(self):
s = build_suggestion("higher_better", 1000.0)
assert s["green"]["label"] == ">=800"
assert s["yellow"]["label"] == ">=500"
assert s["red"]["label"] == "<500"
# 三档构成完整划分
assert calc_level_from_exprs(900, ">=800", ">=500", "<500") == "green"
assert calc_level_from_exprs(600, ">=800", ">=500", "<500") == "yellow"
assert calc_level_from_exprs(400, ">=800", ">=500", "<500") == "red"
def test_lower_better_labels(self):
s = build_suggestion("lower_better", 100.0)
assert s["green"]["label"] == "<=120"
assert s["yellow"]["label"] == "<=200"
assert s["red"]["label"] == ">200"
assert calc_level_from_exprs(100, "<=120", "<=200", ">200") == "green"
assert calc_level_from_exprs(150, "<=120", "<=200", ">200") == "yellow"
assert calc_level_from_exprs(250, "<=120", "<=200", ">200") == "red"
def test_middle_best_labels_partition(self):
s = build_suggestion("middle_best", 100.0, tolerance=10.0)
assert s["green"]["label"] == "90~110"
# 黄 = 外环带 T±2tol（green ⊂ yellow）；因判定顺序 red→green→yellow，绿先命中，
# 等价于严格分区：绿=90~110 / 黄=80~120 / 红=之外
assert s["yellow"]["label"] == "80~120"
assert s["red"]["label"] == "<80|>120"
g = s["green"]["label"]
y = s["yellow"]["label"]
r = s["red"]["label"]
assert calc_level_from_exprs(100, g, y, r) == "green"
assert calc_level_from_exprs(95, g, y, r) == "green" # 90~110 内 = 绿
assert calc_level_from_exprs(85, g, y, r) == "yellow" # 环带
assert calc_level_from_exprs(119, g, y, r) == "yellow" # 环带
# 边界归属与 higher_better/lower_better 保持一致：恰好落在 ±2tol 边界 → 黄（红为开区间）
assert calc_level_from_exprs(80, g, y, r) == "yellow"
assert calc_level_from_exprs(120, g, y, r) == "yellow"
assert calc_level_from_exprs(79, g, y, r) == "red"
assert calc_level_from_exprs(121, g, y, r) == "red"
def test_labels_avoid_scientific_notation(self):
"""大数目标（>=1e6）不得生成 1.6e+06 这类科学计数法（解析器不认）。"""
s = build_suggestion("higher_better", 2_000_000.0)
assert s["green"]["label"] == ">=1600000"
assert s["yellow"]["label"] == ">=1000000"
assert s["red"]["label"] == "<1000000"
for level in ("green", "yellow", "red"):
assert is_valid_threshold(s[level]["label"]), s[level]["label"]
s2 = build_suggestion("middle_best", 1_500_000.0, tolerance=100_000.0)
for level in ("green", "yellow", "red"):
assert is_valid_threshold(s2[level]["label"]), s2[level]["label"]
def test_middle_best_zero_tolerance_guard(self):
"""容忍度退化时不得生成 A~A 之类不可用表达式"""
s = build_suggestion("middle_best", 0.0, tolerance=0.0)
for level in ("green", "yellow", "red"):
assert is_valid_threshold(s[level]["label"])
class TestThresholdValidation:
"""缺陷2：非法格式必须被拒（接口层 422），不再静默入库"""
def test_validate_helper(self):
assert validate_threshold_format({"threshold_green": ">=800"}) == []
assert validate_threshold_format({"threshold_yellow": ""}) == []
assert validate_threshold_format({"threshold_red": None}) == []
errs = validate_threshold_format({"threshold_yellow": "偏离55%"})
assert len(errs) == 1 and "阈值格式非法" in errs[0]
def test_update_kpi_rejects_bad_threshold(self, client: TestClient, db: Session):
create_test_user(db)
token = get_token_for_user(client)
kpi = create_test_kpi(db)
resp = client.put(f"/api/cma/kpis/{kpi.id}", headers=auth_header(token),
json={"threshold_yellow": "偏离55%"})
assert resp.status_code == 422
assert "阈值格式非法" in str(resp.json())
def test_update_kpi_accepts_valid_thresholds(self, client: TestClient, db: Session):
create_test_user(db)
token = get_token_for_user(client)
kpi = create_test_kpi(db)
for good in (">=800", ">=-100000", "52.33~107.67", "<=36|>=124", ""):
resp = client.put(f"/api/cma/kpis/{kpi.id}", headers=auth_header(token),
json={"threshold_green": good})
assert resp.status_code == 200, f"{good} 应被接受"
class TestSuggestApi:
"""接口级：suggest 返回的 label 可解析 + 挂 format_hint"""
def test_suggest_by_type_parseable(self, client: TestClient, db: Session):
create_test_user(db)
token = get_token_for_user(client)
kpi = create_test_kpi(db, kpi_code="F_REVENUE", target_value=1000)
resp = client.get(f"/api/cma/thresholds/suggest/{kpi.id}", headers=auth_header(token))
assert resp.status_code == 200
body = resp.json()
labels = [body["suggestion"][lv]["label"] for lv in ("green", "yellow", "red")]
for lb in labels:
assert is_valid_threshold(lb), f"建议值不可解析: {lb}"
assert body.get("format_hint")
backend/tests/test_threshold_parser_20260911.py
+263
View File
@@ -0,0 +1,263 @@
# -*- coding: utf-8 -*-
"""统一阈值解析器 + 红黄绿判定顺序（红灯优先）单测
任务：project-20260911-threshold-fix
覆盖：解析器（正/负/小数/区间/多条件/非法）、判定顺序、双调用方接线、存量表达式回归。
"""
import os
import sys
import pytest
BACKEND = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
if BACKEND not in sys.path:
sys.path.insert(0, BACKEND)
from app.utils.threshold_parser import ( # noqa: E402
LEVEL_PRIORITY,
calc_level,
calc_level_from_exprs,
describe_level,
eval_threshold,
is_valid_threshold,
parse_clauses,
parse_threshold,
)
class _Kpi:
"""轻量替身：只需要 3 个阈值属性"""
def __init__(self, g=None, y=None, r=None):
self.threshold_green = g
self.threshold_yellow = y
self.threshold_red = r
# ───────────────────────── ① 解析器 ─────────────────────────
@pytest.mark.parametrize("expr,expected", [
(">=800", (">=", 800.0)),
("<=25", ("<=", 25.0)),
(">60", (">", 60.0)),
("<20", ("<", 20.0)),
("=100", ("=", 100.0)),
("!=0", ("!=", 0.0)),
(">=-100000", (">=", -100000.0)), # 缺陷3：负数
("<-100000", ("<", -100000.0)),
("<-1.5", ("<", -1.5)),
(">=1.0", (">=", 1.0)),
(">= 800", (">=", 800.0)), # 允许空白
(" >=800 ", (">=", 800.0)),
("52.33~107.67", ("~", (52.33, 107.67))), # 区间
("-182678~183078", ("~", (-182678.0, 183078.0))),
])
def test_parse_threshold_ok(expr, expected):
assert parse_threshold(expr) == expected
@pytest.mark.parametrize("expr", ["偏离55%", "偏离>55%", "80%", ">=abc", "~", ">=", "52.33~",
">=1>=2", "abc", ">=800元", ">=- 100"])
def test_parse_threshold_invalid(expr):
assert parse_threshold(expr) == (None, None)
assert is_valid_threshold(expr) is False
@pytest.mark.parametrize("expr", [None, "", " "])
def test_blank_is_valid_but_no_clause(expr):
"""空串 = 不设置阈值：视为合法，但无子句。"""
assert is_valid_threshold(expr) is True
assert parse_clauses(expr) == []
assert parse_threshold(expr) == (None, None)
def test_multi_clause():
assert is_valid_threshold("<=36|>=124") is True
assert is_valid_threshold("<=36 | >=124") is True
assert is_valid_threshold("<=36｜>=124") is True # 全角竖线
assert is_valid_threshold("<=36|abc") is False
assert parse_clauses("<=36|>=124") == [("<=", 36.0), (">=", 124.0)]
@pytest.mark.parametrize("actual,expr,expected", [
(800, ">=800", True), (799.99, ">=800", False),
(25, "<=25", True), (25.01, "<=25", False),
(61, ">60", True), (60, ">60", False),
(19, "<20", True), (20, "<20", False),
(-100000, ">=-100000", True), (-100001, ">=-100000", False),
(-100001, "<-100000", True),
(80, "52.33~107.67", True), (52.33, "52.33~107.67", True),
(107.67, "52.33~107.67", True), (107.68, "52.33~107.67", False),
(200, "<24.66|>135.34", True), (10, "<24.66|>135.34", True),
(80, "<24.66|>135.34", False),
(None, ">=0", False),
(5, "偏离55%", False), # 非法表达式 → 不命中（不静默乱判）
(5, "", False),
])
def test_eval_threshold(actual, expr, expected):
assert eval_threshold(actual, expr) is expected
# ─────────────────── ② 判定顺序：红灯优先 ───────────────────
def test_level_priority_is_red_first():
assert LEVEL_PRIORITY == ("red", "green", "yellow")
def test_defect5_yellow_no_longer_masks_red():
"""缺陷5 实测：id=6 G='<=76' Y='>76' R='>91'，值 95 必须判红（原来判黄）。"""
kpi = _Kpi(g="<=76", y=">76", r=">91")
assert calc_level(95, kpi) == "red" # 关键用例
assert calc_level(92, kpi) == "red"
assert calc_level(91, kpi) == "yellow" # 未过 >91 → 黄
assert calc_level(80, kpi) == "yellow"
assert calc_level(76, kpi) == "green" # <=76 → 绿
assert calc_level(70, kpi) == "green"
def test_green_beats_wide_yellow():
"""id=1 实测：G='>=800' Y='>=700'，值 900 不能判黄。"""
kpi = _Kpi(g=">=800", y=">=700", r="<600")
assert calc_level(900, kpi) == "green"
assert calc_level(750, kpi) == "yellow"
assert calc_level(500, kpi) == "red"
assert calc_level(650, kpi) == "info" # 600~700 无命中
def test_negative_thresholds():
"""id=32：Y='>=-100000' R='<-100000' 存量负数表达式（缺陷3）。"""
kpi = _Kpi(g=">=0", y=">=-100000", r="<-100000")
assert calc_level(100, kpi) == "green"
assert calc_level(-50000, kpi) == "yellow"
assert calc_level(-200000, kpi) == "red"
def test_moderate_range_semantics():
"""区间型（middle_best）：绿内区间 → 黄外环 → 红区间外。"""
kpi = _Kpi(g="52.33~107.67", y="24.66~135.34", r="<24.66|>135.34")
assert calc_level(80, kpi) == "green"
assert calc_level(52.8, kpi) == "green"
assert calc_level(124.1, kpi) == "yellow"
assert calc_level(30, kpi) == "yellow"
assert calc_level(200, kpi) == "red"
assert calc_level(10, kpi) == "red"
def test_no_threshold_and_none_actual():
assert calc_level(100, _Kpi()) == "info"
assert calc_level(None, _Kpi(g=">=0", y=">=0", r=">=0")) == "info"
assert calc_level_from_exprs(50, green=None, yellow=None, red=None) == "info"
def test_describe_level():
kpi = _Kpi(g="<=76", y=">76", r=">91")
assert describe_level(95, kpi) == ("red", ">91")
assert describe_level(80, kpi) == ("yellow", ">76")
assert describe_level(70, kpi) == ("green", "<=76")
assert describe_level(0, kpi) == ("green", "<=76")
assert describe_level(None, kpi) == ("info", None)
# ─────────────────── ③ 双调用方接线（消除双实现） ───────────────────
def test_alert_generator_uses_unified_parser():
"""alert_generator 必须委托统一解析器（负数/区间/多条件都生效）。"""
from scripts import alert_generator as ag
assert ag._parse_threshold(">=-100000") == (">=", -100000.0)
assert ag._parse_threshold("52.33~107.67") == ("~", (52.33, 107.67))
trig, msg = ag._check_threshold(95, ">91", "red")
assert trig is True and ">91" in msg
assert ag._check_threshold(95, ">91x", "red") == (False, "")
assert ag._check_threshold(None, ">91", "red") == (False, "")
# 区间 + 多条件（旧实现做不到）
assert ag._check_threshold(124.1, "24.66~135.34", "yellow")[0] is True
assert ag._check_threshold(10, "<24.66|>135.34", "red")[0] is True
def test_alert_generator_loop_order_is_red_first():
"""判定循环顺序必须与 LEVEL_PRIORITY 一致（红灯优先）。"""
import inspect
from scripts import alert_generator as ag
src = inspect.getsource(ag.run_alert_check)
assert "for level in LEVEL_PRIORITY" in src
assert 'for level in ["green", "yellow", "red"]' not in src
def test_kpis_static_threshold_level_uses_unified_parser():
from app.api.kpis import _static_threshold_level
kpi = _Kpi(g=">=0", y=">=-100000", r="<-100000")
assert _static_threshold_level(-200000, kpi) == "red"
assert _static_threshold_level(-50000, kpi) == "yellow"
assert _static_threshold_level(100, kpi) == "green"
def test_kpis_validate_threshold_format():
from app.api.kpis import validate_threshold_format
assert validate_threshold_format({}) == []
assert validate_threshold_format({"threshold_green": ">=800", "threshold_yellow": "",
"threshold_red": None}) == []
errs = validate_threshold_format({"threshold_yellow": "偏离55%"})
assert len(errs) == 1 and "threshold_yellow" in errs[0]
errs2 = validate_threshold_format({"threshold_green": "80%", "threshold_red": "偏离>55%"})
assert len(errs2) == 2
def test_suggest_labels_are_parseable():
"""suggest 生成的 label 必须可解析（缺陷1 根因）。"""
from app.api import thresholds as th
def _walk(obj, acc):
if isinstance(obj, dict):
if "label" in obj and obj.get("label") is not None:
acc.append(obj["label"])
for v in obj.values():
_walk(v, acc)
elif isinstance(obj, list):
for v in obj:
_walk(v, acc)
class _K:
def __init__(self, code, target):
self.id = 1
self.kpi_code = code
self.kpi_name = code
self.target_value = target
for code in ("SALES_TOTAL", "COST_CONTROL_RATE", "TOP5_CUSTOMER_RATIO", "MADE_UP_CODE"):
out = th.suggest_by_type(_K(code, 80.0))
labels = []
_walk(out, labels)
assert labels, code
for lb in labels:
assert is_valid_threshold(lb), f"{code} 生成了不可解析 label: {lb!r}"
def test_existing_266_expressions_still_parse():
"""回归：库里现存的表达式语法必须全部可解析（除已修正的存量 4 条外为 0 不可解析）。"""
from app.database import get_session_local
from app.models import KPIDefinition
try:
db = get_session_local()()
except Exception as e: # 无DB环境（CI）时跳过
pytest.skip(f"DB 不可用: {e}")
try:
rows = db.query(KPIDefinition).filter(
(KPIDefinition.threshold_green.isnot(None)) |
(KPIDefinition.threshold_yellow.isnot(None)) |
(KPIDefinition.threshold_red.isnot(None))
).all()
bad = []
for k in rows:
for f in ("threshold_green", "threshold_yellow", "threshold_red"):
e = getattr(k, f)
if e and str(e).strip() and not is_valid_threshold(e):
bad.append((k.id, k.kpi_code, f, str(e)))
assert bad == [], f"仍存在不可解析阈值: {bad}"
finally:
db.close()