235 lines
10 KiB
Python
235 lines
10 KiB
Python
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"""管理域确定性异常规则(批次B Task B3,v1 §5.4 异常卡片)。
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4 条规则,输入=当前+个人基线 PeriodMetrics,输出异常卡片 dict 列表。
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全部确定性——不使用 LLM 推断,reason/title/suggested_action 均为固定
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中文模板填数,reason 只描述数据事实(百分比/样本数/未回串数)。
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规则表(触发比较均为严格 > / <):
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- reply_p90_worsened : cur.fr_p90_ms > base×1.5 且 cur.cust_threads≥
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soft_threads;>×2.5 升 critical
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- reply_rate_dropped : derive(reply_2h_rate) cur < base 同式×0.7 且
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cur.cust_threads≥soft_threads(率由 scoring.
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derive_metrics 派生,与评分同一口径)
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- activity_drop : cur 日均 (msg_sent+msg_recv)/n_days < base 日均
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(…/28)×0.5 且 base.days_with_data≥min_days
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(基线太稀疏不可比;基线窗口恒 28 天=baseline_window
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语义,当前窗口天数由 n_days 传入)
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- workload_overload : cur.active_friends(周期 distinct) > workload 维
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active_friends_band.high(与评分 band 同源阈值)
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无基线不判:baseline=None 直接返回 [](新员工无个人对比位即不出卡,
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含与基线无关的 overload——新入职负载爬坡期误报多,不判);某侧值
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None/不可算(无串样本的率、无 fact 的分位、0ms 退化基线)→ 对应规则
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不触发。
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纯函数(无 session/IO):B4 dashboard/employees 对全员逐个调用。
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阈值不硬编码:soft_threads/min_days 取 config.sample_guards,过载上限取
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config.dimensions.workload.metrics.active_friends_band.high;乘数
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(1.5/2.5/0.7/0.5)是规则语义本身,定义为模块常量而非可调参数。
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"""
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from __future__ import annotations
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from app.management.employee_metrics import PeriodMetrics
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from app.management.scoring import derive_metrics
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# 规则乘数(语义常量,非可调参数——调整即换规则版本)
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P90_WARN_MULTIPLIER = 1.5
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P90_CRITICAL_MULTIPLIER = 2.5
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RATE_DROP_MULTIPLIER = 0.7
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ACTIVITY_DROP_MULTIPLIER = 0.5
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BASELINE_WINDOW_DAYS = 28 # 个人基线窗口恒 28 天(employee_metrics.baseline_window)
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# ---- 固定文案模板(.format 填数,reason 只含数据事实) ----
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TITLE_P90 = "回复 P90 较个人基线上升 {pct}%"
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TITLE_RATE = "2h 回复率较个人基线下降 {pct}%"
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TITLE_ACTIVITY = "日均消息量较个人基线下降 {pct}%"
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TITLE_OVERLOAD = "活跃客户 {current} 人超过负载上限 {limit} 人"
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REASON_P90 = (
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"近 {n_days} 天首响 P90 为 {cur}(基线前 28 天为 {base}),上升 {pct}%;"
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"期内 {threads} 串客户消息中 {unreplied} 串未回"
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)
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REASON_RATE = (
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"近 {n_days} 天 2h 回复率为 {cur:.0%}({cur_cnt}/{cur_thr} 串),"
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"基线(前 28 天)为 {base:.0%}({base_cnt}/{base_thr} 串);"
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"期内 {unreplied} 串未回"
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)
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REASON_ACTIVITY = (
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"近 {n_days} 天日均消息 {cur:.1f} 条/天,基线(前 28 天)日均 {base:.1f} 条/天,"
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"下降 {pct}%;当前活跃客户 {friends} 人"
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)
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REASON_OVERLOAD = (
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"近 {n_days} 天活跃客户 {current} 人(周期 distinct 口径),"
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"超过配置上限 {limit} 人"
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)
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ACTION_P90 = "查看超时未回客户明细,与员工确认近期负载与接待安排"
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ACTION_RATE = "查看未回串明细,排查回复流程与时段覆盖问题"
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ACTION_ACTIVITY = "与员工确认在岗状态与客户维护情况,并核对账号数据接入是否正常"
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ACTION_OVERLOAD = "评估客户分配与负载均衡,考虑分流部分客户或安排协助"
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def _fmt_ms(ms: float) -> str:
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# 与 scoring._fmt_ms 同式(模块私有不跨文件引用)
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minutes = ms / 60_000
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if minutes >= 60 and minutes % 60 == 0:
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return f"{minutes // 60:g}h"
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return f"{minutes:g}min"
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def _pct_up(cur: float, base: float) -> int:
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"""较基线上升百分比(四舍五入取整;触发后调用,base>0 已保证)。"""
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return int(round((cur - base) / base * 100))
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def _pct_down(cur: float, base: float) -> int:
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"""较基线下降百分比(触发后调用,base>0 已由触发条件保证)。"""
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return int(round((base - cur) / base * 100))
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def detect_anomalies(
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user_id: str,
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current: PeriodMetrics,
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baseline: PeriodMetrics | None,
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*,
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n_days: int,
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config: dict,
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) -> list[dict]:
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"""单员工确定性异常检测(纯函数,卡片结构见 v1 §5.4/模块 docstring)。
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输出按规则表序:p90 恶化 → 回复率骤降 → 活动骤降 → 过载
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(排序/聚合由 B4 调用方按 severity/affected_count 处理)。
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"""
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if baseline is None:
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return [] # 无基线不判(新员工)
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guards = config.get("sample_guards", {})
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soft = guards.get("soft_threads", 10)
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min_days = guards.get("min_days", 5)
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band_high = (
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config.get("dimensions", {})
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.get("workload", {})
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.get("metrics", {})
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.get("active_friends_band", {})
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.get("high")
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)
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cards: list[dict] = []
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unreplied = current.cust_threads - current.threads_answered
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threads_ok = current.cust_threads >= soft
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# ---- 1. reply_p90_worsened:P90 较个人基线恶化 ×1.5/×2.5 ----
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if (
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threads_ok
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and current.fr_p90_ms is not None
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and baseline.fr_p90_ms is not None
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and baseline.fr_p90_ms > 0 # 0ms 基线为退化数据,不可比
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):
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base_p90 = baseline.fr_p90_ms
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if current.fr_p90_ms > base_p90 * P90_CRITICAL_MULTIPLIER:
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severity = "critical"
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elif current.fr_p90_ms > base_p90 * P90_WARN_MULTIPLIER:
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severity = "warning"
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else:
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severity = None
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if severity is not None:
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pct = _pct_up(current.fr_p90_ms, base_p90)
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cards.append({
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"rule_code": "reply_p90_worsened",
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"severity": severity,
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"employee_user_id": user_id,
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"title": TITLE_P90.format(pct=pct),
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"current_value": current.fr_p90_ms,
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"baseline_value": base_p90,
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"unit": "ms",
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"sample_size": current.cust_threads,
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"affected_count": unreplied,
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"reason": REASON_P90.format(
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n_days=n_days, cur=_fmt_ms(current.fr_p90_ms),
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base=_fmt_ms(base_p90), pct=pct,
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threads=current.cust_threads, unreplied=unreplied,
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),
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"drilldown": {"type": "metric", "query": {
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"metric_code": "first_reply_p90", "user_id": user_id}},
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"suggested_action": ACTION_P90,
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})
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# ---- 2. reply_rate_dropped:2h 回复率较基线降 30%+ ----
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if threads_ok:
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cur_rate = derive_metrics(current)["reply_2h_rate"]
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base_rate = derive_metrics(baseline)["reply_2h_rate"]
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if (
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cur_rate is not None and base_rate is not None
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and cur_rate < base_rate * RATE_DROP_MULTIPLIER
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):
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cards.append({
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"rule_code": "reply_rate_dropped",
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"severity": "warning",
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"employee_user_id": user_id,
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"title": TITLE_RATE.format(
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pct=_pct_down(cur_rate, base_rate)),
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"current_value": cur_rate,
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"baseline_value": base_rate,
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"unit": "0-1",
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"sample_size": current.cust_threads,
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"affected_count": unreplied,
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"reason": REASON_RATE.format(
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n_days=n_days, cur=cur_rate, cur_cnt=current.reply_2h_count,
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cur_thr=current.cust_threads, base=base_rate,
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base_cnt=baseline.reply_2h_count,
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base_thr=baseline.cust_threads, unreplied=unreplied,
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),
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"drilldown": {"type": "metric", "query": {
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"metric_code": "unreplied", "user_id": user_id}},
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"suggested_action": ACTION_RATE,
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})
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# ---- 3. activity_drop:日均消息量较基线腰斩 ----
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if n_days > 0 and baseline.days_with_data >= min_days:
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cur_daily = (current.msg_sent + current.msg_recv) / n_days
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base_daily = (
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baseline.msg_sent + baseline.msg_recv
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) / BASELINE_WINDOW_DAYS
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if cur_daily < base_daily * ACTIVITY_DROP_MULTIPLIER:
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cards.append({
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"rule_code": "activity_drop",
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"severity": "warning",
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"employee_user_id": user_id,
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"title": TITLE_ACTIVITY.format(
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pct=_pct_down(cur_daily, base_daily)),
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"current_value": cur_daily,
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"baseline_value": base_daily,
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"unit": "条",
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"sample_size": current.active_friends,
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"affected_count": current.active_friends,
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"reason": REASON_ACTIVITY.format(
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n_days=n_days, cur=cur_daily, base=base_daily,
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pct=_pct_down(cur_daily, base_daily),
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friends=current.active_friends,
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),
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"drilldown": {"type": "employee",
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"query": {"user_id": user_id}},
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"suggested_action": ACTION_ACTIVITY,
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})
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# ---- 4. workload_overload:活跃客户超 band 上限 ----
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if band_high is not None and current.active_friends > band_high:
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cards.append({
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"rule_code": "workload_overload",
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"severity": "warning",
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"employee_user_id": user_id,
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"title": TITLE_OVERLOAD.format(
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current=current.active_friends, limit=band_high),
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"current_value": current.active_friends,
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"baseline_value": None, # 与个人基线无关,阈值在 band 配置
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"unit": "人",
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"sample_size": current.active_friends,
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"affected_count": current.active_friends,
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"reason": REASON_OVERLOAD.format(
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n_days=n_days, current=current.active_friends,
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limit=band_high,
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),
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"drilldown": {"type": "employee", "query": {"user_id": user_id}},
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"suggested_action": ACTION_OVERLOAD,
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})
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return cards
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