浅念
144 lines
7.7 KiB
Python
144 lines
7.7 KiB
Python
"""管理域指标元数据与默认评分配置(批次B,v2 §6/§10)。
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METRIC_DEFINITIONS 是 /api/management/metric-definitions 的单一数据源:
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名称/单位/方向/口径说明(含与现有看板的三方口径差异标注)。
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DEFAULT_SCORE_CONFIG 为 v1.0 试算起点——所有阈值标「试算」,调整走
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新配置版本(employee_score_config),不静默改数。
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"""
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from __future__ import annotations
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import copy
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import sqlalchemy as sa
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# direction: +1 越大越好 / -1 越小越好;caliber 注明与看板差异
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METRIC_DEFINITIONS: dict[str, dict] = {
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"msg_sent": {"name": "发出消息(1:1)", "unit": "条", "direction": 1,
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"caliber": "仅私聊;现有看板为私聊+群聊混合"},
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"msg_recv": {"name": "收到消息(1:1)", "unit": "条", "direction": 1,
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"caliber": "仅私聊"},
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"cust_threads": {"name": "客户消息串", "unit": "串", "direction": 1,
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"caliber": "跨日精确,按串末条归属日;看板为日切口径(多日串重复计)"},
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"threads_answered": {"name": "被回串数", "unit": "串", "direction": 1,
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"caliber": "≡first_reply_fact行数(同源)"},
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"fr_avg_ms": {"name": "平均首响", "unit": "ms", "direction": -1,
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"caliber": "fr_sum_ms/threads_answered"},
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"fr_p50_ms": {"name": "首响中位数", "unit": "ms", "direction": -1,
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"caliber": "周期内 fact 精确分位(窗口函数)"},
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"fr_p90_ms": {"name": "首响P90", "unit": "ms", "direction": -1,
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"caliber": "周期内 fact 精确分位;日切口径会丢跨夜长尾"},
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"reply_2h_rate": {"name": "2h回复率", "unit": "0-1", "direction": 1,
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"formula": "count(fr_ms<=2h)/cust_threads",
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"caliber": "分子分母同源(fact/宽表同一算法)"},
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"reply_24h_rate": {"name": "24h回复率", "unit": "0-1", "direction": 1,
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"formula": "count(fr_ms<=24h)/cust_threads"},
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"active_friends": {"name": "活跃客户(周期)", "unit": "人", "direction": 1,
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"caliber": "期内distinct客户(msg_daily_stats直查),非宽表日值求和"},
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"active_rooms": {"name": "活跃群(日均)", "unit": "个", "direction": 1},
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"new_customer_count": {"name": "新客(期末)", "unit": "人", "direction": 1,
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"caliber": "期末单日持有状态,非期内求和"},
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"cooling_customer_count": {"name": "降温客户(期末)", "unit": "人", "direction": -1,
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"caliber": "距今天数口径(7-29天);频次基线口径二期"},
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"dormant_customer_count": {"name": "沉睡客户(期末)", "unit": "人", "direction": -1,
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"caliber": "≥30天且历史≥5条"},
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"reactivated_customer_count": {"name": "回流客户(期内)", "unit": "人", "direction": 1,
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"caliber": "当日回流且前次间隔>30天,期内求和"},
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"call_count": {"name": "通话数", "unit": "通", "direction": 1,
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"caliber": "语音通话(微信+WA),无消息渠道维度"},
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"answered_rate": {"name": "接通率", "unit": "0-1", "direction": 1,
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"formula": "answered_call/call_count", "caliber": "接通=duration>0"},
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"call_followup_rate": {"name": "通话后跟进率", "unit": "0-1", "direction": 1,
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"formula": "call_followup_count/answered_call",
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"caliber": "接通后24h内任一账号有1:1消息(含客户发来)"},
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"active_share": {"name": "活跃客户占比", "unit": "0-1", "direction": 1,
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"formula": "期末active_friends/(期末active+cooling+dormant)",
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"caliber": "试算口径:期末单日存量;宽表无生命周期active存量列,active以active_friends列充当"},
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}
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# 阈值全部为试算起点(v2 §0 决议:全默认+试算标记);type 三种:
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# target : floor..target 线性 0..100(方向已含在 target>floor)
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# reverse : ≤target_ms 100 分,≥floor_ms 0 分,中间线性降
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# band : 区间内 80..100,低于下限不评分(None),超上限不再加分
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DEFAULT_SCORE_CONFIG: dict = {
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"version": "v1.0",
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"name": "一期试算(3维可用)",
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"normalization": "available_only_rescale", # 可用维度 100/Σweight 再 ×100
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"trial": True,
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"sample_guards": {"min_threads": 30, "soft_threads": 10, "min_days": 5},
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"grade_bounds": {"A": 90, "B": 80, "C": 70, "D": 60}, # 低于 D 线为 E
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"dimensions": {
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"customer": {"weight": 30, "name": "客户经营", "metrics": {
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"active_share": {"type": "target", "floor": 0.20, "target": 0.60},
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}},
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"service": {"weight": 25, "name": "服务质量", "metrics": {
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"reply_2h_rate": {"type": "target", "floor": 0.40, "target": 0.85},
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"reply_24h_rate": {"type": "target", "floor": 0.75, "target": 0.97},
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"fr_p90_ms": {"type": "reverse", "target_ms": 1_800_000,
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"floor_ms": 14_400_000}, # 30min..4h
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}},
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"action": {"weight": 20, "name": "行动执行",
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"status": "pending_data"}, # 批次E 接 followups/任务
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"workload": {"weight": 15, "name": "有效负载", "metrics": {
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"active_friends_band": {"type": "band", "metric": "active_friends",
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"low": 5, "high": 80,
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"basis": "period_daily_avg"},
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"call_band": {"type": "band", "metric": "call_count",
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"low": 1, "high": 200, "basis": "period_total",
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"note": "通话低不扣分仅提示;band 得分下限 80"},
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}},
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"risk": {"weight": 10, "name": "合规风险",
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"status": "pending_data"}, # 批次E/三期接风险事件
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},
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}
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def validate_config(config: dict) -> list[str]:
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"""配置校验:权重总和/结构/引用的 metric_code 有元数据。返回问题清单。"""
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problems: list[str] = []
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dims = config.get("dimensions", {})
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total = sum(d.get("weight", 0) for d in dims.values())
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if total != 100:
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problems.append(f"维度权重总和须=100,当前 {total}")
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for key, dim in dims.items():
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if dim.get("status") == "pending_data":
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continue
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for code, spec in dim.get("metrics", {}).items():
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src = spec.get("metric", code)
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if src not in METRIC_DEFINITIONS:
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problems.append(f"{key}.{code} 引用未定义指标 {src}")
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return problems
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async def _seed_score_config(session) -> str | None:
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"""种子默认评分配置;已存在同版本返回 None(幂等)。纯会话函数可测。
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同 ensure_starter_words 先例:域包提供纯会话种子函数,CLI 只做接线。
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config_json 用 deepcopy 冻结快照——JSON 列须整列重赋值,深拷贝防止
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长驻 API 进程未来的原地修改波及模块常量。
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"""
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from app.db.models import EmployeeScoreConfig
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problems = validate_config(DEFAULT_SCORE_CONFIG)
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if problems:
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raise ValueError(f"score config invalid: {problems}")
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exists = (
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await session.execute(
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sa.select(EmployeeScoreConfig.id).where(
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EmployeeScoreConfig.version == DEFAULT_SCORE_CONFIG["version"]
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)
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)
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).scalar_one_or_none()
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if exists is not None:
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return None
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session.add(
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EmployeeScoreConfig(
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version=DEFAULT_SCORE_CONFIG["version"],
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name=DEFAULT_SCORE_CONFIG["name"],
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status="active",
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config_json=copy.deepcopy(DEFAULT_SCORE_CONFIG),
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created_by="seed-cli",
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)
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)
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await session.flush()
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return DEFAULT_SCORE_CONFIG["version"]
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