feat(management): 批次A六表模型——fact/宽表/通话小表+评分配置/快照/辅导
Co-Authored-By: Claude Code <noreply@anthropic.com>
This commit is contained in:
浅念
@@ -999,11 +999,243 @@ class SensitiveScanState(Base):
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updated_at: Mapped[dt.datetime] = _updated_at()
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# ==========================================================================
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# 经营管理后台(2026-09-08 v2 spec,批次A)——fact/宽表/通话小表 + 三张业务表
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# ==========================================================================
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class FirstReplyFact(Base):
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"""首响事实表(管理域)——一行 = 一个被回的 1:1 客户消息串(v2 §3.1)。
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跨日精确串口径:按会话**全历史**计算,fr_ms = 员工首条回复 ts − 串末条
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客户消息 ts,day = 串末条所在上海自然日。区别于 msg_daily_stats 首响
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三列的日切近似(跨夜串整条丢失,v2 §1.1 #1)。只写 1:1(friend)会话
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——群串语义不成立。由回填 CLI / 4h 滚动 / 夜间全量重算,**不挂写钩子**
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(写钩子零改动)。用途:精确 P50/P90、2h/24h 回复率分子、下钻证据。
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"""
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__tablename__ = "first_reply_fact"
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channel: Mapped[str] = mapped_column(String(16), primary_key=True)
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owner_account_id: Mapped[str] = mapped_column(String(128), primary_key=True)
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peer_id: Mapped[str] = mapped_column(String(128), primary_key=True)
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thread_end_ts: Mapped[int] = mapped_column(BigInteger, primary_key=True)
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reply_ts: Mapped[int] = mapped_column(BigInteger, primary_key=True)
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# 串末条所在上海自然日 yyyymmdd(冗余物化,窗口/趋势索引)
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day: Mapped[int] = mapped_column(Integer, nullable=False)
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fr_ms: Mapped[int] = mapped_column(BigInteger, nullable=False)
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__table_args__ = (Index("ix_fr_fact_day_channel", "day", "channel"),)
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class EmployeeMetricDaily(Base):
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"""员工×日管理指标宽表(v2 §3.2)——1:1 口径,管理域专用。
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列来源(v2 §1.1 #2/#5):消息计数/负载列自 msg_daily_stats
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(peer_kind='friend' 过滤 + account_map 归并);串类三列与首响日值由
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first_reply_facts.compute_conversation 同源输出(串末条归属日)——
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回复率分子分母同口径。metric_version 只影响派生列(生命周期),
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默认 'v1'。account_map 映射不上或不在 staff 的 owner(幽灵员工)
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过滤不落表(v2 §1.3 #11)。通话在 employee_call_daily(无渠道维)。
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"""
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__tablename__ = "employee_metric_daily"
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day: Mapped[int] = mapped_column(Integer, primary_key=True)
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channel: Mapped[str] = mapped_column(String(16), primary_key=True)
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user_id: Mapped[str] = mapped_column(String(64), primary_key=True)
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metric_version: Mapped[str] = mapped_column(
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String(16), primary_key=True, default="v1", server_default="v1"
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)
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# 消息计数列(1:1 口径)
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msg_sent: Mapped[int] = mapped_column(
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Integer, nullable=False, default=0, server_default=sa.text("0")
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)
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msg_recv: Mapped[int] = mapped_column(
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Integer, nullable=False, default=0, server_default=sa.text("0")
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)
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# 负载列(active_rooms 为群负载,唯一计入宽表的群维度)
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active_friends: Mapped[int] = mapped_column(
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Integer, nullable=False, default=0, server_default=sa.text("0")
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)
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active_rooms: Mapped[int] = mapped_column(
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Integer, nullable=False, default=0, server_default=sa.text("0")
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)
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# 串类列(与 first_reply_fact 同源,按串末条归属日)
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cust_threads: Mapped[int] = mapped_column(
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Integer, nullable=False, default=0, server_default=sa.text("0")
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)
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threads_answered: Mapped[int] = mapped_column(
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Integer, nullable=False, default=0, server_default=sa.text("0")
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)
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fr_sum_ms: Mapped[int] = mapped_column(
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BigInteger, nullable=False, default=0, server_default=sa.text("0")
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)
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# 首响日值(趋势图用;评分用周期值从 fact 表窗口函数精确计算)
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first_reply_p50_ms: Mapped[int | None] = mapped_column(BigInteger, nullable=True)
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first_reply_p90_ms: Mapped[int | None] = mapped_column(BigInteger, nullable=True)
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# 生命周期 4 列(距今天数口径,当日持有客户状态分布,当前归属)
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new_customer_count: Mapped[int] = mapped_column(
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Integer, nullable=False, default=0, server_default=sa.text("0")
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)
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cooling_customer_count: Mapped[int] = mapped_column(
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Integer, nullable=False, default=0, server_default=sa.text("0")
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)
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dormant_customer_count: Mapped[int] = mapped_column(
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Integer, nullable=False, default=0, server_default=sa.text("0")
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)
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reactivated_customer_count: Mapped[int] = mapped_column(
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Integer, nullable=False, default=0, server_default=sa.text("0")
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)
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# fact 是否已覆盖当日(宽表生成前置条件;False=串类列可能滞后)
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data_complete: Mapped[bool] = mapped_column(
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Boolean, nullable=False, default=False, server_default=sa.text("0")
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)
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computed_at: Mapped[dt.datetime] = mapped_column(
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sa.DateTime, nullable=False, server_default=sa.func.now()
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)
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class EmployeeCallDaily(Base):
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"""员工×日通话小表(v2 §1.2 #9)——通话无消息渠道维度,独立于宽表。
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phone_call.channel 是通话类型(phone_call/voice_call)非消息渠道,
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塞进 (day,channel,user) 宽表会在 channel=all 聚合时双计。接通 =
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call_duration > 0;followup = 通话后 24h 内该员工任一账号对该客户
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有消息(Task 0 已核实:1:1 通话可匹配率 100%,@chatroom 群通话除外)。
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"""
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__tablename__ = "employee_call_daily"
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day: Mapped[int] = mapped_column(Integer, primary_key=True)
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user_id: Mapped[str] = mapped_column(String(64), primary_key=True)
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call_count: Mapped[int] = mapped_column(
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Integer, nullable=False, default=0, server_default=sa.text("0")
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)
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call_seconds: Mapped[int] = mapped_column(
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BigInteger, nullable=False, default=0, server_default=sa.text("0")
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)
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answered_call_count: Mapped[int] = mapped_column(
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Integer, nullable=False, default=0, server_default=sa.text("0")
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)
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call_followup_count: Mapped[int] = mapped_column(
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Integer, nullable=False, default=0, server_default=sa.text("0")
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)
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computed_at: Mapped[dt.datetime] = mapped_column(
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sa.DateTime, nullable=False, server_default=sa.func.now()
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)
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class EmployeeScoreConfig(Base):
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"""评分配置版本(v1 §10.4/v2 §3.3)——config_json 存维度/指标/权重/
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方向/阈值/样本门槛;激活前须过权重总和与阈值校验(批次B scoring 实现)。
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MVP 无可视化编辑器,受控 CLI 种子维护。"""
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__tablename__ = "employee_score_config"
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id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
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version: Mapped[str] = mapped_column(String(32), unique=True, nullable=False)
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name: Mapped[str] = mapped_column(String(128), nullable=False)
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# draft | active | retired
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status: Mapped[str] = mapped_column(
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String(16), nullable=False, default="draft", server_default="draft"
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)
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effective_from: Mapped[int | None] = mapped_column(Integer, nullable=True)
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effective_to: Mapped[int | None] = mapped_column(Integer, nullable=True)
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config_json: Mapped[dict[str, Any] | None] = mapped_column(JSON, nullable=False)
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created_by: Mapped[str | None] = mapped_column(String(64), nullable=True)
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approved_by: Mapped[str | None] = mapped_column(String(64), nullable=True)
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created_at: Mapped[dt.datetime] = mapped_column(
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sa.DateTime, nullable=False, server_default=sa.func.now()
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)
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activated_at: Mapped[dt.datetime | None] = mapped_column(sa.DateTime, nullable=True)
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class EmployeeScoreSnapshot(Base):
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"""周/月考核快照(v1 §8/v2 §3.3)——不可变:冻结后业务层禁改评分字段;
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重算走 supersedes_id 替代链。同批次×员工唯一。"""
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__tablename__ = "employee_score_snapshot"
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id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
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snapshot_batch_id: Mapped[str] = mapped_column(String(64), nullable=False)
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# week | month | custom
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period_type: Mapped[str] = mapped_column(String(8), nullable=False)
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period_start: Mapped[int] = mapped_column(Integer, nullable=False)
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period_end: Mapped[int] = mapped_column(Integer, nullable=False)
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user_id: Mapped[str] = mapped_column(String(64), nullable=False)
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score_config_version: Mapped[str] = mapped_column(String(32), nullable=False)
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metric_version: Mapped[str] = mapped_column(String(16), nullable=False)
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total_score: Mapped[int | None] = mapped_column(Integer, nullable=True)
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grade: Mapped[str | None] = mapped_column(String(2), nullable=True)
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# scored | insufficient_sample | incomplete_data
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score_status: Mapped[str] = mapped_column(
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String(24), nullable=False, default="scored", server_default="scored"
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)
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dimension_scores_json: Mapped[dict[str, Any] | None] = mapped_column(JSON, nullable=True)
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metric_details_json: Mapped[dict[str, Any] | None] = mapped_column(JSON, nullable=True)
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data_as_of: Mapped[int] = mapped_column(Integer, nullable=False)
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generated_at: Mapped[dt.datetime] = mapped_column(
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sa.DateTime, nullable=False, server_default=sa.func.now()
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)
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frozen_at: Mapped[dt.datetime | None] = mapped_column(sa.DateTime, nullable=True)
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frozen_by: Mapped[str | None] = mapped_column(String(64), nullable=True)
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supersedes_id: Mapped[int | None] = mapped_column(Integer, nullable=True)
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__table_args__ = (
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sa.UniqueConstraint("snapshot_batch_id", "user_id",
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name="uq_score_snapshot_batch_user"),
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Index("ix_score_snapshot_period", "period_type", "period_start"),
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)
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class ManagementFollowup(Base):
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"""管理辅导闭环(v1 §7.3/v2 §3.3)——来源指标→问题→管理动作→结果。"""
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__tablename__ = "management_followup"
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id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
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employee_user_id: Mapped[str] = mapped_column(String(64), nullable=False)
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# metric | anomaly | review | manual
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source_type: Mapped[str] = mapped_column(String(16), nullable=False)
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source_code: Mapped[str | None] = mapped_column(String(64), nullable=True)
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source_period_start: Mapped[int | None] = mapped_column(Integer, nullable=True)
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source_period_end: Mapped[int | None] = mapped_column(Integer, nullable=True)
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source_value_json: Mapped[dict[str, Any] | None] = mapped_column(JSON, nullable=True)
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reason: Mapped[str | None] = mapped_column(sa.Text, nullable=True)
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action_type: Mapped[str | None] = mapped_column(String(32), nullable=True)
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owner_manager_id: Mapped[str | None] = mapped_column(String(64), nullable=True)
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# open | in_progress | resolved | cancelled
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status: Mapped[str] = mapped_column(
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String(16), nullable=False, default="open", server_default="open"
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)
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due_at: Mapped[dt.datetime | None] = mapped_column(sa.DateTime, nullable=True)
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result: Mapped[str | None] = mapped_column(sa.Text, nullable=True)
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employee_feedback: Mapped[str | None] = mapped_column(sa.Text, nullable=True)
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created_by: Mapped[str | None] = mapped_column(String(64), nullable=True)
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created_at: Mapped[dt.datetime] = mapped_column(
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sa.DateTime, nullable=False, server_default=sa.func.now()
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)
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updated_at: Mapped[dt.datetime] = _updated_at()
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closed_at: Mapped[dt.datetime | None] = mapped_column(sa.DateTime, nullable=True)
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__table_args__ = (
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Index("ix_followup_emp_status_due", "employee_user_id", "status", "due_at"),
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Index("ix_followup_owner_status", "owner_manager_id", "status"),
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)
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__all__ = [
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"ChatGroup",
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"Customer",
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"Department",
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"EmployeeCallDaily",
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"EmployeeMetricDaily",
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"EmployeeScoreConfig",
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"EmployeeScoreSnapshot",
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"FirstReplyFact",
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"GroupMember",
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"ManagementFollowup",
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"Media",
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"Message",
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"MomentsPost",
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