freedakgmail
101 lines
3.5 KiB
Python
101 lines
3.5 KiB
Python
"""全量穿透扫描编排（P1.5）。
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把场景检测器的结果转化为线索，记录扫描覆盖范围（证明全量性）与数据版本（可追溯）。
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当前为同步执行；后续可包装为 Celery 异步任务（接口保持不变）。
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"""
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from __future__ import annotations
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import uuid
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from dataclasses import dataclass
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from sqlalchemy.orm import Session
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from app.clues import service as clue_svc
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from app.clues.models import Clue
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from app.scenarios import churn_fraud as cf
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from app.scenarios import split_contract as sc
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MODEL_VERSION = "mock-llm@0.1"
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@dataclass
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class ScanResult:
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scenario_code: str
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scanned_count: int
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clue: Clue | None
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def run_split_contract_scan(
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session: Session,
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contracts: list[sc.ContractRecord],
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approval_threshold: float,
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shared_controller: bool = False,
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data_version_id: uuid.UUID | None = None,
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) -> ScanResult:
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"""场景一拆单扫描：检测→评分→（命中则）生成线索。"""
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finding = sc.detect_threshold_edge(contracts, approval_threshold)
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score = sc.split_risk_score(finding, shared_controller)
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clue = None
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if score > 0:
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rationale = sc.build_rationale(finding, approval_threshold, shared_controller)
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clue = clue_svc.create_clue(
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session,
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title="疑似政企拆单规避审批",
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risk_domain="收入",
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scenario_code="R8",
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score=score,
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rationale=rationale,
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evidence={
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"near_threshold_contracts": [c.contract_id for c in finding.near_threshold],
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"edge_ratio": finding.ratio,
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"near_threshold_amount": finding.total_amount,
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"approval_threshold": approval_threshold,
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"shared_controller": shared_controller,
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},
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subjects={"customers": sorted({c.customer_key for c in finding.near_threshold})},
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amount_involved=finding.total_amount,
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model_version=MODEL_VERSION,
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data_version_id=data_version_id,
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)
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return ScanResult("R8", len(contracts), clue)
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def run_churn_scan(
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session: Session,
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retention_curve: list[cf.CohortPoint],
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commission_paid: float,
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active_ratio: float,
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zero_usage_ratio: float,
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channel_key: str,
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data_version_id: uuid.UUID | None = None,
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) -> ScanResult:
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"""场景二养卡骗补扫描：时序断崖 + 佣金质量不匹配→线索。"""
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finding = cf.detect_pulse_decay(retention_curve)
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mismatch = cf.commission_quality_mismatch(commission_paid, active_ratio, zero_usage_ratio)
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score = cf.churn_risk_score(finding, mismatch)
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clue = None
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if score >= 0.5:
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rationale = cf.build_rationale(finding, mismatch)
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clue = clue_svc.create_clue(
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session,
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title="疑似养卡骗补（脉冲增长+规律退订）",
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risk_domain="成本",
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scenario_code="R9",
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score=score,
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rationale=rationale,
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evidence={
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"cliff_month": finding.cliff_month,
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"max_drop": finding.max_drop,
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"commission_paid": commission_paid,
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"active_ratio": active_ratio,
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"zero_usage_ratio": zero_usage_ratio,
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"mismatch": mismatch,
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},
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subjects={"channel": channel_key},
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amount_involved=commission_paid,
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model_version=MODEL_VERSION,
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data_version_id=data_version_id,
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)
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return ScanResult("R9", len(retention_curve), clue)
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