kx_guo
e8c122e542
- 接入数据库驱动的七维风险评分第一阶段逻辑\n- 增加主模型失败后的 DeepSeek 语义、流式和预防建议兜底\n- 将 AI 预防建议改为基于评分、历史异常和标准规则动态生成\n- 增加无历史案例时的标准库兜底校验和通用规则匹配\n- 修复未知供应商识别及测试占位名称过滤\n- 增加后端滚动日志文件配置和 LLM 请求日志\n- 历史会话直接读取已保存结果，避免重新调用模型和重算风险\n- 补充评分、供应商识别和 LLM 协议测试
100 lines
3.4 KiB
Python
100 lines
3.4 KiB
Python
"""Risk prediction use-case orchestration."""
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.core.exceptions import AppException
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from app.domain.risk_engine import BaselineRiskEngine, IndicatorValue
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from app.schemas.risk_prediction import (
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ModelInfo,
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RiskAssessmentRequest,
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RiskAssessmentResult,
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RiskDimensionScore,
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RiskFactorResult,
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)
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from app.services.database_risk_scoring import DatabaseRiskScoringService
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class RiskPredictionService:
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_engine = BaselineRiskEngine()
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@classmethod
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def model_info(cls) -> ModelInfo:
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return ModelInfo(model_version="database-seven-dimension-v1")
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@classmethod
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async def preview(
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cls,
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request: RiskAssessmentRequest,
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session: AsyncSession | None = None,
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) -> RiskAssessmentResult:
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if session and any(
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value is not None
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for value in (
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request.product_category,
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request.material_type,
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request.supplier,
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request.factory,
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request.stage_new,
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request.defect_level,
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)
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):
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database_score = await DatabaseRiskScoringService(session).score(
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category=request.product_category,
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material=request.material_type,
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supplier=request.supplier,
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factory=request.factory,
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stage=request.stage_new,
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defect_level=request.defect_level,
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)
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dimensions = [
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RiskDimensionScore(
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dimension=item.name,
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score=item.score,
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weight=item.weight,
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status=item.status,
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reason=item.reason,
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sample_size=item.sample_size,
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raw_value=item.raw_value,
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)
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for item in database_score.dimensions
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]
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return RiskAssessmentResult(
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subject_id=request.subject_id,
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score=database_score.score,
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level=database_score.level,
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model_version="database-seven-dimension-v1",
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factors=[
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RiskFactorResult(
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name=item.name,
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value=round(100 - item.score, 2),
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weight=item.weight,
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contribution=round(item.score * item.weight / 100, 2),
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)
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for item in database_score.dimensions
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],
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dimension_scores=dimensions,
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)
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if not request.indicators:
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raise AppException("请填写产品评估参数", code=422, status_code=422)
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score = cls._engine.predict(
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[
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IndicatorValue(name=item.name, value=item.value, weight=item.weight)
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for item in request.indicators
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]
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)
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return RiskAssessmentResult(
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subject_id=request.subject_id,
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score=score.score,
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level=score.level,
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model_version=cls._engine.model_version,
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factors=[
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RiskFactorResult(
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name=item.name,
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value=item.value,
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weight=item.weight,
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contribution=item.contribution,
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)
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for item in score.factors
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],
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)
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