kx_guo
1d58bb8913
- 统一供应商异常统计为近3年数据口径 - 以近3年异常记录数作为合作频次代理值并明确提示 - 实现 A/B 缺陷占比与 S/A/B/C 层级判定 - 按供应商层级输出正常合作、加强巡检、驻厂及备选供应商等管理建议 - 完善风险卡片和供应商卡片的频次、层级、建议展示 - 补充供应商画像接口、测试和历史会话卡片兼容处理
113 lines
3.2 KiB
Python
113 lines
3.2 KiB
Python
"""Risk prediction request and response contracts."""
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from pydantic import Field
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from app.domain.risk_engine import RiskLevel
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from app.schemas.common import ApiSchema
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class RiskIndicator(ApiSchema):
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name: str = Field(min_length=1, max_length=64)
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value: float = Field(ge=0, le=100)
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weight: float = Field(default=1, gt=0, le=100)
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class RiskAssessmentRequest(ApiSchema):
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subject_id: str = Field(min_length=1, max_length=128)
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indicators: list[RiskIndicator] = Field(default_factory=list, max_length=50)
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product_category: str | None = Field(default=None, max_length=100)
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material_type: str | None = Field(default=None, max_length=200)
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supplier: str | None = Field(default=None, max_length=300)
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factory: str | None = Field(default=None, max_length=300)
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stage_new: str | None = Field(default=None, max_length=100)
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defect_level: str | None = Field(default=None, max_length=10)
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class RiskFactorResult(ApiSchema):
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name: str
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value: float
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weight: float
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contribution: float
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class CategoryWarning(ApiSchema):
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category: str
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anomaly_count: int
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risk_level: str
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similar_category: str | None = None
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similar_count: int | None = None
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suggestion: str | None = None
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class SupplierFrequency(ApiSchema):
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supplier_name: str | None = None
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cooperation_count: int | None = None
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anomaly_count: int | None = None
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alpha: float | None = None
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tier: str | None = None
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suggestion: str | None = None
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frequency_basis: str = "近3年异常记录数"
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note: str | None = None
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data_available: bool = False
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class StandardJudgment(ApiSchema):
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category: str
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risk_point: str
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rule: str
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source: str | None = None
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status: str = "待检测"
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class RiskDimensionScore(ApiSchema):
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dimension: str
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score: float
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weight: float
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status: str = "database"
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reason: str | None = None
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sample_size: int | None = None
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raw_value: float | int | None = None
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class SimilarCase(ApiSchema):
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case_id: int | None = None
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date: str | None = None
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product: str | None = None
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category: str | None = None
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material: str | None = None
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anomaly_type: str | None = None
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description: str | None = None
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root_cause: str | None = None
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defect_level: str | None = None
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problem: str | None = None
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solution: str | None = None
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similarity: float | None = None
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class RiskAssessmentResult(ApiSchema):
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subject_id: str
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score: float
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level: RiskLevel
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model_version: str
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factors: list[RiskFactorResult]
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risk_badge: str | None = None
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alert_text: str | None = None
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category_warning: CategoryWarning | None = None
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supplier_freq: SupplierFrequency | None = None
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standard_judgment: list[StandardJudgment] = Field(default_factory=list)
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dimension_scores: list[RiskDimensionScore] = Field(default_factory=list)
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similar_cases: list[SimilarCase] = Field(default_factory=list)
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ai_suggestions: list[str] = Field(default_factory=list)
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class ModelInfo(ApiSchema):
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model_version: str
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score_range: str = "0-100"
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levels: tuple[RiskLevel, ...] = (
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RiskLevel.LOW,
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RiskLevel.MODERATELY_LOW,
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RiskLevel.MEDIUM,
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RiskLevel.HIGH,
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RiskLevel.VERY_HIGH,
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)
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