kx_guo
5c7163d79a
统一对话页与供应商画像的采购订单、异常数、基础分、频次系数、供应商得分和层级计算；补充供应商维度计算结果字段，保留未归类品类标记并优化供应商列表聚合查询；增加响应压缩并完善供应商统计测试。
68 lines
2.0 KiB
Python
68 lines
2.0 KiB
Python
"""Supplier profile API contracts."""
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from pydantic import Field
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from app.schemas.common import ApiSchema
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class SupplierOption(ApiSchema):
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name: str
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anomaly_count: int
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tier: str
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class SupplierListData(ApiSchema):
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items: list[SupplierOption] = Field(default_factory=list)
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default_supplier: str | None = None
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class SupplierMetric(ApiSchema):
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cooperation_total: int | None = None
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anomaly_total: int
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anomaly_rate: float | None = None
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frequency_alpha: float
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supplier_base_score: float | None = None
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supplier_score: float | None = None
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risk_score: float | None = None
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main_category_count: int
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cooperation_count_basis: str = "按近3年采购订单去重单据编号统计"
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anomaly_rate_basis: str = "暂无采购订单/生产批次总量，无法计算真实异常率"
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class SupplierCategoryBreakdown(ApiSchema):
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category: str
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is_unclassified: bool = False
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anomaly_count: int
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anomaly_rate: float | None = None
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top_material: str | None = None
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top_problem: str | None = None
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anomaly_rate_basis: str = "暂无供应商品类合作总量，无法计算真实异常率"
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class SupplierRecentRecord(ApiSchema):
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case_id: int
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month: str | None = None
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product: str | None = None
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anomaly: str | None = None
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defect_level: str | None = None
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class FactoryRecommendation(ApiSchema):
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category: str
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preferred_factory: str | None = None
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alternative_factory: str | None = None
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reason: str
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recommendation_basis: str = "暂无加工厂生产总量，无法计算真实异常率"
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class SupplierProfileData(ApiSchema):
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supplier_name: str
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tier: str
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tier_condition: str
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metric: SupplierMetric
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category_breakdown: list[SupplierCategoryBreakdown] = Field(default_factory=list)
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monthly_trend: list[dict[str, int | str]] = Field(default_factory=list)
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recent_records: list[SupplierRecentRecord] = Field(default_factory=list)
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factory_recommendations: list[FactoryRecommendation] = Field(default_factory=list)
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recommendation_note: str | None = None
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