.env.example

@@ -26,6 +26,20 @@ RISK_DINGTALK_APP_SECRET=
 RISK_DINGTALK_CORP_ID=
 RISK_DINGTALK_AGENT_ID=
 
+# ==================== 百度 OCR ====================
+# 图片调用 WebImage，PDF 调用 PaddleOCR-VL 文档解析；上传原件仅保存在本机目录。
+BAIDU_OCR_API_KEY=
+BAIDU_OCR_SECRET_KEY=
+RISK_BAIDU_OCR_IMAGE_URL=https://aip.baidubce.com/rest/2.0/ocr/v1/webimage
+RISK_BAIDU_OCR_DOCUMENT_URL=https://aip.baidubce.com/rest/2.0/brain/online/v2/paddle-vl-parser/task
+RISK_BAIDU_OCR_TIMEOUT_SECONDS=75
+RISK_BAIDU_OCR_POLL_INTERVAL_SECONDS=2
+RISK_OCR_UPLOAD_DIR=.runtime-uploads/ocr
+RISK_OCR_MAX_FILE_MB=10
+RISK_OCR_MAX_BATCH_FILES=5
+RISK_OCR_REVIEW_CONFIDENCE=0.8
+RISK_OCR_JUDGMENT_CONFIDENCE=0.85
+
 # ==================== 主 LLM 模型配置 ====================
 # 主模型 API 密钥、OpenAI 兼容接口地址、模型名、请求超时时间和生成温度。
 RISK_LLM_API_KEY=

.gitignore

@@ -11,3 +11,4 @@ logs/
 dist/
 build/
 data/
+.runtime-uploads/

README.md

@@ -22,6 +22,10 @@ python scripts/create_user.py --emp-no admin001 --name 管理员 --role admin --
 
 审核接口：`POST /api/v1/risk-items`、`GET /api/v1/risk-items/pending`、`POST /api/v1/risk-items/{id}/review`。
 
+OCR 接口：`POST /api/v1/ocr/recognize` 接收最多 5 个 JPG、PNG 或 PDF 文件，图片调用百度 WebImage，PDF 调用 PaddleOCR-VL 文档解析；返回可编辑字段、检测数值、置信度及标准库判定。`POST /api/v1/ocr/judge` 用于人工修正检测值后重新判定，`GET /api/v1/ocr/files/{file_id}` 通过登录鉴权读取原始附件。启用前配置 `BAIDU_OCR_API_KEY` 和 `BAIDU_OCR_SECRET_KEY`，并执行数据库迁移。
+
+角色采用管理员 `admin`、产品经理 `product_manager`、审核员 `reviewer`、普通用户 `普通用户` 四类。模型权重由管理员通过 `GET/PATCH /api/v1/model-config` 维护，七维权重合计必须为 100%，修改仅对新评估生效。
+
 健康检查：`GET http://127.0.0.1:18474/api/v1/health`
 
 数据库表结构和字段释义通过 Alembic 管理。执行 `python -m alembic upgrade head` 会创建或更新表、索引、外键及 PostgreSQL 表/字段注释。

app/api/router.py

@@ -6,6 +6,8 @@ from app.api.v1.auth import router as auth_router
 from app.api.v1.conversations import router as conversations_router
 from app.api.v1.dashboard import router as dashboard_router
 from app.api.v1.health import router as health_router
+from app.api.v1.model_config import router as model_config_router
+from app.api.v1.ocr import router as ocr_router
 from app.api.v1.risk_items import router as risk_items_router
 from app.api.v1.risk_predictions import router as risk_predictions_router
 from app.api.v1.standards import router as standards_router
@@ -13,6 +15,8 @@ from app.api.v1.suppliers import router as suppliers_router
 
 api_router = APIRouter(prefix="/api/v1")
 api_router.include_router(health_router)
+api_router.include_router(model_config_router)
+api_router.include_router(ocr_router)
 api_router.include_router(auth_router)
 api_router.include_router(conversations_router)
 api_router.include_router(dashboard_router)

app/api/v1/model_config.py

@@ -0,0 +1,30 @@
+"""Administrator-only risk-model configuration endpoints."""
+
+from typing import Annotated
+
+from fastapi import APIRouter, Depends
+
+from app.api.dependencies import DatabaseSession, require_permission
+from app.models.entities import UserAccount
+from app.schemas.common import ApiResponse
+from app.schemas.model_config import ModelWeightConfigData, ModelWeightConfigUpdate
+from app.services.model_config import ModelConfigService
+
+router = APIRouter(prefix="/model-config", tags=["model-config"])
+
+
+@router.get("", response_model=ApiResponse[ModelWeightConfigData])
+async def get_model_config(
+    session: DatabaseSession,
+    _current_user: Annotated[UserAccount, Depends(require_permission("model_weight.manage"))],
+) -> ApiResponse[ModelWeightConfigData]:
+    return ApiResponse(data=await ModelConfigService(session).get())
+
+
+@router.patch("", response_model=ApiResponse[ModelWeightConfigData])
+async def update_model_config(
+    payload: ModelWeightConfigUpdate,
+    session: DatabaseSession,
+    current_user: Annotated[UserAccount, Depends(require_permission("model_weight.manage", operate=True))],
+) -> ApiResponse[ModelWeightConfigData]:
+    return ApiResponse(data=await ModelConfigService(session).update(payload, current_user))

app/api/v1/ocr.py

@@ -0,0 +1,50 @@
+"""Authenticated OCR recognition and source-file endpoints."""
+
+from typing import Annotated
+
+from fastapi import APIRouter, Depends, File, UploadFile
+from fastapi.responses import FileResponse
+
+from app.api.dependencies import DatabaseSession, require_permission
+from app.core.exceptions import AppException
+from app.models.entities import UserAccount
+from app.schemas.common import ApiResponse
+from app.schemas.ocr import OcrBatchResult, OcrJudgmentRequest, OcrStandardJudgment
+from app.services.ocr import OcrService
+
+router = APIRouter(prefix="/ocr", tags=["ocr"])
+
+
+@router.post("/recognize", response_model=ApiResponse[OcrBatchResult])
+async def recognize_files(
+    files: Annotated[list[UploadFile], File(description="1-5 JPG, PNG, or PDF files")],
+    session: DatabaseSession,
+    _current_user: Annotated[UserAccount, Depends(require_permission("risk_item.submit", operate=True))],
+) -> ApiResponse[OcrBatchResult]:
+    return ApiResponse(data=await OcrService(session).recognize(files))
+
+
+@router.post("/judge", response_model=ApiResponse[list[OcrStandardJudgment]])
+async def judge_measurements(
+    payload: OcrJudgmentRequest,
+    session: DatabaseSession,
+    _current_user: Annotated[UserAccount, Depends(require_permission("risk_item.submit", operate=True))],
+) -> ApiResponse[list[OcrStandardJudgment]]:
+    return ApiResponse(data=await OcrService(session).judge(payload.measurements))
+
+
+@router.get("/files/{file_id}", response_class=FileResponse)
+async def get_source_file(
+    file_id: str,
+    session: DatabaseSession,
+    _current_user: Annotated[UserAccount, Depends(require_permission("risk_item.submit"))],
+) -> FileResponse:
+    path = OcrService(session).upload_path(file_id)
+    if not path.is_file():
+        raise AppException("附件不存在", code=404, status_code=404)
+    media_type = {
+        ".jpg": "image/jpeg",
+        ".png": "image/png",
+        ".pdf": "application/pdf",
+    }[path.suffix.lower()]
+    return FileResponse(path, media_type=media_type, filename=file_id)

app/core/config.py

@@ -1,6 +1,7 @@
 """Environment-backed application settings."""
 
 from functools import lru_cache
+from pathlib import Path
 from typing import Annotated, Literal
 
 from pydantic import AliasChoices, Field, SecretStr, field_validator
@@ -24,6 +25,25 @@ class Settings(BaseSettings):
     dingtalk_app_secret: SecretStr | None = Field(default=None, repr=False)
     dingtalk_corp_id: str = ""
     dingtalk_agent_id: str = ""
+    baidu_ocr_api_key: SecretStr | None = Field(
+        default=None,
+        repr=False,
+        validation_alias=AliasChoices("BAIDU_OCR_API_KEY", "RISK_BAIDU_OCR_API_KEY"),
+    )
+    baidu_ocr_secret_key: SecretStr | None = Field(
+        default=None,
+        repr=False,
+        validation_alias=AliasChoices("BAIDU_OCR_SECRET_KEY", "RISK_BAIDU_OCR_SECRET_KEY"),
+    )
+    baidu_ocr_image_url: str = "https://aip.baidubce.com/rest/2.0/ocr/v1/webimage"
+    baidu_ocr_document_url: str = "https://aip.baidubce.com/rest/2.0/brain/online/v2/paddle-vl-parser/task"
+    baidu_ocr_timeout_seconds: int = Field(default=75, ge=10, le=300)
+    baidu_ocr_poll_interval_seconds: float = Field(default=2.0, ge=0.5, le=10)
+    ocr_upload_dir: Path = Path(".runtime-uploads/ocr")
+    ocr_max_file_mb: int = Field(default=10, ge=1, le=20)
+    ocr_max_batch_files: int = Field(default=5, ge=1, le=10)
+    ocr_review_confidence: float = Field(default=0.8, ge=0, le=1)
+    ocr_judgment_confidence: float = Field(default=0.85, ge=0, le=1)
     llm_api_key: SecretStr | None = Field(
         default=None,
         repr=False,

app/domain/model_config.py

@@ -0,0 +1,24 @@
+"""Canonical risk-model configuration shared by scoring and administration."""
+
+DEFAULT_WEIGHTS = {
+    "供应商表现": 25.0,
+    "物料品类": 20.0,
+    "产品品类": 15.0,
+    "加工厂质量": 15.0,
+    "标准符合性": 10.0,
+    "节点阶段": 10.0,
+    "缺陷等级": 5.0,
+}
+
+WEIGHT_DESCRIPTIONS = {
+    "供应商表现": "含频次风险系数 α 修正",
+    "物料品类": "按物料历史异常率",
+    "产品品类": "按具体产品品类历史异常密度",
+    "加工厂质量": "巡检 / 批次合格率",
+    "标准符合性": "按判定标准库量化达标率计分",
+    "节点阶段": "开发阶段越靠后风险越高",
+    "缺陷等级": "A 级缺陷加权",
+}
+
+DEFAULT_FREQUENCY_COEFFICIENT = 0.3
+DEFAULT_FREQUENCY_CAP = 1.5

app/models/__init__.py

@@ -8,6 +8,7 @@ from app.models.entities import (
     ReviewRecord,
     RiskAssessment,
     RiskItem,
+    RiskModelConfig,
     RolePermission,
     StandardRule,
     SupplierProfile,
@@ -22,6 +23,7 @@ __all__ = [
     "ReviewRecord",
     "RiskAssessment",
     "RiskItem",
+    "RiskModelConfig",
     "RolePermission",
     "StandardRule",
     "SupplierProfile",

app/models/entities.py

@@ -119,9 +119,7 @@ class Conversation(Base):
     """A persistent assistant conversation owned by one authenticated user."""
 
     __tablename__ = "conversations"
-    __table_args__ = (
-        Index("ix_conversations_user_updated", "user_id", "updated_at"),
-    )
+    __table_args__ = (Index("ix_conversations_user_updated", "user_id", "updated_at"),)
 
     id: Mapped[int] = mapped_column(BigInteger, primary_key=True, autoincrement=True)
     user_id: Mapped[int] = mapped_column(
@@ -149,9 +147,7 @@ class ConversationMessage(Base):
     """One user or assistant message, including optional structured results."""
 
     __tablename__ = "conversation_messages"
-    __table_args__ = (
-        Index("ix_conversation_messages_conversation_created", "conversation_id", "created_at"),
-    )
+    __table_args__ = (Index("ix_conversation_messages_conversation_created", "conversation_id", "created_at"),)
 
     id: Mapped[int] = mapped_column(BigInteger, primary_key=True, autoincrement=True)
     conversation_id: Mapped[int] = mapped_column(
@@ -176,13 +172,17 @@ class RiskItem(Base):
         Index("ix_risk_items_status", "status"),
         Index("ix_risk_items_submitted_by", "submitted_by"),
         Index("ix_risk_items_submitted_at", "submitted_at"),
+        Index("ix_risk_items_occurred_on", "occurred_on"),
+        Index("ix_risk_items_material_type", "material_type"),
     )
 
     id: Mapped[int] = mapped_column(BigInteger, primary_key=True, autoincrement=True)
     source_type: Mapped[str] = mapped_column(String(20), nullable=False)
     source_file_id: Mapped[str | None] = mapped_column(String(50))
+    occurred_on: Mapped[date | None] = mapped_column(Date)
     product_name: Mapped[str | None] = mapped_column(String(200))
     product_category: Mapped[str | None] = mapped_column(String(50))
+    material_type: Mapped[str | None] = mapped_column(String(200))
     supplier: Mapped[str | None] = mapped_column(String(100))
     factory: Mapped[str | None] = mapped_column(String(100))
     stage_new: Mapped[str | None] = mapped_column(String(50))
@@ -190,6 +190,8 @@ class RiskItem(Base):
     defect_level: Mapped[str | None] = mapped_column(String(5))
     solution: Mapped[str | None] = mapped_column(Text)
     photos: Mapped[list[str]] = mapped_column(JSONB, default=list, server_default="[]", nullable=False)
+    recognition_confidence: Mapped[Decimal | None] = mapped_column(Numeric(5, 4))
+    ocr_payload: Mapped[dict[str, Any]] = mapped_column(JSONB, default=dict, server_default="{}", nullable=False)
     submitted_by: Mapped[str | None] = mapped_column(String(50))
     submitted_at: Mapped[datetime | None] = mapped_column(DateTime(timezone=True))
     status: Mapped[str] = mapped_column(String(20), default="待预审", server_default="待预审", nullable=False)
@@ -323,6 +325,21 @@ class RolePermission(Base):
     )
 
 
+class RiskModelConfig(Base):
+    """Administrator-managed seven-dimension weights and frequency parameters."""
+
+    __tablename__ = "risk_model_configs"
+
+    id: Mapped[int] = mapped_column(BigInteger, primary_key=True, default=1)
+    weights: Mapped[dict[str, float]] = mapped_column(JSONB, nullable=False)
+    frequency_coefficient: Mapped[Decimal] = mapped_column(Numeric(5, 4), nullable=False)
+    frequency_cap: Mapped[Decimal] = mapped_column(Numeric(4, 2), nullable=False)
+    updated_by: Mapped[str | None] = mapped_column(String(20))
+    updated_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), server_default=func.now(), onupdate=func.now(), nullable=False
+    )
+
+
 class OperationAudit(Base):
     """Append-only audit record for authentication, permission, and review actions."""
 

app/schemas/model_config.py

@@ -0,0 +1,32 @@
+"""Risk-model configuration API contracts."""
+
+from datetime import datetime
+
+from pydantic import Field, model_validator
+
+from app.domain.model_config import DEFAULT_WEIGHTS
+from app.schemas.common import ApiSchema
+
+
+class ModelWeightConfigData(ApiSchema):
+    weights: dict[str, float]
+    frequency_coefficient: float
+    frequency_cap: float
+    updated_by: str | None = None
+    updated_at: datetime | None = None
+
+
+class ModelWeightConfigUpdate(ApiSchema):
+    weights: dict[str, float]
+    frequency_coefficient: float = Field(ge=0, le=1)
+    frequency_cap: float = Field(ge=1, le=3)
+
+    @model_validator(mode="after")
+    def validate_weights(self) -> "ModelWeightConfigUpdate":
+        if set(self.weights) != set(DEFAULT_WEIGHTS):
+            raise ValueError("权重必须完整包含七个固定维度")
+        if any(value <= 0 or value > 100 for value in self.weights.values()):
+            raise ValueError("每个维度权重必须大于0且不超过100")
+        if abs(sum(self.weights.values()) - 100) > 0.01:
+            raise ValueError("七个维度权重合计必须为100%")
+        return self

app/schemas/ocr.py

@@ -0,0 +1,73 @@
+"""OCR recognition and standard-judgment contracts."""
+
+from datetime import date
+from typing import Literal
+
+from pydantic import Field
+
+from app.schemas.common import ApiSchema
+
+
+class OcrField(ApiSchema):
+    value: str | None = None
+    confidence: float | None = Field(default=None, ge=0, le=1)
+    needs_review: bool = True
+
+
+class OcrExtractedFields(ApiSchema):
+    product_name: OcrField = Field(default_factory=OcrField)
+    product_category: OcrField = Field(default_factory=OcrField)
+    supplier: OcrField = Field(default_factory=OcrField)
+    factory: OcrField = Field(default_factory=OcrField)
+    material_type: OcrField = Field(default_factory=OcrField)
+    stage_new: OcrField = Field(default_factory=OcrField)
+    description: OcrField = Field(default_factory=OcrField)
+    defect_level: OcrField = Field(default_factory=OcrField)
+    occurred_on: date | None = None
+
+
+class OcrMeasurement(ApiSchema):
+    item: str
+    raw_value: str
+    value: float | None = None
+    unit: str | None = None
+    confidence: float | None = Field(default=None, ge=0, le=1)
+    needs_review: bool = True
+
+
+class OcrStandardJudgment(ApiSchema):
+    measurement_item: str
+    measured_value: str
+    standard_rule_id: int | None = None
+    risk_point: str | None = None
+    standard_value: str | None = None
+    result: Literal["合格", "不合格", "待人工确认", "未匹配标准"]
+    basis: str | None = None
+    reason: str | None = None
+
+
+class OcrFileResult(ApiSchema):
+    file_id: str
+    file_name: str
+    content_type: str
+    engine: Literal["webimage", "paddle-vl-parser"]
+    raw_text: str
+    confidence: float | None = Field(default=None, ge=0, le=1)
+    page_count: int = Field(default=1, ge=1)
+    fields: OcrExtractedFields
+    measurements: list[OcrMeasurement] = Field(default_factory=list)
+    judgments: list[OcrStandardJudgment] = Field(default_factory=list)
+    warnings: list[str] = Field(default_factory=list)
+    requires_review: bool = True
+
+
+class OcrBatchResult(ApiSchema):
+    files: list[OcrFileResult]
+    fields: OcrExtractedFields
+    measurements: list[OcrMeasurement] = Field(default_factory=list)
+    judgments: list[OcrStandardJudgment] = Field(default_factory=list)
+    requires_review: bool = True
+
+
+class OcrJudgmentRequest(ApiSchema):
+    measurements: list[OcrMeasurement] = Field(min_length=1, max_length=50)

app/schemas/risk_item.py

@@ -1,6 +1,7 @@
 """Risk item submission and review contracts."""
 
-from datetime import datetime
+from datetime import date, datetime
+from typing import Any
 
 from pydantic import Field
 
@@ -10,8 +11,10 @@ from app.schemas.common import ApiSchema
 class RiskItemCreate(ApiSchema):
     source_type: str = Field(default="表单", min_length=1, max_length=20)
     source_file_id: str | None = Field(default=None, max_length=50)
+    occurred_on: date | None = None
     product_name: str = Field(min_length=1, max_length=200)
     product_category: str | None = Field(default=None, max_length=100)
+    material_type: str | None = Field(default=None, max_length=200)
     supplier: str | None = Field(default=None, max_length=300)
     factory: str | None = Field(default=None, max_length=300)
     stage_new: str | None = Field(default=None, max_length=100)
@@ -19,11 +22,15 @@ class RiskItemCreate(ApiSchema):
     defect_level: str | None = Field(default=None, max_length=10)
     solution: str | None = None
     photos: list[str] = Field(default_factory=list, max_length=10)
+    recognition_confidence: float | None = Field(default=None, ge=0, le=1)
+    ocr_payload: dict[str, Any] = Field(default_factory=dict)
 
 
 class RiskItemUpdate(ApiSchema):
     product_name: str | None = Field(default=None, min_length=1, max_length=200)
     product_category: str | None = Field(default=None, max_length=100)
+    material_type: str | None = Field(default=None, max_length=200)
+    occurred_on: date | None = None
     supplier: str | None = Field(default=None, max_length=300)
     factory: str | None = Field(default=None, max_length=300)
     stage_new: str | None = Field(default=None, max_length=100)
@@ -31,14 +38,18 @@ class RiskItemUpdate(ApiSchema):
     defect_level: str | None = Field(default=None, max_length=10)
     solution: str | None = None
     photos: list[str] | None = Field(default=None, max_length=10)
+    recognition_confidence: float | None = Field(default=None, ge=0, le=1)
+    ocr_payload: dict[str, Any] | None = None
 
 
 class RiskItemData(ApiSchema):
     id: int
     source_type: str
     source_file_id: str | None
+    occurred_on: date | None
     product_name: str | None
     product_category: str | None
+    material_type: str | None
     supplier: str | None
     factory: str | None
     stage_new: str | None
@@ -46,6 +57,8 @@ class RiskItemData(ApiSchema):
     defect_level: str | None
     solution: str | None
     photos: list[str]
+    recognition_confidence: float | None
+    ocr_payload: dict[str, Any]
     submitted_by: str | None
     submitted_at: datetime | None
     status: str

app/services/baidu_ocr.py

@@ -0,0 +1,179 @@
+"""Small async client for the Baidu OCR endpoints used by the application."""
+
+import asyncio
+import base64
+import time
+from typing import Any
+from urllib.parse import urlparse
+
+import httpx
+
+from app.core.config import get_settings
+from app.core.exceptions import AppException
+
+
+class BaiduOcrClient:
+    token_url = "https://aip.baidubce.com/oauth/2.0/token"
+
+    def __init__(self, transport: httpx.AsyncBaseTransport | None = None) -> None:
+        settings = get_settings()
+        self.api_key = settings.baidu_ocr_api_key.get_secret_value() if settings.baidu_ocr_api_key else ""
+        self.secret_key = settings.baidu_ocr_secret_key.get_secret_value() if settings.baidu_ocr_secret_key else ""
+        self.image_url = settings.baidu_ocr_image_url.strip()
+        self.document_url = settings.baidu_ocr_document_url.strip().rstrip("/")
+        self.timeout_seconds = settings.baidu_ocr_timeout_seconds
+        self.poll_interval = settings.baidu_ocr_poll_interval_seconds
+        self.transport = transport
+        self._access_token = ""
+        self._token_expires_at = 0.0
+
+    @property
+    def configured(self) -> bool:
+        return bool(self.api_key and self.secret_key)
+
+    async def recognize_image(self, content: bytes) -> tuple[str, float | None, int]:
+        token = await self._get_access_token()
+        payload = {
+            "image": base64.b64encode(content).decode("ascii"),
+            "language_type": "CHN_ENG",
+            "detect_direction": "true",
+            "probability": "true",
+        }
+        body = await self._post_form(self.image_url, token, payload)
+        words = body.get("words_result") or []
+        lines = [str(item.get("words") or "").strip() for item in words if isinstance(item, dict)]
+        confidences = [
+            float(item["probability"]["average"])
+            for item in words
+            if isinstance(item, dict)
+            and isinstance(item.get("probability"), dict)
+            and isinstance(item["probability"].get("average"), (int, float))
+        ]
+        text = "\n".join(line for line in lines if line)
+        if not text:
+            raise AppException("图片中未识别到文字，请重新拍摄或手动填写", code=422, status_code=422)
+        confidence = sum(confidences) / len(confidences) if confidences else None
+        return text, confidence, 1
+
+    async def recognize_document(self, content: bytes, file_name: str) -> tuple[str, float | None, int]:
+        token = await self._get_access_token()
+        body = await self._post_form(
+            self.document_url,
+            token,
+            {
+                "file_data": base64.b64encode(content).decode("ascii"),
+                "file_name": file_name,
+                "merge_tables": "true",
+                "return_span_boxes": "true",
+                "angle_adjust": "true",
+            },
+        )
+        task_id = str((body.get("result") or {}).get("task_id") or "")
+        if not task_id:
+            raise AppException("百度文档识别未返回任务编号", code=502, status_code=502)
+
+        deadline = time.monotonic() + self.timeout_seconds
+        query_url = f"{self.document_url}/query"
+        result: dict[str, Any] = {}
+        while time.monotonic() < deadline:
+            await asyncio.sleep(self.poll_interval)
+            query_body = await self._post_form(query_url, token, {"task_id": task_id})
+            result = query_body.get("result") or {}
+            status = result.get("status")
+            if status == "success":
+                break
+            if status == "failed":
+                detail = str(result.get("task_error") or "文档解析失败")
+                raise AppException(f"百度文档识别失败：{detail}", code=502, status_code=502)
+        else:
+            raise AppException("文档识别超时，请稍后重试", code=504, status_code=504)
+
+        parse_url = str(result.get("parse_result_url") or "")
+        markdown_url = str(result.get("markdown_url") or "")
+        if parse_url:
+            parsed = await self._download_result(parse_url, expect_json=True)
+            text, pages = self._text_from_parse_result(parsed)
+        elif markdown_url:
+            text = str(await self._download_result(markdown_url, expect_json=False))
+            pages = max(1, text.count("\n---\n") + 1)
+        else:
+            raise AppException("百度文档识别未返回解析结果", code=502, status_code=502)
+        if not text.strip():
+            raise AppException("文档中未识别到文字，请重新上传或手动填写", code=422, status_code=422)
+        return text[:100_000], None, pages
+
+    async def _get_access_token(self) -> str:
+        if not self.configured:
+            raise AppException("未配置百度 OCR API Key 或 Secret Key", code=503, status_code=503)
+        if self._access_token and time.monotonic() < self._token_expires_at:
+            return self._access_token
+        timeout = httpx.Timeout(15, connect=10)
+        async with httpx.AsyncClient(timeout=timeout, transport=self.transport) as client:
+            try:
+                response = await client.post(
+                    self.token_url,
+                    params={
+                        "grant_type": "client_credentials",
+                        "client_id": self.api_key,
+                        "client_secret": self.secret_key,
+                    },
+                )
+                response.raise_for_status()
+                body = response.json()
+            except (httpx.HTTPError, ValueError) as exc:
+                raise AppException("无法连接百度 OCR 鉴权服务", code=502, status_code=502) from exc
+        token = str(body.get("access_token") or "")
+        if not token:
+            raise AppException("百度 OCR 密钥无效或服务尚未开通", code=502, status_code=502)
+        expires_in = int(body.get("expires_in") or 2_592_000)
+        self._access_token = token
+        self._token_expires_at = time.monotonic() + max(60, expires_in - 300)
+        return token
+
+    async def _post_form(self, url: str, token: str, data: dict[str, str]) -> dict[str, Any]:
+        timeout = httpx.Timeout(self.timeout_seconds, connect=15)
+        async with httpx.AsyncClient(timeout=timeout, transport=self.transport) as client:
+            try:
+                response = await client.post(url, params={"access_token": token}, data=data)
+                response.raise_for_status()
+                body = response.json()
+            except httpx.TimeoutException as exc:
+                raise AppException("百度 OCR 请求超时，请重试", code=504, status_code=504) from exc
+            except (httpx.HTTPError, ValueError) as exc:
+                raise AppException("百度 OCR 服务暂时不可用", code=502, status_code=502) from exc
+        if body.get("error_code") not in (None, 0, "0"):
+            message = str(body.get("error_msg") or "识别失败")
+            raise AppException(f"百度 OCR 返回错误：{message}", code=502, status_code=502)
+        return body
+
+    async def _download_result(self, url: str, *, expect_json: bool) -> dict[str, Any] | str:
+        parsed = urlparse(url)
+        allowed = parsed.scheme == "https" and (
+            parsed.hostname == "baidubce.com"
+            or bool(parsed.hostname and parsed.hostname.endswith((".baidubce.com", ".bcebos.com")))
+        )
+        if not allowed:
+            raise AppException("百度 OCR 返回了不受信任的结果地址", code=502, status_code=502)
+        timeout = httpx.Timeout(self.timeout_seconds, connect=15)
+        async with httpx.AsyncClient(timeout=timeout, transport=self.transport, follow_redirects=True) as client:
+            try:
+                response = await client.get(url)
+                response.raise_for_status()
+                return response.json() if expect_json else response.text
+            except (httpx.HTTPError, ValueError) as exc:
+                raise AppException("无法下载百度 OCR 解析结果", code=502, status_code=502) from exc
+
+    @staticmethod
+    def _text_from_parse_result(body: dict[str, Any]) -> tuple[str, int]:
+        pages = body.get("pages") or []
+        chunks: list[str] = []
+        for page in pages:
+            if not isinstance(page, dict):
+                continue
+            page_text = str(page.get("text") or "").strip()
+            if page_text:
+                chunks.append(page_text)
+            for table in page.get("tables") or []:
+                if isinstance(table, dict) and table.get("markdown"):
+                    chunks.append(str(table["markdown"]).strip())
+        return "\n\n".join(chunks), max(1, len(pages))

app/services/database_risk_scoring.py

@@ -9,18 +9,9 @@ from typing import Any
 from sqlalchemy import select
 from sqlalchemy.ext.asyncio import AsyncSession
 
+from app.domain.model_config import DEFAULT_FREQUENCY_CAP, DEFAULT_FREQUENCY_COEFFICIENT, DEFAULT_WEIGHTS
 from app.domain.risk_engine import RiskLevel
-from app.models.entities import AnomalyCase
-
-DEFAULT_WEIGHTS = {
-    "供应商表现": 25.0,
-    "物料品类": 20.0,
-    "产品品类": 15.0,
-    "加工厂质量": 15.0,
-    "标准符合性": 10.0,
-    "节点阶段": 10.0,
-    "缺陷等级": 5.0,
-}
+from app.models.entities import AnomalyCase, RiskModelConfig
 
 STAGES = ("需求发现", "产品立项", "产品开发", "量产上市", "产品生命管理")
 
@@ -54,6 +45,9 @@ class DatabaseRiskScoringService:
     def __init__(self, session: AsyncSession, *, as_of: date | None = None) -> None:
         self.session = session
         self.as_of = as_of or date.today()
+        self.weights = DEFAULT_WEIGHTS.copy()
+        self.frequency_coefficient = DEFAULT_FREQUENCY_COEFFICIENT
+        self.frequency_cap = DEFAULT_FREQUENCY_CAP
 
     async def score(
         self,
@@ -65,6 +59,7 @@ class DatabaseRiskScoringService:
         stage: str | None = None,
         defect_level: str | None = None,
     ) -> DatabaseRiskScore:
+        await self._load_model_config()
         rows = await self._load_rows()
         category_name = normalize_category(category)
         material_name = normalize_material(material)
@@ -78,14 +73,14 @@ class DatabaseRiskScoringService:
             DimensionScore(
                 name="加工厂质量",
                 score=50.0,
-                weight=DEFAULT_WEIGHTS["加工厂质量"],
+                weight=self.weights["加工厂质量"],
                 status="default",
                 reason="当前数据库没有巡检合格率/批次数据，按 PRD 中性值 50 分计入",
             ),
             DimensionScore(
                 name="标准符合性",
                 score=50.0,
-                weight=DEFAULT_WEIGHTS["标准符合性"],
+                weight=self.weights["标准符合性"],
                 status="default",
                 reason="当前数据库没有检测结果与标准判定记录，按 PRD 中性值 50 分计入",
             ),
@@ -95,6 +90,19 @@ class DatabaseRiskScoringService:
         score = round(sum(item.score * item.weight / 100 for item in dimensions), 2)
         return DatabaseRiskScore(score=score, level=classify_score(score), dimensions=dimensions)
 
+    async def _load_model_config(self) -> None:
+        get = getattr(self.session, "get", None)
+        if get is None:
+            return
+        config = await get(RiskModelConfig, 1)
+        if config is None:
+            return
+        weights = {key: float(config.weights.get(key, value)) for key, value in DEFAULT_WEIGHTS.items()}
+        if abs(sum(weights.values()) - 100) <= 0.01 and all(value > 0 for value in weights.values()):
+            self.weights = weights
+        self.frequency_coefficient = float(config.frequency_coefficient)
+        self.frequency_cap = float(config.frequency_cap)
+
     async def _load_rows(self) -> list[dict[str, Any]]:
         start = self.as_of - timedelta(days=365 * 3)
         statement = select(
@@ -114,7 +122,7 @@ class DatabaseRiskScoringService:
         return [dict(row) for row in (await self.session.execute(statement)).mappings().all()]
 
     def _supplier_dimension(self, rows: list[dict[str, Any]], party_name: str | None) -> DimensionScore:
-        weight = DEFAULT_WEIGHTS["供应商表现"]
+        weight = self.weights["供应商表现"]
         if not party_name:
             return DimensionScore(
                 name="供应商表现",
@@ -155,7 +163,7 @@ class DatabaseRiskScoringService:
             else 0
         )
         base = max(0.0, min(100.0, base + trend_adjustment))
-        alpha = min(1.5, 1.0 + count / 100 * 0.3)
+        alpha = min(self.frequency_cap, 1.0 + count / 100 * self.frequency_coefficient)
         score = min(100.0, round(base * alpha, 2))
         return DimensionScore(
             name="供应商表现",
@@ -167,9 +175,8 @@ class DatabaseRiskScoringService:
             raw_value=count,
         )
 
-    @staticmethod
-    def _material_dimension(rows: list[dict[str, Any]], material: str | None) -> DimensionScore:
-        weight = DEFAULT_WEIGHTS["物料品类"]
+    def _material_dimension(self, rows: list[dict[str, Any]], material: str | None) -> DimensionScore:
+        weight = self.weights["物料品类"]
         if not material:
             return DimensionScore(
                 name="物料品类",
@@ -196,9 +203,8 @@ class DatabaseRiskScoringService:
             raw_value=count,
         )
 
-    @staticmethod
-    def _category_dimension(rows: list[dict[str, Any]], category: str | None) -> DimensionScore:
-        weight = DEFAULT_WEIGHTS["产品品类"]
+    def _category_dimension(self, rows: list[dict[str, Any]], category: str | None) -> DimensionScore:
+        weight = self.weights["产品品类"]
         if not category:
             return DimensionScore(
                 name="产品品类",
@@ -232,9 +238,8 @@ class DatabaseRiskScoringService:
             raw_value=count,
         )
 
-    @staticmethod
-    def _stage_dimension(rows: list[dict[str, Any]], stage: str | None) -> DimensionScore:
-        weight = DEFAULT_WEIGHTS["节点阶段"]
+    def _stage_dimension(self, rows: list[dict[str, Any]], stage: str | None) -> DimensionScore:
+        weight = self.weights["节点阶段"]
         if not stage:
             return DimensionScore(
                 name="节点阶段",
@@ -266,15 +271,15 @@ class DatabaseRiskScoringService:
             raw_value=share * 100,
         )
 
-    @staticmethod
     def _defect_dimension(
+        self,
         rows: list[dict[str, Any]],
         category: str | None,
         material: str | None,
         party_name: str | None,
         defect_level: str | None,
     ) -> DimensionScore:
-        weight = DEFAULT_WEIGHTS["缺陷等级"]
+        weight = self.weights["缺陷等级"]
         if not category and not material and not party_name and not defect_level:
             return DimensionScore(
                 name="缺陷等级",

app/services/dingtalk_auth.py

@@ -170,21 +170,15 @@ class DingTalkAuthService:
 
     @staticmethod
     def map_role(dept: str, title: str, is_admin: bool = False) -> str:
-        """Map DingTalk organization snapshots to the PRD seven-role model."""
+        """Map DingTalk organization snapshots to the four-role administration model."""
 
         text = f"{dept} {title}".lower()
         if is_admin or "it" in text or "信息" in text or "系统" in text:
             return "admin"
         if "审核" in text or "review" in text:
             return "reviewer"
-        if "采购" in text or "procurement" in text:
-            return "procurement"
-        if "品控" in text or "质量" in text or "qc" in text:
-            return "qc"
-        if "管理" in text or "主管" in text or "manager" in text:
-            return "manager"
-        if "运营" in text or "ops" in text:
-            return "ops"
-        if "开发" in text or "产品" in text or "dev" in text:
-            return "dev"
+        if "产品经理" in text or "product manager" in text or (
+            "产品" in dept and ("经理" in title or "主管" in title)
+        ):
+            return "product_manager"
         return "普通用户"

app/services/llm_client.py

@@ -135,6 +135,40 @@ class LLMClient:
                 status_code=503,
             ) from fallback_exc
 
+    async def extract_ocr_fields(self, text: str) -> dict[str, Any]:
+        """Turn OCR text into an editable risk-item draft without inventing absent values."""
+
+        messages = [
+            {
+                "role": "system",
+                "content": (
+                    "你是蝶雅产品风险预估系统的OCR字段抽取模块，只输出合法JSON对象。"
+                    "只能提取输入文字明确出现的信息，不得推测或补造。输出字段：product_name、product_category、"
+                    "supplier、factory、material_type、stage_new、description、defect_level、occurred_on、confidence、"
+                    "measurements。缺失标量用null。defect_level只能是A、B、C或null；occurred_on使用YYYY-MM-DD或null。"
+                    "measurements为数组，每项包含item、raw_value、value、unit、confidence；value必须是数字或null。"
+                    "measurements只允许质量检测指标，例如pH、色差、重金属含量、力矩、喷出量、附着力、"
+                    "不合格率；数量、抽检数量、产品规格、容量、日期、单号和批次不得作为检测指标。"
+                    "confidence为0到1。description应保留异常现象和影响比例，但不得加入原文没有的结论。"
+                ),
+            },
+            {"role": "user", "content": text[:20_000]},
+        ]
+        last_error: Exception | None = None
+        providers: list[ProviderName] = ["primary"]
+        if self.fallback_configured:
+            providers.append("fallback")
+        for provider in providers:
+            try:
+                body = await self._request(messages, provider=provider, stream=False, json_mode=True)
+                return self._parse_json(self._response_text(body, provider=provider))
+            except Exception as exc:  # noqa: BLE001 - deterministic extraction handles provider failures
+                last_error = exc
+                logger.warning("LLM OCR extraction failed provider=%s error=%s", provider, exc.__class__.__name__)
+        if last_error:
+            raise last_error
+        return {}
+
     async def stream_reply(self, messages: list[dict[str, str]]) -> AsyncGenerator[str, None]:
         """Yield reply deltas and fail over only before any primary text is emitted."""
 
@@ -179,9 +213,8 @@ class LLMClient:
             },
             {
                 "role": "user",
-                "content": "以下是本次风险评估数据，请生成预防建议：\n" + json.dumps(
-                    context, ensure_ascii=False, separators=(",", ":")
-                ),
+                "content": "以下是本次风险评估数据，请生成预防建议：\n"
+                + json.dumps(context, ensure_ascii=False, separators=(",", ":")),
             },
         ]
         try:

app/services/model_config.py

@@ -0,0 +1,57 @@
+"""Read and update administrator-managed risk-model configuration."""
+
+from decimal import Decimal
+
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.domain.model_config import DEFAULT_FREQUENCY_CAP, DEFAULT_FREQUENCY_COEFFICIENT, DEFAULT_WEIGHTS
+from app.models.entities import OperationAudit, RiskModelConfig, UserAccount
+from app.schemas.model_config import ModelWeightConfigData, ModelWeightConfigUpdate
+
+
+class ModelConfigService:
+    def __init__(self, session: AsyncSession) -> None:
+        self.session = session
+
+    async def get(self) -> ModelWeightConfigData:
+        row = await self.session.get(RiskModelConfig, 1)
+        if row is None:
+            return ModelWeightConfigData(
+                weights=DEFAULT_WEIGHTS,
+                frequency_coefficient=DEFAULT_FREQUENCY_COEFFICIENT,
+                frequency_cap=DEFAULT_FREQUENCY_CAP,
+            )
+        return self._to_data(row)
+
+    async def update(self, payload: ModelWeightConfigUpdate, user: UserAccount) -> ModelWeightConfigData:
+        row = await self.session.get(RiskModelConfig, 1)
+        if row is None:
+            row = RiskModelConfig(id=1)
+            self.session.add(row)
+        row.weights = payload.weights
+        row.frequency_coefficient = Decimal(str(payload.frequency_coefficient))
+        row.frequency_cap = Decimal(str(payload.frequency_cap))
+        row.updated_by = user.emp_no
+        self.session.add(
+            OperationAudit(
+                actor_user_id=user.id,
+                actor_emp_no=user.emp_no,
+                action="model_weight.update",
+                resource_type="risk_model_config",
+                resource_id="1",
+                audit_metadata=payload.model_dump(),
+            )
+        )
+        await self.session.flush()
+        await self.session.refresh(row)
+        return self._to_data(row)
+
+    @staticmethod
+    def _to_data(row: RiskModelConfig) -> ModelWeightConfigData:
+        return ModelWeightConfigData(
+            weights={key: float(value) for key, value in row.weights.items()},
+            frequency_coefficient=float(row.frequency_coefficient),
+            frequency_cap=float(row.frequency_cap),
+            updated_by=row.updated_by,
+            updated_at=row.updated_at,
+        )

app/services/ocr.py

@@ -0,0 +1,507 @@
+"""OCR upload orchestration, field extraction, and standards evaluation."""
+
+import re
+from datetime import date
+from pathlib import Path
+from typing import Any
+from uuid import uuid4
+
+from fastapi import UploadFile
+from sqlalchemy import select
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.core.config import get_settings
+from app.core.exceptions import AppException
+from app.models.entities import StandardRule
+from app.schemas.ocr import (
+    OcrBatchResult,
+    OcrExtractedFields,
+    OcrField,
+    OcrFileResult,
+    OcrMeasurement,
+    OcrStandardJudgment,
+)
+from app.services.baidu_ocr import BaiduOcrClient
+from app.services.llm_client import LLMClient
+
+_FIELD_ALIASES = {
+    "product_name": ("产品名称", "产品名", "品名"),
+    "product_category": ("产品品类", "品类"),
+    "supplier": ("供应商", "供方"),
+    "factory": ("加工厂", "生产工厂", "工厂"),
+    "material_type": ("物料名称", "物料类型", "物料"),
+    "stage_new": ("发生阶段", "发现阶段", "产品阶段", "阶段"),
+    "description": ("异常描述", "问题描述", "不良描述", "异常现象"),
+    "defect_level": ("缺陷等级", "异常等级", "等级"),
+}
+_MEASUREMENT_WORDS = (
+    "pH",
+    "PH",
+    "色差",
+    "铅",
+    "汞",
+    "砷",
+    "镉",
+    "力矩",
+    "喷出量",
+    "附着力",
+    "含量",
+    "粘度",
+    "黏度",
+    "透光率",
+    "菌落",
+    "温度",
+    "湿度",
+    "不合格率",
+)
+_NON_MEASUREMENT_NAMES = {
+    "数量",
+    "抽检数量",
+    "产品规格",
+    "规格",
+    "容量",
+    "日期",
+    "生产日期",
+    "发生日期",
+    "单号",
+    "订单号",
+    "批次",
+    "批号",
+    "页码",
+}
+_UNIT_PATTERN = r"(?:mg/kg|μg/g|ug/g|g/L|mg/L|N[·.]?m|mPa[·.]?s|Pa[·.]?s|mm|cm|nm|%|级|℃|°C)?"
+
+
+class OcrService:
+    def __init__(
+        self,
+        session: AsyncSession,
+        *,
+        client: BaiduOcrClient | None = None,
+        llm: LLMClient | None = None,
+    ) -> None:
+        self.session = session
+        self.client = client or BaiduOcrClient()
+        self.llm = llm or LLMClient()
+        self.settings = get_settings()
+
+    async def recognize(self, files: list[UploadFile]) -> OcrBatchResult:
+        if not files:
+            raise AppException("请至少上传一个图片或 PDF", code=422, status_code=422)
+        if len(files) > self.settings.ocr_max_batch_files:
+            raise AppException(
+                f"一次最多上传 {self.settings.ocr_max_batch_files} 个文件",
+                code=422,
+                status_code=422,
+            )
+        rules = list(
+            (
+                await self.session.scalars(
+                    select(StandardRule).where(StandardRule.status == "启用").order_by(StandardRule.id)
+                )
+            ).all()
+        )
+        results: list[OcrFileResult] = []
+        try:
+            for upload in files:
+                results.append(await self._recognize_one(upload, rules))
+        except Exception:
+            for result in results:
+                self.upload_path(result.file_id).unlink(missing_ok=True)
+            raise
+        fields = self._merge_fields([item.fields for item in results])
+        measurements = [measurement for item in results for measurement in item.measurements]
+        judgments = [judgment for item in results for judgment in item.judgments]
+        return OcrBatchResult(
+            files=results,
+            fields=fields,
+            measurements=measurements,
+            judgments=judgments,
+            requires_review=any(item.requires_review for item in results),
+        )
+
+    async def judge(self, measurements: list[OcrMeasurement]) -> list[OcrStandardJudgment]:
+        rules = list(
+            (
+                await self.session.scalars(
+                    select(StandardRule).where(StandardRule.status == "启用").order_by(StandardRule.id)
+                )
+            ).all()
+        )
+        return self._judge_measurements(measurements, rules)
+
+    async def _recognize_one(self, upload: UploadFile, rules: list[StandardRule]) -> OcrFileResult:
+        content = await self._read_limited(upload)
+        content_type, suffix = self._detect_type(content)
+        original_name = Path(upload.filename or f"upload{suffix}").name
+        file_id = f"{uuid4().hex}{suffix}"
+        target = self.upload_path(file_id)
+        target.parent.mkdir(parents=True, exist_ok=True)
+        target.write_bytes(content)
+        try:
+            if content_type == "application/pdf":
+                raw_text, provider_confidence, page_count = await self.client.recognize_document(content, original_name)
+                engine = "paddle-vl-parser"
+            else:
+                raw_text, provider_confidence, page_count = await self.client.recognize_image(content)
+                engine = "webimage"
+            extracted = await self._extract(raw_text, provider_confidence)
+            fields, measurements, extraction_confidence = extracted
+            judgments = self._judge_measurements(measurements, rules)
+            warnings = self._warnings(fields, measurements, judgments, provider_confidence)
+            requires_review = bool(warnings) or any(field.needs_review for field in self._field_values(fields))
+            return OcrFileResult(
+                file_id=file_id,
+                file_name=original_name,
+                content_type=content_type,
+                engine=engine,
+                raw_text=raw_text,
+                confidence=provider_confidence if provider_confidence is not None else extraction_confidence,
+                page_count=page_count,
+                fields=fields,
+                measurements=measurements,
+                judgments=judgments,
+                warnings=warnings,
+                requires_review=requires_review,
+            )
+        except Exception:
+            target.unlink(missing_ok=True)
+            raise
+        finally:
+            await upload.close()
+
+    async def _extract(
+        self,
+        text: str,
+        provider_confidence: float | None,
+    ) -> tuple[OcrExtractedFields, list[OcrMeasurement], float | None]:
+        fallback_fields = self._extract_fields_by_labels(text, provider_confidence)
+        fallback_measurements = self._extract_measurements(text, provider_confidence)
+        if not self.llm.configured:
+            return fallback_fields, fallback_measurements, provider_confidence
+        try:
+            data = await self.llm.extract_ocr_fields(text)
+        except Exception:  # noqa: BLE001 - OCR remains usable when the optional entity extractor fails
+            return fallback_fields, fallback_measurements, provider_confidence
+        confidence = self._as_confidence(data.get("confidence"))
+        fields = self._fields_from_llm(data, confidence, fallback_fields)
+        measurements = self._measurements_from_llm(data.get("measurements"), text, confidence)
+        return fields, measurements or fallback_measurements, confidence
+
+    def _fields_from_llm(
+        self,
+        data: dict[str, Any],
+        confidence: float | None,
+        fallback: OcrExtractedFields,
+    ) -> OcrExtractedFields:
+        values: dict[str, Any] = {}
+        for key in _FIELD_ALIASES:
+            value = data.get(key)
+            if key == "defect_level" and value:
+                value = str(value).upper().replace("级", "")
+                if value not in {"A", "B", "C"}:
+                    value = None
+            if value is None or not str(value).strip():
+                values[key] = getattr(fallback, key)
+            else:
+                values[key] = self._field(str(value).strip(), confidence)
+        occurred_on = self._as_date(data.get("occurred_on")) or fallback.occurred_on
+        return OcrExtractedFields(**values, occurred_on=occurred_on)
+
+    def _measurements_from_llm(
+        self,
+        raw: Any,
+        source_text: str,
+        default_confidence: float | None,
+    ) -> list[OcrMeasurement]:
+        if not isinstance(raw, list):
+            return []
+        results: list[OcrMeasurement] = []
+        for item in raw[:50]:
+            if not isinstance(item, dict):
+                continue
+            name = str(item.get("item") or "").strip()
+            raw_value = str(item.get("raw_value") or item.get("value") or "").strip()
+            if (
+                not name
+                or self._normalize(name) in {self._normalize(value) for value in _NON_MEASUREMENT_NAMES}
+                or not raw_value
+                or not self._numeric_appears(raw_value, source_text)
+            ):
+                continue
+            value = self._as_float(item.get("value"))
+            if value is None:
+                value = self._as_float(raw_value)
+            confidence = self._as_confidence(item.get("confidence")) or default_confidence
+            results.append(
+                OcrMeasurement(
+                    item=name[:100],
+                    raw_value=raw_value[:100],
+                    value=value,
+                    unit=str(item.get("unit") or "").strip()[:30] or self._unit_from(raw_value),
+                    confidence=confidence,
+                    needs_review=confidence is None or confidence < self.settings.ocr_judgment_confidence,
+                )
+            )
+        return results
+
+    def _extract_fields_by_labels(self, text: str, confidence: float | None) -> OcrExtractedFields:
+        values: dict[str, OcrField] = {}
+        for field_name, aliases in _FIELD_ALIASES.items():
+            found: str | None = None
+            for alias in aliases:
+                match = re.search(rf"{re.escape(alias)}\s*[:：]?\s*([^\n|｜]{{1,120}})", text, re.IGNORECASE)
+                if match:
+                    found = match.group(1).strip(" ：:;；")
+                    break
+            if field_name == "defect_level" and found:
+                level = re.search(r"\b([ABC])\s*级?\b", found, re.IGNORECASE)
+                found = level.group(1).upper() if level else None
+            values[field_name] = self._field(found, confidence)
+        occurred = None
+        date_match = re.search(r"(?:发生日期|日期)\s*[:：]?\s*(20\d{2})[-/.年](\d{1,2})[-/.月](\d{1,2})", text)
+        if date_match:
+            occurred = self._as_date("-".join(date_match.groups()))
+        return OcrExtractedFields(**values, occurred_on=occurred)
+
+    def _extract_measurements(self, text: str, confidence: float | None) -> list[OcrMeasurement]:
+        results: list[OcrMeasurement] = []
+        seen: set[tuple[str, str]] = set()
+        pattern = re.compile(
+            rf"(?P<item>[A-Za-z\u4e00-\u9fffΔδ（）()·/\-]{{1,32}}?)\s*[:：]?\s*"
+            rf"(?P<value>[+\-]?\d+(?:\.\d+)?)\s*(?P<unit>{_UNIT_PATTERN})",
+            re.IGNORECASE,
+        )
+        for line in text.splitlines():
+            if not any(word.lower() in line.lower() for word in _MEASUREMENT_WORDS):
+                continue
+            for match in pattern.finditer(line):
+                name = match.group("item").strip(" ：:;；-|｜")
+                raw_value = f"{match.group('value')}{match.group('unit') or ''}"
+                key = (name, raw_value)
+                if not name or key in seen:
+                    continue
+                seen.add(key)
+                results.append(
+                    OcrMeasurement(
+                        item=name[:100],
+                        raw_value=raw_value,
+                        value=float(match.group("value")),
+                        unit=match.group("unit") or None,
+                        confidence=confidence,
+                        needs_review=confidence is None or confidence < self.settings.ocr_judgment_confidence,
+                    )
+                )
+        return results[:50]
+
+    def _judge_measurements(
+        self,
+        measurements: list[OcrMeasurement],
+        rules: list[StandardRule],
+    ) -> list[OcrStandardJudgment]:
+        judgments: list[OcrStandardJudgment] = []
+        for measurement in measurements:
+            rule = self._best_rule(measurement.item, rules)
+            if rule is None:
+                judgments.append(
+                    OcrStandardJudgment(
+                        measurement_item=measurement.item,
+                        measured_value=measurement.raw_value,
+                        result="未匹配标准",
+                        reason="标准库中没有找到对应检测项",
+                    )
+                )
+                continue
+            result, reason = self.evaluate_standard(measurement, rule.judge_standard)
+            if measurement.needs_review and result in {"合格", "不合格"}:
+                result = "待人工确认"
+                reason = (
+                    f"识别置信度低于 {self.settings.ocr_judgment_confidence:.2f}，"
+                    "需确认检测值后再采用自动判定"
+                )
+            judgments.append(
+                OcrStandardJudgment(
+                    measurement_item=measurement.item,
+                    measured_value=measurement.raw_value,
+                    standard_rule_id=rule.id,
+                    risk_point=rule.risk_point,
+                    standard_value=rule.judge_standard,
+                    result=result,
+                    basis=rule.regulation,
+                    reason=reason,
+                )
+            )
+        return judgments
+
+    @staticmethod
+    def _best_rule(item: str, rules: list[StandardRule]) -> StandardRule | None:
+        needle = OcrService._normalize(item)
+        if not needle:
+            return None
+        scored: list[tuple[float, StandardRule]] = []
+        for rule in rules:
+            haystack = OcrService._normalize(f"{rule.risk_point} {rule.judge_standard}")
+            if needle in haystack or haystack in needle:
+                score = 2.0 + min(len(needle), len(haystack)) / 100
+            else:
+                left = set(OcrService._bigrams(needle))
+                right = set(OcrService._bigrams(haystack))
+                score = len(left & right) / max(1, len(left | right))
+            threshold = 0.45 if len(needle) <= 4 else 0.3
+            if score >= threshold:
+                scored.append((score, rule))
+        return max(scored, key=lambda pair: pair[0])[1] if scored else None
+
+    @staticmethod
+    def evaluate_standard(measurement: OcrMeasurement, standard: str) -> tuple[str, str | None]:
+        if measurement.value is None:
+            return "待人工确认", "检测值不是可比较的数字"
+        normalized = standard.replace("～", "~").replace("—", "-").replace("至", "~")
+        range_match = re.search(r"(-?\d+(?:\.\d+)?)\s*(?:~|-)\s*(-?\d+(?:\.\d+)?)", normalized)
+        if range_match:
+            mismatch = OcrService._unit_mismatch(measurement.unit, normalized[range_match.end() :])
+            if mismatch:
+                return "待人工确认", mismatch
+            lower, upper = map(float, range_match.groups())
+            passed = lower <= measurement.value <= upper
+            return ("合格" if passed else "不合格"), None
+        comparator = re.search(r"(<=|>=|≤|≥|<|>)\s*(?:±\s*)?(-?\d+(?:\.\d+)?)", normalized)
+        if comparator:
+            mismatch = OcrService._unit_mismatch(measurement.unit, normalized[comparator.end() :])
+            if mismatch:
+                return "待人工确认", mismatch
+            operator, raw_limit = comparator.groups()
+            limit = float(raw_limit)
+            value = abs(measurement.value) if "±" in comparator.group(0) else measurement.value
+            passed = {
+                "<=": value <= limit,
+                "≤": value <= limit,
+                ">=": value >= limit,
+                "≥": value >= limit,
+                "<": value < limit,
+                ">": value > limit,
+            }[operator]
+            return ("合格" if passed else "不合格"), None
+        return "待人工确认", "该标准不是系统可自动计算的数值范围"
+
+    def _warnings(
+        self,
+        fields: OcrExtractedFields,
+        measurements: list[OcrMeasurement],
+        judgments: list[OcrStandardJudgment],
+        confidence: float | None,
+    ) -> list[str]:
+        warnings: list[str] = []
+        if confidence is None:
+            warnings.append("服务未返回文字级置信度，请人工核对识别结果")
+        elif confidence < self.settings.ocr_review_confidence:
+            warnings.append("OCR 置信度低于 0.8，请重点核对标黄字段")
+        if not fields.product_name.value:
+            warnings.append("未识别到产品名称")
+        if not fields.description.value:
+            warnings.append("未识别到异常描述")
+        if measurements and any(item.result == "不合格" for item in judgments):
+            warnings.append("检测数值存在不合格项，请确认后提交审核")
+        return warnings
+
+    async def _read_limited(self, upload: UploadFile) -> bytes:
+        limit = self.settings.ocr_max_file_mb * 1024 * 1024
+        content = await upload.read(limit + 1)
+        if len(content) > limit:
+            raise AppException(f"单个文件不能超过 {self.settings.ocr_max_file_mb}MB", code=413, status_code=413)
+        if not content:
+            raise AppException("不能上传空文件", code=422, status_code=422)
+        return content
+
+    @staticmethod
+    def _detect_type(content: bytes) -> tuple[str, str]:
+        if content.startswith(b"%PDF-"):
+            return "application/pdf", ".pdf"
+        if content.startswith(b"\x89PNG\r\n\x1a\n"):
+            return "image/png", ".png"
+        if content.startswith(b"\xff\xd8\xff"):
+            return "image/jpeg", ".jpg"
+        raise AppException("仅支持 JPG、PNG 或 PDF 文件", code=422, status_code=422)
+
+    def upload_path(self, file_id: str) -> Path:
+        if not re.fullmatch(r"[0-9a-f]{32}\.(?:jpg|png|pdf)", file_id):
+            raise AppException("附件不存在", code=404, status_code=404)
+        root = self.settings.ocr_upload_dir.resolve()
+        return root / file_id
+
+    @staticmethod
+    def _field(value: str | None, confidence: float | None) -> OcrField:
+        threshold = get_settings().ocr_review_confidence
+        return OcrField(
+            value=value,
+            confidence=confidence,
+            needs_review=not value or confidence is None or confidence < threshold,
+        )
+
+    @staticmethod
+    def _field_values(fields: OcrExtractedFields) -> list[OcrField]:
+        return [getattr(fields, key) for key in _FIELD_ALIASES]
+
+    @staticmethod
+    def _merge_fields(items: list[OcrExtractedFields]) -> OcrExtractedFields:
+        merged: dict[str, Any] = {}
+        for key in _FIELD_ALIASES:
+            candidates = [getattr(item, key) for item in items if getattr(item, key).value]
+            merged[key] = max(candidates, key=lambda field: field.confidence or -1) if candidates else OcrField()
+        merged["occurred_on"] = next((item.occurred_on for item in items if item.occurred_on), None)
+        return OcrExtractedFields(**merged)
+
+    @staticmethod
+    def _as_confidence(value: Any) -> float | None:
+        try:
+            number = float(value)
+        except (TypeError, ValueError):
+            return None
+        return min(1.0, max(0.0, number))
+
+    @staticmethod
+    def _as_float(value: Any) -> float | None:
+        match = re.search(r"[+\-]?\d+(?:\.\d+)?", str(value or ""))
+        return float(match.group()) if match else None
+
+    @staticmethod
+    def _as_date(value: Any) -> date | None:
+        if not value:
+            return None
+        try:
+            return date.fromisoformat(str(value).strip()[:10])
+        except ValueError:
+            return None
+
+    @staticmethod
+    def _unit_from(value: str) -> str | None:
+        match = re.search(_UNIT_PATTERN + r"$", value, re.IGNORECASE)
+        return match.group() if match and match.group() else None
+
+    @staticmethod
+    def _unit_mismatch(measured_unit: str | None, standard_tail: str) -> str | None:
+        if not measured_unit:
+            return None
+        standard_unit = re.match(rf"\s*({_UNIT_PATTERN})", standard_tail, re.IGNORECASE)
+        expected = standard_unit.group(1) if standard_unit and standard_unit.group(1) else None
+        if not expected:
+            return None
+        def normalize(unit: str) -> str:
+            return unit.lower().replace(".", "·").replace("°c", "℃")
+
+        if normalize(measured_unit) != normalize(expected):
+            return f"检测值单位 {measured_unit} 与标准单位 {expected} 不一致"
+        return None
+
+    @staticmethod
+    def _numeric_appears(value: str, text: str) -> bool:
+        number = re.search(r"[+\-]?\d+(?:\.\d+)?", value)
+        return bool(number and number.group() in text)
+
+    @staticmethod
+    def _normalize(value: str) -> str:
+        return re.sub(r"[^a-z0-9\u4e00-\u9fffΔδ]", "", value.lower())
+
+    @staticmethod
+    def _bigrams(value: str) -> list[str]:
+        return [value[index : index + 2] for index in range(max(1, len(value) - 1))]

app/services/risk_items.py

@@ -19,8 +19,10 @@ class RiskItemService:
         item = RiskItem(
             source_type=payload.source_type,
             source_file_id=payload.source_file_id,
+            occurred_on=payload.occurred_on,
             product_name=payload.product_name,
             product_category=payload.product_category,
+            material_type=payload.material_type,
             supplier=payload.supplier,
             factory=payload.factory,
             stage_new=payload.stage_new,
@@ -28,6 +30,8 @@ class RiskItemService:
             defect_level=payload.defect_level,
             solution=payload.solution,
             photos=payload.photos,
+            recognition_confidence=payload.recognition_confidence,
+            ocr_payload=payload.ocr_payload,
             submitted_by=user.emp_no,
             submitted_at=datetime.now(UTC),
             status="待审核" if pre_result == "通过" else "待预审",
@@ -111,9 +115,7 @@ class RiskItemService:
             raise AppException("审核人不能审核自己提交的风险项", code=403, status_code=403)
 
         record = await self.session.scalar(
-            select(ReviewRecord)
-            .where(ReviewRecord.risk_item_id == item_id)
-            .order_by(ReviewRecord.id.desc())
+            select(ReviewRecord).where(ReviewRecord.risk_item_id == item_id).order_by(ReviewRecord.id.desc())
         )
         if record is None:
             raise AppException("审核记录不存在", code=409, status_code=409)
@@ -124,7 +126,9 @@ class RiskItemService:
         if action == "通过":
             case = AnomalyCase(
                 product_name=item.product_name,
+                date=item.occurred_on,
                 product_category=item.product_category,
+                material_type=item.material_type,
                 supplier=item.supplier,
                 factory=item.factory,
                 stage_new=item.stage_new,
@@ -132,6 +136,7 @@ class RiskItemService:
                 defect_level=item.defect_level,
                 solution=item.solution,
                 source_type=item.source_type,
+                source_file=item.source_file_id,
                 import_fingerprint=f"risk-item-{item.id}",
             )
             self.session.add(case)

app/services/suppliers.py

@@ -61,7 +61,8 @@ class SupplierProfileService:
         b_count = sum(self.defect_code(row.get("defect_level")) == "B" for row in rows)
         tier = self.tier_for(anomaly_total, a_count, b_count)
         categories = self._categories(rows)
-        score = await DatabaseRiskScoringService(self.session, as_of=self.as_of).score(supplier=supplier_name)
+        scoring = DatabaseRiskScoringService(self.session, as_of=self.as_of)
+        score = await scoring.score(supplier=supplier_name)
         return SupplierProfileData(
             supplier_name=supplier_name,
             tier=tier,
@@ -70,7 +71,11 @@ class SupplierProfileService:
                 cooperation_total=None,
                 anomaly_total=anomaly_total,
                 anomaly_rate=None,
-                frequency_alpha=self.alpha(anomaly_total),
+                frequency_alpha=self.alpha(
+                    anomaly_total,
+                    coefficient=scoring.frequency_coefficient,
+                    cap=scoring.frequency_cap,
+                ),
                 risk_score=score.score,
                 main_category_count=len(categories),
             ),
@@ -122,8 +127,8 @@ class SupplierProfileService:
         return value if value in {"A", "B", "C"} else None
 
     @staticmethod
-    def alpha(anomaly_count: int) -> float:
-        return round(min(1.5, 1 + anomaly_count / 100 * 0.3), 2)
+    def alpha(anomaly_count: int, *, coefficient: float = 0.3, cap: float = 1.5) -> float:
+        return round(min(cap, 1 + anomaly_count / 100 * coefficient), 2)
 
     @classmethod
     def tier_for(cls, anomaly_count: int, a_count: int, b_count: int) -> str:

migrations/versions/20260907_0016_add_ocr_fields.py

@@ -0,0 +1,37 @@
+"""Add OCR extraction and judgment fields to risk items."""
+
+from collections.abc import Sequence
+
+import sqlalchemy as sa
+from alembic import op
+from sqlalchemy.dialects import postgresql
+
+revision: str = "20260907_0016"
+down_revision: str | None = "20260907_0015"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+
+def upgrade() -> None:
+    op.add_column("risk_items", sa.Column("occurred_on", sa.Date(), nullable=True))
+    op.add_column("risk_items", sa.Column("material_type", sa.String(length=200), nullable=True))
+    op.add_column("risk_items", sa.Column("recognition_confidence", sa.Numeric(5, 4), nullable=True))
+    op.add_column(
+        "risk_items",
+        sa.Column("ocr_payload", postgresql.JSONB(astext_type=sa.Text()), server_default="{}", nullable=False),
+    )
+    op.create_index("ix_risk_items_occurred_on", "risk_items", ["occurred_on"])
+    op.create_index("ix_risk_items_material_type", "risk_items", ["material_type"])
+    op.execute("COMMENT ON COLUMN risk_items.occurred_on IS 'OCR或人工录入的异常发生日期。'")
+    op.execute("COMMENT ON COLUMN risk_items.material_type IS 'OCR或人工录入的异常物料类型。'")
+    op.execute("COMMENT ON COLUMN risk_items.recognition_confidence IS 'OCR综合识别置信度，范围0到1。'")
+    op.execute("COMMENT ON COLUMN risk_items.ocr_payload IS 'OCR检测数值、标准判定和人工确认前的结构化结果。'")
+
+
+def downgrade() -> None:
+    op.drop_index("ix_risk_items_material_type", table_name="risk_items")
+    op.drop_index("ix_risk_items_occurred_on", table_name="risk_items")
+    op.drop_column("risk_items", "ocr_payload")
+    op.drop_column("risk_items", "recognition_confidence")
+    op.drop_column("risk_items", "material_type")
+    op.drop_column("risk_items", "occurred_on")

migrations/versions/20260907_0017_four_role_model_config.py

@@ -0,0 +1,103 @@
+"""Apply the four-role matrix and add configurable model weights."""
+
+import json
+from collections.abc import Sequence
+
+import sqlalchemy as sa
+from alembic import op
+from app.domain.model_config import DEFAULT_WEIGHTS
+from sqlalchemy.dialects import postgresql
+
+revision: str = "20260907_0017"
+down_revision: str | None = "20260907_0016"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+PERMISSIONS = (
+    ("普通用户", "risk.assess", "L1", True, True),
+    ("普通用户", "standard.read", "L1", True, False),
+    ("普通用户", "supplier.read", "L1", True, False),
+    ("product_manager", "risk.assess", "L1", True, True),
+    ("product_manager", "risk_item.submit", "L1", True, True),
+    ("product_manager", "risk_item.review", "L2", True, True),
+    ("product_manager", "standard.read", "L1", True, False),
+    ("product_manager", "supplier.read", "L2", True, False),
+    ("reviewer", "risk.assess", "L1", True, True),
+    ("reviewer", "risk_item.review", "L2", True, True),
+    ("reviewer", "standard.read", "L1", True, False),
+    ("reviewer", "supplier.read", "L2", True, False),
+    ("admin", "risk.assess", "L3", True, True),
+    ("admin", "risk_item.submit", "L3", True, True),
+    ("admin", "risk_item.review", "L3", True, True),
+    ("admin", "model_weight.manage", "L3", True, True),
+    ("admin", "rbac.manage", "L3", True, True),
+    ("admin", "standard.read", "L3", True, True),
+    ("admin", "standard.manage", "L3", True, True),
+    ("admin", "supplier.read", "L3", True, True),
+)
+
+
+def upgrade() -> None:
+    op.create_table(
+        "risk_model_configs",
+        sa.Column("id", sa.BigInteger(), nullable=False),
+        sa.Column("weights", postgresql.JSONB(astext_type=sa.Text()), nullable=False),
+        sa.Column("frequency_coefficient", sa.Numeric(5, 4), nullable=False),
+        sa.Column("frequency_cap", sa.Numeric(4, 2), nullable=False),
+        sa.Column("updated_by", sa.String(length=20), nullable=True),
+        sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=False),
+        sa.PrimaryKeyConstraint("id"),
+    )
+    connection = op.get_bind()
+    connection.execute(
+        sa.text(
+            """
+            INSERT INTO risk_model_configs (id, weights, frequency_coefficient, frequency_cap)
+            VALUES (1, CAST(:weights AS jsonb), 0.3, 1.5)
+            """
+        ),
+        {"weights": json.dumps(DEFAULT_WEIGHTS, ensure_ascii=False)},
+    )
+    connection.execute(
+        sa.text(
+            """
+            UPDATE user_accounts
+            SET role = CASE
+                WHEN role IN ('dev', 'manager') THEN 'product_manager'
+                WHEN role IN ('qc', 'reviewer') THEN 'reviewer'
+                WHEN role IN ('procurement', 'ops', '普通用户') THEN '普通用户'
+                ELSE role
+            END,
+            updated_at = now()
+            """
+        )
+    )
+    connection.execute(sa.text("DELETE FROM role_permissions"))
+    statement = sa.text(
+        """
+        INSERT INTO role_permissions (role, func_key, data_level, can_view, can_operate)
+        VALUES (:role, :func_key, :data_level, :can_view, :can_operate)
+        """
+    )
+    for role, func_key, data_level, can_view, can_operate in PERMISSIONS:
+        connection.execute(
+            statement,
+            {
+                "role": role,
+                "func_key": func_key,
+                "data_level": data_level,
+                "can_view": can_view,
+                "can_operate": can_operate,
+            },
+        )
+    op.execute("COMMENT ON TABLE risk_model_configs IS '管理员维护的七维权重与频次系数配置。'")
+    op.execute("COMMENT ON COLUMN user_accounts.role IS '角色：admin、product_manager、reviewer 或 普通用户。'")
+
+
+def downgrade() -> None:
+    connection = op.get_bind()
+    connection.execute(
+        sa.text("UPDATE user_accounts SET role = 'manager', updated_at = now() WHERE role = 'product_manager'")
+    )
+    connection.execute(sa.text("DELETE FROM role_permissions"))
+    op.drop_table("risk_model_configs")

pyproject.toml

@@ -14,6 +14,7 @@ dependencies = [
     "fastapi>=0.115,<1.0",
     "httpx>=0.27,<1.0",
     "openpyxl>=3.1,<4.0",
+    "python-multipart>=0.0.9,<1.0",
     "PyJWT>=2.8,<3.0",
     "pydantic>=2.8,<3.0",
     "pydantic-settings>=2.4,<3.0",

requirements.txt

@@ -3,6 +3,7 @@ asyncpg>=0.29,<1.0
 fastapi>=0.115,<1.0
 httpx>=0.27,<1.0
 openpyxl>=3.1,<4.0
+python-multipart>=0.0.9,<1.0
 PyJWT>=2.8,<3.0
 pydantic>=2.8,<3.0
 pydantic-settings>=2.4,<3.0

tests/test_auth.py

@@ -2,9 +2,9 @@ from app.services.dingtalk_auth import DingTalkAuthService
 
 
 def test_dingtalk_role_mapping() -> None:
-    assert DingTalkAuthService.map_role("产品开发部", "工程师") == "dev"
-    assert DingTalkAuthService.map_role("品控部", "质量主管") == "qc"
-    assert DingTalkAuthService.map_role("采购部", "采购专员") == "procurement"
+    assert DingTalkAuthService.map_role("产品部", "产品经理") == "product_manager"
+    assert DingTalkAuthService.map_role("品控部", "质量主管") == "普通用户"
+    assert DingTalkAuthService.map_role("采购部", "采购专员") == "普通用户"
     assert DingTalkAuthService.map_role("", "审核员") == "reviewer"
     assert DingTalkAuthService.map_role("", "", is_admin=True) == "admin"
     assert DingTalkAuthService.map_role("未知部门", "普通员工") == "普通用户"

tests/test_baidu_ocr.py

@@ -0,0 +1,132 @@
+import json
+
+import httpx
+import pytest
+from app.schemas.ocr import OcrMeasurement
+from app.services.baidu_ocr import BaiduOcrClient
+from app.services.ocr import OcrService
+
+
+@pytest.mark.asyncio
+async def test_webimage_recognition_returns_text_and_confidence() -> None:
+    def handler(request: httpx.Request) -> httpx.Response:
+        if request.url.path.endswith("/token"):
+            return httpx.Response(200, json={"access_token": "token", "expires_in": 3600})
+        assert request.url.path.endswith("/webimage")
+        return httpx.Response(
+            200,
+            json={
+                "words_result": [
+                    {"words": "产品名称：测试面霜", "probability": {"average": 0.92}},
+                    {"words": "pH：5.6", "probability": {"average": 0.88}},
+                ]
+            },
+        )
+
+    client = BaiduOcrClient(httpx.MockTransport(handler))
+    client.api_key = "api-key"
+    client.secret_key = "secret-key"
+    text, confidence, pages = await client.recognize_image(b"fake-image")
+
+    assert text == "产品名称：测试面霜\npH：5.6"
+    assert confidence == pytest.approx(0.9)
+    assert pages == 1
+
+
+@pytest.mark.asyncio
+async def test_paddle_document_recognition_polls_and_downloads_result() -> None:
+    calls = {"query": 0}
+
+    def handler(request: httpx.Request) -> httpx.Response:
+        if request.url.path.endswith("/token"):
+            return httpx.Response(200, json={"access_token": "token", "expires_in": 3600})
+        if request.url.path.endswith("/task/query"):
+            calls["query"] += 1
+            status = "processing" if calls["query"] == 1 else "success"
+            result = {"status": status}
+            if status == "success":
+                result["parse_result_url"] = "https://mock.bcebos.com/result.json"
+            return httpx.Response(200, json={"error_code": 0, "result": result})
+        if request.url.path.endswith("/task"):
+            return httpx.Response(200, json={"error_code": 0, "result": {"task_id": "task-1"}})
+        if request.url.path.endswith("/result.json"):
+            return httpx.Response(
+                200,
+                content=json.dumps(
+                    {
+                        "pages": [
+                            {
+                                "text": "产品名称：测试面霜",
+                                "tables": [{"markdown": "|检测项|值|\n|pH|5.6|"}],
+                            }
+                        ]
+                    },
+                    ensure_ascii=False,
+                ).encode(),
+            )
+        raise AssertionError(f"unexpected URL: {request.url}")
+
+    client = BaiduOcrClient(httpx.MockTransport(handler))
+    client.api_key = "api-key"
+    client.secret_key = "secret-key"
+    client.poll_interval = 0.001
+    text, confidence, pages = await client.recognize_document(b"%PDF-test", "report.pdf")
+
+    assert "测试面霜" in text
+    assert "|pH|5.6|" in text
+    assert confidence is None
+    assert pages == 1
+    assert calls["query"] == 2
+
+
+@pytest.mark.parametrize(
+    ("raw_value", "value", "standard", "expected"),
+    [
+        ("5.6", 5.6, "pH 4.5-6.5", "合格"),
+        ("3.8", 3.8, "色差 ΔE≤3.0", "不合格"),
+        ("+18%", 18, "喷出量偏差≤±15%", "不合格"),
+        ("0.9 N·m", 0.9, "开启力矩 0.8~3.2N·m", "合格"),
+    ],
+)
+def test_standard_numeric_evaluation(raw_value: str, value: float, standard: str, expected: str) -> None:
+    measurement = OcrMeasurement(
+        item="检测项",
+        raw_value=raw_value,
+        value=value,
+        confidence=0.95,
+        needs_review=False,
+    )
+    result, reason = OcrService.evaluate_standard(measurement, standard)
+
+    assert result == expected
+    assert reason is None
+
+
+def test_upload_type_is_detected_from_content_not_filename() -> None:
+    assert OcrService._detect_type(b"%PDF-1.7") == ("application/pdf", ".pdf")
+    assert OcrService._detect_type(b"\x89PNG\r\n\x1a\nrest") == ("image/png", ".png")
+    assert OcrService._detect_type(b"\xff\xd8\xffrest") == ("image/jpeg", ".jpg")
+
+
+def test_standard_judgment_rejects_unit_mismatch() -> None:
+    measurement = OcrMeasurement(
+        item="铅含量",
+        raw_value="8mg/L",
+        value=8,
+        unit="mg/L",
+        confidence=0.95,
+        needs_review=False,
+    )
+
+    result, reason = OcrService.evaluate_standard(measurement, "铅≤10mg/kg")
+
+    assert result == "待人工确认"
+    assert reason == "检测值单位 mg/L 与标准单位 mg/kg 不一致"
+
+
+def test_generic_quantity_does_not_fuzzy_match_a_standard() -> None:
+    class Rule:
+        risk_point = "销售包装可视面及最小销售单元完整标注"
+        judge_standard = "中英文标签内容完整"
+
+    assert OcrService._best_rule("数量", [Rule()]) is None

tests/test_database_risk_scoring.py

@@ -1,7 +1,11 @@
 from datetime import date
+from decimal import Decimal
+from types import SimpleNamespace
 
 import pytest
+from app.schemas.model_config import ModelWeightConfigUpdate
 from app.services.database_risk_scoring import DatabaseRiskScoringService
+from pydantic import ValidationError
 
 
 def _row(**values):
@@ -63,3 +67,52 @@ async def test_supplier_dimension_does_not_count_factory_records() -> None:
     supplier_dimension = result.dimensions[0]
     assert supplier_dimension.score == 70
     assert supplier_dimension.raw_value == 0
+
+
+@pytest.mark.asyncio
+async def test_score_uses_persisted_weight_and_frequency_config() -> None:
+    configured_weights = {
+        "供应商表现": 30,
+        "物料品类": 15,
+        "产品品类": 15,
+        "加工厂质量": 15,
+        "标准符合性": 10,
+        "节点阶段": 10,
+        "缺陷等级": 5,
+    }
+
+    class Session:
+        async def get(self, _model, _identifier):
+            return SimpleNamespace(
+                weights=configured_weights,
+                frequency_coefficient=Decimal("0.5"),
+                frequency_cap=Decimal("1.8"),
+            )
+
+    service = DatabaseRiskScoringService(Session(), as_of=date(2026, 9, 4))
+
+    async def load_rows():
+        return [_row(), _row(product_name="洗发水 250ml", defect_level="B")]
+
+    service._load_rows = load_rows  # type: ignore[method-assign]
+    result = await service.score(category="洗发水", material="瓶子", supplier="祥印", stage="产品开发")
+
+    assert [item.weight for item in result.dimensions] == [30, 15, 15, 15, 10, 10, 5]
+    assert result.dimensions[0].score == 95.95
+
+
+def test_weight_config_requires_exactly_one_hundred_percent() -> None:
+    with pytest.raises(ValidationError, match="合计必须为100%"):
+        ModelWeightConfigUpdate(
+            weights={
+                "供应商表现": 20,
+                "物料品类": 20,
+                "产品品类": 15,
+                "加工厂质量": 15,
+                "标准符合性": 10,
+                "节点阶段": 10,
+                "缺陷等级": 5,
+            },
+            frequency_coefficient=0.3,
+            frequency_cap=1.5,
+        )

tests/test_models.py

@@ -7,6 +7,7 @@ from app.models import (  # noqa: F401
     ReviewRecord,
     RiskAssessment,
     RiskItem,
+    RiskModelConfig,
     RolePermission,
     StandardRule,
     SupplierProfile,
@@ -24,6 +25,7 @@ def test_prd_core_tables_are_registered() -> None:
         "review_records",
         "risk_assessments",
         "risk_items",
+        "risk_model_configs",
         "role_permissions",
         "standard_rules",
         "supplier_profiles",