233 lines
7.9 KiB
Python
233 lines
7.9 KiB
Python
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from __future__ import annotations
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from datetime import datetime
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from pydantic import BaseModel, ConfigDict, Field
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class HealthResponse(BaseModel):
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status: str
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database: str
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class SurgeryStartRequest(BaseModel):
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model_config = ConfigDict(
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json_schema_extra={
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"example": {
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"surgery_id": "123456",
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"camera_ids": ["or-cam-01", "or-cam-02"],
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"candidate_consumables": ["纱布", "缝线", "止血钳"],
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}
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}
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)
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surgery_id: str = Field(
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min_length=6,
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max_length=6,
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pattern=r"^\d{6}$",
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description="手术6位号，只允许6位数字。",
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)
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camera_ids: list[str] = Field(
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min_length=1,
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description="本次手术需要接入的摄像头 ID 列表。",
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)
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candidate_consumables: list[str] = Field(
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default_factory=list,
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description=(
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"本次手术可能使用到的耗材清单。"
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"服务端仅对该清单内的耗材做自动记账与待确认追问；"
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"若为空则不会写入任何消耗（仅拉流推理）。"
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),
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)
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class SurgeryEndRequest(BaseModel):
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model_config = ConfigDict(
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json_schema_extra={"example": {"surgery_id": "123456"}}
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)
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surgery_id: str = Field(
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min_length=6,
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max_length=6,
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pattern=r"^\d{6}$",
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description="手术6位号，只允许6位数字。",
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)
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class SurgeryApiResponse(BaseModel):
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surgery_id: str = Field(description="手术6位号。")
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status: str = Field(description="接口处理状态。")
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message: str = Field(description="返回说明。")
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class SurgeryClientErrorDetail(BaseModel):
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"""与 `HTTPException(detail={...})` 对应；最终 JSON 为 `{"detail": {...}}`。"""
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code: str = Field(description="业务错误码，如 RECORDING_CANNOT_START、RECORDING_NOT_STOPPED、RESULT_NOT_READY。")
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message: str = Field(description="人类可读说明。")
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surgery_id: str = Field(description="手术 6 位号。")
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class SurgeryClientErrorResponse(BaseModel):
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"""FastAPI/Starlette 对 HTTPException 序列化后的常见外形（`detail` 为对象时）。"""
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detail: SurgeryClientErrorDetail
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class SurgeryConsumptionDetail(BaseModel):
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"""单条消耗明细（按事件发生，可能多行）。"""
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item_id: str = Field(description="物品 ID。")
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item_name: str = Field(description="物品名称。")
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quantity: int = Field(ge=0, description="本条记录对应的消耗数量。")
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doctor_id: str = Field(description="医生 ID。")
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timestamp: datetime = Field(description="记录时间（ISO 8601）。")
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source: str = Field(
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default="vision",
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description="记录来源：vision 自动识别；voice 语音确认。",
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)
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class SurgeryConsumptionSummary(BaseModel):
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"""按物品汇总：该手术下该物品消耗数量合计。"""
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item_id: str = Field(description="物品 ID。")
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item_name: str = Field(description="物品名称。")
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total_quantity: int = Field(ge=0, description="该物品在本台手术中的消耗数量合计。")
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def build_consumption_summary(
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details: list[SurgeryConsumptionDetail],
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) -> list[SurgeryConsumptionSummary]:
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"""按 item_id 汇总 total_quantity；名称取该物品首条出现时的 item_name。"""
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totals: dict[str, tuple[str, int]] = {}
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for row in details:
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if row.item_id not in totals:
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totals[row.item_id] = (row.item_name, 0)
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name, acc = totals[row.item_id]
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totals[row.item_id] = (name, acc + row.quantity)
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return [
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SurgeryConsumptionSummary(
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item_id=iid,
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item_name=name,
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total_quantity=qty,
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)
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for iid, (name, qty) in sorted(totals.items(), key=lambda x: x[0])
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]
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class SurgeryVoiceStatusResponse(BaseModel):
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"""手术进行中人工确认（客户端播报）联调状态。"""
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surgery_id: str = Field(description="手术 6 位号。")
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voice_enabled: bool = Field(
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description="是否启用了低置信度人工确认（客户端拉取待确认项）。",
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)
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pending_queue_approx: int = Field(
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ge=0,
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description="待医生确认的追问任务数量（FIFO 队列长度）。",
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)
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last_prompt_snippet: str | None = Field(
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default=None,
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description="最近一次生成的待确认话术摘要。",
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)
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last_asr_text: str | None = Field(
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default=None,
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description="最近一次语音确认接口产生的 ASR 文本。",
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)
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last_error: str | None = Field(
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default=None,
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description="最近一次语音确认错误说明（如 ASR/解析失败）。",
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)
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class PendingConfirmationOption(BaseModel):
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label: str
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confidence: float
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class SurgeryPendingConfirmationResponse(BaseModel):
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"""当前待医生确认的一条低置信度识别。"""
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surgery_id: str
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confirmation_id: str
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prompt_text: str = Field(description="可直接用于 TTS 播报的话术。")
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options: list[PendingConfirmationOption]
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model_top1_label: str = Field(description="模型原始 Top1 标签（可能不在候选清单内）。")
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model_top1_confidence: float
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created_at: datetime
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class SurgeryPendingConfirmationResolveResponse(BaseModel):
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surgery_id: str
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confirmation_id: str
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status: str = Field(description="accepted")
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message: str
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resolved_label: str | None = Field(
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default=None,
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description="解析并确认后的耗材名称；否认全部候选时为 null。",
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)
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rejected: bool = Field(
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default=False,
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description="是否为否认全部候选（不记消耗）。",
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)
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asr_text: str | None = Field(
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default=None,
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description="服务端语音识别得到的文本。",
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)
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audio_object_key: str | None = Field(
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default=None,
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description="MinIO 中原始 WAV 的对象键，用于追溯。",
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)
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class SurgeryResultResponse(BaseModel):
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model_config = ConfigDict(
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json_schema_extra={
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"example": {
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"surgery_id": "123456",
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"status": "completed",
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"message": "结果查询成功。",
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"details": [
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{
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"item_id": "HC001",
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"item_name": "纱布",
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"quantity": 2,
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"doctor_id": "D1001",
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"timestamp": "2026-04-21T10:30:00+08:00",
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},
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{
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"item_id": "HC001",
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"item_name": "纱布",
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"quantity": 1,
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"doctor_id": "D1002",
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"timestamp": "2026-04-21T11:05:00+08:00",
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},
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{
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"item_id": "HC002",
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"item_name": "缝线",
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"quantity": 1,
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"doctor_id": "D1001",
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"timestamp": "2026-04-21T10:45:00+08:00",
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},
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],
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"summary": [
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{"item_id": "HC001", "item_name": "纱布", "total_quantity": 3},
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{"item_id": "HC002", "item_name": "缝线", "total_quantity": 1},
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],
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}
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}
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)
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surgery_id: str = Field(description="手术6位号。")
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status: str = Field(description="结果状态，例如 pending / completed / failed。")
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message: str = Field(description="返回说明。")
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details: list[SurgeryConsumptionDetail] = Field(
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default_factory=list,
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description="消耗明细行：每条含物品、数量、医生与时间；同一物品可多次出现。",
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)
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summary: list[SurgeryConsumptionSummary] = Field(
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default_factory=list,
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description="按物品汇总的消耗合计，应与 details 按 item_id 汇总一致。",
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)
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