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https://github.com/dptech-corp/Uni-Lab-OS.git
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新增批量创建固体物料和从CSV文件入库的功能；更新配置文件中的 report_ip 默认值；新增 solid_materials.csv 文件以支持物料名称导入。
This commit is contained in:
calvincao
@@ -825,12 +825,221 @@ class BioyondCellWorkstation(BioyondWorkstation):
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logger.warning("超时未找到成功的物料转移任务")
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logger.warning("超时未找到成功的物料转移任务")
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return False
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return False
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def create_solid_materials(self, material_names: List[str], type_id: str = "3a190ca0-b2f6-9aeb-8067-547e72c11469") -> List[Dict[str, Any]]:
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"""
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批量创建固体物料
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Args:
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material_names: 物料名称列表
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type_id: 物料类型ID（默认为固体物料类型）
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Returns:
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创建的物料列表，每个元素包含物料信息和ID
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"""
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created_materials = []
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total = len(material_names)
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for i, name in enumerate(material_names, 1):
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# 根据接口文档构建完整的请求体
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material_data = {
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"typeId": type_id,
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"name": name,
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"unit": "g", # 添加单位
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"quantity": 1, # 添加数量（默认1）
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"parameters": "" # 参数字段（空字符串表示无参数）
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}
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logger.info(f"正在创建第 {i}/{total} 个固体物料: {name}")
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result = self._post_lims("/api/lims/storage/material", material_data)
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if result and result.get("code") == 1:
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# data 字段可能是字符串（物料ID）或字典（包含id字段）
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data = result.get("data")
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if isinstance(data, str):
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# data 直接是物料ID字符串
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material_id = data
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elif isinstance(data, dict):
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# data 是字典，包含id字段
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material_id = data.get("id")
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else:
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material_id = None
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if material_id:
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created_materials.append({
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"name": name,
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"materialId": material_id,
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"typeId": type_id
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})
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logger.info(f"✓ 成功创建物料: {name}, ID: {material_id}")
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else:
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logger.error(f"✗ 创建物料失败: {name}, 未返回ID")
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logger.error(f" 响应数据: {result}")
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else:
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error_msg = result.get("error") or result.get("message", "未知错误")
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logger.error(f"✗ 创建物料失败: {name}")
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logger.error(f" 错误信息: {error_msg}")
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logger.error(f" 完整响应: {result}")
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# 避免请求过快
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time.sleep(0.3)
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logger.info(f"物料创建完成，成功创建 {len(created_materials)}/{total} 个固体物料")
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return created_materials
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def create_and_inbound_materials_from_csv(
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self,
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csv_path: str = "solid_materials.csv",
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type_id: str = "3a190ca0-b2f6-9aeb-8067-547e72c11469",
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warehouse_name: str = "粉末加样头堆栈"
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) -> Dict[str, Any]:
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"""
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从CSV文件读取物料列表，创建物料并批量入库到指定堆栈
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Args:
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csv_path: CSV文件路径
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type_id: 物料类型ID（默认为固体物料类型）
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warehouse_name: 仓库名称（默认为"粉末加样头堆栈"）
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Returns:
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包含执行结果的字典
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"""
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logger.info("=" * 60)
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logger.info(f"开始执行：从CSV读取物料列表并批量创建入库到 {warehouse_name}")
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logger.info("=" * 60)
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# 从配置中获取位置ID列表
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warehouse_mapping = self.bioyond_config.get("warehouse_mapping", WAREHOUSE_MAPPING)
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if warehouse_name not in warehouse_mapping:
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error_msg = f"配置中未找到仓库: {warehouse_name}"
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logger.error(error_msg)
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logger.info(f"可用的仓库: {list(warehouse_mapping.keys())}")
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return {"success": False, "error": error_msg}
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warehouse_config = warehouse_mapping[warehouse_name]
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site_uuids = warehouse_config.get("site_uuids", {})
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if not site_uuids:
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error_msg = f"仓库 {warehouse_name} 没有配置位置"
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logger.error(error_msg)
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return {"success": False, "error": error_msg}
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# 按顺序获取位置ID（A01, B01, C01...）
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all_location_ids = []
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position_names = []
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for key in sorted(site_uuids.keys()):
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all_location_ids.append(site_uuids[key])
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position_names.append(key)
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logger.info(f"✓ 从配置文件加载 {len(all_location_ids)} 个位置")
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logger.info(f" 仓库: {warehouse_name}")
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logger.info(f" 位置范围: {position_names[0]} ~ {position_names[-1]}")
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# 读取CSV文件
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csv_file_path = Path(csv_path)
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material_names = []
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try:
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df_materials = pd.read_csv(csv_file_path)
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if 'material_name' in df_materials.columns:
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material_names = df_materials['material_name'].dropna().astype(str).str.strip().tolist()
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logger.info(f"✓ 成功从CSV文件读取 {len(material_names)} 个物料名称")
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logger.info(f" 文件路径: {csv_file_path}")
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else:
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logger.error(f"✗ CSV文件缺少 'material_name' 列")
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return {"success": False, "error": "CSV文件缺少 'material_name' 列"}
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except FileNotFoundError:
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logger.error(f"✗ 未找到CSV文件: {csv_file_path}")
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logger.info("请创建CSV文件，格式：")
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logger.info(" material_name")
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logger.info(" LiPF6")
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logger.info(" LiDFOB")
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logger.info(" ...")
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return {"success": False, "error": f"未找到CSV文件: {csv_file_path}"}
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except Exception as e:
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logger.error(f"✗ 读取CSV文件失败: {e}")
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return {"success": False, "error": f"读取CSV文件失败: {e}"}
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if not material_names:
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logger.error("CSV文件中没有有效的物料名称")
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return {"success": False, "error": "CSV文件中没有有效的物料名称"}
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# 检查物料数量
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if len(material_names) > len(all_location_ids):
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logger.warning(f"物料数量({len(material_names)})超过可用位置数量({len(all_location_ids)})！")
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logger.warning(f"将仅创建前 {len(all_location_ids)} 个物料")
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material_names = material_names[:len(all_location_ids)]
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# 准备位置信息
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location_ids = all_location_ids[:len(material_names)]
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selected_positions = position_names[:len(material_names)]
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# 步骤1: 创建固体物料
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logger.info(f"\n【步骤1/2】创建 {len(material_names)} 个固体物料...")
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logger.info(f"物料类型ID: {type_id}")
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logger.info(f"物料列表: {', '.join(material_names)}")
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created_materials = self.create_solid_materials(
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material_names=material_names,
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type_id=type_id
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)
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if len(created_materials) != len(material_names):
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logger.warning(f"创建的物料数量({len(created_materials)})与计划数量({len(material_names)})不匹配！")
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logger.warning("将仅对成功创建的物料进行入库操作")
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if not created_materials:
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logger.error("没有成功创建任何物料")
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return {"success": False, "error": "没有成功创建任何物料"}
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# 步骤2: 批量入库到指定位置
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logger.info(f"\n【步骤2/2】批量入库物料到 {warehouse_name}...")
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inbound_items = []
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for idx, material in enumerate(created_materials):
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if idx < len(location_ids):
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inbound_items.append({
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"materialId": material["materialId"],
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"locationId": location_ids[idx]
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})
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logger.info(f" - {material['name']} (ID: {material['materialId'][:8]}...) → 位置 {selected_positions[idx]}")
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logger.info(f"\n正在执行批量入库，共 {len(inbound_items)} 条记录...")
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result = self.storage_batch_inbound(inbound_items)
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if result.get("code") == 1:
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logger.info(f"✓ 批量入库成功！")
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logger.info(f" 响应数据: {result.get('data', {})}")
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else:
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logger.error(f"✗ 批量入库失败！")
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logger.error(f" 响应: {result}")
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logger.info("\n" + "=" * 60)
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logger.info("固体物料创建和入库流程完成")
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logger.info("=" * 60 + "\n")
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return {
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"success": result.get("code") == 1,
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"created_materials": created_materials,
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"inbound_result": result,
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"total_created": len(created_materials),
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"total_inbound": len(inbound_items),
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"warehouse": warehouse_name,
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"positions": selected_positions
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}
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# --------------------------------
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# --------------------------------
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if __name__ == "__main__":
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if __name__ == "__main__":
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ws = BioyondCellWorkstation()
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ws = BioyondCellWorkstation()
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logger.info(ws.scheduler_start())
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logger.info(ws.scheduler_start())
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#TODO：新建入库
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# 从CSV文件读取物料列表并批量创建入库
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result = ws.create_and_inbound_materials_from_csv()
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# 继续后续流程
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logger.info(ws.auto_feeding4to3()) #搬运物料到3号箱
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logger.info(ws.auto_feeding4to3()) #搬运物料到3号箱
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# 使用正斜杠或 Path 对象来指定文件路径
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# 使用正斜杠或 Path 对象来指定文件路径
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excel_path = Path("unilabos/devices/workstation/bioyond_studio/bioyond_cell/2025092701.xlsx")
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excel_path = Path("unilabos/devices/workstation/bioyond_studio/bioyond_cell/2025092701.xlsx")
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@@ -0,0 +1,7 @@
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material_name
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LiPF6
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LiDFOB
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DTD
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LiFSI
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LiPO2F2
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@@ -25,7 +25,7 @@ BIOYOND_FULL_CONFIG = {
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# HTTP 服务配置
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# HTTP 服务配置
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"HTTP_host": os.getenv("BIOYOND_HTTP_HOST", "0.0.0.0"), # HTTP服务监听地址（0.0.0.0 表示监听所有网络接口）
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"HTTP_host": os.getenv("BIOYOND_HTTP_HOST", "0.0.0.0"), # HTTP服务监听地址（0.0.0.0 表示监听所有网络接口）
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"HTTP_port": int(os.getenv("BIOYOND_HTTP_PORT", "8080")),
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"HTTP_port": int(os.getenv("BIOYOND_HTTP_PORT", "8080")),
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"report_ip": os.getenv("BIOYOND_REPORT_IP", "172.21.32.57"), # 报送给 Bioyond 的本机IP地址（留空则自动检测）
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"report_ip": os.getenv("BIOYOND_REPORT_IP", "172.21.32.172"), # 报送给 Bioyond 的本机IP地址（留空则自动检测）
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# 调试模式
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# 调试模式
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"debug_mode": os.getenv("BIOYOND_DEBUG_MODE", "False").lower() == "true",
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"debug_mode": os.getenv("BIOYOND_DEBUG_MODE", "False").lower() == "true",
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@@ -112,6 +112,31 @@ WAREHOUSE_MAPPING = {
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"B3": "3a14198c-c2d0-725e-523d-34c037ac2440",
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"B3": "3a14198c-c2d0-725e-523d-34c037ac2440",
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"B4": "3a14198c-c2d0-efce-0939-69ca5a7dfd39"
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"B4": "3a14198c-c2d0-efce-0939-69ca5a7dfd39"
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}
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}
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},
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"粉末加样头堆栈": {
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"uuid": "",
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"site_uuids": {
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"A01": "3a19da56-1379-20c8-5886-f7c4fbcb5733",
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"B01": "3a19da56-1379-2424-d751-fe6e94cef938",
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"C01": "3a19da56-1379-271c-03e3-6bdb590e395e",
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"D01": "3a19da56-1379-277f-2b1b-0d11f7cf92c6",
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"E01": "3a19da56-1379-2f1c-a15b-e01db90eb39a",
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"F01": "3a19da56-1379-3fa1-846b-088158ac0b3d",
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"G01": "3a19da56-1379-5aeb-d0cd-d3b4609d66e1",
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"H01": "3a19da56-1379-6077-8258-bdc036870b78",
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"I01": "3a19da56-1379-863b-a120-f606baf04617",
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"J01": "3a19da56-1379-8a74-74e5-35a9b41d4fd5",
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"K01": "3a19da56-1379-b270-b7af-f18773918abe",
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"L01": "3a19da56-1379-ba54-6d78-fd770a671ffc",
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"M01": "3a19da56-1379-c22d-c96f-0ceb5eb54a04",
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"N01": "3a19da56-1379-d64e-c6c5-c72ea4829888",
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"O01": "3a19da56-1379-d887-1a3c-6f9cce90f90e",
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"P01": "3a19da56-1379-e77d-0e65-7463b238a3b9",
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"Q01": "3a19da56-1379-edf6-1472-802ddb628774",
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"R01": "3a19da56-1379-f281-0273-e0ef78f0fd97",
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"S01": "3a19da56-1379-f924-7f68-df1fa51489f4",
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"T01": "3a19da56-1379-ff7c-1745-07e200b44ce2"
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}
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}
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}
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}
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}
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