基于大语言模型的医院建筑医疗工艺流程问题辅助诊断与分析技术研究OA

The current analysis,evaluation,and diagnosis of medical process flows in hospital buildings rely heavily on manual labor,which suffers from low efficiency,strong professional dependence,susceptibility to omissions,and difficulty in coordinating multiple specifications.This paper proposes a system based on the Dify low-code platform,integrating the Qwen/Qwen2.5-VL multimodal parsing model and the Deepseek-V3 report generation model.Multi-source data,including textual standard specifications,case documents,and image parsing results,are uniformly converted into high-dimensional vectors using the Qwen/Qwen3-Embedding-8B model.Based on the efficient matching capability of the Reranker vector retrieval model,a distributed vector database index is built to construct a medical process knowledge base and a seven-step closed-loop diagnostic workflow,thereby achieving automated diagnosis of primary-level processes.The results show that,compared with manual diagnostic methods,the intelligent assisted diagnostic system reduced the average time spent from 210.0 minutes to 14.7 minutes,and the report generation time from 30 minutes to within 2 minutes,providing a reference for the optimization of hospital building medical process design.