融合知识图谱与检索增强生成的水利抢险预案智能生成方法OA

To address the challenges in emergency response for hydraulic engineering hazards-specifically the low efficiency of historical case consultation,the severe domain knowledge"hallucinations"of general-purpose models,and the difficulty in dynamically coupling static plans with real-time rainfall,water and work conditions-this study constructed a hydraulic domain knowledge graph and a document vector database for retrieval-augmented generation.A collaborative hybrid retrieval algorithm was proposed to accurately retrieve expert knowledge.Furthermore,a multimodal context fusion module was developed to inject real-time sensing data,such as water levels and rainfall,via hydrological data interfaces.This enabled the large language model to generate emergency response plans that integrated historical experience with adaptability to real-time engineering conditions.Experimental results demonstrated that in plan generation tests based on typical hazard cases in Anhui Province,the proposed framework achieved a Top-5 knowledge retrieval accuracy of 94.2％.Additionally,expert evaluation scores for the generated plans increased by 38.5％compared to traditional single-retrieval methods.These findings overcome the limitations of single-modal retrieval and realize a paradigm shift in emergency planning from passive consultation to active generation,providing effective support for decision-making in water conservancy emergencies.