水利信息化Issue(2)：30-35,45,7.DOI:10.19364/j.1674-9405.2026.02.005
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
Intelligent generation method for hydraulic engineering emergency rescue plan fusing knowledge graph and retrieval-augmented generation
摘要
Abstract
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.关键词
知识图谱/检索增强生成/应急预案/大语言模型/水利工程Key words
knowledge graph/retrieval-augmented generation/emergency plan/large language model/hydraulic engineering分类
信息技术与安全科学引用本文复制引用
朱庆辉,蒋静静,赵辉,刘超..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[J].水利信息化,2026,(2):30-35,45,7.基金项目
国家重点研发计划项目(2024YFC3012305) （2024YFC3012305）
安徽省自然科学基金项目(2408055US006) （2408055US006）
