基于大语言模型的水利工程运维决策智能体构建OA

The patrol inspection of water conservancy projects is a core management task for ensuring the safe and stable operation of critical infrastructures.Traditional patrol inspections suffer from high reliance on manual labor,low accuracy in hazard identification,and insufficient dynamic decision-making capabilities.This paper proposes a multi-agent collaborative intelligent decision-making framework that integrates Large Language Models(LLMs)and Graph Retrieval-Augmented Generation(GraphRAG)technologies.Adopting a modular architecture encompassing perception,memory,communication,planning,and action,the framework achieves full automation of the patrol inspection processes.A multi-modal dataset was constructed using multi-source patrol inspection data from the past three years.Domain-adaptive fine-tuning significantly improved the F1 scores of multi-modal large language models in equipment recognition and defect detection by 7.2%and 6.9%,respectively.Furthermore,a dynamic knowledge graph system based on Graph RAG was developed to bridge domain-specific knowledge gaps through knowledge infu-sion techniques,while simultaneously employing an entity-relation reasoning mechanisms to effectively mitigate model hallucinations.Experimental results demonstrate that patrol inspection reports generated by this method,upon dual evaluation by both domain experts and operational maintenance personnel,accurately reflect the professional expertise and technical depth required in operations and maintenance of water conservancy projects.This research provides a novel,interpretable,and reliable technical paradigm for intelligent operation and maintenance of water infrastructure,holding significant engineering application value for advancing digital transformation within the indus-try.