发电技术2025，Vol.46Issue(3)：454-466,13.DOI:10.12096/j.2096-4528.pgt.25084
基于大语言模型的图检索增强生成技术在核电领域的应用与展望
Applications and Prospects of Graph Retrieval-Augmented Generation Technology Based on Large Language Models in the Nuclear Power Field
摘要
Abstract
[Objectives]To address the challenges faced by new types of nuclear power systems in ensuring energy supply,promoting clean energy transition,and achieving"dual carbon"goals,such as structural diversification,load uncertainty,and data complexity,and to solve problems associated with the application of large language models in the nuclear power domain,such as knowledge limitations,hallucinations,and high reasoning costs,the study explores the application potential of combining large language models with knowledge graphs,especially graph retrieval-augmented generation(GRAG)technology.This combination aims to build more intelligent,efficient,and reliable information processing capabilities for nuclear power systems.[Methods]The research status of large language models and knowledge graphs in the nuclear power domain,their respective advantages and disadvantages,and the complementarity of their integration are analyzed.The advantages of GRAG technology over traditional retrieval-augmented generation(RAG)are highlighted,along with its specific applications in scenarios such as nuclear power risk assessment,intelligent question-answering assistance,knowledge management and decision support,and fault diagnosis and prediction.Furthermore,the technical pathway for introducing and fine-tuning large language models,constructing domain-specific knowledge graphs,and implementing GRAG enhancement is outlined.Finally,an outlook on future research is provided,covering areas such as knowledge graph construction under heterogeneous data,cognitive reasoning and decision-making of large language models,and the controllability of human-computer interaction.[Conclusions]GRAG technology combined with knowledge graphs can effectively alleviate the knowledge limitations and hallucination problems of large language models in specialized domains,enhancing the interpretability and reliability of the generated content.The research findings can provide references for the future optimization of knowledge graph construction in the nuclear power domain,enhancing the capabilities of large language models in complex reasoning tasks,and developing artificial intelligence agents for efficient interaction with experts in the nuclear power field.关键词
双碳/核电/智能电网/人工智能(AI)/深度学习/大语言模型/知识图谱/检索增强生成Key words
dual carbon/nuclear power/smart grid/artificial intelligence(AI)/deep learning/large language model/knowledge graph/retrieval-augmented generation分类
能源与动力引用本文复制引用
徐浩然,张瑾昀,马歆,雷文强,曹杰铭..基于大语言模型的图检索增强生成技术在核电领域的应用与展望[J].发电技术,2025,46(3):454-466,13.基金项目
国家自然科学基金面上项目(62272330).Project Supported by General Projects of National Natural Science Foundation of China(62272330). （62272330）
