Applications and Prospects of Graph Retrieval-Augmented Generation Technology Based on Large Language Models in the Nuclear Power Field

XU Haoran; ZHANG Jinyun; MA Xin; LEI Wenqiang; CAO Jieming

doi:10.12096/j.2096-4528.pgt.25084

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Applications and Prospects of Graph Retrieval-Augmented Generation Technology Based on Large Language Models in the Nuclear Power Field

Application of AI in New Power System | 更新时间：2026-06-29

- Applications and Prospects of Graph Retrieval-Augmented Generation Technology Based on Large Language Models in the Nuclear Power Field
- Power Generation Technology Vol. 46, Issue 3, Pages: 454-466(2025)
- 作者机构：
  
  1.中国核动力研究设计院，四川省成都市 610213
  2.四川大学计算机学院，四川省成都市 610065
- 作者简介：
- 基金信息：
  
  General Projects of National Natural Science Foundation of China(62272330)
- DOI：10.12096/j.2096-4528.pgt.25084
  CLC： TK 01;TM 61
- Received：17 February 2025，
  
  Revised：2025-04-30，
  
  Published：30 June 2025
- 稿件说明：
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徐浩然,张瑾昀,马歆等.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(03):454-466.

XU Haoran,ZHANG Jinyun,MA Xin,et al.Applications and Prospects of Graph Retrieval-Augmented Generation Technology Based on Large Language Models in the Nuclear Power Field[J].Power Generation Technology,2025,46(03):454-466.
徐浩然,张瑾昀,马歆等.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(03):454-466. DOI： 10.12096/j.2096-4528.pgt.25084.

XU Haoran,ZHANG Jinyun,MA Xin,et al.Applications and Prospects of Graph Retrieval-Augmented Generation Technology Based on Large Language Models in the Nuclear Power Field[J].Power Generation Technology,2025,46(03):454-466. DOI： 10.12096/j.2096-4528.pgt.25084.

摘要

目的

为应对新型核电系统在保障能源供应、推动清洁能源转型及实现“双碳”目标过程中面临的结构多样化、负荷不确定性、数据复杂性等挑战，并解决大语言模型应用于核电领域时存在的知识局限性、幻觉及推理成本高等问题，研究了大语言模型与知识图谱结合，特别是图检索增强生成(graph retrieval-augmented generation，GRAG)技术的应用潜力，以构建更智能、高效、可靠的核电系统信息处理能力。

方法

分析了大语言模型与知识图谱在核电领域的研究现状、各自优缺点及结合的互补性；重点介绍了GRAG技术相较于传统检索增强生成(retrieval-augmented generation，RAG)技术的优势，并探讨了其在核电风险评估、智能问答辅助、知识管理与决策支持、故障诊断与预测等场景的具体应用；梳理了引入并微调大语言模型、构建领域知识图谱、实现GRAG增强的技术路径。最后，从混杂数据下的知识图谱构建、大语言模型认知推理与决策、人机交互可控性等方面对未来研究进行了展望。

结论

结合知识图谱的GRAG技术能有效缓解大语言模型在专业领域的知识局限性和幻觉问题，增强生成内容的可解释性与可靠性。研究结果可为未来优化核电领域知识图谱的构建、提升大语言模型在复杂推理任务中的能力、开发与核电领域专家高效互动的人工智能体提供参考。

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

Keywords

references

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