面向电力领域的知识图谱与大模型融合关键技术及其典型应用  

Key Technologies and Typical Applications of Knowledge Graph and Large Language Model Fusion in the Power Sector

作　　者：闫玮丹 齐冬莲 闫云凤[2] 彭继慎[1] 郭炳延[2] YAN Weidan;QI Donglian;YAN Yunfeng;PENG Jishen;GUO Bingyan(College of Electrical and Control Engineering,Liaoning Technical University,Huludao 125105,China;College of Electrical Engineering,Zhejiang University,Hangzhou 310027,China)

机构地区：[1]辽宁工程技术大学电气与控制工程学院,葫芦岛125105 [2]浙江大学电气工程学院,杭州310027

出　　处：《高电压技术》2025年第4期1747-1762,共16页High Voltage Engineering

摘　　要：大语言模型(large language model,LLM)及其衍生的多模态大模型因其强大的生成能力、泛化能力引发了AI新变革,但存在幻觉问题、可解释性差等不足。知识图谱(knowledge graph,KG)具备推理结果可解释、可增量知识更新等能力,但交互能力较差。该文综述了知识图谱与大模型技术的发展历程、关键技术、优势与局限。针对电力数据与业务特点,分析了两者应用于电力领域的主流方法,建立了面向电力领域的知识图谱与大模型相融合的技术架构,重点分析了各应用场景的可行性,并指出了未来面临的挑战和可能的研究方向。Large language models(LLMs)and their derived multimodal large models have driven a new AI revolution due to their powerful generation and generalization capabilities.However,they have limitations such as hallucination problems and poor interpretability.Knowledge graphs(KGs)have the capabilities of explainable reasoning results and incremental knowledge updates,whereas,its interactive capabilities are weak.This paper reviews the development history,key technologies,advantages and limitations of KGs and LLMs.Focusing on the characteristics of power data and operational features,this paper analyzes mainstream approaches of applying KGs and LLMs in the power domain.A technical architecture that integrates KGs and LLMs for the power sector is established.The feasibility of each application scenario is analyzed in detail.Finally,the paper points out future challenges and potential research directions in this field.

关 键 词：知识图谱 大语言模型 人工智能 设备运检 电力调度 

分 类 号：TM73[电气工程—电力系统及自动化]