大语言模型融合知识图谱的风电运维问答系统研究  

Research on a wind power operation and maintenance Q&A system based on large language models and knowledge graphs

作　　者：陈庆 柳雨生 段练达 梁好 孙启涛 鲁纳纳 CHEN Qing;LIU Yusheng;DUAN Lianda;LIANG Hao;SUN Qitao;LU Nana(Mingyang Smart Energy Group,Zhongshan 528400,China)

机构地区：[1]明阳智慧能源集团股份公司,广东中山528400

出　　处：《综合智慧能源》2024年第9期61-68,共8页Integrated Intelligent Energy

基　　金：明阳智慧能源集团股份公司科技项目(M0B00022271)。

摘　　要：风电场运维工作高度依赖现场实践经验,而行业内人员高流动性带来经验传递难题,传统知识库和问答系统日益暴露出其局限性。为提高问答系统在专业领域的适用性和可靠性,设计了一种融合大型语言模型和知识图谱的风电运维问答系统,通过语义理解和关联性分析,整合结构化与非结构化数据,提供全面、准确的专业回答。主、客观评价表明,该专业问答模型的准确性、连贯性及信息量均优于某中文大语言模型及ChatGLM模型,不仅提升了风电运维的效率,也为行业知识传承和更新提供了解决方案。The operation and maintenance of wind farms heavily rely on on-site practical experience,while the high turnover rate in the industry poses challenges to the impartment of such experience.Traditional knowledge bases and Q&A systems are increasingly revealing their limitations in this regard.To enhance the applicability and reliability of Q&A systems in professional domains,this paper designs a wind farm operation and maintenance Q&A system that integrates large language models(LLMs)with knowledge graphs.Through semantic understanding and correlation analysis,the system combines both structured and unstructured data to provide comprehensive and accurate professional responses.Both subjective and objective evaluations indicate that the accuracy,coherence,and informativeness of this specialized Q&A model surpass those of a certain Chinese LLM and the ChatGLM model.This not only improves the efficiency of wind farm operation and maintenance but also offers a solution for knowledge transfer and updating within the industry.

关 键 词：风电运维 大语言模型 知识图谱 双基座问答系统 ChatGLM模型 

分 类 号：TK81[动力工程及工程热物理—流体机械及工程] TP24[自动化与计算机技术—检测技术与自动化装置]