电力信息与通信技术2026，Vol.24Issue(5)：23-31,9.DOI:10.16543/j.2095-641x.electric.power.ict.2026.05.03
基于结构化知识图谱检索增强的电力领域长文本生成
Retrieval-Augmented Long-Text Generation for the Power Domain Based on Structured Knowledge Graph
摘要
Abstract
In response to the problems of lack of professional knowledge,illusion generation,and insufficient contextual consistency in the long-text generation in in the power field,this paper proposes a method for generating long-text based on structured knowledge graph retrieval-augmented.Firstly,through graph reinforcement learning,key entities and relationships are searched in the knowledge graph,and accurate knowledge retrieval is achieved by integrating local and global features;Then,a global local multi-agent large model framework is constructed.The global model determines the overall structure by generating summaries and directories,while the local model generates chapter content based on retrieved structured knowledge.Through multi-agent iterative interaction,the professionalism and coherence of long-text are optimized.The experimental results show that the proposed method significantly improves accuracy and consistency in long-text generation tasks in the power field,with a 4.5%improvement in generation quality compared to general large models.It outperforms the comparison method in indicators such as ROUGE-2 and Jaccard similarity,providing an effective solution for intelligent processing in scenarios such as power system operation and maintenance,fault diagnosis,and power office,and helping the digital transformation of the power industry.关键词
电力领域/长文本生成/知识图谱/大语言模型Key words
power domain/long-text generation/knowledge graph/large language model分类
信息技术与安全科学引用本文复制引用
杨定坤,刘小光,薛荣华,刘春艳,张伯雷..基于结构化知识图谱检索增强的电力领域长文本生成[J].电力信息与通信技术,2026,24(5):23-31,9.基金项目
国家自然科学基金项目(62202238). （62202238）
