航天器工程2026，Vol.35Issue(2)：120-127,8.DOI:10.3969/j.issn.1673-8748.2026.02.016
基于大语言模型的航天器健康管理知识图谱构建与应用
Construction and Application of a Spacecraft Health Management Knowledge Graph Based on Large Language Models
刘超 1刘鹏 1张香燕 1陈曦 1衣秀2
作者信息
- 1. 北京空间飞行器总体设计部,北京 100094
- 2. 天津德尔塔科技有限公司,天津 300384
- 折叠
摘要
Abstract
To address the complexity of on-orbit operational data of spacecraft and the dispersion of expert knowledge,this paper constructs a spacecraft fault knowledge graph,which provides associated knowledge assistance and logical reasoning support for anomalous conditions.A knowledge-based question answering method for spacecraft health management is proposed by in-tegrating knowledge graph embedding with large language models(LLMs).First,knowledge graph embedding techniques are employed to perform vectorized representation of entities and re-lations,enabling the deep mining of complex semantic associations among equipment.Second,triple-based vector computation is combined with a Double-Array Trie(DAT)to optimized query efficiency,and domain knowledge alignment and fine-tuning of the LLM are performed.On this basis,a full-link fault QA process is designed,encompassing semantic parsing and candidate an-swer generation,while a vector database is utilized to achieve efficient retrieval and similarity matching of high-dimensional semantic information.Experimental and application analysis dem-onstrate that the proposed method significantly improves the accuracy and response efficiency of spacecraft fault diagnosis,providing a new technical approach for spacecraft health management.关键词
航天器/故障诊断/图谱嵌入/大语言模型/双数组字典树Key words
spacecraft/fault diagnosis/knowledge graph embedding/large language model/Double-Array Trie分类
信息技术与安全科学引用本文复制引用
刘超,刘鹏,张香燕,陈曦,衣秀..基于大语言模型的航天器健康管理知识图谱构建与应用[J].航天器工程,2026,35(2):120-127,8.
