中国铁道科学2026，Vol.47Issue(3)：85-96,12.DOI:10.3969/j.issn.1001-4632.2026.03.08
基于稀疏检索增强生成的城轨车辆监造智能决策方法
Intelligent Decision-Making Method of Urban Rail Vehicle Manufacturing Supervision Based on Sparse Retrieval-Augmented Generation
摘要
Abstract
To improve the identification efficiency of quality issues and the level of intelligent decision-making in the supervision process of urban rail vehicle manufacturing,a Sparse Retrieval-Augmented Generation(SRAG)method dedicated to the manufacturing supervision domain is proposed.Firstly,5 836 technical documents and specifications were screened to construct a knowledge base consisting of 412 authoritative documents and a corresponding knowledge graph.Secondly,a total of 12 248 supervision records from 2019 to 2026 were collected,and professional lexicons combined with word segmentation techniques were employed to reveal the temporal patterns of faults,high-frequency failure modes of components,and the coupling relationships between locations and fault types.Finally,the manufacturing supervision corpus and knowledge graph were embedded into Large Language Models(LLMs)to build a sparse-driven knowledge-augmented generation architecture,enabling accurate knowledge invocation and semantically consistent decision-making in complex contexts.The results show that in terms of the semantic similarity metric,the traditional Back Propagation Neural Network(BPNN),Convolutional Neural Network(CNN),and Recurrent Neural Network(RNN)all achieve scores below 0.50,while the T5 model reaches 0.73.After introducing knowledge augmentation,all mainstream LLMs achieve semantic similarity scores exceeding 0.85.Specifically,DeepSeek R1 is improved from 0.92 to 0.96,and ChatGPT-4o is enhanced from 0.88 to 0.97.Compared with vanilla RAG,SRAG also achieves significant improvements in semantic coherence and structural consistency.This method systematically verifies the effectiveness and engineering promotion potential of the sparse retrieval strategy in the context of industrial manufacturing supervision.It helps promote the intelligent,precise and sustainable development of urban rail vehicle manufacturing supervision.关键词
城轨车辆监造/智能决策方法/稀疏知识增强检索/大语言模型/语义相似度Key words
Urban rail vehicle manufacturing supervision/Intelligent decision-making method/Sparse Retrieval-Augmented Generation(SRAG)/Large Language Models(LLMs)/Semantic similarity分类
交通工程引用本文复制引用
王超,秦进,郭建钦,刘玉涛..基于稀疏检索增强生成的城轨车辆监造智能决策方法[J].中国铁道科学,2026,47(3):85-96,12.基金项目
中国城市轨道交通协会2022年度科研重点专项课题(CAMET-KY-2022105) （CAMET-KY-2022105）
