基于稀疏检索增强生成的城轨车辆监造智能决策方法OA

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.