Equipment maintenance support question and answer method based on fusion RAG big language model

By deploying an offline knowledge base and RAG large language model in the intelligent maintenance support system, the problems of data confidentiality and lack of professional knowledge in the existing system are solved, and efficient and accurate equipment maintenance support question-and-answer functions in complex fields are realized.

CN120671809APending Publication Date: 2025-09-19NORTHWESTERN POLYTECHNICAL UNIV
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Patent Information

Application Number
CN202510658232.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-05-21
Publication Date
2025-09-19

AI Technical Summary

Technical Problem

Due to the confidentiality of industrial data and the lack of professional knowledge in large language models, the existing intelligent maintenance support system is unable to accurately understand and solve the troubleshooting and maintenance problems of industrial equipment, resulting in a lack of basis and reasoning errors in maintenance recommendations.

Method used

An equipment maintenance support question-answering method based on the RAG large language model is adopted. By deploying an offline knowledge base and a large language model, and using RAG technology combined with information retrieval and generation capabilities, the performance of the question-answering system in complex fields is improved.

Benefits of technology

It realizes the equipment maintenance and support question-and-answer function in professional fields, improves the accuracy and basis of the question-and-answer system, and ensures that the generated maintenance suggestions and fault diagnosis results are reliable and operational.

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Abstract

The invention provides an equipment maintenance support question and answer method based on a fusion RAG big language model. The method comprises the following steps: obtaining question information and a corresponding text tag; labels of the corresponding sub-layers are obtained from a local knowledge base through the text labels; retrieving and aggregating K most relevant entities according to the labels of the sub-layers, and generating prompt words; obtaining retrieval information by using a large language model LLM and generating an intermediate reply; and generating a detailed reply in combination with label information of a higher-layer graph in the local knowledge base, and carrying out iterative optimization on the detailed reply until a final reply is generated. According to the method, the offline knowledge base and the large language model are deployed, and the information retrieval capability and the generation capability of the large language model are combined by utilizing the RAG technology, so that the performance of the question-answering system in the complex field is improved, and the question-answering function for equipment maintenance support in the professional field is realized.
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