Product assembly process knowledge pushing method, system and equipment

By combining the Bert-BiLSTM-CRF, LLM-KE and LLM-KGE models, a product assembly process knowledge graph APKG-CP was constructed, which solves the problem of incomplete knowledge in the existing technology, realizes efficient and accurate knowledge push under low hardware resources, and improves the automation efficiency and safety of complex product assembly processes.

CN120611024AActive Publication Date: 2025-09-09HOHAI UNIV
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Patent Information

Application Number
CN202510711618.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-05-29
Publication Date
2025-09-09
Estimated Expiration
2045-05-29

AI Technical Summary

Technical Problem

The existing BERT and LLM models have insufficient semantic recognition when constructing complex product assembly process knowledge graphs, resulting in incomplete knowledge when pushing knowledge, making it difficult to achieve efficient and accurate knowledge matching and reuse under low hardware resources.

Method used

The Bert-BiLSTM-CRF, LLM-KE and LLM-KGE models are combined to construct the product assembly process knowledge graph APKG-CP through structural rationality discrimination module SRDM screening and Bayesian network intelligent reasoning. The semantic similarity calculation of the Bert-BiLSTM-CRF model and the generalization understanding of LLM-KE are used, combined with lightweight models and Bayesian networks for knowledge push.

Benefits of technology

Efficient construction and precise matching of knowledge graphs are achieved with low hardware resources, which improves the efficiency of human-machine collaborative operations, reduces the risk of privacy leakage in cloud operations, and can output assembly process knowledge that precisely matches process requirements under different process design needs.

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Abstract

The invention discloses a product assembly process knowledge pushing method, system and equipment in the technical field of semantic recognition and knowledge graph construction, and the method comprises the steps: carrying out the construction of a knowledge graph through employing a trained Bert-BiLSTM-CRF model and an LLM-KE model according to the obtained product assembly process information, obtaining a first initial knowledge graph and a second initial knowledge graph; and screening the first initial knowledge graph and the second initial knowledge graph by using a structural rationality discrimination module SRDM, performing knowledge graph evaluation on the screened first initial knowledge graph and the screened second initial knowledge graph by using a trained LLM-KGE model, and constructing a product assembly process knowledge graph APKG-CP according to an evaluation result. And pushing assembly process knowledge based on Bayesian network intelligent reasoning. According to the method, knowledge pushing can be completed in different process design requirements, and the efficiency of man-machine collaborative operation is improved.
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