An Intelligent Equipment Training System Based on Incremental Learning

Yanhong Guo (), Yitao Wang, Jiayi Cui and Xin Ye
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Yanhong Guo: Dalian University of Technology, Institute of Advanced Intelligence
Yitao Wang: Dalian Naval Academy
Jiayi Cui: Dalian University of Technology, Institute of Systems Engineering
Xin Ye: Dalian University of Technology, Institute of Advanced Intelligence

A chapter in Proceedings of the 2024 5th International Conference on Management Science and Engineering Management (ICMSEM 2024), 2024, pp 1242-1251 from Springer

Abstract: Abstract Facing complex and lengthy equipment usage instructions and training materials, an intelligent equipment training system can effectively enhance the operational capabilities of personnel and the training capabilities of management departments. This article analyzes the problems existing in equipment training management work and constructs an intelligent equipment training system based on incremental learning. It mainly includes three functional modules: equipment training knowledge base generation module, question generation and score assessment module, and training knowledge base update module. This system overcomes the limitations of traditional manual extraction of expert prior knowledge, enabling the intelligent construction of equipment training knowledge bases and improving the management efficiency of equipment training knowledge resources. Based on training data mining, personalized questions are generated according to the differences of learners, and learners’ scores are intelligently evaluated based on their answers. Considering the generation of new knowledge in equipment training, incremental learning is introduced to dynamically update the equipment training knowledge base. This research represents a new expansion of methods in the field of artificial intelligence applied to equipment training.

Keywords: Equipment Training; Knowledge Extraction; Incremental Learning; Knowledge Graph (search for similar items in EconPapers)
Date: 2024
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DOI: 10.2991/978-94-6463-570-6_124

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