With the rapid development of coal mine intelligent technology, the complexity of coal mine equipment has been continuously improved and the equipment maintenance resources have been continuously enriched. The traditional coal mine equipment maintenance knowledge management technology can no longer meet the current needs of equipment maintenance knowledge management, and the problems of low utilization rate, poor interoperability, and serious loss of knowledge have gradually emerged. It is urgent to study new knowledge system construction and knowledge management application technology for large-scale coal mine equipment maintenance resources. Knowledge graph is a technical method to describe the relationship between things in the objective world by using a graph model. It can effectively solve the problem of knowledge dynamic mining and management under large-scale data. Therefore, this paper focuses on the establishment of a coal mine equipment maintenance knowledge graph system by using knowledge graph technology. The main research contents are as follows: Firstly, based on the current situation that there is no unified basic knowledge system in the field of coal mine equipment maintenance, this paper establishes the coal mine equipment maintenance ontology (CMEMO) to effectively solve the problem that there are no unified representation, integration, and sharing of coal mine equipment maintenance knowledge in this field and provide support for the construction of coal mine equipment maintenance knowledge graph. Then, aiming at the problem that the traditional named-entity recognition method has a poor recognition effect and relies too much on artificial feature design, this paper proposes a named-entity recognition model for coal mine equipment maintenance based on neural network (BERT-BiLSTM-CRF) and applies the model to the coal mine equipment maintenance data set for verification. The experimental results show that, under the same data set, the entity recognition effect of this model is more leading than that of other models. Finally, through demand analysis and architecture design, combined with the constructed ontology model of coal mine equipment maintenance field, the entity identification of coal mine equipment maintenance is completed based on the BERT-BiLSTM-CRF model and the Django application framework is used to build the coal mine equipment maintenance knowledge graph system to realize the functions of each module of the knowledge graph system.

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Based on the analysis of the current challenges and deficiencies in the maintenance and management of coal mine equipment, an intelligent maintenance and health management system framework for coal mine equipment is designed for the big data characteristics of the life cycle of coal mine equipment. Taking the big data processing and analysis of coal mine equipment as the main line, it proposes and elaborates the key technologies of the intelligent maintenance and health management of coal mine equipment driven by big data, including the unified description and analysis of multi-source heterogeneous big data, and intelligent fault diagnosis of coal mine equipment. Technology, health evaluation and prediction technology, intelligent maintenance decision-making technology, etc. Through the implementation of the above-mentioned system architecture and key technologies, data-driven life-cycle intelligent decision-making is realized, which promotes the continuous optimization and improvement of equipment process management and reduces business costs. The proposed system architecture provides a reference model for subsequent development.

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