Architecture and Application of Mine Ventilation System Safety Knowledge Graph Based on Neo4j

To improve the safety management and accident prevention capabilities of mine ventilation systems, the application of knowledge graph technology is proposed. By employing methodologies such as data analysis, entity relationship definition, and entity relationship extraction, and entity extraction using BERT + BiLSTM + CRF model, a safety knowledge graph for the mine ventilation system is constructed. This facilitates the structured processing of historical accident-related textual data and enables the visual analysis and application of accidents based on the knowledge graph. The research results demonstrate that knowledge graph technology can effectively integrate unstructured data and present it in visual graphs or tables. By utilizing Cypher query statements, multi-dimensional accident statistics and the frequency analysis of specific information can be generated, contributing to a comprehensive understanding of accident occurrence patterns. Leveraging the node-to-node characteristics of the knowledge graph, a correlation analysis between entities is conducted, deeply exploring relationships among different types of data, thereby providing new insights to prevent accidents in mine ventilation systems. Moreover, the analysis of mine ventilation accidents and system failure characteristics offers valuable guidance for the safety management of mine ventilation systems.