Mine intelligent ventilation event capture and scheme reasoning knowledge base

Traditional mine ventilation expert systems mainly rely on manually predefined rules and isolated knowledge modules, which limits their adaptability, update efficiency, and support for complex scenario-oriented decision-making. To address this issue, this study proposes an intelligent mine ventilation knowledge base for event capture and scheme reasoning. At the knowledge representation level, a unified framework integrating a ventilation ontology space, a global ventilation knowledge graph, and a task-oriented ventilation event graph is established to organize ventilation entities, semantic relations, abnormal events, and response actions in a structured manner. At the reasoning level, a method combining ontology extraction, rule discovery, and knowledge-based inference is developed to support real-time event identification and scheme generation from multi-source ventilation data. Furthermore, a software prototype is implemented on Neo4j to realize knowledge storage, visualization, and reasoning execution. A simplified real-mine case is used to verify the practical applicability of the proposed method. The results show that the knowledge base can identify coupled abnormal ventilation conditions, trace the corresponding reasoning path, and generate feasible optimization schemes, demonstrating its value for intelligent ventilation decision support and emergency response in coal mines.