Projection of early warning identification of hazardous sources of gas explosion accidents in coal mines based on NTM deep learning network

Abstract

Among all kinds of coal production disasters, the consequences of gas disaster are the most serious. As the existing coal mine gas explosion disaster pre-control management theory and method system is not satisfactory, the neural Turing machine (NTM) deep learning network algorithm is used to calculate and analyse the risk source early warning identification of coal mine gas explosion accidents. Institute with data sets of gas gas accident knowledge base matter each event to cause an (basic or intermediate events) as an example, through the study of the depth of NTM network algorithm calculation analysis shows that self-rescuer failure, personnel peccancy operation, such as downhole safety management does not reach the designated position is easy to cause important hazard of gas explosion accident, the probability to cause an 0.567. Based on the constructed NTM deep learning network algorithm, the risk factors and their weights in the early warning identification of gas explosion accidents are calculated and analysed. Through calculation analysis, it can be seen that the highest weight of risk factors is gas concentration, with a weight of 96. In the early warning identification of hazard sources, the hazard factor next to gas concentration is mine combustibles, with a weight of 75.

Research on coal mine longwall face gas state analysis and safety warning strategy based on multi-sensor forecasting models

“…Current methodologies predominantly depend on manual observation and heuristic approaches. In contrast, the implementation of big data models utilizing deep learning 33 allows for comprehensive monitoring and evaluation of the entire work environment. This advancement not only enhances managerial decision-making but also mitigates human error.…”