Projection of early warning identification of hazardous sources of gas explosion accidents in coal mines based on NTM deep learning network
- Autores: Wenzhe Lai, Liangshan Shao
- Localización: Applied Mathematics and Nonlinear Sciences, ISSN-e 2444-8656, Vol. 8, Nº. 2, 2023, págs. 407-418
- Idioma: inglés
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Resumen
Among all kinds of coal production disasters, the consequences of gas disaster are the most serious. As the existing coalmine gas explosion disaster pre-control management theory and method system is not satisfactory, the neural Turing ma-chine (NTM) deep learning network algorithm is used to calculate and analyse the risk source early warning identificationof coal mine gas explosion accidents. Institute with data sets of gas gas accident knowledge base matter each event tocause an (basic or intermediate events) as an example, through the study of the depth of NTM network algorithm calcula-tion analysis shows that self-rescuer failure, personnel peccancy operation, such as downhole safety management does notreach 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 warningidentification of gas explosion accidents are calculated and analysed. Through calculation analysis, it can be seen thatthe highest weight of risk factors is gas concentration, with a weight of 96. In the early warning identification of hazardsources, the hazard factor next to gas concentration is mine combustibles, with a weight of 75
