<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

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

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Among all kinds of coal production disasters, the consequences of gas disaster are the most serious. As the existing coal mine gas explosion disaster...

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

Data publikacji: 23 gru 2022

Zakres stron: 407 - 418

Otrzymano: 27 lip 2022

Przyjęty: 16 sie 2022

DOI: https://doi.org/10.2478/amns.2021.2.00299

Słowa kluczowe
NTM deep learning network, coal mine accident, gas explosion, hazard warning

© 2023 Wenzhe Lai et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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

Data publikacji: 23 gru 2022

Zakres stron: 407 - 418

Otrzymano: 27 lip 2022

Przyjęty: 16 sie 2022

DOI: https://doi.org/10.2478/amns.2021.2.00299

Słowa kluczoweNTM deep learning network, coal mine accident, gas explosion, hazard warning

© 2023 Wenzhe Lai et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
NTM deep learning network, coal mine accident, gas explosion, hazard warning