The BP Algorithm is used to Predict the Risk of Natural Ignition of Coal Seams to be Mined

In view of the limitations of traditional Hoffman coding in the prediction of natural ignition hazard of coal seams to be mined, a natural ignition hazard prediction scheme based on BP algorithm was proposed. Firstly, the influencing factors were accurately located through the neuron theory, and the indicators were reasonably divided to reduce the interference, and the BP algorithm was used to construct a natural fire risk prediction scheme. Experimental results show that under certain evaluation criteria, the proposed scheme is superior to the traditional Hoffman code in terms of prediction accuracy of natural fire risk and processing time of influencing factors, and has obvious advantages. The prediction of natural ignition risk plays an extremely important role in the coal seam to be mined, which can accurately predict and optimize the growth characteristics and product generation of the coal seam to be mined. However, the traditional Hoffman coding has some limitations in solving the problem of ignition hazard simulation, especially when dealing with complex problems. In this paper, this paper proposes a natural fire risk prediction scheme based on BP algorithm to better solve this problem. In this scheme, the influencing factors is accurately located through the neuron theory, so as to determine the division of indicators, and the BP algorithm is used to construct the scheme. Experimental results show that under certain evaluation criteria, the accuracy and speed of the scheme is significantly improved for different problems, and it has better performance. Therefore, the simulation scheme based on BP algorithm can better solve the limitations of traditional Hoffman coding and improve the simulation accuracy and efficiency in the prediction of natural fire risk of coal seam to be mined.