To better prevent the occurrence of hidden dangers of coal mine accidents and ensure the safety production of coal mine enterprises. This paper mines and analyses the pattern of historical monthly hidden danger quantity in the coal mine and constructs three models: the traditional backpropagation (BP) neural network model, the BP neural network based on the adaptive moment estimation optimization algorithm (Adam-BP) model, and the BP neural network prediction model with the introduction of monthly moderators (Month-Adam-BP). The experimental results show that the Adam-BP model can improve the prediction accuracy, in which the mean absolute percentage error (MAPE) improves by 8.93%, the root mean square error (RMSE) improves by 8.15%, the postdifference ratio C improves by 0.04, and the small error probability P improves by 0.12; the Month-Adam-BP model with the introduction of the monthly adjustment factor further improves the prediction accuracy, in which MAPE improves by 2.61%, RMSE improves by 5.41%, the postdifference ratio C improves by 0.06, and the small error probability P improves by 0.03. And the Month-Adam-BP model prediction accuracy reaches the level 2 standard with credible prediction effect; it can also be used to predict coal mines with periodic characteristics of hidden hazard data. Our prediction results show that the predicted number of hidden hazards in this coal mine for the next month is 29, which is an increase compared to the number of hidden dangers in the previous month. Thus, the coal mine safety managers need to strengthen the management of hidden hazards further to prevent accidents, which can better serve the standardization of coal mine safety production and ensure the smooth production of the coal mine.

Determining the influence scope of the airflow disorder is an important problem after coal and gas outburst accidents in ventilation systems. This paper puts forward the indexes of airflow disorder, including the length of the excavation roadway, the outburst pressure, the pressure difference of the air door, and the air quantity of the auxiliary fan. Using the orthogonal table of L₉ (3⁴) and numerical simulation method, the characteristics of airflow reversal are studied, and the outburst airflow reversal degree is calculated should the ventilation facility fail. Furthermore, on the basis of fuzzy comprehensive optimization theory, the comprehensive evaluation model of the airflow disorder is established. The results show that the length of the excavation roadway is the most important factor affecting the stability of the ventilation system, followed by the outburst pressure, pressure difference of the air door, and air quantity of the auxiliary fan. The influence of a gas outburst accident on the return air system is greater than that on the inlet air system, and a larger air velocity has a greater impact on the ventilation system, especially the air inlet part. Moreover, the airflow reversal degree of the ventilation system increases with the increase of the outburst pressure or decreases with the length of the excavation roadway. This paper provides a basis for the prevention of gas outburst accidents.

The thesis theory combined with computer simulation, according to the thermal theory, the heat transfer process of the surrounding rock in the coal mine roadway is simplified into an unstable state process, and the influencing factors of the unstable heat transfer coefficient of the surrounding rock are found, and the computer experimental system is used as the basis. At the same time, the paper constructs an experimental receipt according to the similarity theory, and uses the concrete specimen simulation like the thermal performance of the surrounding rock of coal mine as the coal mine roadway to realize the real-time simulation of the heat transfer process of the roadway. The unstable heat transfer coefficient of the surrounding rock under different wind speeds and ventilation specimens was tested to find the law of variation. Finally, in the actual coal mining industry, different roadways are selected for borehole temperature measurement, and the temperature record body with temperature recording function is used for measurement. The calculation formula of the original rock layer temperature at different depths of the mine area is obtained, and the accuracy of the model is verified.

Disaster propagation in complex, interdependent, and multilayered networks has attracted considerable research interest in recent years. In this paper, we propose a model that combines two dynamic mechanisms, i.e., the spreading of failure in layer-dependent networks, where each node in a layer depends on one in another layer. We first investigate the robustness of the Erdős-Rényi (ER)-ER, scale-free (sf)-ER, and sf-sf pattern of interdependent networks against cascading failure with different probabilities of triggering, and then use the random link, assortative link, and disassortative link patterns between the networks to analyze the scope of propagation of failure. The numerical results show that with increasing probability of triggering, the number of damaged nodes in both layers increased and the robustness of the scale-free network to random failures decreased due to the interdependence. Regardless of the topological structure, the two layers eventually tended to have similar failure characteristics due to their interdependence. In addition, the different link patterns had a significant effect on enhancing disaster propagation in interdependent networks.