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.

The 110 mining method is a high-efficiency entry-retaining technology without coal pillars or filling materials. At present, there is no precedent for its application in the Huaneng Group. In order to introduce this technology, it is planned to carry out experimental application research in the Yuwang NO.1 Coal Mine of the Huaneng Diandong mining area. The Yuwang NO.1 Coal Mine is a typical coal and gas outburst mine with a coal seam group. In view of the introduction of the 110 mining method under these conditions, first, the geological conditions of the Yuwang NO.1 Coal Mine in the Huaneng Diandong mining area are analyzed, the geological characteristics of the test mining face are summarized, and the practical feasibility of the 110 mining method is analyzed according to the geological characteristics of “one soft, one low, two high, and two complex”. Then, according to the engineering experience, calculations, and analysis, the key parameters of roof cutting of the test mining face in the Yuwang NO.1 Coal Mine are obtained, and with the help of a numerical simulation, the roof-cutting height, the roof-cutting angle, and the blasting parameters are numerically simulated and analyzed. The roof-cutting key parameters of the test face are obtained as follows: the roof-cutting depth is 7 m, the roof-cutting angle is 15°, and the blasting method is continuous hole blasting with 500 mm spacing. After that, according to the coal and gas outburst and the occurrence conditions of coal seams in the Yuwang NO.1 Coal Mine, a variety of gob closure design schemes and gob gas drainage design schemes are put forward, and the field effect investigation scheme is given. After the tunnels and open-off cut of the test coal mine’s first mining face are connected, under the guidance of the above research results, the field blasting test and the 110 mining method entry-retaining test are further carried out to verify the rationality of the design of the roof-cutting parameters and the feasibility of the gangue wall closure and gas drainage design. Furthermore, in the process of the field practice, continuous research is carried out on the stope pressure law and the adjacent layer gas drainage technology under the 110 mining method, and finally, the 110 mining method application technology system in the Diandong Mining Area is formed.

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.