Effectively avoiding coal mine safety accidents has always been an important issue in the process of coal mining. In order to predict mine pressure hazard and reduce the occurrence of mine safety accidents, this paper innovatively combines logistic regression and mine pressure hazard prediction to establish a mine pressure hazard prediction model. By standardizing the data, the model improves the reliability of the mine pressure data and reduces the interference of the prediction effect of random errors. Based on the batch gradient descent algorithm and the Adagrad optimization algorithm, the prediction model is solved innovatively, which greatly improves the calculation speed and prediction accuracy of the model. Accuracy rate, precision rate, recall rate, and F1-score were selected as the evaluation indices to evaluate the prediction effect of the Adagrad optimization algorithm to solve the logistic regression model for mine pressure hazard. Compared with the existing classification algorithms, such as SVM and decision tree, the Adagrad optimization algorithm has the highest four indices when solving the logistic regression prediction model, and it takes the least time to predict. The results show that the model can efficiently predict mine pressure hazard. Finally, C Coal Mine was selected as the example for analysis. The prediction function was added to the mine pressure monitoring interface design. The practical application effect is similar to the theoretical verification. The establishment of this model provides a reliable guarantee for the secure and efficient production of coal mines and provides helpful research for the prediction of mine pressure.

The fleet operation model in which electric vehicles coexist with traditional vehicles is becoming increasingly popular. Because electric vehicles have certain disadvantages and usage limitations, the multi-center management of the distribution of mixed fleets is very complex. There is no research on the multi-center mixed vehicle routing problem based on the integration of reverse logistics and dynamic energy consumption. In response to this challenge, this study proposes a solution to the multi-center mixed vehicle routing problem considering dynamic energy consumption and integrated reverse logistics. Specifically, three studies were carried out: (1) Considering the influencing factors of the operating cost system of the mixed fleet, a system dynamics model was constructed. (2) On the basis of considering delaying the aging of electric vehicle batteries, a new charging station insertion strategy was designed. (3) Based on a novel charging station insertion strategy, a fast non-dominated sorting multi-objective genetic algorithm with an elite strategy was designed to solve this problem. We designed 15 groups of examples to prove the effectiveness of the model and algorithm. The experimental results show that 46.67% of the cases have more than 60% customer satisfaction. The average expenditure cost of 15 groups of cases is CNY 2018.33, which can improve the average customer satisfaction by 22.94%. This method helps companies to formulate transportation plans according to the actual situation, including providing a cost model that considers multiple influencing factors and improving the average customer satisfaction while reducing the total cost expenditure. We believe that the results of this research can provide methods and ideas for logistics companies with multiple distribution centers to formulate large-scale distribution plans.