A Hybrid Coal Flow-Centric Predictive Model for Mining–Transportation Coordination Based on an LSTM–Transformer

This paper addresses the issue of coordination failures in fully mechanized mining equipment under complex operating conditions, which can lead to operational abnormalities and safety hazards. We systematically analyze the dynamic coordination relationships within the equipment system across three dimensions: temporal, spatial, and geometric. Centered on the coal flow, we establish a comprehensive “mining–transportation” coordination mathematical model covering the entire production process from the coal flow cut off by the shearer to the coal flow transported out by the conveyor. Building upon this foundation, a deep learning prediction method integrating long short-term memory (LSTM) and transformer architectures is proposed to construct an intelligent prediction model for the shearer traction speed. This model effectively captures temporal features and long-term dependencies within equipment operation data, enabling the prediction of critical operational parameters for fully mechanized mining systems. It significantly enhances the early identification and warning capabilities for equipment coordination failure states. The experimental results based on the operational data of fully mechanized mining systems show that the LSTM–Transformer model performs excellently in the prediction of traction speed. The mean square error (MSE) of prediction reached 0.041, the mean absolute error (MAE) was 0.122, and the coefficient of determination (R2) was 0.996, fully demonstrating the advantages of the model in terms of prediction accuracy and stability. This article provides a theoretical basis and technical support for the judgment of the operating status of coal mine working faces and the early warning of accident risks, which is of great significance for promoting the intelligent construction of coal mines.