An Entropy-Deep Learning Fusion Framework for Intelligent Management and Control in Open-Pit Mines

This paper proposes a novel fusion paradigm integrating information entropy with deep learning architectures to address the challenges of uncertainty, robustness, and subjectivity in intelligent open-pit mining management. Methodologically, we develop three integrated models: an IE-LSTM model for dynamic ore blending that quantifies system uncertainty to balance efficiency with robustness, an IE-CNN model that utilizes local image entropy to filter false alarms for robust visual detection, and an IE-DNN model employing entropy-weighted objective evaluation for equipment performance. These models form the computational core of an intelligent control system deployed at the Qidashan Iron Mine. Industrial validation conducted over a two-year period demonstrated that the framework led to a blending accuracy of 93.6%, a 73.7% reduction in visual detection false alarms, and objective equipment performance scoring. System-wide outcomes included a resource utilization rate of 98.64%, improvements in overall equipment effectiveness between 4.07% and 22.70%, and cumulative direct economic benefits exceeding 43 million RMB. This research establishes a systematic framework that transitions mine operations from experience-based to data-driven intelligent control, distinguishing itself by explicitly quantifying and managing uncertainty through entropy–deep learning fusion. This provides a replicable blueprint for the mining industry’s digital transformation. It effectively addresses the high-dimensional complexity and dynamic uncertainties inherent in open-pit mining environments.