Autonomous transformation of deep-horizon mining systems: an integrated framework of cyber-physical systems and edge-AI

Abstract

The mining industry is transitioning from traditional mechanization to fully autonomous ecosystems driven by the fourth industrial revolution (Industry 4.0). This research paper investigates the structural integration of cyber-physical systems and multiaccess edge computing within deep-horizon mining operations. Unlike conventional automation, which relies on predefined scripts, the proposed framework utilizes multiagent reinforcement Learning to navigate non-deterministic geomechanical environments. We present a decentralized architectural model that significantly mitigates latency in machine-to-machine communication, ensuring real-time structural health monitoring and optimized fleet coordination. The study demonstrates that digital transformation in mining is not merely a technological layer but a fundamental reconfiguration of the geomechanical risk management paradigm, leading to a theoretical 22% increase in operational efficiency and a substantial reduction in geotechnical hazards. This comprehensive analysis evaluates mathematical models for stress propagation and operational optimization algorithms, formulating a definitive technical roadmap for ultra-deep continuous extraction systems.