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Abstract
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Advancing intelligent mining and enhancing the reliability of mine Cyber-Physical Systems (CPS) are pivotal objectives in the current development of the coal mining industry. System resilience reflects the capacity to respond to external and internal disturbances, hence assessing the resilience of mine CPS can quantify the probability of successful recovery following an attack, informing targeted decisions to improve the system’s resilience, reliability, stability, and safety. In this work, we first delineated the influencing factors and established a factor system through literature review and expert evaluation revision method. Subsequently, we employed the Decision-Making Trial and Evaluation Laboratory (DEMATEL), the Maximum Mean De-entropy (MMDE) method, and the Interpretive Structural Modeling (ISM) method to examine the importance and hierarchical relationships of influencing factors. Finally, incorporating time-varying factors, we developed a dynamic model for assessing mine CPS resilience using Dynamic Bayesian Networks. The results indicate that anti-interference is the most critical factor affecting mine CPS resilience. The findings provide significant insights for practitioners and researchers in optimizing mine CPS resilience, enhancing mine CPS reliability, and formulating strategies for intelligent mine development.
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