Resilience analysis of mine ventilation cyber-physical fusion system

Advancing intelligent mining and enhancing the reliability of the physical information systems in mines is a current objective for the coal mining industry. System resilience reflects the system’s ability to handle external and internal disruptions; therefore, quantifying the resilience of the mine’s cyber-physical system (CPS) allows for the assessment of the probability of successful recovery post-attack, thereby enabling targeted decision-making to improve system resilience, reliability, stability, and safety. In this work, the influencing factors were first identified, and an influencing factor system was established through literature review and the Delphi method. Subsequently, the decision experiment and evaluation laboratory (DEMATEL), maximum mean deviation entropy (MMDE), and interpretive structural model (ISM) methods were employed to study the importance and hierarchical relationships of these factors. Finally, a dynamic Bayesian network considering time-varying factors was used to construct a dynamic CPS resilience assessment model for mining. The results indicate that anti-interference capability is the most critical factor affecting the resilience of the mining CPS. The findings provide valuable insights for practitioners and researchers in optimizing CPS resilience, enhancing CPS reliability, and formulating development strategies for intelligent mining.