Interface access control graph neural network method for flexible automation production of mine ventilation duct

Interface authorization in flexible automated mine ventilation duct production lines is difficult because heterogeneous users and services access safety-critical resources under time-varying cyber-physical risk that static RBAC/ACL rules cannot capture. This study presents HGRAD, a heterogeneous-graph risk-adaptive access control framework for industrial cyber-physical systems. HGRAD models each access event as a dynamic graph with four node types: user nodes encode operator identity, role, and history; interface nodes represent exposed PLC/MES/service access points and protocol/load states; resource nodes denote commands, records, or work-order objects with different sensitivities; and physics-informed risk nodes provide conservative structural-risk proxies for critical resources. A Temporal-HGT encoder and relation-specific hierarchical attention capture temporal context, structural semantics, and abnormal-path salience, while an intervention-inspired log-based filter, adversarial perturbation, Shapley audit weighting, and MC-Dropout uncertainty estimation support adaptive authorization rather than fixed-threshold decisions. In the TON_IoT benchmark mapping, the physics-informed node is derived only from benchmark-log proxy signals, including access intensity, operation criticality, freshness, and resource criticality; it is not generated by real mine ventilation sensors, plant-side finite-element outputs, or field digital-twin measurements. HGRAD achieves the strongest validation and held-out benchmark performance among compared baselines. The results are therefore benchmark-level proof of concept for access-control design, not evidence of completed industrial deployment or field-validated mine-safety performance.