Research on multi-objective optimization of the interface for intelligent coal mine comprehensive mining system based on QN-MHP and Jack dual model validation.

To enhance the usability and operational comfort of the control interface in an intelligent coal mining comprehensive system, this study developed a multi-objective optimization model based on six dimensions: hierarchy, correlation, simplicity, comfort, reachability and visibility. The model was solved using a particle swarm optimization algorithm, and the optimized interface was validated through integration of Jack ergonomic simulation and the QN-MHP cognitive modeling approach. Experimental results showed that the optimized interface significantly improved operator performance: task completion time decreased by 16.2%, error rate was reduced by 75.0% and visual search time decreased by 13.2%. Cognitive load was also alleviated, with reduced utilization of the visual subsystem, central processing module and right-hand operation module, accompanied by increased processing speeds. In addition, the optimized layout improved upper-limb comfort and operational efficiency. The proposed method provides theoretical and methodological support for multi-objective optimization and multimodal validation of industrial human-machine interfaces.