The MMI-HCPS method: Integrating perception-decision-control capabilities of multiple mine inspectors

Abstract

Due to the complex mining environment, the development of equipment intelligence has stagnated, with challenges in the application of key technologies. Additionally, underground inspectors exhibit a lower level of intelligence in monitoring, decision-making, and control aspects. This paper proposes an MMI-HCPS (Multiple Mine Inspectors- Human-Cyber-Physical System) method that integrates the perception, decision, and control capabilities of multiple mine inspectors. Firstly, it reviews the current status of equipment intelligentization in fully mechanized mining faces to highlight the necessity of equipment intelligentization. Secondly, the paper establishes the theoretical framework MMI-HCPS and further refines the overall structure of the entire document based on MMI-HCPS, composed of high-performance multi-site and multi-device communication network (MCN), intelligent inference system (IIS), inspection-human-system (IHS), and center-human-system (CHS). MCN forms the foundation for the overall architecture to operate smoothly, while IIS, IHS, and CHS constitute the core components driving the operation of MMI-HCPS. Both IIS and CHS have their own monitoring and control subsystems, and their decision subsystems are in a loop with IIS, achieving closed-loop inference. Finally, a utilizing the laboratory environment, separate tests were conducted on the network operation status of MCN, monitoring and control capabilities of IHS and CHS, experimental validation of decision-making abilities of multiple inspectors, and ultimately, an experiment was undertaken to assess the overall inspection process. The results indicate that promoting the intelligence of inspectors in the fully mechanized mining face from an intelligent perspective is feasible with this method. The method achieves a shared perception of the fully mechanized mining face among different inspectors, complementing each other’s strengths and weaknesses, enhancing information processing efficiency, and improving individual work efficiency. Furthermore, the method enables an interactive loop between human, computers, and the physical mining face, providing support for further intelligent development of mining equipment.