Content area
Intelligent mining technology, as the core driving force for the digital transformation of the mining industry, integrates cyber-physical systems, artificial intelligence, and industrial internet technologies to establish a “cloud–edge–end” collaborative system. In this paper, the development trajectory of intelligent mining technology has been systematically reviewed, which has gone through four stages: stand-alone automation, integrated automation and informatization, digital and intelligent initial, and comprehensive intelligence. And the current development status of “cloud–edge–end” technologies has been reviewed: (i) The end layer achieves environmental state monitoring and precise control through a multi-source sensing network and intelligent equipment. (ii) The edge layer leverages 5G and edge computing to accomplish real-time data processing, 3D dynamic modeling, and safety early warning. (iii) The cloud layer realizes digital planning and intelligent decision-making, based on the industrial Internet platform. The three-layer collaboration forms a “perception–analysis–decision–execution” closed loop. Currently, there are still many challenges in the development of the technology, including the lack of a standardization system, the bottleneck of multi-source heterogeneous data fusion, the lack of a cross-process coordination of the equipment, and the shortage of interdisciplinary talents. Accordingly, this paper focuses on future development trends from four aspects, providing systematic solutions for a safe, efficient, and sustainable mining operation. Technological evolution will accelerate the formation of an intelligent ecosystem characterized by “standard-driven, data-empowered, equipment-autonomous, and human–machine collaboration”.
Details
Coal mining;
Data processing;
Collaboration;
Edge computing;
Closed loops;
Data integration;
Automation;
Dynamic models;
Monitoring systems;
Technology;
Geology;
Big Data;
Cyber-physical systems;
Internet;
Mining industry;
Decision making;
Standardization;
Data collection;
Artificial intelligence;
Real time;
Hydraulics;
Knowledge representation
; Bi, Lin 1
; Li, Jinbo 2 ; Wu Zhaohao 1 ; Zhao Ziyu 1
1 School of Resources and Safety Engineering, Central South University, Changsha 410083, China; [email protected] (Z.W.); [email protected] (J.L.); [email protected] (Z.W.); [email protected] (Z.Z.)
2 School of Resources and Safety Engineering, Central South University, Changsha 410083, China; [email protected] (Z.W.); [email protected] (J.L.); [email protected] (Z.W.); [email protected] (Z.Z.), School of Mining Engineering and Geology, Xinjiang Institute of Engineering, Urumqi 830023, China