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”.

In the context of Industry 4.0, using a new generation of information technology to activate and transform traditional industries will maintain the long-term competitiveness of traditional industries. The mining industry is also going through the process of informatization transformation. Through a literature survey, we analyze the current situation and challenges faced by mine production and operation management. Many mining companies are expanding in scale, but their operation and management methods are inefficient, and their business processes and organizational management methods need to be reformed. We propose a new mode named CM mode (cloud mining mode). We define this as integrating the core business of the mine (such as production and operation management, mining technology, planning services, etc.) into the cloud through effective use of cloud technologies, cloud resources, and cloud services. A large number of human and intellectual resources move to the cloud. A new mode of operation and management of mining industry clusters is thus formed; it is open, cooperative, and coordinated. We present five elements of the CM mode: data resources, digital technologies, digital talents, cloud business form, and cloud cooperation mode. We establish a “cloud–edge–terminal” technical framework of the CM mode, and describe its technical characteristics. We also describe three main application scenarios of the CM mode, and highlight the development path and key points of construction. The CM mode highlights a new development direction for the production and management of intelligent mines, and is of great significance for giving full play to the value of intelligent construction.

Open-pit mines are becoming more intelligent and unmanned. One of the core technologies for autonomous driving is high-definition map technology, while refined mine maps are also needed to support scenarios such as vehicle–shovel collaboration, loading and unloading, production scheduling, and traffic safety control. To better serve the construction of intelligent mines and address the autonomous driving needs in open-pit mining scenarios, a universal high-definition map data model oriented toward data production and exchange, Open-Pit Map (OP map for short), has been designed. Based on various working scenarios of open-pit mines, eight practical basic elements, eleven location elements, and eight traffic elements in OP map were proposed. A map visualization tool, MapViewer, was developed based on OP map, and the effectiveness and practicality of the data model in the practical application of an intelligent shipping system in a Chinese mine were validated.

To address challenges in automated charging operations of bulk explosive trucks in open-pit mines—specifically difficulties in borehole identification, positioning inaccuracies, and low operational efficiency—this study proposes an intelligent collaborative charging system integrating three modular components: (1) an explosive transport vehicle (with onboard terminal, explosive compartment, and mobility system enabling optimal routing and quantitative dispensing), (2) a charging robot (equipped with borehole detection, loading mechanisms, and mobility system for optimized search path planning and precision positioning), and (3) interconnection systems (coupling devices and interfaces facilitating auxiliary explosive transfer). This approach resolves three critical limitations of conventional systems: (i) mechanical arm-based borehole detection difficulties, (ii) blast hole positioning inaccuracies, and (iii) complex transport routing. The experimental results demonstrate that the intelligent cooperative charging method for open-pit mines achieves an 18% improvement in operational efficiency through intelligent collaboration among its modular components, while simultaneously realizing automated and intelligent charging operations. This advancement has significant implications for promoting intelligent development in open-pit mining operations.

Addressing the challenges in 3D reconstruction of large-scale open-pit mines, such as dramatic terrain undulations, complex texture features, and the difficulty of balancing geometric accuracy with real-time rendering efficiency using traditional methods, this paper proposes a high-fidelity reconstruction framework integrating UAV multi-modal data with the state-of-the-art 3D Gaussian Splatting (3DGS) architecture. First, an integrated air-ground multi-modal data acquisition system is established. Using a UAV equipped with LiDAR and a high-resolution camera, high-quality geometric and textural data of the mining area are acquired through terrain-adaptive flight planning. Second, to tackle the VRAM bottlenecks and loose geometric structures inherent in original 3DGS for large scenes, we adopt the advanced CityGaussianV2 architecture as our core reconstruction engine. By leveraging its divide-and-conquer parallel training strategy, 2DGS planar geometric constraints, and Decomposed Gradient Densification (DGD) mechanism, this framework effectively overcomes memory limitations and significantly enhances the geometric sharpness of slope crests and toes. Finally, engineering validation was conducted at Kambove Mining. Experimental results demonstrate that the proposed method achieves centimeter-level geometric accuracy, a real-time web rendering frame rate exceeding 60 FPS, and a model storage compression rate of over 90%. The digital twin control platform built upon this model successfully achieves deep fusion and visual scheduling of multi-source heterogeneous data, providing a novel technical path for constructing high-precision reality-based foundations for smart mines.

Accurate spatial measurement of blasting areas, typically acquired via Unmanned Aerial Vehicle(UAV) aerial surveys, is a foundational prerequisite for unmanned open-pit mine operations. Driven by such high-fidelity geometric data, intelligent explosive vehicles still face critical challenges in parking efficiency, route planning, and avoiding redundant travel in irregular explosive zones. This paper proposes Thiessen Edge Shift (TES), a parking and route planning strategy based on the Thiessen (Voronoi) skeleton. TES extracts a Voronoi (Thiessen) skeleton for each blasting area and treats edges and vertices as candidate stopping locations. Parking selection is formulated as a topology-aware greedy set-cover problem that favors compact coverage and suppresses dead-end branches. The selected parking points are embedded into a weighted graph, which is subsequently simplified through graph straightening and used for kinematically constrained route optimization via augmented Dijkstra search and 2-opt refinement. The proposed method is evaluated on eight representative blasting areas from a real open-pit mine under six coverage radii ranging from 5 m to 30 m. The results show that TES consistently reduces parking point count and route length compared with a manual heuristic baseline, while maintaining favorable geometric characteristics and practical execution efficiency. In addition, curvature analysis and closed-loop Pure Pursuit simulations indicate that the generated routes are trackable and suitable for further smoothing-based refinement. Runtime evaluation further confirms that TES remains computationally practical for onboard deployment under typical operational settings. Overall, TES provides a geometrically interpretable and computationally efficient framework for joint parking-location selection and route planning in high-density, irregular blasting scenarios.