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.

As a critical enabler of trackless mining operations, load-haul-dump (LHD) vehicles rely heavily on efficient and safe path planning to support the industrial deployment of autonomous driving systems. However, existing planning algorithms often suffer from heavy computational demands, limited adaptability to the constrained kinematics of articulated LHDs, and insufficient robustness in complex underground environments. To address these challenges, we propose a Hybrid A* path planning method guided by high-level reference states. The method first integrates the A* algorithm with generalized Voronoi diagrams to rapidly generate a reference state set along tunnel centerlines. This reference serves as a structural guide for downstream search. Then, a region-aware dynamic node expansion strategy and a multi-objective heuristic cost function are introduced to improve planning efficiency and path quality. Comparative evaluations across three representative underground tunnel scenarios demonstrate that the proposed approach outperforms baseline methods in terms of safety, efficiency, and robustness, providing a viable solution for motion planning of LHDs in confined underground spaces.

The normal estimation of cross-contour polylines largely determines the implicit orebody modeling result. However, traditional methods cannot estimate normals effectively due to the complex topological adjacency relationship of the cross-contour polylines manually interpreted in the process of exploration and production. In this work, we present an orebody implicit modeling method based on the normal estimation of cross-contour polylines. The improved method consists of three stages: (1) estimating the normals of cross-contour polylines by using the least square plane fitting method based on principal component analysis; (2) reorienting the normal directions by using the method based on the normal propagation; (3) using an implicit function to construct an orebody model. The innovation of this method is that it can automatically estimate the normals of the cross-contour polylines and reorient normal directions without manual intervention. Experimental results show that the proposed method has the advantages of a small amount of calculation, high efficiency and strong reliability. Moreover, this normal estimation method is useful to improve the automation of implicit orebody modeling.

In the underground mine ventilation area, the absence of robust solutions for nonlinear programming models has impeded progress for decades. To overcome the enduring difficulty of solving nonlinear optimization models for mine ventilation optimization, a major technical bottleneck, we first develop an advanced linear optimization technique. This method transforms the nonlinear ventilation optimization and regulation model into a linear control model, avoiding the limitation of difficulty in solving the nonlinear mathematical model. The linear strategy opens up a new solution idea for the nonlinear calculation of the mine ventilation optimization and regulation. Furthermore, this study introduces evaluation metrics for ventilation scheme quality, including minimal energy consumption, fewest adjustment points, and optimal placement of these points, enhancing flexibility in ventilation network optimization. By analyzing the ventilation model control objectives and constraints, we formulated a linear optimization model and developed a multi-objective mixed-integer programming model for ventilation network optimization. This paper constructs and verifies a calculation example model for mine ventilation optimization, assessing its reliability based on airflow distribution calculations.

Intelligent ventilation systems can optimize airflow regulation to enhance mining safety and reduce energy consumption, driving green development in mineral resource extraction. This paper systematically elaborates on the overall architecture, cutting-edge advances, and core technologies of current intelligent mining ventilation. Building upon this foundation, a comprehensive intelligent mine ventilation solution encompassing the entire process of ventilation design, optimization, and operation is constructed based on a five-layer architecture, integrating key technologies such as intelligent sensing, real-time solving, airflow regulation, and remote control, providing an overarching framework for smart mine ventilation development. To address the computational efficiency bottleneck of traditional methods, an improved loop-solving method based on minimal independent closed loops is realized, achieving near real-time analysis of ventilation networks. Furthermore, a multi-level airflow regulation strategy is realized, including the methods of optimization control based on mixed integer linear programming and equipment-driven demand-based regulation, effectively resolving the challenges of calculating nonlinear programming models. Case studies indicate that the intelligent ventilation system significantly enhances mine safety and efficiency, leading to approximately 10–20% energy saving, a 40–60% quicker emergency response, and an average increase of about 20% in the utilization of fresh air at working faces through its remote and real-time control capabilities.