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.

Open-pit polymetallic mines commonly encounter challenges such as the asynchrony between mining and processing operations, large fluctuations in ore supply structure, and high haulage volumes, which lead to increased transportation costs and instability in processing plant feed grades. To address these issues, this study, driven by practical production requirements, proposes a two-stage hybrid optimization strategy that combines the global search capability of NSGA-III with the local intensification of particle swarm optimization (PSO), aiming to achieve the coordinated optimization of transportation cost minimization and plant feed grade maximization under constraints imposed by ore supply boundaries and processing plant capacity. To further identify the most suitable solution from the resulting Pareto-optimal set, the VIKOR multi-criteria decision-making method is employed to evaluate and select a blending scheme with optimal balance under the dual objectives of cost and grade. The effectiveness of the proposed approach is validated using a real-world production case, with experimental results showing that the optimized blending scheme achieves a cost reduction of more than 9%, while the gold grades of oxide and sulfide ores are increased to 2.40–3.16 g/t and 2.14–2.17 g/t, respectively, leading to a significant improvement in the overall plant feed grade. Compared with the actual weekly blending plan used in practice, the proposed method enables a comprehensive optimization of transportation cost, feed grade, and ore supply structure within a unified framework.