Jinbo Li

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.

As the demand for multi-scenario semantic updates in intelligent open-pit mining grows, existing semantic segmentation techniques encounter substantial challenges, particularly in handling visually indistinguishable, continuous geometric engineering objects. These approaches frequently fall short of meeting the accuracy standards required for practical applications. To tackle this challenge, the present study introduces a novel semantic segmentation method, CAD-to-PC matching and label propagation (CPMLP). The method constructs unified graph representations from 2D computer-aided design (CAD) drawings and 3D point clouds (PCs), extracts subgraph features using a graph-based feature encoding module, performs cross-graph alignment through contrastive learning, and propagates semantic labels from CAD to PC nodes. Experimental results demonstrate that CPMLP achieves high segmentation accuracy, with an average 3D mean Intersection over Union of approximately 0.7 across different acquisition routes, and robust generalization within complex mining environments. The proposed method provides a feasible solution for high-precision semantic segmentation in intelligent mining scenarios.