Modeling ore body in 3D is the basis of digital intelligent mining. However, most existing three-dimensional mining software uses the contour approach that requires too much man–machine interaction and difficult partial updating. Moreover, accounting for uncertainty and low geometric quality picking is very difficult in the direct contour approach. Therefore, an implicit modeling approach to automatically build the three-dimensional model for ore body by means of spatial interpolation directly based on the geological borehole data with Hermite radial basis function (HRBF) algorithm as the core is proposed. Furthermore, in order to solve the problems of weak continuity of models due to the long-distance original boreholes as well as the boundary-point normal solution error, the densification of original borehole data with the virtual borehole as well as the calculation of point-cloud normal direction based on the adjacent hole-drilling method is proposed. The verification of two mine engineering projects and comparison with the explicit modeling results show that this approach could realize the automatic building of three-dimensional models for the ore body with high geometric quality, timely update and accurate results.

In this paper, we introduce combination constraints for modeling ore bodies based on multiple implicit fields interpolation. The basic idea of the method is to define a multi-labeled implicit function that combines different sub-implicit fields by the combination operations, including intersection, union and difference operators. The contribution of this paper resides in the application of combination of more general implicit fields with combination rules for the implicit modeling of ore bodies, such that the geologist can construct constraints honoring geological relationships more flexibly. To improve the efficiency of implicit surface reconstruction, a pruning strategy is used to avoid unnecessary calculations based on the hierarchical bounding box of the operation tree. Different RBF-based methods are utilized to study the implicit modeling cases of ore bodies. The experimental results of several datasets show that the combination constraints are useful to reconstruct implicit surfaces for ore bodies with mineralization rules involving multiple fields.

The pollution of heavy metals in soil is a problem of great concern to international scholars today. This research investigates the current research activities in the field of soil heavy metal pollution remediation and discusses the current areas of research focus and development trends. We conducted a bibliometric analysis of the literature on soil heavy metal pollution remediation from 1999 to 2020. CiteSpace and Vosviewer were used to conduct document co-citation and cluster analyses on the collected data. The research was mainly carried out based on the following factors: chronological distribution, country and institution distribution, source journal analysis, keyword co-occurrence analysis, and reference co-citation analysis. China (2173, 28.64%) and the United States (946, 12.47%) are the top two countries in terms of the number of articles published, and Environmental Science and Pollution Research (384, 5.06%) and Science of the Total Environment (345, 4.55%) published the most articles. The Chinese Academy of Science (485) is the organization that has contributed the most to the total number of publications. Furthermore, based on a keyword co-word analysis with Vosviewer and CitesSpace, it was concluded that the applications of phytoremediation and biochar in the remediation of heavy metals in soil are current research hotspots. Additionally, future research should focus on repair mechanisms, the development of new repair technology and joint repair systems.

At present, data exchange in China’s digital mine construction process is still based on paper media or electronic documents. The problems of “information islands,” “information versions,” “information faults” and “information preservation” are serious. There are many problems associated with across time and space and multidisciplinary collaborations, blocked business processes and unclear job responsibilities. These problems have seriously hindered the construction of China’s digital mine, thus restricting the safe, efficient production and sustainable development of China’s mining enterprises. Therefore, this paper proposes the concept, connotation, characteristics, architecture and technical requirements of the mining technology collaboration platform and uses it to guide the research and development and implementation of the mining technology collaboration platform of Fujian Makeng Mining Co., Ltd. The results show that the mining technology collaboration platform can solve the information and management problems existing in China’s digital mine construction and realize the centralized storage, interoperability and high sharing of all data, the integration of all involved business, business software and its participants, the clarification of responsibilities and its input and output data of each post and standardization and automation of business process. Therefore, it improves the ability to collaborate across time and space and multidisciplinary among participants, departments and professional posts, ensures high-speed flow of business processes and also improves the working efficiency and quality of mining enterprises and significantly reduces the time for business processing and business process flow and reduces production costs.

In this paper, according to the analysis of optimum circuits, we present an efficient ventilation network solution based on minimum independent closed loops. Our main contribution is optimizing the circuit dividing strategy to improve the iteration convergence and the efficiency of a single iteration. In contrast to a traditional circuit, a minimum closed loop may contain one or more co-tree branches but fewer high-resistance branches and fan branches. It is helpful in solving the problem of divergence or slow convergence for complex ventilation networks. Moreover, we analyze the dividing rules of closed loops and improve the dividing algorithm of minimum independent closed loops. Compared with the traditional Hardy Cross iteration method, the improved solution method has better iteration convergence and computation efficiency. The experimental results of real-world mine ventilation networks show that the improved solution method converges rapidly within a small number of iterations. We also investigate the influence of network complexity, iterative precision, and initial airflow on the iteration convergence.

As microseismic monitoring technology gains widespread application in mine risk pre-warning, the demand for automatic data processing has become increasingly evident. One crucial requirement that has emerged is the automatic classification of signals. To address this, we propose a Transformer-based method for signal classification, leveraging the global feature extraction capability of the Transformer model. Firstly, the original waveform data were framed, windowed, and feature-extracted to obtain a 16 × 16 feature matrix, serving as the primary input for the subsequent microseismic signal classification models. Then, we verified the classification performance of the Transformer model compared with five microseismic signal classification models, including VGG16, ResNet18, ResNet34, SVM, and KNN. The experimental results demonstrate the effectiveness of the Transformer model, which outperforms previous methods in terms of accuracy, precision, recall, and F1 score. In addition, a comprehensive analysis was performed to investigate the impact of the Transformer model’s parameters and feature importance on outcomes, which provides a valuable reference for further enhancing microseismic signal classification performance.