This study proposes a virtual monitoring method for hydraulic supports based on digital twin theory. This method includes an information model, which monitors the attitudes of the hydraulic supports using an information fusion algorithm. This is combined with a virtual digital model that simulates the actual hydraulic support. Important technological aspects of the monitoring system were achieved at the real-virtual interface, which digitizes and modularizes the management and connection processes of the hydraulic supports over its entire life cycle. Finally, experiments were conducted and the real-time virtual images were synchronized with the actual motion of the support within one second, and the error range between the virtual and actual support postures did not exceed 1%. This study demonstrates the potential of this novel combined approach to make it possible to effectively monitor and make decisions regarding the safe and efficient operation of mechanized mine equipment that employ hydraulic supports.

A smart fully mechanized coal mining working face is comprised of various heterogeneous equipment that work together in unknown coal seam environments. The goal is to form a smart operational system with comprehensive perception, decision-making, and control. This involves many work points and complex coupling relationships, indicating it needs to be performed in stages and coordinated to address key problems in all directions and along multiple points. However, there are no existing unified test or analysis tools. Therefore, this study proposed a virtual test and evaluation method for a fully mechanized mining production system with different smart levels. This is based on the concept of “real data processing–virtual scene construction–setting key information points–virtual operation and evaluation.” The actual operational data for a specific working face geology and equipment were reasonably transformed into a visual virtual scene through a movement relationship model. The virtual operations and mining conditions of the working face were accurately reproduced. Based on the sensor and execution error analyses for different smart levels, the input interface for sensing, decision-making, and control was established for each piece of equipment, and an operation evaluation system was constructed. The system comprehensively simulates and tests the key points of sensing decision-making and control with various smart levels. The experimental results showed that the virtual scene constructed based on actual operational data has a high simulation degree. Users can simulate, analyze, and evaluate the overall operations of the smart mining 2.0–4.0 working face by inputting key information. The future direction for the smart development of fully mechanized mining is highlighted.

A mathematical hydraulic support self-tracking model for three-machine cooperative mining is proposed to address low efficiency and difficulties in strategy evaluation of a fully mechanized coal face. The proposed model uses the coordinates and traction speed of the shearer to calculate the frequency of the circular hydraulic support and realize the coordinated operation of the three-machine mining technology. A unity3d hardware-in-the-loop simulation experimental hearer and hydraulic support platform was used to validate the model of autonomous follow-up. The results indicate that collaborative control of coal mining allowed for an efficiency 3.76% higher than under automatic operation mode and 46.03% higher than under manual control; thus, The mathematical model provided an improved production efficiency of the fully mechanized mining face. The mathematical model also provides a more intelligent and reliable security support, and improves the intelligent level of hydraulic support follow-up control.

Scraper conveyor is the main equipment for underground coal transportation, and its high-efficiency and smooth operation is of great significance to safety production. This study simulated the process of transporting bulk coal by the scraper conveyor using the discrete element method. Transporting efficiency of scraper conveyor affected by the chain speed, static frictional coefficient, particle size, and laying angle was studied. Then the relationship between the chain speed, static frictional coefficient and the chute wear was explored. The stress and deformation characteristics of the chute during the transportation were studied by coupling the discrete element method and finite element method. Results showed that the mass flow rate changed significantly with the chain speed and static frictional coefficient, while it varied slightly with the change of particle size and laying angle; the higher chain speed and larger bulk coal led to more serious wear of the chute, and large stress mainly concentrated at the direct contact area and the area under the impact load from the bulk coal. Therefore, when designing the chute structure, it is necessary to ensure the wear resistance and strength of the contact area on the chute. The results could provide a theoretical basis for structural optimization of scraper conveyor.

The undulating coal seam floor ina mine leads to the complicated posture of the hydraulic supports. The existing methods for monitoring the pose of hydraulic supports have a poor monitoring effect, and are especially characterized by a lack of support position information. Therefore, this paper proposes a relative pose reconstruction method for hydraulic supports driven by digital twins. First, the pose plane and relative pose matrix of the hydraulic support are defined to describe the pose state of the support by analyzing the pose deviation between adjacent supports. Then, a digital twin system is built for a hydraulic support model, and a pose deduction method based on ray detection is proposed. The accurate position and attitude of the hydraulic support are deduced in the virtual space driven by actual laser sensor information, and three-dimensional pose reconstruction is realized. Finally, a relative pose reconstruction experiment of the hydraulic support model is designed and conducted. The results show that the relative pose data obtained by the virtual reconstruction method are very close to the experimental measurements, and the highest reconstruction accuracy is found to be 97.44%. The feasibility of the laser sensor layout scheme and the virtual reconstruction method of the pose of the hydraulic support is proved. This method provides a new way to monitor the pose of hydraulic supports, and can provide a reference for the support operation of hydraulic supports in the real fully mechanized mining process.

In practical underground production environments, low light intensity and the presence of dust can disrupt the recognition of coal and gangue. To advance the separation of coal and gangue in underground settings, this paper introduces a coal–gangue recognition approach (DG Module (DGM) + YOLOX-PSB) tailored for conditions of low light intensity and dust. To address the impact of dusty conditions, a preprocessing module (DGM) is proposed. This module employs a dark channel prior dehazing algorithm to mitigate the impact of fog on coal–gangue images. Subsequent steps include white balancing, bilateral filtering, and gamma correction to alleviate noise and distortion issues arising from the dehazing algorithm. To counteract potential drawbacks of the DGM and enhance target recognition accuracy, a polarized self-attention mechanism is integrated during the feature extraction stage to prioritize edge information of coal–gangue targets. By combining the attributes of the weighted bidirectional feature pyramid network, multiple layers of coal–gangue features are efficiently fused to achieve precise identification of coal–gangue targets. Experimental results using a custom dataset demonstrate that the enhanced algorithm outperforms YOLOv3, YOLOv5, YOLOv7-Tiny, and YOLOX, achieving a recognition accuracy of 97.6%, a frames per second rate of 99 and a good smoke concentration robustness. The proposed DGM + YOLOX-PSB serves as a valuable reference for accurate coal and gangue identification in conditions of low light intensity and dust within underground environments.