煤炭科学技术研究院有限公司

Mining wireless short-range communication technology has the characteristics of low latency,high reliability,and flexible networking,and is a key means to address problems encountered in the intelligent mine construction,such as large data transmission delay,complex information interaction processes,and limited equipment control accuracy.At present,research on mining wireless short-range communication mainly focuses on ZigBee,WiFi,and 5G.ZigBee is difficult to meet the requirements of routine applications,WiFi suffers from co-channel interference and large transmission delay,and 5G involves uncontrollable data transmission risks and high costs.To address these problems,this study proposed a mining wireless short-range communication solution with sidelink communication operating in licensed dedicated frequency bands as the core.Two types of architectures were constructed,including an independent networking architecture for mining sidelink communication and a hybrid networking architecture integrating mining sidelink communication and 5G for mining,forming a new full-scenario mining communication mode of"wide-area coverage plus local enhancement".To meet the requirements of time synchronization,resource efficiency improvement,and reliability assurance in the application of sidelink communication in coal mines,key technologies such as GNSS-free time synchronization,dynamic configuration of resource pools based on subframes and subchannels,physical-layer wireless signal transmission,and distributed resource allocation for terminals were investigated,which effectively improved the adaptability and reliability of the system in complex coal mine environments.Application scenarios of mining sidelink communication technology in intelligent equipment control,underground autonomous driving,and emergency command during disasters were also studied,which verified its practical value in promoting the integration of mining operations and communication systems.A test system simulating a GNSS-free underground coal mine environment was built in the laboratory,and the test results showed that the average system latency was less than 23 ms,which effectively met the requirements of critical mining scenarios for wireless short-range communication technology and provided a reliable wireless short-range communication solution for intelligent mine construction.

The electromagnetic environment in underground coal mines is complex,and interference such as the start-up and shutdown of high-power equipment,harmonics of power cables,switching transients,and dense deployment of wireless communication devices mainly couple into communication cables as common-mode signals,posing a serious threat to the stable operation of safety monitoring systems.The symmetry of communication cables is a key parameter that determines their resistance to common-mode interference.The current inspection scheme for coal mine safety monitoring systems uses the physical structure(i.e.,whether the cable is twisted)as the criterion for symmetry determination and treats CAN signal interconnection lines as asymmetric lines by default for testing.This approach potentially conflicts with GB/T 17626.5-2019"Electromagnetic compatibility-Testing and measurement techniques-Surge immunity test",which defines symmetric lines based on Transverse Conversion Loss(TCL).To address this issue,CAN bus cables widely used in coal mine safety monitoring systems were selected as the research object.The TCL test method was used to systematically evaluate the symmetry properties of three types of structures:unshielded parallel wire bundles,unshielded twisted pairs,and shielded twisted pairs.The results showed that the differential-mode-to-common-mode conversion loss of all tested cables exceeded 20 dB,meeting the definition of symmetric lines and indicating that there was no inherent correspondence between the symmetry of cables and physical structure.Accordingly,it was proposed that electromagnetic performance indicators such as TCL should replace the physical structure as the basis for symmetry determination,and it was recommended that mining communication cables be uniformly tested as symmetric lines in electromagnetic compatibility tests,including surge immunity,conducted radio-frequency immunity,and conducted emissions,to better match the actual underground interference environment.

Under the conditions of a composite hard roof,the risk of rockburst at the end of the working face is significantly elevated due to the coupled effects of coordinated fracturing of overlying hard strata,high-stress concentration in isolated coal bodies,and the presence of wide protective coal pillars in the retreating roadway.Analyses indicate that frequent high-energy microseismic events induced by roof fracturing,along with stress concentration in isolated coal pillars,are the primary triggers of rockburst.Through theoretical analysis and field monitoring,the mechanism of roof-blasting-based rockburst mitigation has been elucidated,and a roof blasting control strategy has been proposed to interrupt stress transmission pathways in critical overlying strata,thereby providing effective protection for retreating roadways and isolated coal bodies.Numerical simulations were conducted to compare the stress evolution and plastic zone distribution in the surrounding rock under three scenarios:no blasting,high-position single-hole blasting,and combined"high+low-position"blasting.The results demonstrate that roof blasting substantially mitigates the effects of stress concentration during the end-of-face stage,with the"high+low-position"combined blasting exhibiting optimal performance by achieving the greatest reduction in peak stress and a notable contraction of the plastic zone.Parameter optimization indicates that a combination of high-position(53 m)and low-position(33 m)hole depths,initiation heights of 19.3 m(high)and 13.0 m(low),and inclinations of 60°(high)and 40°(low)simultaneously enables optimal evolution of plastic failure in the strata and effective stress reduction in coal pillars.Field applications have shown that the peak and concentration of support pressures were significantly decreased,with the maximum monitored coal body stress reaching only 12.7 MPa and exhibiting smooth stress variation.Additionally,the frequency and cumulative energy of high-energy microseismic events were reduced by 30%and 35%,respectively,with no events exceeding 104 J.Multi-parameter monitoring confirmed that the proposed approach effectively improved the stress environment during the end-of-face stage,reduced dynamic load intensity,and achieved coordinated protection of retreating roadways and isolated coal bodies.

Current research on gas concentration prediction in working faces of coal mines often suffers from limited feature dimensions and small dataset sizes, making it difficult to extract long-term fluctuation patterns from large-scale time-series data. To address this issue, this study proposes a Principal Component Analysis (PCA)-Transformer-based prediction algorithm for gas concentration in working faces. Firstly, raw gas concentration-related data was cleaned and normalized using min-max scaling. Then, PCA was applied to reduce the dimensionality of seven influencing factors (methane concentration at the upper corner, return airflow methane concentration, oxygen concentration, carbon monoxide concentration, temperature, net flow rate, and wind speed), effectively eliminating weakly correlated features. Finally, the processed training set was fed into a Transformer model, where the encoder and decoder extracted intrinsic patterns and features of gas concentration variations. Using monitoring data from working face 224 of a high-gas mine in Tongchuan as a sample, the PCA-Transformer model was compared with Long Short-Term Memory (LSTM), PCA-Long Short-Term Memory (PCA-LSTM), and Transformer models. The results show that: ① The PCA-Transformer model achieves a Mean Absolute Error (MAE) of 0.020 3, Mean Squared Error (MSE) of 0.047 2, and a runtime of 86 seconds, meeting the accuracy and timeliness requirements for gas concentration prediction in coal production. ② Compared to LSTM, PCA-LSTM, and Transformer models, the PCA-Transformer model better fits gas concentration trends, effectively identifies peak and trough sequences, and requires the least computational time, demonstrating its superior performance.

Most Simultaneous Localization and Mapping (SLAM) systems that utilize tightly coupled laser-inertial odometry are often under-constrained in underground coal mines, leading to mapping failures and hindering the direct deployment of robots in operational frontlines. This paper analyzes the factors contributing to odometry drift in degraded environments, elucidating the mechanisms by which unstructured point clouds induce drift. We propose a targeted detection method for unstructured regions and a two-step denoising approach that includes filtering and interpolation to address dust and fog. An adaptive tightly coupled odometry system is developed, incorporating a factor that characterizes the state of unstructured areas. In a test environment reverse-engineered from the WHU-TLS Tunnel dataset, our system achieves an EAP-ERMS of approximately 0.40 m, an EAP-Mean of 0.37 m, a ERP-ERMS of 0.015 m, and an ERP-Mean of 0.011 m, outperforming other methods significantly. For engineering applications with Ultra-Wide Band devices, we use a nonlinear optimization method to maintain global odometry poses, constructing a factor graph with unstructured state constraints, UWB factors, and loop closure factors. By performing parallel global optimization on all factors, high-precision global localization and mapping are achieved. In a kilometer-scale simulated tunnel with UWB signals, the continuous mapping results show an EAP-ERMS of 1.48 m, an EAP-Mean of 1.32 m, an ERP-ERMS of 0.026 m, and an ERP-Mean of 0.013 m. In a 2 000 m-long, loop-free field test at Cuncaota Coal Mine, the EAP-ERMS was 15.64 m, the EAP-Mean was 14.53 m, the ERP-ERMS was 0.198 m, and the ERP-Mean was 0.037 m, demonstrating the system's potential for practical engineering applications. Our SLAM system, built with a 16-line LiDAR and a 6-axis IMU that meet explosion-proof standards, provides accurate mapping in degraded scenarios, significantly advancing the practical application of coal mine robots.