In order to effectively prevent coal mine accidents, we selected the most serious type of accident in coal mines—gas explosions—as the research object. Based on the accident causation model (24Model), we propose an action path and analysis steps of accidents caused by different employees in the organization. A gas explosion coal mine accident was analyzed using the 24Model and the proposed action path, and 12 unsafe actions, 3 unsafe states, 4 habitual behaviors, 10 safety management systems, and 10 safety cultures were obtained. Case analysis results show that by using the 24Model and path analysis the proposed effect can help employees to clearly identify the cause of the accident, to better understand the logical relationship with the causes of the accident, improve the effectiveness of training, and effectively prevent similar accidents. The 24Model and the proposed path can be used to comprehensively analyze the reasons for and help to effectively prevent coal mine gas explosion accidents.

The existing coal-rock identification technology based on machine vision makes it difficult to accurately identify coal-rock images with low distinguishability. To solve this problem, a special coal-rock environment simulation experimental device was used to conduct simulations, considering various influencing factors such as illumination, air flow, coal dust, and water mist concentration. We characterized the grayscale and texture feature patterns of coal-rock media under varying degrees of interference and established a comprehensive multi-element image training sample library. The simulation experiment results show that illumination, dust, and fog can reduce the distinguishability of coal-rock images, which seriously affects the recognition performance of the network. Based on this, the convolution operation was combined with the Vision Transformer network and the deep convolution algorithm was applied to design a parallel hybrid vision network model, PFVnet. Subsequently, enhanced recognition tests were carried out in combination with the DeepLabV3 + network. The test results show that PFVnet can enhance the features of coal and rock, and achieve a PSNR of 18.90 and an SSIM of 0.58 on the multi-element image training sample library. It can effectively reduce the misjudgment of the DeepLabV3 + network, increasing its accuracy by 0.95%, the mean Intersection over Union (mIoU) by 2.15%, and the mean Pixel Accuracy (mPA) by 2.12%. This research provides new ideas and feasible technical solutions for the improvement of coal-rock identification technology and helps to promote the development of this field.

To address severe mine pressure disasters induced by the coupling of mining-induced dynamic stress and impact disturbance during close-distance coal seam mining, this paper takes the No. 8 and No. 9 close-distance coal seams in the 119 mining area of a coal mine in Ningxia, China, as the engineering background. Theoretical analysis and FLAC^3D numerical simulation methods were adopted to systematically study the evolution of overburden structure, the manifestation law of mine pressure caused by mining disturbance, and the dynamic response mechanism of roadway surrounding rock under impact load. The findings demonstrate: ① Based on key block theory and elasticity mechanics theory, the stress transfer mechanism of the complete bearing type overburden rock in close-range coal seams was clarified. The calculation model of floor plastic zone depth and additional stress was derived, and the influence mechanism of the bearing state of interlayer rock strata on the stability of underlying coal seam roadways was revealed. ② Comparative numerical simulations of mining schemes revealed that both schemes formed a “goaf pressure relief-workface-coal pillar” load-bearing configuration with “upward subsidence and downward bulging” basin-shaped settlement. Scheme A exhibited significantly increased stress peaks and interlayer plastic zones due to repeated mining-induced stress, substantially elevating the risk of strong mine pressure manifestation and surrounding rock instability. ③ Under 8 MPa cosine impact load with a vibration frequency of 50 Hz (peak particle vibration velocity of 9.57 m/s), compared with the unsupported roadway, the bolt–cable collaborative support system reduced the peak displacement of surrounding rock by over 35% and decreased the shock wave propagation velocity by more than 40%, effectively suppressing the expansion of plastic zones and the transfer of impact energy, while significantly enhancing the impact resistance of the roadway. This study not only provides a systematic theoretical basis for close-distance coal seam mining and rock burst prevention but also offers scientific guidance and technical reference for surrounding rock control and dynamic disaster prevention of roadways in similar close-distance coal seam mining projects, which is of important engineering value for ensuring the safe and efficient mining of underground coal resources.