We used the key stratum theory to establish a more realistic thin‐plate mechanical model of elastic foundation clamped boundary and study the fracture mechanism of overlying strata during longwall mining. We analyzed the fracture characteristics and factors affecting fracture of the key stratum combined with the Mohr–Coulomb yield criterion. Besides, we used numerical simulation methods to verify the evolution pattern of the overlying strata fracture. The results show that the fracture mechanisms of the elastic foundation clamped structure’s key stratum varied depending on the position under longwall mining. The advanced coal wall area of the upper surface is a compressive‐shear fracture. The center area of the lower surface is a tensile fracture. With the increase of the excavation length and the load of the key stratum, the central area and the advanced coal wall area of the long side are fractured before the advanced coal wall area of the short side. With the increase of flexural rigidity of the key stratum, the advanced coal wall area of the long side fractures before the central area and the advanced coal wall area of the short side. With the increase of the foundation modulus and the advanced load of the key stratum, the central area fractures before the surrounding advanced coal wall area. The advanced influence distance was positively correlated with the key stratum’s flexural rigidity and advanced load and negatively correlated with the foundation modulus and excavation length. The advanced influence distance was not affected by the load of the key stratum. The numerical simulation results show that, with the increase of the mining area, the fracture trace of overlying strata in goaf extended to the coal wall’s interior. The fracture range of overlying strata is larger than that of the miningd: area. This study has a practical value for water disasters, gas outbursts, and rock strata control.

The dust produced by transportation roads is the primary source of PM2.5 pollution in opencast coal mines. However, China’s opencast coal mines lack an efficient and straightforward construction scheme of monitoring and management systems and a short-term prediction model to support dust control. In this study, by establishing a PM2.5 and other real-time environmental information to monitor, manage, visualize and predict the Internet of things monitoring and prediction system to solve these problems. This study solves these problems by establishing an Internet of things monitoring and prediction system, which can monitor PM2.5 and other real-time environmental information for monitoring, management, visualization, and prediction. We use Lua language to write interface protocol code in the APRUS adapter, which can simplify the construction of environmental monitoring system. The Internet of things platform has a custom visualization scheme, which is convenient for managers without programming experience to manage sensors and real-time data. We analyze real-time data using a time series model in Python, and RMSE and MAPE evaluate cross-validation results. The evaluation results show that the average RMSE of the ARIMA (4,1,0) and Double Exponential Smoothing models are 12.68 and 8.34, respectively. Both models have good generalization ability. The average MAPE of the fitting results are 10.5% and 1.7%, respectively, and the relative error is small. Because the ARIMA model has a more flexible prediction range and strong expansibility, and ARIMA model shows good adaptability in cross-validation, the ARIMA model is more suitable as the short-term prediction model of the prediction system. The prediction system can continuously predict PM2.5 dust through the ARIMA model. The monitoring and prediction system is very suitable for managers of opencast coal mines to prevent and control road dust.

In order to realize the automatic dispensing and remote monitoring of emulsions in the coal mine production process and improve the efficiency and safety of coal mine production, an automatic dispensing and remote monitoring system of emulsions based on the IoT platform was built. The APRUS X industrial network management adapter in this system is the upper computer of Siemens S7-200SMART PLC and the data transmission hub between the dispensing system and the MixIOT cloud platform, while the MixIOT cloud platform realizes the functions of data visualization and remote control terminal; the PLC acts as the control core and monitors the weight sensors, solenoid valves, DC brushless booster pumps, agitators and other equipment at the same time. The data collected by the sensors are uploaded to the cloud through IOT technology and combined with real-time data to realize automatic dispensing and remote control of emulsion. Meanwhile, remote monitoring is realized through Web page and apps, which is convenient for managers to monitor the production site in real time and realize intelligent production. There are 27 groups of liquid dispensing experiments, and the average relative error of emulsion concentration ratio obtained after 27 groups of tests is 4.8%, and the accuracy requirement meets the actual engineering needs. The research results are of great significance to promote the intelligent production of coal mines and the application of industrial IOT technology, and provide specific reference and references for the research and application in related fields.

The fracture characteristics and zoning model of overburden during longwall mining are the basis of coal mine disaster prevention. However, the existing theoretical model is inconsistent with the field measurement. In order to further research into the strata’s fracture characteristics and optimize the overburden’s zoning model, we used the elasticity and Winkler foundation theory to establish first fracture and periodic fracture mechanics models of clamped boundary supported by an elastic foundation with a key stratum as the research object. We analyzed the stress distribution characteristics and fracture evolution pattern of the mining‐induced key stratum. We analyzed the zoning characteristics of mining‐induced overburden and established the zoning model according to different fracture mechanisms. The results show that the key stratum formed a double “O‐X” shaped interconnected fracture zone after the first fracture. The key stratum formed a double “C‐K” shaped interconnected fracture zone after the periodic fracture. We divided the mining‐induced overburden into three zones along the horizontal direction: the original rock zone, the inverted triangular compression‐shear fracture zone, and the trapezoidal tensile fracture zone. The study revealed the mechanism of inverted step fracture in the separation zone, explained the fracture mechanism of the coal pillar support zone, and has significant theoretical value for the prevention and control of water disasters, gas outbursts, and strata movement.

Longwall top coal caving technology is one of the main methods of thick coal seam mining in China, and the classification evaluation of top coal cavability in longwall top coal caving working face is of great significance for improving coal recovery. However, the empirical or numerical simulation method currently used to evaluate the top coal cavability has high cost and low-efficiency problems. Therefore, in order to improve the evaluation efficiency and reduce evaluation the cost of top coal cavability, according to the characteristics of classification evaluation of top coal cavability, this paper improved and optimized the fuzzy neural network developed by Nauck and Kruse and establishes the fuzzy neural network prediction model for classification evaluation of top coal cavability. At the same time, in order to ensure that the optimized and improved fuzzy neural network has the ability of global approximation that a neural network should have, its global approximation is verified. Then use the data in the database of published papers from CNKI as sample data to train, verify and test the established fuzzy neural network model. After that, the tested model is applied to the classification evaluation of the top coal cavability in 61,107 longwall top coal caving working face in Liuwan Coal Mine. The final evaluation result is that the top coal cavability grade of the 61,107 longwall top coal caving working face in Liuwan Coal Mine is grade II, consistent with the engineering practice.

Longwall top coal caving mining is one of the main methods of mining thick coal seams in China. Therefore, carrying out the classification evaluation of top coal caving is of great significance to ensure mining success and reduce the risk of mining technology. In order to realize the classification evaluation of top coal caving, this article introduces the method of using BigML to establish the classification evaluation model of top coal caving. Furthermore, using the data from the CNKI database as sample data, a classification evaluation model of top coal caving is established on BigML. After training, testing, and optimization, the model is used to evaluate the top coal caving in No. 3 coal seam of Gucheng Coal Mine, and the evaluation result is grade 1, which is consistent with the engineering practice. The final research results show that the application of BigML in the classification evaluation of top coal caving is successful; the evaluation of top coal caving through BigML is reliable; BigML provides another scientific reliability way for the classification evaluation of top coal caving.