In order to improve the means of bus dispatch, improve the efficiency of bus operations, and implement a smart campus, the design of an intelligent bus positioning system, which is based on the Internet of Things, is studied. This article first discusses the location awareness of IoT nodes and the location awareness of multi-source positioning to achieve intelligent perception of the environment. Then the overall design of the intelligent bus system of IoT is carried out, and the design idea of the three-tier network hierarchy based on the perception layer, the network layer, and the application layer is given. The system uses RFID technology to track, locate, and monitor buses. The touch screens at bus stops can count the number of passengers waiting for each bus, and the electronic bus-stop boards can publish the arrival time of each bus and some other information. The Zigbee wireless network technology is used to achieve communication between the vehicle terminal, Platform system and dispatch monitoring center. Finally, the simulated annealing algorithm is used to achieve intelligent bus scheduling, improve the quality and efficiency of bus service to meet the travel needs of citizens.

In the environments of various open coal storage sites, mining-affected coalbeds, and goafs, etc., some coal bodies are often affected by external environmental factors. They are highly prone to spontaneous combustion in low moisture content (≤8%). In order to examine the effect of low moisture content on the spontaneous combustion tendency of coals with different metamorphic grade, we conducted a temperature programmed oxidation (TPO) experiment and differential scanning calorimetry (DSC) experiment to study the spontaneous combustion characteristics of coals with different metamorphic grade at four different low moisture contents. The change laws of the characteristic parameters of four different metamorphic grade coals at four different low moisture contents were comparatively analyzed. The experimental results indicate that: (1) Compared other low moisture content, anthracite and fat coal at a low moisture content of 1.2 % show a stronger tendency for spontaneous combustion, and long flame coal and lignite at a low moisture content of 3.4% and 5.6% are more prone to spontaneous combustion. (2) Four different metamorphic grade coals at a low moisture content of 7.8% are less prone to spontaneous combustion.

With the continuous development of society, with the advent of the era of big data, situational awareness systems are gradually becoming well known and play an important role. Situational awareness systems are based on safe big data, and they are environmentally, dynamic and holistically aware of security. A comprehensive system of risk capabilities. Therefore, this paper uses the situational awareness system to study the rescue problem of mine fires, in order to reduce the casualties and economic losses caused by mine fires. On this basis, the convolutional neural network algorithm is used for situational awareness. By optimizing the algorithm, from R-CNN model to Fast R-CNN model, the optimal model of Faster R-CNN is finally proposed and implemented. The mine fire rescue problem.

Immediately and accurately assessing the risk of coal spontaneous combustion and taking targeted action are crucial steps in coal spontaneous combustion prevention and control. A new model, AEM-AHP-LSTM, was proposed to solve the weight calculation problem of multiobjective evaluation in the process of coal spontaneous combustion. Firstly, the key indicators of coal spontaneous combustion were analyzed and used as risk factors to establish an evaluation system. Next, the objective and subjective weights were calculated using AEM and AHP, respectively. The objective and subjective weights were then combined, and TOPSIS was used to calculate the score of the evaluation sample. Finally, the obtained evaluation samples were trained with the BP, RBF, and LSTM model to resolve the problem of model overdependence on historical data and achieve the auto-adapt adjustment of weight with data change. Additionally, data from 15 typical Chinese coal mines were applied to the model. The results indicate that, compared with the BP and RBF neural networks, the LSTM model has higher prediction accuracy, stronger generalization ability, and stronger practicability. The modeling and application findings show that the AEM-AHP-LSTM model was better appropriate for the risk assessment of coal spontaneous combustion. This method can potentially be further applied as reliable approach for the assessment of mine disaster risk.

Accurate quantitative analysis and prediction of dust concentration in mines play a vital role in avoiding pneumoconiosis to a certain extent, improving industrial production efficiency, and protecting the ecological environment. The research has far‐reaching significance for the prediction of dust concentration in mines in the future. Aiming at the shortcomings of the grey GM (1, 1) model in forecasting the data sequence with large random fluctuation, a grey Markov chain forecasting model is established. Firstly, considering the timeliness of monitoring data, the new dust concentration data is supplemented by using the method of cubic spline interpolation in the original data sequence. Therefore, the GM (1, 1) model is established by the method of metabolism. Then, the GM (1, 1) model is optimized by the theory of the Markov chain model. According to the relative error range generated during the prediction, the state interval is divided. Subsequently, the corresponding state probability transition matrix is constructed to obtain the grey Markov prediction model. The model was applied to the prediction of mine dust concentration and compared with the prediction results of the BP neural network model, grey prediction model, and ARIMA (1, 2, 1) model. The results showed that the prediction accuracy of the grey Markov model was significantly improved compared with other traditional prediction models. Therefore, the rationality and accuracy of this model in the prediction of mine dust concentration were verified.

In the environments of all kinds open coal storage sites , mining-affected coalbeds, and goafs, parts of coal body at low moisture content (≤8%) are prone to spontaneous combustion under the influence of some external environmental factors. In order to examine the influence effect of low moisture content on the spontaneous combustion tendency of coals with different metamorphic grade, we conducted temperature programmed oxidation (TPO) experiment and differential scanning calorimetry (DSC) experiment to study the spontaneous combustion characteristics of coals with different metamorphic grade at four different low moisture content, and comparatively analyzed the change laws of the characteristic parameters of four different metamorphic grade coals at four different low moisture content. The experimental results indicate that: 1) Compared other low moisture content, anthracite and fat coal at low moisture content of 1.2% show stronger tendency to undergo spontaneous combustion, long flame coal and lignite at low moisture content of 3.4% and 5.6% are more prone to spontaneous combustion. 2) Four different metamorphic grade coals at low moisture content of 7.8% are less prone to spontaneous combustion; 3) Coals with different metamorphic grade have different tendency to undergo spontaneous combustion.