Gas explosion disaster is the most serious coal mine accidents. In order to summarize the research progress of gas explosion risk assessment，firstly，the risk factors of gas explosion were identified. Then the shortcomings of existing risk assessment methods were analyzed，and the following conclusions were drawn by sorting out relevant literature. The analysis shows that there are subjective problems in identification method and evaluation method of coal mine gas explosion risk sources. There are also some problems with risk factors，such as the uncertainty of gas source and change，the unknown ignition source，the uncertainty of ventilation and air control. The application of objective weighting method and evaluation method based on mathematical theory can improve the accuracy of weighting and evaluation results，but the computational complexity limits its wide application. Although the application of computer models has made the assessment of coal mine gas explosion risk more accurate，it is necessary to solve the problem of expanding the integration of data collection and deep learning. Based on the current research status and existing problems，the future risk assessment of coal mine gas explosion can develop in the direction of multi-source data fusion technology，deeply mining precursory warning information，establishing intelligent models of disaster information based on information depth perception and data mining，and realizing dynamic risk assessment of coal mine gas explosion.

In order to strengthen the governance of online public opinion of chemical emergencies and properly handle the online public opinion crisis caused by chemical emergencies，evolutionary game theory was introduced into the process of network public opinion governance，and a binary evolutionary game model was constructed for local government and network media. Combined with the SD model，a quantitative analysis model was constructed for local governments and online media. Simulation research was conducted based on relevant cases，and the strategic evolution process of each game subject was compared and analyzed. The results indicate that the popularity of online public opinion on chemical emergencies depends on the strategic choices of each party，and the evolutionary game model analysis shows a periodic and recurrent trend. After introducing a punishment mechanism，appropriately increasing the severity of punishment can bring the evolutionary game system into a benign state. Local governments can enhance the emergency warning mechanism for online public opinion，strengthen daily supervision and collaborative governance of online media，and formulate reasonable punishment measures to effectively prevent local governments from inaction and disorderly behavior in the process of responding to chemical emergencies and online public opinion. This can achieve supervision of online media and avoid the vicious evolution of online public opinion.

To reveal the impact of coal metamorphism on the self-ignition process，experimental and quantitative analysis methods was combined to analyze the pyrolysis characteristics，microstructural changes，and their correlations during coal self-ignition. First，Fourier-transform infrared spectroscopy (FTIR) was employed to determine changes in the content of active functional groups in coal samples. Then，thermogravimetric analysis (TGA) was conducted to obtain pyrolysis characteristic parameters for coal samples with varying degrees of metamorphism. Finally，Pearson correlation coefficient method was applied to quantify the relationship between microstructure and characteristic temperatures during coal self-ignition. The results indicate that as the degree of coal metamorphism increases，the content of aromatic and aliphatic hydrocarbons(Ar-CH) rises，while oxygen-containing functional groups(C-O-C) significantly decrease. Low-rank coal samples exhibit the highest proportion of oxygen-containing functional groups，whereas high-rank coal samples contain more than 50% aromatic hydrocarbons. Increased metamorphism delays weight loss curves and shifts them to higher temperature regions，leading to an increase in characteristic temperatures. Ar-CH show a significant positive correlation with characteristic temperatures，while C-O-C exhibit a negative correlation. Overall，the increase in Ar-CH and the decrease in C-O-C in coal samples are identified as the primary microstructural factors responsible for the rise in characteristic temperatures and the reduction in self-ignition propensity.

In order to elucidate the specific causes of abnormal alarms from CH₄ and CO sensors in the return corner of the backfill working face，a systematic investigation was conducted. Initially，a programmed heating-gas chromatography (GC) experiment was carried out on filling materials，complemented by on-site GC measurements，to evaluate whether the alarms were attributable to CH₄ and CO concentrations exceeding threshold limits. Subsequently，a portable gas detector was employed to monitor various filling materials，identifying the primary materials responsible for triggering the sensor alarms. Finally，GC-mass spectrometry (MS) analysis was performed to characterize the volatile components of adhesives and their interference effects on CH₄ and CO sensors. The results indicate that the alarms triggered by CH₄ and CO sensors were caused by the volatile gases from adhesives，rather than by an excessive concentration of CH₄ or CO. The primary constituents of the adhesive VOCs were alkanes，while secondary components included alcohols and esters. Key interfering substances for CH₄ sensor were alkanes such as C₅H₁₂，C₆H₁₂，and C₆H₁₄，with minor contributions from alcohols and esters such as CH₄O，C₂H₄O₂，and C₃H₈O₂. All ten tested combustible gases exhibited cross-interference effects on CH₄ sensor. Interfering substances for CO sensor included CH₄O，C₂H₄O₂，and C₃H₈O₂. While the sensors demonstrated short-term resilience to interference under abnormal gas atmospheres，their stability and anti-interference performance significantly deteriorated with prolonged exposure.

In order to improve the safety production level of coal mining enterprises and ensure a reasonable proportion of safety investment，the CPT method was introduced to establish a dynamic safety investment decision-making model that combined the criteria importance through intercriteria correlation(CRITIC) method improved by the coefficient of variation method and CPT. From the perspective of safety input and output，an evaluation index system was constructed，and the weights of each index were calculated by the improved CRITIC method. Combined with the CPT，the static decision-making ranking results of various safety investment schemes in coal mining enterprises were calculated，and on this basis，the results were dynamically optimized to obtain the final dynamic decision-making ranking results of safety investment. The study shows that the constructed model is reasonable and reliable with high sensitivity compared with VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method. The model considers the influence of the decision maker's subjective risk preference on the actual decision-making results，and the time factor can reflect the dynamic accumulation level during the research period，so the decision-making results are more scientific and reasonable.

In order to investigate the leakage and dispersion patterns of gas-phase CO₂ transport pipelines with different hole sizes，outdoor CO₂ leakage test with apertures of 50 mm，100 mm and 233 mm were carried out based on industrial pipelines. Firstly，according to the pressure drop experiment，the variation law of the pressure in the pipe with the leakage time was analyzed. Then，based on the established theoretical model，the leakage flow and pressure drop changes were predicted and verified by experiments. Finally，the variation of CO₂ volume fraction at different positions of the leakage port was studied. The result shows that the pressure drop rate in the first stage of leakage is the largest. Among them，the pressure drop rate of the three leakage hole sizes is 93.4-1967.5 kPa/s. It takes 14.2-149.8 s for the pressure in the pipe with three leakage hole sizes to drop to the ambient pressure. With the increase of the leakage hole size，the pressure drop process tends to be a cliff type drop. According to the established theoretical model，the relative error between the calculated leakage quality and the experimental actual release is 0.25%-4.54%. The pressure drop curve obtained based on the predictive model is generally slightly lower than the experimental pressure drop curve. However，the variation trend and duration of pressure drop are very close to the experimental results. It shows that the established theoretical method for predicting leakage quality and pressure drop is reliable. In the range of 5-17 m from the leakage port，the peak volume fraction increased significantly with the increase of the leakage hole size. The peak volume fraction of different leakage hole size outside the range of 17 m approaches the same. The dispersion distance of 5% volume fraction of gaseous CO₂ leakage is 26 m.

In order to accurately identify unsafe behaviors during the climbing process of high-altitude workers，this paper proposed an unsafe behavior recognizing method for high climbing workers based on vision，which included the human pose estimation and the one-dimensional convolutional unsafe behavior recognition models. Quantized autoencoder was used to structurally model human key points in human pose estimation，enabling the detection of human key point coordinates. Combining with safety behavior knowledge in high climbing operations，the unsafe behavior recognition model was constructed based on one-dimensional convolutional neural network model，and it was validated by industrial data experiments. Experimental results show that the accuracy of this method is 93.91% and 90.34% on unobstructed and partially obstructed datasets，respectively. Moreover，compared with support vector machines (SVM) and K-nearest neighbor (KNN)，this method has stronger generalization capability.

To enhance the flame retardancy and smoke suppression performance of EP-CE composites，a novel flame retardant was synthesized using DOPO and BPS as raw materials. The flame-retardant DOPO-BPS/OSEP/EP-CE composites were fabricated by the solution casting method，where DOPO-BPS and OSEP were jointly introduced into EP-CE matrix. Based on Fourier transform infrared spectroscopy (FTIR) analysis，nuclear magnetic resonance (NMR) analysis，thermogravimetric analysis (TGA)，limiting oxygen index (LOI) test，UL-94 vertical burning test，and cone calorimeter analysis，the effects of different ratios of DOPO-BPS and OSEP on the flame retardancy and smoke suppression properties of EP-CE composite materials were studied. The results show that the phosphaphenanthrene ring structure of DOPO-BPS could form a stable carbon layer and block the transfer of heat and oxygen. The synergistic effect with OSEP significantly improves the flame retardant effect and smoke suppression performance. When the flame retardant DOPO-BPS and OSEP are added to EP-CE at a mass fraction of 9∶1，the flame retardant performance and smoke suppression performance are the best，the flame retardant performance reaches V-0 level，and the LOI increases to 31.4%. Compared with EP-CE，the peak heat release rate，total heat release and total smoke production decrease by 29.7%，32.2% and 32.7%，respectively.

In order to further scientifically prevent and control coal mine gas accidents and systematically analyze the risk factors and coupling relationships of coal mine gas accidents in my country，an association rule mining model based on the PSO algorithm using Python software was established and verified. The risk factors of coal mine gas accidents were classified in combination with the HFACS accident risk model，and the constructed PSO-FP(Freguent Pattern)-growth algorithm was used to mine association rules for coal mine gas accident investigation reports. The results show that the PSO-FP-growth algorithm has better running speed and association rule effect than the PSO-Apriori algorithm. According to the visualization of association rules of gas accident risk factors and high-support association factors，the main risk factors for coal mine gas accidents in my country are defects in safety supervision and management of coal mine enterprises，inadequate gas prevention and control technology，weak safety awareness of employees，and inadequate management awareness and technology of on-site managers.

In order to improve the inspection efficiency and safety of UAV inspections for wind turbines，a reasonable planning of the UAV inspection path was proposed. A method for UAV inspection path planning based on real-time emergency landing safety constraints was introduced. First，a safety calculation model for emergency landing areas was established. It based on the dynamic endurance capacity of UAV affected by wind speed and direction，as well as constraints such as flight path emergency landing，to assess the safety of the inspection path and establish safety constraints. Then，regarding the objective function of the length of UAV inspection path，an optimal inspection path planning method based on the characteristics of RSA was proposed. This method effectively solved TSP for UAV inspections in wind farms under safety constraints，utilizing the discrete and multi-agent characteristics of RSA algorithm to plan the inspection path for wind farms. Finally，comparative experiments and simulations of wind farms were conducted for different algorithms. results indicated that the real-time emergency landing safety constraint model can comprehensively calculate safe routes by integrating various risk factors，enhancing the safety of the inspection path. RSA algorithm can quickly solve TSP problem for wind farm inspections under safety constraints，improving the level of inspection path planning.