To solve the problem of coal dust leakage from the coal handling system of coal-fired power plants，by comparing different solutions，dust prevention and control equipment and technologies were adopted including curved coal fallout pipe，fully enclosed dust-removing guide chute，pulsed bag dust collector，and wet dust collector. The optimized solution for dust reduction and prevention of the coal handling system was put forward. The dust prevention and control system covering the whole operation process of coal fuel ″turning，stacking，picking，and transporting″ was formed and was applied in Suizhou Power Generation Project. The results of the study show that the method effectively reduces the spillage of coal dust in all aspects of coal fuel transfer and basically realizes a dust-free operating environment and clean fuel transport. It provides a feasible dust prevention technology for controlling the coal dust pollution produced during the operation of the coal handling system.

In order to reveal the effect of water content on the tensile and compressive mechanical properties of coal samples，coal samples from the No.22 seam of the Hanjiawa coal mine were selected. Brazilian disc splitting tests，uniaxial compression tests，and triaxial compression tests were conducted on coal samples with different moisture contents and confining pressures to explore the mechanical characteristics of water containing coal samples. The results show that the strength and elastic modulus of coal samples decreases exponentially with increasing water content. When the coal samples reach saturation，the strength and elastic modulus decrease by 48.3% and 37.6% respectively. With the increase in water content，the plasticity of coal samples is enhanced，and the samples are prone to breakage. Under triaxial compression，the peak value of failure and the strength of coal samples increase with the increase of confining pressure. However，the strength of water-bearing coal samples under different confining pressures is obviously lower than that of dry coal samples. The water content has little effect on the internal friction angle of coal samples but has a significant effect on the cohesive force of coal samples.

In order to investigate the influence of porous materials on the explosion propagation characteristics of LPG in vent pipes，experiments were conducted on a self-built gas explosion test platform. The inhibitory effect of silicon carbide materials with different thicknesses and porosities on the flame propagation characteristics and explosion overpressure in the vent pipe was investigated. The results show that porous materials can effectively prevent the propagation of LPG explosion flame. With the increase of porosity and thickness，the propagation distance，propagation speed and intensity of LPG flame are reduced，and the porosity has a greater effect on the fire resistance than the thickness. The installation of porous materials also reduces the maximum explosion overpressure. When LPG is not ignited behind the installation location，increasing the porosity and thickness of porous materials，the maximum explosion overpressure in front of the material (PT1，PT2) increases，and that behind the material (PT3) is close to the static film-breaking pressure. The firestopping effect of material porosity and thickness shows that increasing the porosity of the material for firestopping should be prioritized when the strength of the material is up to standard in engineering applications.

In order to improve the effectiveness of hidden danger investigation，a reasoning model for hidden danger of explosive accidents of hazardous chemicals was proposed based on BN. First，according to the theory of combustion and explosion and expert experience，the main types of hidden dangers affecting the explosion of hazardous chemicals were determined，and BN model of explosion，leakage and ignition source was constructed. The prior and conditional probabilities of hidden dangers were determined by triangular fuzzy numbers. Then，Bayesian forward reasoning was used to calculate the probability of explosions of hazardous chemicals. Combined with reverse reasoning，the hidden dangers that were most likely to lead to explosion accidents were identified，and the formation of hidden risk factors was traced back. Finally，Bayes sensitivity analysis method was used to determine the key hazards affecting the explosion of hazardous chemicals and the correlation between the accident hazards，and it was verified by an explosion case of oil storage tank. The results show that the explosion probability of the storage tank calculated by causal reasoning is about 4.7%. Equipment overpressure and electric spark have the highest posterior probability，which is the most likely to lead to the explosion of the storage tank. The reason can be traced back to the failure to inspect the storage tank regularly or the failure to use explosion-proof equipment. Electrical spark and valve or flange damage are the key hidden dangers of oil storage tank explosion，and the accident hidden danger is related to the chain of "valve or flange damage → abnormal oil gusher → leakage exceeding limit → fire source (electrical spark) → explosion".

In order to explore the influence of different learning scenarios on the learning effect of learners in the teaching process of fire emergency fire escape in subway stations，and explore the advantages of virtual reality learning. Questionnaires and MP160 physiological instrument were used to obtain different behavioral response data of learners in paper learning，mobile video learning and virtual reality learning scenarios，and these data were processed and analyzed. The results show that there are significant differences in learners' academic performance under the three learning scenarios. Compared with mobile video learning and paper learning scenarios，virtual reality learning scenarios can promote learners' understanding of subway fire escape knowledge and significantly improve academic performance. Under the three learning scenarios，there are significant differences in learners ' interest in learning. Virtual reality learning scenarios show significant advantages in improving learners ' interest in learning subway fire escape knowledge. The virtual reality learning scene reflects a higher level of physiological arousal in terms of physiologic index.

In order to address the increasingly serious cloud-native container security threats arising from large-scale cloud migration of systems，the ISM method merging IPFS，DEMATEL，and method were proposed to identify the key tactical factors influencing cloud-native container security threats and their hierarchical logical relationships from the security intruder perspective. The findings of this research are as follows: the centrality and causality of the persistence and privilege escalation tactical phases are high，positioning them at the core of the entire cloud-native security threat landscape. Security attacks during these two phases require high-priority attention. Threat attacks during the execution and persistence tactical phases constitute essential factors in cloud-native container security. The threats during the initial access，credential theft，and lateral movement tactical phases have the most direct impact on cloud-native container security. In comparison with traditional and triangular fuzzy sets improved DEMATEL-ISM，our proposed method has better performance in identifying container security-related critical factors.

In order to study the reasons for the deterioration of the anchor performance caused by groundwater variation，a series of physical model tests were employed to explore the anchorage enhancement of the expanded anchor in the anti-floating process. Firstly，a single pull-out test was carried out through the indoor model test，and the change of bearing capacity of multi-bell-shaped expanded anchor under different buried depths during the pull-out process was obtained. Secondly，according to the ultimate uplift bearing capacity obtained from the single pull-out test，the cyclic test was carried out to explore the evolution mechanism of the bearing performance of the multi-bell-shaped expanded anchor under different cyclic amplitudes，cyclic times and cyclic frequencies. Finally，the image particle velocity method (PIV) was used to analyze the deformation mechanism of the surrounding soil，and the variation characteristics of the surrounding soil displacement under single pulling-out and cyclic loading were obtained. The test results show that the axial load-displacement curve of the bell-shaped expansion anchor can be roughly divided into three stages: elasticity，vibration and failure. With the increase of buried depth，the ultimate bearing capacity and soil displacement of the anchor increase. Under the action of cyclic loadings，the increase of the cyclic load ratio，the number of cycles and the cycle frequency will weaken the bearing capacity of the anchors.

In order to enhance the stability and safety of railway driving and effectively identify the influence of the dispatcher's fatigue state on the driving organization，a method for identifying the fatigue state of the dispatcher was proposed based on the characteristics of EEG signals. The fatigue state of the dispatcher was divided according to the working time period，and the high-speed rail scheduling simulation experiment was designed to collect EEG data. The three types of brainwave frequency-domain amplitudes of high-speed rail dispatching subjects were extracted as the characteristic value by wavelet series expansion and Fourier transform，and the classification results of fatigue state were verified by combining the operation characteristics and EEG signal characteristics of dispatchers. The ResNet18+SoftMax model and MobileNet V2+SoftMax model were built through the Python language environment. The input features were converted into a three-dimensional rectangular model based on deep learning. The weights were optimized and adjusted to obtain the optimal model，so as to judge the fatigue state of high-speed rail dispatchers. The research results show that the fatigue state recognition accuracy of the participants in the high-speed rail scheduling experiment by ResNet18+SoftMax and MobileNet V2+SoftMax two models is 92.78% and 99.17%，respectively，compared with support vector machines(SVM) model to improve the awake state and fatigue state recognition accuracy，and reduce the model computing time. Among them，the MobileNet V2+SoftMax model can better identify the fatigue state of the dispatcher. With the principle of MobileNet V2+SoftMax model as the core，the potential fatigue risk of high-speed rail dispatchers under long-term working conditions can be identified more quickly and accurately.

To address the low prediction performance of drilling risks in unknown formations during oil and gas development，a drilling risk assessment approach based on grey adjacent well information was proposed. Firstly，a three-parameter interval grey number was proposed to present formation information. Secondly，the depth adjustment and weighted interpolation methods were used to develop a target well gray wellbore information matrix based on adjacent well information. Subsequently，a three-parameter grey safety drilling fluid density window was proposed. Then，a risk assessment model including overflow，well leakage，collapse，and adhesion was developed based on generalized strength-interference theory. Finally，a case study was performed on five wells in an oil field in western China. The results indicated that introduced concept of wellbore information matrix and optimization parameter description of depth adjustment and weighted interpolation effectively presented the profile of the target well's wellbore information. The grey safe drilling fluid density window developed by three-parameter interval grey numbers and wellbore information matrix significantly reduced uncertainty in drilling risk assessment. The evaluation results from drilling risk assessment model based on generalized strength-interference theory agreed well with that in real situations，providing reliable fundamental knowledge for drilling risk assessment.

To investigate the source and proportion of thermal runaway heat transfer of lithium-ion batteries in confined conditions space such as aviation transportation，the 18650 lithium-ion battery (100% state of charge) with lithium cobalt oxide (LCO) as the cathode material was used. The heat transfer model of thermal runaway of lithium-ion batteries was proposed by ANSYS Fluent software. Furthermore，the pyrolysis gas generated by the first battery and its thermal runaway was used as the heat source，and the second battery was heated to thermal runaway through radiation and convection. The results showed that when 2^nd battery reached the thermal runaway temperature，the heat generated by the internal side reaction accounted for 30.01% of the total energy. The gas combustion generated by the 1^st battery thermal runaway provided energy for 2^nd battery thermal runaway，accounting for 5.64% of the total energy. When 2^nd battery reached the maximum temperature，the heat generated inside the battery accounted for 87.39%，and the energy provided by the gas combustion was 1.76%. the pyrolysis gas combustion accelerates 2^nd battery's thermal runaway，though it is a heat source，it is not a heat source. Although the combustion of pyrolysis gas accelerated the thermal runaway process of 2^nd battery，the proportion of energy provided was relatively small.