To effectively evaluate the performance of emergency logistics suppliers，a method for evaluating emergency logistics suppliers was proposed based on supplier evaluation，incorporating prospect theory and interval numbers. Firstly，based on the characteristics of emergency logistics，an evaluation index system for emergency logistics suppliers was proposed from six dimensions: rapid response capability，cost control capability，product quality，delivery service，internal and external conditions of the enterprise，and flexible demand. Then，interval numbers were introduced into the evaluation of emergency logistics suppliers，and an evaluation method based on prospect theory and interval numbers was proposed. The Jaccard similarity coefficient was used to define the similarity of interval number. The maximum sum of similarity with the remaining solutions was used to determine the reference point of the value function for the attribute evaluation value of the corresponding solution. The deviation maximization theory was used to construct a multi-attribute decision weight optimization model based on interval number similarity，from which attribute weights were obtained. Finally，the value function was normalized to expand the scheme discrimination. The prospect value of each scheme was calcuted based on the obtained weight function and value function，and the advantages and disadvantages of the scheme were ranked. The research results indicate that the difference in prospect values between the optimal and worst suppliers calculated using the evaluation method is 0.383 8，while the prospect value difference calculated using statistical inference principles is 0.085 6. The difference of 0.298 2 shows that the proposed evaluation method expands the differentiation between options，helping decision-makers achieve effective decisions.

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.

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.

To address the flight safety risks posed by faults in aircraft control systems，a composite framework for fault risk assessment based on IRPN was proposed. This framework comprehensively considered four key risk factors: fault probability，severity，detectability，and risk damping. First，system fault modes were deduced bidirectionally using FMECA-FTA method. Second，human and environmental factors were incorporated，and a Bayesian network approach was employed to construct a hybrid probability model for calculating fault probabilities. Third，fault severity was categorized into three evaluation parameters，which were comprehensively assessed using the Analytic Hierarchy Process and Fuzzy Comprehensive Evaluation methods. Next，utilizing resources such as pilot quick reference manuals and aircraft type design manuals，a criterion-based reasoning method was applied to establish detectability scoring criteria，allowing for a more scientific evaluation of fault mode detectability levels. Finally，the FRAM was introduced to define risk damping coefficients，characterizing the propagation of risk during the evolution of fault risks. The computational validation was carried out with the case of jamming failure mode of aircraft flap seam wing actuation system. The research results show that its IRPN assessment result is 158，which is in perfect agreement with the actual operation. The validity and accuracy of the failure composite risk index calculated by the IRPN composite risk assessment framework are confirmed by the failure mode example simulation and the real verification of unsafe events.

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.

In order to predict coal gas permeability more accurately and ensure coal mine safety production，a prediction model of coal gas permeability based on ISSA-optimized BPNN was constructed. Firstly，the sparrow search algorithm (SSA) was improved by introducing Sine chaotic mapping and Gaussian mutation to enhance its global search capability and local optimization accuracy，thereby optimizing the weight and threshold configuration of BPNN. Secondly，the data on the factors affecting gas permeability were processed using Pearson correlation coefficient matrix and kernel principal component analysis (KPCA) to improve the computational efficiency and accuracy of the model. Three principal components with a cumulative variance of 88.59% were extracted as model inputs，and permeability was used as the output for the experiment. Finally，the model was applied to a coal mine in Shanxi for case verification. The experimental results show that ISSA-BPNN outperforms PSO-BPNN，PSO-SVM，PSO-LSSVM，and SSA-BPNN models in four indicators: mean absolute error (MAE)，mean absolute percentage error (MAPE)，root mean square error (RMSE)，root mean square error (RMSE)，and coefficient of determination (R²). Compared with other models，ISSA-BPNN has reduced MAE by 0.032 7，0.022，0.017 9，and 0.018 2 in the test samples，respectively. MAPE decreases by 5.15%，3.14%，2.76%，and 2.36% respectively. RMSE decreases by 0.031 6，0.027 9，0.018 8，and 0.022 2 respectively. R² increases by 0.077 5，0.065 8，0.040 1，and 0.049 3，respectively. Finally，the case verification shows that its reliability and stability are high in practical applications.

In order to reduce the failure risk of petrochemical storage tanks in coastal areas under the influence of hurricanes，the failure model for vertical cylindrical tanks under wind load was established based on the strength stress interference theory. The critical wind speed for the buckling failure was determined by an example of a crude oil storage tank. For different random distributions of parameters，such as wind speed，the Monte Carlo method was also used to plot the probability curve of shell buckling failure. The buckling failure mechanism under static conditions was investigated，and the failure probability at different loading levels was determined under static conditions. The results show that the critical wind speeds are 60.67，65.38 and 69.79 m/s respectively when the loading levels are 25%，50% and 75% under the same wind load. According to the failure probability curve，the failure probability of the loading level of 25% is significant greater than that at 50%. Therefore，it can be seen that the buckling failure mechanism of the vertical cylindrical storage tank shell is as follows: the loading level is too low，which can easily lead to buckling failure of the storage tank under wind load. The higher the loading level，and the stronger the wind load resistance of the vertical cylindrical storage tank. Appropriate measures can be taken to increase the loading level to resist strong wind loads in the coastal chemical industries.

In order to assess the resilience of CPS in airport flight areas and provide a reference for rapid recovery in emergencies，a refined and real-time airport flight area CPS network model with flight area control network as the information network and flight area taxi path network as the physical network was proposed. For CPS network in airfield areas，the connectivity of the network was calculated by selecting the relative values of the largest connected subgraphs. The network's resilience was evaluated by combining robustness，performance loss，and comprehensive resilience indicators. CPS situation in airfield areas under different disturbance recovery strategies was compared to determine the optimal recovery strategy. Taking Xi'an Xianyang Airport flight area CPS as an example，the results show that betweenness perturbation causes the greatest damage to the control network，while degree value perturbation causes the greatest damage to the taxiing path network. Using betweenness recovery can enable faster recovery of CPS resilience in airfield areas，and CPS network of the flight area of the airport shows a higher level of resilience under random perturbation. The research results can simulate and predict relevant nodes and provide certain reference significance for ensuring the safety of flight area operations.

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.