In order to study the effect of nitrogen content on pyrolysis process of NC，Fourier Transform Infrared spectrometer (FTIR)，thermogravimetric analysis(TG)-FTIR and pyrolysis(Py) gas chromatography(GC)/mass spectrometry (MS) are used to reveal the structural characteristics，pyrolysis characteristics and process products of NCs with different nitrogen content. The results show that with the increase of nitrogen content，the amount of substituted nitro of NC increases，the pyrolysis reaction rate and reaction degree increase，the proportion of light gas increases and the product types increase，and a variety of chemical recombination forms appear at high temperature. In the pyrolysis process of NC，de-nitration reaction takes place first，and then large molecules are decomposed into small molecules，and then carbon skeleton and ring oxygen bridge fracture occurs. By identifying the common products of NC with different nitrogen content and the main nitrogen oxides in each stage，a mechanism of NC pyrolysis process based on the principle of temperature division is established.

To address the challenge of extracting pulse signals in fault diagnosis of variable-load gearbox caused by redundant features，a pulse feature extraction method based on CAM was proposed. First，a CAM was designed，which consisted of two stages. In the first stage，a multilayer perceptron was used to simulate the channel dependencies and enhanced the important channel features related to faults. In the second stage，the convolutional layers were employed to learn signal segments related to faults. By recalibrating the features in two stages，the module focused on the critical pulse features. Next，based on CAM，this study proposed a CLDN method for extracting fault features in variable-load gearboxes. CLDN further improved the learning and representation of impulse signals by adaptively recalibrating the features at each layer. Finally，the extracted features were fed into a Softmax classifier to validate the feature extraction effect of the proposed method. The results show that CAM's accuracy is on average 3.8% higher than 4 attention mechanisms like Self-Attention，achieving accurate localization of impulse features. Compared with 7 diagnostic methods such as ResNet34，the accuracy of CLDN is 3.7% to 14.6% higher，which significantly enhances the extraction of fault features.

To respond and alleviate the sudden large passenger flow of metro lines in time，a subway passenger flow control optimization model was proposed. Firstly，with the goals of minimizing the total waiting time of passengers and maximizing passenger flow through the interval，permitted inbound passenger flow was used as a decision-making variable to propose a RDM considering constraints such as the supply side，demand side，and passenger flow control intensity. Moreover，the volatility of passenger flow demand was analyzed，and a RM was developed by combining robust optimization theory. The volatility of passenger flow demand was analyzed，and an RM was developed combined with robust optimization theory. Secondly，the robust equivalent transformation theory was used to linearize the nonlinear constraints in RM and solved by the Lingo optimization solver. Finally，a metro line was taken as an example for analysis and verification. The results showed that the RDM model using capacity balance coefficients to decide the permissible inbound passenger flow effectively alleviated the pressure of passenger congestion and improved the efficiency of interval transport. When dealing with uncertain passenger demand，robustness coefficients were introduced in the RM model to adjust fluctuations range of passenger flow demand，thereby reducing the aggregation passenger flow risk and improving the reliability of the passenger flow control scheme.

To deeply analyze the information transmission characteristics of large-scale sports events organizations，the effects of various organizations during the information transmission process were analyzed taking "5·22" cross-country race accident in Gansu Baiyin as an example. Firstly，an event organization information transmission model was proposed based on STAMP model. Moreover，the transmission process was divided into four stages: preparation，incident occurrence，emergency response，and post-incident handling，and analyzed from three levels: individual，enterprise，and government. Then，CN theory was used to develop an organizational information transmission network structure and identify key information nodes and paths. Finally，the entropy weight method was used to propose the information edge weight calculation model. The results indicated that the key information nodes were mainly concentrated at the individual and government levels，especially in the preparation stage when the information load of transmission paths was relatively high. However，enterprises showed insufficient responsibility at this stage，particularly in the acquisition and transmission of weather information，leading to impaired decision-making and actions at critical moments.

To standardize the content and quality of urban lifeline operational monitoring services，a standard system for urban lifeline operational monitoring services was proposed. Based on the mature experience of operational monitoring services and "Guidelines for standardization of organizations in service sector—Part 2: Standard system construction"，the proposed standard was divided into a general service basic standard system，service provision standard system，service guarantee standard system，and position standard system. A standardized and systematic operational monitoring service process was developed from the aspects of standard implementation foundation，operation service content，service quality assurance，and job responsibilities，to comprehensively guarantee the quality of urban lifeline operational monitoring services. The results indicated that the urban lifeline operational monitoring services standard system effectively addressed issues，such as untimely early warning responses and overlapping job functions. However，it should be continuously updated and improved in conjunction with industry development trends to fully promote the standard system implementation.

To enhance the intelligent system's understanding of individual driving behavior under human-machine interaction driving circumstances，an en-route driving style recognition method based on LDA model was proposed. The method explored vehicle trajectory information from multi-dimensions to quickly extract and identify latent driving style features of drivers. Firstly，the semantic understanding rules of driving behavior were established to discretize continuous trajectory data into semantic vocabularies of driving behavior，considering the scene perception layer，pattern layer，operation layer and vehicle status layer. Secondly，according to topic perplexity and consistency，habitual driving styles were classified into four categories: stable，conservative，moderate and aggressive. Finally，each driver's en-route driving style was identified as a probabilistic combination of the aforementioned driving styles. The results show that the proposed en-route driving style recognition method considers drivers' heterogeneity and explains the phenomenon of the same driver exhibiting different driving styles in varying driving environments. Additionally，this research improves the comprehensiveness and comprehensibility of en-route driving style recognition.

In order to facilitate the rapid formation of a disaster and accident trans-regional collaborative governance community in response to major emergencies，this study employed fsQCA. It investigated fourteen cases by selecting seven conditional variables across three dimensions: Incentives，mechanisms，and guarantees. These variables included value consensus，existential threats，administrative mobilization，collaborative linkage mechanisms，interest equilibrium mechanisms，legal guarantees，and digital intelligence technologies. The study explored the core influencing factors and the complex causal relationships in the formation of the disaster and accident trans-regional collaborative governance community. Configurational analysis identified three patterns of community formation: party-government-led，value-driven，and crisis-triggered，with each pattern corresponding to typical cases in the case database. The findings reveal that collaborative linkage mechanisms are a necessary condition for the formation of emergency management communities，while the other six variables cannot individually serve as necessary conditions. Optimizing the functionality of collaborative linkage mechanisms can effectively promote the development of trans-regional emergency management communities.

To improve the emergency preparedness and response capabilities of spent fuel reprocessing nuclear accidents，a spent fuel reprocessing nuclear accident emergency scenario based on knowledge meta was proposed to address the uncertainty of the nuclear accident emergency evolution process，the importance of scenario analysis in emergency response decision-making，the complexity of the evolution process，and the difficulty of organization and implementation. The disaster event，causative agent，causal agent，and emergency response were determined，and then a dynamic scenario model for spent fuel reprocessing nuclear emergencies based on a DBN was developed to calculate the occurrence probability of key scenarios，evaluate the development trend of the scenarios，and analyze the evolution laws and paths. Taking the explosion of the high-release liquid storage tank of the Mayak spent fuel reprocessing plant as an example，the process of deduction of the scenario analysis method of the spent fuel reprocessing nuclear accident based on the knowledge meta theory and DBN was performed，and the results were further analyzed. The results showed that: the loss probability of emergency cooling water supply was 73%，the probability of explosion of the high-release waste liquid storage tank was 86%，the probability of radioactive nuclides transferred to animal and plant products and drinking water through multiple pathways was 87%，the probability of long-lived radioactive nuclides deposition in part of the area was 89%，and the probability of the event calming down and dying out was 72%. The probability of the accident contaminating the air，soil，and river was 89%，85%，and 81%，respectively. The probability of affecting public health and safety was 86%. The scenario evolution process is consistent with the emergency response development of the reprocessing storage tank explosion accident and its impact on the public and the environment，validating the model performance.

To improve the applicability and scalability of the joint reserve model for social emergency supplies，and reduce the reserve risks of the government and manufacturers，raw material suppliers were used to propose a government-led Stackelberg three-tier joint reserve model of government and enterprises under a quantity flexibility contract. In this model，the government and manufacturer engaged in joint a stockpile of physical emergency supplies，while the suppliers were responsible for stockpiling raw materials for the production of emergency supplies. The inverse-order solution method in game theory was used to analyze the optimal reserve quantity of the three parties to balance the expected profit and cost. Moreover，the advantages of the proposed three-tier supply chain model involving government and enterprises were evaluated. Finally，the model performance was validated by numerical simulation and sensitivity analysis. The results demonstrated that the three-tier joint government-enterprise stockpile model not only improved the overall emergency stockpile volume under specific market conditions but also reduced the government's storage risk. Furthermore，it can enhance the profitability of high-performing enterprises and reinforce the structure and efficacy of the emergency stockpile system.

In order to address the "fragmentation" issue in emergency collaboration networks and enhance the effectiveness of emergency management in response to accident disasters，three typical accident disasters were selected. Based on theory of collaborative governance，a "network density-average path distance" analytical framework was constructed using SNA to map the collaborative relationships among various emergency response entities. At the macro level，indicators such as network size，density，shortest path，cohesion，and centralization were employed to portray the overall characteristics of the emergency collaboration networks. At the meso level，cohesive subgroups were analyzed to explore the clustering patterns of emergency collaboration，with the criterion for subgroup division being the professional nature of emergency matters in all three cases. At the micro level，the characteristics of nodes within the networks were evaluated through the triple indices of degree centrality，closeness centrality，and betweenness centrality. The results indicate that emergency collaboration networks of Changsha self-built building collapse，Xiangshui explosion，and Xinjia hotel collapse accidents exhibited tight-collaboration，tight-centralization，and loose-collaboration structures，respectively. It is recommended that the emergency collaboration networks be optimized from three dimensions: clarifying the rights and responsibilities of emergency response stakeholders，adjusting heterogeneous interest demands，strengthening the leadership of core nodes and leveraging their pivotal roles，and promoting the deep embedding of emergency culture to empower endogenous collaborative governance.