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 effectively enhance the transportation capacity of compressed air foam and better address the fire hazards in converter stations，experimental research was conducted on pressure distribution using full-scale compressed air foam transport pipelines of 699，406 and 261 m as examples. Furthermore，the equivalent resistance length method was proposed to consider static pressure loss and local pressure loss，and a comprehensive equivalent resistance coefficient was introduced. An empirical pressure loss relationship suitable for engineering applications was developed. The results indicate that during the compressed air foam transport phase，the pipeline inlet and outlet pressures rapidly increase with time before stabilizing. The time required to reach a steady state is proportional to the length and complexity of the pipeline. After foam transport ceases，the pressure within the pipeline drops sharply. The complexity of the pipeline configuration leads to a nonlinear decrease in local pressure with increasing transport distance. Finally，an empirical relationship between the pressure loss and the equivalent resistance length has been obtained，which can be used to predict pressure drop variations for pipe diameters ranging from DN50 to DN200 and pipeline lengths up to 1 000 m.

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.

In order to ensure the efficiency of response and disposal and reduce the damage caused by the hazardous gas leaks，it was necessary to quickly and accurately trace the location of the leak point as well as the source strength. In this article，a method of hazard location and source strength determination based on a complex algorithm was proposed. The basis of the algorithm was to compare the difference between the concentration of the monitored gas and the concentration calculated by the atmospheric diffusion model，and take the difference as the objective function，so that the parameter with the smallest objective function value was the optimal result of the source intensity and position. The results show that the complex algorithm can quickly and accurately obtain the location and source strength of the leakage source. Compared with the traditional simplex method，the compound algorithm has no restriction on the selection of initial value. Even if the selected initial value has a large deviation from the true value，the position and intensity of the source can be quickly obtained through the iteration of the complex algorithm，avoiding the shortcoming of the traditional simplex method，which has high requirements on the selection of initial value. A comparison of particle swarm optimization，genetic algorithm，simplex and complex algorithm is made in three aspects: traceability efficiency，traceability accuracy and traceability stability，which depicts that the complex algorithm is progressive. The complex algorithm can be applied to trace sources and determine source strength for continuous and instantaneous releases.

In order to reduce conflicts between heterogeneous traffic flows during overtaking，a non-motorized lane width design method that considers avoidance maneuver in heterogeneous traffic was proposed. This method represents an improvement over the traditional design approach. Firstly，the conflict levels of non-motorized vehicles were analyzed through the deceleration group proportions and pedaling cadence. Secondly，a vehicle-to-vehicle force model was established to calculate the forces between different types of vehicles. The additional safety gap was determined based on the relationship between these forces and lateral width. The recommended non-motorized lane width was the sum of the traditional lane width and the additional safety gap. Then，the rationality of the proposed design method was evaluated based on the safety levels of non-motorized lanes with different widths，which was obtained from the safety evaluation model developed in this research. Finally，data from four non-motorized lanes in Xi'an city were analyzed as case studies. The research results show that the deceleration group proportion is 0.93 times，pedaling cadence increases by 0.07 revolutions per second，and the road safety value is 1.07 times compared to traditional non-motorized lanes in non-motorized lanes accommodating heterogeneous traffic. These findings demonstrate that lanes meeting the design width have lower conflicts and greater safety.

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.

In order to effectively reduce the risk of blind zones and lack of control in dust environment monitoring，optimize the node coverage control of the dust environment monitoring system in thermal power plants，and prolong the lifetime of WSN，an energy-saving optimization method based on improved genetic algorithms was proposed. Firstly，based on node coverage，total energy consumption of node deployment and total energy consumption of node communication and transmission，the network coverage quality objective function was constructed. Then，aiming at the problems of the local optimization and coding duplication existing in traditional genetic algorithms，the chromosome combination scheme of integer coding，the adaptive adjustment method of crossover and mutation probability and the elite retention strategy were proposed. Finally，the simulation comparison and analysis were performed to determine the optimized node number and distribution scheme. The results show that the improved genetic algorithm significantly improves the convergence speed. The number of iterations required is reduced to 20，and the fitness value is optimized by 52.18%. In the node deployment and coverage study，the optimized number of nodes is 42，the coverage rate is 97.28%，and the node dormancy rate is 76.19%，which effectively improves the energy-saving effect of the dust environmental monitoring system in the thermal power plant.

A comprehensive and sophisticated multi-algorithm coupled dynamic prediction model is proposed to address the intricate reality and stringent accuracy requirements of predicting tailings dam displacement. Firstly，by employing a time series decomposition model，the cumulative displacement is disaggregated into its trend and cyclical components. The trend term displacement is then forecasted using a Gaussian regression time series prediction model. Secondly，various Copula functions are employed to investigate the overall correlation between the inducing factors and the cyclical term displacement. Owing to the diverse influencing factors and strong nonlinearities associated with the cyclical term displacement，the MISSA-CNN-BiLSTM model is utilized for prediction. Lastly，the predicted trend term displacement from the Gaussian regression model and the predicted cyclical term displacement from the MISSA-CNN-BiLSTM model are merged. The results demonstrate a high degree of consistency between the predicted cumulative landslide displacements and the measured values，with a correlation coefficient of 0.996 and a root mean square error (RMSE) of 0.13 mm. The multi-algorithm coupled model，based on MISSA-CNN-BiLSTM，exhibits remarkable prediction accuracy and effectively captures step changes in tailings dam displacements.

The S-FCN fire image detection method based on feature engineering was proposed to address the issues of high computational complexity and poor real-time performance of deep learning algorithms for fire image detection in complex backgrounds. Firstly，this method extracted color features from images in multiple color spaces and reduced the dimensionality of these features using mutual information. Secondly，the network structure of the deep learning model was simplified by using a single hidden layer of a fully connected network as its backbone. The color features in multiple color spaces can better represent fire smoke and flames，and reducing the dimensionality of color features in multiple color spaces effectively reduces the redundancy of input features. The single hidden layer fully connected network can significantly reduce the number of parameters during the model propagation process. Finally，this method was evaluated on a real and complex background fire image dataset. The experimental results show that the detection accuracy achieved by this method is 93.83%，and the real-time frame rate is 10 869 f/s. This method achieves high accuracy and high-speed fire image detection in complex scenes.

In order to address the challenges associated with characterizing the scenarios of stampede accidents and facilitating comprehension of these scenarios among decision-makers，a method for constructing and combining scenarios of large-scale event stampede accidents was proposed. Firstly，the scene elements of stampede accidents were extracted in large-scale events from the four factors that affect the formation of large-scale activities: people，venue，management，and environment，and a formal expression method for "state" and "trends" of large-scale activities research was established. Secondly，based on Markov model，a deduction description and calculation method for the transformation of situational "state-trends" was provided. Finally，an example analysis was conducted using Shanghai Bund accident. The findings of empirical analyses indicate that deductive results are largely aligned with the actual development process of the 2014 Shanghai stampede. This evidence substantiates the scientific rigour and efficacy of methodology proposed in the paper.