To address the problem of incomplete reflection of network structure characteristics and data distortion in static network models used in dynamic systems，an airport network structure analysis method based on a temporal complex network model was proposed by introducing time attributes. Network characteristic indicators such as nodal temporal degree，temporal centrality，temporal betweenness centrality，and temporal closeness centrality were analyzed. Then，the structural properties of CAN were investigated. Moreover，crucial airport nodes within the temporal network were identified and ranked，and the reasons for the heterogeneous node generation were analyzed. The results indicated that the temporal network structure of Chinese airports presented scale-free network characteristics. The average temporal distance values of CAN vary significantly with the maximum being 6.06 times the minimum. ZWWW (Urumqi Diwopu International Airport) and ZBHH (Hohhot Baita International Airport) represented relatively higher temporal betweenness centrality than other indicators，playing an important central role in the network. However，ZGSZ (Shenzhen Bao'an International Airport) had a higher temporal degree of centrality than other indicators.

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.

In order to prevent transformer fire accidents，short-chain fluorocarbons (FS-50) were combined with non-ionic (Alkyl Polyglucoside(APG)-0810)，zwitterionic (Disodium Cocoamphodiacetate(CAD)-40) and anionic (Sodium Dodecyl Sulfate(SDS) )three kinds of foam solutions，APG-0810/FS-50，CAD-40/FS-50 and SDS/FS-50 were prepared. The basic properties，foamability，foam and liquid film stability of the foam solution in the presence of transformer oil were systematically studied. The curves of foam drainage，volume decay，oil volume and film thickness were obtained over time. The results show that oil has a significant influence on foam properties. Under the action of transformer oil，the foaming ability of the foam solutions decreased significantly，and the foaming ability of APG-0810/FS-50，CAD-40/FS-50 and SDS/FS-50 were reduced by 16.9%，18.4% and 18.8% respectively. The volume of drainage in 60 min increased by 13.6%，74.6% and 15.5%，respectively. Foam volume decay increased by 2.5%，9.8% and 17.9%; The stability of liquid film decreased，and the film thickness decreased by 276.5，414.7 and 552.9 nm，respectively，at 100 s. Among the three foam solutions，the mixture of APG-0810/FS-50 has the best inhibition ability to transformer oil，and the stable foam of this system has the best oil resistance to transformer oil. The research results of this paper can provide a theoretical basis for the development and selection of special extinguishing agent for transformer oil fire.

In order to ensure the safe operation of bag dedust system and prevent the occurrence of dust explosion accident，a real-time quantitative risk evaluation model of bag dedust system was proposed based on deviation degree-FCE. Firstly，based on the monitoring data of the pressure difference between inlet and outlet of the dust，box temperature and lock-in ash discharge fault signal collected by the industrial Internet of Things sensor，the deviation degree was introduced to characterize the risk status of the monitoring parameters of the bag dedust system. Then，FCE was used to calculate the risk status of the bag dedust system. The analytic hierarchy process(AHP) and variable weight theory were combined to assign weights to the evaluation indicators. Based on the membership function and weighted average principle of membership degree，the risk of the bag dedust system was quantified by deviation degree，and the risk evaluation result for the bag dedust system was obtained. Finally，the monitoring data of a certain type of bag dedust system was used to verify the effectiveness of the model. The findings demonstrate that when the monitoring value of the evaluation indicators of the bag dedust system gradually approaches the alarm threshold，and the number of indicators approaching the alarm threshold increases，the risk level is higher. The evaluation results are correlated with the actual operation situation，thus validating the efficacy of the model.

To avoid abnormal attitude problems such as serpentine and axis deviation during shield tunneling affecting construction safety，a large-diameter slurry shield attitude control method combining CatBoost algorithm and MOEAD. A shield posture prediction model was developed with 19 input parameters and 6 output parameters，and the CatBoost algorithm was used to develop a nonlinear mapping relationship between input and output parameters. The SHAP was used to analyze the effects of input parameters on shield posture. The CatBoost-MOEAD shield posture multi-objective optimization model was coupled with the multi-objective optimization algorithm. Then the proposed model performance was validated against the Wuhan Yangtze River large-diameter slurry shield tunnel project. The results showed that the CatBoost prediction model can efficiently predict the posture of large-diameter mud-water shields. The determination coefficients of the six shield posture objectives ranged from 0.931 to 0.974，the root-mean-square errors ranged from 0.030 to 0.880，and the errors ranged from 0.039 to 1.057. The thrust of the propulsion group has the most significant impact on shield attitude among the major construction parameters. The proposed CatBoost-MOEAD multi-objective optimization method for shield attitude had a great performance in optimization effect with a maximum value of 38.86%.

From the perspective of overall high-quality development and high-level security strategy，in order to realize the integration of new quality and productivity security development，research was carried out on the connotation，basic characteristics and capabilities of new quality and productivity security development. First，based on the triangle model，the connotation of safe development of new quality productivity was explained in combination with specific cases. Secondly，using the method of system engineering，the three-dimensional structural model was constructed，and the essential characteristics of the safe development of new quality productivity were analyzed. Finally，the capacities required for enhancing the safe development of new quality productivity were proposed. The research results indicate that the safe development of new quality productivity should focus on science and technology security，information data security，and risk management in new security fields，which are characterized by basic，overall，dynamic，complex and extensive features. The basic is reflected in the time dimension，the overall，dynamic and complex are manifested in the logic dimension，and the extensive features are embodied in the knowledge dimension. To promote safe development of new quality productivity，it is necessary to enhance strategic ability，leadership ability，collaboration ability，risk management ability，and theoretical innovation ability，corresponding to the three-dimensional structure model.

In order to study the invulnerability and key nodes of the metro network in road-rail cooperative urban distribution，so as to support the networking mode of urban distribution and the reliability improvement of the distribution network under road-rail cooperation，the relative network efficiency and relative load entropy were used as the invulnerability measurement indicators，and the network invulnerability changes under different attack modes were studied based on the improved coupling image lattice model first. Secondly，the centrality index of the transportation efficiency of the reaction network and the realistic index of the carrying capacity of the reaction network was selected to construct a comprehensive identification model of key nodes of the network. Then，by analyzing the level of network invulnerability under different index weights，the key node set under the optimal weight value was obtained. Finally，the empirical analysis of the Chengdu metro network was carried out to verify the effectiveness and practicability of the model. The results show that the metro network has a stronger anti-destruction ability in the face of random attacks under the same disturbance intensity. When faced with external disturbances，the relative network efficiency and relative load entropy loss of nodes with a loss of more than 20% is 6.3% and 6.8%，respectively，of which the relative network efficiency loss can reach 56.3%，and the relative load entropy loss can reach 50.2%. Considering the realistic and central indicators，the relative network efficiency loss and the maximum relative load entropy loss caused by each key node are 8.99% and 4.38%，respectively，which need to be paid attention to.

Excessive damage to aircraft braking moving discs will directly threaten flight safety. In order to solve the problems of low detection efficiency and strong subjectivity in the evaluation of damage degree of braking moving disc based on manual experience，an intelligent evaluation algorithm for damage degree and replacement need of aircraft braking moving disc based on image segmentation was proposed. Firstly，the classification and feature information of braking moving disc damage were analyzed. Then，based on the U-Net model，semantic segmentation was performed on the braking moving disc damage image dataset. A quantitative analysis model for the proportion of braking moving disc damage area and a calculation model for the maximum radial width of the fall off area were constructed. Finally，the model was validated using image data of the braking moving disc of Cessna525. The results indicate that the U-Net model has a good image segmentation effect on the types of braking moving disc damage，with average accuracy，average recall，average pixel accuracy，and average intersection to union(IoU) of 90.75%，91.25%，90.25%，and 87.25%，respectively. The evaluation results of braking moving disc are basically consistent with the evaluation conclusions of experienced mechanical engineers，and the evaluation results are objective，accurate，and highly visualized，which proves the rationality and feasibility of the algorithm proposed in this paper.

In order to explicit the distribution characteristics of polluted gases in factory buildings and realize efficient control of industrial building environments，the study analyzed the differences and simplification conditions between the distribution characteristics of multiple gaseous pollutants and single gaseous pollutants，clarified the transportation characteristics of multi-component mixed gases emitted simultaneously from the same source，revealed the feasibility range and principles to be followed for mass transfer simplification of multi-component mixed gases in full scale，and verified the validity of the conclusion through numerical simulation. The results show that the applicable range of the multi-component gas mass transfer simplification model is as follows: when the mixed gas emitted from the same source has no phase change and chemical reaction，and its flow parameter Reynolds number Re is above 10³ and Mach number Ma < 0.3，and the physical property parameters are within the range of Knudsen number Kn < 0.01 and temperature between 0.3 Characteristic vibrational temperature θ_v and T'，the components can be simplified for studying the distribution characteristics of pollutants. The simplified deviation stems from the difference in volume force per unit mass of the mixed gas before and after simplification (ΔF). When simplifying，it is necessary to ensure that the ratio of ΔF to the main driving force is less than 0.01. The theoretical model's application range and simplification principle agree with the simulation results. It is proved that a variety of gaseous pollutants can be simplified into a single type under the condition that the simplification principle is met within the applicable scope to explore the distribution characteristics of pollutants.

In order to accurately predict the material demand in the transitional resettlement stage of earthquakes and improve the efficiency and accuracy of emergency material mobilization，the factors that have a great impact on the number of resettled population were determined based on the historical seismic data in China. A prediction model of the resettled population based on GWO-BP was established，which combined with the quantitative relationship between the population and emergency supplies，to predict the material demand in the transitional resettlement stage after the earthquake. The experimental results show that the GWO-BP neural network model exhibits high accuracy and stability in predicting the number of relocated populations，and can effectively predict the number of relocated populations in disaster areas，thereby calculating the corresponding material demand. GWO-BP neural network model has a certain application value in predicting material demand in post-earthquake transitional resettlement stage，and can provide a reference for the decision-making of emergency material procurement after the earthquake.