In order to better analyze the stable sub-safety state and unstable sub-safety state existing in control sector operation，K-means algorithm was used to cluster three control sector operation stability evaluation indicators of excess-capacity ratio(ECR)，retention degree and flight attitude mixing ratio so that the optimal classification of the operation stability of control sector was determined. The index threshold corresponding to each level was obtained by clustering analysis of a single index. Combined with the index weight calculated by the entropy weight method，the operational stability level of the control sector in each time period was obtained according to the principle of maximum membership degree. Then，the comprehensive evaluation model of control sector operation stability was constructed. The actual flight data of Xiamen No.01 sector was selected to more comprehensively analyze operation situation of control sector from the perspectives of stability and trend. The results show that the best effect is obtained when the control sector operation stability level is divided into three categories. The stability varies with time due to the influences of air traffic flow and control conditions，especially in the two time periods of 7: 30-9: 15 and 20: 00-21: 00，when the change of stability is most obvious. The controllers need to pay great attention to improving the safety of airspace operations.

To solve the crowd congestion problems caused by a large number of phubbers in enclosed one-way long passages in public spaces such as the subway，an intervention model was proposed to calculate the intervention critical value. Experiments were performed to analyze the behavioral characteristics of phubbers and normal pedestrians in enclosed one-way long corridors. Then，different distribution functions were used to propose a small-scale behavioral model. Furthermore，a large-scale congestion intervention decision-making model was proposed based on the proportion of phubbers and crowd density. Finally，the critical value curve between passenger density and the proportion of phubbers was validated against a one-way long passage in a Beijing subway station. The results indicated that behavioral characteristics of phubbers presented as slow following，while normal pedestrians tended to speed up and overtake whenever possible. The simulations calculated the critical value curve between the passenger's density and the phubber's proportion. If the calculated value was lower than the critical curve，it was a low-risk area without any intervention strategies. Otherwise，intervention strategies were required to avoid serious congestion.

To solve the problem that traditional safety design methods based on manual inspection were difficult to cope with the explosion of optional residence solutions caused by the large-scale integration of avionics systems，an avionics system partition model，task model and safety criticality level quantification model were constructed，and the comprehensive design optimization considering safety was modeled as an MDP problem. An optimization method of Soft Action-Critic (SAC) algorithm based on Actor-Critic framework was proposed. In order to obtain the correlation between the parameter selection and training results of SAC algorithm，the sensitivity of the algorithm parameters was studied. At the same time，to verify the superiority of the optimization method based on the SAC algorithm in optimizing the comprehensive design considering safety，optimization comparison experiments were carried out with the Deep Deterministic Policy Gradient (DDPG) algorithm and the traditional allocation algorithm as the objects. The results show that under the optimal parameter combination，the maximum reward after using convergence of SAC algorithm increases by nearly 8% compared with other parameter combinations，and the convergence time is shortened by nearly 16.6%. Compared with the DDPG algorithm and the traditional allocation algorithm，the optimization method based on SAC algorithm has improved approximately 62%，7464%，8370%，2123% and 775% in terms of the maximum reward，cumulative constraint violation rate，partition balance risk effect，partition resource utilization and solution time

In order to solve the problem of determining the distribution of different types of objects in system faults，a method to determine the distribution of objects was proposed. Firstly，the characteristics of the system fault evolution process and object distribution were discussed. Secondly，the method flow chart and implementation process were given. Finally，an example was analyzed. The example studied the basic data matrix composed of 6 factors and 50 objects，and the maximum training set cross-correlation was 0.8，the test set cross-correlation was 1，and the optimal object label distribution (object distribution) was obtained. Finally，the advantages and disadvantages of the method were described. The analysis shows that the database for studying the evolution process is the object set. Methods based on UKSR，combined with K-means and mutual information methods，a randomly distributed object label set is constructed，and the criteria for the optimal object label set are proposed. The optimal object label set is determined through a loop when the correlation between object labels and object data is the largest. The label value of objects in the set is the optimal object distribution. The method overcomes the problem of unsupervised learning and nonlinear mapping. It is concluded that the method can classify the measured objects in the system fault evolution process under unsupervised and nonlinear conditions，and the distribution of class labels of all objects with evolution time. The disadvantage is that it can only be used to study the system fault evolution process represented by two-dimensional.

In order to address the challenges of urban traffic congestion and safety，an ACLR model was proposed. By integrating ConvLSTM，attention mechanisms，and residual structures，the ACLR model effectively enhanced the extraction of spatio-temporal traffic features.The time，space and other characteristics of taxi traffic were processed respectively，and the influence of regional point of interest(POI) data on taxi traffic was mined. Additionally，a specialized learning component was incorporated to capture the impact of external factors and point-of-interest density on traffic flow. Using taxi trajectory data from Beijing，the ACLR model demonstrates superior prediction accuracy compared to other models such as the autoregressive integrated moving average (ARIMA) model，long short-term memory (LSTM)，deep spatio-temporal residual networks (ST-ResNet)，convolutional neural network(CNN)-ResNet-LSTM (CRL)，and attentive crowd flow machines (ACFM) in urban traffic flow forecasting，which is helpful to improve the prediction performance of the model without POI density or considering POI density. The predicted value of the ACLA model is basically consistent with the real value，and it can also be in good agreement with the real value during peak hours，which effectively improves the ability to extract traffic temporal and spatial characteristics，reduces the prediction error，and optimizes the traffic flow prediction performance.

In order to solve the problem of false alarms and missed alarms in pipeline gas leakage detection using a single sensor，timely warning and feedback of leakage status，a multi-source data fusion pipeline leakage detection method based on cross-attention was proposed. Firstly，the pre-trained ShuffleNetV2 model was used to extract spatial features from thermal imaging data. Then，a 1DCNN BiGRU model was constructed by combining a one-dimensional CNN (1DCNN) and a bidirectional gated recurrent unit (BiGRU) to extract temporal features from gas sensors. Finally，cross-attention was used to capture the spatiotemporal correlation of the data and obtain the feature representations of the two data sources. The residual method was used to connect the features and input them into the classification layer to obtain the recognition results. The results show that the constructed SCGA model has a gas recognition accuracy of 99.22%，and the loss value fluctuates between 0-0.04. Compared with support vector machines (SVM)，1DCNN，and BiGRU models that only use gas sensor data，the accuracy is improved by at least 4.12%. Compared with MobileNetV3，ShuffleNetV2，and ResNet18 models that only use thermal image sensor data，the accuracy is improved by at least 1.14%. Compared with the multi-source data fusion model SCG，which simply connects temporal and spatial features，the accuracy is improved by 1%. It was verified that the SCGA model has high accuracy.

To enhance the fire emergency evacuation capability of hospital buildings in fire，a device was designed for timely emergency evacuation and safe escape. UBmap，AHP，and FAST were integrated into the overall design framework to investigate users' needs and relevant product design elements for emergency evacuation devices. The UBmap incorporated the escape behavior of patients during the fire based on the characteristics of hospital buildings，and constructed a behavior journey map to predict and extract needs at each stage. AHP/FAST aggregation methods were then used to rank these needs according to their importance，converting them into primary and secondary functions for analysis and resolution. This determined the accuracy of the product design orientation and functional logic，and finally completed the design scheme. The theoretical model based on UBmap/AHP/FAST was applied to the design and development of fire evacuation device in hospital buildings. The design's overall strength and feasibility were further verified and validated using finite element analysis in ANSYS Workbench software. The results show that the integration of different systematic product analysis，design，and simulation testing methods reduces weak points and uncertainties in the design process. This approach makes the design more systematic and scientific，and achieves the goals of reducing production costs，enhancing safety and efficiency，and improving disaster prevention and mitigation.

In order to investigate the influence of internal and external factors on the safety state of the tailings dam，a method for analysing the safety state of the tailings dam based on heterogeneous hierarchical diagrams was proposed. Firstly，a hierarchical causal graph was constructed based on a priori knowledge to link key factors such as environment，seepage field and stress field with the safety status of tailing dams，and an evaluation index system was established by combining the attribute characteristics of heterogeneous nodes. Secondly，the cloud model and set-pair analysis theory were used to quantitatively calculate the potential logical relationship between the heterogeneous causal graph and the safety stability of the tailings dam. A dynamic interval calculation method for quantitative indicators and a safety status level calculation model were proposed to convert the fuzziness and uncertainty of complex and diverse evaluation indicators into quantitative expressions. Finally，a tailings dam in Luoyang was used as an example to verify the scientificity of the model.The results show that the model can quantitatively analyse the link between factors and states and identify the causes of negative changes in stacked dams. The conclusions of the model analysis can be used for the safety management of the dam-building process.

Frequent heavy rainfall events cause a dramatic increase in attached elevator scaffolding accidents. In order to improve construction safety and reduce the accident rate under heavy rainfall scenarios，an accident causation analysis model based on combination of IFRAM and BN was proposed. The model first qualitatively identified accident mechanisms and explored system functional resonance using IFRAM. Next，IFRAM was mapped to a BN quantitative analysis model，and the prior probabilities of each root node were computed using cloud optimization. Finally，taking the Xi'an "9.10" accident as an example，empirical research was conducted，and corresponding preventive measures were proposed. The results indicate that accidents are most likely to occur when the safety status is IV. The core causes of climbing accidents include workers violating regulations，failure to conduct mandatory supervision such as standing by and heavy rainfall. The combination of factors such as heavy rainfall environment and overloading of the frame after rain is the key to inducing frame climbing accidents.

Aiming at the problem of UAV aerial photography parameters relying on manual experience when collecting image of 3D real scene reconstruction at traffic accident sites，which led to large model measurement errors and low precision，an automatic calculation method of key parameters of UAV aerial photography was proposed. First，the key parameters of aerial photography used by single-lens UAV for images acquisition of traffic accident site were altitude，gimbal angle and shooting interval angle. The numerical relationships of the key parameters with the imaging range，imaging accuracy and overlap rate were analyzed. Then，the aerial photography key parameters computation model was constructed. The key input parameters were the given accident site，UAV technical parameters，image aspect ratio and overlap requirements. On the premise that the accident site was in the effective imaging range and there was no imaging blind zone，the UAV photography parameters were automatically calculated with the goal of improving the accuracy and presentation effect of the image utilization rate model. Finally，combined with case application，the UAV aerial photography parameters calculated by this method were applied to complete the image acquisition at the accident sites，and the constructed 3D real scene model could clearly and completely present the overview of the accident site，with an average measurement error of 1.72%，and a measurement accuracy of 3.54 cm. Compared with the manual empirical method，the average error of the method was reduced by 47.56%，and the accuracy was improved by 48.40%. The study shows that this method can realize the automatic quantitative calculation of aerial photography parameters for 3D real scene reconstruction of traffic accident sites，construct the model with centimeter-level error，and improve the parameterization and automation of UAV aerial photography.