In order to enhance the risk control efficiency of civil aviation flight safety in China and accurately identify the key risk factors that cause flight accidents, multi-source text mining of flight accident risks was conducted using Weibo information, news reports, and aviation accident investigation reports as samples. Flight accident risk factors were identified using the bidirectional encoder representations from transformers for topic modeling(BERTopic) . The semantic correlation of risk factors was analyzed using the Word2Vec model and complex network, from which the key risk factors were determined. The bidirectional encoder representations from transformers(BERT) was adopted to mine the personnel risk factors that trigger the most serious negative emotions among the public. The key personnel risk factors in low-altitude airspace were identified through word frequency statistics. The results indicate that personnel risk is the key risk factor leading to flight accidents. Among them, failure to strictly follow operating procedures, bird strikes, and sickness of flight crew are the key risk factors affecting flight safety. The non-strict implementation of operation procedures by flight crew not only arouses the most negative public emotions but also constitutes the key personnel risk factor leading to low-altitude airspace accidents.

In order to improve the anomaly detection performance of dam monitoring data, a dam abnormal data detection method based on the improved Prophot-long short term memory-particle swarm optimization Prophet-LSTM-PSO was proposed. Firstly, by improving the Prophet method, the trend component features obtained from the decomposition of abnormal data points were clearly visible. Secondly, the decomposed trend, periodic, and residual components were represented in a three-dimensional space, where the original time series data was substituted with the mean distance of the nearest neighbors in this space. Finally, abnormal data points were identified precisely by combining the LSTM network and PSO algorithm to set and optimize anomaly thresholds. The results show that the method proposed in this paper significantly improves detection performance and exhibits high stability compared with traditional methods. Notably, while maintaining a stable recall rate exceeding 95%, both accuracy and precision surpass 95%, thereby validating the effectiveness and practicality of the proposed method.

In order to address potential safety hazards of dust explosions caused by the dispersion of combustible dust during the intelligent transfer of medium and heavy flammable materials (such as coal) in mines, triggered by the movement of robotic arms and materials, this study takes 6-axis industrial mechanical arm as the subject. Referencing the technical parameters of the arm's joint motion and the wind speed threshold for coal dust dispersion as motion constraints, the "4-5-4-4-5-4" polynomial pose interpolation method and the proposed SEKOA were employed as the motion trajectory planning model for the robotic arm. This model analyzed the nonlinear combinatorial engineering optimization problem for achieving safe and efficient operation of the robotic arm. Under the established motion constraints, the SEKOA algorithm demonstrates higher efficiency compared to other algorithms in safely transferring coal powder materials, achieving the fastest time of approximately 9.5 seconds. The material movement is stable, with no tilting or collision. The motion trajectory planned by the "4-5-4-4-5-4" polynomial interpolation method is smooth and continuous. During the material lowering phase, the peak velocities of the main drive joints 1 and 2 are approximately 0.72 rad/s and 0.47 rad/s, respectively, with peak accelerations around 0.5 rad/s². After decelerating for about 2.5 seconds to 0 rad/s, the robotic arm can gently place the material at the designated position. This approach effectively prevents secondary dispersion of coal dust on the sealing container.

To better design the smoke exhaust system for tunnel shafts in high-altitude areas, numerical simulation methods were used to calculate and analyze the effects of environmental pressure and shaft inclination angle on the smoke plug-holing effect during tunnel fires. The temperature fields under different working conditions were obtained using FDS simulations. The plug-holing height was introduced to determine the degree of plug-holing for each working condition. Combined with the degree of plug-holing, the dimensionless Richardson number (Ri') was redefined, and a theoretical calculation model for the plug-holing height was proposed. The critical Ri' number (Ri_c') for the occurrence of plug-holing was derived. The results show that the plug-holing height increases with the increase of the angle and environmental pressure. The Ri' number decreases with the decrease of environmental pressure and increases with the increase of the shaft inclination angle. Under the same environmental pressure, Ri_c' number decreases with the increase of the shaft inclination angle. The relationship between Ri' number and the Ri_c' number can be used to determine whether a complete plug-holing effect occurs.

To enhance aviation operation safety, improve airspace management efficiency, and enhance the defense capabilities of system against spoofing and interference, an anomaly data detection model was proposed based on WGAN-XGBoost. Firstly, WGAN was utilized to learn the intrinsic distribution of the preprocessed ADS-B data, generating abnormal data for augmenting and balancing the training dataset. Then, XGBoost algorithm was employed to train the mixed dataset, building the final abnormal classification detector. Finally, the performance comparisons were conducted through experiments with benchmark models such as Naive Bayes, Logistic Regression, and Perceptron. The results show that the performance of XGBoost is superior to that of all comparison models including accuracy, precision, recall, and F₁ score, with accuracy and precision both exceeding 0.999. The total detection time for 243 792 data points is 2.070 2 s, with an average detection time of 0.008 5 ms per data point. It achieves the optimal balance between detection performance and time cost and has been validated by real abnormal events, demonstrating good practicality and applicability.

In order to prevent fires in urban villages, IGWO and BP neural network were used to predict the risk of fires in urban villages. By introducing nonlinear convergence factors and mutation operators, the traditional grey wolf optimizer (GWO) was improved to enhance its global search capability, convergence speed, and stability. Furthermore, a fire risk prediction model for urban villages based on IGWO optimized BP neural network (IGWO-BP) was constructed. Taking into account the complexity and specificity of urban village fire risk factors, an indicator system was developed to predict fire risk, and an empirical study was conducted for verification. The results show that IGWO has significantly improved global search ability, convergence speed, and stability compared to traditional GWO, particle swarm optimization (PSO), and the Great Wall construction algorithm (GWCA). The IGWO-BP model can predict fire risk in urban villages by processing fire risk indicators.

Community fires exhibit high incidence rates and interconnected risk factors. To establish an effective fire prevention and control system aligned with community risk profiles, a hybrid SNA/N-K model was constructed to calculate risk coupling values and network centrality metrics. By adjusting the out-degree values of closeness centrality using coupling values, critical risk factors were identified, providing evidence-based decision-making insights for community fire prevention. Key findings reveal that management deficiencies dominate risk coupling dynamics. An increase coupling factors significantly elevates community fire risk levels. The top three critical risk factors are failure to investigate fire hazards as required, inadequate safety awareness and knowledge of residents and absence of regular fire safety education. Based on this research, a "three-dimensional four-stage" community fire safety management model is proposed. From the dimensions of institutional optimization, technological empowerment and behavioral intervention, a comprehensive strategy is formulated to decouple and interrupt risk coupling chains in community fire prevention and control, encompassing the full cycle of prevention, early warning, response, and recovery. This study contributes a theoretical framework and empirical intervention pathways for community fire risk mitigation, offering actionable guidance for policymakers and practitioners.

To address the complexity of fire environment and the difficulty in predicting the temperature field in modern commercial buildings, a fire temperature field prediction model was constructed by combining CNN with SVM. Firstly, FDS was used to construct a commercial building fire model, and the sequence data received by the temperature measurement points were obtained. The temperature, position coordinates, and fire duration were used as input parameters to build the dataset. Secondly, the Rime Optimization Algorithm (RIME) was introduced to optimize the number of hidden layer nodes, regularization coefficient, and learning rate in the CNN-SVM, and then the prediction model was established. Finally, experiments were conducted based on the established dataset and prediction model, and the anti-interference ability of the model under different sensor failure rates was discussed. The results show that the model performs optimally in the prediction of the temperature field plane, with an average absolute percentage error of 5.6% and a maximum relative temperature error not exceeding 25%. The anti-interference performance is the best under three working conditions, and the maximum error does not exceed 15% under extreme conditions.

In order to evaluate comprehensive driving risk in interchange ramp areas, a real-vehicle test was conducted on Chongqing interchange groups, focusing on three typical ramps: right-turn directional, left-turn semi-directional, and small-radius loop ramps. Drivers' electrocardiographic data and vehicle operation status were collected by PhysioLAB and Speedbox, respectively, when passing through ramps. Psychological load and vehicle operation risks of the three ramps were analyzed, and an improved entropy-weighted Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS) method was used to construct and evaluate a comprehensive driving risk model. Results show four HRI change patterns: convex curve, continuous increase, continuous decrease and concave curve. Heart Rate Increase (HRI) in right-turn directional ramps first decreases, then increases, in left-turn semi-directional ramps, it first increases, then decreases near diverging/merging points, in small-radius loop ramps, it fluctuates significantly there. Vehicle operation risk is highest in ramp sections, increasing with smaller radii. The level of vehicle operation risk is small-radius loop ramp greater than left-turn semi-directional ramp, greater than right-turn directional ramp. Comprehensive driving risk peaks in ramp sections, widely distributed in split/confluence areas and peaking shortly after diversion points.

In order to realize scientific risk assessment of public opinion on extreme rainstorms, this paper proposed a public opinion risk assessment method of extreme rainstorm disasters based on three-way decisions under the event system theory. From the perspective of event system theory, this method determined the life cycle of public opinion and built indicators for evaluating the risk of public opinion on extreme rainstorm disasters by integrating three dimensions: time, space, and strength. Meanwhile, this paper integrated review helpfulness with hesitant intuitionistic fuzzy sets, effectively quantifying public opinion preferences regarding risk assessment indicators of public opinion. On this basis, the paper extended the three-way decision method to classify public opinion risk into five levels, achieving a scientific assessment of public opinion risk towards extreme rainstorm disasters. Using actual cases and comparative analyses, the effectiveness and adaptability of the risk assessment model of public opinion towards extreme rainstorm disasters were verified. The results show that the proposed method not only successfully realizes the scientific selection of public opinion risk assessment indicators with respect to extreme rainstorm disasters and the effective quantification of public opinion preferences, but also alleviates the decision-making errors caused by the information and cognitive limitations of existing assessment methods to a certain extent.