To explore the feasibility of using AI (artificial intelligence) three-dimensional motion analysis for analyzing the influencing factors on the distance of ski jumping and optimizing athletes’ technical movements, a study was conducted during the 2022 FIS Continental Cup Beijing event. Sixteen athletes’ take-off phase motions were captured within a fixed range using AI-based three-dimensional motion analysis system. This system automatically parsed the videos to obtain biomechanical parameters of the athletes’ take-off phase. By comparing the correlation coefficients and differences between manually processed data and AI-generated data of the three-dimensional coordinates of body joints over time, the validation of the equipment for ski jumping was conducted. The multiple correlation coefficient was found to be greater than 0.91, with an average difference value of less than 1.48 cm, indicating the reliability of the system. Furthermore, a comparison was made between the technical parameters of high-level foreign athletes and domestic athletes. Using t-tests and Pearson correlation coefficient analysis, the relationship between body posture parameters during the take-off phase and sports performance was examined. The results reveal correlations between take-off speed and angles of ankle and knee during the take-off phase, and between the final score and ankle angle during take-off, suggesting that Chinese athletes should focus on achieving full extension during take-off and timing their jumps appropriately to significantly enhance sports performance. Overall, the system demonstrated precise feedback for ski jumping technique analysis. Additionally, it enabled the acquisition of biomechanical parameters from world champion athletes to construct a champion model, providing valuable training references for Chinese athletes.

With China’s Manufacturing 2025 Plan, the military industry to implement the unmanned production line, and AGV (automated guided vehicle) as a fully automated production line of the main logistics carrier, the scheduling of its strengths and weaknesses directly determines the capacity and efficiency of the entire production line. Due to the security requirements of military industrial places, wireless communication and other means cannot be used, and only point-to-point optical communication can be used, which also worsens the real-time communication of AGVs. Based on Plant Simulation software, a simulation system model was established, the real-time data interaction channel between the logistics simulation software and the field controller was opened, the synchronous operation of the simulation system and the reality was realized, and the seamless connection between the logistics simulation software and the field controller was completed, which effectively solves the difficult problem of poor real-time AGV scheduling caused by the lack of wireless power in the military industry. Experiments have proven that this method effectively simplifies the difficulty of writing the scheduling system and improves the overall real-time performance of the system by 0.058 seconds. Compared with the traditional method, the writing time is shortened by 9.7 times, and the debugging time is even shorter by 22 times. This study lays the foundation for the realization of full automation of military production lines and provides technical support for the use of pulsation production lines in hazardous places.

Establishing an accurate rolling bearing performance degradation prediction model plays a crucial role in subsequent processing such as bearing fault classification and life prediction. In order to solve the problem of inaccurate prediction of bearing performance degradation model, an IBA(improved bat algorithm) was proposed to improve the accuracy of degradation model prediction. Firstly, Cat chaotic mapping was applied to the initial position of the population to enhance the traversability of the population and improve the quality of the initial solution. Secondly, an inverse tangent-like control factor was added in the iterative process to improve the algorithm’s accuracy in finding the optimum. Finally, the position updating strategy was improved to prevent from falling into the local optimum. By comparing the results with those obtained from SVR(support vector regression machine) optimized by BA(bat algorithm), SVR optimized by particle swarm optimization algorithm, and SVR optimized by gray wolf optimization algorithm, the results show that the absolute mean error of the prediction model optimized by the IBA decreases by 70.60%, 67.19%, 55.56%, and the root-mean-square error decreases by 76.64%, 76.12%, and 76.12%, respectively. 76.64%, 76.12%, and 30.29%, respectively, further proving the accuracy of the improved prediction model.

The significant advantages of point cloud data are presented in domains such as architectural reverse modeling, 3D reconstruction, and construction progress monitoring. Vast amounts of data are typically involved in the collection of point clouds for architectural structures, with the point clouds of components like beams and columns being particularly crucial. The challenges faced by current semantic segmentation methods for 3D point clouds when processing large-scale data include insufficient extraction of local features and suboptimal recognition accuracy. An enhanced approach for the semantic segmentation of large-scale point clouds of key architectural components using the RandLA-Net deep learning network was proposed. In this regard, the robustness of segmentation results was improved by incorporating a coordinate attention module in the local spatial encoding section. Furthermore, an extended channel attention module has been developed to strengthen the model’s capability in feature discernment, and a focal loss function has been introduced to effectively train the network, while addressing class imbalance issues within architectural point cloud scenes. Consequently, the efficient processing of architectural structure point cloud data and the extraction of key components are enabled. The performance comparisons and analyses conducted through experiments demonstrate that the original RandLA-Net model is outperformed by our model in terms of overall accuracy and component extraction precision in semantic segmentation of large-scale point clouds, thereby confirming the enhanced performance and practical value of the proposed method.

To explore the effects of typical uncertainty factors on the seismic performance of small-and medium-span bridges during design, construction, and service, a four-span continuous small box girder bridge was taken as the engineering background, and a nonlinear dynamic model was built based on OpenSees. The influence mechanisms of seismic uncertainty, modeling parameter uncertainty, and capacity uncertainty on the seismic demand of key components were analyzed. On this basis, combined with the theory of fragility analysis, the linkage effect of the above uncertainty factors in the establishment of component fragility curves was explored, and then the degree of influence of each type of uncertainty factors on the analysis of structural seismic performance was quantified. The results show that the differences in the seismic hysteresis curves of the components are mainly caused by the uncertainties of the modeling parameters, while the differences in the peak seismic response are mainly caused by the combined uncertainties of ground shaking and modeling parameters. The modeling parameter uncertainty and component seismic capacity uncertainty can lead to an increase in the probability of structural damage, which consequently makes some components unable to meet the required damage state. The consideration of the uncertainty factor increases the probability of failure of the bridge system susceptibility curve, and the effect of uncertainty increases as the degree of damage deepens, biasing the analysis results by more than 30 percent.

As industry and agriculture continue to evolve, the threat of organic contamination in groundwater to human health is gaining attention. Following the health risk assessment method recommended by the USEPA, and considering local natural geographical and hydrogeological conditions, organic pollutants in Zhuzhou City’s groundwater were assessed. Using a health risk assessment model, non-carcinogenic and carcinogenic risks from three exposure pathways-drinking water, skin, and inhalation-were evaluated. Results show that key organic pollutants in Zhuzhou City’s groundwater include dichloromethane, 1,2-dichloroethylene, trichloroethylene, tetrachloroethylene, p,p'-DDE, and p,p'-DDD. Non-carcinogenic risks from these pollutants are below specified limits, but every sampling points exceed carcinogenic risk limits, with ZZS119 surpassing the maximum acceptable carcinogenic risk. Inhalation is the primary exposure pathway, contributing to approximately 81% of total risk. Tetrachloroethylene poses the highest carcinogenic risk at 81.08%, followed by trichloroethylene at 11.65%. Urban discharges, volatile organic compounds from chemical plants, insecticide use in forestry, and domestic wastewater discharge have severely contaminated Zhuzhou City’s groundwater, resulting in unacceptable carcinogenic risks for local residents.

The rapid increase in highway tunnel mileage also signifies a gradual rise in operational costs, and the pressing issue of high electricity operation costs for highway tunnels urgently needs to be addressed. To reduce the electricity operation costs of highway tunnels and achieve energy conservation and emission reduction, it is essential to consider optimizing the energy structure under the “dual carbon” background. This involves exploring the application prospects of renewable energy supply systems in highway tunnels and establishing a wind-solar-storage complementary power generation system. Taking a 498 m long highway tunnel load as an example, an optimization based on an improved PSO (particle swarm optimization) algorithm was conducted. The goal was to minimize the full lifecycle costs of equipment construction and maintenance, with constraints on the power shortage load rate and storage capacity, specifically for the wind-solar-storage complementary system. The results show as follows. The improved discrete adaptive particle swarm algorithm obtained the optimal solution after the 20th iteration, while the standard particle swarm algorithm reached the optimal solution near the 300th iteration, indicating a stronger optimization capability of the discrete adaptive particle swarm algorithm. Compared to the standard particle swarm algorithm, the improved discrete adaptive particle swarm algorithm reduced the investment and usage costs of the wind, solar, and storage equipment by 578 300 yuan, approximately 17.37%.Compared to the annual electricity cost of the example tunnel, which is 515 000 yuan, the full lifecycle cost of the wind-solar-storage complementary system is 3 328 800 yuan. The investment cost will be recouped within 7 years, and the investment return rate of this wind-solar complementary system is 10.47%. Over the 20-year lifespan of the equipment, the wind-solar-storage complementary power generation system will save 6 971 200 yuan in electricity expenses.

In order to enhance the output performance of axial flow helical turbine drilling tools, it is crucial to conduct research on the structure and hydraulic performance of the turbine. Firstly, an analytical computational model for the hydraulic characteristics of two types of helical turbines, namely constant thickness blade and variable thickness blade, was established using calculus principles. Secondly, the blade profile equation was constructed to analyze the influence of helix angle on the hydraulic performance of the turbine. Finally, based on the design example of an underground helical turbine generator, the output performance parameters of the two types of helical turbines and the impact of helix angle were analyzed. Furthermore, a mud pulse generator was manufactured based on the optimized analysis results of the helical turbine, and indoor experiments were conducted to study the impact of different displacements on the power generation performance. The research findings show as follows. Under given conditions, the variable thickness helical turbine exhibits significantly higher hydraulic performance compared to the helical turbine with constant blade thickness. The turbine output characteristics curve sharply decreases as the helix angle of the blade increases. However, the decrease in turbine output characteristics becomes insignificant when the helix angle exceeds a certain value (approximately 30°). With 7 blade counts and a helix angle of 38°, the underground turbine generator reaches a maximum power of 300 W with a load of 8 Ω. Through indoor testing, the power of the generator meets the requirements of underground tools. This research provides a reliable power source for underground intelligent drilling equipment.

With the gradual formation of “trunk-branch” linkage multi-level air cargo transportation system, the problem of low turnover efficiency of airport cargo is becoming more and more prominent, and the “trunk-branch” linkage efficient cargo allocation methods have become one of the key technologies to solve the above problems. An integer programming linkage allocation model was established with the maximum loading rate under “trunk-branch” linkage as the optimization objective while simultaneously satisfying constraints such as cabin position, cabin size, center of gravity, and weight for both trunk and branch aircraft. This model was applied based on genetic algorithms to optimize the cargo allocation between the B757-200 trunk aircraft and the ARJ21-700F branch aircraft. It was found that the average loading rate of “trunk-branch” linkage allocation was increased by 5.45% to 72.05% compared with sequential allocation.By demonstrating that the proposed “trunk-branch” linkage allocation method in this study can significantly increase the overall cargo loading capacity, and thus effectively improving the efficiency of cargo turnover at airports, laying a theoretical foundation and providing technical support for the safe and efficient development of the “trunk-branch” linkage multi-level air cargo transportation system.

In order to ensure the safe and stable operation of the power system after large-scale grid-connection of new energy, it is necessary to quantitatively assess the vulnerability of the power grid before and after grid-connection. Therefore, a composite new energy power grid vulnerability analysis method based on complex networks and electrical characteristics was proposed, and the IEEE-39 node was simulated. Firstly, a vulnerability assessment system was constructed by selecting degree centrality, proximity centrality, eigenvector centrality, electric power flow interval, voltage stability and power balance, and then solving each secondary index according to the formula. Secondly, entropy weight method was used to calculate the index weight, TOPSIS method was used to rank the vulnerability of nodes before and after grid connection, and comparative analysis was made. Finally, the obstacle factor model was used to analyze the influence degree of each index on the vulnerability of power grid. The research results indicate that the number and location of new energy sources connected to the grid affect the vulnerability of the power grid. Among the influencing factors of grid vulnerability, the order of influence is voltage stability indicator > closeness centrality indicator > active power balance indicator.