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.

The switching of power devices in servo drive can lead to bus voltage ripple, which may result in performance degradation, electromagnetic interference, and harmonic issues in permanent magnet synchronous motor AC servo systems. To address these issues, the influence of the switching process of power device on the voltage ripple of bus was analyzed and studied for the DC power supply servo drive system. Firstly, based on the working principle of PMSM (permanent magnet synchronous motor)and SVPWM (space vector pulse width modulation) algorithm, the ripple current of the busbar capacitor was analyzed. Then, according to the law of charge conservation, the relationship between the busbar capacitance, busbar voltage and current amplitude under the seven-stage and five-stage SVPWM modulation was analyzed theoretically, and its simplified expression was given. Finally, the theoretical results were verified by simulation experiments.

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.

To analyze the hydrodynamic characteristics of light beacons in wind-wave environments and propose an optimization scheme based on stress concentration points, the VOF (volume of fluid) method was used to calculate the pressure distribution on the surface of the light beacon under wave and wind conditions and the resulting deformations. The results indicate that stress concentration occurs at the base of the central support of the light beacon, with the maximum displacement at the top of the light beacon. Two optimization schemes were investigated based on these findings. In the slanted support scheme, stress concentration shifted to the top of the support, reducing by 32.2% compared to before optimization. The maximum deformation was at the base in the middle, a 40.1% reduction compared to before optimization. In the end-strengthened scheme, stress concentrated at the bottom, with a reduction of 0.08% compared to before optimization, while the maximum deformation was at the top of the light beacon, which was reduced by 23.1% compared to before optimization. It is evident that the light beacon structure with slanted supports can effectively reduce stress intensity and disperse the impact of wind and waves on other parts of the light beacon, representing a better structural optimization scheme.

In order to solve the problem of real-time monitoring and accurate prediction of structural deformation of platform doors on high-speed railway lines, an artificial intelligence-based neural network method was used. Structural deformation data of platform doors, involving 210 different conditions of train length, blocking ratio, installation distance, and speed, were selected as training samples for the network model. Two neural network models, CNN(convolutional neural network) and K-Fold(K-Fold cross-validation) optimized GRNN(general regression neural network), were used to establish predictive models for platform door structural deformation under different working conditions of high-speed railways. These models were compared and verified with the remaining sample data. The research shows that both models effectively predict the operation and maintenance data of railway platform door structures. The K-Fold optimized GRNN model is superior to the CNN model in prediction accuracy. The Mean Square Error of the K-Fold optimized GRNN model is maintained within 0.22, and theRoot Mean Square Error is within 0.27, which is at the leading level in the field. The K-Fold optimized GRNN model better predicts the structural deformation of platform doors when trains pass, providing data references for the design and maintenance of high-speed railway platform doors.

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.

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.

Laser 3D scanning technology rapidly acquires point cloud data of target surfaces, including spatial point coordinates that describe the geometric features of the target and laser reflectance intensity that characterizes the material’s reflectivity. The application of automatic semantic segmentation techniques for 3D point clouds in geological exploration research lays the foundation for depicting regional geological features. To demonstrate the recent advancements of 3D laser scanning technology in large-scale semantic segmentation within geological scenarios, firstly, photogrammetry and LiDAR as two methods for acquiring 3D point clouds were compared, highlighting the advantages of LiDAR in terms of accuracy, versatility, and insensitivity to lighting conditions. By elucidating the principles of lithological semantic segmentation, a comprehensive review and summary of recent methods based on geometric or intensity features were provided. Common large-scale point cloud datasets and evaluation metrics were introduced, and the segmentation performance of different algorithms was compared. Finally, the limitations of existing methods were summarized, and future research directions for lithological semantic segmentation tasks were outlined.