Currently, traditional sports equipment and technical solutions in higher education institutions are unreasonable, and outdated management models for sports training fail to provide scientific guidance for badminton athletes, resulting in suboptimal overall training outcomes. This paper develops a comprehensive contactless motion recognition system based on deep learning algorithms. The system utilizes the Microsoft Kinect V2 smart camera to accurately capture the threedimensional spatial positions of human skeletal joint points in real time and convert them into motion data streams. Additionally, the DTW algorithm is used to calculate the joint angle differences between standard motion sequences and test motion sequences, and an action evaluation formula is defined to assess core strength training movements. After testing, the system’s accuracy in evaluating movement results ranges from 90.20% to 94.69%, enabling effective assessment of core strength training movements. After applying the system to badminton core strength training, students’ foot flexibility significantly improved compared to conventional core strength training (P < 0.05). Therefore, teachers can actively apply the motion recognition system in core strength training when conducting badminton instruction.
Urban management issues have become a significant challenge for urban administrators, and urban management must be based on modern computer technology and network communication environments. This paper investigates objectoriented building extraction methods, proposes a semi-global filtering method based on region growing for filtering remote sensing data, and then presents building 3D modeling methods and rendering methods in 3D remote sensing image modeling scenarios. In error comparison experiments between the classical TIN filtering algorithm and the proposed filtering method, the improved algorithm demonstrates superior performance, accurately identifying ground points at terrain steep changes, complex buildings, and complex roads. Its Type I error and total error are significantly lower than those of the classical TIN point cloud filtering algorithm, thereby demonstrating the superiority of the proposed improved algorithm.
Based on the concepts of matrix arithmetic and big Civic and political education, this paper carries out optimization research on the project-based teaching mode of physical education courses in higher vocational colleges. At the level of teaching design, a teaching mode based on the programization of physical education courses is constructed, and the Civic and Political elements are integrated into the whole process of sports skills training. The inter-frame difference method is used to realize motion detection, and the Fourier transform technology and kernel component analysis are used for feature extraction and dimensionality reduction of the image. Then the hierarchical spatial feature extraction model CNN and temporal feature processing model LSTM are introduced to establish a recognition model based on CNN-LSTM hybrid neural network to recognize sports actions. The results show that the CNN-LSTM hybrid model is less accurate than the combined model in terms of performance metrics compared to the model when only CNN or LSTM alone module is used, and the error value of the combined model for action capture is lower, which proves that the combined model has stronger performance. In addition, the model in this paper is able to capture the spatio-temporal relationship between time series and joints more accurately and quickly, and in the process of realizing the evaluation of the standard degree of different actions, the action recognition error of the CNN-LSTM model is reduced by more than 20% compared with that of the traditional model, which shows that the model in this paper has an excellent recognition effect on different sports actions as well.
This paper combines N-gram models, LDA topic models, TF-IDF algorithms, and clustering analysis methods, and based on the LIA-BiLSTM text sentiment analysis model, collects, organizes, and analyzes a large amount of Chinese language and literature texts. This paper uses big data technology to deeply explore the educational value of traditional culture in Chinese language and literature and discusses its application path in the field of vocational education. Research indicates that the improved LIA-BiLSTM model developed in this study outperforms other models, with an AUC value of 0.9779, approaching 1. Traditional cultural elements in Chinese language and literature help enhance students’ sense of cultural confidence, belonging, achievement, and responsibility. By constructing an integrated educational model of “course integration—cultural immersion—practical experience—evaluation feedback,” this approach not only effectively preserves China’s excellent traditional culture but also infuses vocational education with new cultural significance.
This paper utilizes wireless radio frequency identification (RFID) readers and IoT sensors to monitor and collect relevant data during the construction phase. Taking the construction process of prefabricated buildings as an example, by systematically organizing data on carbon footprint factors such as energy, building materials, transportation, machinery shifts, and labor days, a carbon emission calculation model for the materialization stage of prefabricated buildings and a projection tracing clustering analysis method were established. Subsequently, a carbon emission calculation model for the construction process, including two stages—building material transportation and on-site construction—was constructed to quantitatively analyze carbon emissions during the construction phase of building projects. The results indicate that carbon emissions vary significantly among different residential buildings, with the production stage of building materials accounting for the largest share of carbon emissions (approximately 90%) in all processes. Additionally, taking prefabricated buildings as an example, materials are the primary factor contributing to carbon emissions during the construction stage, also accounting for over 90%. Furthermore, during the entire construction phase, the main structure is the primary carbon emission factor, accounting for over 70% of carbon emissions. Based on the carbon emission calculation framework, the primary causes and main elements of carbon emissions during the construction phase can be calculated and analyzed. By optimizing construction processes and adjusting construction techniques, carbon reduction and control effects can be achieved.
finite element model of the planetary gear transmission system and a parameter model of the planetary gear train were established, and finite element analysis modeling research on the planetary gear transmission system was conducted. Based on this, genetic algorithms were used to optimize the parameters of the transmission system, and simulation studies on the elastic behavior of the optimized transmission system were conducted under specific operating conditions to verify the rationality of the optimized system’s dynamic characteristics. The optimized planetary carrier pin hole node displacement is only 0.312 mm, resulting in minimal positional error. The maximum stress value of the third-stage planetary gear system after optimization is reduced by 0.45 MPa compared to before optimization, while the maximum strain value increases by 0.00058 mm/mm. The input angular velocity, planetary gear angular velocity, and output angular velocity of the optimized system can reach a steady state within the set time, with minimal deviation from theoretical values. The average simulated values of the meshing forces between the sun gear and planetary gears, and between the planetary gears and inner gear ring, are 2916 N and 1275 N, respectively, both close to the theoretical values. This study has preliminarily established an understanding of the transmission characteristics of planetary gear systems, providing a basis for optimizing the design of transmission mechanisms and power configuration in the main spindle drive units of CNC machine tools.
This paper designs an overall scheme for a wearable multi-sensor physiological monitoring system, which monitors physiological parameters such as electrocardiogram signals and blood pressure. It combines CNN, LSTM, and MHA and optimizes them using the NRBO algorithm to construct a CNN-LSTM-MHA hybrid neural network model. The performance of this model in heart rate monitoring and blood pressure prediction is evaluated through experiments. In terms of heart rate detection and blood pressure prediction, the CNN-LSTM-MHA model demonstrates the best overall performance and exhibits superior robustness.
This paper proposes a solution for trustworthy copyright registration of music digital rights using blockchain technology. This solution enables trustworthy copyright registration of digital music and enhances the security of copyright information storage in the music performance industry. The copyright protection system for the music performance industry is designed and implemented on the Hyperledger Fabric platform, and a music performance copyright image encryption algorithm based on wavelet transform and chaos mapping is designed. After generating hash values from the data, it is uploaded to the sample data chain to form a stable blockchain structure. Backup and query functions are performed via Ethereum, and the Camshift algorithm is invoked to track and locate intruders, triggering active warning and defense functions to achieve network threat defense security design. Experimental results show that the copyright registration time for each piece of music increases by approximately 1.9 seconds, and the average feature fingerprint data for each piece of music stored on IPFS consumes approximately 9MB, meeting performance requirements. The use of wavelet transform-based encryption methods enhances the sensitivity of ciphertext, effectively resisting attacks. The final experiment demonstrates the feasibility of the network threat defense solution.
Currently, digitalization has profoundly transformed service industries such as finance, logistics, transportation, tourism, retail, accommodation, and catering, and is gradually penetrating the film industry. This study first constructs an evaluation indicator system for the digital transformation of China’s film cultural dissemination model, comprising five criterion layers and 13 indicator layers, and employs the AHP-objective extension model to measure and determine the weights of the indicator system. The weighting results indicate that the digitalization level accounts for the largest proportion and has a significant impact on the digital transformation of the film cultural dissemination model. From five dimensions—digital technology leapfrog, technology integration and synergy, digital infrastructure, policy and environmental support, and corporate competitive pressure—the study employs the fs QCA method for analysis. The findings reveal that the digital transformation of the film industry’s cultural dissemination model can be categorized into five types, with digital technology leapfrog and policy and environmental support playing crucial roles in the digital transformation of the film industry’s cultural dissemination model.
With the increasing emphasis on fire safety requirements, perfluorohexane fire extinguishing agents have found innovative applications in power distribution systems due to their unique properties. This article discusses the characteristics and advantages of perfluorohexane materials, as well as the application forms of perfluorohexane in power distribution systems. Based on this, a fire extinguishing test was conducted using a specific power distribution system as an example, simulating the initial stage of an indoor power distribution system fire. Gas concentrations of CO, NO, SO₂, and O₂ were measured before and after the fire, and the patterns of temperature and thermal radiation changes at different locations within the fire scene were analyzed. Perfluorohexane fire extinguishing agent was used to suppress the fire in the power distribution system. The test results indicate that in distribution system fires, perfluorohexane extinguishing agents have a fast extinguishing speed, capable of quickly extinguishing fires within 8 seconds, with significant heat absorption and cooling effects, achieving a cooling rate of up to 97.8°C/min. the concentrations of several gases decreased by 65.4% to 100% within a short time, and it did not cause any damage to the power distribution system. This achieved controllable fire suppression and improved extinguishing efficiency in power distribution system fires, representing a new approach to the suppression of initial fires in power distribution systems.