Search

To improve the effect of foreign language teaching in colleges and universities with high efficiency, this paper constructs a BP neural network optimized by multi-data chain by integrating multi-data chain and BP neural network, which effectively solves the shortcomings of BP neural network that is easy to fall into the local optimum. It optimizes the initial value and the threshold value of this neural network. On this basis, the multi-data chain optimized neural network is used to analyze the existing data related to English teaching in colleges and universities, to present the proportion of factors affecting foreign language teaching in colleges and universities in the form of data, and to summarize the reasons for acting the teaching effect and the problems existing in the current foreign language teaching classroom. Teachers and students make positive improvements to foreign language classroom teaching based on the data presented by the neural network to improve the effect of foreign language teaching in colleges and universities. Combined with the BP neural network optimized with multiple data chains, this paper conducted an experimental analysis of students’ concentration, satisfaction with the classroom, and performance. As can be seen from the results of data calculation, after the factors affecting the teaching effect were summarized and corrected, the concentration level of the two students in the experiment was significantly improved, and both remained above 50%. In addition, there was a massive difference in the grades of the same group of students before and after the improvement. The average grade of the control group was 76.47 and the middle grade of the experimental group was 87.6.

In the 21st century, computer technology covers human daily life, including the field of music. This paper uses a quaternion algorithm to describe the gesture posture during piano playing. Combined with the iteratively updated and extended IU-EKF algorithm, it realizes the fusion of piano playing gestures to fix the posture. The recognized piano playing gestures are output to the chord fingering automatic annotation board, the piano audio signal data are preprocessed and input to the 3D space, the annotation area is allocated, and the chord fingering features are extracted using the Boltzmann machine. Through spectral analysis and empirical investigation, we analyze the sound quality effect of the two pianos and the audience’s listening experience. The results show that in the spectral analysis of the two pianos, the time-domain waveforms of the pianos played using the gestures proposed in this paper have durations ranging from 0.75s to 1.25s, and the waveform graphs present triangular shapes, which are better in terms of sound quality. The listeners’ melodic memory of the songs played by two pianos is the best for the two pianos of Chinese art and folk songs, Chinese art and Chinese pop, with the average difference of 5.7899 and 5.6345 respectively. The two pianos form of playing can satisfy the listener’s need of listening to the piano songs to a certain extent.

To realize the safe operation of electric power site, this paper proposes an intelligent recognition technology to automatically identify violations. This study successfully constructs a face detection model for power operation sites by combining deep convolutional neural networks and target detection algorithms. A three-way connected feature pyramid structure containing a neuron self-processing module is adopted, and an accuracy test is completed using a Tri-FPN-based target detection network, significantly improving recognition accuracy. In this paper, we also utilized the on-site images collected by video surveillance equipment, combined with CNN algorithm and HOG feature extraction technology to effectively identify the violations and provide early warning of the breaches of the personnel at the power operation site. MAP curves evaluated the detection performance, and the results showed that the head recognition rate was up to 0.9913, and the accuracy rate of all violations exceeded 0.9350.The high accuracy of CNN-based feature fusion extraction algorithm in the recognition of violations of personnel at the site of electric power operation provides effective technical support to ensure personnel safety.

In this paper, we use space-sensitive crawler technology to obtain the initial data of college students’ employment quality research from the websites or databases of private colleges and universities. We determine the evaluation indexes of college students’ employment quality through data cleaning and conversion of the initial data. On this basis, because of the limitations of the clustering algorithm, grey correlation analysis is used to optimize the clustering algorithm, and the evaluation model of college students’ employment quality based on grey correlation optimization clustering is constructed. Then the clustering analysis of college students’ employment evaluation indexes is carried out. The results show that the main influencing factors of job satisfaction of female graduates in some disciplines such as literature, history and philosophy and education and law are family economic situation Q7 (-1.9328), participation in innovative and entrepreneurial activities Q8 (29.2178), political outlook Q11 (3.0279), and graduation destination Q13 (2.5824), and that this paper’s method is effective in revealing the key factors. This study benefits the enrichment and innovation of the research content of employment quality. It provides theoretical support for solving the problem of future employment quality of college students in private colleges and universities.

With the in-depth development of data intelligence, the design of public environmental facilities has gone beyond mere aesthetics and practicality, and is moving towards a deeper integration with interaction design. This paper addresses this trend, discusses the strategies and principles of interaction design for public environmental facilities, and proposes an innovative single-facility siting problem model (MP) and multi-facility siting problem model (MWP). The article solves and analyzes the optimal siting problems in these models using the advanced new projection contraction algorithm (PC). To practically test the effectiveness of these designs, two sets of empirical experiments are conducted to explore them in depth. The results show that the optimized interaction design of the public facilities substantially increased the frequency of users, especially in the period from 15:00 to 15:10 on rest days, the number of users was as high as 43, of which the highest number of users was 46, mainly among the middle-aged and elderly people, especially the middle-aged group. In addition, this interaction design is excellent in satisfying people’s necessary, spontaneous, and social activities, with satisfaction levels of 0.78, 0.71, and 0.62, respectively. The interaction design strategy proposed in this paper effectively enhances the efficiency of the use of the public facilities and profoundly satisfies the users’ daily needs.

With the development of society, the artistic cultivation requirements for talents are increasing. This paper discusses in depth the main problems in the current reform of art teaching and puts forward targeted solution strategies, aiming to improve the overall quality of art teaching. In the study, the key factors affecting the quality of art teaching in colleges and universities were analyzed using the VAR model, and a multimodal art education knowledge map was constructed using the Viterbi method. Through the ALKPT knowledge tracking model, this paper assessed and scored students’ mastery of art knowledge and incorporated the results into the art classroom quality evaluation module. Based on the scoring data, the art classroom quality features were standardized, and the comprehensive evaluation results were derived through feature fusion dynamic weight assignment. Impulse response analysis showed that the number of returnees who studied abroad to learn about art positively impacted art teaching. The empirical results showed that after the reform of art teaching, the students’ art theory scores increased by 5 points on average, the scores were distributed between 10 and 20 points, and the achievement gap between students was reduced. This study provides a robust strategy and evaluation tool for optimizing art teaching, improving teaching quality and promoting students’ artistic literacy.

This study accurately assessed the overall ecological landscape condition of the city through the environmental landscape pattern index, and specifically explored the NP, LSI, and PD indices of construction land, cultivated land, forest land, grassland, water, and other land. Adopting advanced artificial intelligence and virtual reality technologies, this paper successfully constructs a 3D visualization scene of the urban landscape, which provides a vivid and intuitive perspective for urban planning. Further, based on the planning and design framework of urban landscape ecosystem, this paper clarifies the ecological evaluation elements and landscape design indexes, and uses VR technology to optimize the design of environmental aspects of urban streets. By establishing an optimization strategy database, this paper conducts an in-depth correlation analysis between the optimization scheme’s design indicators and evaluation factors. Comparing the comprehensive ecosystem service area before and after optimization, the results show that applying VR technology in ecological landscape design is highly effective. The integrated ecosystem service area before and after optimization increased from 7611.72m² to 8039.51m², of which the integrated ecosystem service area of “general” grade increased by 324.03m², showing the best effect of ecological optimization. The research in this paper provides scientific basis and technical support for ecological landscape design and new ideas and methods for future urban environmental planning and construction.

This paper proposes a new evaluation model for the current situation and challenges of further media communication of enterprise brands through diversified publicity. A comprehensive evaluation model based on AHP-EWM-TOPSIS is constructed by establishing an evaluation index system for the effect of new media operation and publicity, and preprocessing the indexes with the polar deviation method, combining the hierarchical analysis method and entropy value method for weight assignment. Through practical application to the evaluation of M Company’s publicity strategy, it is found that most employees evaluate the current strategy as average or poor, and the investment in new media operation cost is low. In addition, gender has no significant effect on the use of new media platforms, but the user web video usage rate has reached 90%. Therefore, it is recommended that enterprises adopt scientific and practical theoretical models such as the funnel propaganda model proposed in the paper to improve the efficiency of new media operation and brand communication effect, and promote the development of enterprises.

This paper focuses on the high-quality detection of hidden safety hazards in transmission and OPGW lines, and adopts neural network technology as the research basis. A Faster-R-CNN network structure model is constructed to realize end-to-end target detection by combining RPN and Fast-R-CNN network structure. To further improve the detection accuracy, the BAM algorithm is introduced to enhance the Faster-R-CNN, to realize the accurate detection of hidden dangers in transmission and OPGW lines. This paper also compares the performance of the traditional and improved algorithms, and explores the practical application effect of the constructed model in depth. The experimental results show that the enhanced Faster-R-CNN algorithm significantly improves the correctness of observation in the sky and land regions, with an average accuracy mean value of about 26%, especially when observing field villages, factories, playgrounds, urban areas and swimming pools. Therefore, the improved algorithm proposed in this study effectively enhances the detection capability and accuracy of hidden safety hazards in transmission and OPGW lines.

To cope with the problems existing in the education and Management of students in traditional colleges and universities, improve the management efficiency and realize the informatization of education management, this study proposes a set of optimization paths for the education and Management of students in colleges and universities. In this paper, an intelligent and diversified education management platform is constructed, aiming to strengthen the operation efficiency of the education management team in colleges and universities. By introducing a fuzzy evaluation algorithm to judge the educational management paths of college students and normalizing the input data, empirical evaluation results are derived. Comparative Analysis of the data in the study showed that the optimized educational management path significantly improved students’ daily behavior and mental health status. Students’ average daily online time was effectively controlled between 5.6 and 10.5 hours, the online period was more in line with the healthy work and rest, and the rates of disciplinary behaviors and Internet fraud victimization decreased respectively. At the same time, students’ mental health and learning effects were also improved. In summary, the education and management path for college students proposed in this paper significantly strengthens the management system and comprehensively cultivates students. It provides an effective informatization strategy for education management in colleges and universities.