bluuebunny/crossref_metadata_2025 huggingface dataset:
TARI MENDULANG EMAS DALAM BENTUK AUDIO VISUAL MELALUI APLIKASI YOUTUBE UNTUK SISWA KELAS X SEKOLAH MENENGAH ATAS
Authors: Miranda Aulia , Adlin Dilinar
Published: November 2024

The study was intended to describe the stages in the packaging of golden dancing materials in visual audio for high school x students. The theory used in this study is the theory of packaging tjiptono (2010:106) packaging is a process that deals with the designing and shaping and packaging of a product. The packaging of the gold dance forms on data analysis USES materials validation instruments and media validation performed by the validator of materials experts and the validator of media experts to assess the worthiness of products. The results of the expert assessor's worthiness test score 5 and the results-worthy media expert scores a 4.43, can be deduced from the data analysis techniques indicated that the assessments made by the material expert and the media expert fall to the highest quality. The study produced a gold - study-studded product in visual audio form of dancing via a youtube app that can be used as a dancing medium for other high schools for teacher teaching needs. Assessments of validation by the two validators that gold minute dance materials in visual audio form through this youtube app deserve an immediate test of cocompetition for teacher teaching in high school x class.
Research on the Innovation of Education Management Informatization Based on Deep Learning Models
Authors: Hao Jie
Published: November 2024

In the digital era, the rapid development of information technology has not only changed the education model but also provided new tools and paths for education management. This paper establishes the two most commonly used metrics of support and confidence for association rule analysis. Using the Apriori algorithm, all frequent items in the dataset that satisfy the mining conditions are extracted, and through the constraints of the metrics, the eligible association rules are screened, and the association rule results are parsed. Add the interest degree model to reduce the generation of non-interesting rules. The cluster analysis algorithm and it are combined to apply it in the field of education management, which is empirically examined. The results of cluster analysis and association rule mining show that the support degree of rule 1 is 0.4925, and the confidence level is 1, indicating that if 49.25% of all students score between 2 and 4 in question 3, then the score in question 1 is similar to the score in question 3. Based on the weights calculated by the association rules, the teachers’ basic teaching literacy, classroom teaching ability, teaching and research ability, professional ability and after-school counseling were evaluated respectively, and the overall score of teacher 1 was the highest, with a score of 91.213, which accounted for 10% of the total number of students, and the evaluation method adopted in this paper was scientific and fair.
Abnormal sensing feature detection of DC high voltage power battery for new energy vehicles
Authors: Chen Yuanhua , Yang Yanping , Wang Lifeng
Published: November 2024

As a kind of clean energy transportation, new energy vehicles are widely respected. This topic focuses on the detection of abnormalities in power batteries in new energy vehicles. After combing the common faults of the battery management system, using the basic structure of RBF neural network and the advantages of the reduced clustering algorithm, for a single power battery, the power battery power abnormality detection scheme based on the improvement of reduced clustering algorithm is proposed, and the power battery abnormality detection process is designed. Taking the sensing feature data of the battery management system of a new energy vehicle as an experimental sample, through the battery state estimation experiment and the example application of the model, it is found that the RMSE (0.0018) and MAPE (0.0206) of the model training are lower than that of the comparison model, and the average error rate of the abnormal battery identification is 0.833%. The model’s abnormality detection results in both instances are consistent with the actual maintenance results. The analysis indicates that the RBF neural network model with reduced clustering algorithm has superior accuracy and feasibility for detecting abnormal battery power.
Research on the Relationship between Enterprise Innovation Capability and Industry-University-Research Collaboration in the Era of Digital Economy
Authors: Li Ting , Kong Yusheng , Tao Jingyan , Yu Guo
Published: November 2024

Industry-university-research cooperation has become one of the indispensable and important innovation modes in the construction of today’s innovation system. The article collects the enterprise information system from 2014 to 2021, screens 49 companies that have participated in industry-university-research cooperation as the research object and put forward hypotheses, measures the innovation quality by the number of large groups number and the number of small groups number based on IPC classification number, and measures the level of internal research and development by the two methods of the percentage of R&D investment and the percentage of scientific researchers to carry out empirical evidence to test the impact of the level of industry-university-research cooperation on the quality of innovation of the enterprise. Test. The results of the study show that there is a significant positive correlation between the level of University-Industry Cooperation and the quality of enterprise innovation, with a coefficient of 0.103 and significant at the 1% level, i.e., the higher the level of University-Industry Cooperation, the higher the quality of enterprise innovation. The stronger the driving effect of the level of university-industry research Cooperation on the innovation quality of enterprises in terms of improving the level of internal R&D, the stronger the driving effect.
Study on How Modern Public Art Expresses Digital Themes with the Help of Painting Creation
Authors: Li Maofen
Published: November 2024

Modern public art is an important characteristic form of painting creation, which is constantly given new connotations with the development of the times and the progress of science and technology. Based on the characteristics of modern public art, the article explores the digitalization practice, creation process, and corresponding techniques for painting creation. The TF-IDF algorithm is introduced to mine the digital theme keywords expressed in painting creation, and the BERTopic model is combined to mine the digital theme of painting creation. The K-Means clustering algorithm is used for clustering the digital theme keywords of painting creation, and the digital theme keyword co-occurrence network is integrated to analyze the digital theme research trend of painting creation and expression for modern public art. When the number of digitized themes of painting creation and expression is 50, the F1 value of the BERTopic model can reach 94.36%, and the AUC area for digitized themes of painting creation and expression is 93.37%. The mean value of the keyword clustering profile coefficient of the digitized themes of painting creation and expression is 0.945, and the frequency of “painting creation” in the keyword co-occurrence network is up to 304 times. Relying on digital technology to create modern public art paintings, the digital theme expression of public art can be better realized.
Goal Setting and Instructional Design in Music Education Based on Bloom’s Cognitive Hierarchy Theory
Authors: Li Tiantian
Published: November 2024

This paper first introduces Bloom’s cognitive hierarchy theory of music education goal classification and revises it to construct a new version of Bloom’s educational goal-setting system. After that, it adopts multi-objective hierarchical analysis (AHP) to calculate the weights of the music education objectives assessed by experts and optimizes the convergence of AHP based on Bergh’s improved particle swarm (PSO) optimization algorithm. New music education objectives and teaching contents were created based on the experimental results. The CR values of the six cognitive subprocesses of the second-level evaluation indexes were 0.0001, 0.0011, 0.0014, 0.0014, 0.0023, and 0.0029, respectively. The results of the consistency test of the first-level and second-level indexes were all CR<0.05, indicating that their consistency and trustworthiness were extremely high. The top three indicators in terms of the weights of the secondary indicators are inference, recall and differentiation, whose weights are 0.5723, 0.5276 and 0.5192, respectively; the three indicators with the lowest proportion are interpretation (0.2422), normalization (0.2661) and creation (0.3201). The purpose of this paper is to create a music course instructional design system with a 4-dimensional and 18-indicator system and evaluate the importance of each indicator. It fully embodies the basic spirit of quality education and reflects the common requirements of the new curriculum system of colleges and universities that combines epochal, basic, and selective. The teaching design system proposed in this paper is expected to serve as a reference for the current music teaching reform.
Design of Dynamic Monitoring and Prediction System for Energy Consumption in Public Organizations Based on Energy Efficiency Diagnosis
Authors: Gao Liangfang , Li Junwu , Zhang Li , Hu Pengtao , Yang Zhiping , Kang Zhenning
Published: November 2024

Energy is an important global issue at present, and reducing energy consumption of public organizations can promote the development of a low-carbon economy and low-carbon society, which is of immense significance to both economic and social development. In this paper, we gather dynamic energy consumption data from public institutions using the energy consumption dynamic monitoring platform, preprocess the information, and apply the 3-sigma criterion method to identify abnormal energy consumption points in these institutions. We then propose an energy consumption prediction model based on the PSO-BP neural network, use the Markov model to backtest historical energy consumption data, correct the model’s prediction results, and construct an energy consumption prediction system for public organizations. The study shows that the prediction effect of this system on energy consumption is better than that of the RS model and DS model for 1 hour and 24 hours in advance. This paper’s energy consumption prediction system effectively and timely detects and diagnoses energy consumption anomalies in public organizations’ operations, thereby supporting their energy conservation management. This paper lays the foundation for the establishment of an energy consumption prediction system and the study of energy-saving strategies. It can provide the basis and strong guidance for the optimization of energy-saving operations in public institutions.
Exploration of Monte Carlo Method for Optimization of Energy Consumption in Industrial Enterprises in Energy Efficiency Diagnosis
Authors: Gao Liangfang , Li Junwu , Zhang Li , Hu Pengtao , Yang Zhiping , Kang Zhenning
Published: November 2024

Energy consumption, as one of the most concerned parts of industrial manufacturing costs, has an important impact on the overall operation and development of industrial enterprises. The article takes an air-conditioning manufacturing enterprise as an example, constructs its energy consumption prediction model (ARIMA model), optimizes its parameter estimation method through Bayesian estimation and Markov Monte Carlo method, and finally constructs the ARIMAMCMC model for energy consumption prediction of industrial enterprises. The accuracy of its prediction effect is verified by comparing the load prediction results of this model with other prediction models. The effect of energy consumption optimization under fixed cooling demand and the effect of energy consumption optimization on continuous periods before and after model optimization are analyzed respectively. The model has the highest accuracy in predicting the cooling load of central air-conditioning, and the difference between its predicted and actual values is the smallest. The energy consumption is lower when there is high cooling demand above 70%. At lower levels of 60% and 50% of cooling demand, energy wastage is higher. About 6.09% of the optimized model’s energy consumption is reduced. During the duration period (9:00-20:00), the total energy consumption before and after optimization is 97.218kW and 90.706kW, respectively, and the system energy saving is 6.70%. The period with the worst energy-saving effect is 12:00-13:00 on the 21st, saving energy consumption by 2.27%. The best energy saving is 18:00-19:00 on the 21st, saving 21.05% of energy consumption. During the two days before optimization, the average water temperature was 26.31 and 23.66°C, and the average water temperature after optimization was 24.88 and 23.06°C, respectively.
Architecture Design of Virtual Power Plant Intelligent Scheduling System Based on Multi-Resource Fusion
Authors: Hu Pengtao , Fan Peilin , Xue Jianli , Gao Liangfang , Chen Xiaofang , Yang Zhiping
Published: November 2024

This paper proposes a distributed scheduling model for virtual power plants, and in order to realize the optimal scheduling of multiple virtual power plants, it proposes a reinforcement learning model and establishes a framework of reinforcement learning methods to subdivide the states, actions, and rewards that are generated by the grid scheduling center when it interacts with the power grid. An environment model including multiple virtual power plant operation models is constructed, and the objective function of the control center is set to allocate scheduling commands in the virtual power plants. The algorithmic analysis is based on the actual network framework of the regional power grid and divides the tariffs of power purchased/sold by the VPP to the power market. The centralized optimization method, distributed optimization method, and reinforcement learning optimization method are proposed respectively to solve the MVPP coordinated optimal dispatch model. The optimization objective, deviation control strategy, and carbon trading elements are exemplified, and different scenario scenarios are set to analyze the convergence of the reinforcement learning model (DDPG) and the scheduling results. Reinforcement learning real-time optimal scheduling focuses on the measured values of wind power and loads and avoids the increase of costs or decrease of revenues due to the fluctuation smoothing by the grid by coordinating the internal resources for smoothing or the complementary consumption through the inter-rate of VPP. It can take into account the impact of the current decision on future time, thus achieving scheduling optimization in multiple periods.
Optimization study of unmanned surface craft formation control by fusing BSM and ANN
Authors: Qi Xiaolong
Published: November 2024

With the continuous development of intelligent technology, the application of surface unmanned vessels (USVs) in the field of ocean engineering is becoming more and more widespread, and the cooperative cooperation of multiple USVs can improve the efficiency of task execution and expand the operation coverage, which has a broad application prospect and practical value. In this paper, the kinematic model of USVs formation under three degrees of freedom of longitudinal oscillation, transverse oscillation and bow rocking is established, and the dynamics model is built based on the internal structure of USVs, which lays the foundation for the study of the formation control and collision avoidance problems of USVs. Subsequently, the pilot-follower model is selected as the basis of the formation, the congestion control strategy based on the basic information model (BSM) is proposed to effectively alleviate the congestion of the fleet communication network, and the excellent approximation capability of the ANN-based RBF neural network is utilized to design the adaptive neural network controller. Finally, the event-triggered mechanism is combined with the adaptive dynamic planning algorithm to propose an adaptive optimal control strategy based on BSM event triggering, and at the same time, the neural network weight updating rules determined by the event triggering conditions are set to realize the optimization of unmanned surface boat formation control. Simulation experiments show that the unmanned boat can fulfill the formation task well, avoiding obstacles, maintaining formation, and maintaining target consistency. The proposed control method can significantly reduce the computational burden of the controller, ensuring the stability of the USV cluster system and alleviating the excessive utilization of system communication resources.
Requirements Analysis and Architecture Design of a Nursing Virtual Faculty Technology Support System
Authors: Zhang Meixia , Deng Junfen , Gao Lifang , Wang E , Yang Xuhong , Li Shuxiang , Cheng Beibei
Published: November 2024

Emerging technologies such as virtual reality can provide immersive and interactive experiences for relevant personnel by simulating real clinical environments so that they can obtain adequate professional training experiences. This paper proposes the functional requirements of the nursing virtual teaching and research center under the guidance of NLN Jeffries theory, then combines Marker-SLAM visual augmented reality technology and 3DMax software to construct the virtual interactive system of the nursing virtual teaching and research center, and uses cloud technology and other development platforms to design the nursing virtual teaching and research center technical support system. The results of the nursing practice experiment post-test showed that the nursing skills assessment scores of the students in the virtual teaching and research laboratory system group were higher than those of the students in the traditional model group, and the nursing skills assessment scores of the students in the virtual teaching and research laboratory system group were significantly different before and after the practice (P<0.05). The nursing students’ comprehensive score for the application effect of the system was 22.4, which indicates that the system received more satisfactory feedback from the entire group of nursing students. The nursing virtual faculty technology support system designed in this paper is innovative and practical, and can help nursing students get close to real clinical work, strengthen their comprehensive ability as well as provide more learning opportunities to enhance learning effects.
AI in teaching English writing: automatic scoring and feedback system
Authors: Sun Yiwei
Published: November 2024

Artificial intelligence technology, with its excellent data processing and analyzing ability, has become a new method for scientifically evaluating the quality of students’ writing. In this paper, we construct an automatic scoring model for English writing using a hybrid neural network, then use a random forest algorithm to categorize the assignment utterances and design an automatic writing feedback system. The results of the teaching case show that the mean posttest writing score of the experimental class is 16.832, and the mean value of the posttest writing error rate is 7.278, both of which perform significantly better than the control class. Moreover, the assignments from the experimental class demonstrated a higher level of word performance. As the experiment progressed, the students’ perceptions of this paper’s system grew more positive. This underscores the significance of the research conducted in this paper.
A Study on Synergistic Enhancement of Product Directive Optimization Strategies and Export Trade Performance in the Digital Era
Authors: Wang Guishuang , Dai Honglei
Published: November 2024

The topic of innovation appears again and again in the government work report. The state and enterprises also pay more and more attention to product optimization and the output of innovative products, and more innovative and technological content has emerged from the products. At the same time, China’s foreign trade is transitioning from the traditional big country of foreign trade to the modern foreign trade power. In this paper, we combine natural language understanding and interactive image command mapping networks to improve the command optimization of digital products. The improved Graph-LSTM model is utilized to process the natural language instruction vectors, and the ICF network is utilized to take the environment image and user instructions as inputs to construct a fusion network in which the environment image and the natural language instructions can complement each other to complete the instruction mapping and to generate the location heat map for the execution of the instructions. Using principal component analysis, panel regression modeling, and other methods, we extracted and screened indicators of the influencing factors of export trade performance evaluation and constructed the performance evaluation index system. The synergistic enhancement of product instruction optimization and export trade performance is analyzed through empirical analysis and enhancement effect evaluation. The regression results of the selection equation are analyzed, and in the second column of the model, the coefficients of marketing cost and financial cost are 35.254 and -2.568, respectively, and the P-value is greater than 0.1, which can be analyzed in the next stage. In terms of the level of economic development, the difference between the comprehensive performance of export trade between Guangdong and Heilongjiang is 55.11, which is a large gap, and the optimization of product instructions has a greater impact on the performance of export trade in the eastern region.
Quantitative analysis and model prediction of urban traffic flow optimization by geographic information technology
Authors: Wang Li , Pan Kunyou
Published: November 2024

With the development of intelligent transportation, urban traffic flow prediction faces more complex traffic situations and higher accuracy requirements. In this paper, we collected multi-day traffic data from local cities and used a k-means clustering algorithm to analyze the data by clustering. A BP neural network prediction algorithm combined with k-means clustering analysis is designed to construct an “hour-day-week” urban traffic flow prediction model. The model is trained and applied to real-time urban traffic flow prediction to further analyze the performance effect of this model. The training time of this model is the shortest, which is about 67 minutes, and the MAE value is 3.02. The training speed is ahead of the longest training time in model 2, which is about 163 minutes, and the error is reduced by 8.04 compared with that of the highest prediction error in model 1. With different levels of noise added, the R², ACC, and RMSE values of the model in this paper are maintained at about 0.45, 0.63, and 0.8, respectively, indicating that different levels of noise have a significant effect on the performance of this model. This indicates that different degrees of noise have little effect on the performance of the model in this paper. In addition, the prediction effect is better after clustering analysis. The MAPE value of this paper’s model is the lowest, only 2.64%, which is 3.65%-7.52% lower than the other three control models. The experimental results above fully demonstrate the superior performance of this paper’s model, which suggests that this paper’s model is more effective in predicting urban traffic flow.
Research on automatic UAV path planning technology for complex terrain under neural network perspective
Authors: Zuo Qinghai , Pan Weijun , Zeng Chen
Published: November 2024

UAV path planning originates from robot motion planning, which is the core content of the current UAV application research and plays a key role in improving the operational capability of UAS in low-altitude complex environments. In this paper, the neural network algorithm is utilized to study the path planning problem of UAVs in complex terrain environments. A three-dimensional map model of complex terrain is created using an interpolation composition method, and an adaptation function is introduced to address the smoothness issue in path planning. The UAV’s kinematic model is created by simplifying it into a three-degree-of-freedom mass and using both proportional feedback and feedforward to determine control inputs. The neural network structure adjusts the initial point, and the neural network of an obstacle penalty function and the energy function of the entire path is constructed. With comprehensive waypoint position analysis and the help of adaptive learning factors, this paper completes the path planning for UAVs in complex terrain conditions. This paper’s algorithm for forest fire patrol can reach 100% coverage rate when applying the path planning algorithm to forest fire aviation emergency rescue scenarios. In the forest fire emergency relief material distribution, the path planned by this paper’s algorithm can effectively distribute the relief materials to five target points while successfully avoiding all the obstacles.
Research on high-precision rotational inertia characteristic modeling and control method for auxiliary services
Authors: Wu Yang , Xie Yigong , Zhu Xinchun , Liu Shuangquan , Zhang Bingjin
Published: November 2024

The traditional microgrid electric energy system exhibits low damping and low inertia, making it susceptible to load fluctuations that can significantly impact the system. A new study suggests a way to control a microgrid virtual synchronous generator (VSG) using adaptive regulation of rotational inertia. This lowers the frequency steady-state offset of the VSG control system’s output by using a non-differential frequency regulation method. The virtual excitation regulator regulates the output voltage deviation. Meanwhile, we establish a small-signal model of the grid-connected inverter based on VSG control and explore the effects of rotational inertia and damping coefficient on the stability of the VSG. The MATLAB/Simulink simulation tool is used to simulate the microgrid differential power load mutation, and the effects of the VSG control and the traditional control strategy on several aspects of the system, such as frequency regulation, power regulation, and power quality, are analyzed by comparison. The findings show that the suggested adaptive control method lowers the voltage amplitude from ±25V to ±10V when the system is isolated, stops power fluctuations, and makes the output response curve as good as it can be. Under grid-connected conditions, the VSG adaptive control method still ensures better control performance, but it does not show a significant advantage in this experimental condition.
Measurement and Analysis of Industry Risk-Return Value Measurement and Analysis in the Context of Big Data Technology - A Study Based on VaR and VaB
Authors: Xue Ziyu , Jiang Wenjiang
Published: November 2024

Based on VaR and VaB research methods, this paper constructs a regression model based on industry risk-return value metrics using a Bayesian-expected quantile regression algorithm in the context of big data. The parameters of the Bayesian model are estimated using the decision function, and then the metric effects of the model are evaluated using AIC and BIC. Finally, the relationship between industry risk and return is evaluated using the Bayesian-Expectation Quartile regression model. Under the Bayesian-Expectile model, the difference between the correlation coefficients of AIC (−0.7431, −0.9563, −0.7872, −0.9137) and BIC (−0.0547, −0.0768, −0.0559) is relatively small, reflecting the fact that the Internet financial innovations have not significantly pushed up the risk-free rate of return of the society while pushing up the society’s financing cost. In addition, the three models used in this paper have close EvaR prediction results for information technology, daily consumption and telecommunication services, indicating that the credibility of the industry risk prediction results proposed in this paper is high. The relationship between idiosyncratic volatility and the adjusted return for the next period is related to the time-varying nature of the characteristics of individual stocks, as well as the individual stocks of industry-risk firms. Incorporating factors that affect conditional equity premiums into the time-varying conditions of individual stocks can more accurately reflect the risk-taking of individual stocks of risky firms.
New Forms of Public Art in the Digital Age and Their Impact on Community Interaction Patterns
Authors: Peng Fu , Peng Xiaoyu
Published: November 2024

This paper summarizes the social function of public art under the perspective of digital technology, takes interactive installation art as the main research path, and proposes the design scheme of interactive installation art in community public space with regard to three aspects: sense of context, sense of user participation, and practicality of the installation. Through the way of practical research, analyze the general overview of the subject area and design an installation art scheme that meets the subject area. Set up an example investigation to research and analyze how public art installations affect the interaction of community users. In response to the two installation art interaction methods, users found the operation of the installation MI to be more difficult than that of the installation MT, with the degree of difficulty for both being 3.43 and 4.016, respectively. As for the level of interest, users are more interested in the device MT with a level value of 3.781. The range of [4.3,4.5] is where the mean values of most measured indicators are concentrated in the satisfaction analysis. Meanwhile, the measured values of all the indicators are greater than 4. The overall design of the interactive installation art in the public space is good, and the community is accepting of it.
Developing a Dynamic Marketing Channel Model for Agricultural Cooperatives in the New Media Economy Using the Power of the Internet of Things
Authors: Wang Xuejuan
Published: November 2024

The marketing channel is the lifeline of agricultural product circulation and marketing. Multi-level and multi-channel smooth circulation of agricultural products is an important way for agricultural cooperatives to realize the value of their goal. This paper builds an agricultural Internet of Things service system to lay an informatization foundation for the development of dynamic marketing channels of “new media platform + agricultural cooperatives”. With the use of new media platforms as an operational mode and the marketing strategy of agricultural cooperatives as guidance, the marketing effectiveness of agricultural products can be improved. Adopting Internet thinking and innovation trends, comprehending the characteristics of new media marketing, and encouraging collaboration between new media platforms and agricultural cooperatives. Based on 28,450 agricultural cooperatives in China, the marketing channel mode selection and characteristic data were analyzed, and it was found that among the 2,617 cooperatives using new media platforms for channel marketing, the product price fluctuation was between 0 and 10%, and the difficulty of product sales was stable in the interval of [0,40], which indicated that the new media marketing channel could make the price of agricultural products stable and easier to sell. Evolutionary game analysis found that the choice of cooperation or non-cooperation between agricultural cooperatives and new media platforms is related to the coefficient of benefit analysis, the coefficient of cooperation cost, and the liquidated damages of the cooperation between the two parties. Therefore, in order to improve the intensity of cooperation between new media platforms and agricultural cooperatives, improvements should be made in terms of benefits, costs, and liquidated damages.
Research on Numerical Analysis Methods of New Quality Productivity Development in Colleges and Universities Oriented to Party Building Leadership
Authors: Wei Zhedong , Li Jiayan
Published: November 2024

This paper selects key indicators between party building system inputs and new quality productivity output elements of universities and adopts the correlation method to study the relationship between such elements. From the perspective of mathematical analysis, we can show the interactive relationship between intelligent party building inputs and scientific and technological output elements of universities. The questionnaire method is used to conduct an in-depth investigation and research on the actual situation of postgraduate students’ innovation ability and cultivation. The results of the study show that the elements of funding for the construction of the intelligent party building system show a rising trend from 2020 to 2024. The input of funding for smart party building is proportional to the output, i.e., the greater the input, the more output of new quality productivity in colleges and universities. When investigating the importance of party building in the cultivation of postgraduate students’ innovation abilities, 48.2% of the students believe it is very important. About 58% of the students think that the party building system of their students plays a certain role in cultivating innovation ability.
Informatization Construction of Music Education in Colleges and Universities under the Integration of Big Data Technology
Authors: Xiao Ya
Published: November 2024

The in-depth application of big data technology to the information classroom of music education in colleges and universities can help education managers accurately grasp the dynamics of students’ thoughts and behaviors so as to clarify the direction and focus of education management. This paper takes the intelligent body as the “new subject” in the information classroom, integrates the “knowledge structure”, and provides an opportunity for students to realize their creative learning potential. It interprets the concept of big data from multiple perspectives and grasps the application of big data technology in the field of education. The entropy analysis method is used to depict the discrete degree of learning time and achievement in the information-driven classroom of college music education. K-means and Apriori association rule algorithms in big data technology are integrated into the college music information technology classroom to visualize learning behavior data in the classroom. Through the analysis of learning behavior data, it was found that the distribution of students’ scores in the informatized classroom is mainly between 70 and 90 points, and the teaching effect is better. Students generally have “delayed” in completing homework, and the learning effect is positively correlated with the number of logins and the time of informatized learning. The results of the study have certain reference significance for realizing the goal of education information technology construction in colleges and universities.
Information communication and aesthetic value of data visualization in the field of art and design
Authors: Xie Yuguang , Wang Huijing , Li Jin
Published: November 2024

Data visualization has become a popular discipline in today’s society, and understanding this trend has become a hot topic in the field of design. This paper introduces the visual perception system of the human eye, combining graphic features and dynamic features to reveal the visualization mechanism of information dynamic conveying in the field of art design. Then, the quantitative model of visual communication in color and graphic texture in art design, respectively, and the art design image containing rich plants are taken as an example to realize the visual analysis of information communication in the field of art design. There is a significant negative correlation (p > 0.05) between the purity of the foreground image, the foreground image color gamut area, and the proportion of foreground image color matching. This means that the higher the purity of the foreground image, the smaller the color matching area, leading to more coordinated color matching and a higher aesthetic degree. Simultaneously, the alteration in the gray value of the image texture typically exhibits characteristics such as high frequency, small amplitude, and randomness. The use of visualization technology in art design, reasonable collocation of colors, and graphic aesthetics fully demonstrate its aesthetic value.
Research on Improving the Integration Effect of Implicit Civic and Political Elements in Higher Vocational English Teaching Based on Artificial Intelligence Technology
Authors: Yang Yating , Yuan Bowen
Published: November 2024

The use of artificial intelligence-assisted teaching technology for the teaching reform of higher vocational English courses Civic and political education can not only enhance the topicality and ideology of higher vocational English teaching but also promote the development of teaching in the direction of intelligence and precision. The study aims to extract Civics and Politics elements from the higher vocational English curriculum using natural language processing. The BERT pre-training model is used to obtain the word vectors. Then sentences that can be used as text summaries are selected using cosine similarity calculation. Finally, the selected sentences are fine-tuned by the RoBERTa-Seq2Seq model to remove some repetitive words and obtain text summaries. On the basis of the RoBERTa-Seq2Seq model, an enhanced word vector feature dimension, pointer generation network, and coverage penalty mechanism are added to solve related problems in the process of model application. After the curriculum integration based on the model of this paper, the core literacy competence of higher vocational students has significantly improved. The students’ English scores increased by 23.5 points compared to those before curriculum integration. It shows that the model can effectively help teachers improve the effectiveness and quality of classroom education.
Analysis of three-dimensional modeling technology for complex garment structure design with value of computational geometry application
Authors: Zheng Shouyang
Published: November 2024

At present, clothing structure design technology is experiencing a transformation from two-dimensional to three-dimensional, among which, three-dimensional clothing modeling technology is an important part of three-dimensional clothing structure design research. In this paper, on the basis of the SCAPE method, the global mapping method is adopted to obtain the relationship between human body size parameters and PCA weights, and the deformation law of the human body network is deduced by learning the deformation matrix to realize the establishment of personalized 3D human body model based on personalization. After processing the human polygonal model, the NURBS surface is used to create a 3D human body model that indicates smoothness and high realism. Using the Kinect 3D body scanner, model data is collected to construct a 3D anthropometric database that provides data support for subsequent clothing design. Define the simulation state of fabric organization in the computer, construct the geometric structure model of fabric, and model the clothing fabric. Several mainstream algorithms are selected and compared with the algorithm proposed in this paper. The running time of the algorithm proposed in this paper is reduced by 0.3596s and 0.3022s compared to the geometric model method and the constraint model method, which reflects the superiority of the algorithm. The fabric thickness generated by this paper’s algorithm ranges from 1.94-1.99, and the fabric recovery rate in three minutes is more than 98%, the fabric performance is better, and the three-dimensional garment structure design proposed in this paper is effective.
Exploration of the Path to Improve the Efficiency of Digital Textbook Resource Allocation in Vocational Education Supported by Mathematical Modeling Technology
Authors: Tao Lingyun
Published: November 2024

Digital teaching materials help to promote the vocationalization and intelligence of vocational education, however, the current digital teaching materials have some dilemmas at the resource allocation level. This paper begins with the data exchange link in the digital textbook management system and builds an ERDDS resource allocation system with multiple configuration centers and the introduction of scheduling servers. The resource scheduling optimization problem of the configuration system is mathematically modeled, and the multi-objective optimization mathematical model is solved by using the ant colony algorithm to achieve the purpose of improving allocation efficiency. The experiments on the download task show that the ACO algorithm is less time-consuming and stable, with good convergence. The solution accuracy of the algorithm is improved by 13.38%, 25.68% and 18.97% compared to PSO, ACO and MACO, respectively. The average efficiency of using digital textbooks in each college before and after the experiment has been improved by 19.77%, and the average efficiency of allocating digital textbooks has been improved by 11.83%. It shows that the proposed efficiency improvement path for digital textbook resource allocation is feasible and reasonable.
Design of Intelligent Document Categorization System for Office Software Combined with Neural Networks
Authors: Yuan Sufang
Published: November 2024

Office software documents as a carrier of effective information; the complexity of its text determines the efficiency of information extraction. The current research hot spot involves accurately categorizing the document text. In this paper, the document data are processed by document cut, text segmentation, de-duplication, text feature extraction, word frequency statistics and other data processing, and the Word2Vec model is used to represent the text of office software documents. The improved CNNSVM text classification model was constructed by replacing the classifier and fused the attention mechanism module based on the convolutional neural network. In this way, a document categorization system based on a neural network is designed. On the CR dataset, the model in this paper was trained for 25-90 rounds later, and the loss value, recall rate, accuracy rate, and F1 value converged to about 0.1, 0.91, 0.85, and 0.88 in turn. And the accuracy rate of this dataset has been improved by 18.71% when compared to the worst comparison model. The attention module can be used to display different text sequence weights in the model in this paper to correctly classify text. The above experimental results fully demonstrate the superior performance of the model in this paper and its high matching on multiple datasets.
Construction of Music Education Resource Sharing and Management Mechanism in Colleges and Universities under Cloud Computing Platform
Authors: Zhou Guobin
Published: November 2024

The unbalanced characteristics of the current music education information resources are becoming more and more obvious. This paper builds a resource-sharing model framework with the help of cloud computing technology and designs five major mechanisms for resource sharing in music education. Afterward, the value of educational resources is weighted and calculated, and the incentive mechanism for music education resource points is formulated. Finally, the sharing and value of music education resources are investigated, and the feasibility of the mechanism is verified. Under the point incentive mechanism in this paper, fragmented resources have the lowest economic benefit exchange value, followed by complete resources, while curriculum resources have the highest exchange value, and the exchange value scores of the three resources are 0.069-0.146, 0.345-0.645, and 0.580- 1.0 respectively. Additionally, when the number of resources shared is greater than 5, the benefits of shared resources become more stable. This is related to the fact that participants will adjust the amount of their contribution in the sharing system according to the contribution of other participants. A large number of shared resources with low value will result in a relatively large waste of resources. Not only can they not fully contribute their resources, but they also cannot fully enjoy the resources contributed by other people. Therefore, the value of the subject itself will have an impact on the benefits of the shared resources.
Study on Power Market System Architecture and Clearance Pricing Adapted to Large Scale Access of New Energy Resources
Authors: Wang Haohao , Huang Yuanming , Luo Jinqing , Shu Kangan , Yang Liu , Chen Qing
Published: November 2024

The construction of a new type of power market system structure with new energy as the main body is the direction and goal of China’s power development, and the continuous implementation of the “dual-carbon” goal highlights its importance and urgency. The article is focused on the development stage of the new energy power market and establishes a multidimensional synergistic power market system to adapt to large-scale access to renewable energy sources. A coordinated control strategy for facilitating new energy access to the distribution network is established through the calculation of transient currents in the power grid and the uncertainty of new energy output. Based on the calculation results of the power trading offer and load-side uniform tariff, a clearing and pricing mechanism for large-scale access to new energy sources in the power market is designed based on the consideration of the constraints of energy storage. Example data verify the effectiveness of the mechanism. When the new energy output is 5GW or 10GW, the voltage fluctuation of the power system ranges from 0.5 to 1.5, and the PV new energy output remains unchanged after the fluctuation. The subject benefit obtained from the clearing pricing scenario considering storage charging and discharging rights and capacity rights can be up to 9,428.45 yuan, which is 1.67 times higher than the subject benefit considering only storage charging and discharging rights. The clearing pricing mechanism in the multidimensional synergistic power market system helps to analyze pricing strategies under the new energy access power market and improve the economic benefits of various stakeholders.
The Role and Role of Digital Governance in Improving Government Transparency and Citizen Engagement
Authors: Cao Jiaqi
Published: November 2024

This paper designs relevant questionnaires according to the needs of this research, obtains basic data through the questionnaires, and explores the intrinsic role mechanism between the influencing factors of the dimensions of digital governance and the roles and functions in government transparency and citizen participation through structural equation modeling and regression analysis. The results show that government transparency and citizen participation have a positive effect on digital governance, with a p-value of less than 0.05. In the detailed description, the three dimensions of openness, interactivity, and selectivity of public participation in digital governance in X county positively affect the five dimensions of information integration and scientific decision-making ability, benign communication, and collaborative ability in digital governance capacity. The transparency of government procurement will be positively impacted by the investment of science and technology funding, and the correlation between GDP per capita and transparency of government procurement is 10%.