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In this paper, through the collection and analysis of data, the art of Guangling paper-cutting is theoretically researched, and the general features and shapes of Guangling paper-cutting are summarized. In order to overcome the limitations of traditional algorithms in the extraction of features of Guangling paper-cutting, the combination of spatial pyramid matching and context-dependent histogram methods are used to extract the shape features of paper-cutting images with stronger differentiation. The support vector machine is used to solve the problem of recognizing and classifying the image patterns in the digitized display. The design of the digital promotion of the Shanxi Guangling paper-cutting brand is carried out. Research variables are designed to analyze the effect of digital transformation and promotion of paper-cutting brands. The results show that the discrepancy of Guangling paper-cutting features extracted by the algorithm in this paper is very low, the e1 difference is maintained within 0.1, and the total recognition rate of SVM on Guangling paper-cutting features can reach 88.06%, and the overall effect is more satisfactory, and the regression equation of the influence of paper-cutting = −0.807+0.111*history+0.122* style+0.234*paper-cutting+0.216*display+0.136 * linkage, and propose strategies to enhance the influence of paper-cutting brand. The technical methods used in this study guarantee the quality of the digital transformation of Guangdong paper-cutting from the source.

To deeply analyze the performance of paper-cutting elements in interior space design, this paper explores the artistic expression of paper-cutting elements. The BIM model is constructed based on the TVL algorithm, and a noise-reducing style migration method with an additional total variable loss function is used to fuse the texture features of a specific paper-cutting style so that the paper-cutting texture is generated in the target object area more intensively. The algorithm is supplemented with a U-shaped network structure to obtain the mask map of the target object, modify and expand the ScanNet training data set, and input a content map and a style map to generate the stylized paper cutout image automatically. The results show that compared with other elements, the stability of paper-cut elements reaches 96.31%, the image smoothness reaches 89.34%, and the retention of significant regions reaches 93.26%. In modern design, the peak signal-noise of paper-cut elements improved by 13.418d on average, the structural similarity also improved by 0.34807, and the recognition rate of paper-cut elements in the furnishing category reached 95%. Applying paper-cut art in interior space design can give interior furnishing design better visual effects, provide inspiration and ideas for interior space design innovation, and contribute to improving aesthetics.

Paper-cut elements in the design of boat space can effectively improve the homogenisation tendency of existing boat space design, effectively enhance the characteristics of traditional Chinese culture and enrich the development ideas of boat space design. Therefore, based on the Chinese folk art of paper cutting, this paper combines theoretical analysis and empirical research to deeply analyse the artistic expression of paper-cut elements in the spatial design of boats. This paper mainly studies the artistic integration of paper-cut elements in the decorative design of boats and the artistic integration of paper-cut elements in the practical design of boats. Through the calculation and analysis of the sparse autoencoder algorithm model, it can be seen that the decorative integration of paper-cut elements in wallpaper is 82%, which is higher than that of traditional materials (24%). The integration of paper-cut elements in the space design of cabins and guest rooms is as high as 97% and 94%, respectively, which is far better than the integration of traditional materials in the space design of cabins and guest rooms. This shows that the application of folk paper-cut elements to the space design of boats not only highlights the theme of boats but also enhances the cultural value of boats.

English is a universal language and is widely used. In order to deepen the cognition of language, this paper proposes to analyse the variation of metadiscourse verb patterns in English academic papers from within and between disciplines. By comparing the metadiscourse chunks used in various disciplines, it is concluded that there are obvious differences in the verb types of the subject papers. Analysing various identification algorithms of verb types, it is obtained that the combination of the word movement distance (WMD) model and the R&L density peak clustering algorithm is the best. Using the R&L density peak clustering, identification parameters are easy to determine, and when combined with the WMD model to calculate the similarity of words, it improve the accuracy of verb pattern clustering. By comparison, it is proved that the accuracy of the algorithm combining the WMD model and R&L density peak clustering reaches 30.06%, and the effect of identifying verb pattern variation is the best.

In the realm of Chinese traditional arts, paper-cutting symbols embody deep-seated aesthetic and cultural values. Within the framework of artificial intelligence, the fusion of these conventional symbols with contemporary pattern design through intelligent reconstruction processes promotes innovation in modern design. This paper introduces a novel pattern feature extraction algorithm specifically designed for identifying and extracting the essence of traditional paper-cutting art. Utilizing Deep Convolutional Generative Adversarial Networks (DCGAN), an automated method for generating woven patterns is developed, integrating conventional motifs with modern design elements. Additionally, a segmentation model based on DeepLabV3+ semantics is constructed to facilitate this integration. The study focuses on the fusion of traditional paper-cutting symbols with fabric patterns, providing a new direction for textile design research. The empirical evaluation involved seven experimenters who predominantly favored patterns that blended abstract and traditional styles, with the average style rating exceeding 3.2. Moreover, the color performance analysis of the reconstructed fabric patterns across five regions showed color difference values greater than 4, indicating superior color fidelity. These findings underscore the potential of combining AI-driven techniques with traditional art forms to enhance and revolutionize modern design practices.

The study of metadiscourse word resources is an important way to reveal academic knowledge constructs and genre features in English papers. This paper selects intra- and interdisciplinary English academic papers as data samples, and the text representation is based on the vector space model. The text similarity is calculated using the word movement distance model to complete the clustering based on the density peak, and then the semantic annotation of metadiscourse verb patterns is performed using the conditional random field. Then, the intra- and interdisciplinary metadiscourse annotations are compared to analyze the variation of verb patterns. The intra-disciplinary variation of metadiscourse verb patterns was mainly found using attitude markers, where the log-likelihood ratios of 48.56, 30.25, and 35.64 were obtained for affirmative, critical, and critical declarative attitudes, respectively. In interdisciplinary studies, basic disciplines preferred to use verb patterns as transitions, while applied disciplines preferred to express the author’s point of view. The study of metadiscourse verb patterns effectively enriches the linguistic resources in academic English teaching and deepens the understanding of the characteristics of disciplinary essay genres.

This investigation delves into the evolving patterns and regional variances in industry-academia collaboration (IAC) within Functional Nucleic Acids (FNAs) research. Analyzing publications and patents from 1990 to 2021, we dissected IAC at individual and collective levels, documenting the count and share of IAC endeavors, key partners, and regional differences. Results indicate a rising trend in IAC publications and collaborating institutions, while patent trends display a mix of increases, decreases, and steady states. The United States leads in IAC activities, correlating with its prowess in scientific research and technological innovation. Notably, regional discrepancies in IAC are mainly due to intra-regional variations, with Asia and Europe experiencing the most pronounced disparities.

Leveraging artificial intelligence (AI), this study revolutionizes examination management and education in universities by developing an intelligent system encompassing comprehensive management, pre-examination activities, scheduling, and preparation. The system also features a quality management component for educational outcomes. An enhanced genetic algorithm introduces an adaptation function to optimize intelligent grouping, facilitating effective exam paper distribution. Applied at Y University, our innovative approach significantly refines exam paper difficulty (ranging between 0.5016 and 0.5581) and differentiation (0.3845 to 0.4596), showcasing the intelligent algorithm’s effectiveness in exam management and contributing valuable insights to educational research.

With the innovation and development of commercial shopping centers in China, the shopping environment is becoming more and more colorful. However, in today’s social environment, the central function of low-level consumption experience can no longer meet the needs of the masses. Therefore, in the design of indoor environment, we should start from a new perspective, focusing on the difference of internal space and the experience of consumption mode, so as to meet the development needs of commercial shopping in the future. Based on the research on the indoor environment of comprehensive commercial shopping center, comprehensive commercial shopping center in a certain area as the research target, the corresponding mathematical model was constructed, and using the measured method validation build the model of reliability and validity, thus obtains using CFD numerical simulation software of thermal and moisture environment of building an in-depth exploration, The effective countermeasures are put forward for the original air distribution mode. Comparing the improved scheme with the original scheme, it is found that the former can provide effective basis for the interior environment design of similar comprehensive commercial shopping malls.

Chaotic behaviour of dynamical systems, their routes to chaos, and the intermittency in particular are interesting and investigated subjects in nonlinear dynamics. The studying of these phenomena in non-smooth dynamical systems is of the special scientists’ interest. In this paper we study the type-III intermittency route to chaos in strongly nonlinear non-smooth discontinuous 2-DOF vibroimpact system. We apply relatively new mathematical tool – continuous wavelet transform CWT – for investigation this phenomenon. We show that CWT applying allows to detect and determine the chaotic motion and the intermittency with great confidence and reliability, gives the possibility to demonstrate intermittency route to chaos, to distinguish and analyze the laminar and turbulent phases.

Researching different solutions of nonlinear models has been interesting in different fields of science and application. In this study, we investigated different solutions of fourth-order nonlinear Ablowitz– Kaup–Newell–Segur wave equation. We have used the sine-Gordon expansion method (SGEM) during this research. We have given the 2D, 3D, and contour graphs acquired from the values of the solutions obtained using strong SGEM.

We extend the Boltzmann’s ideas that describe the evolution to the equilibrium of many body systems to the multifractal decomposition of the unitary interval 𝕀, in terms of sets Jα conformed by points with the same pointwise dimension, and obtain the D(α) singularity spectrum.

The study of everyday phenomena involving friction continues to maintain a high level of difficulty despite its long history. The causes of this problem lie in the different scale of the characteristics of the phenomenon, macroscopic and microscopic. Thus, very different models, valid in a narrow scope which prevents generalization, have been appearing. This survey presents the application of network simulation method to the numerical solution to the study of friction at very different scales. On the one hand, on a microscopic scale an atomic force microscope model has been studied, related to the analysis of soft surfaces at the atomic scale. Furthermore, on a macroscopic scale model related to the analysis of an industrial device, such as a brake mechanism has been studied. After presenting herein is a review of the different formulations of the friction force, the nature of the surfaces involved in the phenomenon, as well as the definition of the problems to be analyzed. The design of network models and the implementation of the initial conditions are explained. The results of the application of network models to selected problems are presented. In order to verify the reliability of the proposed models, their results are compared with the solutions obtained by other numerical methods or experimental results, one from a device developed during the preparation of this report.

We analyzed the first set of complete CCD light curves of the W UMa type eclipsing binary IK Boo in the BVRI bands by using the PHOEBE code and deduced its first photometric parameters with, mass ratio q = 0.648 and orbital inclination i = 63o. We have applied a spotted model due to the light curves asymmetry. The system shows a distinct O’Connell effect. The best solution fit to the light curves suggested the influence of star spot(s) on both components. Such presence of star spot(s) is common among the RS CVn and W UMa chromospheric active late type stars.

Distributed power access to the distribution network acceleration, if you can not deal with the relationship between grid operation, grid current balance, energy storage, and other constraints, will significantly reduce the efficiency of grid operation. In this paper, we first model photovoltaic power generation and wind power generation among distributed power sources and summarize the decision-making objectives and constraints according to the impact of distributed power sources on the distribution network voltage, network loss, and other aspects. The traditional particle swarm algorithm is improved and used to construct an optimization model for a distributed power access scheme with active management. Then, the model is compared with the traditional particle swarm algorithm and the NSGA algorithm to verify the effectiveness of the improvement. It is found that the improved PSO algorithms GD and SP in this paper are 0.0042 and 0.0192 in the ZDT1 function test, which is lower than 0.0139 and 0.0652 of NSGA and much lower than 0.0863 and 0.3992 of the basic PSO. In the arithmetic calculations of the model designed in this paper, the DG operation, network loss, power purchase, sewage, and cost are respectively 1,527,100 yuan, 159,800 yuan, 4,792,500 yuan, and 168,300 yuan, totaling 6,647,700,000 yuan, which is much lower in cost than the non-actively managed, non-installed distributed power model. This study provides new ideas and methods for the multi-objective optimization problem of high penetration rate distributed power supply access to distribution networks.

In the context of the era of economic green transformation, enterprises face greater litigation risks, and the opening of the asset market has an important impact on the internal decision-making and control of enterprises under green ecological development. In this paper, the enterprises listed in Shanghai and Shenzhen are selected as research samples, and the explanatory variables, interpretive variables, mediating variables, and control variables are selected for the study. The double difference model is used to construct an empirical model between the variables to explore the relationship between corporate litigation risk and asset opening under green ecological development. The study shows that the correlation coefficients between corporate asset openness (x) and corporate litigation risk dummy variables and numerical variables under green ecological development policy are -0.078 and -0.088, respectively, which are significantly negatively correlated at the 1% level. The robustness test validates this empirical result, indicating a strong correlation between the two. It has also been found that corporate asset openness has a greater negative effect on the litigation risk of heavy-polluting enterprises and state-owned enterprises. The study of enterprise litigation risk and asset opening is rethought in this paper, and it provides a reference basis for the effective management of enterprises under green ecological development.

In the context of economic informatization, the traditional economic management model has been unable to fully meet the actual needs of enterprise development. Enterprises should take effective measures to innovate the economic management model, to achieve the informationization and modernization of economic management work. Based on the needs of economic management innovation, this paper constructs a multi-dimensional digital economic management innovation path based on informationization. Then, according to the fuzzy hierarchical analysis method, construct the evaluation index system of economic management informatization, take company H as an example to carry out empirical evaluation, and analyze the influencing factors of the level of economic management informatization by using the two-sample Z-test. Company H's final evaluation score for economic management informatization is 91.23, and the level of informatization is good. The P-value of the top management's attention to informatization, the cultivation of informatization talents, the innovation ability of informatization talents, the ability of the project to meet the demand, the reliable management system of information system maintenance personnel, and the monitoring system of the information system operation is less than 0.01, and the Z-value is greater than 1.96, and the difference of its results from the hypothesis is very significant, which indicates that these six items have the greatest influence on the informatization of economic management in Company H. Empirical cases and strategy suggestions for the informatization path of economic management are presented in this paper.

Table tennis is regarded as the national ball of China, and in the actual process of competition, athletes should have both high technical and tactical levels and good physical fitness. This paper focuses on the changes in hitting skills and physical fitness of table tennis players after strength training. In order to establish a scientific training system and improve training efficiency, this paper designs a physical fitness monitoring method using big data technology. The monitoring method initially uses the backpropagation algorithm to process the physical fitness data of the athletes. To reduce the computational amount of frequent item sets, it is recommended to use the Apriori algorithm combined with the DC_Apriori algorithm for data mining on processed physical fitness data. Finally, the physical fitness training results analyzed by this fitness monitoring method were synthesized to develop a reasonable strength training program for table tennis players. The athletes were tested for changes in table tennis hitting skills and physical fitness before and after 4 weeks of strength training. By comparing with the athletes who underwent traditional physical training, it was found that the average score of table tennis batting skills of the strength training group based on big data analysis was significantly higher than the average score of the traditional physical training group. Comparative analysis of athletes’ physical fitness from four aspects: speed, strength, sensitivity, and endurance. Strength training based on big data analysis can significantly improve the physical fitness quality of table tennis players.

Diversified teaching is an inevitable requirement of music education reform, and increasing the diversity of teaching content generation is one of the ways to realize it. To this end, this paper proposes a DRMF diversification recommendation algorithm, which regulates the distribution of students’ ratings for different educational resource categories by optimizing the regularization parameter terms so as to realize the diversification of recommendation results. On this basis, a W2V-MMR teaching content generation algorithm is proposed, which utilizes Word2Vec model generation to train the word embedding model with big data text and then the word vectorization learning, which strengthens the semantic connection between the words so as to improve the accuracy of the teaching content generation. It is found that the ILD values of the DRMF algorithm are all higher than 0.215, the RMSDE values are all lower than 0.02, and the AD values are all around 1300. The W2V-MMR algorithm achieves 83.02% of teachers’ satisfaction after multiple rounds of optimization searching, and none of the MDEE values exceeds 0.4. It can be seen that both algorithms proposed in this paper yield good results.

The industrial-education fusion The civics and politics teaching system, a combination of ideological and political education, industrial frontier cultivation, and professional competence education, has the potential to foster a synergistic effect and enhance the talent cultivation mechanisms of applied colleges and universities. In this paper, we look at how applied colleges and universities teach implicit course ideology and politics in a world where industry and education are coming together. To do this, we came up with a design scheme for implicit course ideology and politics, which will be used as a guide for teaching engineering majors at an applied college and university in Hangzhou starting in 2022. An evaluation system of implicit course Civics teaching effect based on AHP hierarchical analysis index empowerment is built to measure the quality of Civics teaching and find out how well this paper’s program works. The comprehensive evaluation of five first-level indicators for college engineering majors yields scores of 8.60, 8.42, 8.51, 8.41, and 8.56 points, with three rated as excellent, two as good, and a total score of 8.50 points rated as excellent. The results reveal a significant difference between the experimental and control classes in five categories following a controlled test, with the average of the five post-test Civics dimensions being 11.43 points higher than that of the control classes. The implicit curriculum civics teaching model proposed in this paper is able to promote the overall improvement of students’ civics literacy. This study has provided a useful exploration of the innovation model of curriculum civics teaching and provides a reference for the construction of curriculum civics in applied colleges and universities.

As an important power equipment indispensable to the normal operation of the power grid, the good or bad working condition of the current transformer directly affects the operational efficiency of the whole power grid. In this paper, an online monitoring platform based on big data technology has been developed to monitor the current status of transformers. First of all, for the temperature of the transformer, through the decoupling of the heat generation mode of the transformer, the state monitoring method based on fiber optic temperature measurement technology and optical multiplexing technology is proposed, and the overall scheme of transformer state monitoring is designed. The data signal of the transformer’s status is collected and processed, and a fault state diagnosis model is constructed based on the decision tree algorithm. Finally, according to the data requirements of the current transformer, an online monitoring platform for transformer status has been constructed using big data technology. Through the simulation test, the variation of ratio error of this paper’s monitoring method is less than 0.60%, and the variation of phase error is less than 0.75 percent, which is in line with international requirements. The average loading time of the monitoring platform is between 123.591s~143.911s, the average loading speed is less than 13s, the average throughput is between 241ops/sec ~257ops/sec, and the average latency of the read and upload operations is 24.225ms. The current transformer condition monitoring platform in this paper has good stability, the overall performance is excellent, and it provides a good solution for the research of the current transformer condition monitoring platform provides practical support for the research of current transformer condition monitoring platform.

Public security heroic model culture is a political-cultural phenomenon with Chinese characteristics formed by public security organs in the process of selecting, recognizing, publicizing, and appeasing heroic models. This paper researches the culture of public security heroic models and proposes a model for disseminating the spirit of public security heroic models through social role perception, called RAIN. The RAIN model unified the two complementary tasks of the information dissemination process and the social roles of users. The model maps the dissemination parameters between users and those between social roles, reducing the number of parameters. The parameters are estimated using a Gibbs sampling-based learning algorithm. At the same time, a continuous influence maximization algorithm is proposed under the independent cascade propagation model, which can calculate the incremental values in each direction by the gradient descent method and get the optimal solution of the objective function value. The communication status of the spirit of public security heroism is analyzed, and the total word frequency of all the communicated high-frequency words is 8350, which is lower than 10,000 and is at the lower-middle communication level. The total communication power value and total influence value of the spirit of public security heroes are 1042 and 4701, respectively, and the number of high communication power and high influence content is only 22 and 21, so there are obvious deficiencies in the work of communication field. This paper takes the shortcomings as a starting point and proposes a communication optimization strategy accordingly.

With the arrival of the digital technology era, virtual reality technology is widely used in the fields of medical care, culture and history, entertainment, and games. In this context, researchers have explored how to organically integrate virtual reality technology into the teaching field, and virtual simulation experiments have been developed with the help of virtual simulation technology. Citespace and meta-analysis are used in the first part of this study to investigate the impact of virtual simulation on the teaching effect of scientific research experiments and to identify the factors that influence the teaching effect of scientific research experiments. On this basis, the control optimization of virtual hands is carried out for the interaction technology of virtual simulation technology, and then DTW is used to optimize the recognition of hand gestures in order to improve the immersion of virtual simulation scientific research experiments. The research experiment teaching model based on virtual simulation technology is constructed jointly with the 5-E teaching model and i-MMLOE teaching design model. The average recognition accuracy of this paper’s gesture optimization recognition method is as high as 99.61%, and the average recognition time is 48.89ms, which can achieve a smooth and comfortable gesture recognition process in the real-time state with good performance. Through the teaching effect test of this paper’s teaching mode, the average score of this paper’s teaching mode compared with the traditional teaching mode is increased by 4.394 points, and the Sig is 0.011 less than 0.05, which is a significant difference, proving that this paper’s virtual simulation scientific research experimental teaching mode has a good teaching effect. It shows that the teaching mode of this paper is helpful for teaching and managing scientific research experiments, and provides certain references and references that are important for scientific research experiment teaching.

Action recognition based on 3D technology is showing a trend of universal application in the field of kinesiology day by day. This paper utilizes 3D action recognition technology to develop an auxiliary teaching system suitable for college basketball teaching and training. The top-down method is used for human pose estimation to provide human skeleton characterization and shield obstacles such as background and illumination for action recognition technology. A new edge connection is added to the original graph convolution algorithm to form the human action recognition algorithm in this paper, and a spatial attention module is introduced to improve the accuracy of model action recognition. The system is equipped with two significant algorithms for human posture estimation and action recognition, and the final design is finished as an auxiliary system for college basketball teaching and training. The system can match the similarity between students’ movements and standard movements with a minimum of more than 80%, and the basketball scores of students are generally improved to more than 90 points after using the system.

The “Guided Pathways” program in community colleges can promote the efficient completion of students’ studies without restricting their choices and promote the popularization of education. This paper proposes a specific strategy for the implementation of the Guided Pathway program, and proposes a quality evaluation model for the Guided Pathway program based on the Sparrow Search Algorithm and Deep Neural Networks to construct an evaluation system. In the construction of the quality evaluation model, transient optimization perturbations are added to the SSA to improve the diversity, searchability, and robustness of the population position transformation. DNN cannot retain all the valuable features, so they are optimized to form DRNN, which is the quality evaluation model based on ISSA-DRNN. In terms of overall performance, the ISSA-DRNN algorithm in this paper has a prediction accuracy of more than 98% in different datasets, and the training and testing time required for evaluation is also significantly shorter than that of SVM and DRNN algorithms, resulting in superior overall performance. The quality evaluation of the Guided Pathways program was conducted at a community college in District S, City Q, China. The primary and secondary indicators have mean values between [3.4,3.7] and [3.4,3.6] that fall short of the respondents’ expectation of more than 4 points and cannot meet the demand. The implementation of Guided Pathways in S community college has deficiencies, and it should be improved and optimized by combining them with the implementation strategies proposed in this paper.

With the emergence of new classroom teaching modes such as STEM education, traditional classroom teaching and student cultivation are facing many new challenges, so this paper explores the use of knowledge mapping in teaching classrooms based on the STEM education perspective. The article first analyzes the basic theory of knowledge graphs and designs a knowledge graph architecture based on the relevant basic theory. It also proposes a personalized learning path generation and recommendation algorithm. Finally, 38 students majoring in educational technology were surveyed to test and explore the knowledge mapping approach proposed in this paper for STEM education. The results of the study indicate that the fifth and sixth groups, which utilized the personalized recommendation algorithm proposed in this paper, achieved the highest learning gains of 0.531 and 0.756, respectively.

Promoting the development of rural financial informatization is an important magic weapon to help rural revitalization, and it is also a necessary way to comply with the trends of the times. This paper constructs a quantitative model of farmers’ investment behavior based on the VaR method and the Morning Star performance evaluation method to provide financial service institutions with ideas for financial product innovation in order to solve the current problems of the increasing level of rural financial service informatization but the uneven degree of informatization and the weak sense of innovation in the construction of rural financial service informatization. In order to make full use of rural financial service informatization resources and reduce the investment cost and time of farmers, this paper designs and implements the financial product recommendation algorithm based on the investment behavior of farmers using the AE_xDeepFFM recommendation algorithm. The analysis finds that farmers’ investment behavior is more significantly affected by economic and social value perception and social influence, and the path coefficients of the two on behavioral intention in the resultant model are 0.411 and 0.407 at the significant level, respectively. Effort expectation, on the other hand, indirectly affects the investment intention and behavior of farmers through performance expectation.

Sentiment analysis is one of the important applications in the field of natural language processing. With the development of science and technology, sentiment analysis is developing in the direction of multi-feature fusion, and multi-feature fusion plays an important value in application in English spoken emotional expression. In this paper, we design a method for extracting multi-features based on multi-networks and propose a sentiment analysis model, ECISA-MFF model, on the basis of a multi-feature extraction framework and feature fusion scheme to solve the problem of data non-alignment and modal noise, and then further classify the sentiments and optimize the model. The article concludes with a comparison of the relevant performance of the models as well as a case study, and it is found that the F-macro value of the model proposed in this paper rises by 22.2% when compared with the BiLSTM+att+C model, which is significantly effective. After applying this paper’s method for emotion recognition, it was found that the average recognition rate of six emotions (happiness, anger, fear, surprise, sadness, and disgust) increased to 65.52%.

In the context of the rapid development of modernization and urbanization, the inheritance and research of music and musical instruments by ethnic minorities are facing great challenges. It is, therefore, of enormous significance to uncover the performance characteristics of ethnic minority musical instruments. This paper focuses on four categories of Yao musical instruments, namely reed instruments, air-pipe instruments, wind instruments, and percussion instruments. We mine the performance features of Yao musical instruments using MFCC feature extraction, perceptual linear prediction parameter extraction, and other methods. The performance of this paper’s algorithm is explored by comparing the detection and recognition accuracy of its algorithm with SVM on four types of musical instruments. The performance characteristics of Yao musical instruments are explored by analyzing the timbre characteristics, auditory characteristics, and beat statistics of the four instruments. This paper’s algorithm significantly outperforms the SVM algorithm in the recognition correct rate of four different types of musical instruments, with differences of 2.1905%, 7.1574%, 5.3758%, and 3.6962%, respectively. The extracted performance features of Yao music instruments reveal that reed instruments and air-pipe instruments have a superior timbral effect than wind instruments and percussion instruments. The sound of pneumatic instruments and wind instruments is the best when it comes to audibility, and the beats of reed instruments and wind instruments are the best among the four instruments.

In this paper, a robust greedy algorithm is applied to select the node with the highest weight value for fault observation in distribution networks. Except for the first observation node, the weights of the remaining nodes are calculated based on the results of the last observation node. The scheduling cycle is divided into several consecutive periods, and the network operation structure is observed for each period, which is gradually merged to finally obtain a set of solution schemes that satisfy the constraints on the number of switching actions for dynamic reconfiguration collaboration. The results show that the line fault system measurability is all in the 81.8%-100% interval. The method in this paper can directly locate 64.3% of the line faults in the 10kV distribution network, which can significantly reduce the number of traveling wave-locating devices, lower the investment cost, and reduce the fault locating error. It has a certain degree of adaptability in the case of the line N-1. Network operation observation can be found that the highest load-interval in a day is from 5:00 to 18:00. Using the method of this paper reduces 1 division period compared with the traditional method, reconfigures the structure to reduce the number of switching actions, and reduces the network loss from 812.06kW·h to 805.69kW·h.

This paper proposes a network security situational awareness model based on graph theory, with the primary goal of improving industrial IoT system security. At the beginning of this paper, graph theory is explained in depth, the mutual transformation of directed and undirected graphs is proposed, the empowerment graph abstracted from practical problems is defined, matrix storage is used to realize graph storage, and an isomorphism function is proposed to realize isomorphism judgment of graphs. Based on the principles of graph theory, we develop a network security situational awareness model and suggest a network risk assessment system. This system utilizes risk indices for vulnerability, services, hosts, and networks and assesses the risk, threat, and posture of a specific asset. The efficacy of the cyber security situational awareness model is examined. The average precision rate, recall rate, and F1 value of this paper’s model reach 99.2%, 98.9%, and 97.05%, respectively. The performance of the recognition precision rate of different cyber-attack types is 1%~8% higher than that of the CN model. The leakage rate and false alarm rate of network attacks are 5.41% and 6.16%, respectively, and the overall accuracy rate reaches 95.48%. In terms of the running effect, the average absolute error and mean squared error of this paper’s model are 0.1302 and 0.2709, which are lower than other comparison models.

The problem of population aging is intensifying worldwide. The proportion of the elderly population is gradually increasing, and the wisdom of elderly care will become a social development trend. This paper explores the status quo and problems of community home care services in the Internet+ era through a questionnaire survey on the supply of intelligent community care services. Based on this basis, a smart community home care system is developed based on Internet of Things (IoT) technology. Embedded control modules such as sensors and buzzer alarms are used to collect information about the posture and vital signs of older adults and to issue an alarm when they fall. Zigbee technology is used to design the communication module of the system so that communication messages can be sent in case of emergency help to older adults. The Mahony fusion-based fall detection algorithm is utilized to determine the falls of older adults and combined with a beeping alarm to ensure real-time monitoring of the personal safety of older people. An empirical study was designed to analyze the satisfaction and perceived importance of the home care system for elderly users, and the results show that the average value of the satisfaction and perceived importance scores of the elderly users participating in the study is above 3.9, and the standard deviation is small. This paper’s smart community home care information system’s feasibility and effectiveness have been fully proven by the result.

People widely use permanent magnet brushless DC motors because of their simple structure, high operating efficiency, simple control, and easy maintenance. This paper constructs an EFID sensor network by merging EFID and sensor technologies and then designs the motor’s intelligent control system and strategy around it. In this paper, the SVR-PSO algorithm is used to localize the motor rotor, which nonlinear SVR trains to construct a nonlinear system of equations for calculating the position of the target tag, and then the PSO algorithm is used to seek the position coordinates of the target tag. The fuzzy PID is utilized to control the brushless DC motor, and the integral separation method is adopted to improve the control strategy. The SVR-PSO positioning algorithm and the improved fuzzy PID control strategy are applied and analyzed. For 12 tags to be measured, the positioning accuracy of SVR-PSO has improved by 56.8% compared to that of LANDMARC. Although SVR-PSO’s positioning algorithm is influenced by mutual coupling and multipath effects, the overall accuracy is still superior to that of LANDMARC. Based on the fuzzy PID controller optimized by the integral separation method, the motor rotational speed has a fast response, a small overshoot, and a small fluctuation of rotational speed, and can automatically regulate and restore stability under the situation of a sudden change of load. The motor speed response is fast, the overshoot is small, and the speed fluctuation is small, and the motor speed can be automatically adjusted and stabilized under the sudden load change, which has better dynamic and static performance than the traditional PID controller and fuzzy PID controller. The purpose of this paper is to provide a strategy reference for the use of RFID technology in the intelligent control of motors.

The growing strength of China’s national power and the rapid advancement of the country’s foreign development make the country pay more and more attention to the development of Chinese excellent traditional culture education. Integrating Chinese excellent traditional culture into English teaching in colleges and universities can help to inherit and promote Chinese excellent traditional culture. This paper explores the path of integrating excellent Chinese traditional culture into English teaching in colleges and universities based on the background of informatization and designs a digital English teaching method of traditional culture based on somatosensory interaction with regard to the carrier of traditional culture teaching materials. The method was adopted for English teaching practice, and the effect of traditional culture dissemination was analyzed from three aspects: cognition, attitude, and action. A questionnaire survey on students’ interest and willingness to learn was conducted, and the results showed that students’ interest and willingness to learn traditional culture increased dramatically after digital English teaching of traditional culture based on somatosensory interaction. By comparing the students’ English scores with the pre and post-tests, the average of the student’s total scores before the implementation of the teaching case is 45.631, and the average of the total scores after the implementation of the teaching case is 68.521, and the student’s scores are significantly improved. By analyzing the effect of traditional culture dissemination, the digital English teaching method of traditional culture based on somatosensory interaction designed in this paper can effectively promote and disseminate excellent Chinese traditional culture.

In this paper, we use VR equipment to collect relevant facial expression images and normalize the angle, scale, and gray scale of the collected images. The direction quantization of image features is realized by 3D gradient computation, and then the histogram of the direction gradient of each video sub-block is cascaded into the final HOG3D descriptor so as to complete the extraction of dynamic expression features. In view of the multi-dimensional problem of the features, it is proposed to use a principal component analysis algorithm to reduce their dimensionality and use a multi-layer perceptron and deep confidence network to jointly construct the facial expression tracking recognition model. The datasets are used to analyze real-time facial expression tracking in virtual reality. The results present that the verification correctness of both datasets A and B reaches the maximum at the 120th iteration. In contrast, the loss value reaches the equilibrium state quickly at the 40th iteration. The dynamic occlusion expression recognition rate of the deep confidence network on dataset A (66.52%) is higher than that of the CNN (62.74%), which fully demonstrates that the method of this paper is able to effectively improve the performance of real-time facial expression tracking performance in virtual reality. This study can help computers further understand human emotions through facial expressions, which is of great significance to the development of the human-computer interaction field.

The rapid development of higher education has made the social supply far greater than the social demand, which has created the problem of difficult employment for college students. The implementation of the national strategy of strengthening human resources and the healthy development of a harmonious society is facilitated by solving the employment problem of college students. In this study, we first constructed a model of college students’ employment through factor analysis and explored the key factors that affect the employment quality of college students. The keywords were used to extract the hotspots of corporate recruitment and to extract measurement variables such as GPA scores. Factor analysis was used to find out the potential variables, extract the effective factors greater than 1, name and number the principal component factors, and identify the items with the highest correlation of each principal component factor, respectively. The model of this paper was validated through exploratory and validation analyses on the obtained data, which were consistent between the validation and exploratory samples. The actual value of X² was 0.7312, which was in line with the evaluation criteria. The evaluation criteria are met by the values of the other five indicators. The model’s high fit and scientific construction are reflected by it. This paper integrates factor analysis with the exploration of college students’ employment pathways, providing a set of application models that can be used as a reference.

Free throw shooting is an important training program for athletes, as well as the most effective way to score in the game. The study uses the 3DRS model to figure out the initial motion, fixes the vectors by figuring out the weighted bidirectional motion and smoothing the motion vectors, adds adaptive motion compensation, and suggests a way to look at training videos for free throw shooting that is based on changing the motion vector field. We test different video sequences using this paper’s algorithm and other algorithms, and compare the effects of free throw prediction before and after the motion vector field transform algorithm. We also select basketball players to conduct control experiments to investigate the effectiveness of this paper’s motion vector field transform algorithm. Our research shows that the average PSNR and SSIM values for different sequences are higher than those of other algorithms by 6.16% to 7.00% and 4.50% to 5.00%, respectively. Using the motion vector field transform also makes free throw prediction 4.3% more accurate overall. Another thing is that the athletes who were taught using the motion vector field transformation algorithm got 19.23% and 16.07% better at hitting free throws and other technical measures. This shows that the algorithm in this paper can be used in real life to teach basketball free throw shooting moves.

The rapid development of urbanization brings a lot of problems, and the protection of the heritage of historical and cultural districts needs to formulate the protection strategy according to the local environmental adaptation and continuously improve and amend it. In this paper, a model is developed for the environmental adaptation mechanism of the heritage of historical and cultural districts, and the spatial structure of the heritage of historical and cultural districts is calculated using spatial statistical methods. Subsequently, an environmental calculation simulation model is proposed based on ENVI-met software, which combines the spatial structure to simulate and analyze the environment of the Shengjin Tower Historical and Cultural District Heritage in Nanchang City, and analyze its environmental adaptability. The test results show that the environmental simulation model proposed in this paper can accurately simulate the environmental change values of the heritage of the historical and cultural district according to the values of external environmental factors. The results of the analysis of the Shengjin Tower Historical and Cultural District Heritage show that the plots in the districts have an average thermal environment adaptation degree of 1.02, and the average air environment adaptation degree is 1.08. Meanwhile, West Street shows higher performance in air environment adaptation degree, with an average of 1.10. The adaptive degree of the plots in the downwind direction is poor, and they are easily affected by the external environment. This paper lays the foundation for other studies on the environmental adaptation of heritage in historical and cultural districts, and the conservation strategies proposed in terms of symbiosis, wholeness, and dynamic sustainability provide references for the conservation and inheritance of heritage in historical and cultural districts.

An in-depth study of the impact of digital technology on the competitiveness of agricultural export trade can provide data support and direction reference for the development of China’s agricultural digital transformation. Based on the theoretical foundation of the research on the impact path of agricultural exports, this paper integrates the entropy power method to select the agricultural digital technology indicator containing the degree of rural digital infrastructure and digital technology as the explanatory variable and the agricultural export competitiveness as the explanatory variable. Then, the measurement scheme for agricultural digital technology levels and agricultural export competitiveness is designed, and regression analysis is carried out after obtaining and testing the data. It is found that the increase in the level of agricultural digital technology (DIG) has a positive promotion effect on agricultural export competitiveness, and for every 1 unit increase in the level of digital economy development, agricultural export competitiveness increases by 1.596 units. The coefficients of digital financial inclusion level, land size, digital talent ownership, digital logistics level, and digital infrastructure construction are 1.855, 2.835, 0.239, 0.942, and 1.516, respectively, which show a positive promotion effect on agricultural export competitiveness. From the sub-sample point of view, the eastern region is leading China in both the development speed of digital technology and the scale of agricultural exports. The competitiveness of agricultural exports in foreign trade can be significantly enhanced by integrating digital technology and the agricultural export industry chain at an accelerated pace. This paper makes a useful exploration of the direction of agricultural digital transformation and the path to enhance the competitiveness of ag\[*\] agricultural exports in the international market.

In today’s era of rapid scientific and technological advancements, the deep integration of artificial intelligence technology and education is paving the way for intelligent development in English education. This paper designs the architecture of the interactive platform for English education in colleges and universities based on artificial intelligence technology, provides relevant tools for interaction assistance, and establishes an analysis model of students’ online learning interactions. To promote the diversity of English test papers, an improved genetic algorithm is utilised for the intelligent grouping of English test papers. A collaborative filtering algorithm is used to intelligently predict students’ English grades by considering the time sequence of English courses. Performance tests were designed to verify the effectiveness of the application of the interactive platform for English education. The intelligent paper grouping took an average of 1.247 seconds, and the discrepancy between the predicted and actual English course grades did not surpass one grade. The interactive platform for English education had a minimum response time of 1.69 ms, and the application server didn’t use more than 10% of its total CPU. The interactive platform, which combines artificial intelligence technology with English education, provides a new path for innovating teaching methods in English education.

When power grid faults occur, especially complex faults, there are many uncertainties such as switch and protection mis-operation, the power system response will be complicated, which causes many difficulties in power grid fault diagnosis. This paper uses the word2vec model vectorization to process the digitized alarm information during grid faults. The processed fault features are input into the DPCNN model to extract global features of the alarm information. Then, the fully connected layer is used to classify grid faults accurately. Subsequently, a convolution module based on the self-attention mechanism is proposed to achieve accurate prediction of grid faults, and the ReLU function and Dropout strategy are used to realize the optimization of the grid fault diagnosis and prediction model. The simulation model test results reveal that the proposed model can effectively diagnose and predict grid faults, with an average accuracy of 97.05% and 95.93%, respectively. The response time for fault diagnosis in this paper’s model for the empirical application of grid diagnosis is reduced from 6.32 minutes to 0.96 seconds, significantly improving diagnosis efficiency compared to the traditional method. This paper provides an effective method for diagnosing and predicting power grid faults and a solution for improving the management of power grids.

University towns and urban economic growth have important interactions, and time series forecasting models can enable urban planners to better clarify the relationship between the two so as to make more scientific and reasonable decisions. Based on the time series forecasting model constructed in this paper, this paper analyzes and forecasts the relationship between university cities and urban economic growth in C. The main conclusions of this paper are that the p-value of ARIMA(0,2,3) and ARIMA(1,1,1) models is lower than 0.05, and the prediction accuracy of ARIMA(1,1,1) model is higher than that of ARIMA(0,2,3) and ARIMA(1,1,1) model in the prediction of the two. ARIMA(0,2,3) with predicted values of 27631.3, 31541.5, and 24412.45, and the error values from the actual values are only 304.98, 1997.49, and 867.95. This paper’s time series prediction model has achieved good results, as evidenced by this. Y1 shows a highly positive correlation with X1, X3, and X4 and a significant positive correlation with X2, which leads to the conclusion that the total amount of investment in urban construction of the university city of C city has a substantial relationship with the economic growth of C city.

This paper takes the application techniques of non-heritage paper-cutting elements on modern clothing fabrics as an example, analyzing and outlining the symbiotic trend of non-heritage elements and clothing design, i.e., personalization, ethnicity, and the unity of tradition and popularity. It is proposed to utilize the unique performance of bionic scale knitting structure fabrics in bionic technology, combined with the combination of non-heritage techniques, to innovate the clothing design process of non-heritage and to form a new way of bionic interaction and symbiosis between non-heritage and clothing textile. Simulate the performance of bionic scale knitting structure fabrics and explore the advantages of bionic technology in clothing textiles from the perspective of flexibility. Explore the market operation effect of clothing design works of non-heritage technology from the aspects of consumer demand, market evaluation, and market operation, respectively. From the load-displacement curve of the flexibility test, it can be obtained that when the loading value is 55N, the displacements of the bionic knitted structural fabric and the separate scale layer are all greater than 11mm, and the bionic scale knitted structural fabric possesses good bending deformability. The evaluation results of the enterprise experts show that the clothing works integrating the non-heritage elements have obvious market demand.

Color is a powerful tool for communication across language and cultural boundaries. Through understanding color emotion and color psychology, designers can establish a deeper emotional connection with the audience and achieve more effective visual communication. The purpose of this paper is to introduce healing colors and their specific applications in visual communication design. In order to explore the specific application value of color healing in visual communication design, this paper mines product color information extracts colors and screen clusters according to the K-Means clustering method, and finally constructs product color data sets. Mining consumer emotional information, combined with the product color data set, calculates the consumer emotional weight and the sense of product safety experience through experimental technical means so as to judge the application value of healing color. According to the final results of the calculation of consumer safety experience, orange (0.6084) and green (0.5718) have higher weights, which also lead to higher product sales. This shows that healing colors affect consumers’ psychological and physiological feelings while also influencing product sales.

Defect detection plays a crucial role in chip quality control, and the chip field has widely researched and applied machine vision-based surface defect detection methods due to their high efficiency, accuracy, and real-time performance. In this paper, we utilize imaging equipment to collect images of high-performance phase noise measurement chips, and we use a mean filtering algorithm and a Sobel operator to preprocess the collected chip images. Then, the PCA method is applied to downscale the extracted chip shape and texture features, and the improved support vector machine algorithm using a genetic algorithm is used to classify and recognize chip defect features. The test results show that the error rate of the defect detection method for high-performance phase noise measurement of chip surface defects is only 1.82% at the highest, and the average error of the measurement of the chip pin width and pitch is much lower than the actual production of the specified error rate. Meanwhile, the design requirement of 3 pcs/s detection efficiency in the actual production of high-performance phase noise measurement chips is satisfied by the theoretical maximum defect detection efficiency of the method. The chip defect detection method presented in this paper has both practical application value and theoretical research significance.

The personalized recommendation of courses and exercises can realize more accurate, tailor-made teaching, significantly improve teaching efficiency, and stimulate students’ learning interest and motivation. In this paper, we first obtain the knowledge point data of computer specialties and then extract the course knowledge point entities using the TF-IDF algorithm. We then use artificial rules to extract the relationships between the entities, design an estimation method for students’ mastery of the knowledge points, combine the knowledge graph to generate different learning sequences for students, and recommend course resources for them. Then, we use a simple Bayesian classification algorithm to classify and characterize the text of exercises and recommend personalized exercises to learners by combining their mastery of knowledge points, degree of difficulty, and other characteristics. The system in this paper is applied to a computer program at a university in Guangzhou to compare the teaching effect. It was found that the average grade of the experimental class was 11.25 points higher than the average grade of the control class, an improvement of 11.92 points compared to the pre-test, and the distribution of the scores clearly progressed from the 75-80 point range to the 85-90 point range. In the three dimensions of perceived usefulness, perceived ease of use, and intention to use, the vast majority of the respondents chose to agree or strongly agree, and the average score of the survey was 4.31, 4.24, and 4.18, respectively, indicating that the system is easy to operate and has a reasonable functional design and that the results of the recommended course resources and exercises basically conform to the learners’ psychological expectations and practical needs. This study proposes a feasible path for the integration of artificial intelligence technology into computer teaching, which will improve the quality and efficiency of computer education and teaching.

In order to fit the new trend of talent cultivation and social demand, this paper proposes a new talent cultivation mode of industry-education integration and utilizes the outstanding advantages of blockchain technology in data preservation and tamper prevention to help preserve and transmit talent cultivation data between schools and enterprises more securely, and promote the realization of the new talent cultivation mode. In this paper, we propose an ECDH-based talent cultivation data sharing method between schools and enterprises, which stores users’ talent cultivation data by constructing a talent cultivation data information format and constructing talent cultivation diagnostic record transaction to lay the foundation for talent cultivation data uplinking. On this basis, the talent cultivation data information is encrypted to protect the privacy of talent cultivation data and ensure the safe sharing of personal talent cultivation data. According to experiments, the new algorithm’s average transmission rate is 6.69Mb/ms, and the last 100 rounds of power grabbing have a success rate of 0.54%. It shows that the algorithm proposed in this paper has the characteristics of being fast and safe, which helps to innovate the existing talent cultivation mode of school-enterprise cooperation. The average score of graduates’ competence has improved by 0.88 points, and more than 85% of employees agree that the new model has a positive effect.

With the reform and development of modern education curriculum, the focus of preschool education has changed from focusing on the “learning results” to the “learning process” of young children, and deep learning, as a hot trend of education reform, is an important way to implement education reform in pre-school education, which is based on problem-solving, emphasizing on the application of migration, and focusing on the reflective construction. Deep learning, a trend of educational reform that is based on problem-solving, emphasizes transfer and application, and focuses on reflection and construction, is an important way to implement educational reform in preschool education. This study first explores the connection between preschool education and deep learning, collects the problems of deep learning in preschool education through a questionnaire survey, and builds a personalized preschool education model based on deep learning in combination with DELC theory. The teaching effect of the deep learning preschool education model is explored through educational comparison experiments in this paper. The mean value of the preschool education model of this paper compared with the traditional preschool education in the four dimensions of learning motivation, learning input, learning strategy, and learning outcome were increased by 2.00~4.00 points, respectively, and the Sig values were all less than 0.05. Meanwhile, the average grade of this paper’s teaching model was 65.124 points, which was increased by 3.112 points compared with the average grade of the traditional cumulative preschool education model, proving that this paper’s effect of the application of deep learning personalized preschool education mode, which has a significant advantage in the degree of deep learning and learning achievement. In addition, this study provides suggestions for the development of deep learning in preschool education from the levels of teachers, teaching materials, teaching curricula, and external support so as to promote the process of deep learning in preschool education.

In this paper, we use word frequency semantic analysis and sentiment analysis to analyze tourists’ review texts of four different brands of cruise ships and extract cruise design elements such as functional service parameters, schedule parameters, and affordable parameters from them. The evaluation factor set of the cruise ship spatial layout division scheme is constructed, and the correlation between the evaluation factors is analyzed through multivariate statistical analysis to establish a comprehensive evaluation method of the scheme in order to determine the optimal module division scheme. After analysis, it can be seen that functional services (service, catering, room, entertainment) are the most concerned elements for tourists, and older people and children are an important part of tourists. Residential functional areas should be concentrated on the 5th-11th floors. The overall module division scheme 3 (module division combination [1,2], [3], [4], [5,6,7,8,9], [10], [11], [12,13], [14]) of the passenger cabin has a correlation of 1 with the efficiency of the spatial layout, and the weight value reaches 0.283, which is the optimal scheme. The effective space utilization rate of cabin interior space layout design scheme 3 is 0.483. The method proposed in this paper, which enhances the efficiency of cabin space layout on cruise ships, has practical significance.

This paper focuses on the “Internet + education” model and describes the feasible strategy of using data mining technology to establish a teaching quality monitoring mechanism in colleges and universities. Through the study of teaching quality monitoring pathways, such as teachers’ teaching level, students’ academic status, and course learning effectiveness, this paper’s main content provides a directional guide. Based on the teaching quality monitoring pathway, we choose the related algorithms of data mining technology, such as cluster analysis, association rule, and factor analysis, to be applied to the various aspects of the teaching quality monitoring pathway. The clustering algorithm is used to classify 1500 teachers in a university into 4 categories, such as excellent teaching effect, good teaching effect, etc., as well as to cluster students into 6 categories, such as excellent in all the courses and low to medium level, according to the course grades. In addition, this paper analyzes students’ course learning effectiveness, academic level, and career guidance using association rule algorithms, factor algorithms, and decision tree algorithms, respectively. From teachers’ teaching to students’ learning and employment, this paper constructs an all-round teaching quality monitoring mechanism using data mining technology, which contributes to the improvement of teaching quality in colleges and universities.