In order to realize the strategic goal of environmental protection and low carbon, designing a set of resource clustering and regulation strategies that take into account energy saving and operating costs has become a research challenge for virtual power plants. In this study, the ICEEMDAN-CNN-SSDAE hybrid model is used to realize high-precision prediction of electricity price and load data in virtual power plants. The objective function and constraints of resource clustering and cooperative regulation of virtual power plants are established under the condition of demand response, and solved by Markov process. Finally, the virtual power plant resource clustering and co-regulation model is constructed on the basis of the deep reinforcement learning model framework by combining the prediction model and objective function. The results show that the ICEEMDAN-CNN-SSDAE model proposed in this paper can guarantee high prediction speed (0.062s and 0.059s) while having high prediction accuracy. It is also found that the average capacity of the output power of each component in the virtual power plant system after the model clusters and optimizes the regulation of a virtual power plant resources increases by 0.535-0.686 MW/h compared with the pre-optimization period, and the economic efficiency and energy utilization are also improved to different degrees. The research in this paper verifies the rationality and effectiveness of the proposed model, and provides certain theoretical basis and guidance method for virtual power plant resource clustering and cooperative scheduling.

In recent years, hyperspectral imaging technology has a large application prospect in quality inspection in the tobacco industry. The study is based on near infrared spectroscopy technology and partial least squares regression method to establish mathematical analysis model of tobacco adulteration ratio of four components, such as expanded tobacco, stalked tobacco, large threaded tobacco and small threaded tobacco, and carry out internal and external inspection. At the same time, TLBO algorithm is used in the optimization of ELM tobacco purity grade determination model to realize the design of tobacco purity monitoring method, and then build the real-time monitoring system of tobacco blending ratio and purity. Tobacco with different purity grades were selected for experimental testing and model comparison analysis. The results show that the constructed PLS model can accurately predict the adulteration content of the four components in tobacco, and the correlation coefficients between the predicted and actual values are above 0.95 (p < 0.01), and the relative deviation of the prediction is below 3%.The accuracy of the TLBO-ELM model for identifying tobacco with different grades of purity is 88%, and the classification accuracy in the validation set is improved by 9.32% compared with the ELM model, which is within the acceptable range. It shows a better classification effect than PLS-DA in an acceptable range, which proves that the proposed method can be used for discriminating and monitoring the purity of tobacco. The monitoring system in this paper can be used in the analysis of tobacco blending ratio and purity detection.

The combination of technology and art in film and television special effects can greatly enhance the visual impact of film and television animation, and improve its commercial and artistic value. The study elaborates on the generation of special effects in 3D modeling technology in film and television production, and based on the application of rigid body special effects, it proposes a highly efficient rigid body crushing mode for optimization in response to the problems such as low real-time performance in rigid body crushing simulation. The model is a particle-based real-time simulation method of object crushing under the impact of external forces, using the discrete unit method to represent the inter-particle force, and proposes an inverse crushing mechanism, which realizes the particle-based DEM simulation on the GPU. Experimental results show that the simulation method of rigid body crushing constructed in this paper can meet the simulation requirements in different scene scales, and the rendering rate in small-scale and large-scale scenes is 90~155FPS and 40~50FPS, respectively, which is not only realistic but also real-time, and can meet the requirements of film and television production.

The lateral quantitative control of paper machine is the key to the quality control of paper machine. In this paper, the paper machine transverse quantitative control system is introduced, for the transverse quantitative control and the model exists of strong coupling, large time lag, multi-dimensional number of characteristics, combined with the predictive control theory, put forward based on the dynamic matrix control and Gram polynomials of the intelligent prediction method. Meanwhile, the speed chain control device of the paper machine control system is designed based on the self-immunity controller and simulated and analyzed. The simulation results show that the slope of the model size and computation in this paper’s method is 1.97, which is smaller than that of the traditional MPC’s 2.85, and has more computational efficiency without affecting the predictive control effect, which is more suitable for online operation. At the same time, the speed chain control system applying the self-resistant control algorithm is better than the traditional PID control in terms of steady state performance, dynamic performance and anti-disturbance performance. The method proposed in this paper facilitates the predictive control and speed chain anti-disturbance of the lateral dosing control system of paper machine and promotes the improvement of paper quality.

The rapid development of natural language processing technology makes machine translation play an increasingly important role in cross-lingual information exchange. In this paper, we propose an English long text translation paradigm based on the self-attention mechanism and introduce various improvement strategies to enhance the model performance. The model’s ability to process English long text is improved by introducing multi-head attention and hierarchical self-attention modules. The long text translation paradigm is optimized by using techniques such as residual linkage, layer normalization and dynamic memory network. A series of experiments are conducted to verify the effectiveness of the improved model on the English long text translation task. The English long text translation paradigm constructed in this paper outperforms the Transformer model and other related variants on both CPU and GPU. And Transformer outperforms this paper’s model in terms of n-gram accuracy in real translation experiments. The BLEU scores of the improved model on News and other datasets are significantly improved compared with the original baseline model, which verifies the effectiveness of the improvement strategy of this paper and provides a reference for the solution of the problem of English long text translation.

Based on the overall demand analysis of intelligent class scheduling system, this paper determines the overall structural design scheme of intelligent class scheduling system, and realizes the intelligent class scheduling system using software development language. Aiming at the problems of overfitting and easy to fall into the local optimum of the benchmark genetic algorithm, the adaptive genetic algorithm optimization in the intelligent scheduling system is realized through the nonlinearization of the fitness function, the crossover operator, and the variational operator. Determine the experimental environment and set up groups (experimental group and control group) to evaluate the optimization performance of the algorithm and the application effect of the system. The program based on Improved Adaptive Genetic Algorithm (IAGA) (class time distribution balance: 0.79) is 0.23 higher than the program based on Adaptive Genetic Algorithm (AGA) (class time distribution balance: 0.56) in terms of class time distribution balance, and IAGA algorithm is more effective and superior in solving the problem of class scheduling in colleges and universities as compared to AGA algorithm. This system can reduce the heavy workload of teaching affairs, and also solve the scheduling difficulties of colleges and universities in the case of teacher shortage.

This paper proposes an accounting statement evaluation model based on hierarchical analysis algorithm (AHP)-fuzzy comprehensive evaluation (FCE) under the theory of combinatorial mathematics. The initial evaluation index system is determined based on the principles of evaluation index system construction, and after the Delphi method screening, the final accounting statement evaluation index system is composed of 14 secondary indicators and 5 primary indicators. Using hierarchical analysis algorithm to calculate the weights of the indicators, and substituting the calculated weights into the comprehensive fuzzy matrix to finalize the task of evaluation and analysis of accounting statements. The first-level indicators are Solvency A2 (0.1680) < Profitability A1 (0.1797) < Operating Capacity A4 (0.1971) < Cash Capacity A5 (0.2093) < Development Capacity A3 (0.2459), while the weights of the second-level indicators are distributed in the range of [0.0174, 0.2079]. The comprehensive evaluation score of the accounting statement of X Breweries Group Company is 73.31, indicating that the overall condition of the company's accounting statements is good.

In the context of continuous innovation in science and technology, consumer demand is becoming increasingly diversified, especially in product packaging design, personalization and uniqueness have become a new pursuit. The article proposes a computer image fusion DPformer-GAN model based on Transformer model and GAN, which is used to realize personalized packaging image fusion and generation. The digital image is then converted into a personalized packaging object through digital printing technology, which then realizes the innovative practice of personalized packaging.The DPformer-GAN model reduces the overlap and occlusion by 12.81% and 2.98%, respectively, compared with the better-performing CGL-GAN model when fusing and generating personalized packaging images. When personalized packaging images fused with computer images are used for digital printing proofing, keeping the percentage of dots lower than 50% can achieve the maximum degree of color reproduction and better retain the visual effect of digital images. Consumers are more favorable to the aesthetics and user experience of personalized packaging, with a favorability rating of 9.16 points and 9.29 points, respectively. It is feasible to use digital printing technology to print the packaging images generated by computer image fusion into personalized packaging, which can also further enrich the image practice options of personalized packaging.

In the field of modern architectural design, the application of artificial intelligence technology and pictorial interaction design is gradually causing revolutionary changes. This paper explores how to integrate these advanced technologies into architectural space design with a view to improving design efficiency and enhancing user experience. Artificial Intelligence Generated Content (AIGC) technology and BIM+VR technology are applied to architectural space design in general, where BIM+VR technology is used for visual modeling of architectural space design solutions and realizing 3D image interaction with users. Specifically, this paper proposes an intelligent assisted design method for architectural space based on Pointnet++ deep learning neural network and a simulation design method for 3D virtual architectural space to realize intelligent and personalized design of architectural space.The average class accuracy and overall assessment accuracy of Pointnet++ trained assessment points reached 83.47% and 76.63% respectively The design scheme given by this model has intelligence, objectivity and authenticity, which can better realize the intelligent assistance for architectural space design. In addition, the 3D virtual architectural space experience system constructed in this paper scores more than 90 points in all experience indicators, with good user experience performance, to meet the user’s image interaction needs, so as to provide a basis for the optimization of architectural space design.

In this paper, the CD4511 chip is selected as the focus of this research to build the LK8820 platform, which mainly consists of the power supply, interface and reference voltage board (IV), power supply and measurement board (PM), digital function pin board (PE), and analog function board (WM). The input and output pins of the CD4511 IC chip are connected to the PIN pins on the PE board of the LK8820 test platform for testing, and the test functions are written in the C language environment. After the test program is written, the LK8820 test platform is used to test the CD4511 integrated circuit chip for the environmental adaptability of electrical parameters. The use of highly integrated chip CD4511 makes the small range of measurement accuracy is very high, but the large current range error is relatively large, due to the external large current range using precision resistors with an accuracy of 0.5% in parallel, after calibration, the error is controlled within the allowable range. 6 input pins of the input high level test results and the input low level test results are in the range of RMS, the number of anomalies is 0, which meets the IC electrical parameters environmental adaptability test. The test results of input high level and input low level of 6 input pins are all within the RMS value range, and the number of abnormalities is 0, which meets the requirement of environmental adaptability test of integrated circuits.

The nation-state is regarded as the basic form of the modern state, but whether the modern state is really a “one nation, one country” type of political community as depicted by the nation-state narrative. This paper explores the influence of national narratives on national identity by constructing an evaluation index system, using a questionnaire survey method, and taking adolescents as the research object. The independent variables of this study are national narrative, including national language, national spirit and national memory, and the dependent variable is national identity, including cognitive tendency, emotional tendency and behavioral tendency. The weights of the indicators and regression results were calculated by AHP-entropy weighting method. The analysis results show that national vocabulary has a great influence on national language, while sense of belonging is the biggest factor affecting emotional tendency, and most of the dimensions of national narrative are positively correlated to national identity with a significant effect. The correlation coefficient between national etiquette and national identity is 0.203, and the correlation coefficient between national history and national identity is 0.254. National history and national etiquette have a significant effect on national identity.

Local colleges and universities are an important part of China’s higher education reform and development, and the quality of cultivation of top-notch innovative talents has a direct impact on the development speed and level of local economy. In this study, the decision tree algorithm is used to establish a prediction and early warning model for students’ performance in the process of cultivating new engineering top-notch innovative talents in colleges and universities, and the K cross-validation method is used to optimize the model parameters and improve the prediction accuracy. Then, based on the intelligent prediction model and the cyclic structure intervention theory, we constructed a dynamic adjustment model for the cultivation system of new engineering top-notch innovative talents. The results of the empirical application of the model show that the hardware and facility conditions of talent cultivation in college D have significant improvement under the application of the dynamic adjustment model. In addition, both graduates (>4.00 points) and employers (>3.67 points) gave a high degree of achievement to the training quality of the university’s top innovative talents in new engineering disciplines. This study helps to meet the demand for high-quality engineering talents for regional economic and social development, enhance the adaptability of higher education and improve the quality of talent cultivation.

The countries in East Asia are neighbors in one country, and their cultures are cross-fertilized with each other, so it is of practical significance to enhance the sense of regional community on this basis. In order to explore the effect of cultural exchange and cooperation on the enhancement of regional community consciousness, this paper constructs a semantic graph for the relevant comments on social platforms, combines GNN and LSTM, and constructs a GNN-LSTM sentiment recognition model to identify and quantitatively represent regional community consciousness. Regression analysis is used to test the enhancement effect of cultural exchange and cooperation on regional community consciousness. The experimental results show that the GNN-LSTM model has a better emotion recognition effect and can provide help for the extraction and quantitative representation of regional community consciousness. The regression coefficients of cultural exchange status on the two models are 0.423 and 0.439 (p<0.01), indicating that cultural exchange has an enhancing effect on regional community consciousness. Cultural distance acts as a mediating variable, the more frequent the cultural exchange and cooperation, the smaller the cultural distance, the more the regional community consciousness can be enhanced.

The development of artificial intelligence enables computers to not only simulate human artistic creations, but also synthesize fine art works with deeper meanings based on natural images. This study digitally parses the fusion of fine art and philosophy visual expressions, and develops a visual expression system based on the fusion of fine art and philosophy by utilizing a variety of key big data algorithms for visual expressions such as adversarial networks. Research on pattern recognition of this system in art creation is carried out through model training, recommendation performance evaluation, pattern recognition strategy application and regression analysis. The model in this paper works best when the number of nearest neighbors k=15, and the recommendation model in this paper can provide a personalized list of artwork recommendations for different people. The recognition of the system in this paper in the five dimensions of “spiritual level”, “value level”, “philosophical level”, “aesthetic level” and “technical level” is distributed between 4.24~4.55. The results of regression analysis indicated that the system in this paper can improve the artistic creation as well as pattern recognition.

The current gemstone jewelry design is not specialized and systematic enough, and most of the gemstone jewelry styles are common and single, which makes the value of gemstone not reasonably reflected. Starting from the evolution of gemstone facets, facet texture and cut classification, the article takes the three-dimensional features of gemstone facets as the basis and optimizes the cut parameters of gemstone facets by combining with geometric transformation theory. The optimized cutting parameters were used as the basis for 3D modeling, and the 3D model of the round faceted gemstone facets was established with the simulation software, and the quantitative analysis of the data was carried out through the brightness, uniformity, and scintillation values. When the table width ratio is 50-60%, the difference between the crown angle and the inclination angle of the star facet corresponding to the circular faceted gemstone facets varies from 9.172° to 20.673°. When the length ratio of the lower girdle facets is 65% to 95%, respectively, the range of the difference between the lower girdle facet inclination and the pavilion angle is between 0.781° and 1.967°. The scintillation value obtained after designing the gem facets using the method of this paper is 4.67 times higher than that of the traditional method. The optimized design model of round faceted faceted gemstones constructed on the basis of geometric transformation theory can provide new ideas for the jewelry design of round faceted faceted gemstones.

Based on the wide application of collaborative filtering algorithm in the current field of graduate employment, this paper introduces it into the employment recommendation mechanism of senior college students and takes it as one of the auxiliary means to formulate the employment policy for senior college students. By studying the implementation effect of employment policy, so as to explore the adaptability of employment policy. Through the time series prediction method based on neural network, the prediction model of employment policy adaptability of higher vocational tertiary students is constructed. Compare the prediction performance of this paper’s prediction model with other models, predict the employment policy implementation effect through this paper’s model, and finally, construct an evaluation system of employment policy prediction results to evaluate the model prediction results. The prediction fit of the model of this paper is 0.8644, and the average relative prediction error is 0.35%, which is the best performance among all prediction models. In the prediction of the employment of higher vocational college students in province A, the number of employment of higher vocational college graduates is positively correlated with the average annual income level and the market share of graduates, and negatively correlated with the total number of gaps between faculty and students in the institutions and the amount of education expenditure. The overall score of the employment policy implementation effect predicted by the prediction model in this paper is 88.8, which is a good evaluation result.

Since the 21st century, the rapid development of artificial intelligence technology, artificial intelligence in many fields have achieved remarkable research results and applications, the integration of AI technology and music has also gradually become an emerging research field. In this paper, first of all, the generation principle of vocal interpretation AI model is studied, in order to realize the digital conversion of vocal interpretation this paper constructs a converter model so as to facilitate the application of artificial intelligence algorithm model. In this paper, in order to match the generated opera vocal music with the given opera performance background, the rhythmic relationship between opera and vocal interpretation is established, and the relationship between motion salience and note intensity is constructed. On this basis, the generator model is changed to a model with a loop structure, and the music theory is mathematically modeled to propose an adversarial network model based on improved multi-track sequence generation. Finally, for the prediction problem in the vocal interpretation AI model, this paper is optimized based on support vector regression. Through empirical analysis, the improved model in this paper has a smaller gap with the real dataset on the metrics of pitch use, pitch shift, note interval and polyphony rate within the track. Meanwhile, the TD distances of this paper’s improved model on the three datasets are 0.655, 0.784, and 0.685, respectively, which is the smallest in the experimental data, and the quality of the improved model’s vocal music generation is excellent. The pitch distribution of this paper’s improved model and the original vocal data basically match, indicating that this paper’s model has better effect on pitch adjustment. In addition, the improved model of this paper generates vocal music with better musicality effect, which has higher musicality while avoiding the generation of more invalid notes. The research work of the paper proves the feasibility of the AI model for opera vocal interpretation and provides a new solution for the current field of vocal music generation.

The study constructs a prediction model to predict the mental health status of innovative entrepreneurs. The real data of mental health assessment of innovative entrepreneurs in S province in 2023 is chosen as the data source. The recursive random forest feature elimination method is used to select the features of the mental health status prediction model. The pre-selection-elimination mechanism was used to construct the mental health state prediction model. The prediction models constructed by support vector machine algorithm, decision tree algorithm and random forest algorithm were trained and evaluated respectively. The AUC value and accuracy corresponding to the random forest algorithm are 0.9126 and 86.39%, respectively, which are better than the other two comparison models. Among the 17 mental health characteristic variables selected in this paper, emotional stress and self-acceptance degree have the greatest influence on the prediction model based on the random forest algorithm.

Taijiquan is a kind of sport that can be used as a national fitness program, and its effect on the training effect of adolescent physical coordination has important research value. In this paper, particle swarm optimization algorithm is applied to the optimization of taijiquan training program, and independent samples test and analysis of variance (ANOVA) are used to investigate the quantitative impact of taijiquan training on adolescents’ physical coordination. The results show that the particle swarm optimization algorithm can effectively improve the effect of taijiquan training, and the algorithm convergence and other properties have obvious superiority compared with other algorithms. At the same time, after the experiment, all the physical coordination test indexes of the experimental group were significantly improved compared with the pre-test and the control group, which explains the important role of taijiquan training in the physical coordination training of adolescents.

Social media as a new type of media has become an important channel for people to obtain information and communicate, which brings new opportunities and challenges for English education. In this study, the Markov chain model is improved by using the weighted ward system clustering method and the fuzzy set theory to improve the prediction performance of the model. Then the ARIMA model and the improved Markov chain model are combined to construct an improved time series prediction model to realize the prediction of the interaction efficiency of social media in English education. The performance of the improved prediction model is superior compared to other comparative models, providing reliability for the subsequent prediction results. The prediction results show that the interactive efficiency of social media interaction data in English education shows an upward trend over time, and the number of readings and playbacks of English courseware resources as well as video resources increases from 18477 and 18147 to 88629 and 84571 in six months. The predicted results of this study indicate that social media has good interactive efficiency in English education, which can be utilized in the future to expand the dimension of education, build an English education platform, expand the teaching space and extend educational thinking, and play a percolating role in English education.

Product awareness can be spread to a wider audience through advertisements, and the introduction of social media platforms has made it easier for marketers to spread brand advertisements and fully attract consumers to generate corresponding purchasing behaviors. Based on fuzzy set theory, the article establishes a fuzzy evidence theory through evidence-weighted fusion, and calculates the utility value of social media advertisements in order to achieve the optimal evaluation of social media advertisements. Then, it explores the influence of social media advertisements on consumers’ purchasing behavior with OLS-LR model, and combines the VAR model to study the dynamic correlation between different types of social media advertising channels and consumers’ purchasing behavior. Without considering the control variables, the regression coefficient of social media advertising on consumer purchasing behavior is 0.438, which is significant at 1% level. With the fourth-order VAR model, CICs social media advertisements have a significant short-term effect on consumer purchasing behavior, while FICs advertisements show a long-term effect. Based on the fuzzy evidence theory, the utility value of social media advertisements can be calculated, and based on the sorting of the calculation results, the construction of optimization paths of social media advertisements can be realized, which provides a new research basis for improving the efficiency of corporate advertising and marketing.

Teaching curriculum design is centered around the three dimensions of affective attitudes and values, processes and methods, and knowledge and skills, which fit with the affective learning model composed of emotion, learning and cognition. This paper brings affective analysis into art curriculum design and proposes a learner affective model for teaching art courses driven by multiple teaching objectives. Through multi-objective optimization, we give an interactive decision-making method based on a hierarchical affective cognitive model to simulate learners’ affective decision-making under multi-objective-driven teaching. Analyze the teaching process of incorporating affective learning strategies in an art course, and examine the interrelationship between affective engagement and learners’ knowledge construction in three rounds of learning activities. To analyze the impact of affective learning strategies on students’ learning outcomes. The experimental group (affective learning strategy group) significantly outperformed the corresponding creativity abilities of students in the control group in the three components of surprise, originality and challenge after the teaching of the art course, and the affective learning strategy succeeded in stimulating students’ creativity. The combination of affective learning model and curriculum design can enhance the effectiveness of art education.

Based on the view that artistic style is mainly reflected in sculpture and painting, the sculpture and painting style of Giacometti is analyzed in depth. Starting from the scope of application of big data technology, the theoretical knowledge based on information theory is proposed to explore the differences in the styles of Giacometti and his contemporaries, and the basic concepts used in the processing are defined, including Shannon entropy, conditional entropy, and interactive information. Redundancy, orderliness, and complexity are set as eigenvalues that can characterize the style of art works, and the eigenvalues of the style of Giacometti and contemporaneous artists are analyzed. The minimum, maximum, and average values of the complexity of Picasso’s works are 207, 991, and 596, respectively, while the values of the three indexes of the complexity of Giacometti’s art works are 446, 990, and 718, respectively, and on the whole, the complexity of Picasso’s works is smaller than that of Giacometti’s works. This paper comprehensively reveals the stylistic differences between Giacometti and his contemporaries through the analysis of quantitative characteristic indexes.

In the context of big data, with the accelerated development of digital technology, enterprises are facing the pressure of digital transformation, and at the same time, big data computing system provides technical support for the digital transformation of enterprises. In this paper, we propose a data analysis system based on iterative computing for the digital transformation of enterprises. In order to avoid the resource consumption caused by unnecessary repeated calculations in iterative computing, this paper proposes optimization based on Spark fault-tolerant mechanism and constructs an enterprise data analysis system based on iterative computing model, which provides technical support for enterprise digital transformation. On this basis, this paper also provides optimization strategies in terms of organizational structure and cultural coordination for enterprise transformation, which provides an effective path for realizing comprehensive digital transformation of enterprises. Through the test of this paper’s iterative computing data analysis system, the speed of Spark optimization based on this paper is increased by nearly 2 times, which illustrates the usefulness of this paper’s optimization based on Sparl fault-tolerant mechanism. Meanwhile, the cache misses of the data analytics system are in the range of 46% to 60%, which provides better performance performance in terms of cache hits and time overhead. In this paper, we provide practical and feasible transformation paths for enterprise digital transformation from three aspects, including digital technology, enterprise organizational structure and culture, and promote the development of enterprise digital transformation.

With the frequent occurrence of global climate change and extreme weather events, meteorological forecasting technology has gradually become an auxiliary technology for production activities. In order to improve the quality of meteorological analysis results, a technology utilizing cloud radar data as the core is proposed. The vertical distribution of water vapor and liquid water in the atmosphere is detected by a ground-based microwave radiometer. The median filtering method is used to further smooth the classified and preliminarily removed reflectance factor data, and computer information processing technology is used for data analysis. The experimental results of Taiyuan ground based remote sensing high altitude detection experiment showed that in the data availability test, the research method had a data availability rate of 97.3% when the height was 2km in humidity data. When conducting accuracy analysis of the results, the root mean square error of the relative humidity profile was only 22.0% when the height increased to 12km. This indicates that the research method can conduct high-quality meteorological analysis and provide assistance for meteorological forecasting.

To solve the problem of identifying intrinsic relationships between objects and mirror segmentation in semantic segmentation of urban scenes using current multi-modal data, this study innovatively integrates color images, depth information, and thermal images to propose a network model that integrates modal memory sharing and form complementarity, and a hierarchical assisted fusion network model. Compared with existing advanced urban scene semantic segmentation methods, the proposed method performed excellently in terms of performance, with an average pixel accuracy and mean intersection over union of over 80% for different objects. In addition, the research method achieved clearer and more complete segmentation results by strengthening contextual associations, and edge processing is also smoother. Even in object segmentation with similarities in distance, shape, and brightness such as “vegetation” and “sidewalk”, the research method still maintained high accuracy. The research method can effectively handle the complexity of urban scenes, providing a new solution for semantic segmentation of multi-modal data in urban scenes.

In the realm of supercapacitor energy storage, multi-element transition metal-oxides with high theoretical specific-capacitance values have been extensively explored. However, their poor electrical conductivity and cycling stability limit their applications. In this study, Co-MoO4@Co3O4/NF was formed by loading Co3O4 on the nickel foam (NF) surface as a substrate by solvent co-precipitation method and annealing treatment first, and then growing CoMoO4 on the surface of Co3O4 by hydrothermal reaction and calcination. Co3O4 nanosheets, which are derived from ZIF-67, offer more active sites and simpler ion/electron transport paths. The electrochemical characteristics of the composite electrode can be substantially boosted by the synergistic effect between Co3O4 as the inner layer and CoMoO4 as the outer layer in the CoMoO4@Co3O4/nickel foam hierarchical composite structural materials. When combined with activated carbon (AC) to form an asymmetric supercapacitor, it exhibits a capacitance normalized to unit area of 0.669 F cm2 at 1 mA cm2. Furthermore, the assembled asymmetric supercapacitor demonstrates an energy per unit volume of 209.29 mWh cm−2 at the current flux of 0.75 mW cm−2, and upholds 89% of its Initial surface capacitance after 6000 cycles.

In this study, a complete two-dimensional anatomical model of the rabbit heart was constructed by Zhang et al. The positive effects of external noise stimulation on cardiac pacemaking and conduction have been studied using computer simulations. Our results show that: 1) the slower pacemaking rate or even oscillation cessation, which is induced by the removal of from the sinoatrial node or a decrease in the active sinoatrial node cell population, can be recovered by adding external noise. 2) Compared to the effect of removal alone, the combined effect of and cell death had a greater impact on slowing down the pacemaking rate. These phenomena indicate that external stimuli play an important role in controlling the initiation and conduction of sinoatrial node pacemaker activity, and that the aging heart may experience obstruction of sinus node conduction and even sudden cardiac death due to decreased cell membrane or partial myocardial cell death. Our results provide insights into the intrinsic mechanisms underlying electroshock healing in some heart diseases.

The expansion of information-based education and the growing need for computer laboratories have made their day-to-day management a key part of teaching and learning management activities. The traditional manual management method is inefficient and difficult to handle the increasingly complex management tasks. The emergence of optimized management systems has effectively improved the effectiveness and quality of computer laboratory management work, but the operational efficiency and security of management systems are also facing enormous challenges. To improve the real-time and security of management systems while efficiently completing management tasks, this paper conducted in-depth research on the design of computer laboratory optimization systems using nonlinear engineering algorithms. To verify its effectiveness, this article tested the system from three perspectives: functional implementation, running load, and security performance. The experimental results showed that at the functional implementation level, the average response time of the system for each test case requirement was 1.0767 seconds. It can be seen from the specific results that the demand response speed of the computer laboratory optimization management system based on nonlinear engineering is fast, which can efficiently handle the daily management tasks of the laboratory and promote the improvement of the quality and level of teaching management.

Due to its advantages in energy conservation, emission reduction and recyclability, air-supported membrane structure (ASMS) has been widely implemented in various applications, including large stadiums, conference centers, warehouses and temporary buildings. Compared to traditional building, the structural and material properties of ASMS are special which significantly affect smoke diffusion and flow behavior during fire scenarios. Moreover, the design methods for smoke management system of ASMS buildings are still lacked and further research is required. In this study, a numerical model of a coal storage bin was built. The impact of several key design parameters, including such as height of natural smoke vent, fire source location and makeup air methods, on the performance of ASMS building`s smoke management system were evaluated. Gas temperature, CO concentration, visibility, smoke exhausting quantity, critical pyrolysis temperature and critical heat radiation intensity were selected as evaluation indexes. The results indicated that better natural smoke exhaust efficiency was achieved when the natural smoke vents were located at the height between 80% and 100% of the building’s total height. Setting natural makeup air vent in the wall of smoke bay 3 can achieve the best cooling effect. However, compared to natural makeup air, the natural smoke exhaust efficiency of mechanical makeup air was much better. When the rate of mechanical makeup air reached 120% of the required natural smoke exhausting quantity, it has significant improvements in temperature reduction, visibility and natural smoke exhausting quantity which are crucial for ensuring safe evacuation during fire events. These findings provide a design refer for enhancing fire protection and construction practices in ASMS buildings.

The development of modern economy encourages the development of enterprises. As an economic accounting system, the financial integration system is not only an indicator to evaluate the income of enterprises, but also an indicator to evaluate the business status of enterprises. In the modern information environment, how to carry out the financial fusion system information of modern information environment is the focus of enterprises. The information contained in the financial integration system provides complete accounting information for the application of communication network and computer technology by combining modern technical means, and effectively analyze accounting and make reasonable economic plans. In the process of dynamic design of financial integration system, it can avoid accounting risks and significantly improve the economic benefits of the company. Firstly, the paper expounded the analysis of the influencing factors of mobile Internet on accounting information system, and briefly analyzed the relevant factors of financial integration construction. It considered the significance of financial integration construction and proposed effective ways to solve problems. After that, the dynamic planning algorithm was introduced into the financial integration construction design. Finally, the investigation and calculation were carried out. The new accounting information system designed by using dynamic programming algorithm and mobile Internet technology could improve the security and efficiency of enterprises by 38.6%.

There are many precious cultural relics collected in the art museum. If it is not properly managed, it will have a great impact on the economy and culture of the country. The construction of a digital art museum that represents the history and culture of the country is very important for the protection, research and dissemination of history and culture. Legacy plays a very important role. Therefore, the current art museum is no longer just a simple display, but more for people to appreciate and understand cultural knowledge, technology and feelings. Under the background and environment of informatization, the main forms of contemporary digital art museums are virtual exhibitions and virtual experiences. From the perspective of art museums, the authenticity of virtual reality is more significant, which can improve the audience’s viewing and experience of various exhibitions. Therefore, under the support of digital technology, the museum combines various exhibits and technologies to present a brand-new “virtual” experience. Based on virtual reality technology, this paper combined digital technology and intelligent technology to establish a virtual display platform for digital art museums with rich content and open sharing. The optimization of the loading mechanism of the scene model can effectively shorten the loading time. When the distance between the viewpoint and the exhibits is within 2m, the optimized shortening time is 509ms. The system can display the virtual art gallery very well. Users can browse the cultural relics exhibits from different perspectives in the scene, so as to realize the real-time roaming of the virtual art gallery scene.

Aiming at the two main sources of threats faced by low-orbit communication satellites, namely, hacker network attacks and spectrum interference, the study builds an adversarial sample detection model based on variational self-encoder and a fast region-based convolutional network based spectrum interference detection model. The outcomes indicated that the detection accuracy of the proposed adversarial sample detection model in the intrusion traffic detection task was 97.68% with an F1 value of 96.86%. The AUC values of the proposed adversarial sample detection model in different typical network attacks were above 95%. The proposed spectrum interference detection model had the best detection effect in single tone interference detection. Its detection accuracy, recall and evaluation precision were 98.65%, 96.21% and 93.14%, respectively. It converged at about 200 iterations with an average recognition accuracy of 95.47%. The experimental results demonstrate the antisample and spectrum interference detection performance of the proposed model. The findings support the continued provision of satellite communication services as well as the safe operation of low-orbit communication satellites.

Fresh items have become an essential necessity for modern people, and the daily diet structure is growing more and more rich as people’s attention to health increases. One of the characteristics of fresh products is that they are hard to retain at room temperature. As a result, IoT logistics technology assistance is frequently needed in logistics linkages including distribution, transportation, and warehousing. Through the scientific and logical planning of the route of fresh food logistics distribution vehicles, this paper aims to effectively lower the overall economic cost of logistics distribution, guarantee the freshness of the fresh food distribution process, satisfy the various individualized needs of customers for delivery time, and enhance logistics distribution. security. This study suggests an enhanced ant colony algorithm in artificial intelligence that can efficiently determine the shortest path. This algorithm can be used to find the best route for new logistics distribution and lower transportation losses. It is based on 5G Internet of Things technology. The ant colony method prior to the enhancement had the longest optimization time of 25. 06 seconds in the 8 search process, according to the experimental data presented in this study. The enhanced ant colony algorithm had the longest optimization time of 17. 89 seconds. In finding the optimal path, after the improvement, the ant colony algorithm takes less time. In the comparison of transportation costs, the cost of the improved ant colony algorithm is reduced by about 1, 100 yuan, the vehicles required are less than those of the ant colony algorithm before the improvement, and the decay rate is also reduced a lot. It can be seen that the improved ant colony algorithm is more suitable for the analysis of the optimal path of fresh logistics distribution.

Financial analysis is a method of analyzing the overall operating status of an enterprise based on financial information, which can help managers judge the company’ s operating risks and adjust the company’ s operating conditions in a timely manner, so as to better achieve business management. This paper aims to study the design of financial indicator analysis system through big data. This paper proposes to find the best clustering center by means of fuzzy identification algorithm, determine the quality of the company’ s operating status, calculate the company’ s overall operating indicators, and determine the company’ s risk level and improvement direction. The experimental results of this paper show that the fuzzy identification algorithm can help the enterprise to determine the overall state of the enterprise’ s operation, improve the financial risk identification ability by 20%, and better realize the enterprise’ s financial analysis and processing.

With the rapid development of China’s economy and the continuous improvement of its international status, “Chinese fever” is quietly emerging all over the world, and the teaching of Chinese as a second language has become an independent discipline in China. There is a lot of room for exploration in research. In the past few decades, with the rapid development of science and technology, “intelligence” has become the development trend of the whole society. The potential of smart devices has become more and more widely used, which has also inspired users’ love and ultra-fast adaptability to devices. This paper uses wireless network communication to study language recognition and its type induction in second language teaching, and proposes a resource allocation mechanism based on bilateral induction. Different expressions are designed with different priorities, and after transformation, the follow-up type induction research can be carried out smoothly, and finally the optimal induction allocation scheme is obtained. The research results show that in the teaching type induction, there are 684 new words in volume I, 778 new words in volume II, and 1462 total words in volumes I and II, and they are all summarized. Compared with traditional methods, the search and extraction speed is increased by 45%. Teaching type induction is more effective in the comprehensive use of multiple teaching methods in primary teaching, but it is still inseparable from traditional teaching methods. Therefore, in the specific teaching practice, we should choose a more suitable teaching method according to the individual factors of the teaching content and teaching objects.

Students in adolescence are not mature in mind, thought, ability and other aspects, which are easily affected by various emotional behaviors. Positive emotional behavior contributes to students’ mental health and academic progress. Negative emotional behavior would lead to psychological problems and academic frustration. If it is not paid attention to, students may act out of control under the control of negative emotions, thus resulting in serious mental illness, which is not conducive to the education and management of students. Due to the rapid development of social information network and science and technology, the analysis of students’ emotional behavior and educational management by pure human intervention has fallen behind, and it is impossible to timely feedback, track and predict students’ status. This paper introduced the general direction and achievements of human-computer interaction research, and discussed the combination of big data and human-computer interaction. The method of applying human-computer interaction technology to students’ emotional behavior analysis and education management was studied. The pure human intervention method was compared with facial emotion recognition, voice emotion recognition, human-computer body feeling interaction and virtual scene education methods under human-computer interaction technology. Five experimental groups were designed to conduct research in three aspects of emotional behavior analysis, education and learning, and supervision and management. It was found that the average accuracy of facial emotion recognition for emotional behavior analysis was 88.0%; the average course learning efficiency of virtual scene education used for students’ educational learning was 82.8%, and the total progress was up to 99.81%; the average success rate of human-computer somatosensory interaction for supervision and management was the highest, which was 68.1%.

In response to the problems of traditional visual design that overly relies on designer experience, poor design effectiveness, and low efficiency, this article studied visual design based on digital technology. In this paper, linear stretch visual design image enhancement method and histogram equalization visual design image enhancement method are used to enhance the image of advertising visual design image, packaging visual design image, beauty visual design image, cultural visual design image. Peak signal to noise ratio (PSNR) was used to analyze the effect of digital technology in visual design. The conclusion was drawn that the linear stretching enhanced visual design image method has better results. This article utilized the linear stretching visual design image enhancement method to enhance 20 visual design images, and analyzed the effectiveness of the linear stretching visual design image enhancement method using a questionnaire survey. The survey results indicated that after processing with the linear stretching visual design image enhancement method, the evaluation scores of the enhanced images by the respondents in School S, School T, and School A were 8.28, 8.76, and 8.47, respectively, which were higher than the original image evaluation scores. The visual design method based on digital technology is beneficial for improving the visual effect of images, and is of great significance for the dissemination of visual images.

Short-term traffic speed prediction in Intelligent Transportation System (ITS) provides an important idea for solving traffic problems. To capture the spatio-temporal properties of traffic speed prediction, we proposes a Graph Convolutional Network-Gated Recurrent Units with Attention (GCN-GRUA) mode for expressway. The Graph Convolutional Network (GCN) and Gated Recurrent Unit (GRU) were used to extract the spatial and temporal features of traffic speed, and the attention mechanism was introduced to improve the prediction performance of the model. Experimental results from the real traffic data set of Qingyin Expressway show that the proposed model has a significant improvement in prediction accuracy compared with GCN, GRU and GCN-GRU models. In addition, the importance of speed characteristic variables and exogenous variables on the traffic speed prediction accuracy show that the speed data with the closest time interval has the greatest influence on the traffic speed prediction, followed by the daily cycle characteristics of traffic speed. As the prediction time increases, the relative importance of the velocity characteristic variable remains above 0.6, while the relative importance of the exogenous variable keeps rising.

In order to provide various disturbance voltage waveforms for the test of power quality event detection and compensation device, it is necessary to develop a device that can simulate power grid faults. VSC power disturbances generator (VSC-IG, Interruption Generator based on Voltage Source Converter) is the importance of theoretical study and compensation device of power quality test tools, the flexibility to produce all kinds of disturbance voltage waveform, And reduce the harmonic pollution to the power grid. In this paper, the power injected by AC power supply into VSC-IG is controlled to stabilize the DC voltage, and a PI controller parameter tuning method considering the change of resistance parameters is proposed to optimize the dynamic performance of the controller. The SPWM rule sampling method is adopted as the underlying control strategy to realize the generation of disturbance voltage waveform. PSCAD/EMTDC platform is used to build the VSC-IG simulation model and carry out the simulation research. The VSC-IG device is designed and implemented by using the physical prototype hardware platform of 30kVA back-to-back converter in dynamic modeling laboratory. The experiment verifies the main circuit structure and control strategy of VSC-IG in this paper, and also verifies the function of digital controller, which lays a foundation for further research on power quality.

This paper aims at resolving the issue that the conventional literature study can’t deal with the large amount of data, the author proposes a research method for theme clustering and text mining of Chinese modern and contemporary literary texts in the network era. The author studied how to effectively improve the thematic clustering performance of literary texts based on keyword clustering ensemble method. Comparing two clustering ensemble methods (K-means based data ensemble and incremental clustering based algorithm ensemble) and four keyword extraction methods (TF-ISF CSI, ECC, TextRank), the effects of various keywords on the results of thematic clustering were analysed. Experiments indicate that the clustering algorithm can greatly increase the topic clustering efficiency, and it is more stable when the key words are less. The author’s research provides new technological means for text mining and thematic clustering in contemporary Chinese literature, which helps to promote the development of digital humanities research.

With the continuous development of the internet age, more and more art images are taking on digital forms, resulting in a new way of survival for art image digitization. However, the digitization process of art images is affected by various factors, resulting in poor results and low digital quality of art images. Therefore, this article conducted research on the digitization of art images based on metadata, and utilized BP (Back Propagation) neural network for metadata processing and analysis to achieve metadata visualization and interactive design. Animation production software was then utilized for image compression, transparent display, and modeling, and finally interactive display technology was used to display the dynamic design of art images. 4000 user feedback data and art image metadata from four age groups were collected and named A art image set. Starting from the visual communication effect, accuracy, and fidelity of art images, the differences in dynamic design of A art image digitization were compared. The experimental results showed that 2820 people were satisfied with the visual communication effect of dynamically designed art images, with a satisfaction rate of 70.5%. Only 1070 people in the control group were satisfied. The metadata accuracy of dynamically designed art images was greater than 80%, and the average accuracy was close to the median line, with small overall fluctuations. The deviation value between dynamically designed art image data and standard images is small, and the overall fidelity is relatively high. In short, the evaluation effect of digital dynamic design of art images is very good.

As AI technology matures, computational intelligence has also been more widely used. Computational intelligence is an important branch of AI. Because of its global search, efficient parallel and other characteristics, it has become a new method to solve complex optimization problems and has received more and more attention. In the meantime, driven by other technologies such as big data, education has gradually broken away from the traditional teaching methods, broken the traditional time and space constraints, and opened a new chapter. With the reform of the national curriculum instructional patterns, the exploration and practice of educational informatization in the educational circle is rising, and the instructional patterns of intelligent service aided design curriculum is also coming. In this paper, a teaching mode of intelligent service aided design with human-computer interaction (HCI) as the core was proposed. Based on the available results, it is an important reference value for its application and promotion in practice. Through the empirical analysis of the course instructional patterns of intelligent service aided design of HCI under computational intelligence, the classroom instructional patterns method of intelligent service aided design of HCI in the intelligent era proposed in this paper has improved 12.7% in promoting students’ understanding of teaching content compared with traditional methods and has increased by 19.7% in improving students’ full satisfaction with course teaching. Besides, in terms of overall teaching effectiveness, it has improved 22.9% compared to the traditional. It illustrates that the teaching methods presented in this paper can better serve students’ development and improve their overall quality, and also meet the teaching needs of teachers, so that teachers can better carry out teaching activities. At the same time, the curriculum teaching mode of human-computer interaction intelligent service aided design in the intelligent era was discussed, which was conducive to promoting the gradual maturity of the development of computational intelligence and making its application in teaching more complete.

The development of economy is inseparable from the construction of traffic buildings. Especially in the current road construction, asphalt mixture is mainly used for pouring. Over time, the asphalt mixture is disturbed by other external factors, resulting in a decrease in the performance of the asphalt mixture. Under this background, this paper mainly studied the mesoscopic angle of the construction asphalt mixture through image processing technology, and analyzed the mesoscopic structural characteristics of the construction asphalt mixture. This paper took the void structure as the research index, and performs image enhancement, image denoising, image sharpening, image segmentation and image edge detection on the collected images of building asphalt mixture in turn. In terms of image enhancement, the image after histogram equalization is clearer in texture, distinct in layers and more prominent than the original image. In terms of image denoising, the median filter method is used, and the noise reduction effect is obviously better than other methods. In terms of image sharpening, the contour of the image sharpened by the Laplacian operator is clearer. In the aspect of image segmentation, the threshold segmentation method has obvious image void boundary and detail information, which is conducive to extracting void information. In terms of edge detection, the image lines under the Canny operator are complete, which greatly reduces the loss of edge information. On this basis, the void structure model was constructed and tested experimentally. The results showed that the average equivalent diameter, average perimeter, and average contour area of the voids in each layer have roughly the same trends as the layers increase. Not only that, the detected void ratio was about 8.14%, which was only 1.17% different from the actual void ratio. This showed that the void structure model constructed under the image processing technology has a significant effect on the porosity detection, and this result brought certain guiding suggestions for the follow-up study of mesostructure characteristics.

The expansion of the big data network has continuously increased the demand for student education and management, and the student education management system has been designed and developed, but the current student education management system does not take care of the core issues of management – teachers and students. As a result, the current management system cannot effectively manage student education information. In this paper, the biggest purpose of applying big data network to the design and development of student education management system is to solve management problems and improve education efficiency. The main algorithms involved in the network increase the development feasibility of the management system, and the design level of the student education management system is analyzed in order to provide a theoretical basis for the later design and development experiments of the management system under the big data network. Through experiments, it is found that the research on the design and development of the management system based on the big data network can effectively improve the quality of online education by 5.39%, which timely found the students who are left behind in learning, and actively followed up the students’ learning situation.

As a new product of artificial intelligence, big data is widely used in daily life. Due to its appearance, people’s lives are more convenient and efficient, but at the same time, there are certain security risks, namely the leakage of private information, especially the financial information problem brought about by financial informatization has a more serious leakage problem. In order to effectively reduce the problems caused by the leakage of financial information privacy, this paper attempted to establish a model of related protection measures for financial big data information security by establishing a three-dimensional encrypted information model of big data or by using differential privacy method and using their own. The three-dimensional encrypted information model of big data overcame the defect that financial information is easy to be broken, while the differential privacy model overcame the defect of inaccurate protection of financial information, both of which can play a better protective role in different applications. The experimental results showed that in the process of accessing financial data information, with the increase of access frequency, the number of sensitive locations changes from 40 to 46. This also meat that a non-sensitive position becomes a sensitive position, which blurs the original sensitive position and achieves the effect of protecting the real sensitive position.

As a kind of humanistic culture, art has existed in people’s daily life long ago. Based on the research of the relevant literature in the art field, this paper found that there were some problems in the art field at present. Combining with the relevant problems, this paper proposed a remote storage system of art painting resources based on artificial intelligence. The system mainly included network security part and image scanning and recognition part. According to these two parts to achieve the purpose of safe remote storage of painting resources, this paper has carried out corresponding tests on the network security rate and image recognition rate of the system. Under the condition of ensuring the normal operation of the system, the data was compared and analyzed with the traditional painting resource storage method. The system method surpassed the traditional method in most performance with 100% security rate and 100% integrity rate. However, based on the particularity of the system method, it has not been accepted by most people at present.

With the application and development of generative AI technologies such as ChatGPT in the field of education and teaching, higher requirements have been put forward to improve the digital ethical literacy of pre-service teachers. However, there are still impediments to the current development of digital ethical literacy among pre-service teachers. Therefore, based on the social cognitive theory, this study aims to discuss the individual-level, behavioral-level, environmental-level, and social-level factors and their relationships that affect pre-service teachers’ digital ethics literacy. A total of 524 pre-service teachers in China were used as the study population. The study found that the factors influencing pre-service teachers’ digital ethics literacy include seven dimensions: personal values and digital ethics awareness at the individual level, digital ethics education competence and digital technology use skills at the behavioral level, resources and environment of the school and related educational policies at the environmental level, and social recognition at the social level. Among them, there are some interactions between the individual and behavioral dimensions, the environment and individual dimensions, the environment and behavioral dimensions, the individual, behavioral and environmental dimensions, and the social and individual behavioral dimensions two by two, and they play a positive influence on improving the digital ethical literacy of the pre-service teachers, but the interactions between the social and environmental dimensions are not significant. On this basis, the improvement of digital ethical literacy of pre-service teachers is discussed to provide some references for the related research on improving digital ethical literacy of pre-service teachers.

Along with the vigorous development of artificial intelligence in all walks of life, artificial intelligence technology has become an inevitable trend in the reform and development of physical education. First of all, this study takes constructivist learning, motor skill learning, and blended learning as the theoretical basis, and selects the three major goals of synergy, intelligence, and wholeness as the basis, and preliminarily constructs the theoretical framework of the health-promoting teaching (SCT) model for the professional disciplines of physical education in colleges and universities. Then, using the gray correlation model, the system characteristics behavioral sequence and the related factors behavioral sequence were established, and the similarity and dissimilarity of the development trends of the two sequences of factors indicator sequences were measured to confirm their correlation degree. Finally, through the implementation condition elements and equipment function elements obtained from the gray correlation analysis, the SCT model applicable to physical education courses was designed, and the effect of the application of the SCT model in physical education classes and its impact on the physical fitness of physical education students were explored. It is found that the SCT model is characterized by timely feedback, strong relevance and abundant resources, so the improvement of all the scores of the students in the experimental class is better than that of the control class, and the physical fitness of the students is also improved to a certain extent. It shows that the teaching effect of SCT mode is better than traditional teaching mode, and can be used in general multimedia classroom, which has certain universality and promotion value.