Common wealth is the essential requirement of socialism, and it is also the goal that countless people have been relentlessly pursuing for thousands of years, and people have never ceased to earnestly aspire to and relentlessly pursue for equal enjoyment and common wealth. This paper studies the impact of coastal wetland tourism resource protection on the realization of common wealth. It analyzes the tourism resources of coastal wetland from the aspects of economic value and resource protection and utilization strategy. On this basis, a multilevel regression model is used to analyze the impact between the two.The tourism economy of each coastal wetland developed rapidly in 2023, which increased by 65%~97% compared with 2015, implying that the conservation and utilization of tourism resources can lead to economic growth and promote economic development. In the multilevel model, resource protection strategy expenditure (0.070), ecological condition (0.265), strategy realization channel (0.053) and institutional trust (0.166) all show significant effects on the level of common wealth. While the regional level variables GDP per capita, provincial ecological level, and tourism resource utilization have unstable effects on the common wealth of urban residents. Based on the multilevel regression model, this study investigates the influence mechanism of tourism resources protection and utilization strategy on common wealth, which provides a basis for the full development of the positive effect of coastal wetland tourism resources protection and utilization strategy on the realization of common wealth.

In the era of digital imaging, the art of photography has undergone profound changes, in which the calculation method of temporal art and symbolic space has become the key to understanding this art form. This paper analyzes the temporal form of photographic art and designs the symbolic space in photographic art, using digital photography technology based on drone remote sensing combined with collage photography technology. And through a variety of calculation methods, the time consciousness and symbolic space of the creative works are quantitatively embodied. The resolution of the photographic works obtained by scanning drone photography and surround photography is 9.448 and 9.966mm respectively, and the error in plane and height is low. The use of collage technology to express different emotions is demonstrated by the audience’s recognition score of more than 4. Digital technology embodies the time consciousness and symbolic space of photography, “storytelling”, “composition and perspective”, “light and color”, with an average increase of 16.9%, 20.36%, and 13.06% respectively. The regression results show that “image capture and processing”, “post-processing”, “high resolution and color reproduction”, “autofocus”, “Digital Signal Processing” can all contribute to the time-conscious and symbolic spatial embodiment of photographic art at the 0.001 level.

Tang Dynasty costumes are regarded as a brilliant brushstroke in the history and culture of Chinese costumes, and the fate of the whole Tang Dynasty can be analyzed through the evolution of Tang Dynasty costumes. In this paper, we have constructed a dress semiotics system from the social level, psychological level and cultural trait level, through the transfer of the imagery dress structure to the real dress, to express its symbolic meaning, and applied the constructed system to the Tang dress symbols, to interpret the meaning of the Tang dress symbols from the two levels of society and culture. Using CiteSpace information visualization software, combined with the literature of “Tang Dynasty Costume”, the study explores the dynamic evolution law of Tang Dynasty costume culture. The results of the study show that the earliest year for the keyword “Tang Dynasty costumes” is 1985, and the frequency is as high as 68 times. The keywords with the highest degree of centrality are dress and Tang Dynasty culture, both of which are 0.38. A number of new keywords with strong salience emerged in 2013-2023, among which the ones with a sudden increase of intensity greater than 5 are artistic features, clothing styles, clothing colors, clothing shapes, sweater, structural design, cheongsam, and knitted fabrics, and therefore the future hotspot of the Tang Dynasty dress research shifts to these keywords.

The combination of deep learning and digital media technology provides great scope for content creation. The article uses Generative Adversarial Network (GAN) in deep learning for content generation. Based on the three major forms of digital media content (image, audio, and video), image, audio, and video are generated by U-Net_GAN model, MAS-GAN model, and SSFLVGAN model, respectively, to construct a digital media content generation model based on generative adversarial networks. Subsequently, the model is validated for performance and the generated images, audio and video are evaluated for effectiveness. By studying the shortcomings of digital media content generation, we propose suggestions to improve its dissemination effect. The U-Net_GAN model outperforms other image generation models in all the indexes of generating images. The performance of speech generation and enhancement of MAS-GAN is much better than other audio generation and enhancement models. The average score of HDR video generated by SSFLVGAN is 4.20, and the average DMOS score is 5.97. The average DMOS score of SSFLVGAN is 5.97. DMOS score is 5.97, which are both 0.16 points higher than the traditional scheme. SSFLVGAN and the traditional scheme are comparable in terms of the picture impact of the generated video. The picture detail effect of the SSFLVGAN generated video is much better than the traditional scheme.

Based on the realization path of new quality productivity on industrial transformation and upgrading as well as talent supply, this paper carries out theoretical analysis in three directions, namely, direct effect, indirect effect and non-linear characteristics, and puts forward relevant research hypotheses. The panel data benchmark regression model, mediation effect model and threshold regression model are constructed respectively to verify the proposed hypotheses. The panel entropy method is used to measure the level of new quality productivity and carry out related research on the driving of new quality productivity. The partial differential decomposition of the spatial Durbin model shows that the direct and indirect effects of the new quality productivity are significantly positive at the 5% and 1% levels, respectively, indicating that the new quality productivity can promote the upgrading of industrial structure in the theoretical provinces. Introducing the variable of technological innovation for the mediation effect test, by observing columns (1) and (2), the regression coefficients of new quality productivity on technological innovation and new quality productivity on industrial structure upgrading are both positive at 0.2484 and 0.2048, respectively, which indicates that regional technology plays a mediating effect in the influence of new quality productivity on industrial structure upgrading.

With the wide application of deep learning in the field of education, student emotion perception has become one of the research hotspots. The study recognizes learners’ facial expressions by face detection algorithm after collecting learners’ data and preprocessing. Algorithms such as convolutional neural network and ConvLSTM are used to recognize learners’ emotions, and learners’ emotions are constructed to be modeled. Evaluate the learner emotion performance of this paper’s model and compare it with other emotion recognition models. The model of this paper is used for practical research to collect students’ emotions in six classes, and statistics and analysis are performed. Finally, by studying the relationship between students’ emotions and behaviors, targeted suggestions for improving students’ behaviors are proposed. The accuracy of this paper’s model in recognizing student emotions on the RAF-DB dataset and classroom dataset is 90.32% and 97.65%, respectively, which is much higher than that of other pre-trained models. The recognition accuracy of this paper’s model for eight types of student emotions is between [0.93, 0.98]. In the statistics of classroom students’ emotions, the main emotions of students in session 1 were concentration, in session 2 were surprise and concentration, in sessions 3, 4, and 6 were surprise and delight, and in session 5 were concentration and disappointment. Focus was significantly positively correlated with “serious attendance”, “thinking”, “answering questions”, “discussing” and “doing tests”, tiredness was significantly positively correlated with “answering questions”, “reviewing” and “deserting”, boredom was significantly positively correlated with “answering questions”, “doing quizzes”, “reviewing” and “desertion”, doubts were significantly positively correlated with “discussing”, “doing quizzes” and “reviewing”, distraction was significantly positively correlated with “reviewing” and “desertion”, happiness was significantly positively correlated with “discussion”, and disappointment was significantly positively correlated with “desertion”.

Under the background of mediatized society, the fusion of reality and reality between the real world and cyberspace has made the role of social network public opinion more and more significant, and the occurrence of any major emergencies will trigger network public opinion. In this paper, the TF-IDF algorithm is used to extract the feature items of social media opinion data, synthesize them into text vectors and input them into the LDA topic model to mine the opinion topic words, and then combine the co-occurrence of the key topic words to draw the semantic maps of the opinion topic words on the web, so as to explore the dynamic evolution of the opinion topic words. The opinion text vectors are then used as inputs to extract the local features of the opinion text through CNN model, combine with BiLSTM model to obtain the global features and temporal information of the opinion text, and realize the dynamic prediction of opinion sentiment through SoftMax classifier. Taking the Xin Guan epidemic event as an example, and divided into three phases: latent period, outbreak period and recession period, the number of public opinion comments on microblog platform during the outbreak period can reach 1942.59 comments/day, and the evolution of public opinion topic words in different public opinion phases are dominated by themes such as “epidemic”, “pneumonia” and so on. When the CNN-BiLSTM model is used to predict the public opinion sentiment dynamics, the prediction accuracy is between 95.84% and 97.56%. Through the effective use of deep learning technology, it can clarify the orientation of public opinion development driven by social media data and provide reliable data support for social media public opinion guidance.

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.

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.

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.