This paper is based on the digital image processing technology, using the undamaged image information to restore and protect the frescoes. The discrete binary wavelet change is used to decompose and denoise the image signal. And decompose and filter the high-frequency component and low-frequency component of the image, choose different components, respectively, carry out coefficient transformation, and solve the OMP least-paradigm for different random matrices. The color space is selected, and the mural color space is channel decomposed according to the grayscale mode and restored separately. Establish an assumed datum for each independent face of the mural, establish a spatial coordinate system for it, realize the transformation of spatial coordinates, and realize the super-resolution three-dimensional reconstruction of the mural based on the generative adversarial network and the self-attention mechanism. Objective evaluation indexes and subjective evaluation indexes are established to compare the protection effect of different algorithms on murals. Compared with the traditional algorithm CDD, this paper’s algorithm improves the restoration time by 9.545~15.625 s, and the peak signal-to-noise ratio index improves by 1.35~4.769 db. In the results of the image extraction and processing, the calculated values of discrete curvature of the mural segments AB, CD, and EF ranges from -0.00945 to -0.00478, and the difference of standard deviation of the curvature from the target curvature is 6.477%. The approximate target curvature is obtained, and the algorithm has strong adaptive ability.

Electricity theft management is closely related to the economy of electric power enterprises. This paper proposes a power theft estimation method based on semi-supervised learning and time series analysis prediction. The electricity consumption data of power theft users are extracted as time series data, and in order to achieve multi-step prediction, MMD is utilized to improve the LSSVR semi-supervised learning algorithm. In addition, a perturbation term is introduced to optimize the convergence effect of the artificial bee colony algorithm, and a time series prediction algorithm based on improved artificial bee colony is established. Bringing in the power theft monitoring process to identify whether the user has power theft behavior, using the real power consumption dataset as the experimental validation data, comparing the identification accuracy of the prediction model. Predict the potential power theft of each user, solve the optimization model with the goal of optimal economic efficiency, and determine the actual ranking order of power theft users. The improved time series prediction algorithm proposed in this paper has a global error of 0.0003 and 0.0027 in dataset 1 and dataset 2, respectively, with the lowest global error and the highest overall accuracy of PSE prediction. And the algorithm predicts the list of users to be scheduled is basically the same as the list of users determined by the real PSE, which can achieve the maximum economic benefits.

Teaching evaluation is the feedback on the teaching effect of teachers and the learning effect of students. It has become a critical link in colleges and universities teaching management and teaching inspection. This paper proposes and applies an improved BT-SVM multi-classification algorithm to the education evaluation model. By calculating the relative distance between classes, the error accumulation phenomenon existing in the traditional SVM when dealing with multi-classification problems is solved. A classifier structure based on an incomplete binary tree is constructed to automatically classify teaching data by gradually dividing the data set and training the SVM classifier. By calculating the decision function value of the test sample in the binary tree, the category to which it belongs can be quickly determined. The education evaluation model follows the principle of legal compliance to improve the quality and efficiency of model evaluation and ensure the rule of law construction in colleges and universities. The research results show that the error rate of the BT-SVM algorithm in machine learning is below 0.1%, the fairness index is between 0.1-2, and the prediction accuracy is 96%. It shows that the machine learning algorithm can effectively improve the efficiency of education evaluation work and has the principle of fair legal compliance.

More and more problems are revealed in the process of popularization of higher education, especially the imperfection of the quality assurance system of higher education, which restricts and hinders the development of colleges and universities to a certain extent. This paper uses structural equation modeling to analyze the influencing factors of higher education quality. And consequently, it combines digital technology to build a higher education quality assurance system. Take a university as an example to practice, through the higher education quality assurance system evaluation index selection and empowerment, combined with the fuzzy comprehensive evaluation method to establish an evaluation model, to assess the effectiveness of the practice of the educational quality assurance system of the sample university. The management level (0.4380) and faculty (0.1472) of the university have the most significant influence on the quality of higher education. Under the constructed higher education quality assurance system, the comprehensive scoring result of the sample colleges and universities is at a good level (8.227), with the highest quality level in the dimensions of teaching effectiveness (8.7341) and student development (8.7000), which indicates that the digitization-based higher education assurance system is able to effectively ensure and promote the quality of education in colleges and universities.

Accompanied by the increasing consumer quality and the exploration of enterprises centered on user experience, the new retail model has emerged, and the emerging retail model also plays an important role in enhancing customer loyalty. This paper establishes an experiential sensory marketing model by combining perceptual theory and emerging technology from the basic features of the new retail model. Multiple linear regression model is used to study the influence of experiential sensory marketing mode on customer loyalty, and the correlation coefficient is used to analyze the correlation between the two. The correlation coefficients between the experiential sensory marketing model and customer loyalty range from 0.457 to 0.669, which is a moderate correlation. For every 1 percentage point increase in experiential sensory marketing mode, there is a significant increase of 0.647 percentage points in customer loyalty, and the average score value of customer attention under experiential sensory marketing strategy is 4.31 points. The sensory marketing strategy in experiential retail environment needs to improve the marketing standardization system, relying on professional service platform to improve the customer’s emotional experience, and then enhance customer loyalty.

Most areas in Hunan Province are rich in shale gas blocks, and their shale gas reservoir physical properties, geological characteristics, and enrichment rules need to be further studied. The article chooses the five # logging data of the Xiaoyanxi Formation in Anhua, Hunan Province, as the research object, preprocesses the logging data by curve environmental influence correction, curve reconstruction, and normalization, calculates the total organic carbon content and mineral composition change of shale gas by multiple linear regression, and uses the multi-mineral content calculated by optimization algorithm combined with the volumetric model to realize the matrix porosity of the variable skeleton. Then, the differential equivalent medium, self-compatible approximation, and K-T models were used to calculate the shale rock skeleton modulus. Then the shale gas reservoir petrophysical model was constructed. The adsorbed gas and free gas of the shale gas reservoir were solved separately to obtain the total gas content of the shale gas reservoir. The average TOC content solved by the model is 1.79%, which is only 2.23% higher than the absolute error of the actual data. When the volume fraction of the organic matter mixture increased from 0 to 0.25, the relative change of the longitudinal and transverse wave velocity ratio was only 0.87%. The shale gas content in Anhua Xiaoyanxi Formation 5# in Hunan Province ranges from 0.87 to 8.41 cm³/g, significantly higher than the lower limit value for shale gas industrial development. Recorded well data can clarify the reservoir’s physical characteristics of shale gas in Hunan Province and provide data support for exploring shale gas.

Supply chain optimization configuration contributes to the improvement and development of enterprise business application system. This paper takes the supply chain of manufacturing enterprises as the research object and analyzes the economic benefits of supply chain optimization of manufacturing enterprises. Aiming at the current development environment of enterprises, it puts forward the necessity of the development of enterprise supply chain flexibility, and establishes the overall supply chain flexibility model that contains the supply flexibility of the supply chain, the manufacturing flexibility and the distribution flexibility of the distributors. Simplify the total cost model of supply chain and establish the demand-driven supply chain optimization model. Analyze and validate the parameter settings of the improved particle swarm algorithm, and obtain the operating efficiency of the improved particle swarm algorithm with the changes of ordering cycle and inventory capacity. Combined with the sample enterprises, analyze the financial savings of each link after supply chain optimization. Further measurements show that after supply chain optimization of Company R, the saving percentage is 10.24%, and the annual saving amount is 562,807 yuan, with obvious economic benefits.

Whether tourism culture and economy develop in a coordinated manner is the key to realize the transformation and interaction of industrial structure. This paper takes the related data of 11 prefecture-level cities in Shanxi Province from 2013 to 2022 as the research object, and after demonstrating the intrinsic mechanism (the relationship of mutual influence) of the development of tourism culture and local economy, it applies the econometric panel Granger causality test to quantitatively test the interactive relationship between the development of tourism culture and local economy. After that, we constructed the index system of tourism culture and local economy, used entropy value method and coupling coordination model to analyze the comprehensive development level and coupling coordination degree of tourism culture system and regional economic system, and used Robust regression analysis to study the influencing factors of coupling coordination degree. The results of the study show that at the 5% significance level, with a lag of 5 and 6 periods, the local economic development is the Granger cause of tourism culture, and the local economic development has an obvious driving effect on tourism culture. In the 10 years of the examination period, the coupling coordination between tourism culture and local economy keeps growing, and the coupling coordination is improved, but there is still a certain gap with the high-quality coordination, meanwhile, the regression results show that focusing on the holistic and balanced development of the influencing factors is conducive to further coordination and interaction between the two systems.

The development of time and technology has made the traditional basic computer teaching unable to meet the needs of current students, and the cultivation of computer thinking ability has become a hotspot of computer education concern. The article combines the flipped classroom model with the MOOC platform of network education, and establishes a MOOC flipped classroom teaching model applicable to computer basic education courses in art colleges. In this model, students’ assessment results data are collected, and the Apriori algorithm optimised by matrix optimisation and prior pruning strategy is used to mine the association relationship of the assessment results, which helps teachers to understand students’ computer knowledge mastery. T College of Fine Arts is used as a research example to illustrate the effectiveness of MOOC flipped classroom through the changes in students’ performance, competence, and satisfaction. The improved Apriori algorithm has an execution time of only 2.93s when its minimum support is 100%, which can be used to understand students’ computer application ability for different question types, majors and skill performances. The mean score of the final exam of the students in the experimental class was 82.79, which reached 111.79% of the score of the control class, and more than 80% of them were satisfied with the MOOC flipped classroom. The use of flipped classroom and network education model can achieve the innovative development of computer basic education courses in art colleges and help to enhance the teaching quality of computer basic education courses in art colleges.

In an energy plan with a high rate of renewable energy acquisition, the comprehensive development of wind and solar energy and flexible power sources such as energy storage will play a key role in this process. The power supply structure in some areas is dominated by coal power, and there is a serious shortage of flexible power supply, which hinders the development of renewable energy. Therefore, this paper proposed a new energy operation and consumption planning method considering the flexible adjustment of the power supply ratio. This paper established a two-layer power planning model with the lowest cost to the whole society and the largest consumption of renewable energy. Then, based on the copula theory, a wind-solar combined consumption probability model is established, and the new energy output curve of the planning year is predicted. Finally, the power supply optimization was solved by the Hooke-Jeeves iterative method. The experimental part took a certain region as the research object, setting the proportion of flexible power supply to 24%. It found out the newly installed capacity of various power supplies, and compared the actual data in the region. The research results have shown that increasing the proportion of flexibly regulated power supply can effectively improve the operation and absorption capacity of new energy, the wind abandonment rate is reduced by 6.21%, and the light abandonment rate is reduced by 5.38%.