Based on the status quo of Sanya Digital Intelligence Tourism Economy, this paper puts forward the strategy of intelligent teaching change under the dual-leader cultivation mode of colleges and universities. Relying on clustering analysis technology to achieve the mining processing of the whole process data of the wisdom teaching platform, to promote the optimization of the process of wisdom teaching change. The catechism data of the basic course of tourism management of a smart teaching platform is collected, and z-score and PCA principal component analysis are utilized to eliminate the quantitative influence of the data. The best cluster values were determined by hierarchical cluster analysis, and the learners were divided into three cluster groups with the help of K-Means clustering algorithm. One-way ANOVA was introduced to compare the achievement data before and after smart teaching of the three groups of students to explore the effect of smart teaching. The results showed that among the paper grades, category 2 students had the greatest change in the mean value of their grades. In practical grades, the mean value of category 2 students’ practical grades was 95.63, which was 20.18 and 26.75 points higher than those of category 0 and category 1 students, respectively. p-value of 1.56951E-17 was less than 0.05, which indicated that the grades of the three categories of students showed significant differences.

This paper combines the necessary functional requirements for teaching system generated by teaching activities in the context of mobile Internet, designs the general framework of the system, users and their rights management, and constructs a set of teaching system. Subsequently, the traditional PSO algorithm is introduced, and the processing scheme of the scheduling problem is defined as particles to form an initial particle swarm, while the particle swarm position in the algorithm is updated by drawing on the crossover idea of the genetic algorithm, so as to optimize and obtain the scheduling algorithm based on DPSO. Then we test the teaching system of this paper from three levels of pressure bearing, response delay and stability performance to ensure the operating environment of the scheduling algorithm of this paper. The courses of three colleges of a university are used as experimental data to analyze the performance of the scheduling algorithm in this paper. In the comparison of course arrangement in different colleges, the adaptability of this paper’s scheduling algorithm is above 0.900, while the highest adaptability of manual scheduling is only 0.8147, which indicates that compared with manual scheduling, this paper’s scheduling algorithm is able to make a more reasonable course arrangement.

Aiming at the potential risks existing in the power market transaction under the new power system, and considering the temporal attributes of the information, this paper proposes to use dynamic Bayesian network to construct the risk monitoring and early warning model of the power market transaction. The dynamic Bayesian network is utilized to calculate the correlation between different risk factors, estimate the risk value of power market transactions, and classify the warning level. Taking the southern regional electricity market as the research object, the relationship between electricity price and transaction volume is explored based on the experimental dataset. A credit grading system is introduced to carry out transaction prediction simulation experiments, relying on the prediction data to determine the link between electricity price and transaction volume. The results show that overall power price and transaction volume show a negative correlation, but in June, when the power price is 0.4370 yuan per kWh, the transaction volume still reaches 19.65 million kWh, and the inverse relationship between the transaction volume and the price is not obvious. The use of dynamic Bayesian network to construct the power market transaction risk monitoring and early warning model can dynamically update and adjust the risk monitoring with the passage of time, making the power market transaction early warning more flexible and real-time.

In this paper, the semantic description framework is used to standardize the extraction of semantic information of non-legacy images. The SIFT algorithm is chosen to calculate the key feature points of non-legacy images. The integrated semantic description framework and SIFT algorithm construct a model to extract the non-heritage image features globally and process them locally, and add the attention feature fusion module to fuse the features that are inconsistent in semantics and scale, so as to realize the accurate extraction of features. Use the algorithmic model of this paper to extract the color features of She ribbons. Develop a website for she color band design and verify its usability. Collect website evaluation data from target users to study the role of digital translation of non-heritage elements. The color feature extraction results are richest and most detailed when the number of She ribbon feature colors extracted is 21. The website usability scale score was 50.31, rating B+, with usability. 65% of the users thought that the website embodied the cultural characteristics of She ribbons. 71% of the users thought that the website was very helpful for understanding the ethnic graphic culture. 88.16% of the users thought that the digital design of She ribbons could effectively promote the dissemination of the ethnic graphic culture.

Increasing urbanization has led to large changes in residents’ consumption behavior, but due to a variety of factors, the overall level of residents’ consumption is low, so that it cannot play its role in promoting economic growth. This paper selects the panel data of China Household Finance Survey from 2011 to 2019, and empirically analyzes the impact and path of changes in residents’ consumption behavior on consumption upgrading by constructing structural equation model and fixed effect model combined with STATA software. The study shows that changes in residents’ consumption behavior and its dimensions can promote residents’ consumption upgrading, and there is regional heterogeneity in the impact of changes in residents’ consumption behavior on residents’ consumption upgrading, and its promotion effect on residents’ consumption upgrading is stronger in the developed regions in the Middle East. Based on the above findings, this paper puts forward feasible suggestions on how to optimize residents’ consumption behavior to better promote residents’ consumption upgrading.

This paper takes the integration of AI technology into piano teaching as the starting point, generates accompaniment rhythms through AI computation, adopts deep learning model to generate accompaniment, and builds a multi-level accompaniment effect generation mechanism. Taking the MuseFlow model as the base model, the generative adversarial network and variational autoencoder are introduced to optimize the structure in a limited arithmetic environment. Quantitative and manual evaluations are used to measure the accompaniment generation effect of the proposed mechanism, and controlled experiments are designed to explore its practical application effect. The results show that the improved MuseFlow model generates accompaniment with an average pitch distance of 0.92, which is 0.15 smaller than that of MMM, and the overall score reaches 4.18. The scores of the experimental group in all six abilities are significantly higher than those of the control group, the degree of students’ positive response to each ability increases to some extent, and the number of students who consider the ability of melodic creation to be at a satisfactory level is 18 more than that of the pre-experiment after the experiment.

Network teaching has become an important way of teaching reform in current higher education and has been applied in the education of various courses. This paper proposes a kind of intelligent auxiliary teaching system based on P2P mode, and researches the realization of the system with the example of Civics course. The construction of “Civics Course Teaching Evaluation System” is systematically discussed by using the fuzzy comprehensive evaluation method, and the weights of the indicators are calculated by entropy weighting method and hierarchical analysis method. Taking the teaching of Civics and Political Science in a university in Guangdong Province as the research object, the intelligent teaching system proposed in this paper is applied to evaluate the interactive effect of teaching with the evaluation system constructed in this paper. The evaluation analysis shows that the school’s evaluation results of all indicators are above 80 points, and the overall teaching rating of its Civics and Political Science course is 86.33, in which social merit, teaching equipment, teaching expression, and professional ethics have the highest scores of 94.37, 92.32, 89.02, and 88.52, respectively. It shows that the intelligent auxiliary teaching system for Civics proposed in the article is well applied in actual teaching.

Aiming at the bridge project in the construction of the development of the status quo of the overdevelopment, maintenance and management level lagging behind, this paper, under the premise of ensuring the safety of the bridge, the bridge surveillance monitoring and risk early warning launched a study to solve the problems of its operation and repair and maintenance. For bridge monitoring and safety monitoring, this paper is based on the vibration acceleration of bridge structure damage identification. On this basis, the damage recognition model constructed by using common neural networks convolutional neural network (CNN), long short-term memory network (LSTM) and deep autoencoder (DAE), and the recognition effect of the three models is compared. This for, for the bridge risk problem, this paper utilizes the Extreme Learning Machine (ELM) and Firefly Algorithm (GSO), constructs the implementation of the GSO-ELM algorithm model for early warning of the bridge safety risk, and the experimental results show that the model proposed in this paper has good effect, which provides support for the future development of the bridge structural safety facilities should be developed in the direction of digitization, automation, and networkization.

A scientific, comprehensive and effective evaluation system of asset management performance of public colleges and universities in the context of high-quality development in the new era contributes to the “asset power” for the construction of high-level and high-quality development of colleges and universities. This paper takes 20 public colleges and universities in Province Y as research samples, and analyzes the asset management performance of public colleges and universities and its influencing factors through the super-efficiency DEA model and SFA model. The results show that the asset management performance of 13 public colleges and universities has reached DEA effective, and the rest of them are DEA ineffective. Human and material inputs have a significant positive effect on the asset management performance of public universities in terms of inputs, and both research income and number of patents have a significant positive effect on the asset management performance of public universities in terms of outputs at the 1% level. Relying on the scientific evaluation index system of asset management performance of public universities, establishing a high-level asset management team and clear budgeting and audit management are effective means to improve the asset management performance of public universities.

With the rapid development of the sports industry, quality traceability and credibility issues have become important issues in the sports industry chain. Traditional quality traceability and credibility assurance methods have shortcomings in efficiency and accuracy, and emerging technologies need to be adopted to solve them. Blockchain technology is considered an important means to address quality traceability and credibility issues in the sports industry chain due to its decentralized, transparent, and tamper resistant characteristics. This article proposed a quality traceability and credibility assurance system based on blockchain technology to address the issues of quality traceability and credibility in the sports industry chain. The system adopted blockchain technology to achieve quality traceability and information credibility assurance in the production, circulation, and consumption processes of sports products. The system adopted distributed ledger technology to record the production, circulation, and consumption records of products, and achieved automated quality inspection and transaction verification through smart contracts. The experiment in this article showed that using this system can improve efficiency and reliability by 80% -95%. The research on methods and systems for enhancing the credibility of quality traceability in the sports industry chain through blockchain can effectively improve the quality traceability ability of the sports industry chain, thereby safeguarding consumer rights and market stability.