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With the rapid development of science and technology, the college music big data management system also needs a computer-aided data model for optimization. In order to improve the efficiency of computer-aided teaching of the music big data management system in colleges and universities, this paper analyzes and processes the music database of music software based on the computer-aided Bayesian algorithm and establishes a Bayesian model. Firstly, this paper introduces the principle and function theorem of the Bayesian algorithm. The data is divided into original music and new music, and a college music data management system with search recommendations as the core is established. We iteratively calculate the optimal hidden semantic matrix and we finally select ReLU function as the activation function of this experiment. After the model of the music management system is established, it is applied to a university. The experimental results show that the experiment has reached the expected standard. When the hidden factor dimension is 11, the system model has the best representation of music features. Students' use of the music search and the recommendation system has greatly improved their music literacy and music coverage. [ABSTRACT FROM AUTHOR]

The data of food quality tracing information have a few features, such as wide coverage range, many circulation links, complex data sources, low authenticity, and difficult information sharing. The continuous development of big data technology provides infinite possibilities for the construction of food quality source tracing systems. Currently, there are many studies on the application of food quality source tracing systems; however, most of them are in the field of food quality databases, and few have concerned about its application in the field of big data. Therefore, to fill in this research gap, this paper aimed to study a dynamic source tracing method for food supply chain quality and safety based on big data. At first, this paper summarized the variables of food supply chain quality and safety, constructed a Petri net model and a Bayesian network model for food quality prediction and source tracing, and realized the prediction of food quality features. Then, this paper applied two data analysis and processing methods—the density-based clustering algorithm and the cosine similarity algorithm—to preliminarily process the collected quality tracing information of each link in the food supply chain and analyzed the influencing factors of food quality. Finally, experimental results proved the effectiveness of the constructed model. Relying on the real-timeliness and authenticity of big data, this paper guarantees the credibility of the traceable information in the tracking process and improves the accuracy through real-time stream processing of the updated data, providing unlimited possibilities for the comprehensive tracking of food sources. [ABSTRACT FROM AUTHOR]

With the research of machine learning technology and big data intelligent processing technology in engineering application becoming more and more mature, people gradually combine machine learning technology and big data intelligent processing technology. Aiming at the problem of innovative employment in colleges and universities, this paper proposes a dynamic decision tree algorithm based on these two technologies and constructs a dynamic model of graduates' behavior. Through the analysis of dynamic decision tree algorithm, a big data analysis system is formed. Finally, simulation experiments verify whether the dynamic model can correctly reflect the behavior of college graduates. The results show that the big data integration system based on big data and dynamic decision tree algorithm has high adaptability. Incremental adaptive optimization of the traditional decision tree model can significantly improve the prediction effect and prediction time of dynamic data and provide theoretical support for the industrialization and social significance of big data technology. The dynamic decision tree algorithm of college employment proposed in this paper has good predictability and provides a certain theoretical reference for college graduates' entrepreneurship. [ABSTRACT FROM AUTHOR]

The rapid development of the Internet in modern society has promoted the development of many different network platforms. In the context of big data, many types of multimodal data such as pictures, videos, and texts are generated in the platform. Through the analysis of multimodal data, we can provide better services for users. The traditional big data analysis platform cannot achieve a completely stable state for the analysis of multimodal data. The construction of multimodal intelligent platform can achieve efficient analysis of relevant data, so as to create greater economic benefits for the society. This paper mainly studies the historical development trend of big data multimodal intelligence technology and the data processing method of multimodal intelligence technology applied to network expression classification, including data acquisition, storage, and analysis. Finally, it studied the fusion algorithm between multimodal data and visual design, as well as the classification of network expression and the application result analysis of visual design in big data multimodal intelligence technology. [ABSTRACT FROM AUTHOR]

Traditional motion data mining models have some problems, such as poor dynamic data capture effect, low information classification effect rate, poor quantitative representation effect, and so on. Based on this, this paper studies the mining method of dynamic motion data based on neural network, constructs a data mining model based on discrete dynamic modeling technology, and realizes the collection of data information from the aspects of motion characteristics and types combined with multilayer sensors. Neural network algorithm is used for comprehensive analysis to realize multivariate analysis and objective evaluation of all data of dynamic motion process and accurate analysis and evaluation according to different data characteristics of different types of motion data. The results show that the data mining model based on discrete dynamic modeling technology and wearable sensor technology has the advantages of high feasibility, high intelligence, and wide application range. [ABSTRACT FROM AUTHOR]

Support vector data description (SVDD) is common supervised learning. Its basic idea is to establish a closed and compact area with the objects to be described as integrity. The described objects are all included within the area or as far as possible. In contrast, other objects are excluded out of the area as far as possible. The inherent nature and laws of data are subsequently revealed, thereby distinguishing the operation state of the machine. In this paper, an orthogonal wavelet transformation-support vector data description (OWTSVDD) is proposed to evaluate the performance of bearings, where the peak-to-peak value of detail signal is extracted through orthogonal wavelet transformation as the set of test samples, thus solving the distance R z from the set of test samples to the center of the sphere. Based on HI = R z 2 − R 2 , its distance to the hypersphere is calculated to judge whether it belongs to the normal state training samples. Finally, the performance and health of bearings are evaluated with HI. According to the classification of two sets of experimental data of rolling bearings, the proposed method better reflects the degeneration of bearing's performance compared with the (SVDD) HI value without extraction of characteristic value, being entirely able to evaluate the entire life cycle of bearings from normal operation to fault and degradation. The HI evaluation result based on experimental data in Xi'an Jiaotong University is consistent with the life-cycle vibration signal of bearings, providing a scientific basis for production and equipment management and improving the prognostics technology-centered prognostics and health management (PHM). [ABSTRACT FROM AUTHOR]

In recent years, with the development of Big Data and dynamic modeling technology, people have gradually deepened the research on the efficient construction of music audio database. Based on this background, this paper first realizes the construction of multilevel music audio database using Big Data technology and collaborative filtering algorithm. Then, through the Big Data analysis technology based on collaborative filtering algorithm, the QRE (quick reaction estimate) model is constructed and the data query system is formed. Finally, experiments are designed to verify whether the music audio database based on collaborative filtering algorithm can correctly retrieve the target music audio data. The experimental results show that compared with the traditional single ∗ input stacked database, the music and audio database constructed by the discrete Big Data dynamic modeling technology based on collaborative filtering algorithm has faster search rate and higher accuracy and can accurately locate the data nodes in the database. This research uses Big Data and collaborative filtering algorithm technology to establish a new multilevel music audio database with discrete dynamic modeling characteristics of complex systems, which can greatly improve the data structure of each level of the database, so as to improve the efficiency and accuracy of retrieving various types of music and audio files in the database. [ABSTRACT FROM AUTHOR]

The arrival of the big data era not only provides corresponding technical support for the development of Educational Networking but also promotes the acceleration of Educational Networking. Therefore, this paper puts forward the research on the impact of big data on the development of education network and constructs a Bayesian knowledge tracking model to collect and analyze the behavior data of teachers and learners in network education. The experimental results show that big data technology provides greater development space for Education Networking. Its market scale has reached 502.47 billion yuan in 2021, and there is a trend of continuous growth. At the same time, the increase in the number of users also makes its teaching content richer and teaching methods more diversified and personalized. And, through the analysis of relevant data, learners and teachers can more comprehensively and truly understand their own level, achieve the purpose of accurate assistance to learners and teachers, and help learners and teachers find their own problems and make targeted adjustments. In addition, the campus intelligent management system based on big data technology can achieve the purpose of multipurpose and information management. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet technology, millions of small, medium, and microenterprises are using Internet recruitment platforms to host their recruitment information. *ey have different job requirements and benefits positions. It is important to understand them for job seekers when choosing a position. Existing Internet recruitment platforms do not provide a detailed analysis of positions and visual methods for multidimensional matching of positions and job applicants. Candidates need to spend a lot of energy to screen out suitable positions. In this paper, we propose an efficient interpretable visualization method of multidimensional structural data matching based on job seekers and positions. First, we extract the keywords of the job seeker's ability and benefits based on personal information, and we generate a job seeker ability table and a job seeker demand table. After that, we calculate the degree of the support, confidence, and promotion of each rule through the association rules generated by each frequent itemset of recruitment data to obtain the association rule table. We further explore the relationship between the skills required for the three types of positions based on the association rule. Finally, we use the regression method to build a salary forecasting model. On this basis, we predict the salary of job seekers based on the work experience, education, and work city provided by the job seeker. Simulation results show that our method has better performance on the job analysis and recommendation. [ABSTRACT FROM AUTHOR]

The driving force of high-quality development of regional economy is inseparable from the support of technology. With the support of big data, we need to solve this problem in order to solve the difficulty of large-scale experimental testing and accurately reflect the feasibility growth of data sample changes. This paper proposes a discrete dynamic modeling technology based on big data background to analyze the development and change of regional economy. The reliability AMSAA model is usually used for dynamic discrete modeling. It can be combined with the change data provided by big data to form a dynamic modeling method for reliability growth evaluation. Then, the Bayesian regression method is used to predict the change parameters of the model, and the spatial econometric method is used to analyze the regional economic change. The results show that compared with the traditional methods, the discrete dynamic modeling method is more accurate and can effectively solve the problem of reliable growth under the condition of big data. After introducing the spatial effect measurement model, it can also reflect the main factors of the growth and change of regional economic real output value. In addition to the development of high and new technology, terrain factors, investment, and government support have also had different effects. Therefore, according to the above results, it is proved that the discrete dynamic modeling technology can accurately obtain the experimental data and provide reliable technical support for dynamic data processing. [ABSTRACT FROM AUTHOR]

The key technology of online travel recommendation system has been widely concerned by many Internet experts. This paper studies and designs a scenario aware service model in online travel planning system and proposes an online travel planning recommendation model which integrates collaborative filtering and clustering personalized recommendation algorithm. At the same time, the algorithm performance test method and model evaluation index are given. The results show that CTTCF algorithm can find more neighbor users than UCF algorithm, and the smaller the search space is, the more significant the advantage is. The number of neighbors is 5, 10, 15, 20, and 25, respectively, and the corresponding average absolute error values are about 0.815, 0.785, 0.765, 0.758, and 0.755, respectively. The scores of the six emotional travel itinerary recommendation schemes are all higher than 142 points. Only the two schemes have no obvious rendering effect. The proposed online travel itinerary planning scheme has potential value and important significance in the application of follow-up recommendation system. It solves the problem of low scene perception satisfaction in the key technologies of online tourism planning system. [ABSTRACT FROM AUTHOR]

Numerous studies have found that there were obvious differences between men and women in research performance, but there is little analysis on teaching. At the same time, the existing studies did not classify the teacher sample but only analyzed the overall sample. This study takes both teaching and research into account, and the sample teachers from a certain university in China are divided into three groups such as outstanding teachers in teaching, outstanding teachers in research, and weak teachers in both teaching and research through cluster analysis based on the discrete data analysis of teaching and research workload from 2018 to 2020. The gender differences in teaching and research performance of male and female teachers in every group are analyzed. It is found that there are obvious differences between male and female teachers in the three groups. By analyzing the correlation between male and female teaching and research performance and gender, age, education, and degree of three groups, it is also found that there are differences between men and women in the correlation of individual factors such as degree and type of graduation university. Based on the results, this paper provides several policy recommendations. [ABSTRACT FROM AUTHOR]

With the development of Internet technology and social model, game products have become an important product of people's life for entertainment and recreation, and the precise marketing of game products has become a winning means for enterprises to improve competitiveness and reduce labor cost consumption, and major game companies are also paying more and more attention to the data-based marketing model. How to dig out the effective information from the existing market behavior data is a powerful means to implement precise marketing. Achieving precise positioning and marketing of gaming market is the guarantee of innovative development of game companies. For the research on the above problem, based on the SEMAS process of data mining, this paper proposes a mining model based on recurrent neural network, which is named as Dynamic Attention GRU (DAGRU) with multiple dynamic attention mechanisms, and evaluates it on two self-built data sets of user behavior samples. The results demonstrate that the mining method can effectively analyze and predict the player behavior goals. The game marketing system based on data mining can indeed provide more accurate and automated marketing services, which greatly reduces the cost investment under the traditional marketing model and achieves accurate targeting marketing services and has certain application value. [ABSTRACT FROM AUTHOR]

Building an information-based rural economic supply chain is an effective way to implement the rural revitalization strategy. The construction of China's agricultural product logistics market is still very backward, mainly including imperfect information system and high cost of intermediate circulation links. How to optimize the supply chain of agricultural products, ensure the normal supply of agricultural products quality and quantity in the market, improve people's living standards, and achieve the purpose of improving benefits is an important topic to be studied today. Aiming at the problems of traditional agricultural products supply chain, the application of Internet of Things technology in supply chain can be effectively solved. Therefore, this paper proposes the research on the environmental effect evaluation of rural economic supply chain based on Internet of Things technology and proposes to build a wireless sensor network for agricultural products for the collection and exchange of agricultural products information. At the same time, RFID (radio-frequency identification) electronic tags integrated with sensors are applied in other links of the supply chain. The experimental results show that the construction of the rural economic supply chain system based on Internet of Things technology can effectively optimize the rural economic supply chain, realize efficient information sharing, improve the quality of agricultural products, reduce the cost of each link of the supply chain, improve the competitiveness of rural supply chain, and promote the good development of rural agricultural products and rural economic supply chain environment. [ABSTRACT FROM AUTHOR]

The manufacturing level directly manifests the comprehensive strength of a country or region. Production efficiency provides an important metric of the competitiveness of the manufacturing industry. Based on the data of China's industrial enterprises of 1999–2011, this paper estimates the production efficiency of manufacturing in Central China's Hubei Province through stochastic frontier analysis (SFA) and thus characterizes the differences between prefectures of Hubei in manufacturing competitiveness. The results show that, on the prefecture level, Xianning and Wuhan saw a decline in manufacturing competitiveness, while Xiangfan and Xiaogan witnessed an increase in manufacturing competitiveness. To enhance local manufacturing competitiveness and make Hubei the forerunner and cornerstone of Central China, different prefectures should adopt different industrial promotion policies, pay attention to cultivating the technological innovation capabilities of enterprises, and strengthen the integration of production, education, and research. [ABSTRACT FROM AUTHOR]