Narrow Results

This study focuses on the programming and creative optimization methods of piano tracks based on big data technology. Through in-depth analysis of the potential application of big data technology in the field of music creation, we propose an innovative framework for piano repertoire programming and creative optimization. This framework fully utilizes the data processing and analysis capabilities of big data technology to mine massive piano repertoire data, extract music elements, style features, and creative patterns. In terms of piano repertoire programming, we use big data technology to quantitatively analyze music elements and construct a data-driven programming model. This model can automatically generate piano repertoire frameworks that match specific styles, emotions, and rhythms based on user creative needs, providing inspiration and direction for creators. In terms of creative optimization, we utilize machine learning algorithms based on big data technology to intelligently evaluate and improve piano tracks. By comparing and analyzing the advantages and disadvantages of different tracks in terms of melody, harmony, structure, etc., we have discovered and extracted the common characteristics of excellent works, providing guidance for creators to optimize their work. This article will focus on the programming and control capabilities of music programming in the design and composition process of big data technology, as well as optimization methods for music creation. Provide precise services for music teaching and research, promote the prosperity and development of music, and accelerate the integration and innovative development of information technology and music education and teaching. In terms of piano repertoire programming, we utilized big data technology to quantitatively analyze music elements and constructed a data-driven programming model. This model can provide creators with rich inspiration and clear direction based on their creative intentions and specific styles. In addition, our study also considered the recognition rates of training models for different instrument types. Through training and optimizing models, we have ensured that piano program programming and creative optimization processes can achieve high recognition rates and accuracy on different instrument types, thereby further expanding the application scope of big data technology in the field of music creation.

This study aims to optimize the sentiment analysis process of user evaluation texts through big data technology and natural language processing technology. This includes developing or improving existing emotion classification algorithms (such as ISOMAP algorithm) to more accurately identify positive, negative, and neutral emotions in user evaluations. Based on sentiment analysis, deeply explore user evaluations with different emotional tendencies and extract key factors that affect user emotions. These key factors may involve multiple aspects such as product quality, service attitude, and logistics speed, which can help companies gain a more comprehensive understanding of user needs and pain points. First, this paper summarizes the theoretical basis and research status of sentiment analysis and clarifies the background and significance of the research. In this study, natural language processing technology and the ISOMAP algorithm were used to classify the emotional tendency of user evaluation texts and three emotional categories were identified: positive, negative and neutral. Then, big data analysis tools are used to dig deeply into user evaluations with different emotional tendencies and extract the key factors affecting user emotions. On this basis, this paper constructs an emotionally-oriented marketing strategy framework. Through the case analysis, the application value of sentiment analysis of e-commerce and the effectiveness of marketing strategy are verified. The research results show that the application of big data technology in user evaluation sentiment analysis can not only provide enterprises with accurate market insights but also guide enterprises to develop more effective marketing strategies and enhance their market competitiveness.

In order to realize the rapid mining of sports data and intelligently formulate the sports training scheme that matches the athletes’ sports state, this paper studies the sports data mining and sports training decision support system based on big data technology. Leveraging MapReduce and OLAP, the system efficiently mines sports data and applies an improved Apriori algorithm to extract frequent itemsets tailored to athletes’ current states. These itemsets directly inform personalized training schemes, enhancing decision support for athletes. Experiments confirm the system’s scalability and precision in large-scale sports databases, effectively matching athletes’ training needs.

In the context of the gradual improvement of the degree of personalized development, College Students’ interest in music shows a diversified trend, which makes the construction of relevant music platforms fully consider the actual needs of individual users and recommend resources corresponding to their interests. This paper puts forward the research on the construction of College Students’ music interest platform based on Internet $+$. On the premise of analyzing the value proposition of the cultural industry platform model and the emphasis on customer goals under the background of “Internet $+$”, this paper designs a platform structure including resource management module, College Students’ user management module, resource recommendation module and resource startup module. The big data technology under the background of “Internet $+$” is used to analyze the MIDI audio characteristics and user behavior characteristics of resources. The binary community is used to establish the matching relationship between the two, determine the recommended resources, and introduce the heat parameter to avoid the cold start problem. The test results show that the processing efficiency and response time of the design platform for user behavior and user requests can meet the needs of practical applications, and has high stability. The collection map, Download map and share map of the recommended results of the platform are the best outputs in the test stage.

The ecological suitability evaluation of low-carbon urban planning is affected by the irregular distribution of ground buildings, resulting in low evaluation accuracy. Therefore, the ecological suitability evaluation method of low-carbon urban planning based on big data technology is proposed. The joint feature detection method of building and block data is used to realize visual remote sensing detection of low-carbon urban spatial planning. The collected remote sensing images of low-carbon urban spatial planning are vertically connected, spatially embedded, vertically stacked, and spatial planning parameters are extracted. The edge contour detection and feature clustering analysis of the remote sensing images of low-carbon urban spatial planning are carried out. It realizes the irregular point marking of the low-carbon city ecological adaptability spatial image and the fusion processing of the multi-spectral image and the full-color image, so as to generate the change detection map of the low-carbon city planning ecological suitability evaluation, and realize the optimization evaluation of the low-carbon city ecological adaptability space through the distribution of the detection map. The experiment shows that the accuracy of ecological suitability evaluation of low-carbon city planning using this method is high, and the model parameter detection effect of urban building space matching is good, which improves the evaluation ability of ecological suitability space of low-carbon city.

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Reform plan for governments’ fiscal role in medical services.

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There are certain risks in the network data of cloud accounting platform, and the accuracy of audit data directly affects the subsequent financial decisions of enterprises. In order to ensure the accuracy of enterprise decision management, this paper constructs a cloud accounting IT audit model based on big data technology. First, the enterprise’s solvency, profitability and operation capacity are taken as the indicators of financial status, and the evaluation index system is constructed. Then the data are preprocessed, after which the analytic hierarchy process is used to analyse the data and determine the audit focus. Finally, $K$-means algorithm, rule association algorithm, SQL statement and audit intermediate table are used to conduct audit analysis, and audit doubts are found, and the effectiveness of the method is verified with specific cases. Through the experimental analysis, we can see that the average accuracy rate of the model built by the research institute is 98.37%, and the average recall rate is 97.67%. This model can accurately identify and judge the abnormal situation of the data in the cloud accounting platform, provide more accurate and effective data analysis results for the financial decision-making of enterprises, and have positive significance for the sustainable development of enterprises.

The growth of smart cities has been driven by digital economy technology, and each city’s economic development has been significantly impacted by the trade and distribution sector. Based on the externality theory, this study develops the MAR externality innovation index, Jacobs externality innovation index, and Porter externality innovation index, and then uses panel data from smart cities from 2013 to 2020 to examine the effects of the three types of indices on the economic standing of cities. First, it is found that Jacobs’ externality innovation in the distribution and commerce industries is better adapted to our current cities. Second, smart cities may innovate just as well as conventional ones in the distribution and business sectors. Third, compared to conventional cities, smart cities’ economic growth is far more influenced by expenditures in research and technology. This study indicates that to support the integration and growth of the distribution of commerce industries, we should build industrial clusters, stimulate the development of industrial clusters and integration, and strengthen the scientific and technological support of cities.