The Role of Digital Information Technology for Social Welfare Computing in the Context of the Internet of Things

• Published in: Mathematical problems in engineering 2022-08, Vol.2022, p.1-12
• Main Author: Lu, Yuqing
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Banded structure; Communication; Digital economy; Dynamic characteristics; Economic development; Economic growth; Efficiency; Feature extraction; Identification methods; Income; Income distribution; Information industry; Information retrieval; Information technology; Internet of Things; Network topologies; Quality of life; Recognition; Subjectivity; Tax rates; Tax reform
• ISSN: 1024-123X; 1563-5147
• DOI: 10.1155/2022/1832343

The paper measures the income distribution effects and welfare effects of VAT itself based on Chip2013 household income and expenditure microdata using the Gini coefficient, Suits index, and Theil index from the perspective of annual income and lifetime income, respectively, and the effective VAT rate. Taking provinces as the basic research unit, we use the first-right method, LISA time path, and Spatiotemporal leap to evaluate China’s well-being level from 2010 to 2020 and analyze its Spatiotemporal dynamic characteristics. At present, most countries adopt a tax rate model with 1∼3 bands. The current VAT rate structure is “13% + 9% + 6%,” which is in line with the international development trend. In the data preprocessing module, the HTTP(S) responses of non-IoT devices are first filtered out and then the text that may contain device information is extracted from the remaining HTTP(S) responses by integrating multidimensional features, and finally, the irrelevant strings in the text are filtered out. At the end of the paper, simulation experiments are designed to verify the function and performance of the identification information extraction system for this heritage connected device, and the results verify that the model identification method in this paper can achieve better identification results compared with existing methods. The results verify that the model recognition method in this paper can achieve better recognition results compared with existing methods.