Big Data Analytics for 6G-Enabled Massive Internet of Things

In recent years, China’s consumer finance has developed rapidly, but the foundation is unstable, and the industry has serious problems of violent competition, excessive credit, and fraud. Therefore, we should attach great importance to the healthy development of consumer finance, especially the management of its credit risk. The application of big data credit investigation can provide early warning of potential risks and prevent the risk of excessive credit investigation. This paper starts with the definition of basic core concepts, such as traditional credit investigation, big data credit investigation, and consumer finance, analyzes the performance and causes of consumer finance credit risk, and combs in detail the relevant theories of the application of big data credit investigation in consumer finance credit risk management. The application of big data credit investigation has optimized the risk management process of consumer financial institutions, deepened the concept of Internet consumer finance, improved the risk management system, created a diversified credit information system, and strengthened the innovation of Internet consumer finance products and services. For example, credit scores provide the most intuitive quantification of consumer credit risk. For consumers with different levels of credit scores, different credit approval processes can be matched. For customers with high scores, the work process can be simplified without affecting the work results. It can reduce the workload of employees by 20% and increase the accuracy of customer credit risk prediction by 16%.

“…en, the High Court Information Network and the Industrial and Commercial Information Network are used to crawl specific data [16][17][18].…”

Section: Consumer Finance Credit Risk Management Experiments Based On Big Data Credit Investigationmentioning

confidence: 99%

Credit Risk Management of Consumer Finance Based on Big Data

Hui-bo

2021

“…How to precipitate the existing research results, combined with artificial intelligence and domain knowledge, to design a more automatic visual analysis system for vocabulary analysis also has strong application value and significance [18]. In the era of big data, all kinds of network big data are in full bloom, with a large amount of information, strong accessibility, and wide communication power, which has become an irreplaceable advantage of network big data [19,20].…”

Section: Overview Of Word Cloud Semantic Analysismentioning

confidence: 99%

Semantic Analysis of Literary Vocabulary Based on Microsystem and Computer Aided Deep Research

Cao

2021

It has great advantages in data processing. Embedded microsystems are widely used in IoT devices because of their specific functions and hard decoding technology. This article adds a literary vocabulary semantic analysis model to the embedded microsystem to reduce power consumption and improve the accuracy and speed of the system. The main purpose of this paper is to improve the accuracy and speed of semantic analysis of literary vocabulary based on the embedded microsystem, combined with the design idea of Robot Process Automation (RPA) and adding CNN logic algorithm. In this paper, RPA Adam model is proposed. The RPA Adam model indicates that the vector in the vector contains not only the characteristics of its own node but also the characteristics of neighboring nodes. It is applied to graph convolution network of isomorphic network analysis and analyzes the types of devices that can be carried by embedded chips, and displays them with graphics. Through the results, we find that the error rate of the RPA Adam model is the same at different compression rates. Due to the different correlations between knowledge entities in different data sets, specifically, high frequency can maintain a low bit error rate of 10.79% when the compression rate is 4.85%, but when the compression rate of high frequency is only 60.32%, the error rate is as high as 11.26%, while the compression rate of low frequency is 23.51% when the error rate is 9.65%.

“…erefore, the focus of the cooperation between the two is to build an Internet marketing and publicity platform. e tourism products and services are put on the Internet platform [10,11]. In this survey, we will first sort out related concepts such as big data and agritourism and then summarize domestic and international surveys on the basic theory of agritourism, research methods, and influential factors.…”

Section: Introductionmentioning

confidence: 99%

Statistical Research on the Development of Rural Tourism Economy Industry under the Background of Big Data

Zhou

2021

China is rich in rural tourism resources. In recent years, rural tourism has been the focus of tourism development in various regions, which has greatly promoted the development of rural economy. But at the same time, low efficiency, unreasonable development, and homogeneous tourism environment have affected rural tourism and reduced the competitiveness of rural tourism. In order to explore innovative ideas and countermeasures for the development of “big data + rural tourism,” improve the development of rural tourism economy and industry, and improve the competitiveness of rural tourism, based on related researches on rural tourism, tourism competitiveness, coordination, etc., starting from the particularity of the rural tourism system, this paper proposes the use of coordination and coupling theory to analyze the status quo of rural tourism economic industry in a certain place in Jiangxi. The interaction of efficiency and competitiveness of rural tourism, such as infrastructure, has been studied in depth. The study found that, in rural tourism, efficiency and competitiveness are positively related. The higher the environmental index, the stronger the competitiveness. When the environment and competitiveness are coordinated, the economic benefits that can be created are about 30% higher than normal. This shows that, for rural tourism to develop better, it is an indispensable condition for the coordination of competitiveness and the environment.