The sharing economy model based on blockchain technology can hand over user data and market data to users and communities. Based on obtaining the consent of users, anyone can use the shared data to create useful products and services to build a new business model and continuously upgrade the user experience in the competition. Due to the imperfection of the trading system, the non-standard trading mode, the uncontrolled trading process, and the digital economic value sold are easy to be damaged. The quality of the digital economy purchased is difficult to guarantee. This paper adopts the decentralized transaction mechanism of asymmetric encryption algorithm, the two-way anonymity mechanism of hash encryption algorithm, the Ownership Authentication Mechanism Based on digital signature algorithm, the data confidentiality mechanism of data encryption technology and interface communication, and the fraud control mechanism based on qualification authentication to realize the point-to-point transaction of the digital economy. It provides a safe and reliable transaction environment for both parties of the digital economy transactions as well as a security guarantee for realizing the value of the digital economy.
With more and more uncertain factors affecting enterprise operation in market economy, enterprises are facing with more and more diversified risks. This requires accurate prediction of the future financial situation of the current enterprises in the market as far as possible, and take preventive measures in advance for the companies with hidden financial crisis. Based on the dynamic CBR model, this paper considers the influence of the year of the training sample in the current year, that is, the distance of the sample from the current year on the model's early warning performance, and builds a dynamic financial crisis early warning model with CBR as the basic classifier considering both concept drift and sample time weight. In this paper, the samples of the cloud financial sharing center of the current year are given a higher weight, while the samples of the distant year are given a lower weight. The results show that the prediction accuracy of THE CBR model considering the time weight of the sample has a stable improvement compared with the CBR model without considering the time weight of the sample.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.