Following the speeding up of a process of financial globalization, the risks faced by financial markets have become more complex and diversified. Correlated patterns among financial assets exhibit characteristics of nonlinearity, asymmetry, and tail correlation. The original linear correlation analysis method is no longer suitable, but relevant information describing financial risks. In order to confirm whether an asset is safe, the key is to study and master its volatility, and this is based on our mastery of volatility measurement skills. This article is based on smart sensor big data security analysis and Bayesian analysis. The risk measurement of financial assets based on the empirical probability model is studied. The GARCH- t (1,1) model is selected according to the Akaike information criterion (AIC) after the generalized autoregressive conditional heteroskedasticity (GARCH) model is established by the EViews software. According to the results of probability integral transformation, a series of correlation coefficients and degrees of freedom of t -copula are obtained by the maximum likelihood estimation method. This paper uses the risk-adjusted return on capital (RAROC) method to evaluate the risk performance of financial assets. Financial institutions can only retain and absorb the financial market risks that cannot be avoided and transferred. The edge user node sends the service request to the edge server node. The edge server uses the model proposed in this paper to evaluate the user’s trust and selects the corresponding service level according to the trust level corresponding to the calculated credibility results. The data show that the edge calculation takes 0.2581 seconds, while the linear search takes about 64 seconds. The results show that intelligent edge computing improves the accuracy and efficiency of financial asset risk measurement.

The emergence of wireless sensor networks connects the physical world with the information world and changes the way humans interact with nature. With the rapid development of modern information technology, accounting information systems (AIS) have emerged at the historic moment. Under the information environment, accounting data exists in paper or paperless form. The use of information technology not only brings convenient and efficient services to enterprises but also has a huge impact on the internal control of the enterprise. Because the network is open and unstable, the system is vulnerable to illegal intrusion and viruses. Based on the above background, the research content of this article is to use DES algorithm to encrypt accounting data. DES (Data Encryption Standard) encryption algorithm is a symmetric password encryption method. It has the advantages of fast encryption speed, simple and practical algorithm, and consideration of both security and efficiency requirements. This paper discusses the application of DES encryption technology to accounting data processing. To achieve data security management goals. Therefore, this paper proposes a DES algorithm based on the logistic chaotic system. Through experimental simulation, the results show that the chaotic discrete model has initial value sensitivity and iterative nonrepetition. The resulting key space is independent and random. In the application, you can perform random key input according to the performance of software and hardware, which is flexible; there is only one “1186828” in the initial DES algorithm encryption process, but each set of plain text in the improved DES algorithm corresponds to a corresponding set of keys and independence. The test results show that they are maintained between 5 and 6.6. It is proved that using the initial value sensitivity of the logistic system and using the initial value as the key can realize the secure management of accounting data on the premise of ensuring efficiency.