Generally speaking, a set of random variables is defined as a random process. When studying stochastic processes, people describe the inherent laws of necessity through superficial contingency and describe these laws in the form of probability, and realize that necessity is the charm of this subject from contingency. The theoretical basis of the whole stochastic process discipline was laid by Andre Andrey Kolmogorov and Du Budian. This discipline originated from the study of physics, such as the study of statistical mechanics by Gibbs, Boltzmann and Poincare, and the pioneering work of Einstein, Weiner and Levy on Brownian motion. Around 1907, Markov studied a series of random variables with specific dependencies, which were later called Markov chains. Wiener gave the mathematical definition of Brownian motion in 1923, and this process is still an important research topic today. Generally speaking, the research on the general theory of stochastic processes began in the 1930s. 193 1 year, Andre Andrey Kolmogorov published the analysis method of probability theory, 1934 A. Qin Xin published the related theory of stationary process. These two works laid a theoretical foundation for Markov process and stationary process. 1953, Dube published his famous book "Theory of Stochastic Processes", which systematically and strictly described the basic theory of stochastic processes.