Embedded task system and Gaussian mixture model in the analysis and application of user behavior in marketing management

With the rapid development of science and technology, embedded mission systems have also penetrated into various industries such as scientific research and military technology. The more and more complex tasks and higher frequency of use also make the embedded task system need to deal with a larger number of related tasks. For the general multiple linear regression analysis based on simple random sampling, we used four different weights to carry out four different modeling, and finally found that they are different in principle and the results of the simulation study, which attracted the attention of researchers. This paper analyzes the status quo of the marketing management of the Z company and improves the marketing management of the Z company. In this process, it summarizes the problems that the Z company has in the marketing management. Among them, the most deadly is that its marketing management concept is somewhat biased. Company Z has invested too much in the development of new customers and insufficient attention to old customers, leading to serious loss of customers and high marketing costs. In addition, the company only pays attention to the improvement of product quality, not enough publicity in terms of brand value, ignoring the huge impact of brand value on overall marketing. The company adopts a non-differentiated marketing concept and cannot meet the different needs of market customers in a timely manner. By improving the Gaussian mixture model commonly used in dynamic panel systems, the error can be reduced and the excessively distorted scale identification test can be corrected. The improved Gaussian mixture model has a better estimation of the limited sample properties.