Construction and research of automotive internationalization system based on multi-database system and XGBoost

The aim of this paper is to better develop an international automotive system platform to promote the globalization of Chinese automobiles. Firstly, this paper provides a detailed explanation of the system's ten underlying databases, which include storage types for text, numerical data, and images. Secondly, it investigates the permutation and combination connection algorithms between databases and proposes an optimization function for database selection and connection. Then, by employing the machine learning model XGBoost, parameters are trained and simulated to calculate the optimal parameters that meet the requirements of the loss function. Finally, a system demo is utilized to test and verify the methods proposed in this paper, and the results indicate that this optimization method can generally meet user requirements. Therefore, the automotive internationalization system platform based on multi-database systems and XGBoost proposed in this paper can further facilitate the advancement of domestic automobiles towards global and international markets.