Research on the Application of Flink Streaming Data Technology in the Construction of Automobile Internationalization Platform

This paper analyzes the core advantages of Flink streaming data technology, including high throughput, low latency and strong scalability, and shows its superiority in processing large-scale automobile data streams. Aiming at the complex business needs of the automobile internationalization platform, a streaming data fusion algorithm is designed, which can integrate multi-source data from different countries and regions in real time, such as user behavior data, vehicle performance data and market feedback data. The algorithm optimizes the data stream processing process to achieve efficient data integration and analysis, and provides support for real-time decision-making of the platform. To verify the effect of the algorithm, this paper conducts model simulation based on Flink streaming data technology. The results show that the algorithm can significantly improve the platform response speed and data processing accuracy when processing cross-border data. Specifically, the simulation data shows that the response time of the platform is reduced by about 30% when the data flow is high, and the data processing accuracy is improved by 15%. In addition, the accuracy of the platform in market demand forecasting is improved by 20%, which enhances the stability and efficiency when facing massive data. The experimental results show that the proposed streaming data fusion algorithm significantly improves the real-time decision-making ability of the platform, especially in cross-border data integration and market demand forecasting.