In order to achieve the goal of independent and controllable key core technologies for Meteo by 2025, the Meteo Big Data Cloud Platform (referred to as Tianqing) establishes a simulation environment based on Hygon X86 CPU and Kylin OS. However, in the operation of simulation platforms, it finds that the docker scheduling performance of data processing and assembly line subsystems based on Kubernetes is poor, which cannot meet the timeliness requirements of user integration algorithms. In response to this issue, this article adopts a comparative analysis method, selecting servers based on three types of CPU and three types of operating systems from the simulation environment and business environment for Tianqing as the research objects. A series of combined comparative test cases are designed. It finds that the kernel is the key factor affecting docker scheduling performance. Further analysis is conducted on the impact of operating system kernel settings on real-time and throughput, as well as the suitable business scenarios. Finally, a method for adjusting the Kylin OS kernel is provided. By adjusting the kernel settings, the docker scheduling performance significantly improves, meeting the timeliness requirements of the data processing system and laying the foundation for achieving self-supporting of the key core technology of Tianqing.