Methodology for the Organization and Application of Satellite Telemetry Data Based on Cloud Database

As the space industry in China experiences rapid growth, the number of satellites in orbit is steadily increasing. Satellites collect various physical and environmental parameters via onboard sensors, collectively known as satellite telemetry data. These data are crucial for real-time monitoring of satellite conditions and hold significant value for operational assessment and maintenance. Given that satellites can remain operational for up to a decade, the volume of telemetry data generated is substantial. To address the challenges of scalable storage and timely query performance for large-scale telemetry data, this paper proposes an organization and application method based on a cloud-native time-series database. The proposed method employs multi-satellite and multi-parameter partitioning and data sharding methods, combined with summary storage method to achieve large-scale telemetry data storage. Additionally, it employs a combination of parallel and summary queries to ensure rapid data query. The Lindorm time-series database is selected for testing and validation. The results show that the proposed method has greatly enhanced both the storage and query efficiency of satellite telemetry data and the query latency for three typical telemetry data application scenarios has been reduced to the second level.