System architecture design of real-time interpretation of satellite telemetry data and intelligent diagnosis of satellite status based on cloud-edge collaborative computing

The timeliness of interpreting satellite telemetry data and the quality of such data serve as critical benchmarks for diagnosing the on - orbit operational status of satellites and forecasting their health trends. Confronted with the exponential growth of satellite telemetry data, contemporary satellite automatic interpretation systems commonly grapple with issues including low interpretation efficiency, a significant absence of abnormal data, and a lack of credibility in real - time satellite status diagnosis. In response, cloud-edge collaborative computing technology is harnessed to reengineer the architecture of the software for real - time interpretation of satellite telemetry data and intelligent diagnosis of satellite health status. Initially, on - board edge computing technology is incorporated to distributed the traditional interpretation between the on-board edge and the ground cloud. Not only the interpretation efficiency is enhanced, but also the telemetry data is refined to a high - quality standard. Subsequently, advanced criteria are introduced into the ground-server interpretation layer. Leveraging cutting-edge AI technologies such as machine learning and deep learning, an intelligent data model for real - time satellite status diagnosis is established. By feeding this model with high - value interpretation data transmitted from the on - board edge interpretation layer, the real - time health status of the satellite can be scientifically determined.