Method for dynamically assessing confidence degree of satellite telemetry data trend prediction

Abstract

The invention discloses a method for dynamically assessing a confidence degree of satellite telemetry data trend prediction. The method comprises the steps of 1) obtaining historical telemetry data information of a satellite telemetry parameter TX123 from a telemetry information historical database, performing outlier elimination processing on the historical telemetry data information of the satellite telemetry parameter TX123, and performing prediction to obtain a telemetry data information set; and 2) performing calculation to obtain confidence information, which comprises a confidence value, a confidence interval and a confidence degree. According to the method, a concept of the confidence information of the satellite telemetry data trend prediction is proposed and comprises the confidence value, the confidence interval and the confidence degree; through the confidence interval and the confidence degree, satellite telemetry data information trend prediction method and result can be dynamically assessed in real time; and compared with the prior art, the method has the advantage that the accuracy of the telemetry data trend prediction method can be quantitatively assessed by calculating the confidence degree.

Description

technical field [0001] The invention relates to an evaluation method for trend prediction of satellite telemetry data, and belongs to the technical field of satellite telemetry data prediction. Background technique [0002] During the satellite's in-orbit operation, its telemetry data contains a large number of objective laws and knowledge that can be used for satellite fault diagnosis and telemetry data trend prediction. Mining the characteristic information of satellite telemetry parameters, effectively understanding, mastering and utilizing its laws is undoubtedly important for satellites. Long-term safe and reliable operation in orbit is of special significance. In the traditional trend prediction of satellite telemetry data, after selecting a certain prediction method to obtain the prediction data, compare the prediction data with the real-time telemetry data. If the deviation is large, the prediction result of the selected prediction method is considered to be inaccura...

AI Technical Summary

Problems solved by technology

The large or small deviation is mostly judged by experience and cannot be quantified to accurately evaluate the rationality of the telemetry data trend prediction method

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