Satellite telemetry data analysis method, system and program product
By performing real-time analysis, time series similarity calculation, and difference processing on satellite telemetry data, confidence intervals are constructed, solving the problems of low accuracy and poor dynamic adaptability in traditional methods, and realizing dynamic anomaly detection of satellite telemetry data.
CN120910745APending Publication Date: 2025-11-07BEIJING AEROSPACE LIANTEST TECHNOLOGY CO LTD
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Patent Information
- Application Number
- CN202511011367.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-22
- Publication Date
- 2025-11-07
AI Technical Summary
Technical Problem
Traditional methods for analyzing anomalies in satellite telemetry data have low accuracy and poor dynamic adaptability, and cannot detect dynamic anomalies within a threshold range.
Method used
By receiving satellite telemetry data in real time, performing analysis and time series similarity calculation, differential processing, constructing confidence intervals for state change parameters, using the confidence intervals to determine abnormalities in satellite state parameters, and outputting early warning information.
Benefits of technology
It achieves efficient and accurate anomaly detection in satellite telemetry data, adapts to the dynamic changes in satellite telemetry data, and improves the accuracy and dynamic adaptability of analysis.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention belongs to the technical field of satellite telemetering, and particularly discloses a satellite telemetering data analysis method and system and a program product, and the method comprises the steps: carrying out the analysis of received satellite telemetering data, obtaining real-time satellite state parameters, constructing a telemetering state time sequence, carrying out the reference time sequence matching verification of the telemetering state time sequence, and obtaining a telemetering state parameter. Performing differential processing and confidence interval calculation on a telemetering state time sequence, determining a dynamic confidence interval, performing anomaly detection on a state change parameter between a real-time satellite state parameter and a satellite state parameter at a previous moment by using the confidence interval, and performing anomaly early warning if anomaly is detected. Therefore, efficient and accurate satellite telemetry data analysis is realized. The method can effectively solve the problems of poor analysis precision and poor dynamic adaptability of a traditional static threshold judgment method, can completely adapt to the dynamic change characteristics of the satellite telemetry data, realizes dynamic analysis and detection of the satellite telemetry data, and accurately judges the abnormal condition of the satellite telemetry data.
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