To ensure the stable long-time operation of satellites, evaluate the satellite status, and improve satellite maintenance efficiency, we propose an anomaly detection method based on graph neural network and dynamic threshold (GNN-DTAN). Firstly, we build the graph neural network model for telemetry data. The graph construction module in the model extracts the relationship between features, and the spatial dependency extraction module and the temporal dependency extraction module extract the spatial and temporal dependencies of the data, respectively. The trained model is then used to predict the data, and the anomaly score between the predicted and actual values is calculated. Finally, the wavelet variance is used to analyze the data period. A dynamic threshold method based on the period time window is used to detect anomalies in the data set. Experimental results of satellite power system telemetry data show that the proposed algorithm's accuracy reaches more than 98%, with good effectiveness and robustness.

为了保证卫星长期稳定运行，评估卫星状态，提高卫星维护效率，提出了一种基于图神经网络和动态阈值（GNN-DTAN）的异常检测方法。首先，我们建立了遥测数据的图神经网络模型。模型中的图形构造模块提取特征之间的关系，而空间相关性提取模块和时间相关性提取模块分别提取数据的空间和时间相关性。然后，将训练后的模型用于预测数据，并计算预测值与实际值之间的异常分数。最后，小波方差用于分析数据周期。基于周期时间窗口的动态阈值方法用于检测数据集中的异常。