Convolutional neural network-based deep space probe state monitoring and identification method

The invention provides a deep space detector state monitoring and identification method based on a convolutional neural network, and the method comprises the following steps: generating detector downlink frequency spectrums corresponding to all possible technical states of a detector in a simulation and frequency deviation mode; classifying the downlink spectrum of the detector according to the target, and adding each classification as a label into the downlink spectrum of the detector to form training sample data; establishing a convolutional neural network, and performing classification recognition training on the convolutional neural network to obtain a trained convolutional neural network; and identifying the actual spectrum data acquired in real time to obtain a detector technical state identification result. According to the method, the workload of ground personnel can be greatly reduced, and automatic support is provided for unmanned state monitoring and judgment of the deep space probe and emergency d