Method for recognizing type of vortex signal of evaporator of nuclear power plant on basis of LSTM-CNN

A method for recognizing the type of a vortex signal of an evaporator of a nuclear power plant on the basis of an LSTM-CNN. The method comprises: calibrating data of each channel of a vortex signal; processing the calibrated data by using a time window; processing time sequence data in a differential manner; extracting time feature information of a time sequence by means of an LSTM network; a CNN network extracting local feature information of the time sequence; fusing the feature information of the LSTM network and that of the CNN network, wherein after the training and learning of a large amount of data, the feature information thereof can be represented by means of an input signal in vector form by using a triple loss principle; and constructing a defect signal feature database, representing same in vector form, comparing same to obtain the Euclidean distance between the vector feature of the input signal and the vector feature in a defect library, determining, according to the magnitude of the Euclidean distance, the category to which the signal belongs, and ultimately achieving the aim of classifying the vortex signal.