Abstract: The present disclosure discloses a tropical instability wave early warning method based on temporal-spatial cross-scale attention fusion, including performing cross-scale spatial map fusion on the multi-scale feature maps by a bilateral local attention mechanism, calculating a prediction loss by the global feature description map, and combining the prediction loss and the regularization loss for optimization training of neural networks; predicting a sea surface temperature at a moment T based on the optimally trained neural networks, selecting data at K moments before the moment T and inputting the data into the optimally trained neural networks, outputting a predicted value of tropical instability waves by the optimally trained neural networks, and drawing a temporal-spatial image of the tropical instability waves by associating the predicted value with coordinates, so as to achieve early warning of the tropical instability waves. The device includes a processor and a memory.

Abstract: The present invention discloses an El Nino extreme weather warning method based on incremental learning, comprising: through supervised representation learning, selectively constraining, by a multi-scale feature frequency domain distillation technology, drift of low-frequency components of the multi-scale features based on incremental training, and memorizing knowledge learned by the parallel convolutional neural networks in old tasks; adaptively learning different fusion parameters according to different time spans of the input multi-scale data by using a multi-scale feature adaptive fusion technology, so as to enhance the ability to learn new tasks; and outputting a Nino3.4 index reflecting a change rule of El Nino through fully connected layers according to the adaptively fused features, establishing a mapping function of an extreme rainfall probability r based on the Nino3.4 index, and in response to predicting that the value r goes beyond a threshold value k.