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.

Abstract: A method and system for decompressing a compressed file is disclosed in this invention, the method comprising: reading a compressed data block from the compressed file; decompressing the compressed data block; outputting the decompressed data for storage into a decompressed file; deleting the compressed data block that was decompressed from the compressed file. The proposed method and system in this invention can reduce the unnecessary repeated data between compressed data and decompressed data. The storage space requirement will be reduced during decompression, and the existing compression/decompression algorithms need not be changed by using this invention. Thus, this invention is easy to be integrated into existing compression/decompression tools.

Abstract: A method and system for decompressing a compressed file is disclosed in this invention, the method comprising: reading a compressed data block from the compressed file; decompressing the compressed data block; outputting the decompressed data for storage into a decompressed file; deleting the compressed data block that was decompressed from the compressed file. The proposed method and system in this invention can reduce the unnecessary repeated data between compressed data and decompressed data. The storage space requirement will be reduced during decompression, and the existing compression/decompression algorithms need not be changed by using this invention. Thus, this invention is easy to be integrated into existing compression/decompression tools.