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Combining actual conditions, power demand forecasting is affected by various uncertain factors such as meteorological factors, economic factors, and diversity of forecasting models, which increase the complexity of forecasting. In response to this problem, taking into account that different time step states will have different effects on the output, the attention mechanism is introduced into the method proposed in this paper, which improves the deep learning model. Improved models of convolutional neural networks (CNN) and long short-term memory (LSTM) that combine the attention mechanism are proposed respectively. Finally, according to the verification results of actual examples, it is proved that the proposed method can obtain a smaller error and the prediction performance are better compared with other models.

Chlorophyll-a (Chla) concentration is an important indicator to monitor eutrophication, which is a persistent problem that affects the ecological health of coastal water or shallow lakes. We have applied a Gaussian-like fuzzy function model for the estimation of Chla concentration in Hong Kong Coastal Waters, based on the spectral signature using the HJ-1A /1B CCD images and measured data. The method of this paper is as follows. Firstly, reflectance peak and fluorescence band were used to figure out the bands or bands combination which impact the Chla concentration significantly; and then calculated the value of all the pixels without measured data according to their similarity degree with the measured points; finally, the full Chla concentration maps in the study area were produced by GIS spatial interpolation. It is concluded in this paper that the method can retrieve the Chla concentration accurately and the result of changes detected coincides with the observed result extremely, what’s more, the maps generated are continuous and smooth which are quite different from traditional RS maps that can only accurate to pixel size.

Through the research on the RFID’s User tag memory and its coding mode, a double-label management mode based on the combination of RFID tag and barcode is proposed, which realizes the binding of information in the warehouse logistics process, the convenient transmission and traceability between the upstream and downstream departments. The interface between the scheduling system and the equipment control system is designed, and the scheduling algorithm based on priority ranking is put forward to realize material outbound in order in warehouse logistics system. This method is an extension of the warehouse and logistics information system, which can solve the problem of the accuracy of the material outbound in sequence to the downstream departments.