3results about How to "Less attention" patented technology

The invention relates to the technical field of data transmission, in particular to a data transmission method and system based on edge computing, comprising: acquiring motion data of a vehicle and channel state information of a communication channel, the motion data comprising acceleration, and the channel state information comprising a signal-to-noise ratio; and based on a plurality of signal-to-noise ratios and accelerations within a set sliding window length. According to the method, the channel-motion coupling fluctuation index is constructed by fusing the vehicle acceleration and the channel signal-to-noise ratio, the time delay sensitivity weighted backlog coefficient reflecting the real business urgency degree is calculated according to the channel-motion coupling fluctuation index, and the self-adaptive parameters of the transmission control algorithm are dynamically adjusted. Therefore, the system can sense the potential channel deterioration risk caused by strenuous movement of the vehicle, the attention on energy saving is automatically reduced when the environment becomes worse or data burst occurs, and the real-time performance of data transmission is preferentially guaranteed, so that the transmission backlog and time delay are remarkably reduced in a high-dynamic edge computing scene.

This invention discloses a multispectral and hyperspectral image fusion method based on a cross-scale Octave convolutional network, comprising the following steps: High spatial resolution hyperspectral images used for training are spatially downsampled and spectrally downsampled to simulate multispectral images (HR-MSI) and hyperspectral images (LR-HSI) captured by a sensor in the same region at the same time; a certain proportion of the resulting simulated multispectral and hyperspectral images are selected as the training set. For the training set, tensors corresponding to each training pair are generated, and each pair of training tensors is input into the convolutional network to obtain the optimal model parameters for multispectral and hyperspectral image fusion; the trained model is used to fuse low spatial resolution hyperspectral images and high spatial resolution multispectral images captured by the sensor to obtain a hyperspectral image with both high spatial and high spectral resolution.

The application discloses an agricultural drought grade prediction method and device, electronic equipment and a storage medium, the method comprises the following steps: extracting time series data of a land surface from a CRU data center; extracting features of the time series data on a time scale, and performing feature fusion and global average pooling to obtain a time feature matrix; extracting features of the time series data on a variable scale, and performing global average pooling to obtain a variable feature matrix; extracting features of the time series data in space, and integrating information of adjacent grid points into the grid point information to obtain a spatial feature matrix; and splicing the time feature matrix, the variable feature matrix and the spatial feature matrix together, and outputting a final drought grade classification result through a Softmax layer. The application improves the prediction accuracy of agricultural drought grades and further reduces agricultural losses caused by drought.