7results about How to "Less attention" patented technology

The invention discloses a multi-granularity instruction generation method and device oriented to visual language navigation. The method comprises the following steps: extracting panoramic image features and action features of coarse granularity and regional features and grid features of fine granularity; coding and fusing the global features by adopting Bi-LSTM and an attention mechanism to obtain coarse-grained trajectory representation; space self-attention and time self-attention based on time constraint and cross attention based on time and local constraint are designed, local features are coded and fused, and fine-grained trajectory representation is obtained; the characterization of the coarse granularity and the characterization of the fine granularity are fused, and multi-granularity track characterization is generated; decoding is carried out through the LSTM-RNN, and a navigation instruction is synthesized; and the synthesized instruction and the sampled trajectory form enhanced data which is used for training a navigation agent. From the perspective of data enhancement, a high-quality natural language navigation instruction is synthesized, the generalization ability of an intelligent agent is improved, and an innovative and practical solution is provided.

The invention discloses a belt running state detection method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring video frame sequence data containing a to-be-detected belt; inputting the video frame sequence data containing the to-be-detected belt into a pre-trained belt running state detection model to obtain a belt running state detection result; wherein the belt running state detection model comprises at least one convolution module, a convolution block attention module, at least one full connection layer and a classifier, and the convolution block attention module obtains a channel attention feature map and a space attention feature map based on the feature map of each video frame in the video frame sequence data. According to the technical scheme, the channel attention feature map and the space attention feature map are obtained through the convolution block attention module, so that the model pays more attention to the belt area, the attention to irrelevant background areas is reduced, and the accuracy of the belt running state detection result is improved.

The present invention discloses a network fault detection method, device, equipment and medium based on semi-supervised learning, the method comprising: collecting a network status data set from a network environment; preprocessing the network status data set, wherein the data set containing a label is recorded as a labeled data set, and the remaining data is recorded as an unlabeled data set; using the Tri-training algorithm to train the three pre-trained base classifiers to obtain a trained network fault model; assigning weights to the three base classifiers in the network fault model respectively; inputting the network status data to be detected into the trained network fault model to obtain a detection result of the network status. The present invention can use a small amount of labeled data to detect whether the network status is faulty, thereby improving the accuracy of detecting network faults, being applicable to a wider range of scenarios, and reducing the cost of annotating data.

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.

The present invention relates to the technical field of equipment failure prediction, and in particular to a method for predicting key equipment failures of airport lighting stations based on digital twins, the method comprising: collecting real-time operation data and real-time physical data of power distribution equipment, and collecting real-time environmental data corresponding to the power distribution equipment; judging the operation failure of the power distribution equipment based on a number of real-time operation data, and judging the physical failure of the power distribution equipment based on a number of real-time physical data; correcting the physical failure judgment result based on a number of real-time environmental data, and then judging the actual failure degree of the power distribution equipment according to the operation failure of the power distribution equipment and the corrected physical failure result; determining the predicted failure degree based on real-time operation data, real-time physical data and a digital twin model of power distribution failure; respectively comparing the actual failure degree with the predicted failure degree to correct the digital twin model of power distribution failure. The present invention improves the accuracy of the fault prediction results of the digital twin model.

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.