8results about How to "Effective association" patented technology

The application discloses a kind of wild fire smoke cluster and its boundary vector automatic identification method, system, storage medium and electronic equipment, belong to satellite remote sensing and disaster monitoring technical field.Method includes: obtaining satellite active fire point, fire event and UVAI data;Fire source area is positioned based on fire event geographic location and initial plume pixel is marked;The spatially continuous smoke cluster connected domain is identified by iterative region growing algorithm;According to the specific distribution form of connected domain pixel in satellite pixel matrix, the accurate closed boundary vector is automatically generated using matching geometric algorithm;Finally, using multiple criteria, the spatial isolated independent smoke cluster is screened out.The application avoids the defects that traditional image method is easily disturbed by environment from physical principle, realizes fully automated processing without training data, and can output vector boundary that can be directly used for diffusion and emission evaluation, significantly improves the accuracy, automation degree and application value of wild fire smoke cluster monitoring.

The application provides an information association method and device, electronic equipment and storage medium, relates to the technical field of communication, and solves the technical problem that in the related art, other users cannot guarantee the security of service data based on the terminal corresponding to a user accessing related services. The method comprises the following steps: receiving an information association request sent by a terminal; in the case that the secret card identifier of the first secret card is the same as the secret card identifier included in the opening information of the first account, and the first field included in the opening information of the first account is empty, sending a first verification code to the terminal, the first field being used to represent the terminal identifier corresponding to the account identifier of the first account; in the case that the first verification code sent by the terminal is received, determining the terminal identifier of the terminal as the terminal identifier corresponding to the account identifier of the first account, and determining the secret card identifier of the first secret card as the secret card identifier corresponding to the account identifier of the first account.

The invention relates to the technical field of neural image analysis, and particularly provides a brain function connection intelligent screening method and system for autism spectrum disorders, and the method comprises the steps: firstly obtaining resting state functional magnetic resonance imaging data and phenotype information thereof, and extracting a blood oxygen level dependence value time sequence of each brain region after preprocessing; then constructing a multi-scale brain network comprising a low-order function connection matrix and at least one high-order function connection matrix; the matrix is converted into a brain function connection graph containing sub-graphs of different scales through threshold sparsification; meanwhile, phenotype embedding features are extracted from phenotype information; the graph data and the phenotypic features are input into a multi-channel neural network model for parallel processing and fusion, and joint feature representation is obtained; and finally, outputting an auxiliary diagnosis result of the autism spectrum disorder through the classifier. According to the method, by fusing the multi-scale brain function connection information and the individual phenotype features, the accuracy of autism classification diagnosis and the generalization ability of the model are effectively improved.

The application provides a license text detection method and system based on spatial calibration and feature fusion, and belongs to the technical field of image processing. The method inputs a license image into a network based on PP-HGNet to extract multi-scale features; performs enhancement processing on the multi-scale feature maps based on spatial calibration, extracts global context, performs shape self-calibration and local detail fusion to strengthen the spatial structure information of the text; performs fusion processing on the enhanced features based on multi-scale hollow convolution, realizes adaptive weighted fusion of multi-scale features by combining multi-branch hollow convolution with a double attention mechanism, and finally generates a binary graph of the text region through differentiable binarization. The application effectively solves the problems of dense text boundary confusion, long text breakage and uneven multi-scale text feature capture in the license, significantly improves the detection accuracy and efficiency, and is suitable for automatic extraction of license information.

The application relates to the field of small sample target detection, in particular to a two-stage small sample target detection method based on an optimized CBAM attention mechanism, and comprises the following steps: training a two-stage target detection network Faster-RCNN by using a base class data set to obtain a base class detection model; freezing parameters of a feature extraction backbone network in the base class detection model; optimizing a CBAM attention mechanism module; placing the optimized CBAM attention module in the feature extraction backbone network to construct a detection network, then inputting a new class small sample data set with a small amount of labeled information to fine-tune parameters of a detection head part of the detection network; and inputting a to-be-detected data set into the detection network to obtain a detection result. Compared with the prior art, the application has the advantages of inhibiting the influence of unimportant spatial information, improving the attention degree of important spatial information, enhancing the sensitivity to different scale features, and having strong generalization ability and robustness and the like.

Embodiments of the present application provide an industrial knowledge generation method and device fusing a large model and a knowledge graph. The method comprises: receiving demand information sent by a client, the demand information comprising at least one keyword; an industrial knowledge document comprising a plurality of paragraph contents; determining at least one target paragraph content associated with the demand information from the plurality of paragraph contents; performing matching processing on the at least one first keyword and a knowledge graph corresponding to the industrial knowledge document to determine at least one chapter content in the industrial knowledge document; wherein the industrial knowledge document comprises a plurality of chapter titles and chapter contents corresponding to the plurality of chapter titles respectively, and the knowledge graph comprises a plurality of knowledge nodes and association relationships between the plurality of knowledge nodes; and generating target content according to the demand information, the at least one target paragraph content and the at least one chapter content. The above method improves the accuracy of generating target content.

This application provides a method, apparatus, and electronic device for cross-camera trajectory association, relating to the field of computer vision technology. The method involves acquiring the trajectories of targets captured by multiple cameras, as target trajectories; acquiring the similarity between image features of every two trajectories within each target trajectory, as an association similarity; associating the two trajectories with the highest association similarity to obtain a trajectory set containing these two trajectories as a new target trajectory; calculating the association similarity between the image features of the new target trajectory and other target trajectories, and then returning to execute the step of associating the two trajectories with the highest association similarity to obtain a trajectory set containing these two trajectories as a new target trajectory, until a first number of target trajectories are obtained. Based on this, it is possible to effectively associate various target trajectories.

The application relates to a method and device for intelligent response text classification, electronic equipment and a storage medium. The method for intelligent response text classification comprises the following steps: S1, data processing, performing an ETL project on data converted through ASR, selecting call data, and then obtaining original corpus data sets required by a model; S2, text composition, performing standardization processing including punctuation symbol processing and stop word removal on the original corpus data sets, using one-hot to encode words, documents and entities, and forming an initial graph; S3, model construction, optimizing a graph convolutional neural network based on a feature projection network Fpnet, improving the graph convolutional network by adopting a multi-head edge node pooling method, forming a multi-head edge pooling graph convolutional network MHSP-GCN architecture, then replacing a common feature learning network C-net and a feature projection network P-net in the Fpnet with the MHSP-GCN, and through a feature projection network OPL, making original features Fp and shared features Fc perform orthogonal projection calculation to obtain purer classification features, so that the accuracy of the intelligent response text classification task is improved.