18results about How to "Effective association" patented technology

The invention discloses a vehicle visual relay tracking method based on spatial-temporal feature fusion, and belongs to the technical field of intelligent traffic. After a multi-target tracking trajectory of each camera on a road is obtained, the method comprises the following steps: establishing a camera space-time constraint based on a topological relation between the cameras and the road, performing trajectory compatibility judgment based on the camera space-time constraint, and filtering out incompatible trajectory pairs; modeling the trajectory of the target vehicle into a graph, and calculating the similarity of compatible trajectory pairs based on a graph convolutional neural network; based on the trajectory similarity, the multi-camera trajectory association problem is decomposed, hierarchical clustering is performed on the trajectory by using a heap data structure, and the global association trajectory of each target vehicle is obtained step by step. According to the method, the efficiency and accuracy of track association are improved, the calculation efficiency is optimized, and vehicle tracking in a complex scene and under multiple cameras can be dealt with.

The present invention proposes a sensitive information analysis and identification method, system, device and readable storage medium, which belongs to the field of information identification technology. The method includes: executing a sample generation strategy according to an original document, generating a sample document, and storing the sample document in a document library; extracting a sample document and its metadata from the document library, converting the sample document into a vector using a pre-trained text embedding model, and storing the vector and the corresponding metadata in a vector knowledge base; adding the extracted sample document and its metadata to the vector knowledge base, performing integration processing, and generating a sample corpus; obtaining a text to be tested, performing block vector similarity matching and statistical feature similarity calculation on the text to be tested; performing mixed reordering on the vector similarity matching results and the statistical feature matching results to generate a final matching result; generating a prompt instruction based on the final matching result, inputting the prompt instruction into a large language model, and generating an analysis result of the text to be tested.

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 provides a visual vibration measurement method with high generalization ability, which comprises the following steps: S1, selecting labels with high generalization ability: collecting historical labels and making the historical labels into a data set; a classic deep learning algorithm is selected for training to decide an optimal label and rule; s2, high-efficiency target detection: designing a high-efficiency detector; a simple target tracker is provided; and converting vibration displacement. According to the method, the practicability, the reliability and the accuracy of vision measurement vibration are remarkably improved, and a solid technical foundation is provided for application in related fields.

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 invention discloses a peripheral circuit state management method and system for digital simulation, and the system comprises a hierarchy division management module which is used for dividing each hierarchy and determining the work function of each hierarchy; the connection state management module is used for matching basic attributes among the components and changing connection states of the components and internal pins of the components; and the signal transmission management module is used for controlling the transmission of the circuit direction and other signal data during excitation triggering in the peripheral circuit. The system is based on the digital simulation technology, and the problems that a peripheral circuit state management method is not high in integration and reusability and has no universality are solved.

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.

The invention provides a data element processing method and device, electronic equipment and a storage medium, and belongs to the technical field of data processing.According to the data element processing method, efficient management of updated data is achieved by constructing a flexible data structure model, entities in data elements and the mutual relation of the entities can be recognized and processed in time, and the data processing efficiency is improved. Through text matching and mutual information value analysis, entities under different labels can be effectively associated, and data identification chaos caused by label inconsistency is reduced, so that the accuracy and speed of data storage and retrieval response are improved, service requirements can be more flexibly adapted, and the complexity and maintenance cost of data management are reduced.

The data processing system performs data processing of raw or pre-processed data. The data includes log files, bitstream data, and other network traffic containing cookies or device identifiers. The data processing system associates devices with device activity histories. The data processing system includes a pairing framework for determining device pairs based on device activity histories, a feature vector generation framework for generating feature vectors corresponding to determined candidate device pairs based at least in part on feature values ​​associated with devices of the determined candidate device pairs within the raw or pre-processed data, a scoring engine for determining scores associated with the determined device pairs based on the generated feature vectors, a graph structure including nodes for representing devices of the determined device pairs (wherein edges between pairs of nodes represent the determined device pairs), and a clustering engine for identifying clusters of corresponding n or more nodes within the graph structure representing corresponding groups of devices.

A development state and application state container mirror image association method based on an OCI specification comprises the steps that a container list in Pod is obtained, and container mirror images are pulled from a container mirror image warehouse in a container running state; a local container mirror image of the node is traversed, the container mirror image is unpacked, and container mirror image information corresponding to all the nodes is obtained and comprises Image ID and Image Digest; and correspondingly associating the development state and the running state of the container mirror image according to the Image ID and the Image Digest of the container mirror image information, and correspondingly storing lists. According to the method, the same container mirror image is identified by using the Image ID, and the relationship between the Image ID and the Digest is reasonably processed, so that the accuracy and the consistency of the container mirror image information among different container mirror image warehouses are ensured; through the design table structure, effective association of the development state container mirror image and the operation state container mirror image is realized, and management and tracing of the container mirror image are facilitated.

The invention discloses an intelligent manufacturing-oriented federal learning client selection method and system and related equipment, and the method and system enable trained models to have better similarity through constructing a client combination with large data relevance and small difference between clients, and improve the accuracy of the data relevance between the clients. Therefore, the federated learning aggregation model obtained through training can effectively reduce the influence of data distribution deviation, and then the problem that when a client carries out federated training, due to the difference between local models caused by manufacturing industry heterogeneous data, deviation occurs in the aggregation model is effectively solved.

The present invention relates to the technical field of data update management, and in particular to a GMP standard dynamic update and management system based on big data, the system comprising: a construction module identifies several historical keywords of historical GMP standard texts as historical cluster centers, and constructs an initial GMP standard text library through clustering division; a text acquisition module obtains a target GMP standard text; a text processing module identifies several actual keywords of the target GMP standard text; an update module compares several actual keywords with several historical keywords to determine whether to update several historical cluster centers, compares the target GMP standard text with the historical GMP standard text, and updates the initial GMP standard text library; a feedback module adjusts a preset acquisition frequency based on the update difference between the target GMP standard text library and the initial GMP standard text library. The present invention improves the timeliness of GMP standard updates.

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 invention discloses an electric vehicle charging management method. The method comprises the following steps: obtaining various new energy power generation branches accessed to an electric vehicle charging station and a multivariate feature fusion vector of each current new energy power generation branch; inputting the multivariate feature fusion vector of each new energy power generation branch into a pre-trained stable supply prediction model corresponding to each new energy power generation branch, and outputting a stability index of each current new energy power generation branch; and obtaining the electricity demand power of each to-be-operated charging pile of the charging station, the rated generation power of each new energy power generation branch and the stability index of the rated generation power, and determining a power supply object of the new energy power generation branch according to a preset power supply association algorithm. Therefore, based on the multivariate feature fusion vector, the stable supply prediction model of each new energy power generation branch is differentiated, and the power supply efficiency and the stability of charging management are improved.

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.