52 results about "Composite graph" patented technology

The invention relates to the technical field of environment monitoring, and provides a collaborative control method and device for a measurable, displayable, adjustable and controllable environment system, and the method comprises the steps: collecting an original data set of a monitoring region, carrying out the preprocessing, and constructing a multi-dimensional environment data knowledge graph containing time-space correlation attributes; through a neural network learning environment parameter and health risk space-time correlation characteristic, outputting a healthy human settlement environment comprehensive evaluation result based on a fuzzy comprehensive evaluation matrix of adaptive threshold adjustment; inputting the comprehensive evaluation result of the healthy human settlement environment into a Kriging interpolation algorithm, generating an indoor four-dimensional space-time dynamic thermodynamic diagram in combination with environment parameter space-time distribution characteristics, generating a health risk prediction thermal layer based on risk weight superposition, and forming a visual composite atlas; outputting a regulation and control strategy by taking energy consumption, equipment cost and a health risk value as optimization targets; and controlling the household cooperative equipment. According to the invention, predictive control is carried out on the environmental equipment, and the accuracy and effectiveness of environmental regulation and control are improved.

The invention relates to the technical field of intelligent power energy monitoring, in particular to a power grid monitoring method. The method comprises the following steps: obtaining power grid monitoring nodes, and carrying out monitoring node graph structure processing to construct a physical feature fusion network graph; performing graph structure knowledge enhancement on nodes in the physical feature fusion network graph to construct a semantic enhancement physical association graph; performing composite diagram operation processing of fault propagation risk assessment and root cause analysis based on the semantic enhanced physical association diagram, and constructing four types of intelligent agents of monitoring, coordination, analysis and decision to obtain a hierarchical collaborative decision network; and executing a local strategy learning process based on Q learning based on the hierarchical collaborative decision network so as to realize agent reinforcement learning optimization and end-to-end deployment implementation. According to the method, automatic and end-to-end construction of the intelligent agent is realized through graph structure semantic fusion and multi-stage reinforcement learning, and the intelligent level of power grid monitoring is remarkably improved.

Technologies are disclosed for managing composite storefront images. The composite storefront images can be generated utilizing portions of media content (e.g., visual content, such as videos, trailers, etc.), and templates generated based on sets of existing storefront images. Objects can be extracted from frames and composited together to generate course composite images. The course composite storefront images can be utilized to map the object portions onto the media content artwork styles to generate refined composite storefront images based on the templates.

The invention discloses a two-way traffic flow dynamic modeling method based on a deep space-time diagram neural network, and belongs to the field of intelligent traffic and data mining, and the method comprises the following steps: S1, constructing a traffic flow dynamic diagram, and capturing the local space-time dependence of a traffic flow; s2, after the local space-time dependency is obtained, extracting time features by using a time convolutional layer; s3, after the time features are obtained, a selection gate is used for screening the effective information obtained in the step S2, and final output is obtained; s4, after a result in S3 is obtained, using a composite graph convolution module to extract spatial features; s5, fusing the two-way traffic, and obtaining a final prediction result through a prediction layer; s6, the loss of the model is calculated, and whether the model meets the requirement is determined, the problems that in the prior art, in traffic flow prediction, bidirectional flow modeling is insufficient, dynamic dependency capture is insufficient, and adaptability to complex traffic scenes is poor are solved, and therefore the traffic flow prediction precision and the model applicability are improved.

The invention discloses a multi-source feature-fused bridge damage deep learning method, which comprises the following steps of: establishing a bridge damage simulation model, and obtaining bridge damage simulation data; associating the bridge damage simulation data with the explosion condition, and integrating the bridge damage simulation data with the bridge feature information to obtain a bridge damage database comprising a training data set and a verification data set; the bridge feature information comprises bridge geometric features and bridge material features; training and optimizing the constructed multi-layer composite graph neural network by using the training data set and the verification data set to obtain a bridge damage prediction model; and completing the prediction of the damage of the target bridge by using the bridge damage prediction model. The prediction method is high in precision and good in adaptability.

The invention provides an image processing method, electronic equipment and a storage medium. The method comprises the following steps: determining a ground area of a virtual scene graph; acquiring a first camera parameter corresponding to the target position under a first visual angle; determining a first figure graph from a preset database based on the first camera parameter; according to a second camera parameter corresponding to the first figure graph, first pixel distribution of the first figure graph and a third camera parameter corresponding to the virtual scene graph under a third visual angle, a transformation matrix is transformed from the second visual angle to the third visual angle, and a second figure graph with second pixel distribution is obtained; based on the depth information of the second figure graph, adding the second figure graph to the target position to obtain a first composite graph; and obtaining a second composite image based on the first composite image and the image generation model. The method can improve the synthesis effect of the synthesized picture.

A method for training a prediction model for a lower urinary tract symptom risk score is disclosed. A method for training a prediction model for a lower urinary tract symptom risk score according to an embodiment comprises the steps of: obtaining numerical data of a urinary system; obtaining graph data of the urinary system; obtaining composite graph data in which the numerical data is reflected in the graph data; obtaining a numerical feature map corresponding to the numerical data; obtaining a composite graph feature map corresponding to the composite graph data; and training the prediction model for the lower urinary tract symptom risk score on the basis of the numerical feature map and the composite graph feature map.

The invention belongs to the technical field of social network privacy protection, and provides a digraph synthesis method and system based on differential privacy, and the method comprises the steps: carrying out the community division of nodes in a digraph, and obtaining a community division result; generating an edge set in the community according to the target in-degree sequence, the target out-degree sequence and the target node similarity set of each community in the at least one community, wherein the edge set in the community comprises at least one edge in the community; generating an edge set between the communities according to the target edge sequence of each community in the at least one community and the target node similarity set, wherein the edge set between the communities comprises edges between the at least one community; according to the method, the edge sets in the communities and the edge sets among the communities are combined to obtain the target composite graph, community information of the directed graph is reserved, and the availability and accuracy of the composite social network graph are improved.

The present invention discloses a composite graph construction method, system, device and storage medium of multimodal data, and relates to the field of computer technology. The present application obtains various user data and resource data on a social network, and then performs information exchange between different modal data in the resource data based on a channel dynamic exchange mechanism, thereby improving the information in each modal data, and constructs a composite graph by improving the resource data, user data and the relationship between user resources after the modal data is improved, so that the composite graph more comprehensively represents various information of users and social resources in the social network, and is more likely to converge in deep learning tasks such as semantic analysis and sentiment analysis based on the composite graph, thereby improving the training effect of various task models.

The invention discloses a face forgery detection method and system based on a graph convolutional neural network. The method comprises the following steps: acquiring a face video of which the authenticity is to be detected; inputting the face video of which the authenticity is to be detected into the trained detection model to obtain a judgment result of whether the video is forged or not; wherein the detection model comprises an encoder used for generating features of each frame of image according to a face video to be subjected to authenticity detection; the space-time composite diagram construction module is used for constructing a space-time composite diagram in which a space diagram and a time sequence diagram are combined according to the characteristics of each frame of image; the space-time composite graph convolution module is used for carrying out feature learning according to the space-time composite graph to obtain classification features; and the classifier is used for predicting the authenticity result of the face video according to the classification features. According to the method, the space-time composite diagram combining the space diagram and the time sequence diagram is constructed according to the space-time consistency requirement of the face video, so that efficient and accurate face counterfeiting detection with high generalization capability is realized.

This invention provides a method and system for constructing a reliable evaluation dataset for power vision models, belonging to the field of intelligent recognition technology for power vision models. The construction method includes: acquiring collected on-site images and obstacle images; constructing virtual 3D models of power equipment and virtual obstacle models; marking the virtual 3D models and virtual obstacle models to obtain corresponding labels; acquiring multiple images from a virtual perspective on the composite model of the virtual 3D models and virtual obstacle models; determining the positional relationship between the virtual 3D models and virtual obstacles in the images based on the images; and generating a composite image and corresponding composite labels based on the positional relationship, the on-site images, and the obstacle images to form a dataset.

The invention provides a preparation method of a composite patterned substrate, the composite patterned substrate and an LED chip, and the method comprises the steps: forming a plurality of holes in the surface of a sapphire substrate, and carrying out the gluing, exposure and development processing, and obtaining a preset pattern; depositing a SiOF layer on the substrate; removing the preset pattern below the SiOF layer through heat treatment to form a cavity; transferring a pattern on the substrate through a nanoimprint technology; and performing inductively coupled plasma etching to form a composite pattern structure. A cavity structure is formed in a sapphire substrate through laser drilling and heat treatment, the light extraction efficiency of an LED is improved by combining with a composite pattern constructed by a nanoimprint technology and utilizing a multi-interface light scattering effect, the propagation direction of light rays can be guided by combining a patterned structure with a cavity, light emitting angle distribution is optimized, and light emitting is more uniform or the directivity is more concentrated; epitaxial growth stress is released through the cavity, dislocation density is reduced, epitaxial layer cracks are effectively inhibited, and crystal integrity is improved.

The invention relates to the field of depth attribute graph clustering, and particularly discloses a depth attribute graph clustering method based on a dual information comparison strategy, and the method comprises the steps: S1, providing a composite graph enhancement strategy which does not destroy original topological information, and generating a comparison view; s2, GAE and MLP are combined to design a linear graph automatic encoder, and information of the two views is transmitted to the linear graph automatic encoder for representation learning; s3, providing a dual information comparison strategy based on the comparison view obtained in S1, and performing comparison learning of the two views; s4, designing a multi-distribution self-supervision strategy, and forming a self-supervision cluster; s5, setting a multi-distribution self-supervision module to integrate the upstream task and the downstream task together, and performing global optimization; and S6, performing an ablation experiment to verify the effectiveness of the DAGC-DICS. According to the method, the clustering quality is remarkably improved, misclassification is reduced, and the clustering performance is effectively improved.

This invention provides a method, system, device, and storage medium for smart contract vulnerability detection, belonging to the fields of blockchain security and artificial intelligence technology. It includes extracting an abstract syntax tree, control flow graph, and data dependency graph from the original smart contract code; obtaining a composite graph object based on the abstract syntax tree, control flow graph, and data dependency graph; processing the composite graph object based on node semantics and function call relationships to obtain a hierarchical heterogeneous graph; constructing a heterogeneous graph Transformer network; using a heterogeneous graph Transformer encoder to process the hierarchical heterogeneous graph for encoding node and edge information, obtaining node representation vectors that fuse semantic and structural information; extracting multi-scale features from the node representation vectors; fusing the extracted multi-scale features using learnable weights; and processing the fused multi-scale features using a base classifier cluster to obtain vulnerability type prediction results and confidence levels. This invention achieves full-process optimization from structural modeling and semantic fusion to decision reasoning, significantly improving the accuracy and interpretability of smart contract vulnerability detection.

This invention provides a fully printed flexible photoresponse array and a parallel pattern recognition method, belonging to the field of semiconductor optoelectronic device technology. Firstly, this invention provides a fully printed flexible photoresponse array, including a flexible substrate and multiple differential dual-branch photoresponse pixels disposed on the flexible substrate. Each differential dual-branch photoresponse pixel includes: a common source electrode, a first branch drain electrode, a second branch drain electrode, a first composite channel branch, a second composite channel branch, and a light-shielding layer disposed above the second composite channel branch. Based on a special structural design and connection relationship, this invention solves the problems of insufficient stability in flexible array recognition, significant background light interference, and insufficient utilization of temporal information in existing technologies. Simultaneously, based on the above structure, a parallel pattern recognition method is provided to achieve parallel recognition and memory retention representation of multiple characters, multiple graphic units, or composite patterns.

This invention discloses a data intelligent sensing and optimization scheduling system and method for aluminum processing, belonging to the field of aluminum processing optimization scheduling technology. The method specifically includes: collecting multi-source heterogeneous data on aluminum processing, preprocessing the collected multi-source heterogeneous data, establishing a composite graph, dynamically generating a feature matrix using the correlation between different types of data, constructing a scheduling model for aluminum processing, performing pre-simulation using a virtual production twin, optimally configuring production resources for aluminum processing, applying the optimization scheduling model to aluminum processing in real time, and continuously optimizing the scheduling model using a reinforcement learning strategy for dynamic adjustment. This application can automatically optimize the allocation and scheduling order of resources according to changes in the manufacturing environment, improving production efficiency and reducing energy consumption and waste.

The invention relates to an antenna array synthesis directional diagram estimation method based on a multi-unit antenna actual measurement directional diagram and application, and the method comprises the following steps: in a standard test environment, measuring an azimuth plane or pitching plane directional diagram of each antenna unit, recording angle gain or power data, and obtaining actual measurement data; establishing a coordinate system, determining antenna reference positions and inter-unit parameters, dividing every two antennas into one group, and scoring groups and parameters; according to the directivity multiplication principle, a double-antenna composite graph computational logic (estimation formula) is established, and data simulation is substituted to obtain a composite graph; and taking the composite diagram as a quasi-unit diagram, and repeating the operation to complete the estimation of the composite directional diagram of the multi-unit antenna array. According to the method, the defects of complex modeling, large calculation amount and poor adaptability of a traditional theory multi-unit antenna array can be avoided, the blank in the prior art is filled, the estimation efficiency and precision can be effectively improved, the requirement for rapid and accurate analysis of the performance of a multi-antenna system in engineering is met, and reliable support is provided for design optimization of the antenna array.

The invention relates to a position privacy protection method and system for a road building image, and belongs to the field of visual image privacy protection. The method comprises the following steps: firstly, extracting a building edge structure edge composite image from an original image, and injecting the image into an encoder of a generator to obtain an intermediate feature; inputting the original image into an encoder and a style learning network to respectively obtain a minimum style representation and a maximum style representation, carrying out style alignment on the two groups of style representations by using intermediate features, and then carrying out up-sampling to obtain false road building images with different style difference degrees; and secondly, identifying the image by using a basic identifier and a pixel-level semantic identifier, performing adversarial training with a generator, and finally generating a false road building image with a privacy protection effect. According to the method, the location privacy of the building image can be effectively protected, the attack of location inference can be resisted, and the generated building image is ensured to maintain relatively high authenticity.

The present invention proposes a vulnerability detection method based on a dual attention mechanism and code similarity, which belongs to the field of vulnerability detection technology. It includes: extracting the program dependency graph corresponding to the unknown function to be tested, expanding the abstract syntax tree corresponding to the statement to each node of the program dependency graph to form a composite graph; using the marked vulnerability statement as a slicing criterion, finding the slicing node in the program dependency graph as the root node, expanding the abstract syntax tree to form a slice composite graph; learning the syntax and semantic information within the statement, generating an initial embedding vector of the statement node composed of semantic and syntax feature vectors; learning the structural features of the source code, obtaining the slice composite graph and the vector representation of the composite graph, and calculating the similarity between the two; when the similarity exceeds a certain threshold, the target function is defined as a potential vulnerability function. This method solves the problem of vulnerability detection methods that lack vulnerability information that can effectively exploit known vulnerability codes while ignoring or even eliminating redundant irrelevant information in the code.

The application provides a drug-target protein binding affinity prediction method and system, relates to the technical field of biological information technology, and comprises the following steps: learning local information of an atomic composite graph at a local level by using an isometric graph neural network; fully utilizing three-dimensional space structure information by using the isometric graph neural network, and keeping rotation and translation of coordinates unchanged during message transmission; then using a node feature extraction model and an atomic interaction matrix to learn deep-level interaction of the atomic composite graph at a global and long-distance level, improving interpretability of feature learning of the protein and the ligand, and making prediction performance of the drug-target protein binding affinity prediction model optimal. The application combines interaction of each distance level of the drug-target protein by fitting a dynamic process of drug-protein combination, simultaneously considers rotation and translation invariance of atomic mutual combination, and effectively improves accuracy of drug-target protein binding affinity prediction.

The invention provides a differential privacy track similarity publishing method. The method comprises the following steps: acquiring position data of a plurality of users at a plurality of moments; constructing a similarity sub-graph of each moment, wherein edges in the similarity sub-graphs represent that the distance between two users connected with the edges is smaller than a distance threshold value; combining the similarity sub-graphs at multiple moments to obtain a similarity composite graph; the weight of each edge is set according to the occurrence frequency of each edge in the similarity composite graph in the similarity sub-graphs at the multiple moments, and a track similar graph is obtained; adding noise in the weight of the edge of the trajectory similar graph to obtain a trajectory similar graph after noise addition; and releasing the noise-added track similar graph. The invention further discloses a differential privacy track similarity publishing device, a computer program product, electronic equipment and a user track similarity analysis method and system. According to the method, the trajectory similarity between the users is visually reflected on the trajectory similarity graph, meanwhile, noise is added to the weight of the edge, and privacy protection is achieved.

The invention relates to a power transmission and transformation project drawing intelligent proofreading method and system, and the method specifically comprises the steps: collecting various historical drawing data of a power transmission and transformation project, and automatically marking the information of electrical elements in a drawing through comparative learning; preprocessing the collected various historical drawing data of the power transmission and transformation project; constructing a composite drawing error judgment model based on deep learning, wherein the composite drawing error judgment model comprises a convolutional neural network and a recurrent neural network; extracting image features of the electrical elements in the target drawing by using a convolutional neural network, extracting text features of the electrical elements in the target drawing by using a recurrent neural network in combination with a self-attention mechanism, and comparing historical data to judge whether errors exist or not; and classifying the errors identified in the target drawing by using a classification algorithm, and performing quantitative evaluation on the severity of the errors identified in the target drawing based on a fuzzy comprehensive evaluation method in combination with a time sequence weight.

The invention discloses a financial risk identification system based on artificial intelligence. The system comprises a data acquisition module, a data preprocessing module, a risk perception model construction module, a hyper-parameter optimization module and a financial risk identification module. The invention relates to the technical field of financial risk data processing, in particular to a financial risk identification system based on artificial intelligence, and the method comprises the steps: obtaining financial risk original data through data collection; a data preprocessing method of data cleaning, data coding, data normalization and data set segmentation is adopted; a composite graph neural network model is adopted to carry out financial risk identification, and through multi-modal data fusion, an interpretable risk cause path is generated, and model transparency and decision support capability are enhanced; according to the method, model hyper-parameter optimization is carried out by adopting an optimization algorithm, a global optimal solution can be efficiently searched, and the model can fully mine a potential mode in financial risk data, so that more accurate identification is realized.

The application discloses a network security-oriented constraint-driven fast topology presentation method, device and medium, which comprises the following steps: connecting subgraphs of a composite graph to form an integral connected graph, generating a preliminary layout based on the connected graph; performing rotation and reflection transformation operations on the entire layout to improve the compatibility of the entire layout with constraints, and executing various constraint conditions to obtain a preliminary constrained layout; on the basis of the preliminary layout, eliminating the overlap between nodes and the nesting relationship in the graph, and maintaining various constraint conditions executed previously to refine and optimize the entire layout; setting a low cooling factor, gradually eliminating the overlap, maintaining the nesting, and maintaining the constraints, and finally obtaining a high-quality layout; and presenting the final network topology on a canvas. The application is suitable for graphs that need to display complex node relationships and hierarchical structures, and the algorithm can ensure high-quality layout while improving the layout speed.

The invention provides an R2 * image synthesis method based on a generative adversarial network. The method comprises the following steps: S1, data preprocessing: calculating and correcting an R2 * value from an MEGRE sequence; s2, ROI segmentation: after matching the R2 * graph with an AALv3 template, extracting an average R2 * value of a region of interest from the composite graph and the real graph; s3, a GAN model: inputting a T1 weighted image and a T2 weighted image by a generator, and generating a corresponding R2 * image; the discriminator distinguishes the synthesized R2 * image generated by the generator from a real R2 * image obtained from a real MEGRE sequence; s4, performing quantitative evaluation on the generated image: evaluating the similarity between the synthetic image and the real R2 * graph by adopting a normalized mean square error, a peak signal-to-noise ratio and a structural similarity index; and S5, statistical analysis: analyzing and comparing the R2 * value of the region of interest in the composite graph and the real R2 * graph. According to the invention, powerful auxiliary diagnosis is provided for neurodegenerative diseases similar to Parkinson's disease from the perspective of medical image synthesis and processing.

Embodiments of the present specification disclose an electric vehicle fast charging method and device, electronic equipment and storage medium. The method comprises: after receiving a charging instruction, obtaining historical test data; constructing a current-capacity hyperbolic curve composite graph and a current-temperature hyperbolic curve composite graph in turn through a first test data set and a second test data set; determining a mutation intersection capacity value and a mutation intersection temperature value respectively; continuously monitoring the charging state of the target battery; comparing each real-time charging parameter with the mutation intersection capacity value and the mutation intersection temperature value; and finally determining the charging mode of the target battery in the current charging stage based on the comparison result, switching between interval charging mode and linear charging mode to keep the target battery at a high battery charging current throughout the charging process, reducing the charging time of the target battery, achieving the purpose of electric vehicle fast charging, and improving user satisfaction.

We provide the system. [Solution] A means for the user to select building elements, A means for the server to search the database based on the selected element and retrieve the corresponding image data, A means of displaying image data acquired by the terminal to the user, A means of reflecting user-selected elements onto the drawing, A means for generating a composite drawing based on a drawing reflected by a server, A means by which the terminal displays a composite diagram, A system that includes this.

According to example embodiments of the present disclosure, a method, device, and medium for graph generation are provided. In this method, a first composite graph is added to a region to be updated in a layout based at least on a first constraint on the layout. The first composite graph includes multiple connected sub-graphs. The first constraint is associated with the layout between different sub-graphs in the composite graph. The method also includes adjusting the position of the first composite graph in the region to be updated based at least on a second constraint on the layout. The second constraint is associated with the layout between different composite graphs in the layout. In this way, a composite graph that satisfies multiple constraints can be generated in the layout.

The invention provides a method, a device, equipment and a medium for replacing a commodity with a commercial picture, and the method comprises the steps: carrying out the matting operation of a first commodity picture uploaded by a user, obtaining a first commodity main body picture, and extracting a first commodity mask; processing to obtain an empty scene graph; carrying out matting operation on a second commodity image uploaded by the user to obtain a second commodity main body image; placing the second commodity main body image at a set position of the empty scene image, performing synthesis to obtain a composite image, and extracting a first commodity main body mask from the composite image; performing negation on the first commodity main body mask to obtain a second commodity main body mask; extracting a total image contour line graph and a depth information graph of the composite image; performing cue word reverse deduction on the composite graph to obtain language description of the composite graph; inputting the second commodity main body mask, the full-graph contour line graph, the depth information graph and the language descriptor into a diffusion model to generate a required replacement graph; the efficiency of merchants is greatly improved, and the cost of the merchants is reduced.