60 results about "Graph generator" patented technology

The invention relates to a traffic flow prediction method and system based on a dynamic perception expert network, and the method comprises the steps: obtaining traffic observation data, constructing a traffic network diagram, carrying out the feature embedding, and generating an initial spatial-temporal feature vector; inputting the initial spatial-temporal feature vector into a double-path time encoder, and respectively extracting a personalized time sequence feature vector and a time sequence dynamic feature vector through parallel channel independent paths and channel mixed paths; carrying out vector fusion through a gating mechanism to obtain a time context feature vector, inputting the time context feature vector into a multi-scale hybrid expert model, activating a plurality of most relevant time scale experts, and generating a corresponding routing weight; and generating a spatial dependency graph through a scale condition dynamic graph generator, performing graph convolution to extract a plurality of spatio-temporal feature vectors, performing weighted aggregation, sending the spatio-temporal feature vectors into a prediction model, and generating a traffic prediction value. Compared with the prior art, the model constructed by the method is relatively high in prediction precision and relatively high in robustness in a complex traffic scene.

The invention discloses a lateral movement detection method and device for deploying a time sequence perception graph neural network based on edge optimization, and the method comprises the steps: S1, capturing a host authentication log in real time through a log collection agent deployed on edge equipment, carrying out the event filtering and key field extraction through a rule matching engine, and forming a standardized entity data set; s2, designing an authentication graph structure by adopting an ontology graph modeling language, and definitely defining node types of a user, a host, a process and the like and a time sequence edge type containing a timestamp and an authentication type attribute; s3, mapping the entities into heterogeneous nodes based on a dynamic graph modeling technology, and constructing a complete transverse movement heterogeneous authentication graph through weighted time sequence edges; s4, importing the constructed authentication graph into a lightweight graph database Neo4j Embedded, and realizing dynamic topology maintenance by adopting an incremental PageRank algorithm; s5, defining each identity verification event as a multi-tuple structure, designing a time perception sub-graph generator, performing optimized and accelerated local neighborhood sampling by setting a time window, and generating a time sequence sub-graph sample required by training; s6, constructing a multi-scale attention coding framework, and integrating a local graph attention network and a global Transform encoder to realize multi-level feature fusion; and S7, aiming at the resource limitation of the edge equipment, implementing model cutting, a hierarchical quantification strategy and a streaming sub-graph loading mechanism, and ensuring the efficient operation of the system in a resource-limited environment.

The invention belongs to the technical field of metabonomics large model construction, and relates to a metabonomics large model construction method based on multi-dimensional database fusion, a global unique ID is allocated for metabolites, and multi-source heterogeneous data is uniformly coded into a multi-modal feature matrix, so that information loss caused by data isomerism in a traditional method is avoided; a dynamic graph generator is adopted, a multi-modal feature matrix and a prior path adjacency matrix are combined, dynamic edge weights are generated through similarity and learnable parameters to construct a metabolic relationship graph, a multi-modal graph attention mechanism is utilized to distribute adaptive weights for different modal features, graph convolution message passing is executed, and the metabolic relationship graph is obtained. Generating a node embedding matrix fused with multi-modal information and an optimized dynamic metabolism relation graph; besides, through a meta-learning method, each database is split into meta-tasks, a dynamic graph is used for training a task exclusive classifier in an inner cycle, a dynamic graph generator and meta-model parameters are jointly optimized through query set loss in an outer cycle, and the cross-database generalization ability of the model is remarkably improved.

The invention belongs to the technical field of artificial intelligence and computer vision, particularly relates to a visual large model emergency management and control system for panoramic situation awareness, and aims to solve the problems of weak multi-source visual data fusion, low risk deduction precision, lagging command decision and the like in urban public safety. The system comprises a global visual perception layer, a space-time alignment engine, a visual large model reasoning center, a dynamic situation map generator, a multi-granularity emergency decision module and an instruction distribution feedback unit. By constructing a high-precision space-time grid and a visual large model for joint reasoning, conversion from pixels to semantics is realized, and a time sequence evolution knowledge graph is generated and dynamically updated; and in combination with a hierarchical response mechanism and a cloud edge collaborative architecture, automatic disposal and man-machine collaborative decision making are supported, and the emergency response efficiency and the system robustness are improved.

The embodiment of the invention provides a face forgery detection method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a pre-training detection model and a to-be-detected face image, and inputting the to-be-detected face image into the pre-training detection model, so that a face key point detector obtains a face key point graph according to the input to-be-detected face image; a semantic segmentation graph generator generates a semantic segmentation graph according to the face key point graph; the feature extractor performs basic feature extraction according to the to-be-detected face image to obtain basic features; the global feature extraction branch performs global feature extraction according to the basic features to obtain a global feature map; the feature learning branch obtains a region-level forgery confidence map according to the basic features and the semantic segmentation map; the feature fusion device carries out fusion processing according to the global feature map and the region-level forged confidence map to obtain classification features; and the classifier obtains a face forgery detection result according to the classification features. According to the invention, the accuracy of face forgery detection is improved.

The invention relates to the technical field of data processing, in particular to a graph neural network-based large-scale knowledge graph construction system, which comprises a node increment receiver, a historical embedded memory, a dynamic neighbor sampler, a local sub-graph generator, a quantum state aggregator, a spatio-temporal topology checker, an embedded propagation controller and a graph updater. A time sequence control closed loop adopts a time window to process new nodes in batches, a historical embedding memory aligns version snapshots and multiplexes historical embedding, and a dynamic neighbor sampler optimizes sampling resources according to time sequence density; a precision adjustment closed loop triggers relation weight correction and quantum line parameter updating, and an embedded propagation controller freezes an inactive node gradient to focus key changes; resource optimization chain adjustment reduces neighborhood complexity, time attenuation coefficient compresses adjacency matrix scale, and historical embedded memory pointer call eliminates vector replication overhead. According to the method, computing resource consumption in the dynamic updating process is remarkably reduced, and knowledge graph representation quality is maintained.

The invention provides a multi-source heterogeneous data fusion processing method and system based on deep learning, and the method comprises the steps: obtaining a multi-source sensor data stream of an Internet of Things terminal, extracting a modal description vector of the multi-source sensor data stream, generating a meta-feature, and independently packaging each data source into a data block; processing paths are dynamically distributed according to element feature vectors in the data blocks, and parallel feature extraction branches are input into the data blocks of different modalities to perform modal feature extraction; inputting the extracted different modal features into a cross-modal semantic graph generator, extracting semantic similarity measurement values among the features through comparative learning, and obtaining a dynamically updated cross-modal knowledge graph; performing multilayer graph convolution calculation on the modal features based on a cross-modal knowledge graph to generate a joint feature tensor; according to the method, the problems of low fusion efficiency, poor adaptability, poor consistency and the like caused by multi-source, heterogeneous and strong time-varying characteristics of sensor data in an existing Internet of Things system can be effectively solved.

A communication analysis system includes: an information receiver that receives information indicating analysis target communication performed by a monitoring target; an information obtainer that obtains past communication information indicating communication by the monitoring target; a WL determiner that determines, using the obtained analysis target communication and the whitelist, that non-WL communication has been established in the analysis target communication; a similar terminal extractor that extracts one or more terminals similar to destination and source terminals included in a non-WL communication link determined; a primary similar communication link extractor that extracts a past communication link similar to the non-WL communication link as a primary similar communication link from the obtained past communication information using the extracted similar terminals; and a NW graph creator that creates a NW graph for analysis as graph information for analyzing the non-whitelist communication using the extracted primary similar communication link and the obtained past communication information.

A travel map creating apparatus includes a position sensor obtaining the positional relationship of an adjacent object relative to the apparatus; a floor map creator creating a floor map based on the positional relationship; a self-position calculator calculating the self-position on the floor map; a marker identifier identifying a marker; a marker position calculator calculating the relative position of the marker to the apparatus; a mode switcher switching the mode of the apparatus between a floor map creation mode and a marker identification mode; a restricted access information generator, based on the floor map, the self-position, and the relative position of the marker, defining a boundary of a restricted area to which the entry of an autonomous mobile robot is prohibited and generating restricted access information including the boundary; and a travel map creator creating a travel map including the defined restricted area based on the restricted access information.

An apparatus for diagnosing an autism spectrum disorder (ASD) based on a graph neural network includes a preprocessor configured to acquire brain image data, designate a region of interest (ROI) of the brain, and generate preprocessed data for neural network input, a spatial feature extractor configured to extract spatial features from the preprocessed data, a temporal feature extractor configured to analyze changes in brain activity over time and extract attention-based temporal features, a spatial and temporal convergence feature unit configured to analyze spatiotemporal correlations by combining the spatial features and the temporal features, a graph generator configured to convert connectivity between regions of interest into a graph structure and implement the same as nodes and edges, and a graph classifier configured to analyze a spatiotemporal pattern of the connectivity through the graph structure and classify whether or not there is an ASD.

The present application relates to the technical field of data processing, and more particularly to a large-scale knowledge graph construction system based on a graph neural network, comprising a node incremental receiver, a historical embedding memory, a dynamic neighbor sampler, a local subgraph generator, a quantum state aggregator, a space-time topology verifier, an embedding propagation controller and a graph updater. The time sequence control closed loop adopts time window batch processing of new nodes, the historical embedding memory aligns version snapshots and reuses historical embeddings, and the dynamic neighbor sampler optimizes sampling resources according to time sequence density; the precision adjustment closed loop triggers relationship weight correction and quantum circuit parameter updating, the embedding propagation controller freezes the gradient of non-activated nodes to focus on key changes; the resource optimization chain adjusts to reduce neighborhood complexity, the time decay coefficient compresses the size of the adjacency matrix, and the historical embedding memory pointer call eliminates vector copy overhead. The present application significantly reduces the computational resource consumption of the dynamic updating process and maintains the quality of the knowledge graph representation.

The present disclosure provides an apparatus and method of guided neural network model for image processing. An apparatus may comprise a guidance map generator, a synthesis network and an accelerator. The guidance map generator may receive a first image as a content image and a second image as a style image, and generate a first plurality of guidance maps and a second plurality of guidance maps, respectively from the first image and the second image. The synthesis network may synthesize the first plurality of guidance maps and the second plurality of guidance maps to determine guidance information. The accelerator may generate an output image by applying the style of the second image to the first image based on the guidance information.

A system and method employed by an infrastructure-as-code (IaC) tool that is configured for defining IaC configurations comprising containers of interconnected components of a cloud computing platform. A system includes a graph generator configured to generate a graph of dependencies between the interconnected components of the cloud computing platform based at least in part on user-defined input-output relationships between the interconnected components. The IaC tool is configured to use the graph of dependencies to automatically determine a sequence of the provisioning or deployment of the interconnected components of the cloud computing platform. The system further includes a remote procedure call interface configured to enable communications between the IaC tool and the cloud computing platform, the remote procedure call interface further configured to enable the provisioning or deployment of the interconnected components of the cloud computing platform, in the determined sequence.

The invention relates to a composition scheme generation method, electronic equipment and a storage medium. The method comprises the following steps: acquiring an original scene image and composition preference information; determining an image feature tag set based on the original scene image, and determining a preference feature tag set based on the composition preference information; determining at least one style fine tuning model based on the image feature tag set and the preference feature tag set; loading the at least one style fine tuning model to a preset composition generator to obtain a composition generation model; and generating a composition scheme based on the original scene image, the preference feature tag set and the composition generation model. According to the scheme provided by the invention, the scene graph can be fully analyzed to generate the composition scheme meeting the photographing requirements of the user, and the aesthetic level of the composition scheme is improved, so that the photo photographing quality and the user experience are improved.

Methods, systems, and apparatus for automatic detection of software bugs are disclosed. An example apparatus includes a comparator to compare reference code to input code to detect a source code error in the input code; a graph generator to generate a graphical representation of the reference code or the input code, the graphical representation to identify non-overlapping code regions; and a root cause determiner to determine a root cause of the source code error in the input code, the root cause based on the non-overlapping code regions.

The embodiment of the application discloses a test data processing system, an algorithm graph generator, a storage chip automatic test device, a link protection code generation method, a test data processing method and a computer program product, and belongs to the technical field of storage chip testing. The system comprises: a configuration register group, used for storing static preset parameters of a current test mode, wherein the static preset parameters at least comprise a storage interface protocol rule used by the current test mode; a burst context extraction circuit, used for detecting a start signal of burst transmission in a dynamic running process of an algorithm graph generator, extracting dynamic burst information and original test data of current burst transmission; and a link protection code generation circuit, used for analyzing the dynamic burst information and the original test data according to the storage interface protocol rule obtained from the configuration register group, and dynamically generating a link protection code of current burst transmission. The application can greatly reduce test complexity and significantly improve test efficiency.

The application discloses a kind of real-time APT attack dynamic detection methods based on traceability graph matching, to solve the technical problem that APT attack is not easy to be identified, belong to computer information security technical field.The application includes the following steps: step one, by traceability graph generator component, the data collected is modeled as traceability graph and stored in data storage component, according to existing threat intelligence to generate query graph;Step two, data storage component maintains a dynamic sub-traceability graph in memory, receives data from hard disk in streaming, and data storage component inputs the dynamic sub-traceability graph to threat detection component;Step three, threat detection component calculates graph matching on the input sub-traceability graph and query graph, and transmits the result to alarm and tracking component;Step four, alarm and tracking component receives graph matching result and updates posterior probability for measuring whether there is an anomaly.

This invention discloses a method for inferring relationships in dynamic systems based on bidirectional attention, relating to the fields of artificial intelligence, deep learning, and complex network modeling. The relationship inference model includes a graph generator and a neurodynamics surrogate model. The graph generator generates interaction structures between nodes based on structural parameters and represents them using an adjacency matrix. The neurodynamics surrogate model models the dynamic system based on node trajectory data under a given adjacency matrix. The training of the relationship inference model involves training the graph generator to generate the adjacency matrix while simultaneously training the neurodynamics surrogate model using unsupervised learning to maximize the likelihood of observed node trajectory data under the adjacency matrix. The total loss function includes a likelihood-based dynamic system prediction loss term and a regularization term applied to the inferred interaction structure. This invention is used to infer potential inter-node interaction structures in dynamic systems.

The present disclosure provides an apparatus and method of guided neural network model for image processing. An apparatus may comprise a guidance map generator, a synthesis network and an accelerator. The guidance map generator may receive a first image as a content image and a second image as a style image, and generate a first plurality of guidance maps and a second plurality of guidance maps, respectively from the first image and the second image. The synthesis network may synthesize the first plurality of guidance maps and the second plurality of guidance maps to determine guidance information. The accelerator may generate an output image by applying the style of the second image to the first image based on the guidance information.

The application provides a hard negative sampling contrast recommendation method based on a graph generation diffusion model, including the following steps: encoding a user-item interaction graph through a graph convolutional neural network to obtain a node embedding vector, and then performing inner product on the node embedding vector to obtain a recommendation probability; model parameters of the graph convolutional neural network are obtained through the following training: S1, respectively generating contrast view 1 and contrast view 2 through an inner diffusion model view generator and a parameter topological denoising view generator; the inner diffusion model view generator comprises an inner diffusion model, and the parameter topological denoising view generator comprises a parameter topological denoising network; S2, performing contrast learning on the contrast view 1 and the contrast view 2. The application combines a signal alignment strategy to build a double-view generator contrast learning framework, aiming to improve the problems of insufficient model generation capacity and great data noise influence, so as to improve the performance of a recommendation system.

This application provides an OPC model training method and an OPC model training device, relating to the field of model training technology. The method includes: cutting a chip design layout file to obtain multiple basic blocks; adding noise to the multiple basic blocks to obtain multiple noisy blocks; training a graph generator based on the multiple noisy blocks and corresponding real noise; using the graph generator to generate multiple test graphs based on multiple preset noise vectors; and training the model based on the multiple test graphs to obtain an OPC model. By training the graph generator based on the multiple noisy blocks and corresponding real noise, the graph generator implicitly learns the data distribution of the graphs, that is, it learns the rules of chip design. This results in multiple test graphs generated by the graph generator having more diverse forms and conforming to design rules. The OPC model trained based on these multiple test graphs is also more accurate and reliable.

The invention discloses an intelligent view blanking method and device suitable for a two-dimensional engineering drawing of chemical static equipment, equipment and a medium, and relates to the technical field of view blanking, and the method comprises the steps: generating an initial contour line set of a two-dimensional state corresponding to a three-dimensional assembly model according to the three-dimensional assembly model of the chemical static equipment and a current drawing view angle; determining a target part pair with a mask relation based on the initial contour line set, and judging whether a user-defined mask rule of the target part pair exists in a mask rule base or not; if yes, storing the user-defined mask rule of the target part pair into a target mask rule table, and if not, storing the user-defined mask rule of the target part pair into the target mask rule table by utilizing the default mask rule of the target part pair; and generating a two-dimensional view of the three-dimensional assembly model under the current drawing view angle through a two-dimensional view generator on the basis of the target mask rule table and the initial contour line set. The efficiency and accuracy of view blanking can be improved, and engineering habits are met.

The invention discloses a multi-view comparative learning recommendation method fusing semantic perception. The multi-view comparative learning recommendation method comprises the steps of semantic feature preprocessing, semantic perception view generation and semantic optimization comparative learning. The system mainly comprises four core modules: a semantic feature preprocessing module, a semantic perception view generator, a semantic optimization comparison learning module and a collaborative optimization recommendation generation module. The method is widely applied to Internet platforms and application systems requiring personalized recommendation services, such as e-commerce, online videos, social networks, news pushing and the like.

The application discloses a carbon emission management method, system, storage medium and computer program product, relates to the technical field of carbon emission management, and the method comprises the following steps: acquiring a carbon emission management instruction input by a user; calling a hierarchical digital twin sub-model corresponding to the carbon emission management instruction in a pre-constructed multi-level digital twin model system, wherein the multi-level digital twin model system is divided into a plurality of hierarchical digital twin sub-models in a manner combining administrative region division and project division; if the called hierarchical digital twin sub-model is a project-level digital twin model, collecting project energy consumption data, and calculating annual period carbon emission total amount based on the project energy consumption data; calling a pre-constructed graph generator to extract carbon quota parameters; and generating a carbon quota scheme according to the carbon quota parameters and the annual period carbon emission total amount. The multi-level digital twin model system is adapted to regional policy differences, and supports zero-carbon target quantitative management.

A wafer map generation device includes a dimension reducer and a map generator. The dimension reducer is configured to receive a plurality of polarization matrices associated with a plurality of target positions of a target wafer substrate and to generate a plurality of reduction result data through dimension reduction of one or more elements of each of the plurality of polarization matrices. The map generator is configured to generate a wafer map associated with the target wafer substrate based on the plurality of reduction result data.

The application discloses a kind of instance relationship-driven large-scale garden scene point cloud segmentation methods, comprising: first using the method of downsampling and clustering obtains the instance object annotation data of garden scene point cloud, then using annotation data trains instance segmentation network, adopts the instance object in point cloud that is segmented out using trained instance segmentation network, then using garden point cloud and instance object input to instance relationship graph generator generates the instance relationship graph of garden scene, again through graph convolution generates the instance relationship graph feature of each garden scene, finally the instance relationship graph feature of garden scene and point cloud connection input to trained point cloud segmentation network obtains the point-by-point segmentation data of large-scale garden scene.The application can cope with large-scale garden scene point cloud, can accurately obtain the context information in garden point cloud scene, provide sufficient prior knowledge for the point cloud segmentation of garden scene, so as to improve the segmentation efficiency and accuracy of garden point cloud.

This invention provides a graph neural network business process anomaly detection method and system based on multi-attribute graphs, including the following steps: Step 1: Converting trajectories in event logs into multiple graphs using a multi-graph generator; Step 2: Inputting the converted multiple graphs into a multi-graph encoder for one-hot encoding and positional encoding to obtain the hidden representation of each node; Step 3: Decoding the hidden representation of each node into a probability distribution using a multi-sequence decoder; Step 4: Calculating an anomaly score based on the probability distribution using an anomaly score calculator, and marking trajectories and attributes as abnormal or normal based on a threshold. This invention solves the technical problems of anomaly detection in the business process field, capable of automatically detecting abnormal trajectories in event logs and identifying the root cause of the anomaly.

The application provides an automatic accounting document generation device and method. The device comprises a data source management module, a meta-database construction module, a view set construction module, a view generator, a document template configuration module and a document generation module. The data source management module constructs a meta-database to manage the metadata of a plurality of heterogeneous data source tables. The view set construction module constructs a view set for each business scenario, configures a view data source table for each view set, and defines a view field for each view. The view generator is used to write a view field acquisition program of the view and to obtain a view field data value. The document template configuration module sets a document template for each business scenario, configures a view set for each document template, and obtains a value of a document element through a view in the view set. The document generation module obtains a corresponding document template according to an input business type, and outputs an accounting document. In the case of flexible and changeable business data, the business data can be introduced into a corresponding data source table for document generation without system upgrade, and when the business requirement changes, the data source, the view and the document template only need to be configured accordingly.

Several examples describe a technique for generating a route plan that involves receiving a semantic map representing a physical environment. The technique also includes generating a route graph based at least on the semantic map, where the route graph contains one or more route graph edges, each edge having an associated location in one or more map regions of the semantic map and associated costs. The technique further includes determining the cost of each graph edge based at least on a region type, which is determined at least on the basis of a map region in which the respective route graph edge is located.The technique also includes generating a route plan for a mobile robot using the route graph to move from a specific starting point on the semantic map to a specific destination on the semantic map.

A system for generating a floating image for a plurality of passengers within a vehicle includes a passenger monitoring system adapted to monitor the position of head and eyes of each passenger, a compute engine in communication with the passenger monitoring system and adapted to calculate a holographic image and encode the holographic image to a display of a picture generating unit hologram generator, and a beam steering device adapted to receive information related to a position of the head and eyes of each of the passengers from the passenger monitoring system, and the display is adapted to project the holographic image to the beam steering device and the beam steering device is adapted to re-direct the projected holographic image to the eyes of each of the passengers, based on the information received from the passenger monitoring system.