46 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 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.

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 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.

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 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.

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.

One or more infographics are acquired by agricultural machinery. These infographics map the values ​​of one or more agricultural characteristics at different geographic locations in the field. Field sensors on the agricultural machinery sense these agricultural characteristics as the machinery moves through the field. A predictive map generator generates predictive maps of the agricultural characteristics at different locations in the field, based on the relationships between the values ​​in the infographics and the agricultural characteristics sensed by the field sensors. These predictive maps can be output and used for automated machine control.

One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.

The invention discloses a knowledge graph-based conference content structured recording method, which comprises the following steps of: acquiring conference multi-source data, executing cleaning and standardization processing, and generating a standardized data set; constructing a conference fact behavior map, and establishing a hierarchical structure and an association relationship; constructing a self-growing standard graph generator, extracting a behavior mode, and evolving to generate a standard knowledge graph; bidirectional consistency reasoning is executed, an expected link is generated, and the link consistency degree is calculated; inputting the atlas and the structure to a Grapher network, executing global coding, and generating a conference hidden sequence vector; and generating a structured record based on the implicit sequence and the consistency, executing a filling prompt, and updating the model and the map. According to the invention, by constructing the conference fact behavior graph, the self-growing specification knowledge graph and the improved Grapher network, intelligent structured recording and specification consistency verification of the content of the whole process of the conference are realized.

The invention provides a subgraph federated learning method based on supervised comparative learning and a graph encoder. The subgraph federated learning method comprises the steps that a client side calculates global category reliability in combination with a server side so as to quantify data heterogeneity; the client learns all classes, especially minority classes, by means of supervised contrast learning, obtains feature representation with higher distinction degree, then screens high-quality data through a graph encoder and a decoder, and transmits the screened coded data to a server; the server constructs a pseudo-graph through a graph generator based on the encoded data and optimizes the global model using data distillation. Therefore, the local model not only can strengthen the self representation capability, but also can provide reliable high-quality knowledge for the server, so that the global model performance is effectively improved. According to the method, the problems of data heterogeneity, insufficient minority class feature representation and high-quality information mining in the subgraph federation are effectively solved, and the generalization performance of a global model is improved while data privacy is protected.

One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural property values at different geographic locations of a field. An on-board sensor on the agricultural work machine senses the agricultural property as the agricultural work machine moves through the field. A prediction map generator generates a prediction map that predicts the agricultural property at different locations in the field based on a relationship between values in the one or more information maps and the agricultural property sensed by the on-board sensor. The prediction map can be output and used for automated machine control.

The application provides an image restoration method and system based on reverse cascade energy structure cooperative evolution, a medium and equipment, belonging to the technical field of image processing and image restoration. The system comprises: a front-end CNN for visual feature extraction to obtain an initial feature map; a distance map generator for calculating the shortest Euclidean distance from each pixel in the damaged area to the known area to obtain a distance map; and dividing the damaged area into equidistant concentric ring-shaped bands; a boundary statistical encoder for calculating the statistical moments of each ring-shaped band and the joint features of each pixel in each ring-shaped band; a reverse band cascade HDNN for calculating the Hamiltonian of each ring-shaped band using a Hamiltonian function, performing half-step symplectic evolution in order from the outer band to the inner band; a Transform repair network for using the evolution results as input, using the Transform repair network to repair the damaged area, and outputting a repaired image. The scheme can guarantee the structural consistency of the damaged image restoration, avoid distortion, and improve the repair effect.

The invention relates to the technical field of man-machine interaction, in particular to a dynamic self-adaptive interaction system and method for a generative large model, and the system comprises an intention deconstruction engine, a dynamic canvas controller, a constraint propagation module, and a traceability graph generator. The method has the beneficial effects that a core hub of technical capability and user experience is broken through, and real-time performance is realized; 100-millisecond-level response is generated to generate process intervention; interpretability: visually presenting a decision path; and the robustness is high, and cross-modal constraint transfer is supported.

The invention discloses a real-time APT attack dynamic detection method based on traceability graph matching, is used for solving the technical problem that an APT attack is not easy to identify, and belongs to the technical field of computer information security. The method comprises the following steps: step 1, modeling collected data into a traceability graph through a traceability graph generator component, storing the traceability graph in a data storage component, and generating a query graph according to existing threat intelligence; step 2, the data storage component maintains a dynamic sub-traceability graph in a memory, receives data from a hard disk in a streaming manner, and inputs the dynamic sub-traceability graph to the threat detection component; step 3, the threat detection assembly performs graph matching calculation on the input sub-traceability graph and the query graph and transmits a result to the alarm and tracking assembly; and step 4, the alarm and tracking component receives a graph matching result and updates a posterior probability for measuring whether an anomaly exists.

The application belongs to the technical field of metabolomics large model construction, and relates to a metabolomics large model construction method based on multi-dimensional database fusion, which assigns a global unique ID to metabolites, and uniformly encodes multi-source heterogeneous data into a multi-modal feature matrix, thereby avoiding information loss caused by data heterogeneity in traditional methods; a dynamic graph generator is adopted, a multi-modal feature matrix and a prior path adjacency matrix are combined, a dynamic edge weight is generated through similarity and a learnable parameter to construct a metabolic relationship graph, a multi-modal graph attention mechanism is used to assign adaptive weights to different modal features, graph convolution message passing is performed, a node embedding matrix fused with multi-modal information and an optimized dynamic metabolic relationship graph are generated; in addition, through a meta-learning method, each database is split into a meta-task, a dynamic graph is used to train a task-specific classifier in an inner loop, and a dynamic graph generator and a meta-model parameter are jointly optimized through a query set loss in an outer loop, thereby significantly improving the cross-database generalization ability of the model.