486 results about "Graphical data" patented technology

Techniques for intelligent prefetching of database pages for improved query processing are described. Page access patterns for queries are collected over time and used to update a graph data structure to include collections of nodes corresponding to database pages that are often and / or recently accessed together. When processing a query, the graph data structure can be used to identify scenarios when prefetching may be helpful and to identify particular pages that should be prefetched. These pages are prefetched and placed in a local cache storage that can be rapidly accessed by query processors in comparison to the original storage location for this data, which may be across a network.

Training and executing machine learning pipelines for graph data sets may be orchestrated. A request for machine learning pipelines on a graph data set may be received via a machine learning pipeline building system interface. The machine learning pipeline building system may generate a profile of the graph data set from characteristics determined from the data set by the machine learning pipeline building system. Machine learning algorithms may be selected according to the profile of the graph data set and may be used to generate an execution plan that is executes the machine learning pipeline.

In a method for providing a visual representation of search results, a natural language query may be obtained from a user. The natural language query may be processed to obtain key query terms. Based on the key query terms, a relevance-ranked search on a plurality of documents may be performed to obtain a subset of the plurality of documents relevant to the natural language query. Answers to the natural language query may be extracted from relevant documents among the subset of documents, and graph data may be obtained using document links associated with the relevant documents from which the answers were extracted. The graph data may include entities and relationships identified in the relevant documents. The graph data may be used to generate a graph that may be displayed to the user to enable the user to discover information previously unknown to the user.

The invention discloses an automatic monitoring method for infrastructure construction operation violation based on multi-modal data fusion, and belongs to the technical field of graphic data processing and pattern recognition, and the method comprises the steps: obtaining multi-modal source data of a target scene, carrying out the data recognition and standardization processing of the multi-modal source data, and generating preprocessing feature data; acquiring an on-site environment condition, and generating an environment state parameter; performing dynamic weight distribution on the preprocessed feature data based on the environment state parameters to generate weighted feature data; performing multi-source data conflict judgment and resolution on the weighted feature data to generate conflict resolution event data; and based on a preset behavior rule pattern library, performing behavior pattern matching on the conflict resolution event data to generate a violation judgment result. According to the method, a multi-modal spatial-temporal feature fusion and dynamic weight distribution mechanism is adopted, and an environment adaptive perception and multi-source data conflict resolution technology is combined, so that automatic monitoring and pattern recognition of illegal behaviors in a construction environment can be realized.

A method is provided including updating a graph database based on generating one or more new nodes and edges for the graph database associated with onboarding information for a first user. The method includes extracting, from the updated graph database, extracted graph data for the first user and inputting at least a portion of the extracted graph data for the first user into one or more machine learning-artificial intelligence (ML-AI) models to obtain one or more recommended user applications. The method includes causing display of the one or more recommended user applications on a display device, receiving second user input indicating approval for granting access to at least one recommended user application from the one or more recommended user applications, and providing instructions to direct a second computing platform to grant access to the at least one recommended user application.

The invention discloses a feature fusion processing method for anesthesia depth multi-modal data, and belongs to the technical field of graphic data processing and pattern recognition, and the method comprises the steps: obtaining a multi-modal physiological signal and an electromyographic signal of a patient; performing time axis calibration on the physiological signal to generate an alignment signal; extracting a multi-modal feature vector and calculating an anesthesia depth index; performing deviation analysis on the basis of the electromyographic signal and the index to obtain an electromyographic response deviation index; performing graphical feature mapping on the real-time electroencephalogram signal to generate a real-time time-frequency map; when the deviation index exceeds a safety threshold value, performing graph pattern matching with a pattern template library to calculate a similarity score; and outputting the current anesthesia state mode in a classified manner. According to the method, a multi-modal signal graphical feature fusion technology is adopted, and a dynamic map generation and pattern matching mechanism is combined, so that the problem of complex pattern recognition of physiological signal graphic data can be solved, and the accuracy and timeliness of anesthesia state classification are improved.

Systems and methods are described for dynamically allocating memory for responding to requests. In some aspects, a thread may be obtained, where the thread includes a request to use a portion of system memory to perform a query of a database, such as a graph database. The system may determine that the portion of memory is greater than available memory of a maximum managed memory size of the system, and may block the thread until additional memory becomes available. The maximum managed memory size may be configurable, and may be less than a total memory of the system. The system may then determine, based at least in part on a change in the available system memory, that the available system memory is equal to or greater than the portion of memory to satisfy the request, and cause the system to process the thread to provide a response to the query.

The various implementations described herein include methods and devices for providing an instant messaging interface for data analytics. In one aspect, a method includes displaying a user interface for a communication application, including displaying a shared link to a graphical data visualization. A snapshot button near the link is used to generate a link snapshot comprising a version of the graphical data visualization. In response to user activation of the snapshot button, the method generates the link snapshot. To generate the link snapshot the method first determines if the user or users have proper security access to view the data contained in the graphical data visualization. If a user has proper security access to a subset of the data from the data source, the method dynamically generates the link snapshot for the subset of data to which the user has security access.

The invention discloses a real estate registration information real-time query system based on graphic data, and relates to the technical field of information real-time query, and the system comprises a knowledge graph generation unit which is used for processing an established multi-modal data set, generating a joint vector, coding the joint vector, and storing the coded joint vector in a preset graph database, synchronously constructing a dual dynamic index mechanism to generate a real estate knowledge graph; the knowledge graph updating unit is used for capturing change events in the multi-modal data set in real time, identifying graph structure differences of a change area and generating a topology correction strategy by positioning the change area and a neighborhood range, and updating a real estate topology knowledge graph in combination with transaction characteristics of a graph database; a query result display unit; and a data synchronization unit. According to the method, the real-time data channel is constructed, capturing and incremental processing of the real estate business change event are achieved, and it is ensured that the real estate knowledge graph and graphic data are dynamically consistent in topological structure and attribute information.

The invention relates to the technical field of industrial internet of things operation and maintenance, and particularly provides a remote maintenance auxiliary method integrating video monitoring and three-dimensional modeling. The method comprises the following steps: acquiring engineering graphic data and point cloud scanning data of maintenance equipment, and collecting video stream data of a maintenance equipment site; the video stream data is used for describing the operation state of maintenance equipment; the video stream data comprises a plurality of video frames; matching the point cloud scanning data with the engineering graphic data, and constructing a watertight three-dimensional grid model according to a matching result; mapping texture features of the maintenance equipment in a target video frame to the surface of the watertight three-dimensional grid model to obtain a target three-dimensional model; and receiving a first maintenance instruction marked in the target three-dimensional model by a remote expert, and sending the first maintenance instruction to a video picture of a client of an on-site maintainer. According to the technical scheme provided by the invention, the time consumption for positioning the overhaul part can be reduced.

In one implementation, a device may facilitate the hunting, detection, and forensic assessment of threats utilizing a knowledge graph. The device obtains telemetry data collected within a cloud computing environment. The device forms a temporal graph that represents changes in the cloud computing environment over time based on the telemetry data. The device maps the temporal graph into a knowledge graph for storage in a graph database. The device makes the graph database available to an artificial intelligence model that issues queries to detect security threats to the cloud computing environment.

The embodiments of the present application relate to the technical field of terminals. Provided are a rendering processing method and an electronic device. The method comprises: determining a plurality of rendering instructions corresponding to first graphic data to be rendered of a first application program, wherein the plurality of rendering instructions include a first rendering instruction; and then reading, from a graphics processing unit (GPU) memory, a first rendering resource corresponding to the first rendering instruction, and executing the plurality of rendering instructions on the basis of rendering resources respectively corresponding to the plurality of rendering instructions, so as to render the first graphic data. In this way, a rendering resource is read from a GPU memory, so as to reduce data interaction between a GPU and a main memory, thereby reducing the bandwidth consumption of a terminal device.

Systems and methods are provided for generating an index of a set of vectors as a distributed proximity-based graph data structure comprising representative vectors corresponding to subsets of the set of vectors, storing the set of vectors across a plurality of storage devices of a distributed storage system based on the distributed proximity-based graph data structure, and loading, in response to a vector query, a plurality of subsets of the set of vectors from the distributed storage system based on the distributed proximity-based graph data structure.

A method including providing a graph database representation, wherein the graph database representation represents a plurality of nodes in a graph which are interconnected by respective edges, wherein each node of the plurality of the nodes represents a data sample and is assigned to at least one node feature, wherein each edge of the plurality of the edges represents a relationship between the data samples, transforming the graph database representation into a data matrix using a first machine learning algorithm suitable for graph data and an architecture as first layers of a joint machine learning architecture, determining at least one node of the plurality of nodes based on the transformed data matrix using a second machine learning algorithm and a second architecture as second layers of a joint machine learning architecture, and providing the at least one determined node. Further, a computing unit and a computer program product is provided.

The invention relates to the technical field of system application, and discloses an online document processing method and system based on structured storage, and the method comprises the steps: splitting content elements of an online document into blocks, and carrying out structured storage as independent objects; querying basic information of the document, loading data according to a document block pageBlockId list, creating a component according to the loaded data, constructing a component tree, and performing document rendering by using a lazy loading technology; the method comprises the following steps: abstracting documents and document blocks as objects, synchronizing the objects to a graphic database, modeling a relationship between the documents and the document blocks as an edge in the graphic database, and converting the relationship between the documents into a graphic data structure; when the document content changes, the storage interface is called to store the changed document block, and the online document is automatically stored, the document content is structurally stored in the block form, dynamic updating of the online document is achieved, the reference relation between the documents is stored and managed, and the relation between the documents is visually and clearly displayed.

Disclosed is a mutually generative artificial intelligence system based on multi-dimensional spatiotemporal information vector graphics, relating to the field of artificial intelligence and engineering applications. Based on a geographic information system (GIS) or a computer-aided design (CAD) platform and a data source, a multi-dimensional vector spatiotemporal large model terminal, a multi-dimensional spatiotemporal information processing agent terminal, and an intelligent information system application terminal are constructed. For multi-dimensional spatiotemporal data such as two-dimensional and three-dimensional vectors and temporal states, a multimodal spatiotemporal large model having an understanding capacity for an engineering professional knowledge system, a data processing flow, multi-dimensional vector graphics, and thematic graphics-text documents is pre-trained to achieve the mutual expression and generation of engineering multi-dimensional vector graphics and thematic graphics-text documents and to form an intelligent engineering graphic data processing application.

An apparatus and method for the generation and improvement of efficiency data is disclosed. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive a user profile from a user, wherein the user profile comprises occupational data. The memory instructs the processor to determine efficiency data as a function of the occupational data. The memory instructs the processor to generate a plurality of graphical data as a function of the efficiency data. The memory instructs the processor to identify a plurality of efficiency clusters associated with the efficiency data as a function of the plurality of graphical data. The memory instructs the processor to identify an ideal arrangement of each cluster of the plurality of efficiency clusters as a function of the efficiency data and generate improvement data as a function of a comparison.

The invention relates to an artificial intelligence identification method for optical printing characters on the surface of a plastic package electronic component, and the detection method comprises the steps: 1), obtaining an image, obtaining a clear graphic data set sample, carrying out the size transformation of a graphic data set, and dividing the graphic data set into a training set, a verification set and a test set according to a proportion for model training verification and test; step 2), constructing an improved DBNet character detection network based on an attention mechanism; 3) constructing an improved convolutional recurrent neural recognition network based on the spatial transformation network; 4) combining the detection network in the step 2) and the recognition network in the step 3) to obtain an optical character recognition network model; and 5) training and testing the optical character recognition model in the step 4) by using the training set. The device has the advantages of high detection precision, high stability, high speed and high efficiency, and can be used for non-contact nondestructive detection.

The concepts and technologies disclosed herein are directed to software runtime assisted co-processing acceleration with a memory hierarchy augmented with compute elements. An example system disclosed herein includes one or more switches and a plurality of hardware compute nodes connected via the one or more switches. Each hardware compute node of the plurality of hardware compute nodes includes an in-memory compute (IMC) element configured to perform in-memory processing operations on data, such as graph data. The system also includes a near-memory compute (NMC) element configured to perform near-memory processing operations on the data. The system also includes a far-memory compute (FMC) element configured to perform far-memory processing operations on the data.

The invention relates to the technical field of mineral engineering, in particular to an underground waterproof closed wall deformation monitoring system and method. The system comprises a monitoring device arranged in front of an underground waterproof closed wall; the upper computer is arranged on the ground and is in communication connection with the monitoring device; the monitoring device comprises a light projection module which generates a structured light pattern and projects the structured light pattern to the underground waterproof airtight wall, and the light projection module generates the structured light pattern to form a plurality of light spots on the underground waterproof airtight wall; the two light receiving modules are used for receiving the structured light patterns reflected by the light spots; the data processing module is used for acquiring structured light patterns reflected by all the light spots and performing deformation calculation according to the structured light patterns reflected by all the light spots; the image editing module is used for acquiring deformation calculation results of all the light spots and drawing a waterproof closed wall full-surface deformation three-dimensional graph based on the calculation results; and the data transmission module is used for transmitting a calculation result and the waterproof closed wall full-surface deformation three-dimensional graph to an upper computer. Full coverage of surface deformation of the waterproof closed wall is achieved, meanwhile, the laying workload is small, and maintenance is easy.

Disclosed is a search result provision method performed by at least one processor, comprising the steps of: receiving a natural language query; generating graph data through a machine learning model on the basis of the natural language query; acquiring a first embedding vector from the generated graph data by using a graph embedding algorithm; identifying an index from an index database on the basis of the acquired first embedding vector; extracting data from a source database on the basis of the identified index; and providing a search result for the natural language query on the basis of the extracted data.

The invention discloses a processing method for paleontoid epidermis graphic data, which is characterized in that in a data acquisition link, three-dimensional scanning equipment is applied to grasp original three-dimensional graphic data for subsequent processing, an analysis foundation is laid, a preprocessing stage is of great significance, high-frequency noise caused by a scanning environment and equipment precision is eliminated through de-noising processing, and the processing efficiency is improved. And when the obtained smooth three-dimensional data is converted into the two-dimensional texture mapping graph, errors and distortion are reduced, and the data availability is improved. In the textural feature extraction step, a gray-level co-occurrence matrix algorithm is applied to the field, textural feature parameters such as contrast, correlation, energy and entropy are calculated, quantitative description of paleontoid epidermis textures is realized, abstract textures are converted into comparable and analyzable numerical values, and key characteristics for identifying species are mined. In the texture classification stage, based on the extracted feature parameters, a model is constructed by means of machine learning algorithms such as a support vector machine and a random forest, and paleontological epidermis textures are accurately classified.

The invention discloses a composite insulator fault detection method based on YOLOV8, and relates to the technical field of graphic data reading, and the method comprises the steps: synchronously collecting an infrared image and a visible light image of a composite insulator, taking the infrared image and the visible light image as dual-mode images, carrying out the cooperative preprocessing, obtaining a registered dual-mode image, and generating a significance hot area graph; constructing an improved target detection network, and inputting the registered bimodal image into the improved target detection network to obtain a detection result of the composite insulator and a pixel-level contour of a fault area; obtaining a final overheating grade based on a physical mechanism diagnosis model, and calculating an intensity loss rate estimated value of a current fault based on a pixel-level contour of a fault area; and generating and outputting a structured diagnosis report based on the fault type, the absolute temperature data matrix, the final overheating level, the strength loss rate estimated value and the fault area pixel level contour. According to the invention, the technical problem of poor detection effect caused by incomplete composite insulator fault detection in the prior art is solved.

A visual effects system includes a first display, a second display, and a graphics generation system. The graphics generation system is configured to provide graphics data to the first display and the second display to define a combined image. The graphics generation system includes a processing system and a memory, the memory encoded with instructions configured to be executed by the processing system to cause the first display and the second display to display a first image on the first display based on the graphics data and a user location, a user gaze orientation, or both, and display a second image on the second display based on the graphics data and the user location, the user gaze orientation, or both.

Disclosed are a method of forming an optical proximity correction (OPC) model and / or a method of fabricating a semiconductor device using the same. The method of forming the OPC model may include obtaining a scanning electron microscope (SEM) image, which is an average image of a plurality of images taken using one or more scanning electron microscopes, and a graphic data system (GDS) image, which is obtained by imaging a designed layout, aligning the SEM image and the GDS image, performing an image filtering process on the SEM image, extracting a contour from the SEM image, and verifying the contour. The verifying of the contour may be performed using a genetic algorithm. Variables in the genetic algorithm may include first parameters related to the image alignment process, second parameters related to the image filtering process, and third parameters related to a critical dimension (CD) measurement process.

The invention provides an artificial intelligence-based city scene generation method and a computer readable storage medium, and the method comprises the steps: firstly, carrying out the automatic recognition and extraction of different functional regions based on input graphic data, and carrying out the vectorization of the different functional regions into polygons with structural features; matching a three-dimensional model from a model library according to polygon features and generating an initial layout containing positions and orientations; constructing a differentiable boundary constraint function for each functional region to define a legal region; then defining a differentiable objective function including a boundary loss item and a collision loss item; and finally, by taking the initial layout as a starting point, optimizing an objective function through a gradient descent method, and iteratively adjusting the layout of each three-dimensional model to obtain a final city scene. According to the method, the spatial layout rule is converted into the computable loss function through the differentiable optimization framework, the automatic and intelligent generation of the city scene is realized, the layout generation efficiency is effectively improved, and the layout rationality is ensured.

The invention discloses a laser processing error compensation method and a processing system, and the method comprises the following steps: based on target processing graphic data, setting a decomposition step length and a galvanometer refresh cycle of laser processing, and planning a target axis track and a target galvanometer track; before machining is started, the shaft is driven to move along a preset empty track, actual motion coordinate information on a plurality of preset points is recorded, and coordinate information of the next point position of the shaft is predicted according to the coordinate information; calculating deviation information based on the actual coordinate information of the current point location on the target axis track, and correcting the track between the current point location and the next point location of the axis by using the deviation information; obtaining the correction information of the galvanometer on the corresponding galvanometer track based on the correction information of the track between the current point location and the next point location of the shaft; and the correction information of the galvanometer is issued to the galvanometer to compensate the actual position of the galvanometer, and then laser machining is started. Various machining errors are effectively compensated, and the machining precision and the product quality are improved.

A system for grouping architectural information within a plurality of layers for common formatting in a database. The system may include at least one processor; and at least one memory configured to execute the instructions to perform operations. The operations may include group according to the meta-model, architectural datasets together to structure database architectural information into a common format; establish an architectural domain for grouping the database architectural information in a plurality of layers; organize the meta-model to establish possession of information in the plurality of layers; and provide architectural data corresponding to each data domain for each of the plurality of layers from one or more data sources.

The invention provides a laser imaging system and a laser imager, and the system comprises a graph scanning module which comprises a laser scanner, an image sensor and a controller, and the controller comprises a gray value distribution calculation unit, an optical loss adjustment unit, an exposure parameter adjustment unit, a graph fusion unit and a graph optimization unit; the graph conversion module is used for converting the scanned graph data into machine data; the graphic imaging module comprises a laser emitter used for projecting machine data on a photosensitive material; the mechanical control module comprises a guide rail for driving the laser transmitter to move; according to the laser imaging system provided by the invention, proper laser performance can be selected when the graph is acquired, so that the effect of the graph is improved.

The invention discloses a power grid marketing and distribution graphic data batch treatment method and system based on a power grid topological structure, and the method is characterized in that the method comprises the following steps: collecting power grid equipment graphic data, and carrying out the data association and integration through the power grid topological structure; performing regional distribution analysis on a power grid graph data checking result, and collecting problem scenes; performing meter box recall on the abnormal marketing meter box data of the problem scene in batches; the data is visually displayed based on a geographic information system, the data is further analyzed according to the interactive operation of the user, and batch treatment is automatically carried out.