207results about "Vectoral format still image data" patented technology

Embodiments of the present disclosure relate to multimodal AI-based search for digital assets via an indexing and / or search pipeline. With respect to the indexing pipeline, some embodiments obtain first data and second data associated with a first digital asset. Such data represents different data types or modalities of the same digital asset. After obtaining the first and second data, some embodiments then generate a composite index. After the composite index is built such index can then be used to execute a query via the search pipeline. To execute the query some embodiments compute a relevance score for each digital asset, of multiple digital assets, based at least in part on a measure in which each digital asset satisfies one or more parameters or conditions for two or more data types of the query. Various embodiments then rank each digital asset and present one or more associated indicators.

The invention discloses a data container storage and management method based on a multi-scale space subdivision grid, and the method comprises the steps: constructing a multi-scale space subdivision grid coding system covering the earth surface and the height dimension based on an earth space subdivision theory, and endowing each grid with a unique code to form a unified space-time framework; multi-source data of images, vectors, non-space and the like are collected and transmitted to a processing center, and after consistency detection and normalization processing are carried out, unified grid codes are generated for various kinds of data based on the coding system, and mapping is established; and finally, storing the coded data into a database, updating an index, realizing dynamic scheduling, logic aggregation and visual display of multi-source data by means of unified coding, realizing efficient management and control and cross-domain collaboration of massive heterogeneous data, and improving data storage consistency and retrieval and display efficiency. According to the method, the problems of fragmentation of spatial big data, slow retrieval response, difficulty in cross-domain fusion and the like are effectively solved, and the management efficiency and the sharing depth of the spatial data are remarkably improved.

A system and a method are disclosed for classifying features from an input image. The method includes generating, by a processing circuit, a segment feature from a first input image, the segment feature corresponding to a group of pixels associated with an object represented in the first input image, and being an out-of-vocabulary segment feature; performing, by the processing circuit, a retrieval of a first feature vector, corresponding to the segment feature, from a database of feature vectors, the first feature vector representing an object-specific segmentation mask; and generating, by the processing circuit, an output segmentation mask based on the first feature vector.

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.

The disclosure relates to systems and methods automatically extracting an image and related image components, computationally determining an understanding of the image, and generating mathematical vector embeddings via sentence encoders based on the computationally determined understanding. The mathematical vector embeddings may be used for semantic image retrieval that enables image searching based on a semantic understanding of input images and / or input text. The mathematical vector embeddings may be used for training and executing generative Artificial Intelligence (AI) models to create new content that includes retrieved images and / or generate new images.

The invention relates to the technical field of channel monitoring and underwater topography analysis, in particular to a multi-beam sounding data driven underwater topography profile extraction method and device and a medium, and the method comprises the steps: obtaining channel region multi-beam sounding original data, carrying out the standardized preprocessing, and outputting a sounding data set; performing spatial interpolation and vectorization processing on the sounding data set to generate channel geological data, and issuing the channel geological data as a similar three-dimensional channel bottom graph through a geographic information service platform; generating a profile path, and generating interpolation points according to a preset interval; constructing a spatial index structure based on the sounding data set, and establishing an adjacent sounding point retrieval channel for each interpolation point; and in allusion to the same profile path, acquiring multiple periods of sounding data, calculating water depth difference, and dynamically and visually displaying an abnormal movement phenomenon by using a broken line graph. According to the method, efficient extraction, dynamic comparison and automatic early warning of the multi-period sounding data can be realized, and the analysis efficiency and accuracy of the channel topographic change are remarkably improved.

Example implementations include a method, apparatus and computer-readable medium of object / action recognition using a text-based classification model, comprising generating an image vector configured to represent one or more features of the first image. Additionally, the implementations further include computing a vector distance between the image vector and each of a first text vector and a second text vector, wherein the first text vector is configured to represent a first text, and wherein the second text vector is configured to represent a second text. Additionally, the implementations further include classifying the first image according to the first text or the second text based on which computed vector distance indicates a highest similarity between the image vector and either the first text vector or the second text vector relative to the other of the first text vector or the second text vector.

The invention discloses an automatic administrative boundary fusion charting method and a meteorological charting method based on a python language. Belongs to the technical field of meteorological charting, and particularly relates to the cross technical field of meteorological data processing and geographic information system visualization. The method starts with the construction of a multi-scale adaptive boundary fusion map, so as to solve the problems of cross-scale (such as province-to-village) precision faults and large interpolation errors of complex terrains, and achieve a cross-level administrative division boundary seamless aggregation technology (village level-town level-county level-province level). Therefore, the problem that precision is easy to lose when cross-scale conversion (such as from province to village) is carried out during meteorological mapping is solved.

A machine-learning algorithm is trained on images with a set of diverse items to produce as output feature vectors in a feature-vector space derived for the set. New item images for new items are passed to the algorithm and new feature vectors are projected into the vector space. A classifier for each new item is trained on the new feature vectors to determine whether the new item is new item or is not that new item. During a transaction, an item code scanned for an item and an item image are obtained. The item image is passed to the algorithm, a feature vector is obtained, a corresponding classifier for the item code is retrieved, the feature vector is passed to the classifier, and a determination is provided as to whether the item image and item code matches a specific item that should be associated with the item code.

Disclosed herein is a system and method for matching products detected in an image of a shelf. The match or non-match of the products is then used to make a determination that the products are correctly positioned on the shelf of if the positioning of the products represents a plug or spread situation.

The invention relates to the technical field of tire manufacturing and detection, in particular to a tire appearance defect detection method and system based on a retrieval enhancement generation technology, and the method comprises the steps: collecting image data, and completing the preprocessing of the image data through an edge calculation end; the retrieval enhancement generation module is used for sequentially carrying out picture vectorization processing on the preprocessed data, carrying out vectorization retrieval in a historical defect vector library and then sending the data into a multi-modal large model through the Ethernet to complete data fusion and an image defect judgment result; and the retrieval enhancement generation module outputs a judgment result to the production management end and the edge calculation end, and the production management end archives the judgment result. Compared with the prior art, the detection automation and intelligence are realized, the detection accuracy and efficiency and the production line quality control capability are also remarkably improved, and the production efficiency is improved. The method is suitable for defect identification and quality evaluation tasks of tire appearance detection.

A natural language image searching method is applied to an image searching system including an image analysis unit, a data management unit and an instruction input unit. The natural language image searching method includes an image feature vector encoder of the image analysis unit receiving a detection image and generating an image feature vector, the image analysis unit transmitting the detection image, the image feature vector, and a time stamp associated with the detection image to the data management unit, a text encoder of the instruction input unit generating and transmitting a text feature vector to the data management unit in accordance with a query statement, and the data management unit determining whether the query statement generated by the instruction input unit via natural language conforms to the detection image in accordance with a comparison result between the text feature vector and the image feature vector.

The invention relates to the technical field of intelligent analysis of drawings, in particular to an AI intelligent drawing auditing system for architectural drawings based on deep learning. The system comprises a drawing structured analysis module, a building component feature fusion module, a drawing violation drawing examination analysis module and a violation drawing examination optimization visual module, a drawing primitive data set, an attribute feature set and a standard rule base corresponding to a building engineering drawing can be obtained, and meanwhile a building component multi-dimensional feature matrix is generated; inputting the multi-dimensional feature matrix of the building component into a deep learning model for intelligent identification, compliance verification and drawing violation examination analysis, and generating a building drawing violation feature vector; based on the building drawing violation feature vector, violation point location aggregation analysis and reverse drawing checking optimization application are carried out, so that a corresponding building drawing intelligent drawing checking result is output, and a building drawing compliance thermal bitmap is displayed. According to the invention, intelligent and standardized review of the architectural engineering drawings can be realized, so that the drawing review efficiency of the architectural drawings can be improved.

A system and method for building first-party identity graphs using third-party data employs deep learning to enable privacy-preserving identity resolution. The system includes a deep learning model trained with transformer architecture and contrastive learning on a comprehensive third-party identity graph containing personally identifiable information (PII). Custom tokenizers process PII data by leveraging hierarchical structures and domain-specific characteristics to handle variations in spellings, abbreviations, and data formats. An identity matcher generates vector embeddings from first-party PII inputs, which are stored in a vector database for efficient similarity searches. A distributed similarity computation component compares first-party embeddings with third-party data embeddings, outputting similarity scores that enable fuzzy matching capabilities. A graph builder constructs accurate first-party identity graphs based on similarity thresholds while incorporating relevant third-party data.

Systems and methods for natural disaster damage analysis. Aerial images are converted (710) into a set of property images. The property images are then clustered (720) using vector embedding. Clustering the property images includes classifying the property images into separate categories, determining a symmetric similarity between the property images in each category, and clustering the property images based on the similarity. A first artificial intelligence agent extracts (730) damage information from a set of representative members of the clustered property images, where the damage information includes damage level information and damage reasoning information. The damage information is then stored (740) in a datastore with at least two databases, where at least one of the databases stores damage reasoning information and at least one other one of the databases stores damage level information.

The invention discloses a GIS-based channel digging and filling amount acquisition method, and relates to the technical field of channel engineering, and the method comprises the steps: firstly, obtaining DEM data after framing processing, channel GIS basic data and cross section parameters, and obtaining a channel digging and filling amount based on the channel GIS basic data, the cross section parameters and the DEM data after framing processing; drawing related cross section terrain sectioning lines, cross section characteristic lines and sloping lines on the channel plane map; and finally, according to the cross section terrain sectioning line, the cross section feature line and the sloping line, obtaining a channel body digging and filling amount, a channel adjacent road digging and filling amount and a channel sloping digging and filling amount, and adding the channel body digging and filling amount, the channel adjacent road digging and filling amount and the channel sloping digging and filling amount to obtain a target channel digging and filling amount. The earth-rock volume can be quickly calculated only by inputting GIS basic topographic data and a small amount of parameters of longitudinal and transverse sections, the earth volume calculation complexity is reduced, and the earth volume calculation efficiency is improved.

Some embodiments of an example method disclosed herein may include receiving point cloud data representing one or more three-dimensional objects; receiving a viewpoint of the point cloud data; selecting a selected object from the one or more three-dimensional objects using the viewpoint; retrieving a neural network model for the selected object; generating a level of detail data for the selected object using the neural network model; and replacing, within the point cloud data, points corresponding to the selected object with the level of detail data.

To provide a computer system and a learning data generation method for generating learning data for efficiently learning a model that outputs a feature quantity vector of data to which a numerical value is given as a label.SOLUTION: A computer system that holds information of a model that calculates a feature amount vector of data to which a label that is a numerical value representing a feature of the data is assigned, calculates the feature amount vector by inputting the data to the model, and executes a process of selecting a negative example and a positive example using the feature amount vector a plurality of times, the computer system comprising: For each candidate data, an index representing the difference between the query and the label of the candidate data is calculated, the candidate data having a large index is selected as a negative example, and the candidate data having a small index is selected as a positive example.SELECTED DRAWING: Figure 5

A large language model (LLM) system includes a personal AI agent, an enterprise AI agent, and a public AI agent. A method for operating the LLM includes: executing a first action plan to obtain a first integrated answer and a first integrated score based on a first query using the personal AI agent; and executing a second action plan to obtain a second integrated answer and a second integrated score based on a second query using the enterprise AI agent. A method for applying the LLM system includes: transforming a bitmap of a block diagram into a scalable vector graphics and integrating a semantic tag into the scalable vector graphics; training the large language model by using the scalable vector graphics integrated with the semantic tag and using a functional specification document; and generating a structured text description of the block diagram using the large language model which is trained.

The invention relates to the technical field of computers, and discloses a document retrieval method, device and equipment, a storage medium and a computer program product.The method comprises the steps that in response to a document retrieval request, the document retrieval request is converted into a problem retrieval vector, and the vector similarity between the problem retrieval vector and a document image retrieval vector is calculated, the document image retrieval vector is obtained by analyzing a document into a document image set and encoding the document image set, and a document retrieval result corresponding to the document retrieval request is generated according to the vector similarity; according to the document retrieval method, the document is analyzed into the document picture set in advance, the document picture set is encoded to obtain the document image retrieval vector, and the document retrieval is performed through the problem retrieval vector and the document image retrieval vector, so that the document retrieval in the form of the document image is realized; therefore, more space structure information, fine-grained information and context information can be reserved, and the document retrieval precision is improved.

Data processing techniques and systems for processing telematics data associated with a vehicle, such as an automobile, to classify how the vehicle is being operated by a driver. The telematics data can include the use of image data captured by a camera of the vehicle. The image data is processed in conjunction with vehicular telematics data such as position, speed, and acceleration data of the vehicle obtained from, for example, smartphone sensors. The image data is processed and used by a processing system to provide a context for the telematics data. The image data and the telematics data are classified by the processing system to identify driving behavior of the driver, determine driving maneuvers that have occurred, scoring driving quality of the driver, or a combination of them.

A method of searching a content scene includes receiving an image file of content; dividing the image file into scene units to generate scene images, and storing the scene images in a first database; extracting vector data of the scene images by using an embedding model, and storing the vector data in a second database; receiving a query for an image search by using a search tool, and converting the query into a vector to detect data having high similarity from the vector data of the second database; and extracting a scene image corresponding to the data having high similarity among the scene images from the first database, and providing the scene image as a search result of the search tool.

The invention discloses a geological map scene implementation method and system based on measure potential tapping, relates to the technical field of oil and gas field development, and is used for solving the problems that conflicts among a caliber layer, a method layer and a data layer are accurately positioned based on the same reference system, and a minimum recalculation set is difficult to stably solve according to the conflicts. Establishing a strong primary key index of a well ID, a layer ID, a grid unit and a time step, and registering and generating a caliber; a bidirectional Hausdorff distance is minimized through a multi-resolution pyramid and elastic registration, isolines are extracted and vectorized into a boundary set, scale factors and registration evidences are recorded, and co-located grids are generated; calculating four components including space overlapping, boundary consistency, trend correlation and abnormal co-location of CI in co-location grids, and performing structured output; and forming a conflict graph, locally replaying and generating a factor importance sequence by using a single factor, and solving a minimum recalculation set.

This invention discloses a distributed linking system for urban and rural planning modules, including a distributed linking processing platform connected to a front-end interface. The platform also includes interconnected regional distributed modules, a distributed vector creation module, a link index module, a front-end and back-end distributed linking calculation module, and a modeling calculation module. The regional distributed modules create vector data for urban and rural plans within the entire planning area and store it in a vector database as block files. The link index module includes a front-end tag storage module, a back-end tag storage module, and an index connection module. This invention proposes a distributed linking system for urban and rural planning modules, employing distributed linking to enable concurrent multi-point construction and storage of a specific urban and rural planning area and concurrent multi-point access to that area, achieving lightweight loading and reduced computational load.

Systems and methods for natural disaster damage analysis. Aerial images are converted into a set of property images. The property images are then clustered using vector embedding. Clustering the property images includes classifying the property images into separate categories, determining a symmetric similarity between the property images in each category, and clustering the property images based on the similarity. A first artificial intelligence agent extracts damage information from a set of representative members of the clustered property images, where the damage information includes damage level information and damage reasoning information. The damage information is then stored in a datastore with at least two databases, where at least one of the databases stores damage reasoning information and at least one other one of the databases stores damage level information.

This application discloses a method, apparatus, device, and storage medium for heterogeneous topographic map reconstruction based on spatial indexing and multidimensional ray detection, relating to the field of geographic information system processing technology. The method includes: emitting scanning rays within the geographic range corresponding to the target mapping area and obtaining their intersections with vector boundaries; classifying the target mapping area based on the intersections and scanning rays to identify boundary depressions and cavity edges; extracting background elevation points from a preset background topographic map library based on the identified boundary depressions and cavity edges; constructing a constrained Delaunay triangulation network and then smoothing it to obtain the target topographic file. This solves the problem of not being able to automatically identify irregular boundaries and cavities during stitching, which leads to elevation faults. It achieves automatic identification of the mapping area boundary morphology and accurate positioning of the elevation points to be filled without manual intervention.

This application discloses a plug-in-based method and apparatus for loading network digital earth data, including acquiring network digital earth data and user data; classifying the network digital earth data to obtain default data types and acquiring custom data types corresponding to the user data; writing default data plugins for the default data types and custom data plugins for the custom data types according to pre-defined data plugin writing specifications; and uploading the default data plugins and custom data plugins to a server so that the server adds the default data plugins and custom data plugins to a data plugin list. This application, based on data plugin writing specifications, writes data plugins corresponding to data types and uses a data plugin mechanism to dynamically load data, which can meet the needs of users to customize data plugins and improve the flexibility and breadth of data loading methods in the network digital earth system.

A system and method for improving identity resolution match rates using deep learning techniques that enable privacy-preserving fuzzy matching of personally identifiable information (PII) employs a deep learning model trained with transformer architecture and contrastive learning on third-party identity graph data. Custom tokenizers process different Pll types by leveraging hierarchical structures and domain-specific characteristics. The trained model generates vector embeddings that enable fuzzy matching to account for variations in spellings, typographical errors, and data inconsistencies without requiring adherence to strict data schemas. A vector database stores embeddings for nearest neighbor searches to identify potential identity matches based on distance calculations between embeddings. A match filter applies logic to determine valid matches, and the system can operate without requiring movement of raw Pll data from secure environments.

An information processing apparatus (1) includes: an acquisition unit (101) configured to acquire an object image that contains one or more objects; a first inference unit (105) configured to infer one or more rectangular areas that respectively surround the one or more objects in the object image, and a type of each of the one or more objects, by applying the object image to a first learning model; a second inference unit (105) configured to infer an area of a target object in the object image by applying the one or more rectangular areas and the type of each of the one or more rectangular areas to a second learning model; and an extraction unit (105) configured to perform color determination on each pixel in the area of the target object and extract colors of the target object.

The application provides a geometric figure processing method and device, equipment and a storage medium. The method comprises the following steps: acquiring a to-be-recognized image, and recognizing and cutting the to-be-recognized image to determine an initial geometric illustration; performing optimization processing on the initial geometric illustration to determine a target geometric illustration; performing denoising processing on the target geometric illustration to obtain a denoised geometric illustration, acquiring image features of the denoised geometric illustration, classifying the image features to determine a target geometric element, and determining a first vector element according to the target geometric element and a geometric element relationship obtained by disassembling the target geometric element; editing a to-be-edited vector element in the first vector element based on an editing instruction, determining a vector figure according to the to-be-edited vector element and a non-editing vector element, and exporting the vector figure to a figure resource library in a standard format. Thus, high-precision recognition is performed on the geometric figure, and the subsequent labor cost for adjusting the geometric figure is greatly reduced.