331results about "Vectoral format still image data" patented technology

The invention discloses a visual scene method, system and equipment based on digital twinning and a medium, and relates to the technical field of digital twinning and visual modeling, and the method comprises the steps: collecting multi-format source data, carrying out the field standard mapping, and carrying out the consistency verification of the multi-format source data after the field standard mapping; writing the multi-format source data after consistency verification into a to-be-fused buffer area, selecting model component data in the to-be-fused buffer area to execute coordinate reference conversion, and performing association binding on the converted model component data and the structured graphic and text information by constructing an identification field; and synchronously loading the associated and bound model component data and the structured graphic and text information to a predefined template container, generating a scene configuration file according to a container structure, and calling a release engine to register to a multi-terminal rendering service channel. According to the method, accurate alignment of multi-format model components in a unified space coordinate system is realized by constructing an affine transformation coordinate reference conversion scheme.

A method for managing a framework includes: receiving, by a GUI, a query that is sent to a first module; analyzing, by the first module, the query to infer intention; making, by the first module, a determination that the intention is not searching for an object in a database; sending, by the first module, the query to a second module; transforming, by the second module, the query into a vector that is sent to a third module; performing, by the third module and using the vector, a search for a nearest image in the database; identifying, by the third module, a path associated with the nearest image, which is sent to an analyzer, in which the analyzer further sends the path to a fourth module; and fetching, by the fourth module and based on the path, an image from a store, which is sent to the GUI.

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 present disclosure provides a method, a device, and a product for retrieval. The method includes acquiring context information related to an image and determining a representation of the image based on image data and the context information of the image, where the context information includes at least one of environment parameters, user behavior data, time elements, or field metadata. The method further includes encoding the representation as an image vector in a high-dimensional vector space and storing it into an image vector database. When retrieval is performed, a query that includes text information and that is for the image vector database is received, and an image associated with the text information is determined from the image vector database. The method according to the present disclosure can improve accuracy and efficiency for image retrieval.

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.

A method of clustering patterns of an integrated circuit includes; providing a pattern image and numeric data, as input data corresponding to a first pattern to a first model, wherein the first model is trained by a plurality of sample images and a plurality of sample values, obtaining a content latent variable using the first model, and grouping a plurality of content latent variables corresponding to a plurality of patterns into a plurality of clusters based on a Euclidean distance, wherein the numeric data represents at least one attribute of the first pattern.

The invention discloses a visual question-answering system based on multi-modal knowledge autonomous learning and a construction method, and belongs to the technical field of artificial intelligence. The invention provides a visual question and answer model construction method fusing image information, natural language questions and external knowledge. A knowledge dynamic screening and replacement strategy is constructed by introducing an autonomous evolution mechanism, and whether knowledge is inserted or updated can be judged according to a current training state and knowledge similarity, so that the problem that a traditional static knowledge base cannot adapt to task changes is solved. According to the method, through pseudo data construction and multi-modal triple extraction, structured and high-association-degree'image-question-answer 'knowledge representation is established, so that knowledge has better semantic expression ability, and redundancy and noise are effectively reduced. According to the method, rapid retrieval and active calling of knowledge are achieved by means of the vector indexing technology, key support information can still be obtained in a complex scene, and the expression of the model in a visual question-answer task based on knowledge is remarkably improved.

The invention discloses a perspective analysis method and system based on multi-element space superposition, and belongs to the technical field of geographic information processing.The perspective analysis method comprises the steps that analysis range data input by a user is obtained, and the analysis range data is generated through map drawing or external vector file import; selecting at least two analysis object layers; performing spatial alignment, coordinate system conversion and attribute field standardization processing on the analysis range data and the analysis object layer; a parallel spatial indexing technology is adopted, all analysis object layers and analysis range data are superposed at a time, and a fine-divided result library is generated; storing a space unit ID and an original layer attribute field in the fine crushing result library; calculating a target field value according to the area proportion of the space unit based on a preset adjustment rule, and outputting the target field value to the space data file and the table file; generating a multi-dimensional statistical report according to user-defined dimensions and statistical indexes through a perspective analysis engine on the basis of the fine crushing result library; and outputting the fine space data file and the analysis report according to the filtering condition.

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 invention discloses an agricultural insurance data acquisition and management system based on satellite remote sensing. The system comprises a data acquisition module, a preprocessing module, a verification module and a management module. The data acquisition module covers basic, universal and vector data acquisition according to data types; the preprocessing module improves the quality of collected data; the verification module verifies a cultivated land vector and insured user information, and data accuracy and reliability are enhanced; the management module builds two libraries, and constructs an agricultural insurance data large model by utilizing knowledge graph modeling. In the technical application, on the basis of satellite remote sensing and A I, large-user plot data is collected by means of an image segmentation algorithm, and the cost is reduced. A full-coverage model is constructed through abstract materialization, and business application is supported. Technical means such as satellite remote sensing, weight dispatch data superposition and AI image recognition are used for verifying data, manual operation is reduced, verification accuracy is guaranteed, data value is mined, and the agricultural insurance data management level is improved.

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 geographic information systems, in particular to an incomplete road network repairing and reconstructing method based on AI recognition. Comprising the following steps: S1, road surface data geometric restoration and road width calculation; s2, reconstructing a target road network topology; s3, road attribute matching; and S4, topology simplification and output. According to the method, through the dynamic distance threshold value and topology verification, broken roads are intelligently connected, the disconnection phenomenon is eliminated, a topology connected road network is constructed, and a reliable data basis is provided for path planning. The width is calculated through correlation between the road surface and the center line, and the external road network attributes are fused, so that the output road network has key information such as the road width and grade, and the trafficability evaluation requirement in mechanical construction is met.

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.

The present invention discloses an image retrieval method based on hyperdimensional vector calculation, comprising: preprocessing a collected image data set; dividing the preprocessed data set into a training set and a test set; based on the training set, training a ResNet18 network using a small sample learning method to obtain m+1 feature extraction networks; using the trained ResNet18 network to extract features from each image in the test set, generating a hyperdimensional vector of the test set image, and obtaining a hyperdimensional vector library; using the trained ResNet18 network to extract features from an input query image, generating a hyperdimensional vector corresponding to the query image; matching the hyperdimensional vector corresponding to the query image with all vectors in the hyperdimensional vector library, and outputting the vector with the highest matching degree. The present invention realizes image retrieval with low resource consumption and high robustness, can effectively complete image matching tasks in the field of the Internet of Things, and meets the demand for high robustness image retrieval in scenarios with limited hardware resources.

The application discloses a trademark retrieval library generation method and device based on a multi-modal feature generation model, constructs a trademark image-text pair according to the Vienna graphic element classification standard, and trains a multi-modal feature generation model; an initial trademark retrieval library is constructed by using the trained multi-modal feature generation model; trademark rejection data is obtained, and the trained multi-modal feature generation model is fine-tuned by using an adapter technology and a triple comparison learning method, so that an optimized multi-modal feature generation model is obtained; the optimized multi-modal feature generation model is used to regenerate a trademark image feature vector, and the vector is input into a vector database to obtain a final trademark retrieval library. The multi-modal feature generation model is fine-tuned twice by using the trademark image-text pair and the trademark rejection data, so that the overall retrieval effect of the trademark retrieval library constructed by using the multi-modal feature generation model is improved, and thus the trademark retrieval result is more accurate.

The present disclosure provides a memory device and a data search method thereof. The data search method of the memory device includes vectorizing a data to be searched to obtain a data to be searched vector according to a compression mode recorded, wherein the data to be searched and a plurality of objects of a database are compressed by the compression mode; setting a search condition; searching the objects of the database with the data to be searched vector to determine whether the data to be searched matches the objects in the database; and recording and outputting at least one matched object in the database that matches the data to be searched.

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.

A method for subdivided representation reinforcement of an image / text representation vector through an attribute value of an object in an image-language alignment model is provided. The method for training an image-language alignment model, according to an embodiment of the present invention, generates, in an input image, object-specific representation vectors of the image, generates, in an input text, object-specific representation vectors of the text, and uses the generated object-specific representation vectors so as to train an image-language align model through a contrast loss function. Therefore, object-specific attribute representation is reinforced such that each attribute is represented to be subordinate to the objects, and thus accurate image searches can be performed for more complex natural language queries by means of the image-language alignment model, and accurate natural language searches can be performed for images having various objects.

The invention relates to the technical field of data processing, in particular to an urban green visual rate spatial distribution simulation method based on a green facility environment feature framework, which comprises the following steps of: acquiring a streetscape image, a satellite remote sensing image, road network distribution data and land use type grids of a whole city domain, marking a vegetation coverage area and generating an initial green visual rate grid map layer; and performing semantic segmentation on the streetscape image to calculate a vegetation pixel proportion so as to generate a green vision rate observation value of the road network region. And combining the road network weight parameter and the land use type coding parameter to form an environment parameter index when the multi-scale environment feature framework is constructed. And then constructing a spatial interpolator based on the frame, and completing non-road region green vision rate calculation in combination with an input observation value and an environmental parameter index to generate a global interpolation result. The spatial limitation of existing street scene sampling is broken through, global coverage is achieved through public geographic data, the implementation threshold of small and medium-sized cities is lowered, and a scientific decision basis is provided for heat island effect analysis and green land planning.

A method for managing a framework includes: receiving, by a GUI, a query that is sent to a first module; analyzing, by the first module, the query to infer intention; making, by the first module, a determination that the intention is not searching for an object in a database; sending, by the first module, the query to a second module; transforming, by the second module, the query into a vector that is sent to a third module; performing, by the third module and using the vector, a search for a nearest image in the database; identifying, by the third module, a path associated with the nearest image, which is sent to an analyzer, in which the analyzer further sends the path to a fourth module; and fetching, by the fourth module and based on the path, an image from a store, which is sent to the GUI.

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.

The invention discloses a GIS-based river network water conservancy object upstream and downstream determination method. The method comprises the following steps: preprocessing river vector data to obtain river network vector data; constructing a river network directed graph by using the river network vector data and the hydraulic supervision object vector data; respectively calculating a static OD cost matrix and a dynamic OD cost matrix according to the edge weight of the river network directed graph; when the weight cost is smaller than infinity, if the flow direction from the starting point to the target point is consistent with the river reach, the target point is downstream relative to the starting point, and if the flow direction from the target point to the starting point is opposite to the river reach, the starting point is upstream relative to the target point. According to the method, the directed graph is constructed according to the river network vector data, the static and dynamic OD cost matrixes are calculated, the upstream and downstream relation between the starting point object and the target point object is calculated, and the efficiency and accuracy of data processing of the hydraulic supervision objects distributed in the river network are improved. The invention further discloses a device for implementing the method, electronic equipment and a storage medium.

The invention discloses an RAG document splitting optimization method and system in the operator field, and belongs to the technical field of large model optimizing.The method comprises the steps that a document is uploaded, and source files and file information are stored through minio and paradeDB respectively; constructing a document loader, automatically selecting a corresponding loader according to a file type to carry out file processing, converting the file into a uniform Markdown text, analyzing a picture in the document, and converting the picture into a base64 format; constructing a picture processor, extracting a picture base64 character string in the text, processing a bitmap and a vector diagram, and converting the bitmap and the vector diagram into a Markdown picture reference format; constructing a document divider; vector conversion; and recalling the text. According to the method, the problem that picture extraction, conversion and processing are complex in the current RAG is solved, vector retrieval, full-text retrieval and mixed retrieval functions can be achieved through one database, and the operation and maintenance cost is reduced.

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.