1320results about "Still image data indexing" patented technology

The invention discloses a transformer equipment oil leakage monitoring method and system based on image recognition, relates to the technical field of image processing, and effectively eliminates static background interference by constructing a standardized image sequence S (t), performing gray gradient analysis and pixel feature extraction, recognizing candidate oil spot areas and constructing a disturbance active score Sx. The edge structure and the morphological stability coefficient of the dynamic oil stain suspected area are further extracted, and the credible level quantitative judgment of the dynamic oil stain suspected area is realized by integrating the characteristic indexes and the morphological stability coefficient, so that the dynamic identification capability and the anti-interference capability are realized; the oil leakage identification accuracy and the system automation risk response capability in a complex environment are improved, false alarm and missing alarm are effectively avoided, and the operation safety of equipment is guaranteed.

Techniques are generally described for query evaluation for image retrieval and image generation. In various examples, a first encoded representation of first natural language input data may be generated. An image retrieval process may be selected from among the image retrieval process and an image generation process based at least in part on the first encoded representation of the first natural language input data. A second natural language encoder may generate a second encoded representation of the first natural language input data. The second encoded representation may be used to determine first image data stored in a first data repository. The first image data may be sent for output on a display of a first computing device.

The invention discloses a defect file and image number association method based on multi-format analysis, and relates to the technical field of semiconductor detection and data analysis, and the method comprises the following steps: constructing a multi-format rule base, receiving batch Klarf files and corresponding defect image folders, and extracting image numbers in defect image file names; reading field names of a preset number of Defect data of the Klarf file, matching the field names with the multi-format rule base, and selecting an analysis strategy according to a matching result by adopting a self-adaptive analysis algorithm; the analyzed Defect data are preprocessed, the association confidence coefficient is calculated through a three-layer verification algorithm, and an association result table is generated; according to the invention, automation, standardization and high reliability of semiconductor defect detection data processing are realized.

A method, computer program product, and computing system for generating a plurality of chunks for a plurality of text portions of a document, wherein the document includes the plurality of text portions and a plurality of images. Each chunk is indexed using a word embedding. Each of the plurality of images is indexed based upon, at least in part, a position of a respective image relative to a corresponding chunk. An image placeholder is generated for each of the plurality of images. A plurality of image-enhanced embeddings is generated by inserting the image placeholder for each of the plurality of images into a respective word embedding for the corresponding chunk. The plurality of image-enhanced embeddings are provided for processing a query using a generative artificial intelligence (AI) model.

The invention provides an index construction method for a medical ultrasonic database, and relates to the technical field of ultrasonic data processing, and the method comprises the steps: reading a multi-frame image sequence from a DICOM ultrasonic database, and generating a diagnosis intention vector for each frame; analyzing the diagnosis report, and segmenting the report into a plurality of segments; lesion detection and segmentation are performed on each frame of image to obtain lesion information, and a cross-frame aggregation strategy is adopted to identify a unified lesion object; extracting a feature vector of each focus object to obtain a focus object feature vector; establishing an alignment mapping between the focus object and the report fragment by adopting triple constraints, and generating a focus report mapping table; and constructing a multi-layer index structure, and storing the focus report mapping table and the multi-layer index structure in parallel. According to the method, a multi-layer index structure comprising a metadata index, a text index, a focus object vector index and a cross-modal embedding index is formed, so that efficient organization and focus-level accurate retrieval of medical ultrasonic data are realized.

The invention discloses an appearance patent graph retrieval method and infringement detection system based on a multi-modal large model, and relates to the technical field of intelligent retrieval, and the method comprises the steps: carrying out the feature extraction of each patent picture in an appearance patent database through a multi-modal large language model, and storing the features in a vector database; generating an embedded vector of the target picture and a description text for describing the appearance characteristics of the target picture by utilizing a multi-modal large language model; calculating the similarity between a target picture embedding vector and each picture embedding vector in a vector database, performing retrieval to form a preliminary candidate set, and then calculating the semantic similarity between a target picture description text and a patent text corresponding to each picture in the preliminary candidate set; performing fusion to obtain a retrieval result of comprehensive sorting; correlating the corresponding patent law text, positioning and marking related text description, and forming a visual evidence chain combining vision and text evidence. According to the method, the vision and text bimodal information is fused, so that the whole-process intelligentization from image understanding to infringement judgment is realized.

The invention provides an image data and pattern spot management system and method and electronic equipment, and relates to the technical field of geographic information data processing, and the image data and pattern spot management system comprises an image data indexing module which is used for carrying out three-dimensional indexing on a remote sensing image to obtain a space-time-data source association chain of the remote sensing image, the remote sensing image contains pattern spot data with an identifier; the pattern spot complete cycle construction module is used for performing multi-temporal analysis on the pattern spot data to obtain an analysis result, and constructing a pattern spot change life cycle map of the pattern spot data in combination with the association chain; and the tracing module is used for positioning the target pattern spot when the target pattern spot needs to be traced to obtain a change node of the target pattern spot and a space-time-data source index corresponding to the change node, and searching in the association chain according to the change node and the space-time-data source index to obtain a traceable data chain. According to the invention, the storage and management of the geographic image data and the pattern spot data are more systematized and refined.

The disclosed techniques provide improved methods of operating system (OS) search. Users are enabled to search for documents, emails, presentations, content they entered into a web form, meetings they participated in, and other interactions they had with their computing device. To accomplish this, screenshots are periodically captured and indexed. Machine learning models are used to infer embeddings for visual elements of the screenshots and / or text extracted from the screenshots. A full text index of a relational database may also be populated with text extracted from the screenshots. The embeddings and full text index may then be used to retrieve screenshots in response to a user history query. For example, screenshots of embeddings within a defined distance of an embedding of the user history query may be selected. Query results from different embedding indices and relational databases may be ordered by applying different weights to different kinds of search scores.

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 provides a three-dimensional visualization analysis method for a real scene of an ancient earthquake exploring groove, which belongs to the technical field of seismic geological survey and comprises a data acquisition module used for acquiring point cloud data of target exploring grooves in different regions. And the model building module is used for carrying out scene format conversion on the point cloud data of the target exploring grooves in different regions, and carrying out exploring groove three-dimensional model building by utilizing the converted SLPK file. And the visualization module is used for carrying out live-action three-dimensional display on the three-dimensional models of the target probe grooves in different regions through a three-dimensional scene Web server based on the SLPK files in different regions, and rendering node data of different detail levels of the probe grooves according to user requirements. The ancient earthquake event can be better understood, and a reliable three-dimensional digital file and a new shared analysis normal form are provided for the ancient earthquake.

A computing system that is configured to identify computer-readable items based upon input queries is described. The computing system receives a query and creates a semantic embedding of the query. The computing system searches over multiple indices based upon a keyword in the query and additionally searches over multiple indices based upon the semantic embedding of the query. Search results are identified based upon the multiple searches.

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 an intelligent fusion processing method and system for forest and grass wet resource pattern spots and annual land change survey data, and belongs to the field of space-time big data in the natural resource industry, and the method comprises the steps: completing the pattern layer fusion based on the annual land change survey data in combination with forest and grass wet pattern spot survey results; superposing county and rural boundaries to form an administrative fusion layer; performing subcompartment division based on the remote sensing image; merging broken classes according to a principle of adjacent positions and similar types; eliminating a pattern spot sharp angle; land classes are refined, and attribute information is perfected; the public welfare forest range and the protection level are optimized; automatically checking and summarizing to form a result layer; and finally performing statistics to generate various thematic reports. According to the method, technologies such as database storage, spatial index construction and a fusion algorithm are adopted, the data processing efficiency is improved by more than 50%, and the sharp angle processing time is shortened; and the labor cost and the time investment are obviously reduced.

The invention relates to DICOM image processing, in particular to a DICOM image preprocessing and quality control system based on Spark, and the system comprises a data collection module which collects a DICOM medical image from a data source, and automatically recognizes and extracts a key metadata field in the DICOM medical image; the index construction module is used for constructing a comprehensive and accurate index system according to key metadata fields of the DICOM medical images; the data standardization module is used for carrying out standardization processing on key metadata fields of the DICOM medical images, supporting version control and audit tracking on the key metadata fields and carrying out optimization processing on the DICOM medical images; the scoring module is used for carrying out objective quality evaluation on the DICOM medical image based on a multi-dimensional image quality evaluation model and calculating a corresponding comprehensive quality score; according to the invention, the defect that the DICOM medical images from different equipment manufacturers are difficult to efficiently preprocess can be overcome.

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.

The invention relates to the technical field of computer vision and multi-modal data processing, and discloses a multi-modal picture data processing method and device, equipment and a storage medium, and the method comprises the steps: carrying out the multi-dimensional feature extraction of a pre-obtained standardized picture set, and obtaining composite feature data; inputting the composite feature data and a preset cue word into a multi-modal large model to generate a natural language description text, and extracting key semantic information from the natural language description text to form a structured semantic set; and performing associative storage on the standardized picture set, the composite feature data and the structured semantic set, and constructing a multi-modal index based on associative storage data. According to the method, deep understanding of multi-modal picture data is realized based on multi-dimensional feature extraction and semantic understanding of a multi-modal large model.

The invention belongs to the technical field of brain-computer interfaces, multi-modal feature alignment and information retrieval, and discloses a visual content retrieval method based on electroencephalogram signals. Cross-subject standardized electroencephalogram samples are obtained and input into an electroencephalogram encoder to extract low-dimensional electroencephalogram signal feature vectors; an image encoder is adopted to process the corresponding retrieval images to extract visual feature vectors; the low-dimensional electroencephalogram signal feature vector and the visual feature vector are jointly input into a prototype attention enhancement module to form a dynamic prototype pool, and prototype enhanced electroencephalogram signal representation is obtained through processing; the method comprises the following steps: performing classification training according to existing prototype enhanced electroencephalogram signal representation and image data pairs; for a plurality of time slices of the to-be-queried electroencephalogram signal sample, calculating a reconstruction error or a signal-to-noise ratio of each time slice to obtain a confidence coefficient; and adopting a weighted average or voting mechanism to fuse a plurality of time slice results, and outputting a stable and robust final visual retrieval classification result.

The invention discloses a two-dimensional code full-life-cycle tracking management system based on dynamic encryption, relates to the technical field of two-dimensional code tracking management, and is characterized in that a target object life cycle state sequence diagram is constructed, and a two-dimensional code coding index is dynamically generated based on a current state, an equipment environment and a time factor; and dynamic generation and secure binding of two-dimensional code content are realized in combination with a multi-factor asymmetric encryption mode. Meanwhile, through path consistency verification and state chain continuity judgment, validity verification is carried out on a two-dimensional code access behavior, and the validity of the two-dimensional code is judged in combination with an environment deviation detection mechanism; compared with the prior art, the method can effectively prevent the two-dimensional code from being counterfeited, replayed and visited unauthorized, improves the uniqueness, verifiability and safety of the two-dimensional code in the whole life cycle, and is suitable for high-safety product tracking, anti-counterfeiting authentication, multi-stage identity recognition and other scenes.

The invention provides a multi-modal semantic fusion image and text relevance dynamic analysis method and system, and belongs to the technical field of multi-modal data processing. The method comprises the steps of image and text data preprocessing, feature extraction, fusion semantic vector generation through a bidirectional cross attention mechanism, dynamic relevance score calculation through a time sequence attention long-short-term memory network and combined loss function end-to-end training. According to the method, accurate alignment of image and text features can be realized through a bidirectional cross attention mechanism, the cross-modal matching accuracy is improved, the dynamic correlation analysis capability is enhanced by using time sequence modeling, and the problems of insufficient feature coding suitability and limited time sequence modeling capability in the prior art are solved.

The invention discloses an image retrieval method and device, computer equipment and a storage medium, belongs to the technical field of artificial intelligence, and is provided with a retrieval system applied to an insurance marketing scene image. According to the method and the device, the to-be-processed image is segmented and coded, the content features of the image are combined with the position information to form high-quality image representation, and the corresponding image index information is generated by utilizing the preset image index generator, so that efficient indexing and retrieval of the image are realized. In the encoding process, local features of the image are reserved, spatial position information is fused, discrimination and uniqueness of image indexing are improved, generated image indexing information and an original image are stored in an image retrieval database in an associated mode, it is ensured that the image retrieval process has high matching precision and response speed, and the image retrieval efficiency is improved. The image retrieval accuracy and processing efficiency are effectively improved, and the method is suitable for large-scale image library management and rapid retrieval.

The invention discloses an image auditing method and device, a storage medium and a program product, and relates to the technical field of computers. The method comprises the following steps: acquiring an input image, a historical image library and an auditing rule library; obtaining at least two target historical images in a historical image library according to the input image and the historical image library; obtaining at least one target auditing rule in an auditing rule base according to the input image and the auditing rule base; and through the multi-modal large model, according to the input image, the at least two target historical images and the at least one target auditing rule, generating an auditing result of the input image, the auditing result of the input image being used for indicating that the input image is one of a violation image, a compliance image and an uncertain image. According to the method, the learning value of few samples is fully utilized, the open-source multi-modal large model can directly perform a security auditing task without fine tuning, the applicability of the multi-modal large model in a content auditing scene is improved, and the training cost of image auditing is reduced.

The invention relates to a dynamic construction method and system for an integrated energy large model knowledge base. The method comprises the steps that a directed graph is constructed according to physical equipment or energy nodes on a target integrated energy system; performing knowledge association and filling on nodes and edges in the directed graph to form a node static knowledge set; accessing multi-source real-time data, and performing anomaly detection and state analysis on the multi-source real-time data to obtain a corresponding event; mapping the event into a directed graph, and updating the directed graph and the node static knowledge set to obtain an updated graph database and an updated vector knowledge base; and constructing an input prompt to the comprehensive energy field large model based on the query statement and the corresponding knowledge set to output an answer. According to the method, a dynamic knowledge base tightly coupling a physical world and an information world is constructed, the systematization degree, the cross-system analysis capability and the real-time performance of comprehensive energy system knowledge are remarkably improved, and powerful support is provided for deep application of a large model in scenes such as fault diagnosis and operation optimization.

The embodiment of the invention discloses a building engineering crack detection method and system based on image recognition, and the method comprises the steps: obtaining a building surface image, carrying out the preprocessing of the image, obtaining a standardized image, and carrying out the multi-scale decomposition extraction and integration of various features, and forming a multi-dimensional feature descriptor set; after feature importance is evaluated, a compact feature vector is generated through dimension reduction, quantization coding and compression, and then a multi-level feature index mechanism for optimized compression is constructed. A query feature vector is extracted from a newly collected image, searching and screening are completed by means of an index mechanism and a tolerance threshold, and a crack matching result is obtained; and based on the result, positioning cracks, classifying types, measuring parameters and evaluating severity, and generating a crack state report. The crack trend is analyzed in combination with the historical data time sequence, a multi-stage early warning mechanism is designed, maintenance suggestions are provided, and a real-time monitoring and early warning system is formed. According to the embodiment of the invention, the technical problems of high storage pressure and low real-time detection efficiency in the prior art can be effectively solved.

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 scene generation method and device based on multi-source GIS data fusion, and relates to the technical field of digital twinning and programmed generation. The method comprises the following steps: acquiring GIS data, preprocessing the GIS data, and storing the preprocessed GIS data in a geographic information resource library; a structured resource library is constructed, semantic parameters are added to the three-dimensional model in the structured resource library through the configuration file, and three-dimensional model resources with structured semantics are constructed; on the basis of the three-dimensional model resources with structured semantics and GIS data in a geographic information resource library, building and road generation and terrain processing are carried out in a programmed modeling engine through a configuration file, and scene data are generated; and importing the generated scene data into a real-time rendering engine, carrying out dynamic environment interaction and biocenosis simulation, and generating a city scene. The problems that in the prior art, an urban three-dimensional modeling method is low in efficiency and insufficient in environment interaction reality sense are solved.

The invention relates to the technical field of ocean structure monitoring, in particular to a method for monitoring the scouring degree of a pile foundation of an offshore wind power station through bubble tracing, which comprises the following steps of: 1, constructing a bubble feature data acquiring an underwater image, manually segmenting and selecting key bubbles, extracting contour features of the key bubbles and expressing the contour features by vectors, a Zernike moment is used for grouping according to shapes, and a bubble feature database is constructed; and 2, identifying underwater bubbles: obtaining an underwater background image, constructing a dynamic background, obtaining an image containing suspended particles, extracting a foreground main body through a background subtraction method, comparing contour features of the foreground main body with a bubble feature database, and screening out the bubbles. By introducing an innovative method combining image recognition, feature modeling and physical inversion, the key problems of bubble misjudgment, track loss, inaccurate flow velocity and the like in a traditional bubble tracing technology are effectively solved, and high-precision, low-cost and automatic monitoring of the scouring process of the offshore wind power pile foundation is achieved.

The invention discloses a structured information extraction system for a mental disorder electronic medical record based on a knowledge graph and a natural language, and belongs to the technical field of medical information processing. According to the system, firstly, core concepts such as diseases, symptoms and drugs are extracted from an authoritative guide to construct a mental disorder knowledge graph, and a standardized clinical knowledge base is established; then pre-training and fine-tuning the deep learning model on a large number of biomedical texts and desensitized medical records to enable the deep learning model to have a medical language understanding ability; performing preprocessing, named entity recognition and entity linking on the electronic medical record text, and mapping spoken expressions to standard medical terms; inference is carried out by utilizing a relation extraction model and combining with a knowledge graph to complement implicit clinical information; and finally, structured data output meeting the standards of FHIR and the like is generated. According to the method, through deep fusion of knowledge driving and data driving, the problems of insufficient semantic understanding and weak generalization ability of a traditional method are effectively solved, and the accuracy and clinical value of electronic medical record structured information extraction are remarkably improved.

The embodiment of the invention provides a picture display method and a related device, and the method comprises the steps: firstly, storing thumbnails according to the arrangement order of the thumbnails in a thumbnail display interface when electronic equipment stores the thumbnails corresponding to shot or stored pictures; in this way, the electronic equipment has the capacity of inquiring and obtaining the thumbnails in batches. And then, when the electronic equipment displays the thumbnail display interface, the electronic equipment can query and obtain the thumbnails in batches. In the embodiment of the invention, the electronic equipment can splice the plurality of obtained thumbnails to obtain the spliced thumbnail, and then the electronic equipment can display the spliced thumbnail through the view control. Through the picture display method provided by the invention, the performance and the frame rate of loading and displaying a large number of thumbnails by the electronic equipment can be improved, so that the problems of white blocks and lagging in the process of browsing a large number of thumbnails by a user can be avoided or reduced.

The invention discloses a PCB (Printed Circuit Board) defect detection method based on machine vision, which relates to the related technical field of computer vision, and comprises the steps of multi-modal data acquisition, data preprocessing and feature extraction, feature fusion, classification, positioning and marking of extracted feature vectors by adopting a support vector machine as a classifier, and establishment of a defect sample library. According to the method, each modal data reflects the state of the PCB from different aspects, the fusion of the multi-modal data can be mutually verified and supplemented, misjudgment and missed judgment are reduced, the related samples in the sample library can enable the model to know the performance of the defects on different materials, so that the defects can be identified more accurately in the detection, and the detection accuracy is improved. By acquiring the image sequences of the same circuit board in different production stages and processing the image sequences by using the long-short-term memory network model, the change trend of the defects in time sequence can be captured, so that the development of the defects in the future production stage can be predicted.

The invention discloses a method and device for automatically designing a gas path diagram. The method comprises the following steps: carrying out element identification on an input three-dimensional model to obtain the element type of each element in the three-dimensional model; based on the element type, selecting elements contained in an action group through interaction setting of a client, and configuring corresponding action instructions for the action group to obtain action group configuration data; performing legend matching on elements contained in the action group to obtain model element legend data; and carrying out legend feature recognition on the model element legend data, and carrying out legend connection and content labeling on the basis of a legend feature recognition result and the action group configuration data so as to generate a gas path diagram. The technical problems that an existing gas circuit diagram is low in design efficiency and prone to errors are solved.