198 results about "Content retrieval" patented technology

The invention belongs to the technical field of air traffic control and conduction, and particularly relates to an intelligent question answering system method for air traffic control and conduction business knowledge. According to the method and the system, the refined and context-aware information retrieval capability of the air traffic control communication service knowledge and the expert-level air traffic control communication service knowledge question and answer capability are provided, the knowledge management and application efficiency is effectively improved, and powerful support is provided for digital transformation of related services. Comprising the following steps: S1, a document content extraction and storage process; s2, a content retrieval and generation process; s3, content slice optimization is carried out through extraction and similarity calculation of adjacent paragraphs or contents, complete contents are in the same slice, and the content retrieval efficiency is improved; according to the method, the label library and the business rule library are continuously iteratively optimized, so that the provided service is always in the optimal state.

A method for using a local large language model (LLM) within a user's secure computing environment is disclosed. The LLM operates behind a firewall to prevent transmission of sensitive data, and utilizes an encrypted vector database and artificial intelligence techniques for content retrieval, response generation, and task anticipation. This system can be used on mobile, wearable, vehicle, or IoT devices and offers various services such as health monitoring, financial advice, automated communications handling, and personalized daily activity optimization. It also has the ability to detect fraud, fine-tune responses using augmented user data, assist in negotiations, identify personal interests, and provide health recommendations based on dietary and physical activity data.

The invention provides a game strategy retrieval method and device based on event-driven knowledge graph embedding. The method comprises the following steps: performing structured analysis on strategy data, and updating entities and relationships; performing graph embedding calculation on entities and relationships in the knowledge graph to obtain vector representation among the entities, and constructing a searchable vector index; vectorizing a strategy query request of a user, and performing similarity retrieval on the strategy query request and the vector index to obtain a first candidate entity set similar to the query vector; performing relation reasoning by taking the first candidate entity set as a starting point in the knowledge graph to obtain a second candidate entity set in semantic association with the first candidate entity set, and combining the second candidate entity set with the first candidate entity set to form a target entity set; and performing correlation sorting on the target entity set to obtain a strategy result. According to the method, the strategy data can be automatically and structurally managed, and deep semantic retrieval is supported, so that the accuracy of game strategy content retrieval is improved, and the dynamic expansion capability is achieved.

Disclosed are methods, systems, devices, apparatus, media, and other implementations for improved search-time content retrieval performed by an information retrieval platform. The implementations include a method including determining by a searching system, at a first time instance, one or more first search results for a first query comprising one or more query terms associated with a first concept, modifying the query, based on the determined one or more first search results, to include one or more modified query terms associated with one or more concepts hierarchically related to the first concept, and determining at a subsequent time instance one or more subsequent search results for the modified query comprising the one or more modified query terms.

A method for using a local large language model (LLM) within a user's secure computing environment is disclosed. The LLM operates behind a firewall to prevent transmission of sensitive data, and utilizes an encrypted vector database and artificial intelligence techniques for content retrieval, response generation, and task anticipation. This system can be used on mobile, wearable, vehicle, or IoT devices and offers various services such as health monitoring, financial advice, automated communications handling, and personalized daily activity optimization. It also has the ability to detect fraud, fine-tune responses using augmented user data, assist in negotiations, identify personal interests, and provide health recommendations based on dietary and physical activity data.

Devices and techniques are generally described for LM-based content retrieval. First query data including a first request related to first content may be received. First action data associated with the first query data may be determined. First prompt data including a representation of the first query data and data representing the first action data may be generated. The first prompt data may instructs a first LM to recognize entities in the first query data relevant to the first action data. The first LM may determine a first recognized entity from the first request. The first recognized entity may be associated with the first content. A request to resolve the first recognized entity may be generated. A first resolved entity for the first recognized entity may be determined. The first LM may generate first instructions to perform the first action data using the first resolved entity.

Systems and methods for verifying content delivery in a content delivery network (CDN) are described. Content retrieval and routing in a CDN are tested using multiple test sequences that are each assigned to a different value of a parameter P and are generated using the assigned value of P. A client device can verify whether a test sequence received at the client device matches a test sequence requested by the client device (e.g., the correct content was) and verify that the received test sequence is assigned to the same value of P as the requested test sequence. The value of P assigned to the received test sequence can be extracted, by the client device, from the received test sequence to determine which test sequence was actually received, thereby providing information that can be used to help debug content delivery errors.

One example method includes enabling a user to define a query that comprises a hybrid search syntax (HSS), and the HSS is in the form of a single string that comprises a lexical component and a semantic component, receiving the query from the user, parsing the lexical component and the semantic component into respective groups of one or more sub-components, performing a lexical search based on the sub-components of the lexical component, and performing a semantic search based on the sub-components of the semantic component, ranking, separately from each other, results of the lexical search and results of the semantic search, combining the results of the lexical search and the results of the semantic search to create aggregate results, and re-ranking the aggregate results.

The invention relates to an information retrieval method and device for hierarchical planning reinforcement learning based on retrieval enhancement. The method comprises the following steps: acquiring a natural language query request of a user; calling a large language model of hierarchical planning reinforcement learning training based on retrieval enhancement to generate semantic keywords; the high-level strategy splits the natural language query request into a series of sub-query requests; the low-level strategy generates a retrieval query according to the context of the current sub-query request, and obtains a result verification condition from an external knowledge base through an RAG model; calling a search engine to execute multiple rounds of fine-grained information retrieval to obtain a plurality of candidate retrieval results; and performing deep semantic matching analysis on the candidate retrieval results and the result verification conditions by utilizing the post-trained large language model to calculate a matching degree score, and screening the candidate results according to the matching degree to obtain a final information retrieval result. Compared with the prior art, the method has the advantages that high-quality content retrieval and result display of complex query problems of users in various scenes can be realized.

Disclosed in the present application are a data processing method and apparatus, and a device and a readable storage medium. The method comprises: obtaining M candidate results which are sorted and displayed on a page, wherein the M candidate results are retrieved on the basis of an initial query statement; if an interaction operation meets a re-retrieval condition, acquiring historical interaction information, which comprises N historical query statements and a set of historical interaction results, wherein the N historical query statements comprise the initial query statement, and the set of historical interaction results comprises a candidate result which is included in an interaction record corresponding to an object; and generating context content vectors corresponding to the historical interaction results in the historical interaction information, and generating an updated query statement by means of the context content vectors and the N historical query statements, wherein the updated query statement is used for being displayed in a retrieval prompt box, and the retrieval prompt box is used for guiding the object to perform re-retrieval by means of the updated query statement. By means of the present application, the accuracy and efficiency of content retrieval can be improved.

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.

An example may provide at least one first generative machine learning model (GMLM) instruction and an intent to a GMLM. The at least one first GMLM instruction is to cause the GMLM to use the intent to generate first GMLM output. The first GMLM output includes GMLM-generated output sections. A device may provide the first GMLM output including the GMLM-generated output sections and at least one second GMLM instruction to the GMLM. The at least one second GMLM instruction is to cause the GMLM to use the intent, the GMLM-generated output sections, and a first data set to generate second GMLM output including at least one first digital element. A device may validate the second GMLM output by comparing the at least one first digital element to at least one second digital element. The at least one second digital element is accessible via a second data set.

The invention discloses an unsafe behavior monitoring system, and the system comprises a real-time monitoring module which is used for obtaining a video image of a construction site scene in real time; the early warning module is used for detecting unsafe behaviors of the video picture and performing early warning on the unsafe behaviors; the personnel safety file management module is used for identifying identity information of construction personnel and recording working states and unsafe behaviors of the construction personnel; the site safety management module is used for calculating a coverage area and a blind area of the camera according to the planar graph information of the construction site, the position of the camera and the visual field; the conversational query module is used for analyzing the query request content of the user, retrieving the related video content according to the query request content and outputting a retrieval result meeting the query requirement; and the safety education training module is used for generating education training contents according to the detected unsafe behaviors. The monitoring efficiency is improved; and meanwhile, analysis of video contents based on different fine granularities can be completed, so that the safety monitoring precision and the monitoring effect are improved.

The invention relates to the technical field of artificial intelligence, in particular to an intelligent file editing and auditing system and method and a storage medium, and the method comprises the following steps: receiving a purchase file; vectorization processing is carried out on the purchase file; newly establishing a purchase document; relevant data are recalled from the vectorized data according to target information required by the chapters, structured conversion is carried out, and the data are added into the corresponding chapters, so that compilation of the purchase document is completed; the compiled purchase document is preprocessed; the method comprises the following steps: reading review requirements from a preset document review point library, extracting related review contents from vectorized data of a purchase document, performing review judgment on the related review contents according to the review requirements by utilizing a large language model, and outputting a review judgment result, thereby completing intelligent review of the purchase document. According to the method, manual content retrieval, extraction, integration, filling and auditing work can be replaced, the workload and time are greatly saved, and meanwhile the accuracy and speed of information retrieval are obviously improved compared with manual information retrieval.

Devices and techniques are generally described for LM-based content retrieval. First query data including a first request related to first content may be received. First action data associated with the first query data may be determined. First prompt data including a representation of the first query data and data representing the first action data may be generated. The first prompt data may instructs a first LM to recognize entities in the first query data relevant to the first action data. The first LM may determine a first recognized entity from the first request. The first recognized entity may be associated with the first content. A request to resolve the first recognized entity may be generated. A first resolved entity for the first recognized entity may be determined. The first LM may generate first instructions to perform the first action data using the first resolved entity.

The invention discloses a bridge engineering intelligent question answering method, device and equipment and a storage medium, and relates to the technical field of artificial intelligence and natural language process.The bridge engineering intelligent question answering method comprises the steps that a large model conducts natural language processing on an input question, and preprocessing information of the question is obtained, the preprocessing information comprises the bridge engineering field related to the problem; the question is divided into a plurality of sub-tasks according to the preprocessing information, the corresponding sub-tasks are executed through a plurality of agents sharing and transmitting intermediate results so as to complete question answering, and the sub-tasks comprise content retrieval, data structured processing and knowledge fusion and complementation. According to the method, professional and detailed bridge engineering knowledge question-answering services can be provided for users in multiple rounds of interaction, and the intelligent level and response efficiency of a question-answering system in bridge engineering application can be improved.

The invention relates to an academic paper abstract generation method based on an image-text fusion index, and belongs to the technical field of artificial intelligence and multi-modal information processing. The method comprises the following steps: acquiring academic paper data from a public data source; according to academic papers in the academic paper data, segmenting paper contents according to logic, establishing association between fragments and images, and constructing an image-text fusion index; performing semantic retrieval on four dimensions in the academic paper to obtain a final retrieval content set of all the dimensions; and generating an image-text fusion abstract based on the retrieval content set of the four dimensions. According to the method, by constructing the image-text fusion index, the image-text association strength and the chapter context coherence are enhanced, deep coupling of the image and the text in the semantic level is achieved, and the image-text collaborative expression ability of the abstract is improved; by designing a structured dimension template and a content retrieval mechanism driven by a standard problem, the structured coverage of key information is ensured, and the logic continuity and readability of the abstract are remarkably improved.

Apparatus and methods for automating pre-procedural coordination workflows in a digital environment include at least a processor and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to generate, using a trained content retrieval data structure, a plurality of content retrieval parameters, receive, from a first entity, an input including an input data structure as a function of the plurality of content retrieval parameters, populate, using the input data structure, a content queue comprising a plurality of content elements, query a second entity using at least a content element of the plurality of content elements, update the content queue as a function of at least a query response received from the second entity, generate a recommended course of action as a function of the updated content queue, and perform the recommended course of action.

The invention relates to an infant teaching resource intelligent searching and recommending system based on an AI large model. The system comprises a resource acquisition module, a label indexing module, an A I semantic understanding module, a resource screening and adapting module, a recommendation sorting module and a compliance control module. Teachers input teaching requirements through natural languages, voices, teaching plans or pictures, and the system performs semantic analysis and keyword extension based on a large language model and completes multi-modal content retrieval in combination with a resource map. The system introduces a complexity analysis and right age adaptation mechanism, intelligently simplifies and reconstructs contents, and generates a personalized recommendation list based on teacher portraits and a collaborative filtering algorithm. And the resource use process is constrained by the compliance control module to ensure that the content copyright is legal. The system can significantly improve the lesson preparation efficiency of kindergarten teachers and the teaching resource adaptation degree, and is suitable for a digital teaching platform and an intelligent education system.

The invention relates to a document recognition method and device, electronic equipment and a storage medium. The method comprises the steps that a target document and title information of the target document are acquired; performing hierarchical analysis on the title information, and establishing a directed acyclic graph corresponding to the target document according to a hierarchical analysis result; based on the directed acyclic graph, a tree diagram corresponding to the target document is constructed, and each node of the tree diagram correspondingly records a document title of the target document; and according to the corresponding relationship between each node in the tree diagram and the document title, obtaining the document content of the target document by using the tree node of the tree diagram as an index. According to the method, the tree diagram corresponding to the document is constructed according to the support relationship, and the clear hierarchical structure is analyzed through the tree diagram, so that a document management system with the hierarchical structure is formed, the analysis requirements of different types of documents are met, and the legal content retrieval and analysis efficiency is remarkably improved.

The invention discloses an investigation report generation method based on large language model deep thinking and content retrieval, and belongs to the technical field of artificial intelligence. Generating requirement information input by a user is obtained, cue words are constructed according to the generating requirement information, the cue words are input into a preset large language model, and an article generating plan is generated; analyzing and partitioning the generated plan to obtain a plurality of structured chapter abstract contents and corresponding networking search keyword data; circulation is carried out until all the chapters are generated, retrieval is carried out according to networking search keywords needed by the current chapter, and reference content needed for writing the current chapter is obtained; and the reference content obtained through retrieval, the written previous chapter content and the abstract content needing to be written by the current chapter are used for constructing cue words required by writing of the current chapter. And inputting the cue word into a preset large language model, carrying out deep reasoning and thinking, generating the content of the current chapter, and giving a corresponding reference in a place where the related retrieval content is cited.

The embodiment of the invention provides a ship industry standard document intelligent processing method, which comprises the following steps: S1, document analysis: carrying out structured analysis and restoration on a ship industry standard document to generate a structured text supporting vectorization storage and semantic retrieval; s2, database construction: constructing a document vector index database based on the structured text to form a semantic retrieval task-oriented efficient data structure; and S3, content retrieval: performing user-oriented query, constructing a semantic retrieval mechanism, and completing high-precision matching from a natural language problem to a structured document content and result return. According to the embodiment of the invention, the document structure can be accurately restored, the content segmentation is more reasonable, the retrieval matching is more accurate, the retrieval result has more content depth, the technical path is clear, and the project landing is easy.

The invention provides a multi-path voice stream real-time separation and content retrieval method, which comprises the following steps of: constructing a local voiceprint library by using a registered voice sample, and extracting and normalizing voiceprint characteristics of a target speaker through a deep voiceprint encoder; performing frame-level feature analysis on the mixed voice signal, calculating a semantic distance between a current voice frame and a target voiceprint, and generating a dynamic confidence score; feature conflict detection is carried out by combining a confidence coefficient threshold value and a de-jitter mechanism, and a conflict perception attention gating module is triggered to realize frequency band enhancement and suppression; a continuous feature confusion region is dynamically identified based on a sliding window, and the separation precision of the confusion region is improved through local reconstruction and time-frequency mask optimization iteration. According to the method, high-precision recognition and separation of the voice of the target speaker in the mixed voice are realized, non-target components are effectively inhibited, and the separation effect and robustness are improved.

The embodiment of the invention provides a content retrieval method, device and equipment based on a machine learning model. The method comprises: in response to a received query request, determining a first content object associated with the query request, the first content object comprising a plurality of content segments and a plurality of groups of identifiers respectively corresponding to the plurality of content segments, and each group of identifiers at least comprising a first index corresponding to the content segment; providing the query request and the first content object to a machine learning model to obtain at least one second index output by the machine learning model, the at least one second index respectively indicating at least one target content segment in the plurality of content segments; and based on the at least one second index and the multiple groups of identifiers, extracting at least one target content fragment from the first content object to respond to the query request.

Aspects of the present disclosure relate to hierarchical content organization techniques, which may thus enable improved content indexing and / or retrieval, among other benefits. In examples, a content platform maintains a content hierarchy, where, for example, each category within the content hierarchy has an associated set of categories / subcategories. It will be appreciated that any number of hierarchical levels may be used. Accordingly, a post or other content item may be associated with one or more categories / subcategories (e.g., one or more nodes) of the content hierarchy, thereby facilitating subsequent retrieval of the content (e.g., responsive to a user query and / or to provide to a search platform). Thus, in contrast to discussion boards and other content management systems, the disclosed aspects ensure content is organized when created, thereby improving later retrieval and / or any of a variety of subsequent processing, among other benefits.

The invention discloses an unstructured document multi-modal analysis and semantic metadata deep extraction method and system based on multi-agent collaboration. The method comprises the steps that multi-modal elements such as texts, images and tables are extracted in parallel through a multi-format unified analysis engine; a four-agent (table structure analysis, multi-modal image analysis, semantic text understanding and metadata extraction) collaborative architecture is adopted, and task decomposition, asynchronous scheduling and result fusion are realized based on a multi-agent collaborative scheduling algorithm; the performance is improved by combining dynamic concurrency control, memory optimization and an intelligent cache mechanism; and finally generating structured output. The system integrates the modules and supports micro-service architecture and stability guarantee. According to the method, multi-modal deep semantic understanding is achieved, the processing efficiency is improved by 3-5 times compared with that of a traditional method, the text / table analysis precision reaches 98% and 95% respectively, localized efficient processing and incremental updating are supported, and a high-quality data source is provided for enterprise knowledge management and content retrieval.

The invention relates to a data retrieval method and device, a storage medium and electronic equipment, and relates to the technical field of artificial intelligence. The method comprises the steps that to-be-retrieved content is acquired, and multi-modal to-be-retrieved features of the to-be-retrieved content on multiple different dimensions are determined; determining target attention weights of the multi-modal to-be-retrieved features and an original document vector index in a preset three-dimensional feature vector container on a plurality of different dimensions; based on the target attention weights on the multiple different dimensions, determining a comprehensive matching score between the multi-modal to-be-retrieved feature and an original document vector index; and determining a target document vector index from the original document vector indexes based on the comprehensive matching score, and retrieving a result according to the target document vector index and the content corresponding to the to-be-retrieved content. According to the method and the device, the accuracy of the obtained content retrieval result is improved.

A method of retrieving visual content includes receiving user input defining an initial search query from a client application. The initial search query and a meta prompt are then delivered to a refined query generating model which is trained to analyze the initial search query to determine user intent and to generate a refined search query based on the initial search query and the meta prompt. The refined search query is delivered to a visual content retrieval model which retrieves aesthetic visual content with reference to a visual content index. Retrieved aesthetic visual content is returned to the client application.

The invention discloses a multi-mode private illegal content retrieval method and system based on semantic granularity perception, and belongs to the technical field of image-text multi-mode content retrieval. In order to solve the technical problem that private and illegal contents are difficult to effectively retrieve through explicit text description, the method comprises the following steps of: constructing an image identifier consisting of clustering, matrix decomposition, quantization and unique coding, and realizing generative learning of an image and the image identifier by utilizing a multi-modal large model; and constructing a soft label based on the similarity between the image and the text representation, and outputting a corresponding image identifier through text-driven generative retrieval learning. According to the method, the illegal image content with semantic matching can be retrieved from the private text description, and the method is suitable for scenes such as illegal content examination, risk identification and content filtering.

The invention discloses a document question and answer method and device, a storage medium and a program product, and the method comprises the steps: carrying out the content retrieval of a first document database based on an inquiry statement, so as to determine a corresponding intermediate retrieval result; performing content retrieval on a second document database based on the inquiry statement and the intermediate retrieval result to determine a retrieval matching result, the first document database and the second document database being databases constructed based on at least one document; detecting whether the retrieval matching result meets a reply condition of the inquiry statement or not; and when the retrieval matching result meets a reply condition, constructing a reply answer according to the retrieval matching result. Therefore, the question answering system can adapt to questions with different semantic complexities and logic levels through a document retrieval mechanism mixed with the neural symbols, and high-quality answers can still be efficiently produced when complex questions are processed.