1500results about "Metadata text retrieval" patented technology

Apparatuses, systems, and methods for shared codeword in two-pass access operations. The memory may use a read read modify write write (RRMWW) cycle to write data and metadata to the array. Metadata and a data codeword are read out as part of two read access passes and combined into a shared codeword. Error correction is performed on the shared codeword, and then the corrected shared codeword is modified with write data and metadata. Updated parity is generated based on the modified shared codeword and the modified data and updated parity and the metadata are written as two write access passes.

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.

The invention relates to the technical field of information retrieval, and discloses a multi-modal fusion and reinforcement learning collaborative retrieval enhancement generation method and system. The method comprises the following steps: receiving an original query input by a user, and generating a sub-query based on a large language model in combination with a multi-modal context of a current iteration step; forming a current state in combination with the sub-query and the multi-modal context, modeling a retrieval enhancement generation task as a Markov decision process, and adaptively selecting an optimal action from a predefined action set in the current state by utilizing a large language model according to a decision strategy; executing a corresponding multi-modal retrieval operation according to the optimal action, fusing the obtained multi-modal information, generating an intermediate answer or a final answer of the sub-query, and updating a multi-modal context by using the intermediate answer; off-line training optimization is carried out on the large language model through imitation learning and a calibration chain, and decision strategies and sub-queries are inferred online through the model after fine adjustment. According to the invention, more efficient and accurate complex query processing is realized.

A system and method for improving computer functionality by retrieving answers / responses to questions / input from a cache such as those used with chatbots and generative AI systems. Disclosed is a multi-layered caching strategy that focuses on the relevance of a cache hit by improving the quality of the answer. The approach demonstrates that response latency is significantly reduced when using caching and how a caching strategy could be applied in various layers of increasing relevance for a simple Question-and-Answer system with the possibility of extending to more complex generative AI interactions.

The invention provides a multi-modal-based data retrieval enhancement method, which solves the problems of semantic gaps and outdated information among different modal data through cross-modal semantic alignment and real-time data source integration. And secondly, by constructing a multi-modal feature space and introducing a dynamic retrieval layer, the retrieval speed and accuracy are improved. In addition, by combining sparse retrieval and dense retrieval methods, the accuracy and the recall rate are balanced, and the retrieval result is optimized. And finally, through the steps of post-processing and result optimization, the correlation of the retrieval result and the user experience are further improved. The method is not only suitable for data retrieval of common modes such as texts, images and videos, but also can be expanded to any multi-mode scene, and provides powerful technical support for cross-mode retrieval.

The invention relates to the technical field of intelligent retrieval and matching, in particular to a property intelligent retrieval and matching system based on a knowledge graph. The method comprises the following steps: a data acquisition and processing unit acquires property right data and preprocesses the property right data; the knowledge modeling and graph construction unit constructs the preprocessed property right data into a knowledge graph based on entity extraction, relation extraction and attribute labeling; the retrieval analysis and semantic understanding unit is used for receiving a retrieval request input by a user, converting the retrieval request into a structured retrieval statement matched with the knowledge graph based on a dynamic multi-hop reasoning intention path discovery scheme, and analyzing a retrieval intention of the retrieval request through a context modeling and semantic disambiguation method. According to the method, a semantic understanding mechanism combining a dynamic multi-hop reasoning intention path discovery scheme with context modeling and semantic disambiguation is introduced, so that a complex retrieval request input by a user in a natural language form can be accurately analyzed.

The invention provides a data reordering retrieval method and system based on RAG, and the method comprises the steps: carrying out the dynamic semantic partitioning processing of an original document, generating corresponding document blocks, storing the document blocks in a vector database, constructing a hierarchical index, analyzing a received query request, extracting keywords in the query request, and carrying out the retrieval of the query request. Boolean keyword matching is carried out through an inverted index in the hierarchical index, semantic retrieval of keywords is carried out in a vector database, an initial candidate set is generated after multi-source retrieval results are fused, a query request and the initial candidate set are input into a generative reordering model, and a query result is obtained; according to the method, the initial candidate set is selected, the corresponding score is generated through the cross-modal attention mechanism in the generative reordering model, the initial candidate set is reordered according to the score, the final retrieval result is obtained, and the reordered document blocks are displayed, so that the overall retrieval speed is increased, the limitation of a single retrieval mode is avoided, and the accuracy of the retrieval result is improved.

A method of performing data analysis according to a natural language query from a user includes: receiving, from a user terminal, a user input including the natural language query requesting the data analysis; determining, based on the user input, at least one database among a plurality of databases as a target database; generating a prompt based on the user input and the target database; inputting the prompt into a code generation model to obtain a structured query (SQL) statement; executing the SQL statement to generate a result of the data analysis on the target database; and transmitting the result of the data analysis to the user terminal, where the result of the data analysis is displayed on a screen of the user terminal.

Systems and methods include pre-processing documents in cloud storage using query embeddings, providing personalized prompts to users based on documents in cloud storage, real-time anticipation of user interest in information contained in documents in cloud storage, and providing generative answers including citation to source documents in cloud storage. The system and methods generate generative machine learning model (MLM) prompts based on document portions of documents in a cloud-based content management platform. The systems and methods use the generative MLM to generate responses to prompts, and the responses include citations to the document portions used to generate the responses in order for users to verify the responses.

The invention provides a multi-modal heterogeneous model retrieval enhancement method and system, and the method comprises the steps: building a knowledge and application example double-corpus based on user multi-modal query, and designing a joint retrieval mechanism to obtain a result set; mapping and scheduling to obtain feature representation through special processing channels for texts, images and audios and a Spiking neural network with a segmented trapezoidal topological structure; constructing a three-stage cascade architecture of a basic model, an advanced model and human experts, and obtaining a decision path and answer candidate set in combination with a recursive and discarding decision mechanism; a Hamiltonian graph network is used for representing a multi-modal relation, and a gradient-free descent method is used for rapidly training and optimizing model parameters; an enhanced retrieval result is obtained through cross-modal semantic alignment and dynamic retrieval window adjustment; and high-quality response is obtained through context-aware sorting and retrieval enhanced reasoning. According to the method, the multi-modal information retrieval processing efficiency and the heterogeneous model reasoning response quality are improved.

A confidence estimation tool uses a calibrated confidence mapping model to estimate confidence for a model-generated candidate root cause. The tool uses a generative artificial intelligence (“AI”) model to determine, based on a description of a current event, a candidate root cause of the current event. The tool determines a description-based confidence score using the description of the current event and descriptions of a set of relevant historical events in a target domain. The tool also determines a cause-based confidence score using the candidate root cause of the current event and root causes of the set of relevant historical events. Finally, the tool determines a final confidence score using the description-based and cause-based confidence scores. Even if the generative AI model is configured for general-domain applications, by referencing relevant historical events, the tool can accurately estimate confidence for a model-generated candidate root cause within the target domain.

The invention provides a minority language professional question and answer method based on mixed retrieval and retrieval enhancement generation, which comprises the following steps: S1, constructing a multi-language knowledge constructing a vector index database and a term knowledge graph by using a multi-language model according to a related minority language document; s2, multi-layer mixed retrieval: cross-language document recall is realized through a multi-layer mixed retrieval module, and the multi-layer mixed retrieval module is composed of keyword retrieval, semantic retrieval and vector retrieval; s3, answer generation: performing answer generation through an adaptive multi-language model by using a retrieval enhancement generation module, and introducing rule constraint decoding and a dynamic attention mechanism in the generation stage to improve the professionality and accuracy of the answer; and S4, self-adaptive optimization and knowledge updating: through a user feedback reinforcement learning module, optimizing the model based on user error correction data and supervising updating of the knowledge base. According to the method, professional questions and answers of the minority language can be realized based on a small amount of professional data of the minority language, the recall rate of the document of the minority language is improved, and the generation quality is improved.

The invention discloses an intelligent legal instrument retrieval system based on a vector technology, which relates to the technical field of information retrieval and comprises an instrument vector generation module, an index construction module, a query vector generation module, a similarity calculation module and a result optimization module, comprising but not limited to word segmentation and stop word removal operations, and converting the legal document into vector representation by using a pre-trained word vector model; through the intelligent legal instrument retrieval system based on the vector technology, semantic meanings of the legal instruments can be effectively understood, and even in the face of fuzzy or inaccurate query input of a user, related legal instruments can be quickly and accurately positioned by means of similarity calculation in a vector space; the efficiency, correlation and accuracy of legal document retrieval are greatly improved, and the urgent demands of the legal industry and related demanders for efficient legal information retrieval are met.

The Trust-Informed Engagement and Restraint (TIER) system is a modular behavioral governance architecture for conversational AI that enables dynamic, real-time enforcement of trust-aligned interaction policies. The system comprises a Behavioral Governance Framework defining policy rules and an Enforcement Wrapper hosting Core Modules that operate externally to the AI model. These modules regulate behavioral traits including trust calibration, emotional tone, conversational containment, authority modulation, and cross-modal consistency. TIER continuously monitors interaction metrics and applies policy-driven constraints during live sessions without requiring model retraining or internal access. Unlike static filters or post-hoc moderation systems, TIER provides proactive, session-aware behavioral governance with auditable enforcement across domains. The architecture supports model-agnostic deployment in regulated and sensitive environments such as healthcare, finance, legal services, and intelligent assistance platforms.

Provided are a data analysis method and system, and a related device. In the method, a physical mapping relationship between metadata and table data of an underlying physical table is established; a semantic mapping relationship between nodes and the metadata is established, wherein the semantics of labels of the nodes are related to the semantics of the metadata; and a model mapping relationship between an analysis model and the nodes is established. In this way, a plurality of mapping relationships are established, so that the analysis model can be decoupled from underlying table data, an underlying data change does not affect the analysis model, and the analysis model is thus highly flexible and adaptable; in addition, since nodes in a knowledge graph include semantic labels, a user can complete data querying by means of natural language.

The invention discloses an industrial pump fault intelligent diagnosis method based on a GraphRAG large model, and the method comprises the following steps: collecting fault records and vibration signals of an industrial pump, and constructing a fault and characteristic spectrum; obtaining a fault type of the feature parameters by using a convolutional neural network, and generating a fusion knowledge graph in combination with a Word2Vec model and cosine similarity; the method comprises the following steps of: inputting map data into an LLM large model, and optimizing a prompt template by using a prompttune function; extracting node information of entities, communities and the like by using a GraphRAG index construction module; extracting related entities in a user request through a convolutional neural network or LLM, and matching the related entities with the map nodes to obtain information; and mapping the information to index data, and outputting a diagnosis report by utilizing a retrieval generation module. Through the GraphRAG technology, the fault diagnosis precision and the maintenance decision-making efficiency are improved, and the industrial production stability and safety are enhanced.

The invention relates to a hospital database management method and system based on adaptive data desensitization. The method comprises the following steps: S1, uniformly integrating multi-source data of a hospital into a database; s2, performing sensitivity grading on the data of the database through a data classification algorithm, and automatically generating corresponding labels; s3, establishing a desensitization model based on role permission, access content and data features; s4, the user inquires data and inputs related parameters through the front-end system, whether fields or data in the request contain high-sensitivity fields or not is judged, and if the fields or the data contain the high-sensitivity fields, a proper desensitization strategy is dynamically generated based on a desensitization model according to the user role, the data sensitivity and the access context; and S5, monitoring access behaviors in combination with data using log records, carrying out anomaly detection based on a GANopen model, and if an abnormal behavior is detected, triggering an alarm or stopping access. According to the invention, efficient utilization and sharing of data are realized, and the intelligent level of hospital data management is improved.

The invention relates to a federal knowledge retrieval and large language model enhancement system and method. The system comprises a server and a hardware accelerator. The server preprocesses an input query and extracts a head word of the query; the server retrieves the head word in the caching process through a cache module of the server, and if the cache of the cache module is not hit, the server sends a retrieval instruction to the hardware accelerator so as to retrieve in a local cache of the hardware accelerator; if the local cache is still not hit, the hardware accelerator generates parallel subtasks associated with the head word in a mode of segmenting a local knowledge graph, so that deep search is carried out, and noise is added into knowledge items retrieved by the parallel subtasks to protect sensitive information; and the hardware accelerator integrates the knowledge items added with the noise, performs reasoning through a large language model and generates an enhanced answer. According to the method, a two-stage dynamic cache system is constructed, the cross-device communication frequency can be effectively reduced, the query request is responded preferentially through the local high-frequency cache, and the overall network load of the system is reduced.

The invention relates to the technical field of semantic analysis, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an intention analysis and strategy generation method, device, equipment and medium, and the method comprises the steps: obtaining interaction data, recognizing an interaction type according to metadata features, selecting a corresponding analysis model to analyze and process data, and generating a text corpus; current user information is extracted from the text corpus, user intention score analysis is executed based on an intention analysis strategy corresponding to the interaction type, and a current user intention score is generated; and generating a demand list according to the user information, the intention score and the interaction type, and generating and outputting an interaction strategy based on the user information and the demand list. Through interaction data processing and automatic intention scoring analysis, the demand list and the interaction strategy of the customer can be quickly and accurately generated, the customer information processing efficiency and accuracy are improved, the workload of manual input and analysis is reduced, and the customer service quality and the response speed are improved.

The invention discloses an electronic archive intelligent retrieval method and system based on a block chain, and relates to the technical field of archive retrieval, and the method comprises the steps: extracting text features of an electronic archive, and generating metadata; generating a content hash value from the electronic file original text; writing the metadata, the content hash value and the ABE access strategy into the block chain smart contract; constructing a reverse index based on the metadata semantic tag, wherein an index entry is associated with a block chain storage address; calculating a root hash and anchoring the root hash to the block chain; analyzing a keyword and a digital identity certificate in the user retrieval request; calling an intelligent contract to verify whether the user attribute accords with the ABE access strategy of the target file; retrieving an encrypted hash list matched with the file in the distributed index; acquiring the encrypted file fragments from the distributed storage system; verifying data integrity; and combining the fragments to generate a final retrieval result. The method has the advantages that through deep coupling of the block chain and attribute encryption, an electronic archive management system considering security and intelligent retrieval is constructed.

The invention provides a transformer substation AI aided design method based on LLM and RAG, and the method comprises the steps: carrying out the demand analysis through a large language model LLM, and extracting key information; performing knowledge retrieval by using an RAG technology, wherein the knowledge retrieval comprises transformer substation aided design knowledge base construction, transformer substation aided design knowledge retrieval and knowledge reordering; according to the demand analysis and the knowledge retrieval result, the LLM is utilized to generate a preliminary design scheme of the transformer substation, and the design scheme comprises an electrical main wiring diagram, equipment type selection and layout. By adopting the combination of the LLM, RAG and AI aided design, the comprehensive improvement of the design efficiency, standardization and innovation of the transformer substation can be realized, and the problems of low efficiency, insufficient standardization and lack of innovation in the prior art are solved.

The invention discloses a large model-based government affair official document quotation retrieval method, device and equipment and a medium, and relates to the technical field of artificial intelligence, the method comprises the following steps: obtaining a government affair official document, determining a document slice corresponding to the government affair official document, and creating a document knowledge base and a vector knowledge base based on the document slice; identifying the terminologies in the government affair document by using a target pre-training natural language processing model to obtain a corresponding identification result; based on the document knowledge base and the vector knowledge base, performing keyword matching on the recognition result by using a target information retrieval algorithm to obtain a plurality of corresponding candidate quotation documents; and performing semantic correlation scoring on each candidate citation document and the recognition result through a Rianker model, reordering each candidate citation document according to a corresponding scoring result, and outputting a target citation document according to a corresponding reordering result. Therefore, the quotation retrieval efficiency can be improved.

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.

The invention relates to the technical field of equipment operation and maintenance intelligent retrieval, and provides an equipment operation and maintenance data enhanced retrieval method based on a knowledge graph and a large language model, which comprises the following steps: (1) constructing and dynamically maintaining the knowledge graph containing equipment operation and maintenance field entities and relationships; (2) analyzing natural language query of a user, performing multi-hop association retrieval in the knowledge graph, and screening out a related evidence set; (3) constructing a structured cue word based on the evidence set, and driving a large language model to generate a preliminary diagnosis report; and verifying, correcting and formatting the preliminary diagnosis report into a final visual report. According to the method, deep intention understanding and multi-hop association mining of natural language query of a user are realized by constructing a dynamically evolved equipment operation and maintenance knowledge graph, and a reliable evidence chain and a structured cue word constraint mechanism based on the knowledge graph are introduced to ensure that the generated diagnosis report is strictly based on field professional knowledge; the problem that the real semantic intention of user query cannot be deeply understood in traditional retrieval is effectively solved.

The invention provides a cross-modal retrieval model training method and a remote sensing image text retrieval method, and is applied to the technical field of remote sensing cross-modal retrieval. The remote sensing image text retrieval method comprises the following steps: performing feature extraction on a preprocessed remote sensing image by utilizing a trained cross-modal retrieval model to obtain image global features and image local features, and performing feature extraction on a preprocessed target text to obtain text global features and phrase global features; calculating the global similarity between the image global features and the text global features by using the trained cross-modal retrieval model, and calculating the local similarity between the image local features and the word group global features; and carrying out weighted calculation on the global similarity and the local similarity by utilizing the trained cross-modal retrieval model, and screening a matching result between the remote sensing image and the target text based on the weighted similarity to obtain a retrieval result between the remote sensing image and the target text.

The invention discloses a traditional Chinese medicine clinical research scheme generation method and system, and relates to the field of medicine. The method comprises the following steps: aiming at traditional Chinese medicine clinical research, constructing a knowledge graph covering evidence-based evidence elements PICO; establishing a traditional Chinese medicine cue word project and an element template based on evidence-based evidence elements PICO covered by the knowledge graph; identifying element information selected in the element template by adopting a traditional Chinese medicine cue word project, and generating a cue word feature vector; retrieving data matched with the cue word feature vector from the knowledge graph by adopting a trained language model; when the feature in the cue word feature vector cannot retrieve the matched data from the knowledge graph, automatically generating the matched data by using the trained language model; and integrating the matching data automatically generated by the trained language model and the matching data retrieved from the knowledge graph to obtain a traditional Chinese medicine clinical research scheme. The preparation efficiency and quality of the traditional Chinese medicine clinical research scheme can be effectively improved.

The invention discloses a scientific paper academic value measurement method based on semantic fusion and reference behavior analysis. The method comprises the following steps: determining a literature database according to a target research field, constructing a field keyword list, and retrieving and collecting an initial literature data set; the internal innovation value is quantified; quantizing an external approved value; the step of constructing the academic value comprehensive measurement model comprises the sub-steps of applying an entropy weight method to distribute index weights; calculating an academic value comprehensive score through linear weighting; checking and evaluating through a Spearman correlation coefficient; according to the method, a comprehensive evaluation system based on multi-dimensional semantics and behavior characteristics is constructed, internal knowledge contribution and external recognition value of the papers are systematically fused, objectivity and precision of scientific research paper academic value evaluation can be effectively improved, and therefore the problems of evaluation time lag, reference behavior deviation and subjective weighting existing in a traditional method are solved, and the scientific research paper academic value evaluation efficiency is improved. And the method has good universality and popularization value.

The invention discloses a construction site safety monitoring method and system based on a large model and RAG, and the method comprises the steps: carrying out the intelligent analysis of real-time video data of a construction region based on a visual large model, and recognizing the behavior safety, violation operation, dangerous facilities and environment risks of construction personnel; adding a time label to the analyzed security event text to form a construction site security event log; reasoning and analyzing the security event log, determining whether security early warning is generated or not, and pushing early warning information; carrying out on-site intelligent voice broadcasting according to the early warning information; through a multi-RAG architecture, a construction site safety log database, a public construction safety management database and an enterprise internal safety management database are constructed, and construction safety information is stored by using a vector database and a graph database. By adopting the technical scheme of the invention, the problems of insufficient real-time monitoring capability, limited intelligent analysis capability and lack of effective knowledge base support in the existing construction site safety supervision are solved.

The invention discloses a text retrieval method and device, equipment, a storage medium and a product. The method comprises the steps of obtaining a question input by a user and at least one candidate text; for each candidate text, determining semantic similarity and keyword similarity between the question and the candidate text; sentence features of the questions and document features of the candidate texts are extracted respectively, and the weight of semantic similarity and the weight of keyword similarity are determined according to the sentence features and the document features; based on the weight of the semantic similarity and the weight of the keyword similarity, according to the semantic similarity and the keyword similarity of the question and each candidate text, calculating the comprehensive similarity of the question and each candidate text; at least one candidate text with the maximum comprehensive similarity is screened out, and an answer candidate set is obtained; and inputting the answer candidate set and the question into a preset large language model, and outputting a retrieval result. According to the method, the accuracy and efficiency of long text retrieval can be improved.

The invention discloses a power distribution network first-aid repair operation safety check scheme generation method and system and a related device. The method comprises the steps that a power distribution network first-aid repair operation safety check document is read and subjected to segmentation processing to form a plurality of text blocks; converting the text blocks into vectors through an embedded model, and storing the vectors into a power distribution network emergency repair operation safety inspection knowledge base; obtaining an input query problem, converting a query problem text into a problem vector through an embedded model, filtering and screening the problem vector by using the power distribution network metadata, and calculating the similarity between the filtered and screened problem vector and a vector in a power distribution network emergency repair operation safety inspection knowledge base by using a similarity measurement method; selecting a target vector construction prompt word from the power distribution network first-aid repair operation safety inspection knowledge base; and splicing the input query question and the prompt word as the previous text by using the large language model, and generating an answer. According to the method, retrieval and generation are combined, the retrieval data correlation is improved through power distribution network metadata filtering, and the effect of a large language model in scheme generation is enhanced.