819 results about "Data retrieval" patented technology

The present invention discloses an artificial intelligence-based adaptive big data storage and retrieval optimization system and method designed to intelligently manage and optimize large-scale distributed data environments. The system integrates data acquisition, distributed storage, metadata processing, adaptive learning, and retrieval optimization units configured to work collaboratively for continuous self-optimization. The invention employs deep reinforcement learning and predictive neural network techniques to dynamically analyze system telemetry, workload behavior, and data access patterns in real time, enabling proactive adjustment of data placement, caching, replication, and compression parameters across distributed nodes. The metadata processing framework utilizes graph-based dependency modeling to maintain semantic and contextual relationships among datasets, facilitating intelligent and context-aware data retrieval. The retrieval optimization unit interprets user queries semantically and computes the optimal retrieval route using latency prediction models and dynamic routing techniques.

The invention relates to the technical field of data recording, in particular to a data recording system for an automatic driving vehicle, and the system comprises a multi-source signal synchronization module, a behavior stage recognition module, a track abnormity elimination module, a dynamic compression adjustment module and a segmented index marking module. According to the method, a unified time alignment mechanism is constructed in a signal synchronization processing link, a strict corresponding relation between acquisition sequences is ensured, key time sequence characteristics of driving behavior switching are extracted by adopting a multi-behavior parameter comprehensive judgment method, segmented expression of a complex driving process is realized, and the accuracy of driving behavior switching is improved. According to track anomaly identification, through collaborative screening of inertial data and a positioning track, abnormal fragments which do not conform to physical rules are effectively eliminated, data compression dynamically sets interval length according to a parameter combined fluctuation trend, the fidelity of a key period is remarkably improved, non-key data redundancy is inhibited, and after a segmented index structure is established, the data compression efficiency is improved. And the data retrieval process is clearer.

A multimodal metadata retrieval-augmented generation method and system, relating to the technical field of artificial intelligence. In the present invention, the method comprises: determining data modalities of query content, performing feature extraction on data of each modality on the basis of the determined data modalities, converting extracted features into feature vectors, and fusing the feature vectors of the modalities to generate a multimodal feature vector; extracting, from data of each modality in the query content, key metadata of the data of each modality, converting the key metadata into a key metadata vector and connecting same to the multimodal feature vector, and constructing an augmented vector; and performing retrieval by means of the augmented vector. The method helps improve knowledge coverage and information utilization efficiency, and enhances the accuracy, relevance, and credibility of generated content.

The invention discloses a data analysis method and system based on a large model. The method comprises the steps that natural language query of a user is received; performing semantic coding and intention analysis on the natural language query based on a pre-trained large language model; based on metadata retrieval of vector similarity, obtaining metadata related to natural language query semantics from a vector database; generating corresponding structured query parameters through the large language model in combination with the obtained metadata and the natural language query; according to the structured query parameter, generating an executable structured query language statement; and executing the structured query language statement to obtain a data query result. According to the method, the problems of insufficient semantic comprehension ability, low metadata retrieval precision and poor query generation controllability in the prior art are solved.

Disclosed are a data query method and system, and a related device, relating to the field of artificial intelligence. The method comprises the following steps: a data query system acquires input data; obtains, on the basis of the input data, metrics to be queried as requested by a user; uses, on the basis of said metrics, a multi-agent collaborative processing strategy to confirm a data query result, the multi-agent collaborative processing strategy being used for indicating a manner of implementing data retrieval on the basis of at least one of a graph model and a vector database; and presents the data query result, wherein the graph model expresses, in a graph-based form, correspondences between the metrics and between the metrics and database table data, and the vector database expresses, in a vector-based form, descriptive texts and computational formulas of the metrics. In this way, even if the expression of input data differs from the expression of fields in the database, or if metrics to be read are not present in the database, corresponding SQL statements can still be obtained by means of the graph model or the vector database, thereby reducing the error rate of natural language query, and meeting the query requirements of users.

The invention relates to the field of data retrieval, and particularly provides a dynamic retrieval enhancement generation method and system based on a knowledge graph, a terminal and a medium, and the method comprises the following steps: extracting a structured triple from multi-source heterogeneous data through a large language model, and constructing a global knowledge graph by means of an entity linking technology; integrating a real-time data stream interface, and dynamically updating graph nodes and attributes based on an event-driven mechanism; adopting a RotatE model to respectively encode the entity and the relationship to a complex number space, and fusing to generate a mixed vector to construct an efficient index; after user query is received, topic nodes are positioned through semantic analysis, related entities are retrieved through mixed indexes, and multi-hop reasoning is executed along a relation path to generate reasoning sub-graphs and extended contexts; and finally, generating structured text answers by using a large language model, and adaptively outputting multi-modal results such as texts, charts and the like according to user requirements. According to the method, the knowledge updating timeliness, the complex query reasoning capability and the retrieval precision are effectively improved.

The invention provides a data retrieval method, system and device, and relates to the technical field of big data processing, the method comprises the following steps: obtaining a search text of a user; performing data permission assembly according to the search text to obtain a user permission set of the user; according to the user permission set, generating a retrieval filtering condition of the search text; according to the retrieval filtering condition, performing multi-source data search in combination with the search text to obtain an initial retrieval result corresponding to the search text; and performing unauthorized rejection operation on the initial retrieval result to obtain a final retrieval result of the search text. According to the method, the range is defined through permission assembly, the condition focusing target is filtered, the efficiency is improved through pre-matching, and the precision is guaranteed through post-removing, so that the retrieval data volume is compressed to improve the efficiency, and the matching pertinence is enhanced to improve the precision.

The invention relates to the cross technical field of artificial intelligence and data retrieval, and particularly provides an RAG question and answer optimization method and device.Firstly, original knowledge documents of various formats and natural language query of a user are received on an input layer, then a knowledge base construction module and a knowledge retrieval module are arranged on a processing layer, and a knowledge retrieval module is arranged on the processing layer; the middleware layer is provided with an MCP Server module and a vector database, the MCP Server module is a standard interface agent, and the vector database is a distributed storage engine for efficient approximate nearest neighbor search and mixed retrieval; and finally, deploying an LLM generation module at an output layer for generating a final answer based on the retrieved enhanced context. Compared with the prior art, the method has the advantages that irrelevant or low-signal-to-noise-ratio knowledge contacted by a large language model (LLM) can be effectively reduced, and the accuracy, the reliability and the practicability of knowledge base questions and answers are comprehensively improved.

The invention provides a multi-modal retrieval enhancement generation method and system, and relates to the technical field of computer vision. According to the method, better cross-modal semantic alignment can be realized in an embedding space through a multi-modal vector model; the semantic correlation between the target knowledge retrieved by the data of different modalities in the user input request and the user input request is ensured, so that the target knowledge is more accurate, the accuracy of an output result generated by the large language model is improved, and the generation quality of the large language model is improved. Through a mode of vectorizing the structured description text of the dynamic data, cross-modal semantic alignment can be realized, semantic information of a complex mode can be ensured to be fully captured, high-dimensional original data of the dynamic data is prevented from being directly processed, the calculation overhead required when the dynamic data is coded and retrieved is greatly reduced, and the efficiency of encoding and retrieving the dynamic data is improved. Faster retrieval and generation are supported, the generation efficiency of the large language model is improved, and the method is suitable for a real-time or resource-constrained environment.

The invention discloses a model-based data retrieval method and electronic equipment, and relates to the technical field of large model data retrieval, the method comprises the following steps: receiving a retrieval request of a user, the retrieval request carrying request data; according to reward estimation values corresponding to a plurality of strategies pre-stored in the target language model, a target retrieval strategy and initial retrieval parameters corresponding to the target retrieval strategy are determined, and the reward estimation values are used for representing the efficiency of the strategies; retrieving the request data through the target language model according to the target retrieval strategy and the initial retrieval parameters to obtain an initial retrieval result; outputting the initial retrieval result, and determining feedback reward data of the initial retrieval result in response to a feedback operation of the user on the initial retrieval result; determining a target retrieval parameter according to the feedback reward data and the initial retrieval parameter; according to the target retrieval strategy and the target retrieval parameters, the request data are retrieved through the target language model, and a target retrieval result is obtained.

The invention provides a data retrieval system and method based on a large language model and a readable storage medium. The system comprises a data analysis module, a multi-mode integration module, a semantic retrieval module and a post-processing module which are arranged in sequence. The data analysis module analyzes the original engineering data to generate structured data; the multi-modal integration module maps the structured data to the same semantic vector space to construct a vector database, and establishes a cross-modal association index to construct a multi-modal engineering knowledge graph; the semantic retrieval module is based on a large language model and a retrieval enhancement technology, responds to a natural language query of a user, retrieves related information from a vector database and a multi-modal engineering knowledge graph and generates answers; the post-processing module performs abstract generation on the natural language answers based on a large language model and generates associated recommendation content. According to the method, the query accuracy, the retrieval efficiency and the user query interaction experience can be remarkably improved; and the scheme reuse rate can be improved based on the recommended content.

The invention provides a data retrieval and chart generation method based on a large language model, and relates to the field of data processing. According to the method, natural language query, interface images and interaction behaviors are fused through a large language model, context features are generated by combining historical query modeling, a query semantic graph is constructed and matched with a database semantic structure graph to obtain field mapping, and a retrieval plan with caliber and time rules is generated and executed to obtain a result. Results are cleaned, an index set is generated according to the index dictionary and the life cycle library, chart types are determined in combination with interaction and data changes, and coding specifications are generated to output chart results. By implementing the technical scheme provided by the invention, the processing efficiency of data retrieval and chart generation can be improved.

The invention relates to a laboratory safety detection method and device, equipment and a medium. The method comprises the steps that a networking retrieval module is adopted to select a tool for large-scale data retrieval; guiding an intelligent agent RAG module to screen and verify information by adopting a thinking chain technology; in the generation stage, a contrast decoding method is used, the confidence difference between the reference lexical elements and the illusion lexical elements is calculated, and confidence reweighting is conducted on the reference lexical elements and the illusion lexical elements so as to strengthen real safety information and inhibit model illusion content. And inputting the laboratory scene image, the user input text instruction, the structured comprehensive query instruction and the effective information of all rounds into a multi-mode large language model reinforced by a thinking chain technology to generate a laboratory safety detection report, and proactively providing an optimization suggestion of the laboratory safety layout based on the laboratory safety detection report. According to the invention, the reasoning capability of the model in a complex security scene is enhanced, and the accuracy and reliability of a detection result are improved.

Apparatus for predicting an outcome of user engagement and methods used therein include a processor and a memory connected to the processor, wherein the memory contains instructions configuring the processor to receive user data that include a plurality of data attributes, populate a data retrieval module using the user data, the data retrieval module including assessment metrics, wherein populating the data retrieval module includes assigning a weight to each data attribute of the data attributes as a function of at least an assessment metric of the assessment metrics, rank the data attributes as a function of the weight, and generate an outcome prediction, as a function of the ranked data attributes.

In general, techniques for efficient data retrieval from a backup system are described. An example computing system includes one or more storage devices and processing circuitry having access to the one or more storage devices and configured to: process an input to generate a filter, wherein the input indicates a context for one or more queries; apply the filter to backup data to obtain filtered data from the backup data; generate an index of embeddings from the filtered data; process, based on the index of embeddings, a query to generate a response for the query; and output the response.

The invention discloses a dynamic multimedia data hash retrieval method and system based on extensible increment, and relates to the technical field of multimedia data retrieval. The method comprises the following steps: acquiring dynamic multimedia data to be retrieved; a pre-trained Hash retrieval large model is constructed, the Hash retrieval large model is trained by taking a bit extensible Hash center as global supervision information and taking tag cosine similarity as local supervision information, and specifically, generalization feature representation of new multimedia data maintaining new and old class discrimination is obtained through forward propagation; constructing a linear mapping relationship between the generalization feature representation and the hash code, and introducing an auxiliary variable to continuously update the hash function without playback; and utilizing the trained Hash retrieval large model to generate a query Hash code for the dynamic multimedia data to be retrieved, and utilizing the query Hash code to retrieve. According to the method, low memory occupation, high updating efficiency and non-forgetting retrieval of the dynamic multimedia data stream in the open environment are realized.

The invention discloses a data retrieval method and system for a distributed database, and relates to the technical field of data storage and data retrieval, and the method comprises the steps: obtaining the operation state data periodically sent by each node through a heartbeat signal collection interface of the nodes of the distributed database, carrying out the time sequence analysis for the signal interval fluctuation, and obtaining the operation state data; if it is detected that the node signal interval exceeds a preset threshold value, marking the node as an abnormal node, determining an abnormal node list, and recording final task metadata analysis information of the abnormal node; according to the abnormal node list, screening out a node with the lowest current load from a standby node pool; according to the data retrieval method and system for the distributed database, quick self-healing, stable operation and continuous response of a distributed database retrieval service in a complex network environment are achieved, and the reliability of the system and the retrieval experience of a user are fundamentally improved.

A system and method are disclosed for interfacing with one or more databases using natural-language queries. The system translates a natural-language input into an intermediate formal representation, such as a Concept Query Language (CQL), which references domain concepts rather than database-specific structures. A data access subsystem maps these domain concepts to database-specific queries using a domain dictionary, enabling seamless translation across heterogeneous databases and database management systems (DBMSs). The system supports distributed data retrieval, error recovery, and dynamic query planning. A presentation subsystem formats the results into user-friendly outputs such as charts or tables. The architecture allows for modular grammar and dictionary configuration, enabling rapid adaptation to new domains, schemas, or user roles without procedural code changes. This approach improves accessibility, maintainability, and scalability of database interactions by abstracting technical complexity from end users.

The invention provides a universal method for realizing multi-condition and search based on LDAP, and belongs to the technical field of data retrieval of directory service and identity management. A user defines query conditions including attributes, values and logical relationships through a configuration file, a visual interface or an interface; the system parses the query condition model into a query condition model supporting nested combination of AND, OR and NOT by using a parser, and performs legality check and error prompt; and then a generator dynamically generates a filtering statement conforming to the LDAP grammar specification according to the model, and query is executed through an LDAP client. In order to improve the performance, common condition caching and high-frequency statement pre-compiling optimization are introduced, and an asynchronous query mode is supported. And the query result is returned in the form of structured data, and is matched with a display interface to support screening, sorting and exporting, so that the flexibility and maintainability of multi-condition query are improved.

The present invention extends to methods, systems, and computer program products for data retrieval and validation for asset onboarding and deriving asset characteristics. A first set of data associated with an asset is collected. Identifiers associated with the first set of data are created. A second set of data associated with the asset is collected based on the identifiers. The first set of data set and the second set of data are compared based on the identifiers. The first set of data is validated based on the comparison. An artificial intelligence module is trained as part of a continuous training cycle in view of validation findings. Concurrently with artificial intelligence module training, one or more characteristics of the asset are derived from the first set of data using one or more AI agents.

The invention relates to the technical field of data retrieval, in particular to a FastGPT-based intelligent question-answering system for construction project quality inspection and detection standards. According to the technical scheme, the method comprises the steps of external knowledge acquisition, document processing, vectorization, vector database, retrieval and context generation and intelligent question and answer, data can be collected by a system, a semantic segmentation technology, a QA pair generation technology and a high-dimensional vectorization technology are utilized, in combination with M3E and OpenAIEmbedding models, Top-K similarity retrieval and context dynamic recombination are achieved, and structured and hierarchical professional answers are generated. According to the method, the intelligent management and real-time application capability of the construction project quality inspection and detection standard can be remarkably improved, the manual retrieval and interpretation cost is reduced, and the digital and intelligent requirements of an engineering project are met.

The invention relates to the technical field of big data retrieval, and discloses an AI financial audit chain and intelligent inquiry knowledge graph system, which comprises a metadata index storage unit, a topology trigger monitoring unit and a dynamic sub-graph instantiation engine, and is characterized in that a discrete record index is maintained by the system by utilizing Hash mapping, a trigger instruction is generated through streaming numerical value verification, and the dynamic sub-graph instantiation engine is generated; according to the method, a temporary local directed graph structure containing an affair-related subject is only constructed in a memory as required, and resources are released after isomorphic matching is completed to recover an index residence state, so that the contradiction between storage redundancy and computing power divergence caused by full-amount graph construction under mass data is solved by establishing an asymmetric architecture of a normal index and a transient graph, and the construction efficiency is improved. And the response certainty of deep hidden fund link retrieval is ensured while the system resource overhead is reduced.

Methods, systems, and machine-readable media user generative artificial intelligence (AI) to generate a pre-work brief for display on a field service technician mobile device. An instruction to generate a pre-work brief is received. Work order data based on a field service technician user identifier associated with the instruction to generate the pre-work brief is retrieved. A generative AI prompt template is retrieved based on the work order data. A generative AI prompt is generated based on the prompt template and the work order data. The prompt is provided to a generative AI. The pre-work brief is received as an output of the generative AI. The pre-work brief is transmitted to the mobile device of the field service technician.

Systems and methods are disclosed for retrieving information from legacy storage media with high fidelity and minimal contact. A read sensor with multiple read elements detects electromagnetic fields from tracks on a storage medium. Readings from different elements are combined to generate a composite interpretation, which can be used to, for example, generate digitized waveforms and determine if portions of tracks are damaged.

Domain-specific system and method for enhancing firmographic search through query understanding and expansion presents an innovative approach to revolutionizing firmographic search processes. This system employs advanced domain-specific query understanding and expansion techniques to bridge the gap between natural language queries and structured firmographic data, significantly improving precision and relevance in search results. This method utilizes a multi-model approach to dissect natural language queries into named entities, subsequently mapping them to specific structured attributes within a database. This refined query understanding enhances the alignment of unstructured queries with the structured format required for accurate data retrieval. The system also introduces custom confidence scoring to assess the reliability of model outputs, further improving the accuracy of structured query formulation. The presented system stands as a pioneering advancement in enhancing the firmographic search experience, catering to diverse user needs in the ever-evolving domain of business data retrieval.

The invention relates to the technical field of image semantic segmentation, in particular to an intelligent landslide identification method based on multi-source remote sensing image fusion. Comprising the following steps: calculating a gradient matrix and a water flow convergence index based on a digital elevation model, searching and determining a physical prior interval of cohesive force and an internal friction angle based on geological lithology data, and constructing an input tensor; extracting a surface semantic feature map of the optical image by using visual perception branches through a double-branch coupling network; based on a physical prior interval, mapping branches by using physical parameters, and carrying out nonlinear mapping to obtain a limited rock-soil mechanical parameter diagram; and calling a micro infinite slope stability layer, executing forward derivable calculation based on a limit equilibrium equation, generating a slope stability coefficient field, constructing physical attention gating, executing Hadamard product operation, and generating a semantic feature tensor. Through deep coupling integration and collaborative optimization of the visual perception branch and the physical parameter mapping branch, the true effectiveness of the obtained position data is ensured.

The invention discloses a multi-protocol dynamic adaptive industrial equipment data integration system and method, and relates to the field of industrial equipment data processing, and the system comprises an identification module which is used for receiving a protocol signal sent by industrial equipment, analyzing a protocol type identifier carried in the signal, and synchronously creating a data connection channel between the equipment and the system; the generation module is used for automatically generating corresponding data interaction adaptation logic based on the signal analysis result in the identification module; according to the method, optimized interaction adaptation logic is generated by combining equipment protocol parameters and historical interaction data, reliability and real-time performance of data interaction are improved, invalid fragments are filtered during data collection, collection frequency is dynamically adjusted, data quality and equipment load balance are considered, different protocol data are processed in a standardized mode according to a unified model so as to achieve format unification, and data interaction efficiency is improved. Storage resources are dynamically allocated according to the data generation frequency and the key degree, the associated index is established, and the storage utilization rate and the data retrieval efficiency are improved.

The invention discloses a material database system with enhanced artificial intelligence. The material database system comprises an interactive user interface module, a data management and service module, a visual processing module and an AI analysis and intelligent dialogue module. Structured and unstructured material data are managed in a centralized manner through a unified database and cloud storage, and a standardized data center is constructed; by integrating a chemical structure rendering library and a 3D graphic engine, real-time rendering from a CIF file to an interactive 3D model is realized, and a dynamically generated XRD spectrogram is displayed through an interactive chart library, so that the interpretability of data is greatly improved; by introducing an AI application development framework and an LLM, the system is endowed with intelligent analysis ability exceeding simple data retrieval, and professional material interpretation can be provided; by integrating the RAG system, a traditional search mode is upgraded into an intelligent dialogue mode, so that a user can explore material data like a dialogue with an expert.

The invention relates to the technical field of data retrieval, in particular to a retrieval method, device and equipment, a medium and a product, and the method comprises the following steps: generating a query feature vector according to a query request of a user; according to the method, retrieval is performed on the basis of a text index structure, an entity index structure and a semantic index structure, different types of semantic units and corresponding correlation scores are obtained, retrieval is performed through a multi-level index structure, and comprehensive semantic units can be obtained; determining a text index weight, a semantic index weight and an entity index weight corresponding to each semantic unit according to the query feature vector; and determining a comprehensive sorting score according to the weight and the correlation score, and finally screening and outputting a sorting semantic unit. According to the method, multi-aspect factors such as texts, entities and semantics are comprehensively considered, and the weights of the multi-dimensional indexes are dynamically predicted and fused in combination with the query feature vectors, so that a final semantic unit is screened out, the query requirements of a user can be more accurately matched, and the accuracy of a retrieval result is greatly improved.

The present invention extends to methods, systems, and computer program products for data retrieval and validation for onboarding private credit assets and deriving private credit asset characteristics. A first set of data associated with a private credit asset is collected. Identifiers associated with the first set of data are created. A second set of data associated with the private credit asset is collected based on the identifiers. The first set of data set and the second set of data are compared based on the identifiers. The first set of data is validated based on the comparison. An artificial intelligence module is trained as part of a continuous training cycle in view of validation findings. Concurrently with artificial intelligence module training, one or more characteristics of the private credit asset are derived from the first set of data.