1615 results about "Data retrieval" patented technology

The invention relates to an intelligent question-answering system optimization method and device based on a knowledge graph, and the method comprises the steps: obtaining original knowledge data of a target knowledge domain, and constructing a knowledge graph structure model; extracting term information of entity nodes in the knowledge graph structure model, and constructing an entity term set; receiving a natural language question input by a user, executing a semantic understanding operation based on the standardized expression set to obtain a structured question semantic representation, and matching the question semantic representation with the case training set to obtain context semantic features; constructing a cue word template, and executing a query instruction generation operation to obtain a target query statement of the graph database; submitting the target query statement to a graph database to execute data retrieval operation, and obtaining query result data corresponding to the question semantic representation; and performing personalized rendering processing on the query result data based on the user portrait information to generate final question and answer return content. The method has the effect of improving the query accuracy.

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 an intelligent operation and maintenance method and apparatus based on a knowledge graph and a large model, and an electronic device. The method comprises the steps of collecting multi-source runtime data of a Kubernetes cluster; constructing a knowledge graph with time dimension based on the resource change event, recording termination time in response to graph relationship failure, recording starting time in response to a newly added relationship and not setting the termination time, and associating the entity with the performance index and the log data; responding to the diagnosis request, scheduling a specialized agent by a coordination agent through multi-agent cooperation to retrieve associated information from multi-source data, and iteratively integrating to generate a structured context; and inputting the generated structured context information into a large model reasoning service to output a fault root cause diagnosis and solution. The technical problems that information dispersion and relevance are weak, root cause positioning is difficult and time-consuming, comprehensive context sensing ability is lacked and expert experience is excessively relied on are solved.

The embodiment of the invention provides a multi-modal knowledge graph construction method in cross-media retrieval. The method comprises the steps that extracted multi-modal features are mapped to a multi-modal feature space through a linear transformation layer; in the multi-modal feature space, the intra-modal attention weight of each modal feature is calculated according to a self-attention mechanism, the cross-modal attention weight of different modal features is calculated according to a cross attention mechanism, the two weights are fused to obtain a final fusion weight, each modal feature is weighted and input into a graph attention network, and the multi-modal feature is obtained. Obtaining a multi-modal fusion graph structure; performing semantic analysis on each modal feature, matching with a preset multi-modal semantic knowledge base, determining potential semantic association, performing semantic alignment on the multi-modal fusion graph structure according to a preset graph matching algorithm and the potential semantic association to obtain a multi-modal knowledge graph, performing cross-modal data retrieval according to the multi-modal knowledge graph, and performing cross-modal data retrieval according to the multi-modal knowledge graph. The multi-modal data cross-media retrieval method and device can improve the efficiency and accuracy of multi-modal data cross-media retrieval.

The invention discloses a course data intelligent multi-dimensional retrieval system based on deep learning, and relates to the technical field of course retrieval. The course data retrieval platform is in communication connection with a knowledge graph construction module, a user intention analysis module, a user portrait construction module, a personalized recommendation engine module and a retrieval display module, and all the modules are in electric signal connection; the knowledge graph construction module is used for collecting course data from multiple sources and constructing a course domain knowledge graph. According to the natural language processing technology based on deep learning, deep semantics queried by a user can be deeply analyzed, fuzzy or implicit requirements can be understood, limitation of traditional keyword matching is broken through, intent features and entity relationships can be extracted by combining a knowledge graph, specific entities and relationships queried by the user can be accurately mapped, and user experience is improved. Therefore, a more accurate retrieval result is provided, and personalized requirements of users are met.

The invention provides a knowledge question and answer rapid processing method and system based on artificial intelligence, and relates to the field of artificial intelligence. A multi-modal knowledge graph is constructed, collected multi-source teaching data is fused through a mixed retrieval strategy, and the mixed retrieval strategy comprises semantic retrieval, vector retrieval and metadata retrieval; multi-level question and answer processing is executed based on an RAG enhancement framework, a multi-modal input intention is analyzed, cross-library joint retrieval is performed, and an optimization answer is generated in combination with a teaching scene; distilling the global model to a lightweight TinyBERT architecture, dynamically optimizing question and answer quality through a cognitive reinforcement learning framework, positioning a key document from a comprehensive retrieval list, evaluating an optimized answer, and reconstructing an answer with a key document verification score; according to the invention, the professional skill level of teachers and students in the fields of artificial intelligence and large model application can be improved, and the personalized requirements of teachers and students in teaching, scientific research and innovation courses can be met.

The invention relates to the technical field of artificial intelligence large language models, and discloses a data attribution analysis task processing method, system and device based on a large language model and a storage medium. The data attribution analysis task processing method is applied to data attribution equipment and specifically comprises the following steps that S101, user input is received through a multi-mode input interface, and the user input comprises natural language problems, structured data files or API data streams; by means of the natural language understanding ability of the large language model, the system can directly analyze service problems put forward by a user in a daily term, professional data query languages are not needed, non-technical personnel can conveniently use the system, the data retrieval time is shortened to be within 3 minutes from 30 minutes on average through the automatic SQL query generation technology, the efficiency is improved by 10 times, and the method is suitable for large-scale popularization and application. The system can intelligently identify the database fields corresponding to the business indexes and generate optimized query statements.

The invention discloses a multi-modal data retrieval method and device, a storage medium and computer equipment. The method comprises the following steps: collecting multi-modal original data; on the basis of description information of metadata of original data, all metadata belonging to the same associated items and logic relations among all the metadata are obtained, a metadata chain is constructed, a distributed graph database is constructed on the basis of the metadata chain, the metadata chain is expressed in the distributed graph database in the form of a graph, the graph comprises nodes and edges, the nodes represent the metadata, and the edges represent the metadata. The edge represents a logical relationship between the metadata; and when a data retrieval instruction is received, traversing each node in the metadata chain along the logical relationship of the metadata chain in the distributed graph database, obtaining a target node matched with a data retrieval requirement corresponding to the data retrieval instruction, and returning original data corresponding to metadata represented by the target node. Multi-modal data dynamic association retrieval can be realized, and cross-modal information mining efficiency and accuracy are improved.

The invention discloses a structured data retrieval system and method based on semantic matching and hierarchical indexing, and the related retrieval system comprises a first construction module which is used for extracting slice data in a preset vector library and meta-information corresponding to the slice data, and constructing a text node object containing an id; the second construction module is used for traversing a text node object to obtain meta-information subjected to hierarchical structure processing, and constructing a nested index tree; the directory decomposition module is used for receiving an input text, performing decomposition based on a hierarchical structure and generating a corresponding query vector; the retrieval module is used for performing semantic retrieval and hierarchical retrieval on the text in sequence to obtain a retrieval result; the grouping and sorting module is used for grouping the retrieval results according to the hit hierarchy, sorting the retrieval results in each group according to a descending order, and combining all groups to obtain a final retrieval result list; and the data backtracking module is used for acquiring an original text field from the vector database according to the id corresponding to the retrieval result.

The invention discloses a file retrieval method, device and equipment based on multi-modal AI and a storage medium, relates to the technical field of data retrieval, and aims to solve the problem that traditional file retrieval is low in efficiency and precision. The method comprises the following steps: performing content analysis on a multi-modal file including a picture file, a video file and a document file of text and / or visual information, and extracting a text semantic feature, a picture visual feature and a video time sequence feature; mapping the text semantic feature, the picture visual feature and the video time sequence feature to a cross-modal semantic space used for representing a high-dimensional vector associated with different modal features, and generating a cross-modal association vector; according to the modal type of the retrieval information, a corresponding retrieval module in a mixed retrieval engine is called to conduct retrieval in a cross-modal semantic space, an initial retrieval result is obtained, and the mixed retrieval engine comprises a text retrieval module, a visual retrieval module and a cross-modal fusion module; and performing dynamic weight distribution sorting on the initial retrieval result, and outputting a target matching result.

A Retrieval-Augmented Generation (“RAG”) framework may include an RAG vector data store with information about vector embeddings. An RAG pre-processing platform may access unstructured data and perform vector embedding to generate a vector embedding for unstructured data to be stored in the RAG vector data store. The RAG pre-processing platform may also access structured data from the knowledge base and create a summary and metadata about the structured data to be stored in the RAG vector data store. An RAG retriever platform may receive a user prompt from a user, perform vector embedding, and retrieve context-relevant information for unstructured and structured data by searching for similar embeddings in the RAG vector data store. An RAG reader platform may combine the context-relevant information with the user prompt and an RAG prompt to create a LLM prompt. A context-aware response is then output to the user.

A generative artificial intelligence system includes a retrieval augmented generation (RAG) assistant that utilizes function calling to facilitate multi-source data retrieval to enhance user queries transmitted to a large language model (LLM). The RAG assistant Transmits, to the LLM, a function selection instruction prompt that includes conversation history data, a function list including function definitions that each correspond to a data source, and instructions directing the LLM to return a function call to at least one function defined on the function list identified as relevant to the conversation history data based on a corresponding function descriptor. In response to receiving a function selection response from the LLM that includes the function call, the RAG assistant selects and executes a conditional operation based on a name of the at least one function.

The invention provides a knowledge base and business system collaboration method under an AI platform, and belongs to the technical field of AI platform digital data processing.The method includes the steps that triple semantic analysis is conducted on query by constructing a multi-level semantic vector representation module, a parallel data retrieval engine is started, vector retrieval, graph reasoning and real-time data pulling are executed at the same time, and the query efficiency is improved; establishing a dynamic confidence evaluation mechanism to evaluate the quality of the data source, executing a weight distribution algorithm based on reinforcement learning, dynamically calculating the weight of the data source according to a query type by adopting a graph convolutional network intelligent routing decision model, and implementing multi-source data fusion and consistency verification to solve data conflicts through a weighted voting mechanism. An intelligent result sorting and filtering system is established, a multi-dimensional evaluation strategy is adopted to output high-quality answers, a continuous learning and feedback optimization loop is constructed, system performance is continuously optimized through active learning and a graph shortest path algorithm, and the technical problem that knowledge base data and a service system cannot effectively and uniformly make decisions is solved.

The invention discloses a relational database-oriented data retrieval enhancement generation method and system, and relates to the technical field of internet data retrieval services. The relational database-oriented data retrieval enhancement generation method comprises the following steps: S1, collecting original structure data, meta-information data and semantic interference data, and constructing a standardized multi-granularity expression set after preprocessing; s2, analyzing a semantic structure queried by a user, and dynamically adjusting an information extraction range according to the semantic structure; s3, comprehensively evaluating field matching strength and time coverage suitability, and switching a context construction strategy; s4, evaluating the semantic integrating degree and the information coverage degree of the candidate segments, and constructing a multi-level prompt context structure; and S5, tracking the semantic granularity and the context adaptation degree, and performing response quality diagnosis and dynamic correction. The problems that in a relational database, natural language query semantic granularity is difficult to switch in a self-adaptive mode, structure fragment matching is not accurate, and context prompt construction is redundant and scattered are solved.

The invention provides a data intelligent question and answer ChatBI method and device based on a large model. The method comprises the following steps of 1, constructing a database knowledge vector library; and step 2, question semantic transliteration and retrieval. According to problem transcription and data retrieval supplementation, a large language model is utilized to generate a corresponding visual chart according to the characteristics and problems of data, automatic visualization of any data is achieved, meanwhile, a more detailed and clearer expression mode is provided in a dialogue mode, and the method has the advantages of being high in practicability and easy to popularize. The flexible, intelligent and automatic process is realized, and the trend and relationship of the data are more intuitively displayed for customers. By combining a big language model and a data visualization technology, a proper visual chart is automatically generated according to natural language cue words and data, so that the data analysis and presentation efficiency is greatly improved, the learning cost of a user is reduced, and a personalized data display solution is provided for users in different fields.

The invention discloses a personalized learning system based on multiple agents and retrieval enhancement generation. The system comprises a knowledge extraction agent, a retrieval enhancement generation module, a planning teaching agent, a knowledge consolidation agent and a test evaluation agent. The knowledge extraction agent extracts document information from the original learning materials and converts the document information into semi-structured data; the retrieval enhancement generation module is used for storing the semi-structured data into a vector library in a vector form, retrieving information in the vector library according to a request sent by a user, and inputting enhancement information combined by the retrieval information and the request into a downstream agent; and the planning teaching agent, the knowledge consolidation agent and the test evaluation agent are respectively used for constructing a knowledge graph according to the received information to generate a personalized teaching plan, generating exercise questions to consolidate learned knowledge, evaluating a learning effect and providing feedback suggestions. The learning efficiency, the learning effect and the learning experience of the learner can be remarkably improved, and personalized learning can be met.

The invention relates to the technical field of data processing, and provides a high-dimensional data library falling method, device and equipment based on a hybrid index and a medium, a configuration interface is called to convert multi-modal high-dimensional data into a high-dimensional vector in a configuration format, and efficient conversion and interaction among different modal data can be realized through a unified vectorization interface; the high-dimensional vector is compressed and dimensionally reduced, so that the storage overhead can be effectively reduced; an HNSW index and an IVFFLAT index of a vector to be subjected to library falling are constructed based on a hybrid index strategy, a hybrid index of the vector to be subjected to library falling is obtained, and a hybrid index structure can support rapid indexing of large-scale high-dimensional vector data; according to the method, the mixed index and the vector to be subjected to database falling are subjected to database falling to the centralized relational database, high-dimensional data are subjected to compression and dimension reduction before database falling, so that the database falling overhead is reduced, the database falling efficiency is improved, and meanwhile, the mixed index structure is combined, and the data retrieval efficiency can be improved after database falling.

The invention belongs to the field of query optimization, and particularly relates to a fragmentation storage and query optimization method and system for a high-concurrency database, and the method comprises the steps: analyzing business features through a preset decision tree model, and selecting an optimal fragmentation key, constructing a fragmentation rule engine and initializing a database in combination with hash, range and list fragmentation rules and a fragmentation splitting-merging strategy; generating a query abstract syntax tree by using an ANTLR4 analyzer, and detecting whether a preset Cube is hit or not to directly return a result; if not, dynamically routing to a target fragment list based on a fragment key field, load balancing and a failover strategy, accelerating data retrieval in combination with a high-frequency index, and combining fragment-level results through a T-TopK algorithm; according to the method, the self-adaptive matching of the fragmentation strategy and the service requirement, the calculation push-down of the query process and the result optimization are realized, and the low delay and the high availability in a high-concurrency scene are ensured.

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.

A metadata access method, apparatus, and computer-readable storage medium for efficient metadata retrieval through cache management. The method receives metadata query requests including target index information from processes and performs matching operations on a global cache file containing records with index information and slot identifiers. Each cache description array corresponds to memory blocks caching metadata. Upon successful matching, the target cache description array is accessed using the target slot identifier. The data state of target metadata is determined from the cache description array, and target address information indicating the location of the target memory block in shared memory is obtained and returned to the requesting process, enabling efficient shared memory-based metadata access.

The invention discloses an automatic report generation method based on cross-modal agent collaboration, and relates to the technical field of automatic document generation, and the method comprises the steps that a central scheduling engine coordinates an outline generation agent, a data query agent, a chart drawing agent and a data analysis agent; the central scheduling engine drives the outline generation agent to generate a structured framework of a report based on a user demand, and dynamically schedules each agent according to a task dependency relationship; the data query agent realizes data retrieval by fusing retrieval enhancement generation and a thinking chain technology; the chart drawing agent adaptively generates a chart according to the data type; the data analysis agent is combined with an expert system and a knowledge base to generate an analysis conclusion; wherein the outline generation agent, the data query agent, the chart drawing agent and the data analysis agent are coordinated in parallel under the coordination of the central scheduling engine, and exception handling and dynamic adjustment are realized through a cross-agent coordination mechanism.

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.

The embodiment of the invention provides a multi-modal low-altitude grid data dynamic indexing method and device for improving a cache mechanism. The method comprises the following steps: mapping multi-modal low-altitude grid data to a unified vector space, and constructing a cross-modal semantic association hybrid index for the mapped low-altitude grid data; dividing the mapped low-altitude grid data into a plurality of dynamic sub-regions according to data distribution density and data access popularity, and managing a mixed index corresponding to each dynamic sub-region by combining a Loki log index fragmentation mechanism and tagged metadata; the mapped low-altitude grid data is cached by adopting an EHCD hierarchical multi-level cache architecture; and carrying out cross-modal low-altitude grid data retrieval by combining SIMD instruction set parallelization vector similarity calculation and GPU acceleration complex space relation operation. In this way, the real-time retrieval efficiency, the system expansibility and the energy efficiency ratio of the low-altitude grid data can be remarkably improved.

Systems and methods are provided to improve data retrieval from a cache memory by using semantic matching to retrieve data from the cache memory. The system includes a two-tiered cache system, with a first tier implementing “key-value” pairs, and a second tier that includes a table that is configured as an artificial intelligence (AI) search indexed source. When a new input does not have a matching “key” at the first tier, the system performs a semantic search at the second tier of the cache to determine if relevant data is stored in the cache. The current systems and methods increase the likelihood of obtaining data for queries from the cache memory, reduce the response time to the queries, improve search consistency, reduce computing resource utilization, improve system performance, and reduce costs.

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 discloses a thermal management control method for a power battery. The method comprises the following steps: establishing a basic model for calculating heating and cooling of the battery; basic control logic is set, and the basic model is substituted into different temperatures to select different control modes; data management and analysis including real-time state monitoring, historical data calling, cloud platform data calling and big data comparison rule searching; a model learning algorithm is introduced, historical working conditions, environmental parameters and user behavior data are fused, the system temperature trend is predicted, and temperature management is started in advance; a temperature control model is dynamically optimized through big data; the optimal energy consumption of the control temperature is used as a target function; and 5) fault early warning: obtaining characteristic laws of temperature, voltage, time and the like during a thermal event and each fault event, and realizing early warning when the vehicle has similar characteristics. According to the invention, temperature uniformity control in the whole life cycle of the power battery can be realized, the service life of the battery is prolonged, and timely early warning can be carried out on thermal safety control.

The invention discloses an electric power multi-modal corpus construction query method and system based on a sliding window, which is applied to the field of electric power data query, and comprises the following steps: obtaining a structured document according to electric power multi-modal data, segmenting the structured document to obtain a plurality of segmented text blocks, and storing the segmented text blocks into a database; inputting each segmented text block into a large language model to generate a to-be-stored text vector and construct an electric power multi-mode corpus, when a user query request is received, generating a plurality of query variants according to query data, performing nearest neighbor search on each query variant in the electric power multi-mode corpus to obtain a corresponding nearest neighbor search result, and storing the nearest neighbor search result in the electric power multi-mode corpus. And fusing each nearest neighbor search result to generate an electric power related document set comprising a multi-modal association mark. According to the method, the semantic units can be accurately captured, semantic breakage is avoided, the retrieval continuity and coverage rate are improved, the comprehensiveness and context adaptability of retrieval results are improved, and the data retrieval requirement under the complex scene of the power industry is met.

The disclosure provides systems and methods for identifying salient test runs involving an autonomous vehicle system. A processor receives sets of run data, each set representative of a driving scenario. For each set, an output set is generated, the output set comprising time-indexed events generated in response to a detected behaviour of at least one challenger agent, and a sequence of decision indicators indicating whether a driving action by an ego agent would be permissible. A data retrieval component is coupled to a results database and retrieves output sets based on the time-indexed events and the sequence of decision indicators. The processor generates the sequence of decision indicators by generating a planned trajectory of the ego agent, and determining whether the predefined driving action by the ego agent would be permissible.

The invention relates to the technical field of geographic information data, in particular to an ecological remote sensing data processing system which comprises a remote sensing layer acquisition module, a boundary position checking module, a region coding integration module, a spatial change splicing module and an ecological trend drawing module. According to the method, by accurately matching the geographic identification information and the spatial boundary value, the coverage range and the positioning precision of the ground feature pattern spots are remarkably improved, the time information and the spatial information are effectively integrated, the continuity and the accuracy of the data are enhanced, the boundary resetting technology effectively reduces the errors of the geographic information, and the time-space continuity of the data is improved; the processing efficiency is greatly improved by constructing a continuous layer structure and optimizing a data retrieval process, the fine boundary splicing technology also allows fine tracking of ecological changes, more detailed data support is provided for ecological monitoring, the ability of ecological trend analysis is enhanced, and thus the overall effect and application value of ecological monitoring are improved.