5458 results about "Data query" patented technology

Disclosed herein are methods and systems for providing individualized responses and recommendations based on a shared knowledge language. A method of receiving a user query for a personalized response associated with a profile; interpreting the query by executing a machine-learning model; generating a data query corresponding to the user query by executing the machine-learning model, the data query configured for execution in a computer model comprising one or more nodes having an identifier corresponding to a series of nouns and verbs generated in accordance with a schema associated with a shared knowledge language; receiving a first node of the computer model, wherein the first node is associated with the profile and generated based at least in part on an application accessed by the profile; presenting the personalized response, wherein the personalized response comprises an indication of the first node of the computer model.

The invention provides a cross-modal similarity-based text mining data query method and system, relates to the technical field of data mining, comprises a cross-modal similarity-based text mining data query method and system, and belongs to the technical field of natural language processing. The method comprises the following steps: constructing a multi-level feature extraction network to respectively obtain text, image and time sequence feature vectors; feature fusion is realized by using an adaptive cross-modal contrast learning network, and a cross-modal similarity matrix is constructed; performing hierarchical screening on the target text by adopting a multi-level screening model to obtain a candidate text set; a text relation reasoning module is constructed based on a graph neural network, and text clustering and relation prediction are completed through a multi-task learning framework. According to the method, the accuracy of cross-modal text data query is improved, the adaptivity of feature fusion is enhanced, and efficient text relationship mining is realized.

A data query system and methods are provided that are configured to intelligently generate structured data queries from natural language questions using large language models (LLMs). The system includes a processor and a computer readable medium operably coupled thereto, the computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, to perform operations which include receiving a natural language question for structured data, converting the natural language question to embeddings, matching the embeddings to pre-generated questions from a user questions repository (UQR), determining an accuracy of the matching meets or exceeds a threshold similarity, determining, using an LLM and metadata corresponding to the pre-generated questions from the UQR, a structured data query for querying for the structured data, and querying the structured database system using the structured data query.

Systems and methods for generating output data based on a data graph are provided. An output request for the output data based on the data graph is received. The output request comprises one of a natural language request from a target user or an application request from an application of the target user. The data graph has nodes and edges between the nodes. The nodes represent entities associated with an enterprise organization and the edges represent relationships among the entities. A graph data query is generated with a large language model (LLM) using the output request as a first input to the LLM. The graph data query is performed against the data graph to obtain a graph data output that represents a sub-portion of the data graph. The output data is generated with the LLM using the graph data output as a second input to the LLM.

Operators managing a cloud RAN collect vast amounts of data, e.g., node-level data, gNodeB level data, user level data, and flow-level data, which are utilized for network monitoring, evaluating key performance indicators (KPIs), and nodes management. Retrieving and visualizing information and values of different metrics is critical to managing network operation; however, data retrieval on large datasets is challenging. While foundation models perform poorly on large datasets, an accurate answer to a data query is generated by providing semantically similar metrics as context to a foundation model, thereby limiting the number of counters needed for processing the data query. The foundation model then generates a first output of metrics relevant to answering the data query and, based on the first output, generates a second output comprising query code (e.g., SQL or KQL) for computing the answer, thereby improving mathematical accuracy of the answer.

Disclosed are a data query statement generation method, apparatus and device, and a storage medium. The method comprises: acquiring a process presentation requirement and a natural statement to be converted; on the basis of the process presentation requirement, constructing a prompt instruction; and on the basis of the prompt instruction, inputting said natural statement into a pre-trained data query statement generation model, and on the basis of a model output result, determining and outputting an intermediate generation result and a target data query statement which correspond to the process presentation requirement, wherein the pre-trained data query statement generation model is a generative large language model that is trained on the basis of a pre-constructed chain-of-thought, and the pre-constructed chain-of-thought is a chain-of-thought that is constructed after logic segmentation is performed on a logic plan corresponding to a data query statement.

The invention discloses an agent long-term memory modeling method based on a memory network, and relates to the field of agents, and the method comprises the steps: storing vector data to a vector database, and storing structured metadata to a relational database; receiving an input request of a user, and executing vector similarity retrieval in the vector database to obtain semantic similar fragments; executing structured data query in the relational database to obtain structured metadata; the memory abstract is retrieved; forming a candidate data set; taking a filtered result as memory information, inputting the memory information into a large language model through a cue word project, and generating reply content; the input request of the user and the generated reply content are combined to form a new interaction record; processing the new interaction record and historical interaction records stored in a vector database and a relational database through a large language model to generate a memory abstract; aiming at the insufficient long-term memory emotion interaction coherence of the intelligent agent, the emotion interaction coherence is improved.

The invention discloses a data query method and system based on a natural language text. The method comprises the following steps: dynamically shielding the natural language text to generate a semantic vector sequence; key entities and association relationships thereof in the semantic vector sequence are extracted, and a structured entity relationship set is obtained; matching the structured entity relationship set with knowledge graph nodes to generate a heterogeneous semantic graph; performing heterogeneous fusion on the heterogeneous semantic graph and text syntactic features to generate intermediate query representation; processing the intermediate query representation to generate an executable statement carrying a semantic association identifier; and verifying the semantic consistency of the response content of the executable statement and the original input, and iterating to regenerate the executable statement when the semantic consistency does not pass the verification. And efficient and accurate natural language query conversion and response generation are realized through dynamic shielding enhanced semantic robustness, knowledge graph path extension and weight iterative optimization, heterogeneous feature fusion and a multi-modal verification mechanism.

The invention belongs to the technical field of data query in the electric power field, and particularly relates to an electric power field SQL intelligent agent construction method based on a KMDI chain. According to the method, a plurality of field knowledge bases are constructed based on professional knowledge such as a data structure, data coding and a data relation in the electric power field, and a chain type process composed of three links of knowledge matching and decision making, key knowledge distillation and key knowledge injection is designed and an SQL intelligent agent is constructed; under the support of a large language model, the data coding conversion of the electric power technical terms in the natural language questions is realized, and the data query SQL statements matched with the requirements are accurately generated, so that the business personnel in the electric power field can directly realize the access and operation of the data through the natural language.

The invention relates to the technical field of information, and discloses a ciphertext execution exception handling method and system for a privacy computing algorithm, which quantifies the data query sensitivity of each tenant by collecting differential privacy query parameters of a plurality of tenants, and performs time sequence analysis on the sensitivity quantification result by using an ARIMA model to predict the future trend of the tenant, so as to improve the accuracy of the ciphertext execution exception handling of the privacy computing algorithm. The residual error between the actual sensitivity and the predicted sensitivity is calculated to identify abnormal tenants; a sliding window method is adopted to extract statistical features of the abnormal tenants to construct a feature set, and clustering analysis is carried out on the feature set to identify abnormal modes and abnormal fingerprint feature clusters corresponding to the abnormal tenants; and training a model based on the positive and abnormal fingerprint feature clusters of the abnormal tenants, and quantifying the influence degree of each abnormal mode on the privacy protection level of the abnormal tenants by using the model, so as to adjust and execute the privacy budget allocation proportion of each abnormal tenant according to the obtained quantitative analysis result. And therefore, the exception in the ciphertext execution of the privacy calculation algorithm can be effectively processed.

The invention provides a cache-based data query analysis result optimization method and system, and relates to the technical field of data query analysis result optimizing.The cache-based data query analysis result optimization method comprises the steps that user query statements are understood through a semantic understanding model and an intention recognition model, query features are constructed and matched with a preset template, and query rules are determined; based on data distribution prediction and index optimization technologies, an optimal execution path is determined, a database executor is scheduled to obtain a query result, the result is cached, anomaly detection and resource scheduling optimization are performed according to performance data, and a preloading strategy is generated to improve query efficiency.

A large language model (LLM) may receive a query via a user interface of a client device. The LLM may generate one or more data queries from the query to query one or more data sets. The LLM may then transmit the one or more data queries to the one or more data sets. The LLM may then receive information associated with the query and a source for the information from the one or more data sets. The source may be indicative of a location within the one or more data sets from where the information was obtained. Following, the LLM may generate a response to the query that includes the information associated with the query and the source for the information and transmit the response to the user interface of the client device for display.

The invention provides an SQL (Structured Query Language) statement generation method and system based on a large language model. Target database metadata are collected, a structured dictionary is constructed, cue words adaptive to the large language model are generated in combination with a user query strategy, and accurate SQL generation is realized through a closed-loop execution optimization mechanism after model reasoning and SQL analysis. Through a large language model intelligent analysis technology, a multi-source heterogeneous database metadata dynamic adaptation technology and a closed-loop optimization mechanism for SQL statement generation, execution and feedback, the efficient and accurate SQL statement automatic generation function is achieved through the characteristics of cross-database grammar automatic adaptation, complex service requirement accurate analysis, intelligent error correction and the like. The system not only improves the working efficiency of developers, but also reduces the resource occupation and operation and maintenance cost of the system, and improves the accuracy and safety of data query and processing.

The invention discloses an intelligent data query method and device based on a large model and a medium, and relates to the technical field of large model application. The method comprises the steps of receiving a natural language query text input by a user, and determining a target interface group from pre-associated interface groups through a preset business line classification rule and a keyword matching algorithm; inputting the natural language input text into a pre-trained large model to analyze a parameter set required by interface calling; wherein the parameter set comprises at least two of the following items: a time range field, a business main body field and a statistical dimension field; calling an API interface in the target interface group according to the parameter set, and obtaining original business data; performing context supplementation on the original business data based on the business logic rule to generate a standardized data result containing business background description; and reconstructing the standardized data result according to a display format preset by a user, and outputting a combined visual result containing charts, tables and text description.

The invention discloses a report chart automatic generation method based on natural language analysis and data query, which comprises the following steps: acquiring a natural language text, and constructing a slot set and an initial semantic fragment; executing consistency verification and ambiguity identification, and constructing a semantic framework; executing field standardization and mode alignment, generating a data query statement and establishing a traceability chain identifier; querying a target database to obtain an original data set, completing data quality self-correction, and generating a cleaned data set; calling an improved Mamba model to construct a constraint judgment matrix and outputting a target chart subset; generating a chart description grammar and an image annotation template, and executing image rendering, feature point recognition and image sidebar construction; and performing layout optimization and double compilation, and outputting a structured report file. According to the method, the chart generation automation level and the report compiling efficiency are improved, and the accuracy, traceability and specialty of the output content are ensured.

The invention provides an interactive data query and display method and system based on a large language model. The method comprises the steps that a user query request and a service database used for query are obtained; based on the user query request, obtaining a query intention classification result of the user query request and a key entity in the user query request; generating a structured query parameter based on the obtained query intention classification result and the key entity; based on the generated structured query parameters, through interactive processing, rewriting a user query request to conform to a standard query grammar; constructing a query cue word based on the rewritten user query, and generating an SQL statement through a pre-trained large language model based on the constructed query cue word; obtaining a data query result from a business database for query based on the generated SQL statement; based on the query result, a chart is automatically generated to display the query result, and the chart type is determined according to the chart display type of the historical query result of the user.

The invention relates to a database query method and device based on a large language model agent and electronic equipment, and the method comprises the steps: obtaining and analyzing query request information, recognizing a query request intention type, carrying out the problem splitting of the query request information according to the query request intention type, and obtaining sub-query request information; searching and obtaining an SQL statement corresponding to the sub-query request information in the target knowledge base according to the category of the query request intention, constructing and generating an SQL statement prompt word and sending the SQL statement prompt word to the large language model when the SQL statement is not searched, executing according to the returned SQL statement to obtain a sub-query result, integrating the sub-query result and obtaining a query result of the query request information, and generating a data visualization code based on the large language model, executing and outputting a query result and a visualization display result thereof. By combining natural language processing, a large language model and a multi-item optimization technology, the efficiency and usability of data query are greatly improved, and non-technical users can conveniently analyze complex data.

The invention provides a semantic enhancement and dynamic completion method and system for a power market data graph. The method comprises the following steps: acquiring target entity information and external data of a power market data graph by responding to a data query instruction; converting external data into structured semantic tags, then traversing the topological structure, establishing a mapping relationship between the tags and entity attributes, and combining query instruction influence logic to calculate topological integrity indexes and mark missing entities or correlate to form a missing tag set; establishing attribute mapping based on a target entity and external data, generating a semantic vector fusing cross-domain features, and projecting the semantic vector to a topological space according to a missing mark set to form cross-domain feature topological nodes and connections; embedding a missing position to reconstruct a topological structure, and checking logic connectivity; the dynamic conduction chain is analyzed, and a feedback interface containing the semantic relation and the completion structure is generated to respond to query. According to the method, conduction logic closed-loop verification of the missing elements in the power market data graph is realized.

The invention relates to the technical field of computers, in particular to a multi-modal data search method, device and equipment based on a generative AI large model, a storage medium and a product. The method comprises the following steps: acquiring various types of data from a plurality of heterogeneous data sources to obtain acquired data; performing feature information extraction on the acquired data based on a preset generative AI large model to obtain feature data; constructing a multi-modal index structure based on the feature data; matching the search instruction with index data in the multi-modal index structure; and fusing and sorting the matching results to obtain a target search result. According to the method, various types of data are collected from a plurality of heterogeneous data sources, a multi-modal index structure is constructed after feature information extraction is performed on the data by adopting a generative AI large model, and the search instruction is efficiently matched with the various types of data, so that the efficiency and accuracy of data query are improved.

The invention provides an intelligent data query method based on a natural language, and relates to the technical field of intelligent data processing and natural language interaction.The intelligent data query method comprises the steps that firstly, enterprise original data is subjected to standard treatment, and a standardized theme database and a data directory and index definition document are constructed; key semantics are extracted based on unstructured knowledge, and a domain knowledge vector library is fused and constructed by combining document text fragments and vector representation of a mapping relation between historical questions of a user and an SQL (Structured Query Language). And after receiving a natural language question of a user, calling a large language model to identify a task type, and distinguishing knowledge questions and answers, data query and complex analysis. Executing corresponding operations according to different types: directly retrieving a vector library by knowledge questions and answers to generate answers; extracting keywords in data query and generating a query request in combination with context; and in the complex analysis, predefined workflow is judged and executed or intelligent agent processing is called, and query or analysis requirements are output.

The invention discloses a medical and educational informatization comprehensive supervision and diagnosis platform system based on multi-modal data fusion. A data acquisition module acquires multi-modal data from a medical and educational information system and preprocesses the multi-modal data; the data storage module constructs a hybrid architecture to classify and store data and establishes an index. The multi-modal data fusion module deeply fuses medical records, images and physiological monitoring data of medical treatment and teaching resources, learning behaviors and teaching evaluation data features of education by means of a feature extraction technology and a fusion algorithm. The supervision and diagnosis module constructs models of medical service quality evaluation, disease diagnosis assistance, teaching quality monitoring and the like based on fusion features to realize accurate analysis. The user interaction module provides interaction interfaces for different roles and supports operations such as data query and uploading. The platform system breaks the data barrier, fully mines the data value, provides powerful support for informatization management, decision making and service optimization in the medical and education fields, and promotes the innovative development of the two fields.

According to the artificial intelligence-based data analysis system and method provided by the invention, a set of novel data analysis system architecture is designed, and key technical components such as natural language processing, a large language model, database query optimization, multi-modal visualization and a semantic knowledge graph are fused; data transmission between modules is realized through unified intermediate data objects such as a structured query intention, an analytic tree and a structured query language template object, and asynchronous collaboration is realized through event driving and a message queue mechanism, so that a non-professional user can input an analysis request through a natural language; semantic analysis, structured query language query statement generation, data query and result visualization presentation are automatically completed, the method can be widely applied to data analysis scenes in the industries of government affairs, traffic, finance, education and the like, the data use efficiency is improved, the technical threshold is reduced, and the digital decision-making ability is enhanced.

The invention provides a method and a device for converting a natural language statement into an SQL (Structured Query Language) and a storage medium, relates to the technical field of data processing, and is used for improving the accuracy of converting the natural language statement into the SQL statement. The method comprises the steps that semantic analysis is conducted on a data query text, a target entity and at least one query field are obtained through extraction, and the target entity indicates a query intention corresponding to the data query text; determining a comprehensive similarity between each query field and each first candidate node according to a semantic similarity and a structural similarity between each query field in the at least one query field and each first candidate node in a pre-stored knowledge graph; determining a target node corresponding to each query field according to the comprehensive similarity between each query field and each first candidate node; according to the information, the data query text can be converted into the structured query statement.

The invention relates to the technical field of artificial intelligence, in particular to a ChatBI dynamic data query and visualization system and method.The ChatBI dynamic data query and visualization system comprises a natural language interaction and intention analysis module, a metadata knowledge graph module, a dynamic SQL generation and optimization module, an intelligent visualization recommendation module and a multi-source data connection and real-time calculation module; the method has the beneficial effects that by fusing natural language interaction, intelligent query generation and self-adaptive visualization technologies, the core pain points of high technical threshold, large response delay, complex visualization configuration and the like of a traditional BI tool are thoroughly solved.

The invention provides a data query method and device, equipment and a product, and the method comprises the steps: recalling knowledge description and table knowledge corresponding to a data query request based on a preset industry knowledge base, and storing knowledge description corresponding to each term in a target scene and description information of each table in the target scene in the industry knowledge base; generating prompt information of data query based on the knowledge description corresponding to the data query request, the table knowledge and the data query request; and generating a structured query statement based on the prompt information through the large language model, and performing data query in a database based on the structured query statement to obtain a data query result. Therefore, the data query efficiency and accuracy can be improved.

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 a 3DTiles data storage method and a map data query method.The 3DTiles data storage method comprises the following steps that (1) an LOD hierarchy pyramid type hierarchy table is established, and an LOD range index is created; 2) generating a three-dimensional object into an agent point, and segmenting a tile according to a KD-tree structure to construct a three-dimensional KD-tree index tree; and 3) storing the data of the KD-tree leaf node into a GridFS of MongoDB multi-mode storage, mapping a file path into a unique hash value, writing the unique hash value into a corresponding node according to a KD-tree node storage structure, and creating a geographic spatial index. The method creatively solves the problems that file system management is difficult due to the fact that the number of files is large, a hierarchical structure based on a file directory is difficult to support efficient spatial range retrieval, and the spatial query response time is reduced to the millisecond level in combination with a three-dimensional KD-tree index and a MongoDB geographic spatial index.

The invention belongs to the technical field of information retrieval, and discloses a multi-source cross-domain data query method and system. The method comprises the following steps: firstly, carrying out data identification, extraction and classification by utilizing a large language model, and constructing a multi-source knowledge graph; secondly, performing deep aggregation and reconstruction of multi-source cross-domain data based on a large language model to obtain a homologous knowledge line graph; thirdly, calculating the confidence degree of the query nodes and the subordinate sub-graphs according to query requirements, rejecting low-quality nodes, and extracting credible candidate sub-graphs from the knowledge graph so as to ensure that a multi-domain retrieval query result is effective; and finally, embedding the screened credible candidate sub-graphs into a cue word template, and carrying out data query by utilizing a vertical large model. According to the multi-source knowledge fusion and query method based on the large language model, cross-domain data in different sources are integrated to form domain knowledge, and accurate and efficient query of the multi-source cross-domain data is achieved through the vertical large model.

The embodiment of the invention discloses an RAG data query method and device based on privacy protection, equipment and a medium. The scheme can comprise the steps of obtaining a data query request provided by a query party; obtaining target data from the database based on the data query request; processing the target data by adopting a large model of a model party according to a preset privacy protection mode to obtain a query result; the preset privacy protection mode is used for preventing the target data from being leaked to the model party; and feeding back the query result to the query party.

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.