3462 results about "Data query" patented technology

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 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.

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 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 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.

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 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 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 provides a data query method and system for converting a natural language into a database query language, and the method comprises the steps: analyzing the natural language input of a user through a multi-modal understanding agent on the basis of constructing a dynamic knowledge graph based on metadata, combining a historical session with a business term table, eliminating ambiguity, and generating a standardized Query, a retrieval routing agent selects a query strategy according to Query complexity, simple query directly matches a cache template, complex query traverses a knowledge graph, and related tables, fields and service constraints are returned; then, an expert committee agent generates an SQL (Structured Query Language) by adopting multi-stage collaboration, executes plan pre-evaluation, and selects a version with the highest comprehensive score; the test agent simulates and executes the SQL in the isolation environment, and verifies the grammar legality and the field permission; and finally, executing the detected SQL, and processing a result. According to the method, the accuracy of converting the natural language into the SQL (NL2SQL) in a complex database scene can be effectively improved.

The invention discloses a cross-domain heterogeneous data query system and method based on a large model and a knowledge graph, belongs to the technical field of information retrieval, and aims to solve the technical problem of complex relation reasoning in cross-domain heterogeneous data query. Comprising a data input and preprocessing module used for collecting multi-modal data to obtain feature vectors; the knowledge graph construction and management module is used for constructing a knowledge graph and providing query service based on the knowledge graph; the bidirectional enhancement module is used for writing the reasoning result of the large language model into a knowledge graph and carrying out version management; the domain adaptation layer is used for carrying out model training on the large language model based on a lightweight adapter and a domain adaptation mechanism; the real-time query and reasoning module verifies and supplements the candidate answers based on a knowledge graph to generate an initial answer, and explains a reasoning path based on a causal reasoning network to generate a final answer; and the interpretability and transparency module is used for displaying knowledge in the knowledge graph through a visual interface and providing auditing service based on the operation day.

The invention discloses a data query caching method, device and equipment and a storage medium, and is applied to the field of big data, and the method comprises the following steps: obtaining a query request, and analyzing the query request to obtain a query condition; judging whether a matched query record matched with the query condition exists in the cache library, and if yes, directly returning the matched query record as a query result; otherwise, executing the query task based on the query condition to obtain a query result, and updating the cache library according to the query condition and the query result. By analyzing the query condition, the intention of the user can be more accurately understood, and a foundation is laid for cache matching and query execution. When the cache library is hit, the result can be quickly returned without repeated calculation, so that the waiting time of the user is shortened, the query efficiency is improved, and the calculation resource consumption is reduced. When the query result is not hit, the query task is executed to obtain the query result, and the new result is stored in the cache library, so that cache support is provided for subsequent same query, effective cache data is accumulated step by step, and the query efficiency is continuously optimized.

The invention discloses a verifiable medical data query method based on a DAG block chain architecture, and belongs to the technical field of block chains and medical information management. According to the method, medical events such as diagnosis and treatment records and image reports are modeled into DAG nodes, an on-chain topology with time sequence and causal association is constructed by encrypting a hash pointer, dynamic indexing and multi-keyword verifiable query are achieved in combination with an off-chain authentication data structure, a verification object containing a matching node set and a verification path is received, and the verification object is subjected to dynamic indexing and multi-keyword verifiable query. The authenticity, integrity and timeliness verification of the result is independently completed based on the authentication root stored on the chain without depending on a third party. According to the method, through an on-chain and off-chain collaborative storage and incremental authentication updating mechanism, on the premise of ensuring privacy and tamper-proofing of medical data, query efficiency, verification performance and expandability under high concurrency are remarkably improved, communication and resource expenses are lower, and the method is suitable for large-scale popularization and application. The method is suitable for application scenes such as cross-institution medical data sharing, remote diagnosis and treatment and compliance auditing.

The invention discloses a structured query language generation method and device and a nonvolatile storage medium. The method comprises the steps that a data query instruction of a user is received, and keywords in the data query instruction are extracted; related nodes are determined in a knowledge graph according to the keywords, the nodes in the knowledge graph are used for recording entity information in a database, and edges in the knowledge graph are used for recording the relation between entities; obtaining node information of related nodes and node information of associated nodes of the related nodes; and inputting the node information of the related nodes, the node information of the associated nodes and the data query instruction into a structured query language generation model to obtain a structured query language output by the structured query language generation model. According to the method and the device, the technical problem that the generated structured query language is inaccurate due to low retrieval accuracy and incomplete information during retrieval in the prior art is solved.

The invention provides a supply chain product traceability system, method and device based on a block chain, and a medium, and belongs to the technical field of block chains. The system comprises a data acquisition module which is arranged in a plurality of information terminals in a supply chain and is used for collecting product information, carrying out preliminary format conversion and encryption processing, and sending the product information to a block chain network; the block chain network is used for realizing data consistency through a consensus algorithm and storing product information by adopting a distributed account book technology; the data query module is used for providing a product traceability information query service by initiating a query request to a block chain network, and obtaining product supply chain information according to the authority of a query terminal; and the intelligent contract module is deployed on the block chain network and is used for automatically executing the business rules in the supply chain according to predefined intelligent contract code logic. According to the invention, real and reliable recording and efficient query of the full life cycle information of the product are realized by using the block chain technology, and accurate product traceability information is provided for consumers.

The invention discloses a natural language-to-SQL (Structured Query Language) method and system based on RAG (Random Access Gateway) enhancement, and the method comprises the steps: constructing a query intention graph through dependency syntax analysis, recognizing and complementing semantic missing components, and forming complete semantic representation; vector representation is carried out on the business term segments by adopting vectorization coding, accurate definitions of business terms are obtained from a factory structured knowledge base, and an enhanced context set is formed through expansion retrieval in a low-confidence region; an SQL template mapping network is established based on historical query records, a mapping relation matrix is generated through field candidate matching, mapping conflict positions are identified, and multiple SQL candidate sequences are generated; grammar verification is carried out on the candidate sequence to identify grammar errors, semantic consistency verification is carried out to calculate the intention alignment degree, and an optimal SQL statement is selected through a deviation correction factor; and performing formatting processing and statistical abstract on an execution result, and providing data query capability for scenes such as factory quality management and equipment maintenance.

A computer-based system and corresponding computer-implemented method process input data queries in a manner enabling advanced functionality for data analytics. The method transforms a query plan tree into a query strategy tree. The query plan tree is constructed from an input data query associated with a computation workload. The method compiles the query strategy tree into dataflow graph(s) (DFG(s)). The method transmits the DFG(s) for execution via a virtual platform. The method monitors execution of the DFG(s). The method outputs, based on a result of the execution monitored, a response to the input data query. The result is received from the virtual platform and represents at least one computational result of processing the computation workload by the virtual platform. The system and method enable rapid and efficient retrieval and analysis of data stored in data lakes and other data storage systems, in response to user queries.

The invention relates to a data query analysis method based on a natural language, which comprises the following steps of: analyzing a natural language query request input by a user into a dynamic semantic intention tensor which comprises an operation type, a comparison relationship, a time constraint and a data role semantic dimension, and dynamically combining according to task semantics to form a complete data query and analysis operation expression; mapping the natural language intention to a controllable data operation space of a database, and generating a preliminary data operation chain; performing side effect perception processing on the operation tensor, predicting a potential execution consequence of the operation, generating a side effect tensor for simulating a side effect of the database operation, and if any index in the side effect tensor exceeds a preset threshold value, performing local correction on the operation tensor; according to the method, a complex natural language query task is disassembled into a plurality of minimum semantic units by adopting a step-by-step semantic resolution mechanism, each unit is mapped into a specific tensor operation, and a dependency relationship between operation units is dynamically reasoned to generate an operation execution chain.

The invention provides a dynamic rule combination-based data query method and apparatus, and an electronic device.The method comprises the steps of firstly obtaining query parameter information input by a user, and then analyzing fields, operators and values in each query parameter group; based on the analyzed fields, operators and values, a corresponding target processing rule is matched for each query parameter group from a preset processing rule set, and then the query parameter group is processed according to the corresponding target processing rule for each query parameter group; and generating an SQL query statement based on a preset logic operator set and a processing result of each query parameter group, and finally executing the SQL query statement to query the database to obtain a database query result. By adopting the method and the device, the query parameter information can be automatically converted into the SQL query statement, the operation complexity of multi-condition complex query can be reduced, and meanwhile, the universality of the multi-condition complex query in diversified query scenes is improved.

Latency, response times, and efficiency improvements for data querying are provided herein, particularly in the context of querying large database systems and data tables from disparate data sources. There are provided systems and methods for automatic query and data retrieval optimization through procedural generation of data tables from query patterns. A service provider may utilize different computing services for query processing and data retrieval for different applications and services used by internal and / or external users. Instead of querying large database systems and numerous data tables, pre-aggregated data tables may instead be used and searched by procedurally generating such tables based on precomputation rules and query patterns. Once patterns have been identified in queries, corresponding data may be aggregated from data sources in a pre-aggregated data table. Query optimization rules may then be used to have these data tables queried in place of their original sources.

The invention discloses an intelligent data query system and method based on natural language processing, and relates to the technical field of natural language processing and database query. According to the method, natural language input and database mode information are received, a historical query log is combined to construct mode knowledge representation, a query skeleton is generated on the basis, a fine-tuned large language model is called to generate candidate SQL statements, error detection and ambiguity recognition are carried out on the candidate statements, and the query result is obtained. And if necessary, triggering interaction clarification and updating a query result according to user feedback. And meanwhile, the wrong clauses are locally repaired through a gating mechanism, and the clauses are returned and regenerated when multiple times of repair fails, so that the correctness of the query statement is ensured. And finally, after the SQL is executed in the database, a result is fed back to the user. Besides, the system records generation and repair tracks in the operation process, and continuously optimizes the model based on reward shaping, comparative learning and self-game training, so as to improve the generalization ability in different business scenes.

The invention relates to the field of data query, and particularly provides a medical big data query method and system based on dynamic desensitization, a terminal and a medium, and the method comprises the steps: collecting multi-source heterogeneous medical data, carrying out semantic normalization, constructing a unique identifier of a patient, and generating standardized intermediate plaintext data; constructing an adaptive isomorphic index structure based on the data, constructing a semantic hash B + tree index for structured data, constructing a feature vector index for unstructured data, and dynamically optimizing index hierarchy and parameters according to access frequency; and after a query request is received, the permission context is analyzed, intermediate plaintext data is positioned and read by using the index, and a differential privacy protection strategy is dynamically applied according to the access permission. According to the method, the problem of balance between privacy protection and data effectiveness as well as between retrieval efficiency and storage cost in medical data processing is effectively solved, and data availability and access performance are improved while safety compliance is guaranteed.

The invention relates to a data query method and device based on a natural language and a medium, and belongs to the technical field of data analysis. Receiving a natural language query input by a user; constructing task planning guide information based on the natural language query, and inputting the task planning guide information into the large language model to identify a target task type corresponding to the natural language query; when the target task type is a data query type task, determining a query target index corresponding to the natural language query from an index library; on the basis of a DSL dimension normal form corresponding to the query target index, dimension fields related to semantics of the natural language query and matched enumeration values are recognized from the multiple dimension fields and enumeration value sets; and constructing a structured intermediate representation, generating an executable code corresponding to the natural language query, and executing the executable code to obtain a corresponding structured query result from a data source. According to the method and the device, the accuracy and the interpretability are considered, and meanwhile, the SQL generation flexibility is improved.

The invention discloses an automatic data modeling and optimizing system and method fusing a knowledge graph and ChatBI, and relates to the technical field of data analysis. In order to solve the problems of high interaction threshold and insufficient analysis depth of a traditional BI tool, the scheme adopted by the invention comprises a data layer which has the capabilities of multi-source data access, domain knowledge graph construction, intelligent mapping recommendation, federal calculation and data mild governance, and realizes data integration and semantic unification; the analysis layer realizes accurate conversion from a natural language to an SQL and semantic reasoning of a knowledge graph through graph vectorization, multi-model cooperation, natural language understanding, intelligent SQL generation and graph dynamic updating, and supports efficient data query and analysis; and the application layer has the functions of natural language interaction, visual recommendation and generation, root cause analysis and intelligent early warning, and provides a visual interaction interface and data display service for a user. According to the invention, intelligent data analysis and visualization based on natural language interaction can be realized.

The invention discloses a dynamic multivariate time sequence-oriented infrastructure load prediction method, which comprises the following steps of: predicting an infrastructure load by adopting a trained prediction model, segmenting acquired multivariate time sequence data, querying based on time steps and nodes of input segmented data to obtain a space-time pattern vector, and predicting the load of the infrastructure by adopting a dynamic multivariate time sequence-oriented infrastructure load prediction model. A sequence mode vector is obtained based on input segmented data query, the space-time mode vector and the sequence mode vector are spliced into a dual-mode vector output by a dual-mode pool module, and then in a dynamic MTS encoder, the dual-mode vector and segment embedding representation are spliced to obtain a final feature vector; and finally, inputting the final feature vector into a multi-scale decoder for decoding to obtain a prediction result. Aiming at challenges such as data imbalance, data missing and space-time dependence erosion, the accuracy of a prediction result is improved under the condition that repeated training is not needed, repeated training needed during topological change is avoided, and the robustness, prediction precision and calculation efficiency of the model are improved.

The invention relates to a financial field data intelligent dialogue analysis system based on an AI large model, which relates to the field of artificial intelligence and financial data analysis, and comprises a database preposed preprocessing module and a financial data intelligent dialogue analysis module. The financial data analysis module is used for normalizing financial data, and the financial data analysis module is used for realizing natural language question analysis, data query and analysis, dialogue answer generation and visual display based on a Qwen-72B model and a tool set. The system improves the accuracy through professional knowledge injection, supports data attribution and user feedback optimization, and is suitable for complex financial data analysis scenes.