1120 results about "Table (database)" patented technology

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 provides a Text2SQL generation method and system driven by a large language model, and the method comprises the steps: recognizing key entities and relationships in a natural language question through the large language model, and carrying out the construction to obtain a query graph; constructing a hierarchical graph representation model based on the database mode of the target database; searching candidate sub-image sets which have the same structure as the query graph and are in semantic association with the query graph from the constructed graph representation model; and constructing a structured Prompt template based on the candidate sub-image set and the user question, generating an initial SQL statement, and outputting a final query result after grammar and semantic detection correction. According to the method, a traditional database is converted into the graph knowledge base, so that the SQL generation accuracy and performance are improved; guiding the large model to generate an SQL (Structured Query Language) meeting business requirements through knowledge graph storage and retrieval table and column information; and the accuracy of the SQL is continuously optimized by utilizing a self-adaptive feedback mechanism, so that the method is particularly suitable for a large-scale complex query scene, and self-improvement and customization of the model are realized.

The invention provides an SQL statement analysis method and device, equipment and a storage medium, and relates to the technical field of databases. According to the specific scheme, the method comprises the steps that SQL statements and metadata thereof are obtained, wherein the metadata comprises table names, column names and predicates of the SQL statements; target statistical information corresponding to the metadata is obtained from a preset statistical information base, and the target statistical information comprises table statistical information, column statistical information and index statistical information. And loading the statistical information to a test environment database to obtain a simulation database corresponding to the SQL statement. And based on the statistical information, determining an execution plan of the SQL statement, and executing the SQL statement in the simulation database according to the execution plan to obtain a statement analysis result. The method can effectively solve the problem of analysis distortion caused by the distribution difference of test and production environment data, and can obtain a more accurate SQL analysis result without complex data preparation.

Provided are system, apparatus, device, method and / or computer-program product embodiments, combinations and / or sub-combinations thereof for using an AI model to facilitate natural language interactions with databases. An example method can include receiving a natural language prompt associated with a user and identifying tables in a database based on the natural language prompt. The method can further include determining a table schema(s) of each of the tables identified, generating, using a large language model, a query to the tables in the database based on the natural language prompt and the table schema(s), and obtaining, using the query, data from at least one table of the tables in the database. The method can include generating, using the large language model or another large language model, a response to the natural language prompt based on the data obtained from the at least one table of the tables in the database.

The invention discloses a method and a system for generating an SQL (Structured Query Language) query based on a natural language question. The method comprises the following steps: converting mode information of a target database and the natural language question into semantic vector representation; based on the semantic vector representation, a simplified database mode set related to SQL query is screened out through an attention mechanism; based on the natural language problem, the simplified database mode set and preset database constraint information, generating an SQL structural skeleton, and filling specific elements of the SQL structural skeleton to form a preliminary SQL query; and performing dynamic correction and verification on the initial SQL query by utilizing a large language model, and outputting a final SQL query. According to the method, association mining between user query and a database mode is effectively enhanced by utilizing a context-aware cross-encoder mechanism, and an implicit corresponding relation between a natural language problem and a database table / column can be more accurately identified and utilized.

A computer-implemented method for improved schema matching of two databases is disclosed. The method can receive a schema of a source table from a first database and a schema of a plurality of target tables from a second database, identify one or more matching tables among the plurality of target tables based on comparison of the schema of the source table and the schema of the plurality of target tables using a large language model, obtain first sample attribute data from the source table and second sample attribute data from a selected matching table, and identify one or more pairs of matching attributes between the source table and the selected matching table based on comparison of the first sample attribute data and the second sample attribute data using the large language model. Related systems and software for implementing the method are also disclosed.

The invention discloses a query method based on a knowledge base and a large model. The method comprises the following steps: constructing the knowledge base of a database; obtaining target language information which is input by a user and contains a user question; based on the knowledge base, generating a target pseudo mode corresponding to the target language information; based on the target pseudo mode, screening out a minimum table set associated with answering the user question from the knowledge base; packaging the multi-table connection logic in the minimum table set into a query view; based on the query view and the target language information, a reference example pair list which is most similar to the user problem and indicates the mapping relation between the problem and the query statement is retrieved from a knowledge base, and each example pair in the reference example pair list represents the mapping relation between the problem and the query statement; and generating a target query statement corresponding to the target language information based on the user problem, the target pseudo mode, the query view and the reference example pair list, and performing retrieval in a database based on the target query statement to obtain a query result.

A computerized method is provided for using large language models to integrate data from multiple sources including technical metadata (e.g. column and table names) with a dictionary of standard abbreviations employed in the metadata, representative data from the columns rows of the table, a business glossary of terms in the data, and representative queries used to interrogate the data, to create a non-technical or natural language description of business value of the table and its columns and data.

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 an alloy field-oriented text and table double-track collaborative data extraction method and system. The method comprises the following steps of: obtaining an alloy related document DOI; taking the DOI as an index to obtain a full-text literature and storing the full-text literature in a JSON format; performing document screening based on an LDA topic distribution model and establishing a target topic document library; preprocessing the unstructured table, and obtaining structured table data by using a large language model; performing automatic text segment division on the JSON format text through rule matching analysis; and sorting the text segments by using a large language model to generate semi-structured text data, fusing and normalizing the text and table data, and outputting a component-process-organization-performance-strengthening mechanism alloy data chain. According to the method, through a double-track collaborative data extraction mechanism, the problems of multi-source heterogeneity, format fragments, information islands, semantic layering and the like of material subject data are effectively solved, structured treatment of literature data in the alloy field is achieved, and a unified technical path is provided for constructing a high-quality alloy database.

The invention discloses a method for generating statements from natural languages to SQL (Structured Query Language) based on a large language model agent. The method comprises the following steps of: constructing an LLM (Logical Language Model) guide table mode index for a target database; obtaining a user natural language question and extracting a keyword; searching in an LLM guidance table mode index according to the keyword, and screening related data tables; splicing the table mode information and the keywords of the related data table into a user natural language problem, and constructing an enhanced cue word; based on the large language model intelligent agent, generating SQL statements according to the enhanced cue words and verifying the SQL statements, if the SQL statements succeed in verification, obtaining the required SQL statements, and if the SQL statements fail in verification, performing iterative closed-loop correction on the generated SQL statements through an SQL statement closed-loop correction workflow; if the corrected SQL statement is successfully verified, the required SQL statement is obtained, and if the corrected SQL statement fails and the number of times of correction reaches a threshold value, the suggestions that the natural language problem of the user has defects and is modified are output. According to the method, the accuracy and success rate of SQL statement generation can be improved.

Methods, systems, and devices for data management are described. A method may include obtaining a new snapshot of a relational database of a source computing environment, comparing the new snapshot of the relational database to a prior snapshot of the relational database, updating, in response to the comparison indicating that data of a row of a relational database table changed between the prior snapshot and the new snapshot, a field of a second versioning control column of a corresponding row of a backup database table of the set of backup database tables to indicate a version of the new snapshot, and adding, in response to the comparison indicating that the data of the row of the relational database table changed, an additional row to the backup database table, where a field of a first versioning control column of the additional row includes an indication of the version of the new snapshot.

The invention discloses an association mapping method and system based on an API and a database sensitive field. Comprising the following steps: generating a tracking identifier for a current HTTP request through a global interceptor, and storing the tracking identifier and request context information into a thread context; intercepting an SQL (Structured Query Language) statement sent to a database by the application program, and analyzing the SQL statement to extract a data table name and a field name set accessed by a current HTTP (Hyper Text Transport Protocol) request; identifying sensitive fields in all access fields in the field name set; generating an associated log record based on the accessed sensitive field set, the SQL statement, the tracking identifier and the request context information; and analyzing the API response data to obtain a response field set, and establishing an association mapping relationship between the accessed sensitive field set and the response field set, so as to create complete data flow link information based on the association mapping relationship and the association log record. The data security of the sensitive data can be improved.

The invention discloses a cross-heterogeneous database data migration method and system, and the method comprises the steps: building a multi-data-source connection among multiple data sources, and enabling the multi-data-source connection to be used for representing the incidence relation between a source database and a target database; obtaining a source data table based on the migration sql, and querying source data corresponding to the source data table from a source database; converting the source data based on a preset model mapping rule to obtain target data; determining a target database into which the source data is migrated according to the multi-data source connection, and writing the target data into the target database to realize data migration; and performing data verification on the target database according to the checking rule to obtain a migration result, and generating a result report according to the migration result. According to the method, the source data is converted into the target data through the preset model mapping rule, migration obstacles caused by structural differences among heterogeneous databases are effectively solved, the consistency of the data is ensured through the checking rule, and the accuracy of data migration is improved.

The invention provides an NL2SQL database table dynamic screening method and system based on multi-strategy fusion. The method comprises the steps that a preliminary candidate set is generated, a semantic encoder is used for encoding a user natural language problem and table and column information in a database into vectors, the semantic similarity is calculated, a preset number of tables are screened out from a whole database table based on the similarity, and a preliminary candidate table set is formed; performing deep analysis and reordering, performing multi-dimensional analysis on the tables in the preliminary candidate table set, and calculating a graph relationship compactness score and a content correlation score of each table; and performing fusion decision, inputting the semantic similarity score, the graph relationship compactness score and the content correlation score of each table into a fusion model to obtain a final comprehensive score of each table, and screening out a final correlation table set based on the final comprehensive score. The method has high accuracy and high recall rate, the generation speed and response efficiency of the NL2SQL system are improved, meanwhile, the robustness is high, and the practicability and generalization ability of the system are improved.

The invention relates to a table information retrieval method and device, computer equipment and a storage medium. The method comprises the following steps: determining a table picture and a table title of a searchable table; based on the cue word template, according to the position information of the table link of the searchable table, the table title, the table abstract generation instruction and the table abstract specification, generating a table cue word; generating a table summary text according to the table prompt word, determining summary fragments, performing vectorization processing on the summary fragments, and determining summary text vectors; storing the table abstract text, a text identifier corresponding to the table abstract text, a link sequence number of a table link, a table picture address of the searchable table and an abstract text vector into a vector database; and obtaining an information retrieval request sent by a user, performing information retrieval on the vector database, and determining an information retrieval result corresponding to the information retrieval request. According to the scheme, the accuracy and integrity of information retrieval can be improved.

The invention relates to the technical field of database development, in particular to an SQL generation method and device based on a large model and an ER graph. The method comprises the following steps: analyzing a user query statement to obtain a database candidate table set; querying table field information of each table related to the user query statement from a database candidate table set; according to the table field information of each table, querying an association relationship among the tables in the database candidate table set from a preset ER map, and determining intermediate table information, the intermediate table being used for associating candidate tables without direct association; and generating an SQL query statement according to the intermediate table information and the database candidate table set. Through combination of candidate set generation and SQL optimization, logic errors of the generative large model are reduced, and execution efficiency is improved. The method does not depend on manual annotation of training data of a query language and an SQL template, the labor cost is greatly reduced, and good mobility is achieved.

The invention discloses an SSD B + tree indexing method and system based on wear leveling, and relates to the technical field of database indexing structure optimizing.The method comprises the steps that B + tree logic nodes and SSD physical addresses are decoupled, and a dynamic node mapping table is introduced; replacing local updating with additional updating, and writing the new version of the node into a new position of the SSD; when a write-in address is allocated to the node, an allocation strategy considering a block wear state is adopted, and write operation is guided to an area with relatively light wear; during garbage collection, high-wear blocks are preferentially treated to integrate wear. According to the method, the problems of writing hot spots and unbalanced abrasion of the B + tree on the SSD are effectively solved on the database software level, the service life of the SSD can be remarkably prolonged while the high performance of the B + tree is kept, and the method has very high universality and practical value.

The invention discloses an anaphora disambiguation method and system based on big language model enhanced text and structured query language generation. The method comprises the following steps: receiving a natural language question of a user, and analyzing and generating structured table field information through mode information; identifying and rewriting fuzzy time expression in the problem; extracting ambiguous entities, and replacing the ambiguous entities with database standard values through semantic matching and fuzzy matching; synthesizing the information to generate a structured query language statement and executing the structured query language statement; and if the execution result is null, automatically triggering an anaphora disambiguation process, performing error correction and re-matching on field values in the statement through a dual matching mechanism, and generating and executing a corrected query statement. According to the method, the problems of fuzzy anaphora, indefinite time expression, low matching accuracy, resource waste and the like in a traditional SQL system are effectively solved through a strategy of combining pre-processing and post-processing, and the accuracy, robustness and execution efficiency of complex query are remarkably improved.

Provided are a method and device for parsing a database query statement, and a storage medium. A query statement for a database table to be queried is obtained, and an empty database table corresponding to the database table to be queried is created; the query statement is executed in the empty database table, and a parser installed in a database is called to parse the query statement to obtain a syntax tree, and an execution plan is generated based on the syntax tree for execution in the empty database table; and the syntax tree is obtained from the parser in an execution process of the query statement, and a syntax tree serialization result is generated and output as a parsing result of the query statement.

The invention belongs to the technical field of database query and natural language processing, and provides an NL2SQL method based on foreign key completion and a knowledge graph, and the method comprises the steps: extracting a candidate foreign key relationship from database metadata and data content, and trimming and completing the candidate foreign key relationship through a three-layer funnel verification mechanism, a Schema knowledge graph is constructed; based on the natural language query, determining a seed table set directly related to the query intention; on the basis of the Schema knowledge graph, calculating a connection path between nodes of a seed table, complementing an intermediate table on the path, and expanding the seed table into a sub-graph set containing a complete connection relationship; generating a plurality of candidate SQL statements based on the sub-chart set; and executing the plurality of candidate SQL statements, and voting according to the consistency of execution results to select a final SQL statement.

The invention discloses a method for generating a text system, and aims to solve the problems of long development cycle, disjunction of requirements and technology implementation and the like of traditional software and realize automatic generation from a natural language requirement to a deployable service system based on a large language model. The method is divided into two stages: in the first stage, a system is disassembled into a logic subsystem through man-machine cooperation, core business logic is extracted, a large language model is called by adopting a divide-and-conquer strategy, and a detailed specification containing a functional process and an interface form is generated; in the second stage, a large language model is called to generate a database table structure and a DDL script, an engineering framework of a basic technology stack is initialized and integrated through a predefined scaffold, CRUD codes are generated, the codes are analyzed to generate a transformation plan for structured data, and target logic codes are incrementally integrated. According to the method, control is achieved through Python, the problem of large model input limitation is solved, and generated codes are standard and extensible. The development cycle is shortened, the requirement-design-code consistency is guaranteed, the threshold is lowered, platform locking is avoided, and enterprise digital transformation is assisted.

Example implementations include a system and a computer-implemented method for breaking computational graph lineages configured for identifying at least one mutable variable of a given database table in a block of structured query language (SQL) instructions, the block containing at least one loop, the at least one mutable variable including one or more of a first variable declared and initialized outside a given loop and used and updated in the given loop or a second variable defined inside the given loop and assigned value as a result of an operation in the given loop. The implementations further include inserting a breakage of a computational graph lineage of at least one of the first variable or the second variable.

The invention provides a Text-to-SQL (Structured Query Language) conversion method based on semantic modeling, which relates to the technical field of natural language processing and data analysis, and comprises the following steps: designing a structured semantic model comprising a fact table and a dimension table by adopting a dimension modeling method, and defining business field division, data model definition and model dictionary construction; performing word segmentation processing on the natural language question input by the user, matching the natural language question with dimensions, indexes and dimension values in the structured semantic model based on the model dictionary library, and generating Schema information containing database types, table names, field definitions and filtering conditions; calling the large model, and converting the natural language problem into a preliminary semantic SQL in combination with Schema information and a preset rule; calling the large model again, performing logic verification and grammar optimization on the preliminary semantic SQL, and generating a corrected semantic SQL; based on Schema information and data model definition, the corrected semantic SQL is converted into an executable database SQL statement.

The invention discloses a database query system and method for converting a natural language into an SQL (Structured Query Language) statement based on a Transform architecture, and the system captures a long-distance dependency relationship and complex semantic information in the natural language based on the Transform architecture, so that the semantic accuracy is improved, the wrong and repeated operation of generating the SQL statement is reduced, and the data acquisition efficiency is improved. On the other hand, the problems of grammar errors, semantic inconsistency and the like are verified, recognized and corrected through the SQL error correction module, database query failures are reduced, the user operation experience is improved, meanwhile, the error correction module can conduct compensation through reverse verification and correction, the dependence of the system on the precision of a single module is reduced, and the overall stability is improved; and for similar related problems, SQL statements can be directly extracted from the mapping table, so that time-consuming processing links such as Transform semantic analysis and SQL generation are omitted, and the waiting time and the computing power consumption of a user are remarkably reduced.

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

Methods, systems, and computer program products are provided that implement vector-related requests using query operators. For example, a system includes a database, a parser, a converter, an optimizer, and an execution engine. The database is associated with query operators that are non-vector-specific. The database stores a table with vector embeddings. The parser is configured to parse a request indicating a vector operation associated with the vector embeddings. The converter is configured to convert the vector operation into a logical operator tree comprising a representation of the request as a logical flow of the query operators, enabling the vector operation without vector-specific executable code or operators. The optimizer is configured to convert the logical operator tree into an executable plan. The execution engine is configured to execute the executable plan against the table with vector embeddings.

In some embodiments, there is provided generating a logical query plan for a query that includes a single fact table and multiple reducer tables; optimizing the logical query plan for query execution; and executing the optimized logical query plan.

The invention relates to the technical field of query characterization, and discloses a database query feature vector generation method, which comprises the following steps of: obtaining a connection bitmap and a database mode graph of a database queried by a target query statement; determining a database mode sub-graph related to the target query statement according to the database mode graph; encoding the database mode sub-graph through a preset gating graph neural network to generate a mode item vector; and determining a query feature vector corresponding to the target query statement according to the connection bitmap and the mode item vector. According to the method, by obtaining the database mode graph and the connection bitmap, the structure association information between the table and the column in the database and the predicate condition features of the query can be synthesized, the semantic association represented by the features is enhanced based on the coding capability of the graph neural network, and the expression capability of the query feature vector for query intention and data association is effectively improved.

The invention discloses a data warehouse table structure generation method and device, a medium and a product, and belongs to the technical field of data warehouses. According to the method, the Excel template configured with multiple pieces of source database table structure information is obtained, table field definition is automatically analyzed, the source data type is intelligently mapped and converted into the target data type according to the source database type, meanwhile, partition key fields are automatically recognized based on the preset strategy, and finally a complete and accurate data definition language (DDL) statement is generated. According to the method, remarkable technical breakthrough and value are achieved in practical application, the table building efficiency can be improved by 90% or above, batch processing of hundreds of tables is supported, and manual errors are reduced by 95% or above; mainstream database type conversion is comprehensively supported, and various data models such as Dores are adapted; the method has the capabilities of automatic type detection and conversion, intelligent key field selection and partitioning and bucket dividing strategy optimization.