197 results about "Database querying" patented technology

The invention belongs to the technical field of equipment knowledge engineering and natural language processing, and discloses an LLM-based constraint and self-repairing uniformization equipment knowledge graph automatic construction method. The method comprises the following steps: firstly, acquiring equipment related document data through network collection, document arrangement and database query; then, utilizing an equipment domain ontology and constraints as preposed soft and hard constraints, driving LLM to generate semantic intermediate representation, and obtaining candidate triples through structured compiling; then, a self-repairing closed loop is formed through semantic unit testing, logic consistency detection and evidence binding verification, and triples which violate constraints and have conflicts or illusions are automatically recognized and repaired; and finally, entity standard identification, cross-document duplicate removal combination and conflict resolution are realized through cross-segment unification and incremental alignment. According to the method, the fragile path that the LLM directly generates the triple and blindly stores the triple is avoided, the illusion and inconsistency problems are effectively inhibited, and the correctness, interpretability and maintainability of the equipment knowledge graph are remarkably improved.

The present application discloses a database query method and apparatus, an electronic device, and a non-volatile storage medium. The method comprises: determining a knowledge graph corresponding to a database to be queried, wherein the knowledge graph is used for representing a logical structure and an association relationship of data in said database; determining similarity scores between user question text and graph nodes in the knowledge graph, and determining a target node from among the graph nodes of the knowledge graph on the basis of the similarity scores, wherein the similarity scores are used for representing the degree of association between the graph nodes and the user question text; and on the basis of the target node, generating database schema information corresponding to said database, and using a large language model to generate, on the basis of the database schema information, a structured query language statement corresponding to the user question text.

A system and method for querying, processing, and ranking governmental data across heterogeneous databases may include integrating large language models (LLMs) and multi-agent orchestration to enhance accuracy and mitigate bias. The system receives a natural language query, processes the query using an agent orchestration LLM to generate search query instructions, and executes specialized data record processing agents to retrieve data from diverse sources, including relational, vector, and graph databases. A plurality of candidate natural language responses is generated and validated by data verification LLMs based on relevance and accuracy metrics to reduce hallucinations. The system further employs cross-source validation, statistical and linguistic consistency analyses, and weighted scoring to refine responses, which may include textual summaries, tables, or downloadable files presented via a graphical user interface. Accordingly, the approach ensures precise and contextually relevant results, particularly for applications involving sensitive or regulated data such as legislative and governmental records.

A database query processing method includes receiving a natural language request for information contained within a database from a user in an application session, prompting a large language model to generate a SQL request, and receiving a particular SQL request from the large language model that is parsed to identify a command to access one or more database structures. A security predicate is appended to the command, creating a modified SQL request, to enforce one or more database access constraints constraining a user-authenticated client device that submitted the request that is not enforced in a database session between the application and a database. The modified SQL request is used to access data in the database session, and a visualization of the accessed data is caused to be displayed in the application session.

The present disclosure provides an artificial intelligence-based alarm management based on structured time series data and unstructured contextual information where the conventional methods fails to do. Initially, the system receives a user query associated with an industry automation system and classifies into one of a) data request query b) an anomaly detection and diagnosis query c) a log request query and d) feedback for previous response using a query classifier. Simultaneously, the user query is converted into an associated timeseries query representation. Furthermore, a plurality of database queries is generated based on the timeseries query representation using query generator. Further, data pertaining to the user query is retrieved from an associated database based on database queries using Deep Learning models, thereby identifying a critical alarm, and to take appropriate actions if there is any anomaly. Finally, the retrieved data is displayed to the user in a user readable format.

A multimodal content management system having a block-based data structure can include a query engine configured to perform automatic data discovery for natural language queries. The system can generate and render, at a computing device, a page comprising a graphical user interface (GUI) with a displayable item from a first block of a block-based data structure. The system can generate and bind, to the page, a schema definition comprising a first reference to the first block and a second reference to a set of blocks, wherein the first block is relationally linked to the set of blocks via the second reference. The system can use at least a portion of a natural language prompt, received at the GUI, to generate an input feature for a large language model, the input feature having a schema-question pair that includes at least a portion of the schema definition. The large language model can generate a query configured to operate on the block-based data structure.

A database query processing method includes receiving a natural language request for information contained within a database, categorizing the natural language request in a particular category from a plurality of categories, and selecting a large language model and one or more database structures for which a SQL request is to be directed based in part on the particular category. The selected large language model is prompted to generate a SQL request directed to the selected set of one ore more database structures and the received SQL request is validated for the particular category. The validated particular SQL request is used to access data within the database and one or more visualizations is caused to be displayed based at least in part on a result of the validated particular SQL request.

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 natural language structured query generation method and system based on pattern reasoning, and relates to the technical field of natural language processing and database query. The method comprises the steps of mode knowledge graph construction in an offline stage and three-step reasoning generation in an online stage. In the offline stage, through structure analysis, semantic enrichment and data portrait processing, a database mode is converted into a knowledge graph Schema-KG rich in structure and semantic information. The online stage adopts three-step reasoning: firstly, decomposing a natural language of a user into an intention component through LLMParser, and linking the intention component to a graph entity; secondly, reasoning an optimal JOIN path on the Schema-KG based on a graph search algorithm, and generating a mode perception intermediate representation SA-IR; finally, the SA-IR is compiled into an SQL, and pre-verification is executed. According to the method, the accuracy of complex query is remarkably improved through a structured reasoning mechanism, errors during operation are effectively avoided through pre-execution verification, a clear reasoning process is provided through SA-IR intermediate representation, and the interpretability and debugging efficiency of the system are greatly improved.

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.

The invention relates to the field of databases, and provides a method for converting a natural language into a database query language, a database and electronic equipment. Performing intention recognition on the natural language information, and determining a service scene corresponding to the natural language information; obtaining a table set and a field slice corresponding to the business scene through hierarchical recall based on the business scene, and forming candidate context information in combination with a preset constraint slice; and sorting the candidate context information based on a preset weight index, and generating a database query language based on the sorted candidate context information. The method and the device are used for solving the defects of inaccurate natural language to database query language and low reliability in related technologies, and can realize more efficient and more accurate knowledge recall, and provide sufficient and related context support for a large model, so that the accuracy and the reliability of natural language to SQL are improved, and the user experience is improved. And the illusion risk is effectively reduced.

The invention belongs to the field of cross technologies of natural language processing and database query technologies, and provides a method for generating database query through a natural language. Comprising the steps that table names and field names of all data tables are obtained, a Schema attribute graph used for describing the incidence relation between the tables is constructed based on the table names and the field names, and a structured Schema index database is established; receiving a query request input by a user, and calling an entity recognition model to recognize entity information related to the business from the query request to obtain corresponding candidate table names and candidate field names; determining an associated sub-graph structure, and constructing a local structured Schema index database; constructing a candidate association path set, and determining a target association path according to a preset priority rule; performing grammar and logic verification on the structured query statement to obtain an executable structured query statement; and updating to obtain the target local structured Schema index database. According to the invention, the stability and accuracy of query execution are improved.

Systems and methods are disclosed for converting natural language queries to a query instruction set for searching a data warehouse. To generate a query instruction set from a natural language query, a system iteratively uses a generative artificial intelligence (AI) model and database query tools to generate a query instruction set in a stepwise manner. The system and generative AI model do not require a priori knowledge of data table contents in the data warehouse, which may include sensitive information. In addition, the system does not require access to the data warehouse to generate the query instruction set. Instead, the system is implemented to use structure information from the data warehouse, including table lists (such as table names) and table format information (such as column names) of tables in the data warehouse, and the generative AI model is a generally trained model to generate the query instruction set.

Methods and systems for retrieving data from at least one database based on a query input related to a clinical trial. A system processes a received query input related to a clinical trial using a pre-trained language model neural network. The neural network generates a structured representation of the query input. The system maps a first data field of the structured representation to a first column name and maps a second data field of the structured representation to a second column name. The system generates a database query based on (i) a database schema, (ii) the first column name, (iii) the second column name, (iv) data values associated with the first data field, and (v) data values associated with the second data field. The database query specifies an operation for joining data associated with the first column name with data associated with the second column name.

Techniques are disclosed for augmenting training data for training a machine learning model to generate database queries. Training data comprising a first training example comprising a first natural language utterance, a logical form for the first natural language utterance, and associated first metadata is obtained. From the first training example, a template utterance is generated. A second natural language utterance is generated by filling slots in the template utterance based on a database schema and database values. Updated metadata is produced based on the first metadata and the second natural language utterance. A second training example is generated, comprising the second natural language utterance, the logical form for the first natural language utterance, and the updated metadata. The training data is augmented by adding the second training example. A machine learning model is trained to generate a database query comprising the database operation using the augmented training data set.

A database query processing method includes receiving a natural language request for information contained within a database, categorizing the natural language request in a particular category from a plurality of categories, and selecting a large language model and one or more database structures for which a SQL request is to be directed based in part on the particular category. The selected large language model is prompted to generate a SQL request directed to the selected set of one ore more database structures and the received SQL request is validated for the particular category. The validated particular SQL request is used to access data within the database and one or more visualizations is caused to be displayed based at least in part on a result of the validated particular SQL request.

The invention relates to the technical field of databases, in particular to a system and a method for querying a database with different databases and different tables based on a large model. The system comprises a data source reading module, a sub-library and sub-table strategy inference module, an SQL (Structured Query Language) analysis module, an SQL rewriting module, a query execution module and a result aggregation module. According to the method, the use threshold is reduced, a user does not need to know a sub-library and sub-table strategy of a bottom layer, and the fragmented data can be queried only by writing a standard SQL; the query efficiency is improved, and the time for manually compiling complex queries is shortened through automatic SQL rewriting and result aggregation processing; the adaptability is enhanced, and when the library and table division strategy is changed, the system can automatically adapt without modifying historical query statements; complex query is supported, and cross-library table complex query scenes including multi-table association, sub-query and the like can be processed.

Methods and systems for retrieving data from at least one database based on a query input related to a clinical trial. A system processes a received query input related to a clinical trial using a pre-trained language model neural network. The neural network generates a structured representation of the query input. The system maps a first data field of the structured representation to a first column name and maps a second data field of the structured representation to a second column name. The system generates a database query based on (i) a database schema, (ii) the first column name, (iii) the second column name, (iv) data values associated with the first data field, and (v) data values associated with the second data field. The database query specifies an operation for joining data associated with the first column name with data associated with the second column name.

The invention discloses a natural language database query method and system based on semantic closed-loop verification. The method comprises the following steps: receiving an initial natural language; semantic analysis is carried out through the semantic analysis model, and query elements are extracted; generating a first SQL statement based on the query element; reversely analyzing the first SQL statement into a first natural language; performing consistency comparison on the first natural language and the initial natural language; and when the semantics are inconsistent, generating comparison difference information, triggering a correction cycle step, and if the comparison results are consistent or the comparison results become consistent after one or more correction cycles, executing a corresponding SQL statement to obtain a query result. Through a closed-loop verification mechanism of SQL reverse analysis and semantic consistency comparison, SQL generation errors caused by natural language ambiguity or LLM understanding deviation can be effectively recognized and corrected, and the problem of poor accuracy is solved.

A database query processing method includes receiving a natural language request for information contained within a database from a user in an application session, prompting a large language model to generate a SQL request, and receiving a particular SQL request from the large language model that is parsed to identify a command to access one or more database structures. A security predicate is appended to the command, creating a modified SQL request, to enforce one or more database access constraints constraining a user-authenticated client device that submitted the request that is not enforced in a database session between the application and a database. The modified SQL request is used to access data in the database session, and a visualization of the accessed data is caused to be displayed in the application session.

The application is suitable for the technical field of data processing, and provides a database query method, system, device and medium based on natural language. The method comprises the following steps: acquiring a natural language text; based on the natural language text and a field library, calling a root matching strategy, a semantic matching strategy and a fuzzy matching strategy through a hierarchical cooperation mechanism to perform field mapping and determine a target field; splicing the target field to obtain an initial semantic query statement; loading and executing multiple correctors in sequence through a service discovery mechanism to obtain a target semantic query statement; converting the target semantic query statement to obtain a target physical query statement; executing the target physical query statement to obtain a target query result and return the query party. Through hierarchical cooperation of the root matching strategy, the semantic matching strategy and the fuzzy matching strategy, the application improves the scalability and adaptability of the matching strategy, improves the field recognition accuracy, expands the field matching range, and optimizes the matching efficiency.

The application discloses a kind of intelligent SQL generation method and system based on multistage intention recognition, comprising: receiving user natural language query;Vector matching step: vector matching is carried out based on knowledge base, obtain Top-K candidate query object, and knowledge base contains multi-layer structure description information;Large language model intention understanding step: determine final query object in combination with candidate query object description;Load branch workflow, parse field and table structure, dynamically configure database table and field information;SQL generation step: generate SQL sentence in accordance with syntax rule, including automatically adding field, time condition conversion, field priority matching operation;Execute SQL sentence and return result, and record log if it fails to prompt correction.The application provides an efficient, accurate and user-friendly database query solution by natural language processing technology combined with database knowledge base.

The invention discloses a database query statement generation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining query demand information which is used for representing a demand which is described in a natural language and is used for querying a target database; inputting the query demand information into a query statement generation model, and generating an initial structured query statement by utilizing the query statement generation model; statement execution time of the initial structured query statement is determined, and the statement execution time is time for executing the initial structured query statement on the target database; and inputting the statement execution time into a query statement generation model, and generating a target structured query statement by utilizing the query statement generation model. By the adoption of the technical scheme, the problem that the execution efficiency of the database query statement generated in the related technology is low is solved.

The invention relates to the technical field of laboratory safety management and control, and particularly discloses a laboratory safety comprehensive management and control method based on a multi-modal model, and the method comprises the following steps: S1, obtaining a material list and an operation time sequence table of an experiment, querying hazard parameters of a material in different physical states based on a preset safety database, calculating the hazard intensity value of each experimental operation; s2, collecting laboratory environment data in real time, and generating current spatial state distribution; according to the hazard intensity value, positioning in spatial distribution and calculating a risk value when each operation is executed; s3, pre-defining a strategy function, and determining hazard clearing efficiency required by different risk levels; and according to the risk value, matching a clearing efficiency requirement, solving and outputting an equipment control parameter sequence meeting the requirement, and finally, accurately adjusting the clearing equipment according to the equipment control parameter sequence.

The application belongs to the cross technical field of natural language processing and database query technology, and provides a method for generating a database query from a natural language. The method comprises the following steps: obtaining table names and field names of each data table, constructing a Schema attribute graph for describing the association relationship between tables based on the table names and the field names, and establishing a structured Schema index library; receiving a query request input by a user, calling an entity recognition model to identify entity information related to a business from the query request, and obtaining corresponding candidate table names and candidate field names; determining an associated subgraph structure, and constructing a local structured Schema index library; constructing a candidate association path set, determining a target association path according to a preset priority rule; performing syntax and logic checking on a structured query statement, and obtaining an executable structured query statement; and updating the target local structured Schema index library. The application improves the stability and accuracy of query execution.

The invention discloses a multi-source information processing method, system and device based on dynamic perception and intelligent response and a medium, mainly relates to the technical field of information processing, and is used for solving the problems of low information acquisition efficiency, insufficient information processing intelligence degree, lack of effective summarization and poor system flexibility in the existing scheme. Comprising the following steps: crawling latest information, and storing the latest information to an article data table of a database; obtaining natural language query information of a current user, and determining a preset intention category corresponding to the natural language query information; extracting keywords from the natural language query information according to a preset intention category; constructing database query conditions by utilizing the keywords; the database query condition is executed, and a plurality of corresponding information messages are retrieved from an article data table of a database; and utilizing a preset induction cue word to guide the large language model to integrate a plurality of pieces of information into an abstract report in a preset format.

A machine-readable medium stores instructions that, when executed by a processor, cause the processor to execute operations. The operations include receiving information identifying a database table and preprocessing the table. The preprocessing includes extracting primary and foreign key relationships and generating sample data by combining frequency-based sampling of common values with random sampling. The operations also include generating, using an LLM, a unified metadata schema. The generating includes processing the table and the sample data using domain-specific prompts and outputting the unified metadata schema. The operations include embedding and indexing the unified metadata schema and converting a natural language question output from an end-user device into a SQL query. The converting includes identifying relevant metadata in the unified metadata schema matching query intent and generating the SQL query using the filtered metadata. The operations also include executing the SQL query and providing query results to the end-user device.

The invention discloses a poultry breeding pedigree coding method. The method comprises the following steps: firstly, determining strain codes of individuals; determining the appearance codes of the individuals; determining batch codes of the individuals; determining family numbers of the individuals; determining individual type codes of the individuals; determining individual sequence numbers of the individuals; generating a check code; combining the strain code, the provenance code, the batch code, the family number, the individual type code, the individual sequence number and the check code in sequence to form an individual code; according to the method, the structured coding rule containing information such as strains, generations, batches, families, types, individual serial numbers and the like is constructed, so that the information capacity borne by a single code is greatly improved, and key individual identity information which originally needs to depend on database query can be directly reflected in a coding character string; therefore, the problem that the traditional serial number information amount is too low is solved.

A database system receives a database language query specifying a grouping clause and one or more aggregate result values. The database system generates a pre-aggregation table based on the query. The database system generates an ingest query configured to generate data for populating the pre-aggregation table and a completion query configured to processes data stored in the pre-aggregation table. The database system periodically receives a set of input records and executes the ingest query to generate a set of pre-aggregation records. The set of pre-aggregation records are combined with the pre-aggregation table. The database system executes the completion query on the data stored in the pre-aggregation table to generate a result set and sends the generated result set to the requestor of the results.

The invention discloses a database query method, device and equipment based on a WebAPI interface and a storage medium, in the query method, WebAPI interface input parameters comprise an input parameter object and a task number, after a client sends a request message comprising the WebAPI interface input parameters, the WebAPI interface converts the input parameter object into dictionary data in a standard format, and the dictionary data in the standard format are stored in the database. The method comprises the following steps: acquiring a task number from a data center, acquiring a corresponding execution script from the data center according to the task number, assigning dictionary data to the acquired execution script, executing query of a database according to the execution script to obtain a result array, and finally converting the obtained result array into an output format and sending the output format to a client. The input parameters of the WebAPI interface are compatible with a plurality of interface requirements of different queries, different input parameters and different outputs by setting the input parameter objects and the task numbers, so that the design and development cost is effectively reduced while the personalized interface requirements are quickly responded, and code recompiling and redeployment online are not needed every time the requirements are changed.