253 results about "Database structure" patented technology

The invention provides an SQL (structured query language) statement generation method based on large-model multi-stage iteration, which is used for database structure understanding and natural language SQL query generation, constructing multi-level semantic embedding of a database structure through advanced semantic modeling, and deeply analyzing a natural language query request of a user by utilizing an intention recognition model; a query intention frame is extracted in combination with a pre-trained large model, the query intention frame and database semantics are embedded and fused by adopting a calibration correction model and in combination with a multi-stage feedback mechanism, and a query intention and model parameters are optimized step by step; an optimal practice SQL statement is generated through deep optimization and a retrieval strategy model, model parameters and retrieval strategies are continuously optimized through self-adaptive evolution and user preference feedback, and a knowledge base is updated. According to the method, the problems of much manual intervention, inaccurate intention understanding, insufficient structure understanding and lack of a feedback mechanism are effectively solved, and efficient and accurate SQL query generation is realized.

A system and method for automatic generation of database queries using zero-shot, context-based machine learning may output and / or execute database queries and / or analytics insights or plots based on text prompts, and may include or involve: wrapping a text prompt to include database structure information; generating, by a large language model (LLM), a query based on the wrapped prompt, where the query may include one or more database operations; and extracting data or information items from a database based on the query. Some embodiments may include additional prompt or query processing operations such as, e.g., wrapping queries to include corresponding database operations, validating that queries do not include malicious or undesirable commands, and automatically performing appropriate computer actions based on generated queries. Some embodiments of the invention may relate to databases and text prompts describing user actions input to a computer and collected by a desktop data collection software.

A database query processing service is provided for efficiently processing query workloads with natural language statements and native database commands. The database query processing service may receive some database queries that do not contain a natural language marker and process these database queries without using large language models to generate replacement query content. The database query processing service may also receive other database queries that do contain the natural language marker and process the other database queries using large language model(s) to generate replacement query content or leverage replacement query content already generated by the large language model(s). The replacement query content is checked to ensure the content is natively valid for the content to retrieve data from database structures referenced in the content. The database query processing service may use the natively valid replacement query content to cause execution of operations responsive to the other database queries.

A database structure obtained by means of information retrieval, and a reasoning system, which structure and system specifically relate to a gut microbe knowledge graph system, comprising: a gut microbe knowledge graph consisting of a gut microbe knowledge base, a gut microbe and small-molecule drug therapy association knowledge base, and a clinical medicine database; and a multimodal uncertainty reasoning system, using the gut microbe knowledge graph. The gut microbe knowledge graph system predicts potential diseases, drugs, genes, etc., which are associated with gut microbes.

Computer-implemented methods and systems are disclosed for Information Technology Service Management (ITSM). The pioneering AI-driven system revolutionizes IT service management by uniquely validating, suggesting, and inferencing incident and problem data. At its core are cutting-edge generative AI techniques like GANs and LLMs, requiring intricate training and iterative refinement on varied data sets, showcasing a depth of expertise in database structures and AI concepts. It bridges critical gaps in incident resolution and classification through cognitive computing, AI, NLP, and deep learning, applied to both historical and current data. The system comprises modules for Incident Validation & Classification, Resolution Validation, Generative Intelligence, Problem Probability Calculation, and Prevention Recommendation, each employing AI to enhance standard compliance, predictive analysis, and proactive management, thereby setting new standards for IT service management efficiency and effectiveness.

The invention provides a cross-database unified management and operation method and system, and relates to the technical field of databases, and the method comprises the following steps: establishing a connection by obtaining database connection information; extracting a database structure by using a dialect strategy processor and mapping the database structure into a unified description; converting the operation instruction into a standard intermediate statement; converting the intermediate statement into a target database execution statement by using a grammar analysis module and a conversion rule; and executing and monitoring in a transaction unit mode. Unified management and operation of different types of databases are realized, the consistency and reliability of database operation are improved, and the development difficulty of cross-database operation is reduced.

The invention relates to the technical field of databases, can be applied to the field of science and technology finance / digital medical treatment, and discloses a database migration method and device, electronic equipment and a storage medium. The method comprises the steps that database structured query language identifiers are defined, strategy classes corresponding to all databases are packaged, and a strategy context management container is created; receiving a target database type, dynamically loading a strategy type instance according to the type, and generating an executable query strategy chain according to the strategy type instance; when a database query request is received, matching a corresponding strategy type instance, and processing an original database query statement to generate a target database executable instruction; controlling a query strategy chain to execute a single-path or double-write-path mode through a common switch, and transmitting the instruction to a strategy type instance for execution; and analyzing a return result of the target database, performing conversion adaptation through the strategy type instance, and returning a conversion adaptation result. The method realizes automatic migration, reduces cost and risk, and improves system performance.

The invention provides an intelligent number asking method for large model index query, and belongs to the field of natural language processing and database query. An auxiliary information base is constructed to store a database structure and historical question and answer examples, and key entities and fields are extracted by conducting structured format processing on natural language questions; a large language model guided by a small number of examples is utilized to generate an intermediate expression close to an SQL grammar and a plurality of SQL query candidates, and a consistency alignment mechanism is designed to verify candidate SQL in the aspects of field matching, aggregation functions, data tables and the like so as to screen out an optimal SQL query. Finally, the generated correct SQL and the corresponding question and answer pairs are fed back to the auxiliary information base, and self-learning enhancement is achieved. According to the method, under the condition that only a small number of training examples are needed, the accuracy and expandability of large language model text generation SQL query are improved.

The invention belongs to the technical field of high-performance composite materials, and discloses a carbon fiber reinforced thermoplastic composite material performance database, a construction method and application thereof, and the method comprises the following steps: S1, database structure construction; s2, experimental sample collection and data standardization; s3, feature engineering and variable reduction; s4, training a machine learning model; s5, constructing and verifying an adaptive model; and S6, data expansion and feedback optimization. According to the method, material performance prediction and formula parameter reverse design under target performance are realized through systematic acquisition and normalization processing of three types of data of material components, preparation process and performance characterization and building of a nonlinear mapping model among a material structure, a process and performance through a machine learning method. The database can be used for intelligently recommending a high-performance composite material combination scheme, is suitable for rapid screening and customized development of various thermoplastic composite materials, effectively reduces the research and development cost and development cycle, and improves the material design efficiency.

The invention discloses a structured data quality evaluation and optimization system using a large language model, which comprises a data access module used for formatting and preprocessing data, a data quality detection module used for establishing data quality evaluation indexes, performing statistical analysis and rule verification to detect the data and calculating the overall quality grade, and a big language model, the semantic analysis and SQL generation module is used for analyzing a database structure through a large language model, converting a natural language instruction into an executable SQL statement and is based on the large language model, and the anomaly detection and optimization suggestion module is used for positioning anomaly by utilizing a mixed detection algorithm and generating natural language interpretation and optimization suggestions in combination with the large language model. And the user feedback and dynamic adjustment module is used for absorbing adjustment suggestions through a feedback loop mechanism, and optimizing a prompt project and an SQL generation strategy. According to the method, semantic analysis is carried out on a database structure by adopting a large language model, and high-precision SQL query is automatically generated.

The invention relates to the technical field of computers, in particular to a dynamic configuration initialization and version synchronization method and system for multi-agent service, and the method comprises the following steps: obtaining a database update lock, constructing a database migration script dependency relationship, determining a current version of a database mode, upgrading and deploying the database mode, and verifying agent versions. The method has the beneficial effects that a rigorous database updating lock control mechanism, perfect migration script dependency chain verification, agent version management, port availability detection, idempotence and transaction safeguard measures are introduced, so that the processes of database structure upgrading and agent version initialization in a distributed deployment environment can be effectively supported; and the data consistency, the operation stability and the service continuity of the system are ensured.

The invention discloses a natural language database query method and system based on large-model two-channel context enhancement, and the method comprises the steps: carrying out the parallel execution of a business link and a mode link on a query statement, and respectively outputting business context information and mode link information; carrying out vectorization fusion on the two types of information to generate combined context information; generating a query intention vector based on the combined context information, and outputting a complexity label of the query statement through a complexity calculation model; and generating a prompt template according to complexity label differentiation, inputting a large model to generate an SQL statement, and executing query. Through a double-channel cooperation mechanism, the problems of semantic ambiguity, database structure complexity, poor field adaptability and the like in natural language query are solved, query accuracy and response efficiency are improved, and the method is suitable for multi-industry complex database scenes.

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 invention relates to a query method based on multi-agent collaboration, and the method comprises the steps: generating a query structured representation after obtaining a natural language query text input by a user, and obtaining an associated database structure representation in combination with a constellation data model of a target database; and inputting the query structured representation and the database structure representation into a preset query statement generation agent to generate a plurality of first candidate query statements. And checking and correcting the first candidate query statement, and carrying out iterative checking and correcting on the corrected query statement which does not meet the condition. And obtaining a second candidate query statement with higher reliability through an iterative correction mechanism. And then, carrying out confidence evaluation on each second candidate query statement, selecting the second candidate query statement with the highest confidence as a target query statement, and executing query in the target database. According to the embodiment of the invention, the accuracy, reliability and practicability of natural language query are improved, and more convenient, efficient and accurate query experience is provided for users.

The invention discloses a heterogeneous database structure dynamic synchronization method and device and a medium, belongs to the technical field of database migration, and is used for solving the defects of the existing database migration technology in the aspects of environment without native change log support, structure synchronization and fault tolerance. The technical problems of log dependence, low structure repairing efficiency and weak migration fault-tolerant capability exist in the prior art. The method comprises the following steps: performing data structure differentiation comparison between a source library and a target library to generate a structure difference atlas; traversing and converting the abstract syntax tree after syntactic analysis in the source library to generate a DDL statement conforming to the target library; performing incremental synchronization processing on the migration task between the source library and the target library to obtain incremental synchronization information; if structural difference information is detected in the incremental synchronization information, carrying out structural repair processing on the target library; and if the structure repair result information is repair operation failure information, performing data fusing processing on the migration task.

A system for a multi-tenant API framework in scalable cloud-based database solutions, which includes the following: an API layer module that enables access to database services for multiple tenants; a tenant management module configured to handle tenant authentication, authorization, and role-based access control; a data partitioning module for partitioning tenant-specific data into isolated database structures; a resource allocation module for the dynamic allocation of computing resources to individual tenants based on usage patterns; a security module configured to protect tenant data through encryption and secure data transmission; A monitoring and analysis module for real-time monitoring of tenant activities and system performance.

The invention provides an after-batch stock land data management method and system. The method comprises the steps of designing an after-batch stock land management database structure, and constructing a land supply data graph layer, a batch land data graph layer, a batch land data graph layer, a batch unsupplied land data graph layer, an overdue working land data graph layer and an idle land data graph layer; collecting multi-source data, performing preprocessing, problem treatment and quality inspection, and generating standardized land supply data and land batch data; constructing a land supply and approval database, and comparing and updating the land supply and approval database with data of a natural resource department system; generating a batch of unsupplied land, an overdue working land and an idle land data graph layer through overlay analysis, and constructing a batch stock land database; automatically updating graph and attribute data based on data such as territorial change investigation and the like; the functions of automatic reminding, land use unit interaction and space query are executed; and carrying out visual rendering and display on the database data, and superposing and displaying the batch stock land and planning and remote sensing image data. According to the invention, the intensive land utilization supervision efficiency can be improved.

The invention relates to the technical field of database migration, in particular to a cross-database SQL automatic adaptation detection method and system. According to the cross-database SQL automatic adaptation detection method, a source database and a target database are installed and configured, structured query language SQL statements during source code running are captured in a multi-dimensional mode, and the method comprises the two parts of static code analysis and dynamic running monitoring; a difference knowledge base is constructed, and cross-database structured query language (SQL) automatic conversion is achieved through an intelligent conversion engine; bidirectional conversion verification is carried out, and result comparison and boundary testing are executed; and establishing a risk scoring mechanism, quantifying conversion risks, and performing grading processing according to risk scores. According to the cross-database SQL automatic adaptation detection method and system, full-automatic structured query language SQL grammar detection and intelligent repair during database migration are achieved, and the problems that traditional manual migration is low in efficiency and insufficient in test coverage rate are effectively solved.

The invention provides a data migration method which can be applied to the technical field of artificial intelligence, and relates to application of a large model in the fields of information security and financial science and technology. The data migration method comprises the steps that in response to a migration request for migrating target data in a first database to a second database, a target agent is called to split the migration request into N migration tasks, the database structures of the first database and the second database are different, and N is an integer larger than or equal to 2; calling a target agent to sequentially execute N migration tasks based on a preset tool set, so as to migrate the target data in the first database to a second database; wherein at least one non-target tool in the preset tool set is masked in advance based on the to-be-executed target migration task, and the target agent is configured to perform tool calling based on the masked preset tool set. The invention further provides a data migration device and equipment, a storage medium and a program product.

The invention relates to the technical field of data query, can be applied to business scenes such as financial science and technology and medical health, and discloses a data query statement dynamic generation method and device, equipment and a medium, and the method comprises the steps: receiving query request information, matching business strategies, completing conflict resolution, and generating a target business strategy set; obtaining dynamic context information formed by a database structure, an index, statistical information and a system load; and generating an initial data query statement based on the target business strategy set, the dynamic context information and the query request information, and obtaining an executable data query statement through grammar verification, logic optimization and execution cost analysis. According to the method, service rule analysis, conflict resolution and environment perception are combined, so that the query statement generation process can be adaptively adjusted along with the change of the service rules and the change of the running state of the database, and the accuracy and execution efficiency of query statement generation are improved while the correctness of service logic is kept.

A database query processing service is provided for efficiently processing query workloads with natural language statements and native database commands. The database query processing service may receive some database queries that do not contain a natural language marker and process these database queries without using large language models to generate replacement query content. The database query processing service may also receive other database queries that do contain the natural language marker and process the other database queries using large language model(s) to generate replacement query content or leverage replacement query content already generated by the large language model(s). The replacement query content is checked to ensure the content is natively valid for the content to retrieve data from database structures referenced in the content. The database query processing service may use the natively valid replacement query content to cause execution of operations responsive to the other database queries.

The invention provides a heterogeneous database structure dynamic synchronization and fault-tolerant migration method, system and device, and belongs to the technical field of database migration. The method comprises the following steps: detecting a structural difference through combination of active detection and passive triggering; realizing DDL statement conversion of different databases by utilizing AST analysis through a cross-platform DDL converter; defining a migration process by adopting a migration state machine model so as to ensure orderly migration; and a breakpoint resume and consistency guarantee mechanism is utilized to ensure that migration fault tolerance is consistent with data by means of a vernier triple, an intelligent recovery strategy and a bidirectional check protocol. According to the method, the problems of structure synchronization and migration fault tolerance of the heterogeneous database in an environment without native change log support are effectively solved, the automation, high efficiency, stability and reliability of database migration are improved, and the method has wide application value.

A computer-implemented database management system for communicating with two or more databases, at least two of the two or more databases having a different database structure, is provided. The system comprises a semantics component, a knowledge graph component, and a query interface. The semantics component obtains data items from the databases and determine relationships therebetween. The knowledge graph component generates a knowledge graph based on the identified relationships. Each node of the knowledge graph is associated with a respective data item stored in the databases and defines a structured database query for accessing the respective data item in the databases. The query interface receives a user input, determines a knowledge graph query based on the user input, and provides the knowledge graph query to the knowledge graph component for identifying at least one node of the knowledge graph corresponding to the knowledge graph query, thereby obtaining a corresponding database query.

The invention belongs to the technical field of medical information, and provides a standardized electronic medical record shared document automatic generation method and system, and the method comprises the steps: obtaining related specifications, constructing a document directory structure, and generating directory data; the method comprises the following steps: loading a pre-constructed standard data element dictionary and a local business system database structure, respectively analyzing the pre-constructed standard data element dictionary and the local business system database structure to obtain standard data elements and local fields, and establishing a mapping relationship between the standard data elements and the local fields through an intelligent matching algorithm to obtain a field mapping table; acquiring configuration information including node names, data types, whether the user must be filled or not and value ranges, and binding the configuration information with local fields to generate complete node configuration data; and generating a document template according to the node configuration data, generating a shared document file based on the basic information of the patient, the doctor-seeing record and the selected document template, and performing verification. According to the method, the full-process automation of the shared document from directory definition to content generation is realized, and the method has high efficiency, standard performance and intelligence.

The invention relates to the technical field of equipment control, and provides an equipment control method and system based on a natural language and electronic equipment. According to the application, the machine language vector is generated through the intention recognition result of the natural language instruction, and the graph query statement is generated according to the semantic concept after the machine language vector is deconstructed, so that the equipment knowledge graph of the original equipment is queried through the graph query statement to obtain the equipment operation path meeting the semantic concept; the knowledge graph has a mesh topology structure, the equipment knowledge graph is used for replacing a rigid relational database structure, deep requirements in natural language instructions are mined through intention recognition, machine language vectors are obtained, the machine language vectors are deconstructed, and the control of the original equipment is achieved. And generating a graph query statement corresponding to the equipment knowledge graph according to the deconstructed semantic concept, thereby accurately and comprehensively querying the complex context semantic relationship between the nodes, and improving the understanding accuracy of the natural language instruction.

A database business system upgrade management method, apparatus, device and medium, the method comprising: creating a fixed version table in a database, the version table comprising a table field for storing a version number of a current database structure; wherein the initial value of the table field is set to be 1, and 1 is increased progressively after upgrading is successfully completed every time; each program version is upgraded, and if the database structure is changed, an upgrading file is newly added; database upgrading: querying the version table before upgrading, and obtaining the current version number recorded in the database; traversing the names of all the upgrade files, and if the upgrade file of the next version of the current version number exists, executing the upgrade file; if the execution of the upgrade file fails, the upgrade fails; and if the database upgrade fails, querying the version table, obtaining the current database version number according to the table field in the version table, and carrying out a rollback operation. According to the method, one-time upgrading across multiple versions can be realized, and the actual version state of the database can be quickly confirmed.

The embodiment of the invention discloses an intelligent number asking method and system based on dynamic feedback, a storage medium and electronic equipment. The method comprises the steps that a subject domain and a semantic model are constructed, a data view is established, data mapping is configured, and a natural language problem and a standardized index are analyzed; generating an SQL (Structured Query Language) logical expression based on the Few-shot example and the large model, and carrying out semantic correction on the SQL logical expression; sQL query is executed, dynamic feedback storage is carried out based on an execution result, and SQL generation parameters are updated by utilizing a self-adaptive learning mechanism. According to the method, through semantic modeling and data mapping, precise alignment of a natural language and a database structure is achieved; few-shot examples and large model generation are combined with semantic correction rules, so that the accuracy and stability of SQL generation are improved; and a continuously optimized intelligent number-asking closed loop is constructed by executing a feedback and adaptive learning mechanism, so that the self-learning and evolution capabilities of the system are improved.

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 provides a structured query statement generation method and device, equipment, a medium and a program product, and can be applied to the field of artificial intelligence and the field of financial science and technology. The method comprises the steps that in response to a natural language statement input by a user, the natural language statement is analyzed through a natural language processing model, and semantic elements are obtained; performing syntax analysis on the semantic elements to generate an abstract syntax tree; based on the abstract syntax tree, database structure metadata coding is conducted on the natural language statement through a deep learning model, and a first structured query statement is generated; and performing security verification on the first structured query statement to generate a second structured query statement.

According to the complex nested SQL generation method based on KG-LLM two-way reasoning, a framework of'knowledge graph (KG) reasoning path constraint and large language model (LLM) semantic generation optimization 'is adopted, it is ensured that SQL statements conform to a database structure by means of deterministic constraint of KG, complex nested query is flexibly processed by means of semantic understanding capacity of LLM, and the method has the advantages of being simple in structure, high in efficiency and high in efficiency. The defect of flexibility of an existing method is overcome, and diversified query requirements and dynamically changing query logic can be flexibly coped with; besides, through an information interaction mechanism of KG reasoning and LLM reasoning, core constraint information of KG is injected into LLM to generate context, SQL fragments generated by LLM are verified and subjected to conflict resolution, it is ensured that generated SQL statements conform to database structure constraints and also conform to user intentions, and the user experience is improved. The problem that accuracy and reliability are limited when complex SQL statements are generated through an existing method is effectively solved.