148 results about "Database structure" patented technology

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 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 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.

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.

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.

The invention relates to the technical field of natural language processing, and discloses a structured query statement generation method and device.The method comprises the steps that a natural language problem input by a user and related information of a target database are received; performing mode linking processing on the natural language question and the related information of the target database to obtain database structured mode information related to the semantics of the natural language question; based on the natural language problem and the database structured mode information, self-driven exploration operation is executed, and a corresponding exploration result is generated; inputting the database structured mode information and the exploration result into a preset language model to generate an initial structured query statement; and performing verification and iterative correction on the initial structured query statement to generate a final structured query statement. According to the method, the structured query statements which are matched with natural language question semantics and can be effectively executed can be efficiently generated, and the accuracy and effectiveness of generation of the structured query statements are effectively improved.

Provided herein are database structures, systems, and methods for an anonymous, information exchange platform. The information exchange platform described herein may comprise blockchain structures, decentralized networks, peer-to-peer technology, cryptographic techniques, and / or a combination thereof. Past and present actors associated with a topic of information (e.g., a product) may flexibly exchange information on the information exchange platform by implementing question and answer protocols.

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 relates to the technical field of database structures, in particular to a semi-cut mountain modeling database construction method and system based on multi-dimensional parameters, and the method comprises the following steps: extracting slope elevation and cutting angle fields, arranging the sequence of related fields and unifying formats, calculating a text relationship and generating numbers, dividing blocks, and writing the blocks into a structural text. And forming a database structure construction atlas. According to the method, a regularized field cluster is formed by constructing a field value set and a structure sequence, a field jump path is established, the sequence direction is identified, clear expression of a field combination mode and continuing logic is achieved, content blocks are divided in combination with node information, the pairing relation is adjusted, and a unified format is set to enhance field normalization; the method comprises the following steps: binding elevation and strength sections through attribute injection, completing classified undertaking and structure reconstruction of text records, realizing field system output in cooperation with directory naming and path registration, enhancing the ordered organization and linkage ability among fields, and improving the expression efficiency and expansion adaptability of structure construction.

The invention provides an enterprise knowledge graph construction and intelligent training method and system based on a large model, and belongs to the technical field of information retrieval and database structures. The construction method comprises the following steps: loading a current enterprise knowledge database based on a first data identification large model, and dividing the enterprise knowledge database into a static knowledge sub-database and a dynamic knowledge sub-database; calibrating the static knowledge sub-database as a standard static knowledge base based on the second data calibration large model; based on the standard static knowledge base and the dynamic knowledge sub-database, an enterprise knowledge graph is constructed, and the enterprise knowledge graph comprises static knowledge nodes and dynamic knowledge nodes; when the enterprise knowledge database is updated, updating content is uploaded to the dynamic knowledge sub-database; and updating the enterprise knowledge graph based on the updated dynamic knowledge sub-database. According to the technical scheme, the efficiency of enterprise knowledge graph construction and intelligent training can be improved.

An object access service is implemented on a computer system for configuring and enforcing multi-dimensional data security using security assignments beyond role-based access controls. The computer system accesses a request submitted on behalf of a user for access to database structure(s). The computer system requests predicate(s) mapped to role(s) assigned to the user and stored in association with the database structure(s). Predicate(s) submitted to the database may be dynamically filled in by the database pursuant to retrieving data from the database structure(s) to reference security assignment field(s) and security assignment value(s) of the security assignment. The predicate(s) are used to retrieve a security assignment that restricts access to the database structure(s) beyond the role(s) assigned to the user. The security assignment is used to restrict data accessible from the database structure(s) to generate a result set, which is transmitted to a client consumer system for consumption via a consumer interface.

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.

Systems, methods, and computer-readable media are provided for using generative AI enriched with metadata about historical document characteristics to transform documents of various formats, including images, to the fields and values they represent. A prompt template may be selected in association with a type of document. The prompt template indicates field definition(s) of field(s) to be detected in the document and location(s) in which the field(s) have been detected in prior documents. A large language model is prompted with a prompt generated using the prompt template to generate a result that assigns value(s) to the field(s). Output from the language model is used for identifying the field to value mapping for the document, such that data detected from the document may be stored in appropriate database structures of a database. Metadata stored in association with the prompt template is updated based on location(s) in the document in which the field(s) were detected, and the value(s) of the field(s) are stored in a database. Outbound documents may be similarly translated to detect values of corresponding fields requested by third parties, even if those values are not stored in the database. In this scenario, values for fields may be detected in outbound documents using the prompt templates enriched with metadata as processed by the large language model before such information is prepared to be sent to a third party.

The invention discloses a method for questioning and answering. The method comprises the steps of receiving a natural language question input by a user; determining a target node set according to the natural language question and correlation between nodes in a question and answer database relation graph generated on the basis of a question and answer database in advance; mapping the target node set into a database Schema; taking the database Schema and the natural language question as input of a target model to generate an SQL query statement; and determining a natural language answer of the natural language question according to a query result of the SQL query statement in the question and answer database. By dynamically constructing the database Schema related to the natural language question input by the current user, the target model directly understands the question and answer database structure based on the database Schema and generates the accurate SQL query statement, end-to-end training does not need to be performed on the target model, the structural change of the question and answer database can be automatically adapted, and the calculation burden is effectively reduced.

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.

Comprehensive systems and methods manage software lifecycle processes in an enterprise environment, and ensures consistent, secure, and efficient handling of software applications, packages, and distribution methods. The system features a web application interface that validates user input, enforces multi-level authentication, and manages user roles and permissions. A directory system dynamically selects stored procedures based on input data and current database structure, executing operations entirely within the database. Stored procedures enforce business logic, manage complex data relationships, support data encryption, and anonymize sensitive information. Hierarchical data structures organize records with multi-level granularity, temporal tracking, and partitioning. The database management system optimizes operations, implements automatic failover, and supports real-time data synchronization. The system also includes advanced error handling, real-time monitoring with predictive alerts, a secure audit trail, and a reporting module integrating with external business intelligence tools. Automated maintenance and disaster recovery ensure scalable, secure software lifecycle management.

A graph database system stores and analyzes connections and relationships between entities using a database structure that represents users, messages, threads, and attachments as nodes with interconnecting edges. The system implements a hybrid approach to electronic communication classification combining self-hosted language models with computer server clustering for pattern matching and suggestion generation. Additionally, the system includes mechanisms for creating and suggesting reusable electronic communication components based on semantic analysis of communication patterns. The system architecture enables relationship analysis, digital content management, and privacy-preserving electronic communication classification.

The invention discloses a business model establishment method and device, computer equipment and a product, and relates to the technical field of business model development. The business model establishment method comprises the following steps: associating a corresponding first wide table according to an entity attribute of a first business model newly established by a user; in response to an editing operation of a user on the first business model, setting a field of the first business model and establishing a mapping relationship between the field of the first wide table and the field of the first business model; in response to a model relationship definition operation of a user on the first business model, establishing an association relationship between the first business model and an existing second business model; and in response to a template configuration operation of a user, loading the first business model through the configured template. According to the method, the hard dependence of the business model on a new table or a new field is broken, the complicated approval process required by database structure adjustment in the traditional business model development process is thoroughly avoided, and the business model development period is greatly shortened.

The application relates to the field of knowledge graphs, and discloses a method and system for generating a resource map of a person's network based on a relationship graph. Multi-source interaction behavior data is collected to construct a heterogeneous graph structure, and a graph neural network is used to jointly encode the interaction frequency, time decay factor and spatial trajectory co-occurrence degree to calculate the weight of a strong implicit relationship. A relationship graph is generated based on the weight of the strong implicit relationship, and a space-time dimension index is embedded in the database structure. The space-time reachable probability of a resource node is calculated based on the combination of the geographical topological constraint, the weight of the strong implicit relationship and the space-time dimension index. The resource nodes are visualized and rendered based on the space-time reachable probability to generate a resource map of a person's network. The application overcomes the defect that a static graph cannot represent dynamic relationships, improves the accuracy of strong implicit relationship mining, realizes quantitative calculation of resource space-time reachability, and reduces the redundancy of graph search traversal.

The invention discloses an NL2SQL-based power intrinsic safety question-answering system construction method, system and device and a medium, and belongs to the technical field of power safety. The method comprises the following steps: collecting fine tuning data and constructing a fine tuning data set; performing low-rank adaptive fine tuning on a pre-trained large language model through the fine tuning data set to obtain a fine tuning large language model, and triggering an annealing strategy for optimization; building prompts through the fine-tuning large language model in combination with user questions and database structure information; and generating a structured query language statement based on the prompt, querying a database to obtain a query result, and adjusting the prompt according to the query result to process an error. According to the method, low-rank fine tuning in the field of electric power intrinsic safety is carried out on a general pre-training large model by introducing the LoRA technology, so that the model has general language understanding ability and industry exclusive knowledge at the same time, and the problem that a traditional model is high in universality but low in specialty is solved.

System and methods for evaluating, developing, and continuously updating quantum sensor designs are disclosed. The system can include a database structure configured with plural quantum sensor evaluation models. Each quantum sensor evaluation model can be designed to evaluate quantum sensor types (e.g., a qubit-based sensor design or an atom-based sensor design). The system can include a processor configured to perform functions. The functions can include receiving information including one or more selected operational specifications related to an operational environment within which a quantum sensor will be operated. The functions can include evaluating effects of implementing the one or more selected operational specifications on one or more qubit-based sensor designs and / or one or more an atom-based sensor designs of quantum sensor evaluation models. The functions select a quantum sensor design based on results of the evaluation. Leveraging adapted AI / ML modeling optimizes selection of quantum sensor design, among other technical advantages.

This invention relates to a Text-to-SQL method, system, and medium, and pertains to the field of database technology. Addressing the issues of long SQL lengths and low accuracy in SQL generation due to existing database structures, this application solves these problems by using information tagging and applying SQL syntax rules. This improves the accuracy of SQL generation by addressing the difficulties in remembering information and the weak information correspondence capabilities inherent in existing database structures.

The invention discloses an AI-driven bank derivative index configuration and decision-making method. The index configuration and decision-making method comprises the following steps: determining general derivative index configuration; generating a derivative index; and verifying the derivative indexes, and establishing a verification mechanism. On the basis of fusing the AI technology, comprehensive construction is carried out from the aspects of database structure design, algorithm module development, user interface development, system deployment and integration and the like, and high efficiency, stability and expansibility of the system are ensured.

The invention provides a database storage method and device, electronic equipment and a storage medium, and the method comprises the steps: pre-storing a database structure table and a data storage table in a database, under the condition that an instruction for creating a target database table is received, determining fields contained in the target database table from preset fields of the data storage table, adding a first record in the database structure table, wherein the first record at least stores names of fields contained in the target database table; when the instruction of adding the target data to the target database table is received, the target data is stored in the target field of the data storage table, and the target field is the field corresponding to the target data, so that the operations of adding the database table in the database and adding the data in the database table can be simplified; therefore, the adjustment efficiency of the database table is improved, and the structure of the database table is not changed, so that the adjustment of the database table can be realized under the condition that a user does not feel the database table, and the user experience is improved.