203 results about "Database structure" patented technology

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 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 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.

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 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.

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.

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.

The invention discloses a database structure comparison method and device, a storage medium and a program product, and relates to the technical field of data processing. Target table structure information about a to-be-compared database can be obtained from a preset storage space in response to a database structure comparison request; the table structure information of different types of databases is stored in the preset storage space, and the data formats of the table structure information stored in the preset storage space are the same, so that the compatibility problem caused by difference of different database structure definition grammar and metadata storage modes can be eliminated by adopting the mode; according to the technical scheme, no matter what types of the databases to be compared are, the corresponding table structure information can be obtained from the preset storage space, so that the comparison result can be obtained by comparing the target table structure information, and the technical problem that the database structure comparison efficiency is low can be solved.

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 big data processing, in particular to a big-model-driven efficient NL2SQL conversion system and method based on big data training, and the system comprises a data collection and preprocessing module, a big model training module, an NL2SQL conversion module, an optimization and verification module, and a deployment and interaction module. The method has the beneficial effects that the big model is trained through big data, and the conversion efficiency and accuracy from the natural language to the SQL statement are remarkably improved in combination with technologies such as multi-modal fusion and reinforcement learning. A large-scale diversified data training and continuous updating mechanism enables the model to have very strong generalization ability, various complex, fuzzy and ambiguous natural language queries can be processed, and the method adapts to different database structures and business scenes.

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.

Methods and apparatus for enhancing Large Language Models for IT Operations (Ops) are provided. Logs, alerts, infrastructure configuration, traces, and other telemetry data from a variety of data sources including cloud based IT platforms are selected and dynamically schematized into a database in response to a query by IT personnel. The database is leveraged by a structured LLM to generate insights and responses to queries, enhancing diagnostic and troubleshooting capabilities. The approach aims to streamline data management, improve accuracy, and reduce operational inefficiencies by utilizing dynamic virtual tables and curated data sources while addressing potential challenges such as LLM hallucinations.

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.

The invention relates to the technical field of language generation algorithms, and discloses a natural language generation SQL method based on ES, knowledge base and interaction enhancement, and the method comprises the following four sequentially executed core steps: data feature preprocessing: carrying out deep analysis (including field type, constraint relationship, data distribution and the like) on a database table structure and data features, and carrying out data feature preprocessing; and after data de-duplication is executed, the ES is imported, and an independent structured index is established according to a table-level isolation principle to provide basic data for subsequent retrieval. According to the method, the keyword matching efficiency and the semantic understanding depth are both considered through collaborative retrieval of the double-storage-layer knowledge base in combination with Elasticsearch keyword inverted index and Milvus vector library semantic retrieval. And the results are fused through the RRF algorithm, so that the retrieval precision is remarkably improved, the limitation of a single retrieval mode is solved, and the generated SQL better fits the database structure and the user intention.