11313results about "Database design/maintainance" patented technology

A method to be executed on a computing device comprising (i) accessing and / or modifying a database to be automatically curated, (ii) optionally accessing additional data or information sources for further useful data or information, (iii) using one or more pre-trained large language models (LLMs) accessed via API or other connections, issuing prompts and retrieving prompt-answers and executing database curation requests that specify database curation tasks to be performed on at least one sub-structure of the database, the tasks comprising (a) a database enrichment task to compute new data records to be inserted into the database sub-structure, (b) a database verification task to verify, using the one or more LLMs, data contained in the sub-structure, and identify incorrect data, (c) a database update, and (d) a null-value or a missing value replacement task. The requested tasks are automatically performed via a computation comprising an adaptively generated prompt sequence.

A computing system is disclosed for transforming document data into schema-conformant structured outputs. The system obtains document data comprising multi-format structured documents and classifies each document by type and class using vector-based modeling and structural feature analysis. An extraction configuration is selected for each document, the configuration comprising machine-executable instructions for parsing based on semantic and layout characteristics. The system extracts semantic data using structured inference, transforms the semantic data into schema-conformant outputs, and validates the outputs using temporal and domain-specific constraints. Validated structured data may be used for downstream processing, visualizations, or optimization based on performance metrics.

The invention provides an HBase client main-standby switching method and system based on fault perception, and relates to the technical field of database management, and the method comprises the steps: collecting system operation data through a distributed fault monitoring module, building an anomaly detection baseline model, carrying out fault diagnosis based on a system health assessment distribution matrix, and generating a fault characteristic spectrum; and switching is triggered according to a risk level evaluation result, and the optimal standby node is selected for sequential switching by using a deep strategy learning algorithm, so that rapid and accurate fault detection, diagnosis and intelligent switching can be realized, and the reliability and performance of the system are improved.

The invention provides a time sequence knowledge graph federation collaborative optimization method, system and device based on causal inference and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: generating an enhanced knowledge unit with a causal mark through the real-time access of a multi-field heterogeneous data stream and the execution of a space-time alignment operation; by calculating new and old knowledge conflict scores, conflict resolution and version management are realized through a decision tree mechanism, and a time sequence knowledge graph with history tracing is output; node weights are dynamically distributed among distributed nodes based on knowledge entropy, a hierarchical aggregation strategy is adopted to update an entity embedding layer and a relation prediction layer, and a global optimization model is output; the method comprises the following steps: analyzing a natural language query containing an anti-fact condition, extracting a factor sub-graph from a time sequence knowledge graph, executing intervention calculation, and generating an anti-fact influence report, thereby solving the technical problems of a traditional time sequence knowledge graph in the aspects of multi-source heterogeneous data fusion, knowledge conflict resolution and privacy protection; and the accuracy and the interpretability of the knowledge graph are improved.

The present invention relates to a digital twin processing method and system, and a cloud platform. The method comprises: acquiring production system elements, carrying out abstraction definition and parameterization description on the production system elements by means of digital-to-analog knowledge fusion of a mathematical model and a mechanistic model, so as to construct a digital twin ontology model; analyzing and reconstructing model data to obtain a mapping model of which object variables can be directly accessed and operated by a collective motion control method, so that the model is visualized at the cloud; and using an external data source to drive parameter update and operation matching of the model by means of a motion control method, so as to complete cooperative deployment and synchronous evolution of an actual physical device and the model in the production process on a cloud server. According to the present invention, a model is constructed by means of digital-to-analog knowledge fusion of a mathematical model and a mechanistic model, the model is mapped to achieve motion visualization, model parameter update and operation matching on the cloud are achieved, and then cooperative deployment and synchronous evolution of a physical device and the model are completed.

A computer-implemented for information processing based on a large language model (LLM) includes: obtaining a query request; determining, by the LLM and based on the query request, a query condition, a source of a query result, and a select field for generating a structured query language (SQL) statement; generating by the LLM, the SQL statement by combining the query condition, the source of the query result, and the select field; and obtaining the query result corresponding to the query request, by inputting the SQL statement into the LLM for execution

A data query system and methods are provided that are configured to intelligently generate structured data queries from natural language questions using large language models (LLMs). The system includes a processor and a computer readable medium operably coupled thereto, the computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, to perform operations which include receiving a natural language question for structured data, converting the natural language question to embeddings, matching the embeddings to pre-generated questions from a user questions repository (UQR), determining an accuracy of the matching meets or exceeds a threshold similarity, determining, using an LLM and metadata corresponding to the pre-generated questions from the UQR, a structured data query for querying for the structured data, and querying the structured database system using the structured data query.

A method, apparatus, and computer-readable medium of a migration platform for analyzing a legacy database based on applying a large language model to the legacy database and legacy application code to extract legacy database values and legacy application values, determining destination database values corresponding to a destination database based on the legacy database values and the legacy application values, generating migration code for migrating the legacy database to the destination database based on code generation templates, user preferences, and code generation procedures configured to create one or more files and one or more directories based on the code generation templates, validating the migration code based on generating mock data, storing the mock data in the legacy database, and applying the migration code to the mock data to migrate the mock data to the destination database, and executing the validated migration code to perform a migration of the legacy database.

The invention relates to the technical field of data processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a database migration method, device, equipment and medium. And deploying a double-write control module of the application program layer to write the source database and the target database in parallel, and completing consistency verification through a data verification comparison task. And controlling a target database write-in channel according to a difference analysis result, closing a source database write-in channel in combination with a service takeover state, and completing a migration process from the source database to the target database. According to the method, full migration, application layer double writing, differential data verification and dynamic write-in channel control are combined, data consistency of the source database and the target database in the migration process is guaranteed, the source database and the target database are safely switched to after the service takeover capacity is verified, and the migration safety and continuity are improved.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for linking a database schema to a natural language query. In particular, in some embodiments, the disclosed systems determine, from tables in a database schema, a subset of tables relevant to a natural language query by comparing embeddings for the tables in the database schema and embeddings for the natural language query. Additionally, in some implementations, the disclosed systems select, from a schema graph comprising nodes that represent the tables in the database schema, an additional table along a path between a pair of nodes representing a pair of tables from the subset of tables. Moreover, in some embodiments, the disclosed systems determine a set of relevant tables by appending the additional table to the subset of tables. Furthermore, in some implementations, the disclosed systems generate, from the set of relevant tables, a response for the natural language query.

The invention discloses a method and a system for realizing data synchronization between heterogeneous databases based on logs. The method comprises the following steps: acquiring connection information of a source database and a target database; querying a table structure of the source end database and generating an SQL statement to establish a data mapping rule; matching a corresponding rule model from a mapping rule model library; if the matching is not successful, the system abnormally quits and prompts the user to create a new mapping model and add the new mapping model to the mapping rule model base; converting a table structure of the source end database into a JSON character string based on a standard field type; an SQL statement required by the target database is output through the data mapping engine module; the message monitoring program reads the data in the temporary storage area and converts the data into data meeting the requirements of the target database; formatting the converted data into SQL statements supported by a target database; and sending the generated SQL statement to a target database to complete data migration. Accurate and controllable synchronization of the data and the model in a heterogeneous environment is realized.

This disclosure includes a process for merging legacy databases having different data schema into a principal data store. A computing device retrieves a legacy database from each of a plurality of domains of an organization to form a plurality of legacy databases, where at least two legacy databases in the plurality of legacy databases have a different data schema. For each of the plurality of legacy databases, the computing device retrieves data from the respective legacy database, places the data from the respective legacy database into a respective unstructured database, and merges the respective unstructured database into a principal data store designed to hold data for every domain of the plurality of domains.

A system allows generation of information used by an integrated development environment using a machine learning-based language model, for example, a large language model. The integrated development environment is for developing applications, e.g., database applications. The system receives a natural language request for information related to development of the database application. The system determines contextual information describing a development task associated with the database application and generates a prompt for a machine learning based language model based on the natural language request and the contextual information and receives a response. The system extracts the information related to development of the database application from the response. In response to a natural request, the system may generate a database query, provide a resultset by executing the database query, automatically determine a type of chart and generate one or more charts for visually displaying the result.

The invention relates to the technical field of artificial intelligence and Internet of Things, and discloses an AI interaction intelligent module based on a hybrid architecture, comprising a user interaction unit which supports voice, text and image multi-modal input and integrates intention recognition and context understanding algorithms; the data processing unit is used for carrying out structured processing on the electric appliance specification and the historical fault data and constructing a dynamically updated knowledge graph; the hybrid architecture core unit comprises a deep learning subunit for realizing natural language understanding and generation based on a Transform model, and a knowledge reasoning subunit; a fault diagnosis unit; and a feedback optimization unit. According to the method, seamless cooperation of deep learning and symbol logic is realized through a dynamic routing strategy, a high-confidence-coefficient scene generates a response through a Transform model, a medium-confidence-coefficient scene calls a knowledge graph rule for verification, and a low-confidence-coefficient scene supplements information through multiple rounds of interaction, so that the effect of improving balance efficiency and safety is achieved.

A method of performing data analysis according to a natural language query from a user includes: receiving, from a user terminal, a user input including the natural language query requesting the data analysis; determining, based on the user input, at least one database among a plurality of databases as a target database; generating a prompt based on the user input and the target database; inputting the prompt into a code generation model to obtain a structured query (SQL) statement; executing the SQL statement to generate a result of the data analysis on the target database; and transmitting the result of the data analysis to the user terminal, where the result of the data analysis is displayed on a screen of the user terminal.

The invention provides an intelligent analysis and execution method and system for database cascading operation, and relates to the technical field of database management.The intelligent analysis and execution method comprises the steps that a target table and an association table are extracted through a recursive query algorithm, field mapping analysis is carried out to generate a dependency weight matrix, cyclic dependency is recognized based on the matrix and decomposed into directed acyclic subgraphs, and the directed acyclic subgraphs are analyzed; node depth values and breadth values are calculated for topological sorting to generate an execution sequence; performing node classification on the execution sequence, organizing the execution sequence into a batch processing task group, calculating an optimal execution path through an adaptive path optimization algorithm, generating a distributed transaction control instruction, and allocating the task group to an execution thread pool for execution by using a consistent Hash algorithm; monitoring an execution process, dynamically adjusting thread pool resources according to a performance prediction value, executing cascade rollback when an exception occurs, and reconstructing a transaction execution path; according to the method, the execution efficiency and stability of the database cascade operation can be effectively improved, and the system resource consumption is reduced.

The invention provides an industrial internet equipment operation and maintenance knowledge question-answering method, system and equipment and a medium, and belongs to the technical field of intelligent operation and maintenance. The method comprises the steps of obtaining an operation and maintenance document, extracting text data through a MerU tool, and generating a structured text file; based on the structured text file, using a langchain library to carry out text partitioning, and carrying out vectorization processing to generate an operation and maintenance knowledge base; obtaining a query problem of a user, and retrieving target text data from the operation and maintenance knowledge base by adopting a mixed retrieval strategy combining a sparse vector and a dense vector; and generating cue word information by utilizing the cue word template according to the query question and the target text data, and inputting the cue word information into the large language model to generate an answer. According to the method, related knowledge of operation and maintenance problems is automatically retrieved according to the equipment operation and maintenance document and the historical knowledge base, and automatic intelligent answering is performed by using a large model, so that the operation and maintenance cost of massive equipment in the industrial internet is reduced.

The invention provides a method for diagnosing a structural service behavior based on a high dam accident mode and a failure path, and the method comprises the steps: constructing a high dam accident case database, and extracting accident feature parameters; by applying multi-scale layered adaptive corresponding analysis, through feature weight calculation, risk-out mode layered weighting, physical similarity index establishment and structural difference compensation, the problem of unbalanced feature weight is solved, and a weighted similar project sorting list is generated; constructing a multi-failure mode coupled time sequence adaptive network model, solving the problem that a failure path time sequence evolution mechanism is imperfect, and outputting a time sequence failure path prediction model; and in combination with the similar project sorting list and the time sequence failure path prediction model, performing project analogy analysis, and generating a high dam safety state evaluation report. The technical problems that the operation time of the ultra-high dam is short, the monitoring information is insufficient, the numerical analysis result is difficult to completely reflect the engineering practice and the like are solved, and a new technical path and a new solution are provided for high dam safety assessment and risk management.

The invention discloses an artificial intelligence-based bid invitation file automatic auditing method and system, and the method comprises the steps: setting an online database and a local database in the system, carrying out the format auditing, compliance auditing and other abnormal auditing of a bid invitation file through a bid invitation file auditing module, generating auditing suggestions, and carrying out the automatic auditing of the bid invitation file. After the auditing opinions are manually confirmed by a system using unit, a final bid invitation file manuscript is generated, and the system comprises the following modules: a database module, a file uploading module, a bid invitation file auditing module, a suggestion modification module, a bid invitation file generation module, a user interaction interface and a learning module. The method is convenient to use, intelligent auditing of the bid invitation file is achieved through the artificial intelligence technology, and the overall quality and efficiency of the bid invitation file are improved.

The invention relates to the technical field of process management, and discloses a lightweight process management method, which comprises the following steps of: defining a business process and analyzing the business process through a process engine; a graphical process design interface is provided, and a user can dynamically configure business process nodes through dragging and clicking operations; the business process is disassembled into a plurality of task nodes, and each task node is packaged into an independent function module through modularization design; the task nodes are combined according to needs and executed in parallel on different processors, and efficient execution of the task nodes is ensured; the execution state of the business process is monitored in real time, and potential problems are found and processed in time through log recording and an exception processing mechanism. According to the technical scheme of supporting dynamic adjustment of the task nodes and optimizing parameters based on the historical abnormal mode, execution parameters of the task nodes are dynamically corrected, association logic between the nodes is adjusted in real time, and the effects that process management is flexible and adapts to complex service scenes, and operation stability is improved are achieved.

The invention discloses a carbon emission intensity inversion and traceability method and system based on numerical simulation and a reduced-order model. The method comprises the following steps: constructing a full-size three-dimensional map; numerical simulation of pollutant diffusion is carried out according to map information and known environment conditions; reducing the order of the analog data through an intrinsic orthogonal decomposition method; establishing a carbon emission simulation database under the multi-working-condition variable environment based on the reduced-order model; the carbon dioxide concentration and meteorological data are measured in real time and on site, a simulation database is used as a supplement, and the intensity of a known emission source and the intensity and position of an unknown emission source are calculated by adopting a source item estimation method, a particle swarm algorithm and the like. According to the invention, full-size reduction of a limited area and high-precision small-scale carbon emission monitoring can be realized, and effective emission source tracing, intensity analysis, potential emission source prediction and the like can be carried out according to a small amount of field actual measurement data.

The invention provides a carbon database construction method and system based on regional energy carbon emission prediction, and the method comprises the steps: eliminating the space-time reference deviation of an original data set, and generating an energy activity hologram spectrum with a unified space-time label; inputting the energy activity holographic spectrum into a non-linear correlation program, extracting a carbon release intensity characteristic curve of each energy carrier through a self-adaptive weight distribution mechanism pre-stored in the non-linear correlation program, and constructing a spatial-temporal characteristic network in combination with industrial operation cycle parameters and climate response factors in a target area; the spatial-temporal characteristic network establishes a dynamic coupling relationship between an energy flow mode and a carbon emission hot area to form a carbon emission associated topological structure with a self-organizing characteristic; a three-dimensional carbon database is formed, and the three-dimensional representation and deduction capability of space-time evolution of carbon emission is realized. The system comprises an atlas generation module, a structure construction module and a content fusion module. According to the invention, normal form transformation from static carbon accounting to dynamic carbon metabolism simulation is realized.

The invention provides a distributed real-time database intelligent management method and system based on big data, and relates to the technical field of database management, and the method comprises the steps: predicting a resource demand through a neural network model, selecting a target data node, generating a data fragment mapping table by adopting an annular data structure, and constructing a fragment routing table to realize writing and migration of the data fragments. After a data processing task is received, data distribution is determined according to the fragment routing table, sub-tasks are split and distributed to target data nodes based on priorities, the resource occupancy rate is monitored, the tasks are redistributed when necessary, and finally a data processing result is generated. The method improves the database management efficiency and the resource utilization rate.

The invention discloses a large model application construction method based on a configurable workflow and a domain knowledge base. The method comprises the following steps: S1, constructing the domain knowledge base, establishing a semantic map and generating a knowledge embedding data set; s2, defining a configurable workflow, setting a jump rule based on a semantic process language, and binding a task semantic tag; s3, configuring a Prompt adaptive generation mechanism, and generating a Prompt input text in combination with the semantic tag and the knowledge embedding data set; s4, calling a knowledge embedding data set, retrieving semantic segments and embedding Prompt to form enhanced Prompt input; s5, inputting the large language model to obtain a return result and an index, and executing jump judgment; s6, scheduling a large language model service instance, and dynamically selecting a service interface according to an index and a task state; and S7, processing by a process termination node, arranging an output result, recording a log and calling data. According to the method, intelligent scheduling and application construction of the large model based on the workflow and the knowledge base are realized.

An example computer system for data pipeline orchestration configured to manage and coordinate the end-to-end process involved in moving and transforming data from various sources to designated target repositories can include one or more processors; and non-transitory computer-readable storage media encoding instructions which, when executed by the one or more processors, causes the computer system to create: a user interface configured to receive metadata configuration requirements; a parsing module programmed to parse the metadata configuration requirements into one or more constituent components; and a template selection module programmed identify and select appropriate templates from a template repository used to fulfill the one or more constituent components.

The invention relates to the technical field of distributed resource management and optimization, in particular to a distributed resource life cycle evaluation and dynamic updating method, which comprises the following steps: data acquisition: collecting multi-modal data of distributed resources, including resource state data, use behavior data, environment data and historical maintenance data; feature extraction: carrying out feature engineering processing on the multi-modal data, and extracting key indexes such as a resource health state, a degradation rate and environmental sensitivity; building a life cycle evaluation model, building the model by using the time sequence data of the resources based on a multi-task learning framework, and predicting the health state and the residual life of the resources; and optimizing a dynamic updating strategy, and monitoring and dynamically adjusting in real time. The method provided by the invention overcomes the problems of insufficient life cycle dynamics and rigid updating strategy in a traditional method, has the characteristics of high efficiency, intelligence and accuracy, and is suitable for intelligent management and optimization of new energy equipment, industrial assets and other distributed resources.

The invention relates to the technical field of database operation and maintenance, in particular to a database operation and maintenance decision-making method and device based on a large language model.The method comprises the steps that real-time operation indexes and historical log data of a database are collected, and multi-dimensional time sequence features are extracted; and analyzing the characteristics by using a large language model, judging whether a performance bottleneck or an abnormal risk exists or not, and determining the relevance between the performance bottleneck or the abnormal risk and a database performance problem. If the risk exists, acquiring a system load sudden change trend and a resource request rate, quantifying a system pressure degree, and inputting a performance optimization model to generate an adaptive tuning strategy; calculating statement complexity through an SQL audit log, and selecting an index adjustment strategy in combination with an index optimization model; evaluating the influence of the tuning strategy and the index strategy on the throughput, the response delay and the resource utilization rate, and determining a target high-performance strategy; according to the scheme, accurate optimization and efficient operation and maintenance of database performance are realized through intelligent analysis and dynamic adjustment and optimization.

A system and method for generating a database query based on a natural language query improves database utilization is presented. The method includes receiving a natural language query directed to a security database, wherein the security database includes a representation of a computing environment; selecting a first database query from a plurality of database queries; generating a second database query based on the first database query adapted by the received natural language query; and executing the second database query on the security database.

Systems and methods for migrating and integrating data from one or more external locations to an internal location are disclosed. The system comprises multiple temporary locations to sort, transform, or store the data in the migration process. During the process, the data could be assigned various attributes, which are determined based on metadata associated with the original data. The system will track these attributes as the data moves through the various temporary locations.

The invention provides a database adaptive migration method and system based on a credential environment, and relates to the technical field of data processing.The method comprises the steps that an intelligent migration model is used for analyzing the structure, including a table structure, index configuration, constraint conditions and data types, of a source database, data size distribution is calculated, and a source database structure feature result is generated; according to a source database structure feature result, automatically selecting and configuring a target database type, adjusting environment parameters of a target database, and generating target database configuration information; according to the structure of a source database and configuration information of a target database, a data mapping rule is automatically established by utilizing an intelligent migration model, and the data mapping rule comprises automatic correspondence of table names and field names, automatic conversion of data types, and an automatic migration scheme of indexes and constraints. According to the method, automatic and intelligent migration from the source database to the target database is realized through the intelligent migration model, the performance is automatically optimized, the migration report is generated, and the migration efficiency and quality are improved.