2133 results about "Target database" patented technology

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 invention provides a method for generating an SQL (Structured Query Language) by a natural language based on bidirectional mapping and semantic parsing, which relates to the technical field of database query and comprises the following steps of: extracting natural language query elements and packaging the natural language query elements into structured data, and establishing a mapping relationship from a query field to a service attribute and a physical data table by adopting a bidirectional Hash index technology; and automatically identifying multi-table association keys, performing semantic extension and compliance verification, generating an abstract syntax tree, performing processing according to user permission, and finally converting the abstract syntax tree into an SQL statement conforming to a target database syntax specification. According to the method, the accuracy and the efficiency of converting the natural language into the SQL are improved, and the flexibility and the safety of the system are enhanced.

The invention provides an SQL (Structured Query Language) statement generation method and system based on a large language model. Target database metadata are collected, a structured dictionary is constructed, cue words adaptive to the large language model are generated in combination with a user query strategy, and accurate SQL generation is realized through a closed-loop execution optimization mechanism after model reasoning and SQL analysis. Through a large language model intelligent analysis technology, a multi-source heterogeneous database metadata dynamic adaptation technology and a closed-loop optimization mechanism for SQL statement generation, execution and feedback, the efficient and accurate SQL statement automatic generation function is achieved through the characteristics of cross-database grammar automatic adaptation, complex service requirement accurate analysis, intelligent error correction and the like. The system not only improves the working efficiency of developers, but also reduces the resource occupation and operation and maintenance cost of the system, and improves the accuracy and safety of data query and processing.

The embodiment of the invention discloses a database multi-source heterogeneous data synchronization method and device.The method comprises the steps that a data change event is monitored through an RPA robot deployed in a server where a source database is located, and when change operation of structured data or unstructured data is detected, an initial data set is generated; performing fragmentation processing on the initial data set to generate a plurality of data fragmentation tasks, and allocating the data fragmentation tasks to distributed computing nodes to execute parallel format conversion operation so as to generate a target data set; detecting a task execution state of the distributed computing node, and when a generation completion event of a target data set is detected, activating a data synchronization channel and transmitting the target data set to a target database; and performing conflict detection and identification based on an RPA conflict processing strategy configured in the target database, when a primary key conflict or a uniqueness constraint conflict is identified, updating the target database based on preset conflict merging indication information, and generating a data synchronization log.

The invention discloses a report chart automatic generation method based on natural language analysis and data query, which comprises the following steps: acquiring a natural language text, and constructing a slot set and an initial semantic fragment; executing consistency verification and ambiguity identification, and constructing a semantic framework; executing field standardization and mode alignment, generating a data query statement and establishing a traceability chain identifier; querying a target database to obtain an original data set, completing data quality self-correction, and generating a cleaned data set; calling an improved Mamba model to construct a constraint judgment matrix and outputting a target chart subset; generating a chart description grammar and an image annotation template, and executing image rendering, feature point recognition and image sidebar construction; and performing layout optimization and double compilation, and outputting a structured report file. According to the method, the chart generation automation level and the report compiling efficiency are improved, and the accuracy, traceability and specialty of the output content are ensured.

The invention relates to the technical field of intelligent agricultural equipment, and discloses an automatic agaricus bisporus picking method, device and equipment based on artificial intelligence and a medium. The method comprises the steps that RGB images and three-dimensional point cloud data of the agaricus bisporus growth environment are collected in real time through a binocular vision camera and a depth sensor which are installed at the tail end of a picking robot, and environment illumination parameters and cultivation bed coordinate information are obtained; inputting the RGB image into a pre-trained lightweight convolutional neural network, identifying a pileus contour, a stipe position and a maturity level of the agaricus bisporus, calculating a space coordinate, a height and a growth inclination angle of the agaricus bisporus based on the three-dimensional point cloud data, and constructing an agaricus bisporus target database; a mechanical arm picking path is generated based on the agaricus bisporus target database, and path weight parameters are optimized online through a preset reinforcement learning model; according to the maturity grade and morphological characteristics of the agaricus bisporus, the clamping force and the rotating angle of the end effector are dynamically adjusted, and automatic picking of the agaricus bisporus is completed.

The invention relates to the technical field of natural language processing, and discloses a structure prompt-based reasoning collaborative SQL generation method. According to the method, a small model carries out structured analysis on natural language query and a target database mode, SQL substructures and combination sequence prompts are generated, a large model constructs a multi-path inference tree based on the prompts, and path nodes sequentially generate SQL substructure fragments. Top-k paths are screened through path node scores and path cumulative scores, comprehensive sorting is performed by applying a heuristic scoring function, and candidate SQL with the highest score is selected. And verifying and correcting the candidate SQL according to execution feedback, and outputting a final SQL result after ensuring that semantics and structures are correct. According to the method, the problems of insufficient reasoning ability of a small model, poor reasoning stability and illusion of a large model and the problems of Tree-of-Though reasoning path redundancy and frequent focus switching are solved, the reasoning reasonability and execution efficiency in a Text2SQL task are improved, and the method is suitable for an application scene from a complex natural language to structured query.

The invention provides a structured processing method and device for a contract text, equipment and a storage medium, and the method comprises the steps: carrying out the preprocessing of the contract text, generating a standardized word sequence, and enabling the standardized word sequence to comprise a plurality of word sequences and part-of-speech tagging information of each word sequence; the standardized word sequence is input into a named entity recognition model to recognize key entities in the contract text, key terms in the contract text are extracted based on a machine learning algorithm, the key entities include both parties of the contract, the amount of the contract and the name of a product, and the key terms include payment terms, delivery terms, default responsibility terms and force majeure terms; through dependency syntactic analysis and a semantic role labeling model, analyzing to obtain a semantic relationship between the key entities and the key terms; and storing the extracted key entities, key terms and semantic relationships in a target database in a structured form, and establishing a target index to support quick query. According to the invention, the error rate of manual processing is reduced.

The invention discloses a query statement conversion method and device based on a large model, equipment and a medium, and relates to the field of computer science, and the method comprises the steps: obtaining a natural language query statement sent by a user side, comparing the natural language query statement with a high-frequency problem in a target knowledge base, and obtaining a query statement conversion result; whether a high-frequency problem with the similarity with the natural language query statement exceeding a preset similarity threshold value exists in the target knowledge base or not is judged; if the high-frequency problem that the similarity with the natural language query statement exceeds a preset similarity threshold does not exist, determining a target database table corresponding to the natural language query statement based on the target knowledge graph, and extracting a creation statement corresponding to the target database table, so as to determine target information corresponding to the target database table according to the creation statement; and analyzing the target information by using a preset large model to generate an SQL statement corresponding to the natural language query statement. According to the method and the device, the natural language query statement can be efficiently and accurately converted into the structured SQL statement.

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

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

The invention belongs to the field of SQL generation, and particularly discloses an SQL generation method and system based on a large language model, a terminal and a medium, a natural language query statement and a target database mode are transmitted into the large language model, and N candidate tables most relevant to user query are selected and fed back by the large language model according to a first prompt word; selecting and feeding back a plurality of candidate data columns most relevant to the user query from each candidate table by the large language model according to the second cue word; retrieving an example question and answer pair similar to the user query from the vector database, wherein the example question and answer pair comprises an example question and an example SQL statement; generating a plurality of candidate SQL statements by the large language model according to the third cue word; and carrying out confidence coefficient filtering on the plurality of generated candidate SQL statements, and selecting a final SQL statement. According to the method, the natural language query statement is converted into the SQL statement through the large language model, the SQL statement generation efficiency and accuracy are improved, and then the usability and accuracy of database query are improved.

The invention discloses a webpage generation method and device, electronic equipment and a storage medium, and relates to the technical field of computer software or other related fields, the method comprises the following steps: receiving demand information of a user for an expected webpage, and inputting the demand information into a target language model to obtain N pieces of component information in the expected webpage, N being a positive integer; retrieving a target database based on the N pieces of component information to obtain a retrieval result; inputting fusion demand information of the retrieval result and the demand information into the target language model to obtain a configuration code which is used for describing the page layout, the component attribute and the application logic of the expected webpage; and analyzing the configuration code to obtain a corresponding source code, and rendering the page based on the source code to obtain a target webpage corresponding to the expected webpage. According to the method and the device, the technical problem that the generated webpage does not accord with the intention of the user due to understanding limitation of a low-code platform fused with a large language model in related technologies is solved.

The invention discloses a method for realizing SQL (Structured Query Language) grammar conversion of different databases based on middleware and the middleware, belongs to the technical field of databases, and aims to solve the technical problem of how to realize SQL grammar conversion of different databases through the middleware. The following operations are executed through the middleware to realize the grammar conversion of the SQL between the source database and the target data constructing an abstract syntax tree AST by taking various SQL elements as nodes; operating nodes in the AST on the basis of requirements of a target database; based on the data type in the target database, performing data type mapping by applying a data type conversion rule, and generating an SQL statement adapted to the target database; performing security check on the generated SQL statement; caching the query result and the generated SQL statement based on a caching mechanism; and recording the conversion process and query processing of the SQL statement.

The invention provides a Text2SQL generation method and system driven by a large language model, and the method comprises the steps: recognizing key entities and relationships in a natural language question through the large language model, and carrying out the construction to obtain a query graph; constructing a hierarchical graph representation model based on the database mode of the target database; searching candidate sub-image sets which have the same structure as the query graph and are in semantic association with the query graph from the constructed graph representation model; and constructing a structured Prompt template based on the candidate sub-image set and the user question, generating an initial SQL statement, and outputting a final query result after grammar and semantic detection correction. According to the method, a traditional database is converted into the graph knowledge base, so that the SQL generation accuracy and performance are improved; guiding the large model to generate an SQL (Structured Query Language) meeting business requirements through knowledge graph storage and retrieval table and column information; and the accuracy of the SQL is continuously optimized by utilizing a self-adaptive feedback mechanism, so that the method is particularly suitable for a large-scale complex query scene, and self-improvement and customization of the model are realized.

The invention relates to the technical field of database migration verification, in particular to a cross-data-platform database migration verification method and system applied to autonomous controllable transformation. The method comprises the following steps: configuring a source database and a target database in a data source management module; newly establishing a comparison task in the task management module; a comparison database is connected through a dynamic data source assembly, different databases are connected through JDBC, and comparison verification is executed after database connection parameters are configured; database comparison is carried out in the task management module, wherein the database comparison comprises data volume verification and data consistency verification; a comparison report is generated and stored in a built-in mode. The problem of data integrity in the cross-platform data migration process is solved, the system fully considers the difference between the MySQL database and the local database in character set, function and technical characteristics, data verification and comparison work after migration of the MySQL database source and various local target databases is supported, consistency verification before and after data migration is achieved, and the verification efficiency is improved. And the risk of data loss caused by data migration is avoided.

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 data verification method which comprises the following steps: configuring source database connection parameters and target database connection parameters, setting comparison task parameters, and associating the comparison task parameters with corresponding database connection parameters; determining a source table and a target table corresponding to the source table identifier and the target table identifier, executing table structure comparison to generate a structure difference record, determining a data range according to a comparison mode parameter, executing data volume comparison to generate a quantity difference value, determining a data sampling strategy, and performing content comparison to generate a feature identifier comparison result; and finally, integrating the structure difference record, the quantity difference value and the feature identification comparison result to generate a verification report. According to the method, the data range and the sampling strategy are driven by comparing the mode parameters, so that the integrated processing of structure comparison, quantity comparison and content comparison is realized, and the accuracy and efficiency of data synchronization verification are improved in a heterogeneous database environment.

The invention discloses a multi-database compatible query processing method and device and a medium, and relates to the technical field of data query processing. The method comprises the steps that according to configuration information of all compatible databases, dialect types of all the compatible databases are dynamically recognized, and corresponding dialect configuration rule sets are loaded; when an original SQL statement of the database operation is received, intercepting the original SQL statement before the database operation is executed through a preset interceptor mechanism; according to a dialect configuration rule set of the target database to be operated corresponding to the original SQL statement, reflecting and modifying the original SQL statement in the BoundSql object to obtain a standard SQL statement; and submitting the standard SQL statement to the to-be-operated target database for execution, and returning an execution result.

The invention discloses a new energy grid-connected path optimal configuration method and system based on a knowledge graph technology, and relates to the technical field of power distribution network dispatching. The method comprises the following steps: obtaining power grid operation data, and carrying out scene similarity analysis and attribute similarity analysis on the power grid operation data to determine an abnormal map; expanding the capacity of the native database through the abnormal map, and extracting texture information and primitive information in the expanded target database to construct a power distribution network knowledge graph; and determining an abnormal symbiotic region according to the abnormal situation in combination with the power distribution network knowledge graph, obtaining a load transfer path according to an abnormal transition factor of the abnormal symbiotic region, and performing transfer situation analysis on the load transfer path to determine a target transfer strategy. According to the scheme, a multi-target path search algorithm driven by an abnormal transition factor is introduced to search an optimal transfer path meeting contact constraints, a transfer strategy considering reliability, economical efficiency and risk controllability is generated, and the generation efficiency and reliability of the transfer strategy are remarkably improved.

The invention discloses a model training method and device and a data processing method. The model training method comprises the following steps: acquiring a plurality of structured query language templates; traversing the plurality of structured query language templates, and searching a target function having a mapping relationship with the structured query language templates in a target database; replacing placeholders in the structured query language template with random characters to obtain a target statement; determining a data object corresponding to the target statement in a target database, and determining an answer message in the large language model according to the data object; and receiving a question message matched with the answer message, and training the large language model according to the answer message and the question message matched with the answer message. According to the method and the device, the technical problem of relatively low training efficiency of a large language model caused by low labeling speed and easiness in making mistakes due to the fact that labeling personnel need to understand a natural language problem and a structured query language at the same time in a related labeling method for a training set is solved.

The invention relates to the technical field of data import, in particular to an intelligent Excel data import method and system based on dynamic mapping, and the method comprises the steps that the system traverses all Excel fields for each database field, calculates the feature similarity and field matching degree of the Excel fields, and generates a field mapping template set according to all field matching results; reading the Excel content according to rows, cleaning each row of content into a standard value, performing quality scoring, and outputting a target format data structure set; screening a structure alignment data row and a field structure exception record set which conform to a threshold value; the system reads the structure alignment data rows line by line, processes the extraction value of each field and outputs a legal data set; and extracting a field sequence set, searching whether fields marked as primary keys or unique keys exist in the field sequence set, and combining the fields into a uniqueness judgment set to judge whether the currently written Excel data exists in the target database table.

The invention relates to the technical field of natural language processing, and discloses an intelligent slot filling and mode linking method based on RAG and M-Schema, and the method comprises the steps: rapidly matching a preset FAQ knowledge base through an RAG-FAQ module, and extracting a predefined medical intention and a slot structure; if the matching fails, starting an NLU module to perform intention classification and slot position extraction; performing entity linking and normalization on the slot value by utilizing an RAG-medical field knowledge base context enhancement module, performing multi-dimensional verification in combination with M-Schema metadata of a target database, and outputting a matching column and a confidence score; aiming at uncertainty types such as medical term ambiguity and business rule conflict, a clarification problem is dynamically generated, user feedback is received, SQL query conforming to business rules is generated after slot position information is corrected, and finally the SQL query is transmitted to a medical database execution module to output an analysis result. According to the invention, the availability and accuracy of the general framework in the scene facing the hospital management field can be improved.

The invention relates to the technical field of data transmission, and discloses a multi-key-based industrial internet security management method and system, and the method comprises the steps: collecting to-be-encrypted storage data, carrying out the analysis, obtaining an importance score, and determining an encryption mode; when composite encryption is carried out, the resource utilization rate is collected to determine a composite encryption scheme, and whether time sequence chaos temporary storage is carried out on the to-be-encrypted storage data or not is judged; the encrypted data and the vacancy rate after encryption are collected, a storage target sequence of the encrypted data is determined, and a similar voting value of each database in the encrypted data and the storage target sequence is obtained to determine a target database; and calculating a first hash value and a second hash value of the encrypted data, comparing the hash values, and verifying the integrity of the stored data. According to the method, the encryption failure and system delay risks are reduced, and the storage efficiency of the encrypted data under the resource shortage condition is improved.

The invention provides a data conversion method which can be applied to the technical field of big data. The data conversion method comprises the steps of obtaining metadata and relational statements in a source database; analyzing the metadata, converting the metadata into an intermediate format, and constructing a metadata intermediate model; analyzing the relation statement, extracting semantic levels in the relation statement, and constructing a semantic intermediate model; and associating the metadata intermediate model with the semantic intermediate model, converting the metadata intermediate model into metadata corresponding to a target database according to a preset conversion rule, and converting the semantic intermediate model into a relation statement corresponding to the target database. The invention further provides a data conversion device and equipment, a storage medium and a program product.

The invention discloses a method and a system for generating an SQL (Structured Query Language) query based on a natural language question. The method comprises the following steps: converting mode information of a target database and the natural language question into semantic vector representation; based on the semantic vector representation, a simplified database mode set related to SQL query is screened out through an attention mechanism; based on the natural language problem, the simplified database mode set and preset database constraint information, generating an SQL structural skeleton, and filling specific elements of the SQL structural skeleton to form a preliminary SQL query; and performing dynamic correction and verification on the initial SQL query by utilizing a large language model, and outputting a final SQL query. According to the method, association mining between user query and a database mode is effectively enhanced by utilizing a context-aware cross-encoder mechanism, and an implicit corresponding relation between a natural language problem and a database table / column can be more accurately identified and utilized.

The invention discloses a dynamic form generation and cross-database adaptation method based on metadata driving, and belongs to the technical field of data architectures. The method comprises the steps that an original metadata attribute graph is established; on the basis of the original metadata attribute graph and user roles and equipment parameters for calling the forms, generating an optimal equipment form interaction data graph corresponding to each user permission; verifying an interaction path between the forms and a cross-database demand of a target database type to carry out cross-database mapping adaptation analysis, and generating an optimal form data adaptation database of the corresponding equipment of each user permission; and performing rule compiling based on multi-source heterogeneous data input by a user in real time and execution logic in the original metadata attribute graph, marking an incremental propagation path in the optimal form interaction data graph of the corresponding equipment of each user permission, and updating the optimal form data adaptation database of the corresponding equipment of each user permission. The method has the advantages that the data interaction efficiency is improved, and the interaction round-trip times and redundant data transmission are reduced.

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 real-time data acquisition buffer throughput management method and device and electronic equipment, and relates to the field of data processing. Receiving a data object containing a sampling value, a timestamp, a measuring point number and a state mark through an input memory queue, and constructing a logic mapping identifier by a mapping agent unit to generate a logic write-in state; the logic write-in state is stored in a delay sensing buffer area, and a transaction confirmation delay sequence returned by a target database is monitored in combination with a distributed delay monitoring unit; establishing a time regression model based on the delay sequence, and generating a prediction projection window of transaction confirmation time; when the window meets a confidence threshold condition, releasing the logic write-in state to an output memory queue; and finally, writing into a database through an output thread, generating a completion mark table, and feeding back for delay buffer dynamic management. By implementing the technical scheme provided by the invention, the buffer data release rhythm and the database processing capability are conveniently coordinated, so that the overall throughput efficiency is improved.

The invention relates to a medical health big data exploration system, and the system comprises a demand understanding module which is used for converting a data exploration demand into a structured exploration task; the compliance review module is used for performing compliance review on the probing task; the code generation module is used for generating a query code according to the probing task and the target database characteristics; the query execution module is used for querying query result data corresponding to the query code from a target database according to the query code; the query result analysis module is used for performing multi-dimensional analysis on the query result data to obtain a multi-dimensional analysis result; a report generation module; and the result presentation module is used for presenting the exploration report in an interactive mode in a user interface and supporting iterative improvement of the report by providing a feedback mechanism, so that the problems of data isomerism, difficulty in quality evaluation, low exploration efficiency, high professional threshold, insufficient privacy protection and the like in the existing medical data exploration process are solved.

The invention discloses a complex SQL query dynamic rewriting method based on AST and a server, and relates to the field of data rewrite.The method comprises the steps that in response to a received target database type, an original SQL query statement is analyzed into AST; an SQL processor is initialized; recursively traversing a selection statement structure of the main query, and recursively traversing a sub-query structure nested in the selection statement structure; recursively traversing each selected statement structure in the joint query; recursively traversing conditional expressions in the updated statements, the deleted statements and the inserted statements or nesting and selecting statement structures; converting the rewritten AST into an SQL (Structured Query Language) query statement after dynamic rewriting; and returning the dynamically rewritten SQL query statement, and rewriting the SQL expression and the SQL statement structure in the AST according to a preset dynamic modification logic. The technical problem that a complex nested structure cannot be processed in a traditional SQL enhancement or modification mode can be effectively solved.