Heterogeneous database advanced object conversion method based on large model

Abstract

The invention relates to the technical field of heterogeneous database advanced object conversion migration, in particular to a heterogeneous database advanced object conversion method based on a large model, which comprises the following steps: S1, collecting data required by database conversion as a professional knowledge base of the database; s2, by means of a large model development tool, generating a database professional large model by importing the knowledge base; s3, sorting the training data set, and training the database large model to make the conversion result more accurate; s4, continuously optimizing a model conversion result until the conversion result of the model can meet requirements, and performing model deployment; and S5, performing heterogeneous database high-level object conversion by means of the deployed model, and completing the conversion migration work of the whole high-level object. The method can effectively solve the problems that during existing database conversion, the manual conversion workload is large, the conversion is complex, and the cost is high when a syntactic analyzer generator adapts to various databases.

Description

Technical Field

[0001] The present invention relates to the technical field of high - level object conversion and migration of heterogeneous databases, and specifically to a method for converting high - level objects of heterogeneous databases based on a large model. Background Technique

[0002] At present, the high - level object migration of heterogeneous databases in enterprises is itself a particularly difficult task. In the traditional way, developers rewrite high - level objects such as stored procedures according to the syntax of the migrated database, which is an extremely labor - intensive task. Currently, there is no mainstream high - level object migration tool. Some rely on grammar parser generators for conversion, but the writing of grammar files for each database is a huge project, and the grammar conversion and splicing of different databases are extremely complex, making it difficult to conveniently and quickly support multiple database adaptations.

[0003] Therefore, a method for converting high - level objects of heterogeneous databases based on a large model is needed to solve the problems raised in the above background technique. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for converting high - level objects of heterogeneous databases based on a large model to solve the problems raised in the above background technique.

[0005] To achieve the above - mentioned purpose, the present invention provides the following technical solutions: A method for converting high - level objects of heterogeneous databases based on a large model, comprising the following steps: S1, collect the data required for database conversion as the professional knowledge base of this type of database; S2, with the help of a large - model development tool, generate a professional database large model by importing the knowledge base; S3, organize the training data set and train the database large model to make its conversion results more accurate; S4, continuously optimize the model conversion results until the conversion results of the model can meet the requirements, and then perform model deployment; S5, with the help of the deployed model, perform high - level object conversion of heterogeneous databases to complete the entire high - level object conversion and migration work.

[0006] As a preferred solution of the present invention, the data required for database conversion in S1 includes the official syntax structure of the supported databases and its definition and explanation documents, as well as various high - level object instances.

[0007] As a preferred solution of the present invention, the large - model development tool in S2 includes, but is not limited to, Alibaba PAI.

[0008] As a preferred solution of the present invention, the professional database large model in S2 deeply masters various database syntax rules and semantics.

[0009] As a preferred solution of the present invention, in S4, when optimizing the model conversion result, the unsatisfactory conversion instances are added to the BadCase for correction.

[0010] As a preferred solution of the present invention, the specific method for optimizing the model conversion result in S3 is as follows: preset a high-quality question set according to the data required for database conversion, train the database large model, obtain the training result and the optimization solution, and feedback the training result and the optimization solution to the database large model respectively, so that it can continuously learn the difference between the excellent conversion result and the bad conversion result corresponding to the question set until the conversion result of the database large model can meet the requirements.

[0011] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. In the present invention, by collecting the data required for database conversion as the professional knowledge base of this kind of database, with the help of the large model development tool, by importing the knowledge base, generating the database professional large model, sorting out the training data set, training the database large model, making its conversion result more accurate, continuously optimizing the model conversion result until the conversion result of the model can meet the requirements, and then performing model deployment, and using the deployed model to perform heterogeneous database high-level object conversion to complete the entire high-level object conversion and migration work, and using the large model development tool to develop a professional database high-level object conversion large model, the problems of large manual conversion workload, complex conversion, and high cost of adapting the syntax analyzer generator to multiple databases during the existing database conversion are solved. Detailed implementation manners

[0012] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0013] To facilitate the understanding of the present invention, the present invention will be described more comprehensively below. Several embodiments of the present invention are given. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0014] It should be noted that when an element is referred to as "fixed to" another element, it can be directly on the other element or there can also be an intermediate element. When an element is considered to be "connected to" another element, it can be directly connected to the other element or there may be an intermediate element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used in this article are only for illustrative purposes.

[0015] Unless otherwise defined, all technical and scientific terms used in this article have the same meaning as those commonly understood by those skilled in the technical field to which this invention belongs. The terms used in the description of this invention in this article are only for the purpose of describing specific embodiments and are not intended to limit this invention. The term "and / or" used in this article includes any and all combinations of one or more of the related listed items.

[0016] Embodiment, the present invention provides a technical solution: A method for advanced object conversion of heterogeneous databases based on large models, comprising the following steps: S1, collect the data required for database conversion as the professional knowledge base of this type of database; S2, with the help of large model development tools, generate a professional large model for the database by importing the knowledge base; S3, sort out the training data set and train the database large model to make its conversion result more accurate; S4, continuously optimize the model conversion result until the conversion result of the model can meet the requirements, and then deploy the model; S5, with the help of the deployed model, perform advanced object conversion of heterogeneous databases to complete the entire conversion and migration work of advanced objects.

[0017] Further, the data required for database conversion in S1 includes the official syntax structure of the supported databases and its definition and explanation documents, as well as various advanced object instances.

[0018] Further, the large model development tools in S2 include but are not limited to Alibaba Pailian.

[0019] Further, the professional large model for the database in S2 deeply masters various database syntax rules and semantics.

[0020] Further, when optimizing the model conversion result in S4, the unsatisfactory conversion instances are added to BadCase for correction.

[0021] Further, the specific method for optimizing the model conversion result in S3 is as follows: Preset a high-quality question set according to the data required for database conversion, train the database large model, obtain the training result and the optimization plan, and feedback the training result and the optimization plan to the database large model respectively, so that it continuously learns intensively the difference between the excellent conversion result and the bad conversion result corresponding to the question set until the conversion result of the database large model can meet the requirements.

[0022] The working process of the present invention: When the advanced object conversion method for heterogeneous databases based on a large model designed by this solution is running, collect the data required for database conversion as the professional knowledge base of this type of database. The data required for database conversion includes the official syntax structure supported by the database and its definition and explanation documents, as well as various advanced object instances. With the help of a large model development tool, the large model development tool includes but is not limited to Alibaba Pailian. By importing the knowledge base, generate a database professional large model, and the database professional large model deeply masters various database syntax rules and semantics. Sort out the training data set, train the database large model to make its conversion result more accurate, add the unsatisfactory conversion instances to BadCase for correction, preset a high-quality question set according to the data required for database conversion, train the database large model, obtain the training result and the optimization plan, and feedback the training result and the optimization plan to the database large model respectively, so that it continuously learns intensively the difference between the excellent conversion result and the bad conversion result corresponding to the question set until the conversion result of the model can meet the requirements. Perform model deployment, and with the help of the deployed model, perform advanced object conversion for heterogeneous databases to complete the entire conversion and migration work of advanced objects.

[0023] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirits of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An advanced object conversion method for heterogeneous databases based on large models, characterized in that, Including the following steps: S1. Collect the data required for database conversion to form a professional knowledge base for this type of database; S2. With the help of large model development tools, generate a professional database large model by importing the knowledge base; S3. Organize the training data set and train the database large model to make its conversion results more accurate; S4. Continuously optimize the model conversion results until the conversion results of the model can meet the requirements, and then perform model deployment; S5. With the help of the deployed model, perform heterogeneous database high-level object conversion to complete the entire conversion and migration work of high-level objects.

2. The advanced object conversion method for heterogeneous databases based on a large model according to claim 1, characterized in that: In S1, the data required for database conversion includes the official syntax structure of the supported database and its definition and explanation documents, as well as various high-level object instances.

3. The advanced object conversion method for heterogeneous databases based on large models according to claim 1, characterized in that: In S2, the large model development tools include but are not limited to Alibaba Pailian.

4. A method for advanced object conversion of heterogeneous databases based on large models according to claim 1, characterized in that: In S2, the professional database large model deeply masters various database syntax rules and semantics.

5. A method for advanced object conversion of heterogeneous databases based on large models according to claim 1, characterized in that: In S4, when optimizing the model conversion results, add the unsatisfactory conversion instances to BadCase for correction.

6. The advanced object conversion method for heterogeneous databases based on large models according to claim 1, characterized in that: The specific method for optimizing the model conversion results in S3 is as follows: Preset a high-quality question set according to the data required for database conversion, train the database large model to obtain the training results and optimization solutions, and feedback the training results and optimization solutions to the database large model respectively, so that it can continuously learn the difference between the excellent conversion results and the bad conversion results corresponding to the question set until the conversion results of the database large model can meet the requirements.