Bidirectional mapping method and device for database model and programming language data model

By building loss functions and training large models, the two-way mapping between the database model and the programming language data model is realized, which solves the problem of difficulty in realizing two-way mapping in the existing technology, improves the efficiency and quality of software development, and enhances flexibility.

CN119718299APending Publication Date: 2025-03-28SHANGHAI CAIYUE XINGCHEN INTELLIGENT TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202411873269.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-18
Publication Date
2025-03-28

AI Technical Summary

Technical Problem

The prior art is difficult to realize the bidirectional mapping between the database model and the programming language data model, resulting in the impact of software development efficiency and quality and lack of flexibility.

Method used

By obtaining sample data of the database model and programming language data model, a loss function is constructed, including structural consistency loss, data type mapping accuracy loss and semantic maintenance loss, and using these data and loss functions to train the big model to obtain a bidirectional mapping model.

Benefits of technology

The two-way mapping between the database model and the programming language data model is realized, which reduces manual workload and error rate, improves software development efficiency and quality, enhances flexibility, and adapts to diverse project needs.

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Abstract

The invention relates to a bidirectional mapping method and device for a database model and a programming language data model. The method comprises the steps of obtaining sample data for bidirectional mapping of a database model and a programming language data model, constructing a loss function for bidirectional mapping of the database model and the programming language data model, performing normalization processing on the sample data, training a large model by using the normalized sample data and the loss function, and obtaining a database model and a programming language data model. Obtaining a bidirectional mapping model; and carrying out bidirectional mapping on the database model and the programming language data model by utilizing the bidirectional mapping model. By adopting the method, bidirectional mapping of the database model and the programming language data model can be realized.
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