Method and system for automatically generating table data

Abstract

The invention provides a method and a system for automatically generating table data. The method comprises the steps of obtaining table data generation configuration items used for limiting how to generate data of at least one data table; and generating data of the at least one data table based on the obtained table data generation configuration item, wherein the table data generation configuration item comprises a data set parameter configuration item used for configuring the name and the main key value number of the main key of the at least one data table, and a data table parameter configuration item corresponding to each data table and used for limiting how to generate the name and the field value of the field in the data table. According to the method and the system, a user does not need to construct a specific data creation scheme for each service scene, and only needs to set each table data generation configuration item according to the specific service scene so as to automatically generate the table data meeting the requirements of the user; and on the other hand, multi-table data association generation can be supported, that is, generation of data tables with association relationship based on association fields between every two data tables can be supported.

Description

technical field [0001] The present invention generally relates to the field of data processing, and more specifically relates to a method and system for automatically generating table data. Background technique [0002] Machine learning is an inevitable product of the development of artificial intelligence research to a certain stage. It is committed to improving the performance of the system itself by means of calculation and using experience. In computer systems, "experience" usually exists in the form of "data". Through machine learning algorithms, "models" can be generated from data. Model, when faced with a new sample, the model will provide the corresponding judgment, that is, predict the result. [0003] The design of machine learning algorithm application solutions often depends on the data format of the data. The data format of the data used in different application scenarios varies greatly, and in many application scenarios, due to some reasons, it is impossible t...

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Problems solved by technology

[0003] The design of machine learning algorithm application solutions often depends on the data format of the data. The data format of the data used in different application scenarios varies greatly, and in many application scenarios, due to some reasons, it is impossible to obtain real data in advance (for example, bank data, etc. Sensitive information) for off-site development, but on-site development directly faces problems such as tight project time. In this case, modeling engineers generally generate fake data (that is, artificial data) off-site, and develop based on fake data. The corresponding machine learning algorithm application plan is then brought into the field for improvement and tuning

[0004] However, the existing number creation methods often have the following problems: On the one hand, the number creation methods are limited to specific business logic, that is, different business scenarios have different data requirements, and after the business scene is switched, the data format of the required data is often large. Therefore, it is necessary to design its own number creation scheme for each business scenario, and the repetitive workload is large; on the other hand, it only supports independent generation of single-table data, and cannot support multi-table data association generation, and the actual situation often requires Generate associated multi-table data, for example, associated multi-table data can be used to test situations such as splicing between tables

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