Data storage method, apparatus and device, and computer readable storage medium

Abstract

The present invention discloses a data storage method. The method comprises: obtaining input sample data and corresponding database template data with the same data structure as the data source, and generating a corresponding data conversion model according to the input sample data and the database template data; obtaining raw data of the data source and inputting the raw data into the data conversion model; and converting the raw data of the data source into corresponding stored data through the data conversion model, and importing the stored data into the database. The present invention further discloses a data storage apparatus and device, and a computer readable storage medium. According to the technical scheme of the present invention, analysis and comparison are carried out between the data structure of the data source and the data structure of the system database, a data conversion model is constructed, and the data storage is automatically completed through the data conversionmodel, so that the data can be automatically stored without human intervention, and the work efficiency is improved.

Description

Technical field [0001] The present invention relates to the technical field of data processing, in particular to a method, device, equipment and computer-readable storage medium for data storage. Background technique [0002] With the development of technology, risk assessment methods based on computer risk control engines have begun to appear on the market. This method relies on the Internet to accumulate a large amount of user data, and based on these data and computer risk control models, the evaluation results are obtained to determine whether to lend . [0003] When adopting this big data evaluation method, it is necessary to import the large amount of raw data obtained on the Internet into the system database. However, due to the diverse source channels of these data, they have the characteristics of different structures, high data redundancy, and non-linearity; and the data storage format required by the database is often different from the original data format, which leads...

AI Technical Summary

Problems solved by technology

However, due to the various sources of these data, they have the characteristics of different structures, high data redundancy, and non-linearity; and the data storage format required by the database is often different from these original data formats, which leads to data storage. The process is extremely cumbersome, requiring staff to manually convert the format of the original data into the format required by the system database, increasing the workload of the staff to put the data into the database

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