SQL statement generation method, device and equipment and storage medium

Abstract

The invention relates to the field of financial science and technology, and discloses an SQL statement generation method and device, SQL statement generation equipment and a storage medium. The SQL statement generation method comprises the steps: obtaining each feature binning rule, and carrying out the analysis of each feature binning rule according to a preset binning rule library, so as to obtain the feature type of each feature binning rule; obtaining a target grammar matching rule corresponding to each feature binning rule according to each feature type; and generating SQL statements corresponding to the feature binning rules based on the feature binning rules and the target grammar matching rules corresponding to the feature binning rules. The characteristic binning model is automatically converted into the SQL statement system, the labor cost and the time cost for outputting the model to run in the production environment are reduced, and the generation efficiency of the SQL statement is improved.

Description

technical field [0001] The present invention relates to the technical field of financial technology, in particular to a method, device, equipment and storage medium for generating an SQL statement. Background technique [0002] With the development of computer technology, more and more technologies (big data, distributed, blockchain, artificial intelligence, etc.) The industry's security and real-time requirements also put forward higher requirements for technology. [0003] Feature binning is a feature processing method that divides interval eigenvalues ​​into multiple discrete values ​​or groups discrete eigenvalues, which can make the eigenvalues ​​more stable and improve the robustness of the model. The feature binning algorithm is widely used in various scene modeling. After the modeler builds the binning model, he needs to write the SQL code according to the binning rules and send it to the database administrator to deploy it to the production environment for operatio...

AI Technical Summary

Problems solved by technology

[0004] However, the current method of manually writing SQL codes is inefficient and has a high error rate, especially when users need to frequently perform interactive binning adjustments, the binning rules will be iteratively updated frequently, resulting in excessive pressure on manual SQL

At the same time, feature binning generally involves a large number of feature processing. The number of each feature binning is tens to hundreds, which makes the number of feature binning rules very large, resulting in the high cost of manually writing SQL statements.

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