Data processing method and server

Abstract

The invention discloses a data processing method and a server. The method comprises the following steps: receiving request data, and obtaining feature variables to be processed from the request data, wherein the feature variables to be processed have first feature attributes; obtaining a derivative strategy, according to the derivative strategy, performing intersection combination of combinable features on the feature variables to be processed to obtain derivate variables, wherein the derivative variables have second feature attributes, and the second feature attributes are closer to target expected demands compared to the first feature attributes; obtaining a screening strategy, and according to the screening strategy, screening the derivative variables to obtain candidate target variables; and adding the candidate target variables into a variable pool used for storing the feature variables to be processed so as to perform a next round of variable derivation and variable screening according to the derivative strategy and the screening strategy until the target expected demands are observed.

Description

technical field [0001] The invention relates to Internet technology, in particular to a data processing method and server. Background technique [0002] With the development of Internet technology and the popularization of smart terminals, data transmission and interaction are becoming more and more convenient. The transmission and interaction in the mobile Internet era are more convenient than in the traditional Internet era. For example, users can now use mobile terminals to pay water, electricity or gas bills, and purchase wealth management products, etc. All of these require the use of mobile payment, which brings convenience to people's lives, but also easily brings hidden dangers to data security. In order to improve security, it is necessary to analyze the data to distinguish the risky data that will bring hidden dangers to data security. When using the existing risk control strategy for analysis, due to its own limitations, most of the obtained raw data are weak va...

AI Technical Summary

Problems solved by technology

When using the existing risk control strategy for analysis, due to its own limitations, most of the obtained raw data are weak variables after analysis, and the weak variables are not enough to reflect the real intention of the user expressed by the data, nor can they That is to say, through weak variables, it is impossible to distinguish which risk data needs to be shielded or restricted, that is, the data triggered by malicious users is likely to block or restrict the data triggered by non-malicious users, affecting users normal usage needs

However, in related technologies, there is no effective solution to this problem

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