Data processing method and equipment based on recurrent neural network, and medium

Abstract

The invention discloses a data processing method, device and equipment based on a recurrent neural network and a medium, and the method comprises the steps: obtaining to-be-processed time sequence data, and inputting the to-be-processed time sequence data into a preset data processing model; wherein the data processing model is obtained by performing federated forward training on a recurrent neural network model at different moments and performing federation reverse training on the recurrent neural network model at different moments after the federation forward training is completed, and eachtraining of the recurrent neural network model at the same moment is combined with the second sample time series data and the first sample time series data of the first equipment at the same target moment; and executing a preset data processing flow on the to-be-processed time series data based on the preset data processing model to obtain a target prediction label of the to-be-processed time series data. According to the method and the device, the technical problems of overlong model construction time and low calculation efficiency caused by incapability of directly performing time series data interaction among different data parties in the prior art are solved.

Description

technical field [0001] The present application relates to the technical field of artificial intelligence in financial technology (Fintech), and in particular to a data processing method, device and medium based on a recurrent neural network. Background technique [0002] With the continuous development of financial technology, especially Internet technology and finance, more and more technologies (such as distributed, blockchain, artificial intelligence, etc.) Requirements, such as the financial industry also has higher requirements for data processing. [0003] The Recurrent Neural Network (RNN) model is often used to process time-series data. Time-series data refers to data arranged based on time series. Time-series data includes features of the same user at the same moment in different data sources such as different servers. Data, for example, user X purchased product A in a convenience store, and a new sales record is generated in the server of the convenience store. At...

AI Technical Summary

Problems solved by technology

[0005] The main purpose of this application is to provide a data processing method, device, device, and medium based on a cyclic neural network, aiming to solve the problem that in the prior art, different data parties cannot directly interact with time series data, resulting in excessive model construction time. Long, technical problems with low computational efficiency

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