Flow prediction model training method, flow prediction method, device, equipment and medium

Abstract

The present application provides a traffic forecasting model, training method, forecasting method, device, equipment and medium. The method includes: obtaining a traffic data set, intercepting the traffic data set through a sliding window operation, and obtaining the sample data characteristics of the traffic with the size of the sliding window , and the sample label corresponding to the sample data feature of the flow; the next flow value after the sample data feature of the sample label flow; obtain the sample attribute feature corresponding to the sample label; Processing, and input the output memory state and sample attribute features into the fully connected network of the model for regression prediction; according to the sample label and prediction results, the parameter weights are updated through the back propagation algorithm to obtain a trained traffic prediction model. In this way, the prediction results are associated with the attributes of different flows, and the problems of lag and low accuracy in flow peak prediction are solved.

Description

technical field [0001] The present application relates to the field of network technology, in particular, to a traffic prediction model, training method, prediction method, device, equipment and medium. Background technique [0002] With the popularization of the network, the scale of network traffic is constantly updated, and efficient and reasonable allocation of network resources has become particularly important. On the one hand, the unreasonable allocation of network resources may cause some network resources to be unable to be used normally due to exhaustion, and even cause network paralysis; while another part of network resources is in a state of excess, which seriously affects the user's online experience. On the other hand, although the network allocates network resources reasonably in the early stage, the network traffic is sudden. At this time, the originally sufficient network resources may be in short supply. To solve this problem, the existing SDN (Software D...

AI Technical Summary

Problems solved by technology

[0006] The model trained through the above implementation process can combine the processing results of the sample data characteristics of the previous traffic with the gated recurrent neural network and the sample attribute characteristics during prediction when performing traffic prediction, so that it can be used in traditional gating On the basis of cyclic neural network prediction, combined with more abundant flow sample attribute characteristics, the prediction of flow is no longer based solely on the flow evolution law at historical moments, but will combine different attribute characteristics of different flows Make predictions so that the prediction results are associated with attributes at different moments to be predicted, so as to solve the problems of hysteresis and low accuracy in the prediction of traffic peaks

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