Research on mine wireless network traffic prediction based on deep learning

In order to accurately predict the change of mine wireless network flow, ensure the safe and stable operation of underground wireless network and ensure the safety of mine production. Based on the analysis of the traffic characteristics of wireless network, this paper proposes a kind of CL-FCCNet, which is a traffic prediction model based on residual network （ＲｅｓＮｅｔｓ） and circular neural network（ＲＮＮ）. The prediction model can forecast traffic for complex wireless network working environment, model the temporal and spatial characteristics of traffic data, and help to realize the abnormal automatic alarm of traffic monitoring. The experimental results show that the prediction effect of the model is improved compared with the existing prediction methods.