Research On Mobile Phone Traffic Prediction Algorithm Based On Hadoop

In recent years,mobile Internet has entered a period of rapid development,with the number of mobile phone users from the GB level to the level of TB or even PB-level,the emergence of mass mobile phone user traffic to the operators in the data analysis and processing to bring challenges.How to predict the use of mass mobile phone users in the next stage,and then to stimulate business growth has become a research hotspot.Because the traditional data analysis method can not analyze the massive data quickly,it is imperative to set up a platform based on large data prediction algorithm to deal with massive data-Hadoop is a distributed framework,which can easily realize the mining and processing of massive data,so the processing framework based on the Hadoop platform provides a new solution for solving the above problems.By comparing the prediction effect of common intelligent algorithms on mobile phone traffic,the BP neural network algorithm is superior to other algorithms.But BP algorithm also has some shortcomings.In order to overcome the problem that BP neural network algorithm falls into local minimum,this paper combines BP neural network and phase space reconstruction technology,calculates the best embedding dimension and delay time to improve the accuracy of mobile phone traffic prediction.For the shortcoming that training process needs a lot of time,this paper proposes a parallel processing of BP algorithm based on Hadoop platform to shorten the training time,in order to improve the efficiency of processing massive data.In order to reflect the forecasting effect of data more intuitively,this paper builds a simulation platform based on Hadoop platform,which combines the MapReduce distributed programming framework with the CC-BP algorithm,and adopts the SSM framework technology to visualize the forecasting results of mobile traffic data.By comparing the experimental results,it is proved that the improved BP algorithm based on Hadoop is more efficient and has higher prediction accuracy.By comparing the data efficiency of different nodes,we can see that with the increase of the number of platform nodes,the processing efficiency of massive data will be improved,thus verifying the feasibility of the simulation platform.