摘要
以准确估计舰船通信网络流量为研究核心,提出基于大数据驱动与分析的舰船通信网络流量智能估计方法。该方法以大数据驱动和分析领域中的经验模态分解、卷积神经网络为技术核心,使用基于改进集合经验模态分解的舰船通信网络信号降噪方法,去除舰船通信网络信号中噪声;构建去噪后舰船通信网络信号中的流量时间序列图,凸显通信网络流量在各个时间段的变化特征之后,将舰船通信网络流量时间序列图中的时间序列,作为基于卷积神经网络的网络流量预测方法的预测样本,提取舰船通信网络细节信号的时间序列,估计流量的趋势信息与时间信息,实现舰船通信网络流量智能估计。经测试:该方法对舰船通信网络流量智能估计后,估计结果与实际流量基本一致。
Taking the accurate estimation of ship communication network traffic as the research core,an intelligent estimation method of ship communication network traffic based on big data driving and analysis is proposed.This method takes empirical mode decomposition and convolution neural network in the field of big data driving and analysis as the technical core,and uses the signal denoising method of ship communication network based on improved ensemble empirical mode decomposition to remove the noise in the signal of ship communication network.After constructing the traffic time series diagram in the denoised ship communication network signal and highlighting the change characteristics of the communication network traffic in each time period,take the time series in the ship communication network traffic time series diagram as the prediction sample of the network traffic prediction method based on convolution neural network,extract the time series of the ship communication network detail signal,and estimate the trend information and time information of the traffic,Realize the intelligent estimation of ship communication network traffic.The test shows that the estimation result is basically consistent with the actual traffic after the intelligent estimation of ship communication network traffic by this method.
作者
孟祥瑞
MENG Xiang-rui(College of Applied Technology,Dalian Ocean University,Dalian 116300,China)
出处
《舰船科学技术》
北大核心
2022年第10期123-126,共4页
Ship Science and Technology
基金
辽宁省教育科学“十三五”规划课题(JG20EB022)
辽宁省大学生创新创业训练计划项目(S202110158001X)。
关键词
大数据驱动
舰船
通信
网络
流量
智能估计
big data driven
worship
signal communication
network
flow
intelligent estimation
