Research On Business Modeling And Traffic Forecasting Of High-speed Rail Private Network Of The Integrated Ground-air-space Network

Due to the limited resources of the integrated information network of space and earth,there is a situation in which resources are limited.Therefore,how to allocate network resources more reasonably under the premise of ensuring service quality is a problem worthy of in-depth study.It analyzes and predicts the service traffic of each private network,and allocates corresponding resources according to the predicted value,which can effectively ensure the Qo S requirements of private network services and improve the overall resource utilization efficiency of the network.In the aspect of business flow model construction,the existing research is often limited to pure theoretical research and rarely considers practical application.The high-speed rail private network is the core component of the ground private network of the integrated information network of the sky and the ground.So far,there are few studies on high-speed rail business analysis and traffic modeling,and the pertinence is not strong.The service transmission characteristics and traffic size of the high-speed rail private network are different from those of the traditional Internet,which will inevitably lead to changes in its business traffic characteristics.It provides theoretical basis and basis for online perception","cooperative allocation of network resources",and "network anomaly detection".Based on the analysis of the space-ground integrated network,the hardware and software structure of the high-speed rail private network,and the existing high-speed rail services,this thesis determines the connotation of the analysis and modeling of the space-ground integrated network service,and the high-speed rail service suitable for transmission through the space-ground integrated network.Then,starting from the requirements of traffic forecasting,traffic supervision,network resource allocation,etc.,conduct business analysis of high-speed rail private networks for control(mainly including train control,dispatch and command,emergency rescue,traffic safety monitoring and disaster protection)and passenger service.Modeling with flow.Starting from the implementation principle and process of control services,analyze the information transmission content and flow direction of each type of service,Qo S requirements such as real-time performance/packet loss rate,and security level and other transmission services and security requirements,and abstract the information of each type of transmission packets.Parameters such as packet type,arrival time interval,and packet length are used to construct a high-speed rail service traffic model.The method of combining theoretical analysis and actual traffic fitting is used to carry out passenger service business analysis and traffic modeling.First,the existing network traffic modeling research results,especially the business division and traffic modeling results of 3GPP and 3GPP2 are: For reference,initially determine the traffic model of the passenger service business,and then perform fitting and verification through the actual network traffic.The traffic characteristics of the high-speed rail private network is analyzed by simulating traffic.Based on the traffic module,the high-speed rail private network control business simulation traffic is generated,and the passenger service business traffic is generated by combining self-capturing and network download,and the two are synthesized to generate the high-speed rail private network simulation traffic.The characteristics of self-similarity,burstiness and periodicity of high-speed rail private network traffic are analyzed qualitatively and quantitatively.This thesis proposes an LSTM network traffic prediction model IPA-LSTM with attention mechanism based on traffic characteristics.The periodicity,self-similarity and burstiness of the high-speed rail private network traffic are used as the influencing factors of the attention mechanism to predict,and the model can better capture the traffic characteristics by adding weight coefficients to the LSTM hidden layer.The experimental results show that the prediction model is better than the traditional LSTM model.Finally,the traffic prediction results are applied to the design of the network traffic monitoring algorithm,and the limited network resources are allocated reasonably.