Communication Optimization Technique For Spatio-temporal Information In Military Analytic Simulation

Moving entities widely exist in military analytic simulation applications such as attack-defense system combat simulation.Its continuous motion behavior leads to the updating of spatio-temporal state,which occupies most communication content,and becomes it becomes the key factor that affects and restricts the operation efficiency.Traditional communication optimization techniques mainly aim at improving data transmission speed or filtering redundant communication links.However,they cannot effectively overcome the bottleneck of communication caused by large-scale spatio-temporal data transmission.Therefore,it is of great practical value and theoretical significance to carry out researches on correlation techniques aiming at reducing the information traffic of spatio-temporal state,to improve the communication performance,make effective use of the existing computing and communication resources,meet the increasing performance requirements of military analytic simulation,and promote the further development of simulation theory and technology.To overcome the bottleneck of spatio-temporal information communication in military analytic simulation,this dissertation focuses on an optimization idea of"Computation-for-Communication".we constructs the agent model corresponding to the behaviors of moving entities by using the data laws and behavioral knowledge contained in the spatio-temporal information in the simulation,and introduces an approach of “Sender construction,recipient calculation" to reduce the amount of spatio-temporal information traffic.Thereby the performance of analytic simulation can be enhanced.The specific works and innovations are as follow:(1)An online spatio-temporal data management method based on trajectory data flow modeling is proposed.Spatio-temporal data are the basis of analyzing and understanding the physical movement behavior and constructing the agent model.Due to the dynamic difference of spatio-temporal data,the update characteristics of flow data,and the frequent I/O operation requirements,the traditional methods of file storage and database support to collect relevant data would seriously affect the performance of simulation,which is difficult to effectively adapt to the requirements of continuous collection of spatio-temporal information in the process of operation.According to the above problem,this paper proposes a method of online spatio-temporal data management based on trajectory data flow modeling.This method can reduce the overhead of spatio-temporal data management by converting the prosess of continuous reception and preservation of spatio-temporal information into a discrete trajectory management model construction procsss.The experimental results show that the proposed method can improve the simulation performance compared with the traditional method.(2)An optimization method of spatio-temporal information communication based on dynamic local computing mechanism is proposed.Spatio-temporal data describe the continuous motion state of moving entities.Due to the characteristics of "long link","high frequency","short content" and "continuous communication" shown in the communication interactions,it is difficult for traditional communication optimization technology to effectively alleviate the bottleneck of spatio-temporal information communication in military analytic simulation.This paper presents a dynamic local computing mechanism based on a full-time motion model,which transforms the communication optimization into an agent model construction process.Based on the historical spatio-temporal data collected and saved online,the sender can online construct a full-time motion model with the trajectory as an agent,by taking advantage of the similarity of the motion behaviors during the analytic simulation experiment of multi-samples and multiple runs.The receiver can obtain the spatio-temporal state information of the sender only by locally running the motion model.The experimental results show that the proposed method can effectively avoid the real spatio-temporal data transmission,reduce the actual traffic of spatio-temporal information,and improve the simulation performance.(3)An optimization method of individual spatio-temporal information communication based on adaptive motion prediction mechanism is proposed.When the collected historical spatio-temporal data cannot provide an effective modeling basis due to the small number of simulation runs or the high autonomy of the physical movement behavior,the agent-based agent model cannot guarantee the communication optimization effect based on the "computation-for-communication",and the traditional motion modeling method is also difficult to apply to the description and prediction of individual motion behavior in a single simulation run due to the sparsity of spatio-temporal data and motion mode.This paper proposes an adaptive motion prediction mechanism based on dynamic hybrid motion prediction model,which uses the information of online extracted individual behavior to improve the motion prediction ability of the agent model and dynamically adapts to individual behavior changes by actively switching internal predictors.The experimental results show that the proposed method can effectively reduce the communication frequency of individual spatio-temporal data.Based on the above research results,this dissertation takes the simulation of Naval Air Defense Simulation as an example.Firstly,the communication characteristics are analyzed to verify the related theories and ideas in this thesis.Then the comprehensive testing of the optimization technology and tradtitionlal medhods is carried out based on this military analytic simulation application.The experimental results show that the simulation application exhibits good scalability,and the performance can achieve an upgrade of about 45.53%.