基于大数据分析技术的战场态势分析及预测

Battlefield Situation Analysis and Prediction Based on Big Data Analysis Technology

针对信息化条件下联合作战产生的海量、多源、复杂的战场数据,提出一种Hadoop分布式数据处理平台。收集海量数据进行战场态势(battlefieldsituation,BS)要素分析,用粒子群算法(particleswarmoptimization,PSO)优化极限学习机(extremelearningmachines,ELM)的方法对战场态势历史数据进行训练,构建战场态势预测模型;并采用Matlab2018对战场态势进行模拟仿真。仿真结果表明:Hadoop处理海量战场数据效率更高,可有效提高战场态势的预测精度,为辅助指挥员快速掌握复杂战场态势提供新的方法和途径。

Aiming at the massive,multi-source and complex battlefield data produced by joint operations under the condition of informationization,a Hadoop distributed data processing plat form is proposed.Massive data are collected to analyze the elements of battle field situation(BS),and the particle swarm optimization(PSO)is used to optimize the extreme learning machines(ELM).The method of ELM is used to train the historical data o f battlefield situation and construct the prediction model of battlefield situation,and Matlab 2018 is used to simulate the battlefield situation.The simulation results show that Hadoop is more efficient in processing massive battlefield data,and can ef fectively improve the prediction accuracy of battlefield situation,which provides a new method and way for assisting commanders to quickly grasp the complex battlefield situation.