面向LVC训练系统的云边协同服务架构

doi:10.16182/j.issn1004731x.joss.23-0681

摘要/Abstract

摘要：

LVC训练作为军事训练的一种重要手段得到军事专家和建模仿真专家的高度关注。随着虚实要素类型越来越丰富和深度融合，LVC训练系统变得越来越复杂。针对LVC训练系统物理虚拟空间互联互通、信息交互、仿真计算和运行控制等问题，参考信息物理系统、云边计算架构设计面向LVC训练系统的云边协同服务架构（CESA-LVC）。CESA-LVC从智能实时互联、联合仿真计算、训练辅助服务、训练认知决策和动态配置优化等几个方面规范LVC训练系统的结构，为设计开发面向服务、云边灵活部署、高效运行的LVC训练系统提供参考模型。探讨了CESA-LVC的联合仿真计算、复杂网络通信、复杂系统控制等3个方面的关键技术问题。实现了跨越3个省区的LVC训练原型系统，对CESA-LVC架构各层次主要指标进行了验证，证明了架构设计的合理性。

关键词: LVC, 训练, 云边计算, 协同服务, 仿真

Abstract:

LVC training, an important means of military training, has received great attention from military and M&S experts. As the virtual and physical elements become more abundant and deeply integrated, LVC training systems become increasingly complex. Aiming at physical-virtual connection, information interaction, simulation computation, run-time control, etc., this paper designs a cloud-edge collaborative service architecture for LVC training systems (CESA-LVC) by reference to cyber-physical systems and cloud-edge computing architectures. CESA-LVC standardizes the structures of LVC training systems from several aspects of intelligent real-time interconnection, joint simulation computation, training auxiliary service, training cognitive decision, and dynamic configuration optimization. It provides a reference model for the design and development of a service-oriented, flexible deployment, and efficient LVC training system. The paper discusses the key technical problems of joint simulation computation, complex network communication, and complex system control. The prototype system for LVC training spanning three provincial-level regions is realized, and the main indicators of each level of CESA-LVC architecture are verified, which proves the rationality of the architecture design.

Key words: LVC, training, cloud-edge computing, collaborative service, simulation

中图分类号:

TP391.9

彭勇,张淼,胡越 . 面向LVC训练系统的云边协同服务架构[J]. 系统仿真学报, 2023, 35(9): 1825-1836.

Peng Yong,Zhang Miao,Hu Yue . Cloud-Edge Collaborative Service Architecture for LVC Training System[J]. Journal of System Simulation, 2023, 35(9): 1825-1836.

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主要参数	量值	部署位置
实兵实装(L)数量	>600	A市外场
模拟器(V)数量	>20	A市外场、B市
构造兵力(C)数量	>8 000	A市外场
异地互联网络带宽/Mb	100	A市内场、B市
内部局域网带宽/Mb	1 000	A市内场、B市
5G基站数量(台)	2	A市外场
云服务器数量(台)	30	A市外场
人机交互终端设备(台)	120	A市内场、B市

功能层	主要参数	量值
智能实时互联	平均接入终端实体数量	600
	终端接入平均时间开销/ms	105
	点对点数据传输平均时延/ms	76
	现地AR显示帧率/(帧/s)	35
联合仿真计算	分布仿真节点数	133
	仿真时间同步精度/s	1
	点对点数据传输平均时延/ms	236
	点对点数据传输最大平均时延/ms	630
	点对点数据传输最小平均时延/ms	102
	并行仿真模型实体数量	708
	并行仿真加速比	3.6:1
	1 000次毁伤裁决服务调用平均时间开销/s	3.05
训练辅助服务	服务调用响应平均开销/ms	208.3
训练辅助服务	100次服务调用平均时间开销/s	708.9
训练认知决策	综合态势显示实体数量	8 368
	综合态势显示刷新频率/(帧/s)	32
	评估指标数量	26
动态配置优化	虚拟机数量(台)	130
	系统开设平均时间开销/s	6 356
	虚拟网络平均时延/ms	23

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