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在建设大规模的视频监控网络时,面对监控终端随时传输过来的动态数据流问题,提出一种动态的数据量预测方法和优化设计方案.通过对系统均衡状态的理论分析,建立多层排队网络模型用以分析和预测视频监控网络的视频动态上传行为.基于此模型,对监控网络建设中的两个资源配置问题提出了优化方案.针对满足需求的最少资源配置问题,通过建模给出一种量化计算方法.对因突发事件随机暴增的上传数据流,设计了一种动态调整计算量的算法,可将超出负荷的数据流动态转移到其他可用的计算点,以保持整个系统的负载均衡,保证有效的响应时间.最后,通过一组实际环境下的实验验证了该分析和算法的有效性.
When building a large-scale video surveillance network, a dynamic data volume prediction method and an optimized design scheme are proposed in the face of the dynamic data flow problem that the surveillance terminal transmits from time to time.Through the theoretical analysis of the system equilibrium state, a multi-layer queuing The network model is used to analyze and predict the video upload behavior of the video surveillance network.On the basis of this model, an optimization scheme is proposed for the two resource allocation problems in the surveillance network construction.Aiming at the least resource allocation to meet the demand, the model is given by modeling A method of quantification calculation is proposed in this paper.An algorithm to dynamically adjust the amount of computation is proposed for uploading data streams that burst randomly due to emergencies and to dynamically transfer overloaded data streams to other available computing points to keep the entire system Load balancing to ensure effective response time.Finally, the effectiveness of this analysis and algorithm is verified by a set of experiments in real environment.