摘要
多传感器系统通过整合多种传感器数据,实现了全面且精准的环境感知,然而,如何有效融合异构数据并实现实时处理的高效性,仍然是当前研究的热点和难点问题.为此,围绕多源异构传感器的数据融合和算力优化展开研究,提出了一种创新的解决方案.首先,基于主/从架构设计数据融合系统,解决多源异构数据处理难题;其次,构建了"云—边—端"3 层架构,利用边缘服务器分担云服务器的计算压力,权衡任务调度策略,实现网络资源与计算资源的协同管理;最后,针对任务的时延与能耗需求进行建模,在资源约束下构建最小化系统成本的优化问题,将问题转化为马尔可夫决策过程(MDP,Markov decision process),使用深度确定性策略梯度(DDPG,deep deterministic policy gradient)算法进行求解.仿真结果表明,所提出的架构和调度策略在降低时延和能耗方面表现优异,为多传感器系统中的高效数据融合与算力优化提供了新思路.
Abstract
Multi-sensor systems integrate diverse sensor data to achieve comprehensive and accurate environmental per-ception.However,how to effectively fuse heterogeneous data and realize the efficiency of real-time processing is still a hot and difficult issue in current research.Therefore,focusing on data fusion and arithmetic optimization of multi-source heterogeneous sensors,an innovative solution was proposed.Firstly,a data fusion system based on master-slave architec-ture was designed to solve the problem of multi-source heterogeneous data processing.Secondly,a three-layer"cloud-edge-end"architecture was implemented,leveraging edge servers to offload computational pressure from cloud servers,optimizing task scheduling strategies,and enabling coordinated management of network and computing resources.Fi-nally,the delay and energy consumption requirements of tasks were modeled,and the optimization problem of minimiz-ing system cost was constructed under resource constraints,which was transformed into Markov decision process(MDP)and solved with deep deterministic policy gradient(DDPG)algorithm.Simulation experiments show that the proposed ar-chitecture and scheduling algorithm exhibit excellent performance in reducing both latency and energy consumption,and provide a new idea for efficient data fusion and arithmetic optimization in multi-sensor systems.
国家科技期刊平台
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