基于雾计算的煤矿全场景监测系统研究
Research on coal mine full scene monitoring system based on fog computing
【索引】曹健萍,李敬兆.基于雾计算的煤矿全场景监测系统研究[J].工矿千亿国际app下载,2020,46(2):50-53.
【Reference】CAO Jianping,LI Jingzhao.Research on coal mine full scene monitoring system based on fog computing [J].Industry and Mine Automation,2020,46(2):50-53.
【DOI】10.13272/j.issn.1671-251x.2019050031
【作者】 曹健萍1,2,李敬兆1,2
【Author】 CAO Jianping1,2,LI Jingzhao1,2
【作者机构】1.安徽理工大学 电气与信息工程学院, 安徽 淮南232001;2.工业节电与电能质量控制协同创新中心, 安徽 合肥230000
【Unit】1.College of Electrical and Information Engineering, Anhui University of Technology, Huainan 232001, China; 2.Collaborative Innovation Center of Industrial Energy-saving and Power Quality Control, Hefei 230000, China
【摘要】目前煤矿全场景监测系统主要依赖于云计算实现数据处理、存储与决策,云计算需实时处理海量监测信息,严重影响系统决策层的时效性与精确度。针对该问题,提出一种基于雾计算的煤矿全场景监测系统,以神经元感知节点为单元设计雾计算神经网络,缓解云计算数据处理压力。针对基于粒子群优化算法(PSO)的节点部署方法存在过早收敛现象和局部最优解的问题,通过改进的PSO算法优化神经元感知节点部署,实现网络结构优化。仿真结果表明,与经典PSO算法相比,改进PSO算法能够更快寻得最优解,整体通信覆盖率的最优值、最差值和平均值分别提高了3.19%,3.31%,3.25%,具有收敛快速有效、适应性强、稳定性高等优势。
【Abstract】At present, coal mine full scene monitoring system mainly depends on cloud computing for data processing, storage and decision-making. Cloud computing needs to process massive amounts of monitoring information in real time, which seriously affects timeliness and accuracy of system decision-making layer. In view of the above problem, a coal mine full scene monitoring system based on fog computing was proposed. Fog computing neural network is designed with neuron sensing nodes as a unit to alleviate the pressure of cloud computing data processing. In view of problem of premature convergence and local optimal solution of the node deployment method based on particle swarm optimization algorithm, improved particle swarm optimization algorithm is used to optimize the deployment of neuron sensing node to achieve network structure optimization. Simulation results show that compared with the classic PSO algorithm, the improved PSO algorithm can find the optimal solution faster, and the optimal value, the worst value, and the average value of overall communication coverage have increased by 3.19%, 3.31%, and 3.25%, respectively, which has the advantages of fast and effective convergence, strong adaptability and high stability.
【关键词】 煤矿全场景监测系统; 雾计算; 边缘计算; 神经元感知节点; 粒子群优化算法
【Keywords】coal mine full scene monitoring system; fog computing; edge computing; neuron perception node; particle swarm optimization algorithm
【文献出处】工矿千亿国际app下载,2020年2期
【基金】国家自然科学基金资助项目(51874010);矿山巡检机器人非线性多智能体协同控制策略研究资助项目(2018IRS16);安徽省工业节电与电能质量控制协同创新中心开放课题基金项目(KFKT201406);重大创新平台及高校创新人才团队、矿山物联网研发团队资助项目(2017A053)
【分类号】TD76
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