Research on coal mine full scene monitoring system based on fog computing
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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.
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期刊类型引用(4)
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百度学术
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