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Smart safety early warning system of coal mine production based on WSNs

机译:基于WSN的煤矿生产智能安全预警系统

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Safety is the primary consideration of underground operation. At present, many large mineral countries are committed to using wireless sensors to reduce the accident rate and prevent casualties and major economic losses through early warning. The existing monitoring and early warning system of wired coal mine can only be used in the main tunnel, while the complex underground working area is still the "blind area" of monitoring and early warning. This paper proposes a smart safety monitoring system based on improved DV-Hop localization algorithm for randomly deployed wireless sensor networks (WSNs), which can position accurate personal tracking in industrial fields, such as mines, underground operations, tunnel engineering, and underground engineering. By choosing anchor nodes and calculating the average per-hop distance between anchor nodes are considerably different from the original DV-Hop. Based on the improved DV-Hop positioning algorithm, the critical safety data related to underground coal mining and associated with the location of the staff can be extracted precisely and analyzed quickly. The simulations show that the enhanced algorithms are able to effectively improve localization accuracy for randomly distributed sensor networks and ensure its safety in complex environment and improve the level of safety production.
机译:安全是地下操作的主要考虑因素。目前,许多大型矿产国家致力于使用无线传感器来降低事故率,并通过预警来防止伤亡和主要经济损失。有线煤矿的现有监测和预警系统只能用于主隧道,而复杂的地下工作区仍然是监测和预警的“盲区”。本文提出了一种基于改进的DV-Hop定位算法的智能安全监控系统,用于随机部署的无线传感器网络(WSN),可以在工业领域进行准确的个人跟踪,例如矿山,地下操作,隧道工程和地下工程。通过选择锚节点并计算锚节点之间的平均每跳距离与原始DV跳相当不同。基于改进的DV-Hop定位算法,可以精确地提取与地下煤矿和与工作人员位置相关的关键安全数据,并快速分析。模拟表明,增强算法能够有效地提高随机分布式传感器网络的本地化精度,并确保其在复杂环境中的安全性,提高安全生产水平。

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