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基于多传感器物联网的岩溶塌陷监测与预警评估方法。

Karst Collapse Monitoring and Early Warning Evaluation Method Based on Multisensor Internet of Things.

机构信息

Institute of Karst Geology, CAGS/Key Laboratory of Karst Collapse Prevention, CAGS, Guilin, Guangxi 541004, China.

China University of Geosciences, Beijing 100083, China.

出版信息

Comput Intell Neurosci. 2022 May 18;2022:2099268. doi: 10.1155/2022/2099268. eCollection 2022.

Abstract

The international community has paid extensive attention to the numerous engineering problems faced by karst areas caused by the increasingly frequent human activities. China has a wide variety of karst forms. Among them, carbonate karst is the most widely distributed, and the development of carbonate karst is relatively strong in many areas. Countless property losses are caused by karst disasters every year. This article aims to study the real-time monitoring and timely early warning of karst collapse through the use of multisensor Internet of Things technology. To this end, this article proposes an improved method for multisensor data fusion. It optimizes and improves the transmission and delivery efficiency of its data. This makes the improved multisensor more in line with the research content of this article in terms of monitoring efficiency. At the same time, related experiments and analyses are designed to compare and analyze the karst collapse and the monitoring efficiency of the sensor. The experimental results of this article show that after the improvement, the anti-interference ability of the monitoring system is increased by 34%. The frequency of early warning has also been improved by 24%, which has high practical application value.

摘要

国际社会广泛关注人类活动日益频繁给岩溶地区带来的诸多工程问题。中国岩溶形态多样,其中碳酸盐岩溶分布最广,许多地区碳酸盐岩溶发育较强。每年岩溶灾害都会造成无数财产损失。本文旨在通过多传感器物联网技术研究岩溶塌陷的实时监测和及时预警。为此,本文提出了一种改进的多传感器数据融合方法,对其数据的传输和投递效率进行了优化和改进,使得改进后的多传感器在监测效率方面更符合本文的研究内容。同时,设计了相关实验进行分析,对比传感器的岩溶塌陷和监测效率。本文的实验结果表明,改进后监测系统的抗干扰能力提高了 34%,预警频率也提高了 24%,具有较高的实际应用价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6be6/9132626/1349a0d3ea5e/CIN2022-2099268.001.jpg

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