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Karst Collapse Monitoring and Early Warning Evaluation Method Based on Multisensor Internet of Things
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 carbona...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9132626/ https://www.ncbi.nlm.nih.gov/pubmed/35634044 http://dx.doi.org/10.1155/2022/2099268 |
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author | Zhang, Xin Deng, Longfu Li, Na |
author_facet | Zhang, Xin Deng, Longfu Li, Na |
author_sort | Zhang, Xin |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9132626 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-91326262022-05-26 Karst Collapse Monitoring and Early Warning Evaluation Method Based on Multisensor Internet of Things Zhang, Xin Deng, Longfu Li, Na Comput Intell Neurosci Research Article 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. Hindawi 2022-05-18 /pmc/articles/PMC9132626/ /pubmed/35634044 http://dx.doi.org/10.1155/2022/2099268 Text en Copyright © 2022 Xin Zhang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Zhang, Xin Deng, Longfu Li, Na Karst Collapse Monitoring and Early Warning Evaluation Method Based on Multisensor Internet of Things |
title | Karst Collapse Monitoring and Early Warning Evaluation Method Based on Multisensor Internet of Things |
title_full | Karst Collapse Monitoring and Early Warning Evaluation Method Based on Multisensor Internet of Things |
title_fullStr | Karst Collapse Monitoring and Early Warning Evaluation Method Based on Multisensor Internet of Things |
title_full_unstemmed | Karst Collapse Monitoring and Early Warning Evaluation Method Based on Multisensor Internet of Things |
title_short | Karst Collapse Monitoring and Early Warning Evaluation Method Based on Multisensor Internet of Things |
title_sort | karst collapse monitoring and early warning evaluation method based on multisensor internet of things |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9132626/ https://www.ncbi.nlm.nih.gov/pubmed/35634044 http://dx.doi.org/10.1155/2022/2099268 |
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