Use of community mobile phone big location data to recognize unusual patterns close to a pipeline which may indicate unauthorized activities and possible risk of damage

Abstract Damage caused by people and organizations unconnected with the pipeline management is a major risk faced by pipelines, and its consequences can have a huge impact. However, the present measures to monitor this have major problems such as time delays, overlooking threats, and false alarms. T...

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Autores principales:	Shao-Hua Dong, He-Wei Zhang, Lai-Bin Zhang, Li-Jian Zhou, Lei Guo
Formato:	article
Lenguaje:	EN
Publicado:	KeAi Communications Co., Ltd. 2017
Materias:	Pipeline Big location data Third-party damage Model Prevention Science Q Petrology QE420-499
Acceso en línea:	https://doaj.org/article/44b0e3b5880e448c90295fe6e462779b
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spelling	oai:doaj.org-article:44b0e3b5880e448c90295fe6e462779b2021-12-02T05:20:31ZUse of community mobile phone big location data to recognize unusual patterns close to a pipeline which may indicate unauthorized activities and possible risk of damage10.1007/s12182-017-0160-71672-51071995-8226https://doaj.org/article/44b0e3b5880e448c90295fe6e462779b2017-04-01T00:00:00Zhttp://link.springer.com/article/10.1007/s12182-017-0160-7https://doaj.org/toc/1672-5107https://doaj.org/toc/1995-8226Abstract Damage caused by people and organizations unconnected with the pipeline management is a major risk faced by pipelines, and its consequences can have a huge impact. However, the present measures to monitor this have major problems such as time delays, overlooking threats, and false alarms. To overcome the disadvantages of these methods, analysis of big location data from mobile phone systems was applied to prevent third-party damage to pipelines, and a third-party damage prevention system was developed for pipelines including encryption mobile phone data, data preprocessing, and extraction of characteristic patterns. By applying this to natural gas pipelines, a large amount of location data was collected for data feature recognition and model analysis. Third-party illegal construction and occupation activities were discovered in a timely manner. This is important for preventing third-party damage to pipelines.Shao-Hua DongHe-Wei ZhangLai-Bin ZhangLi-Jian ZhouLei GuoKeAi Communications Co., Ltd.articlePipelineBig location dataThird-party damageModelPreventionScienceQPetrologyQE420-499ENPetroleum Science, Vol 14, Iss 2, Pp 395-403 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Pipeline Big location data Third-party damage Model Prevention Science Q Petrology QE420-499
spellingShingle	Pipeline Big location data Third-party damage Model Prevention Science Q Petrology QE420-499 Shao-Hua Dong He-Wei Zhang Lai-Bin Zhang Li-Jian Zhou Lei Guo Use of community mobile phone big location data to recognize unusual patterns close to a pipeline which may indicate unauthorized activities and possible risk of damage
description	Abstract Damage caused by people and organizations unconnected with the pipeline management is a major risk faced by pipelines, and its consequences can have a huge impact. However, the present measures to monitor this have major problems such as time delays, overlooking threats, and false alarms. To overcome the disadvantages of these methods, analysis of big location data from mobile phone systems was applied to prevent third-party damage to pipelines, and a third-party damage prevention system was developed for pipelines including encryption mobile phone data, data preprocessing, and extraction of characteristic patterns. By applying this to natural gas pipelines, a large amount of location data was collected for data feature recognition and model analysis. Third-party illegal construction and occupation activities were discovered in a timely manner. This is important for preventing third-party damage to pipelines.
format	article
author	Shao-Hua Dong He-Wei Zhang Lai-Bin Zhang Li-Jian Zhou Lei Guo
author_facet	Shao-Hua Dong He-Wei Zhang Lai-Bin Zhang Li-Jian Zhou Lei Guo
author_sort	Shao-Hua Dong
title	Use of community mobile phone big location data to recognize unusual patterns close to a pipeline which may indicate unauthorized activities and possible risk of damage
title_short	Use of community mobile phone big location data to recognize unusual patterns close to a pipeline which may indicate unauthorized activities and possible risk of damage
title_full	Use of community mobile phone big location data to recognize unusual patterns close to a pipeline which may indicate unauthorized activities and possible risk of damage
title_fullStr	Use of community mobile phone big location data to recognize unusual patterns close to a pipeline which may indicate unauthorized activities and possible risk of damage
title_full_unstemmed	Use of community mobile phone big location data to recognize unusual patterns close to a pipeline which may indicate unauthorized activities and possible risk of damage
title_sort	use of community mobile phone big location data to recognize unusual patterns close to a pipeline which may indicate unauthorized activities and possible risk of damage
publisher	KeAi Communications Co., Ltd.
publishDate	2017
url	https://doaj.org/article/44b0e3b5880e448c90295fe6e462779b
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Use of community mobile phone big location data to recognize unusual patterns close to a pipeline which may indicate unauthorized activities and possible risk of damage

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