Latest revision as of 16:16, 12 February 2021

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.

Document type: Article

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The different versions of the original document can be found in:

• https://link.springer.com/content/pdf/10.1007%2Fs12182-017-0160-7.pdf under the license https://creativecommons.org/licenses/by

• http://link.springer.com/article/10.1007/s12182-017-0160-7,

https://doaj.org/toc/1672-5107,

https://doaj.org/toc/1995-8226 under the license cc-by

• http://link.springer.com/article/10.1007/s12182-017-0160-7/fulltext.html,

http://link.springer.com/content/pdf/10.1007/s12182-017-0160-7.pdf,

http://dx.doi.org/10.1007/s12182-017-0160-7

• https://link.springer.com/article/10.1007%2Fs12182-017-0160-7,

https://paperity.org/p/79444542/use-of-community-mobile-phone-big-location-data-to-recognize-unusual-patterns-close-to-a,

https://academic.microsoft.com/#/detail/2609746640 under the license http://creativecommons.org/licenses/by/4.0