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

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.

Keywords

• Pipeline
• Big location data
• Third-party damage
• Model
• Prevention