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Application of Data Mining Technology Based on Apriori Algorithm in Remote Monitoring System

  • Chenrui Xu
  • Kebin Jia
  • Pengyu Liu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 891)

Abstract

At present, the theoretical analysis of gas station oil and gas data is weak, and there is no unified platform for collecting and uploading. In view of these problems, a set of data acquisition and mining scheme is proposed. The Apriori algorithm is used to correlate the current environmental data of oil and gas, focusing on the correlation between oil and gas concentration and liquid resistance pressure, tank temperature, tank pressure, time, and treatment unit emission concentration. In addition, we designed and implemented a remote online monitoring system for oil and gas recovery based on the SSH framework. The results of the application obtained in a gas station in Beijing show that this system can provide the reference basis for the intelligent construction for the gas station to monitor the large oil and gas data. The results of data mining and analysis can provide accurate and objective data support for the monitoring personnel of gas stations, and higher priority monitoring for the heavy point data segment. It has reference value and provides a good technical foundation for the statistics and processing of oil and gas data in the follow-up gas stations.

Keywords

Apriori algorithm Correlation analysis Remote detection system Data mining SSH 

Notes

Acknowledgment

This paper is supported by the Project for the National Natural Science Foundation of China under Grants No. 61672064, 81370038, the Beijing Natural Science Foundation under Grant No. 4172001.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Beijing Laboratory of Advanced Information NetworksBeijingChina
  2. 2.Department of InformationBeijing University of TechnologyBeijingChina

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