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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 16 Issue 5, 2025.
Abstract: An important trend in the development of digitalization is the expansion from digitalization to intelligence. For the intelligence of oil and gas facilities, it is to apply the multi-intelligent judgment and analysis technology to the actual production of oil and gas facilities, so as to realize the real-time data acquisition, analysis and “monitoring tube” integration of remote oil and gas facilities. As a flexible monitoring configuration software, force control provides a good development interface and a simple engineering implementation method under the trend of intelligent oil and gas facilities, which can facilitate users to realize and complete the functions of the monitoring layer. Its humanized application, such as trend curve, report output, limit alarm and other system functions, can also be more intuitive for users to monitor and process control on-site production data, and provide strong technical support for the intelligent process of oil and gas facilities. Based on this, this paper studies the design of remote monitoring and management system of oil and gas station by integrating the Internet of Things, artificial intelligence and big data analysis technology.
Shu Haowen, Zhang Bin, Gao Shiyu, Gu Li and Jia Yanjie, “Remote Monitoring and Management System for Oil and Gas Facilities with Integrated IoT and Artificial Intelligence Data Analysis” International Journal of Advanced Computer Science and Applications(IJACSA), 16(5), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160594
@article{Haowen2025,
title = {Remote Monitoring and Management System for Oil and Gas Facilities with Integrated IoT and Artificial Intelligence Data Analysis},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160594},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160594},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {5},
author = {Shu Haowen and Zhang Bin and Gao Shiyu and Gu Li and Jia Yanjie}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.