智能化油气管网技术的应用分析
Application Analysis of Intelligent Oil and Gas Pipeline Network Technology
摘要: 伴随智能化的迅速发展，智能化建设已成为管道行业未来发展的重要趋势，一方面，通过智能化技术，可以实现管道生产全过程的数据采集和监控，这样在作业的每一环节都提高了生产效率和生产品质，其次，智能化技术还可以为企业提供更加准确的市场预测和决策支持，帮助企业更好地把握市场趋势和客户需求。本文主要针对基于物联网和大数据的油气管道数据采集和基于人工智能的用户用气预测两个技术，分别阐述智能化在油气管网中的应用分析，进一步激发智能化技术在管道行业中的应用和发展，为管道行业的可持续发展贡献力量。
Abstract: With the rapid development of intelligence, intelligent construction has become an important trend in the future development of the pipeline industry. On the one hand, through intelligent technology, data collection and monitoring of the entire pipeline production process can be achieved, which improves the production efficiency and quality of production in every aspect of the operation. Secondly, intelligent technology can also provide enterprises with more accurate market forecasts and decision-making support, helping enterprises to better grasp market trends and customer needs. This article mainly elaborates on the application analysis of intelligence in oil and gas pipeline networks based on two technologies: oil and gas pipeline data collection based on the Internet of Things and big data and user gas prediction based on artificial intelligence, to further stimulate the application of intelligent technology in the pipeline industry and development and contribute to the sustainable development of the pipeline industry.
文章引用：朱佳丽, 吴宇蕊, 蔡国林, 张建. 智能化油气管网技术的应用分析[J]. 矿山工程, 2025, 13(1): 1-4. https://doi.org/10.12677/me.2025.131001

参考文献

[1] 张海峰, 蔡永军, 李柏松, 等. 智慧管道站场设备状态监测关键技术[J]. 油气储运, 2018, 37(8): 841-849.
[2] Gustavo, C. and Markom, S.C. (2017) Intelligent Digital Oil and Gas Fields. Gulf Professional Publishing, 1-10.
[3] 徐波, 李博, 宋小晖, 等. 油气管道智能化运行解决方案的思考[J]. 油气储运, 2018, 37(7): 721-727.
[4] Austin, W. (2017) GE Predix the Future of Manufacturing. Assembly, 60, 70-76.
[5] Monostori, L., Kádár, B., Bauernhansl, T., Kondoh, S., Kumara, S., Reinhart, G., et al. (2016) Cyber-Physical Systems in Manufacturing. CIRP Annals, 65, 621-641. [Google Scholar] [CrossRef] 
[6] Cain, J.R. (2018) Use of Exposomes to Assess Astronaut Health. Journal of the British Interplanetary Society, 71, 112-116.
[7] 李柏松, 王学力, 王巨洪. 数字孪生体及其在智慧管网应用的可行性[J]. 油气储运, 2018, 37(10): 1081-1087.
[8] 王琳, 商周, 王学伟. 数据采集系统的发展与应用[J]. 电测与仪表, 2004(8): 4-8.
[9] Toksari, M. (2010) Predicting the Natural Gas Demand Based on Economic Indicators: Case of Turkey. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 32, 559-566. [Google Scholar] [CrossRef] 
[10] Farabet, C., Couprie, C., Najman, L. and LeCun, Y. (2013) Learning Hierarchical Features for Scene Labeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, 1915-1929. [Google Scholar] [CrossRef] [PubMed]

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