%0 Journal Article
%T 输气管道高后果区智能识别技术<br>Intelligent Identification for High Consequence Areas of Gas Pipelines
%A 夏志伟
%A 高春元
%A 彭名超
%A 周泽山
%A 邱旭
%J Mine Engineering
%P 334-338
%@ 2329-731X
%D 2023
%I Hans Publishing
%R 10.12677/ME.2023.113040
%X 随着城市化的快速发展，输气管道的建设和运营也日益频繁，而管道事故也是造成财产损失和人员伤亡的主要原因之一。高后果区是指在事故发生后可能对周边环境造成严重影响的区域，传统人工识别存在识别效率低下、更新速度慢等问题，因此采用智能识别方式开展管道高后果区识别对于事故的预防和应对具有重要意义。本文对输气管道高后果区智能识别的相关技术进行了综述，指出了目前高后果区识别中存在的问题及未来的发展方向。研究表明，结合遥感技术和地理信息系统，利用人工智能、机器学习等技术，可以有效识别输气管道高后果区，提高事故预防和应对的能力。<br />
With the rapid development of urbanization, the construction and operation of gas pipelines are becoming more and more frequent, and pipeline accidents are also one of the main reasons for property losses and casualties. The high-consequence area means the area that may cause a serious damage to the surrounding environment after the accident. Traditional manual identification has problems such as low recognition efficiency and slow update speed. Therefore, the use of intelligent identification to carry out the identification of pipeline high-consequence areas is important for accident prevention. This paper summarizes the relevant technologies of intelligent identification of high-consequence areas in gas pipelines, and indicates current problems and future directions in the identification of high-consequence areas. Studies have shown that combination of remote sensing technology and geographic information system, using artificial intelligence, machine learning and other technologies, can effectively identify high-consequence areas of gas pipelines and improve the ability to prevent and respond to accidents.
%K 高后果区，遥感技术，数据挖掘，机器学习，智能识别<br>High Consequence Area
%K Remote Sensing Technology
%K Data Mining
%K Machine Learning
%K Intelligent Identification
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=68209