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[1]唐浩然熊俊楠雍志玮陈文杰刘傲儒王启盛肖慧文王荣康.多源数据融合的燃气管道高后果区识别与分级方法[J].油气储运,2024,43(09):1-9.
　TANG Haoran,XIONG Junnan,YONG Zhiwei,et al.Identification and grading method of HCAs in gas pipelines based on Multi source Data Fusion[J].Oil & Gas Storage and Transportation,2024,43(09):1-9.

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多源数据融合的燃气管道高后果区识别与分级方法

《油气储运》[ISSN:1000-8241/CN:13-1093/TE] 卷: 43 期数: 2024年09期页码: 1-9 栏目: 出版日期: 2024-09-25

Title:: Identification and grading method of HCAs in gas pipelines based on Multi source Data Fusion

作者:: 唐浩然熊俊楠雍志玮陈文杰刘傲儒王启盛肖慧文王荣康

Author(s):: TANG Haoran; XIONG Junnan; YONG Zhiwei; CHEN Wenjie; LIU Aoru; WANG Qisheng; Xiao Huiwen; WANG Rongkang

关键词:: 燃气管道; 高后果区; 多源数据; 识别与分级

Keywords:: gas pipeline; high consequence areas; multi source data; identification and grading

分类号:: TE88

文献标志码:: A

摘要:: 【目的】传统燃气管道高后果区识别方式受限于人员熟练程度与人为主观判断影响，存在效率低以及人为误差较大等问题。近年来，卫星影像数据与地理信息系统（Geographic Information System，GIS）的引入在一定程度上改善了上述问题，但单一数据依然难以满足燃气管道高后果区识别与分级对数据多样性的需求，限制了识别和分级工作的定量化与自动化程度。通过多源数据获取建筑物空间及属性信息，提出燃气管道高后果区识别与分级方法。【方法】基于无人机采集的高分辨率正射影像，构建一种优化建筑物边缘提取效果的深度学习模型SEU-Net（Squeeze-and-Excitation U network）从正射影像中提取管道周边建筑物轮廓；在引入兴趣点（point of interest，POI）获取建筑物种类信息的同时，通过三维点云获取高精度数字高程模型（Digital Elevation Model，DEM）与数字表面模型（Digital Surface Model，DSM），计算建筑物高度；基于GIS的空间分析功能与属性处理能力，计算建筑物面积，整合建筑物空间与属性信息。设计了燃气管道高后果区识别与分级算法。【结果】以四川省遂宁市某燃气管道为例验证该方法，与传统识别结果相比，应用该方法识别出人工漏识别建筑物17座，减少误识别高后果区管段0.369 km。【结论】该方法具备更高的准确度和效率，在燃气管道完整性管理领域具有很好的应用潜力。

Abstract:: [Objective]Accurate identification and classification of high-consequence areas (HCAs) in natural gas pipelines are crucial for the safe operation of high-risk systems. Traditional HCA identification methods are limited by the expertise and subjective judgment of personnel, resulting in low efficiency and human errors. In recent years, the introduction of satellite imagery data and geographic information system (GIS) has partially addressed these issues. However, a single data source cannot meet the quantitative and automated requirements for identifying HCAs in gas pipelines. [Methods]Therefore, we propose a multi-source data-based method for the automatic identification and classification of HCAs in gas pipelines. Firstly, potential HCA areas are identified using satellite imagery data, and high-resolution orthophotos are obtained through unmanned aerial vehicles (UAVs). Then, we construct a deep learning model called SEU-Net (Squeeze-and-Excitation U network) to extract the outlines of buildings surrounding the pipelines. Simultaneously, we utilize point of interest (POI) to obtain information on the types of buildings, and filter the three-dimensional point cloud data using the cloth simulation filter (CSF) to generate a digital elevation model (DEM). The height and area of buildings are obtained by combining the DEM data with digital surface model (DSM) data. Based on this, we integrate the spatial and attribute information of buildings using the spatial analysis and attribute processing capabilities of GIS, and design an automatic identification and classification algorithm for HCAs in gas pipelines. We validate the accuracy and reliability of the proposed method using a pipeline in Suining City as a case study.[Results] Comparative analysis with traditional identification results shows that our method successfully identifies 17 buildings that were previously missed by manual recognition and reduces the misidentification of HCA pipeline segments by 0.369 km. [Conclusion]Our method demonstrates higher accuracy and automation, and has broad potential applications in the field of gas pipeline integrity management.

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更新日期/Last Update: 2024-07-17