【摘要】 随着油气管道安全运营要求的不断提高，高后果区的精准识别成为保障管道安全的关键环节。传统人工识别方法主观性强，容易产生误判，且难以应对大规模管道的安全评估。现有智能方法虽然引入自动化，但依赖于多个异构软件平台的集成，数据交互复杂，难以实现端到端的自动化流程。为此，本研究提出了一种融合多注意力机制的天然气管道高后果区智能识别方法。通过构建包含POI场所类别数据、无人机正射影像和三维重建结果的高后果区识别数据库，采用融合多注意力机制的U-Net模型，实现了对高后果区建筑物轮廓的精准识别。实验结果表明，该方法在城市、城乡结合部及乡村三类典型场景下的识别准确率分别达到97.54%、97.78%和94.44%。结合数据库中集成的地理坐标、特殊场所属性及三维高度信息，实现了对山西某高后果区段地区等级与高后果区等级的精准分级，为天然气管道的安全运营与管道完整性管理的智能化发展提供了科学依据。更多还原

【Abstract】 As the demand for the safe operation of oil and gas pipelines grows, precise identification of high consequence areas has become crucial for pipeline safety. Traditional manual methods are highly subjective, prone to errors, and unsuitable for large-scale pipeline assessments. While existing intelligent methods offer automation, they depend on the integration of multiple heterogeneous software platforms, leading to complex data interactions and challenges in achieving fully automated, end-to-end processes. To this end, this study proposes an intelligent identification method for high consequence areas of natural gas pipelines integrating multiple attention mechanisms was proposed. A high consequence area identification database containing POI venue category data, UAV orthophotos, and three-dimensional reconstruction results was constructed, and the U-Net model integrating multiple attention mechanisms was employed to achieve precise identification of building outlines within high consequence areas. The experimental results indicated that the identification accuracy of this method reached 97.54%, 97.78%, and 94.44% in urban, urban-rural fringe, and rural settings, respectively. By integrating geographical coordinates, special venue attributes, and three-dimensional height information from the database, precise classification of the regional and high consequence area levels for a specific area in Shanxi was achieved, offering a scientific foundation for the safe operation of natural gas pipelines and the intelligent development of pipeline integrity management.更多还原