The concentration data obtained by the unmanned aerial vehicle

With the rapid pace of global urbanization and rising energy demands, efficient gas leak detection is vital for public safety. This study proposes an efficient and sensitive gas leak detection method based on reinforcement learning to enhance localization speed and robustness. The approach includes critical area identification, reinforcement learning model training, and leak point localization. Simultaneously introducing noise and missing data to test the robustness of the model. This method has also been applied to the pipeline network center of Chongqing Jiangjin Natural Gas Co., Ltd., further confirming its effectiveness and adaptability in complex environments. In the case of multiple leakage points, the average relative deviation of each leakage point in the X and Y directions calculated by this method is 2.1 percentage points, and the average deviation distance is 0.75 meters. These results demonstrate the method's adaptability to complex environments, contributing to a comprehensive and efficient environmental management system.

随着全球城市化进程的加速和能源需求的不断上升，高效的燃气泄漏检测对于公共安全至关重要。本研究提出了一种基于强化学习的燃气泄漏检测方法，旨在提升定位速度和鲁棒性。该方法包括关键区域识别、强化学习模型训练以及泄漏点定位。同时，通过引入噪声和缺失数据来测试模型的鲁棒性。此外，该方法已被应用于重庆江津天然气有限公司的管道网络中心，进一步证实了其在复杂环境中的有效性和适应性。在存在多个泄漏点的情况下，通过该方法计算得出的每个泄漏点在X和Y方向上的平均相对偏差为2.1个百分点，平均偏差距离为0.75米。这些结果充分展示了该方法对复杂环境的适应性，为构建全面高效的环境管理体系做出了贡献。