Establishing an accurate and efficient source term estimation (STE) system is of great significance for identifying unknown gas leakage sources in urban environments. Many successful STE methods have been proposed, while most of them assume there is only one point source. However, the complicated urban atmospheric dispersion and the massive sensor data in distributed edge devices pose new challenges. To address these issues, this paper proposes a method to convert measured concentrations into visual features, which retains the characteristics of diffusion and the layout of obstacles. Then, a federated STE (FL-STE) framework is proposed to extract knowledge from local models collaboratively without collecting all privacy data, in which a deep neural network is used to recognize the relationship between visual features and source terms. Furthermore, we construct an urban dispersion dataset with multiple obstacles and sources by FDS simulation. Various empirical studies prove the efficiency of the proposed method.

建立准确高效的源项估计（STE）系统对于识别城市环境中的未知气体泄漏源具有重要意义。已经提出了许多成功的 STE 方法，而大多数方法都假设只有一个点源。然而，复杂的城市大气扩散和分布式边缘设备中的海量传感器数据带来了新的挑战。针对这些问题，本文提出了一种将测量浓度转换为视觉特征的方法，该方法保留了扩散和障碍物布局的特征。然后，提出了一种联合 STE（FL-STE）框架，在不收集所有隐私数据的情况下，协同从本地模型中提取知识，其中使用深度神经网络来识别视觉特征和源术语之间的关系。此外，我们通过 FDS 模拟构建了具有多个障碍物和来源的城市分散数据集。各种实证研究证明了所提出方法的有效性。