Leaking source localization approach in multi-obstacle scenarios based on CNN and attention mechanism

Hazardous gas leaks seriously threaten human life, property, and the ecological environment. A timely and accurate approach to locating the leaking source can prevent further expansion of the leakage and facilitate subsequent rescue and repair work. Therefore, the localization of leaking source is of great significance. However, gas diffusion in multi-obstacle scenes is highly random and complex. Most of the traditional localization approaches do not consider the multiple factors that affect gas diffusion and lead to low accuracy. In this paper, FLUENT simulation software is used to build a three-dimensional scene with complex obstacles and simulate several SO₂ leaking scenes based on the real chemical industry park firstly. Then multiple feature maps are constructed using concentration data collected from several monitoring points, serving as input data for the neural network. And a CNN model with attention mechanism is designed to identify the leakage scenes. The final experimental results verify the effectiveness of the localization approach proposed.