Method for calculating dispersion of point source explosion of pollutants based on local micro-meteorological environment
By constructing a coupled micro-meteorological-explosion-diffusion model and combining high-density monitoring and numerical simulation techniques, the accuracy and real-time performance of pollutant point source explosion diffusion calculations under local micro-meteorological conditions were solved, achieving high-precision and rapid pollutant diffusion simulation and visualized decision support.
Patent Information
- Application Number
- CN202610522596.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-04-20
- Publication Date
- 2026-07-14
AI Technical Summary
Existing methods for calculating point source explosions and diffusion of pollutants lack accuracy and real-time performance under local micro-meteorological conditions. They cannot effectively reflect the diffusion trajectory and concentration distribution of pollutants in areas with complex terrain or variable weather. Furthermore, the explosion intensity and diffusion process are not tightly coupled, resulting in large calculation errors and poor real-time performance.
By constructing a coupled micro-meteorological-explosion-diffusion model, and combining high-density micro-meteorological monitoring, UAV monitoring, satellite remote sensing data and lidar technology to obtain high spatiotemporal resolution data, the explosion shock wave and pollutant release parameters are inverted. Numerical simulation methods are used to simulate the interaction of the three, and the calculation process is optimized by combining fluid dynamics equations and machine learning to achieve accurate and rapid pollutant diffusion simulation.
It enables refined input and dynamic coupling of local micro-meteorological elements, improves the accuracy of pollutant diffusion concentration calculation, meets the real-time requirements of emergency command, adapts to parameter adjustments in different local scenarios, and provides visualized decision support.
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