基于GPU的空气污染数据可视化方法研究

Air Pollution Data Visualization Method Based on GPU

传统空气污染浓度可视化方法无法显示完整的视觉结果,且容易导致可视化感知问题,如出现遮挡或杂乱无章甚至伪像等。针对上述问题提出一种高效的可视化方法,基于图形硬件的可编程性,统计分析大气风场区域特性进行种子点预采样,通过四面体点定位算法快速构建场线,结合箭头跟踪和等值面绘制与分析污染物的可扩散运动。同时利用聚类算法实现场线抽取,设计光照模型以减少场线与等值面之间的相互遮挡,有效解决了三维流场中出现的感知问题,呈现出清晰的流场模式、空气污染物浓度分布及区域性变化,大大提高了空气污染物浓度数据的可读性。

Traditional air pollution concentration visualization methods cannot display complete visual results, and easily lead to visual perception problems, such as occlusion or chaos or even artifacts. In view of the above problems, an efficient visualization method is proposed.Based on the programmability of graphics hardware, the regional characteristics of atmospheric wind field are statistically analyzed, seed points are pre sampled, field lines are quickly constructed through tetrahedral point positioning algorithm, and the diffusible movement of pollutants is analyzed by combining arrow tracking and iso-surface rendering. At the same time, field line extraction is realized by clustering algorithm, and lighting model is designed to reduce the mutual occlusion between streamline and iso-surface, which effectively solved the perception problems in the three-dimensional flow field, shows a clear flow field mode, air pollutant concentration distribution and regional changes, and greatly improves the readability of air pollutant concentration data.