Abstract—The process of smart gird operation and management produces big volume of isomeric and polymorphic data, known as power big data. Since the data has a property of panoramic, the combination of data visualization and 3D scene is an efficient solution to comprehensible analysis and demonstration, while the highly detailed 3D models challenge rendering performance and the speed of human-machine communication. Therefore, this paper proposes a fast scene constructing method aiming at scenes that are composed of electrical equipment models. This method is designed based on a weighting function, which takes several factors that contribute to the complexity of a model into account. First, we select several specific factors of an edge. These factors are special because their values affect the surface of electrical equipment 3D models a lot, while affect common 3D models not obviously. A proper scenario is designed to quantize these factors and their corresponding weighting parameter. Second, in order to ensure the weighted contribution of each factor is balanced, we adjust the weighting parameter of each factor by restricting the range of the parameter. At last, we use two stacks for one model to record the edges that are to be optimized and have been optimized in sequence, sorted by the contribution value. Experiment shows that method in this paper meets the demand of power big data visualized analysis system by finely retaining figuration and high rendering performance.
Index Terms—Power big data, visualized analysis
Cite: Zhao-Yang Qu and Jing-Yuan Huang, “A Fast Scene Constructing Method for 3D Power Big Data Visualization," Journal of Communications, vol. 10, no. 10, pp. 773-777, 2015. Doi: 10.12720/jcm.10.10.773-777
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