This study aims to address the challenge of lightweighting point cloud data at the part level in substation equipment, aligning with the requirements of digital twin applications for grid substations and considering t...
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ISBN:
(纸本)9798350375145;9798350375138
This study aims to address the challenge of lightweighting point cloud data at the part level in substation equipment, aligning with the requirements of digital twin applications for grid substations and considering the hierarchical structure of equipment. Utilizing the importance of equipment parts, we construct a priority queue and integrate the draco algorithm to automatically lightweight transformer equipment point cloud into semantic models with multiple levels of detail at the part level. This approach effectively resolves the lightweighting issue of point cloud data for substation equipment at the part level, achieving a differential treatment where key parts maintain high original data retention rates while minor parts occupy fewer resources. In comparison to applying the global draco algorithm to equipment, our method offers the advantage of flexibly allocating storage and transmission resources to user-focused key targets while keeping the overall compression rate unchanged.
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