The invention relates to the technical field of digital twinning, particularly provides a digital twinning scene
adaptive optimization method based on
point cloud data, and solves the problems that smoothness and detail presentation cannot be balanced during network fluctuation, and a scene prediction optimization
mechanism based on space-time semantic analysis is not established. The method comprises the following steps: data preprocessing and hierarchical construction: carrying out preprocessing of abnormal point
elimination, missing value
complementation and density adjustment on original
point cloud data, dividing key and common regions according to scene requirements, and constructing a multi-scale and multi-resolution hierarchical
point cloud data system; the method can deeply analyze the spatio-temporal dynamic characteristics of the point
cloud data through the technologies of spatio-temporal dynamic semantic segmentation, dynamic change monitoring, prediction optimization and the like, processes the continuous
time sequence point
cloud data after adaptive transmission, models a
time dependency relationship and extracts local and global characteristics in time and space dimensions respectively, and improves the accuracy of point
cloud data processing. Accurate identification of object types, positions and dynamic change information is realized.