Spatial environment data multi-scale display method, model building method and device
A technology for spatial environment and model building, applied in image data processing, character and pattern recognition, image analysis, etc.
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Embodiment 1
[0081] The flow of the method for establishing a multi-scale model of spatial environment data in this embodiment is as follows Figure 4 As shown, the specific implementation steps are as follows:
[0082] 1. Obtain the spatial environment data, and determine the K neighboring point information of each point in the spatial environment data.
[0083] In the spatial environment data, the calculation of the eigenvalue of a point generally requires the surrounding K-nearest neighbor points to participate in the calculation, and a single point cannot be calculated. The simplest way to search for K-nearest neighbors is to calculate the distance between the target point and all points, then sort them, and finally keep K points, but when there are a lot of point data, this method is very time-consuming. At present, the nearest neighbor indexing methods mainly include the K neighbor point calculation method, the KD tree neighbor point index method, the Voronoi neighbor point index, e...
Embodiment 2
[0123] As time goes by, space environment data will be constantly updated. For example, the total electron content of the ionosphere over my country changes with time, such as Figure 5 As shown, therefore, the multi-scale expression model established with data at one time cannot be applied to other times. In addition, spatial environment data, especially field data, are huge in volume. If the method of visualizing massive terrain data is used to process the data in advance to establish a multi-scale expression model of spatial environment data, on the one hand, the workload is huge and affects timeliness. On the other hand, , advance processing can only generate discrete layer data, and cannot realize seamless multi-scale expression, so the present invention optimizes the multi-scale expression of the space environment to support real-time expression.
[0124] This embodiment is a further optimization on the basis of embodiment 1, according to Figure 4 For the established m...
Embodiment 3
[0126] Another time-consuming step in the multi-scale model building process is the calculation part of the integrated feature parameters. The feature calculation of each space environment data is only related to the original K adjacent points, which has parallel characteristics. Therefore, in order to further improve the construction efficiency of the multi-scale model of space environment data, the present invention adopts the GPU acceleration method of CUDA architecture to calculate the feature parameters. Calculation is optimized, and GPU development uses the Compute Unified Device Architecture (CUDA) launched by NVDIA in June 2007, which can use C-like language to complete the development of high-performance general-purpose computing for GPUs without using traditional graphics APIs Once launched, it has become a research hotspot in various fields and has been well applied in many aspects. Therefore, the present invention uses the CUDA architecture to complete the calculati...
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