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High accuracy surface modeling method based on big data

A surface modeling, high-precision technology, applied in 3D modeling, image data processing, instruments, etc., can solve the problem of small storage space, achieve low storage requirements, stable results, and ensure linear independence

Inactive Publication Date: 2017-05-17
INST OF GEOGRAPHICAL SCI & NATURAL RESOURCE RES CAS
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  • Abstract
  • Description
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Problems solved by technology

[0004] Aiming at the defects of the existing HASM model in solving large-scale problems, the present invention provides an improved HASM, which requires a small storage space and can solve large-scale problems while ensuring accuracy

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  • High accuracy surface modeling method based on big data
  • High accuracy surface modeling method based on big data
  • High accuracy surface modeling method based on big data

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Embodiment Construction

[0048] The principles and features of the present invention are described below in conjunction with the accompanying drawings, and the examples given are only used to explain the present invention, and are not intended to limit the scope of the present invention.

[0049] Such as figure 1 As shown, a large data-based high-precision surface modeling method provided by an embodiment of the present invention includes the following steps:

[0050] Step 1: Create geographic coordinate information of each sampling point and the sampling value of the variable to be measured, including the longitude information of the sampling point and the latitude information of the sampling point in the geographic coordinate information;

[0051] Step 2: discretize the space of the area to be measured into a grid point form, obtain the discrete value of the grid point, and establish a sampling equation according to the geographic coordinate information and the sampling value of the variable to be m...

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Abstract

The invention relates to a high accuracy surface modeling method based on big data. The method comprises the following steps: creating geographic coordinate information and to-be-tested variable sampling values of all sampling points; turning a to-be-tested regional space into a grid point form through discretization, and establishing a sampling equation; conducting high order difference discretization on a partial differential equation set of a surface, obtaining a corresponding algebraic system, combining the algebraic system and the sampling equation to form an equality constrain least squares problem, converting the problem into a solve cross cutting object function minimum value problem, and then converting the minimum value problem into a solve symmetry indeterminate equation set; randomly selecting iteration initial values; blocking a coefficient matrix, and storing the block matrixes after decomposition; solving an HASM equation set through a block line projection iterative method, and determining whether a solving result is convergent; determining whether a solution meets demands of a Gauss and C.odazzi equation set; and outputting a high accuracy simulation surface model. Large-scale problems can be solved, the needed storage space is small, and defects of HASM for solving large-scale problems are prevented.

Description

technical field [0001] The invention relates to a method for spatial surface modeling for large-scale problems, which can be used in the fields of digital elevation model generation under the background of big data, meteorology, biomass, soil spatial distribution simulation, etc., and can also be regarded as a surface grid A method of approximation, which can be used for large-scale surface modeling in physics, chemistry, medicine, etc. Background technique [0002] In order to solve the error problem and multi-scale problem that have plagued surface modeling for half a century, we have established a global approximation data (including remote sensing data and global model coarse resolution) based on the theory of differential geometry and optimal control theory. Simulation data) as the driving field, the High Accuracy Surface Modeling (HASM for short) method with local high-precision data (including monitoring network data and survey sampling data) as the optimal control co...

Claims

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Application Information

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IPC IPC(8): G06T17/20G06T17/30G06T17/05
CPCG06T17/05G06T17/20G06T17/30
Inventor 赵娜岳天祥
Owner INST OF GEOGRAPHICAL SCI & NATURAL RESOURCE RES CAS
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