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.