Under a large data amount of sampling points, Delaunay triangulation can be adopted to establish a triangulation network and then employ local neighborhood sampling points for Kriging interpolation. However, this algorithm requires fitting a semi-variogram to each interpolation point, which incurs significant overhead in the condition of a large interpolation point scale. Therefore, this study proposes a Kriging interpolation method that fits the semi-variogram on a triangular basis. Additionally, it utilizes CPU-GPU load balancing to optimize some calculations and fully considers the influence of non-uniform samples on the Kriging interpolation effect. The results show that the proposed algorithm can ensure the interpolation effect of non-uniform sample sets, improve computational performance, and ensure high accuracy.