Method and system for data-driven prediction based on spatial information constraints

Abstract

Provided herein is a method and a system for data-driven prediction based on spatial information constraints, belonging to the technical field of intelligent information processing. The method comprises: prediction target interpolation based on collocated Co-Kriging; sample weight calculation based on sequential Gaussian simulation and loss function construction based on spatial information constraints; optimization of loss function and data-driven prediction based on deep fully connected neural network. The system comprises: data acquisition module, data preprocessing module, prediction target interpolation module, sample weight calculation module, loss function construction module, loss function optimization module, data-driven prediction module. It realizes the expansion of learning samples under the restriction of spatial information, and uses the spatial information to optimize the loss function, thus improving the utilization rate of data information, facilitating guiding the learning process to converge to reasonable assumptions, thereby improving the performance of the prediction method based on data-driven.

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