Temperature field data reconstruction method

Abstract

Provided is a temperature field data reconstruction method. The temperature field data reconstruction method includes the steps of 1, obtaining a temperature field data matrix; 2, conducting random sampling on the temperature field data matrix; 3, on the basis of sample data, filling the temperature field data matrix to reconstruct temperature field data. Before random sampling is conducted on thetemperature field data matrix, rank reduction processing can be conducted on an original matrix by adopting such a singular value decomposition algorithm. A singular value threshold algorithm or a singular value projection algorithm can be adopted to fill the temperature field data matrix on the basis of the simple data to reconstruct the temperature field data. By the adoption of the temperaturefield data reconstruction method and a computer readable storage medium, the problem is solved that in traditional methods, only mathematic relations among stations or correlations among locals are taken into consideration; therefore, global temperature field data can be recovered by using little temperature field station data; at the same time, reconstruction errors are low, the signal-to-noiseratio is extremely high, and not only is a traditional interpolation method overcome, but also data storage space can be saved.

Description

technical field [0001] The invention relates to a data processing method in the field of meteorology, and more specifically, to a method for reconstructing temperature field data. Background technique [0002] With the advent of the era of big data, people have higher and higher requirements for the quality of life and the accuracy of weather forecasts. However, due to insufficient or damaged sensors, improper storage of weather data, and equipment conditions are affected The temperature field data is missing and incomplete due to many problems such as the limit. How to obtain accurate temperature field information has become an urgent problem to be solved. [0003] In order to obtain more accurate temperature field data outside the site, different scholars have proposed different spatial interpolation methods. The most common method is the bilinear interpolation algorithm, which has a simple process and easy calculation results; the inverse distance ratio method proposed ...

AI Technical Summary

Problems solved by technology

However, the application of compressive sensing theory to image reconstruction needs to meet the restricted isometric characteristics, and its accuracy will seriously affect the quality of the reconstructed temperature field image

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