Research Of Confidence-controlled Adaptable Interpolation Algorithm And Its Application In Meteorological Data Processing

In the production and life of today's times,people have more and more standards for the precision and volume requirements of various types of data.However,by means of science and technology and the objective conditions of cost acquisition,we can not easily get the data that meet the demand directly.For example,in the fields of meteorological observation,remote sensing,medical radiography,geological exploration and so on,the raw data obtained by people are often limited in precision and dispersed.The data users prefer to transform the raw data into more regular,more compact and clearer data.For example,two-dimensional image data are spatially interpolated and reconstructed in order to get more visual results.Most of the commonly used spatial interpolation algorithms do not control confidence level,and there is no real-time adjustment and process optimization in the interpolation process,so the problem of poor reliability or low operation rate is often appeared.In this paper,the existing spatial interpolation algorithms are analyzed,and the limitations and deficiencies of the existing spatial interpolation algorithms are summarized.A new interpolation algorithm named "Confidence-Controlled Adaptable Interpolation Algorithm" is proposed.The algorithm is used in meteorological data processing to compare and contrast the interpolation results of other classical spatial interpolation algorithms.In this paper,the existing classical interpolation algorithms are compared and analyzed,the design idea and construction principle of existing algorithms are summarized,and the indexed storage structure is adopted in the implementation of this algorithm,which provides support for fast data access and data relationship construction.This paper sets up the confidence degree model to introduce the confidence degree control and the interpolation mode selection in the classical spatial interpolation two links,it realizes the controllable and adaptive transformation of the spatial interpolation algorithm;In the process of optimizing the algorithm,the operation rate of this algorithm is improved by adding two kinds of optimization methods,such as search strategy transfer and data reduction template.The efficiency and reliability of the proposed algorithm are validated by comparing the process and result of the application of the algorithm and other classical interpolation algorithms in the problem of meteorological data interpolation.