Research On Key Technologies Of Meteorological Data Quality Control Based On Random Observational Methods

Accurate meteorological data is of great significance to the country's ecological and military security.Therefore,the quality control of meteorological data is an important part of meteorological observations.There are two key technical issues in the quality control of meteorological data.One is the quality of data transmission.For example,the satellite image is transmitted in a noise-containing channel,and the other is the observation quality of the data.For example,ground-based observatory failure sensors produce conflict data.However,given the strong timeliness of the information contained in the meteorological data,the large-scale nature of meteorological data poses serious technical challenges to the above problems.Using random observational methods combined with some prior knowledge of observations can effectively reduce the amount of data to be processed.This idea has been used and verified in the field of compressed sensing.In view of this,this paper attempts to apply the idea of random observation method to the key technical issues of meteorological data quality control.The main-work of this article is as follows.First of all,this paper proposes a Compressed Sensing Blocking(BCS)method to infer the effect of the number of image blocks on image reconstruction for data transmission quality problems.The core idea of this method is to use the characteristics of the random observation matrix to find the optimal number of image matrix blocks.Based on the previous theory,suppose that the original image obeys a Gaussian distribution,the paper theoretically analyzed the influence of noise under compressed perspective,derived the range of error probability,and proposed the BCS algorithm.Experimental results show that the number of blocks of an image has a strong correlation with the image recovery process.Then,this paper presents the Random Sampling-Arithmetic Mean(RS-AM)method for data observation quality problems.This method is based on a random sampling model.By repeatedly extracting random observation vectors,the goodness of fit between the expected distribution and the sampling distribution is evaluated to find the closest random observation vector to the desired distribution.This paper used the distance formula to calculate the distance between the median and the arithmetic mean of each set of observations in the RS-AM approach and selected the set of observations with the smallest distance.In this set of observations selected,randomly continued until the stop condition was met.In addition,the advantage of the RS-AM method is that there is no need to estimate the sources reliability,and the truth calculation is simple.Experimental results show that the proposed RS-AM method can effectively solve the quality problem of data observation.