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Meteorological data complement method based on automatic convolutional encoding and decoding algorithm

A meteorological data, encoding and decoding technology, applied in the computer field, can solve the problem of low precision

Active Publication Date: 2017-02-22
XIDIAN UNIV
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Problems solved by technology

The present invention utilizes convolutional automatic encoding and convolutional automatic decoding to learn the correlation of surrounding data features in the process of filling data, effectively overcoming the problem of low precision of meteorological data filling

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  • Meteorological data complement method based on automatic convolutional encoding and decoding algorithm

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Embodiment Construction

[0082] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0083] Refer to attached figure 1 , further describe in detail the steps realized by the present invention.

[0084] Step 1, preprocessing the meteorological data.

[0085] In the first step, a 3×2 two-dimensional matrix is ​​constructed using a one-dimensional data containing six attribute values.

[0086] The six attribute values ​​refer to atmospheric pressure, dry bulb temperature, dew point temperature, wind speed, wind direction, and total cloud cover.

[0087] The construction method of the 3×2 two-dimensional matrix is ​​that the first two values ​​of the six attribute values ​​are sequentially used as the first row of the two-dimensional matrix, and the middle two values ​​of the six attribute values ​​are sequentially used as the first row of the two-dimensional matrix. In the second row, the last 2 values ​​of the 6 attribute values ​​are used a...

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Abstract

Provided is a meteorological data complement method based on automatic convolutional encoding and decoding algorithms. The method comprises the steps that meteorological data is preprocessed; the number of iterations is set; automatic convolutional encoding is conducted on a four-dimensional matrix; automatic convolutional encoding is conducted on a feature matching matrix; automatic convolutional encoding is conducted on the feature matching matrix; automatic convolutional decoding is conducted on the feature matching matrix; automatic convolutional decoding is conducted on the feature matching matrix; automatic convolutional decoding is conducted on the feature matching matrix; a loss function is calculated; a convolution kernel and an offset weight are updated; a meteorological data attribute value is updated; meteorological data complement is completed. According to the meteorological data complement method based on the automatic convolutional encoding and decoding algorithms, the problem that the precision of data complement is low is solved, and the method has the advantages of being high in robustness and missing value filling accuracy.

Description

technical field [0001] The invention belongs to the technical field of computers, and further relates to a method for filling meteorological data based on a convolution automatic encoding and decoding algorithm in the technical field of meteorological data processing. The present invention can be applied to specific application scenarios such as meteorological data processing and meteorological data analysis. Aiming at the limitations of existing meteorological data filling algorithms, the missing meteorological data can be filled according to the correlation between the attribute characteristics of meteorological data learned many times. its weather data value. Background technique [0002] Fill Gap is a method due to missing data. Missing data refers to the data that should have been obtained but were not obtained for some reason at the time of data collection. It refers to the incomplete value of one or some attributes in the existing data set. In scientific research, ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
CPCG16Z99/00
Inventor 刘惠杜军朝姚士民韩俊王静刘泽宇赵一凡
Owner XIDIAN UNIV
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