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Water quality data cleaning method based on empirical wavelet transform and multi-scale entropy

An empirical wavelet, multi-scale entropy technology, applied in the fields of electrical digital data processing, special data processing applications, complex mathematical operations, etc., can solve problems such as poor data robustness, and achieve strong data robustness and good data cleaning effect. Effect

Pending Publication Date: 2021-02-05
HANGZHOU DIANZI UNIV
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Problems solved by technology

[0005] Aiming at the poor robustness of data in the existing cleaning technology, the present invention provides a method for automatic cleaning of water quality data based on empirical wavelet transform and multi-scale fuzzy entropy

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  • Water quality data cleaning method based on empirical wavelet transform and multi-scale entropy
  • Water quality data cleaning method based on empirical wavelet transform and multi-scale entropy
  • Water quality data cleaning method based on empirical wavelet transform and multi-scale entropy

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Embodiment Construction

[0037] In order to solve the problem of data noise generated by sensors in the water quality monitoring system, the present invention proposes a novel adaptive time series data cleaning method based on empirical wavelet transform-multiscale entropy (EWT-MFE), such as figure 1 Shown, the present invention comprises the following steps:

[0038] Step 1: Calculate the frequency spectrum of the original noisy time series water quality data using fast Fourier transform.

[0039] Step 2: Set an appropriate initial boundary according to the frequency characteristics of the spectrum, and then use an adaptive segmentation method to segment the spectrum.

[0040] Assume that the Fourier spectrum support interval [0, π] is divided into N consecutive sub-intervals, and the boundary is Γ={ω k} k＝0,1,2...N (where ω 0 = 0, ω N = π), then each interval is defined as Λ k =[ω k-1 ,ω k ], apparently for each ω k As the center, define the width as 2τ n The transition phase interval T ...

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Abstract

The invention relates to a water quality data cleaning method based on empirical wavelet transform and multi-scale entropy. According to the method, after the frequency spectrum of original data is subjected to adaptive segmentation through fast Fourier transform, the original data with noise is decomposed into different intrinsic mode functions IMF through empirical wavelet transform. According to different characteristics of the whole IMF, adaptive adjustable parameters based on multi-scale entropy are introduced into a threshold function, so that the noise removal performance is improved. Finally, the high-frequency noise of the points over the entire IMF is filtered, which includes more effective data amplitudes and less noise retention. The method is more suitable for time series datacleaning, and achieves a better effect on the noise removal precision of synthetic analog data and field water quality data.

Description

technical field [0001] The invention relates to an automatic cleaning method for water quality data, in particular to an automatic cleaning method for water quality data based on empirical wavelet transform and multi-scale fuzzy entropy. Background technique [0002] In the field of water quality monitoring, the safety of surface water resources has always been a solid foundation for human development and survival. The time series data collected by water quality monitoring sensors can help environmental protection departments monitor, collect and analyze water quality data. Time series data mining can extract useful information from a large amount of historical data, thus providing decision makers with important and extremely valuable information or knowledge. [0003] In traditional water quality monitoring systems, water quality time series data mining, such as water quality prediction, water quality assessment and water quality modeling, is crucial to the realization of w...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06F17/14G06F17/15
CPCG06F17/142G06F17/148G06F17/156G06F2218/18G06F2218/06Y02A20/152
Inventor 蒋鹏陈锃许欢余善恩林广
Owner HANGZHOU DIANZI UNIV
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