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Air quality anomaly detection method based on distributed online principal component analysis

A principal component analysis, air quality technology, applied in the direction of analyzing materials, measuring devices, instruments, etc., can solve problems such as inappropriateness

Active Publication Date: 2018-09-04
ZHEJIANG UNIV
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Problems solved by technology

In this case, the data is scattered and collected by multiple sensor nodes, so the conventional PCA method of using the data covariance matrix to solve the principal components is obviously not suitable

Method used

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  • Air quality anomaly detection method based on distributed online principal component analysis
  • Air quality anomaly detection method based on distributed online principal component analysis
  • Air quality anomaly detection method based on distributed online principal component analysis

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Embodiment Construction

[0061] The technical solution of the present invention will be further elaborated below in conjunction with the accompanying drawings.

[0062] as attached figure 2 Shown, the inventive method realizes steps as follows:

[0063] 1. Obtain air pollutant concentration data

[0064] The air pollutant concentration data includes daily multi-location air pollutant concentration data of a certain city within a certain time period.

[0065] Daily multi-site air pollutant measurement data for a certain city over a certain period of time can be obtained from the Ministry of Environmental Protection Data Center and related websites. Specific Embodiments Obtain the historical data of the daily air pollutant concentration in Hangzhou at the Qingyue Open Environment Data Center. After obtaining the original data, label the obtained original data as the air pollutants that are finally used for subsequent target function processing and calculation. The concentration data (the data is als...

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Abstract

The invention discloses an air quality anomaly detection method based on distributed online principal component analysis. The method comprises acquiring known air pollutant concentration data of a city, designing a distributed online principal component analysis model, and processing air pollutant concentration data through the air quality anomaly detection method based on distributed online principal component analysis to obtain a judgment result so that the air quality anomaly is detected. The method can conveniently predict the overall air quality in a certain range, can suppress the influence caused by the abnormal training data, has the function of denoising, and has a fast processing speed and shorter time.

Description

technical field [0001] The invention belongs to the field of distributed signal processing, machine learning and anomaly detection, in particular to an air quality anomaly detection method based on distributed online principal component analysis. Background technique [0002] Principal Component Analysis (PCA) is a commonly used unsupervised dimensionality reduction method. The main direction of the data distribution can be obtained by PCA. Conventional PCA implements the principal component analysis process by constructing the data covariance matrix and calculating its eigenvectors. These eigenvectors are the more informative directions in the original data space and are therefore considered principal components or principal directions. For conventional PCA, it is generally necessary to obtain all the original data and generate the covariance matrix of the original data. Obviously, the principal component analysis method can be applied to the field of anomaly detection, ...

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Application Information

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IPC IPC(8): G01N33/00
CPCG01N33/0062
Inventor 李春光苗雪丹王涛
Owner ZHEJIANG UNIV
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