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Total fitting error threshold estimation method and system for sliding window segmented straight line representation

A technology of fitting errors and segmented straight lines, applied in the field of automation, can solve problems such as losing advantages

Active Publication Date: 2018-10-02
SHANDONG UNIV OF SCI & TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

One of the more common methods for determining the total fitting error threshold is based on wavelet transform. The disadvantage of this method is that it is difficult to carry out online, which makes the sliding window segmentation method that can be operated online lose the advantage of online operation.

Method used

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  • Total fitting error threshold estimation method and system for sliding window segmented straight line representation
  • Total fitting error threshold estimation method and system for sliding window segmented straight line representation
  • Total fitting error threshold estimation method and system for sliding window segmented straight line representation

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Embodiment Construction

[0066] It should be pointed out that the following detailed description is exemplary and intended to provide further explanation to the present application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0067] It should be noted that the terminology used here is only for describing specific implementations, and is not intended to limit the exemplary implementations according to the present application. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural, and it should also be understood that when the terms "comprising" and / or "comprising" are used in this specification, they mean There are features, steps, operations, means, components and / or combinations thereof.

[0068] In a typical implementation of the present application, such as figure 1 As shown, the total fitting err...

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Abstract

The invention discloses a total fitting error threshold estimation method and a total fitting error threshold estimation system for a sliding window segmented straight line representation. The total fitting error threshold estimation method specifically comprises the steps of: selecting a historical time series data acquired in an industrial alarm system as training data; selecting a typical datatrend time scale; and estimating distribution of fitting residuals according to statistical properties of the training data, and calculating a total fitting error threshold value corresponding to a sliding window segmentation method according to the distribution of the fitting residuals, wherein the determined total fitting error threshold value needs to make a straight line segment average time length after the sliding window segmentation method is implemented equal to a typical data trend time scale. The total fitting error threshold estimation method and the total fitting error threshold estimation system determine the appropriate total fitting error threshold value quickly, can effectively assist the operator to implement the sliding window segmentation method quickly, and achieve thepurpose of improving the working efficiency of process monitoring. Through implementing the sliding window segmentation method based on the threshold estimation method, the obtained segmentation result is independent of signal amplitude and noise level, only depends on the trend variation of a time sequence, and has good reliability.

Description

technical field [0001] The invention relates to the field of automation technologies such as industrial process monitoring and alarm systems, in particular to a method and system for estimating a total fitting error threshold of a sliding window segmented straight line representation. Background technique [0002] In the design of modern industrial alarm systems, the collected time series data has the characteristics of "high-dimensional and massive", which makes it difficult to implement the traditional process monitoring method directly on the original data. At present, one way to deal with it is to extract the time series The effective features of the algorithm are used for data compression, and the compressed data is used for calculation, so as to improve the efficiency of the algorithm and realize the applicability of the algorithm to "high-dimensional, massive" data. In the data feature extraction method, the segmented straight line representation method is an efficien...

Claims

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Application Information

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IPC IPC(8): G06K9/00
CPCG06F2218/18
Inventor 王建东陈矿
Owner SHANDONG UNIV OF SCI & TECH
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