Sewage aeration tank foam identification method based on GoogLeNet-SVM (Support Vector Machine)

A sewage aeration and recognition method technology, applied in the direction of nuclear method, neural learning method, character and pattern recognition, etc., can solve complex problems, increase enterprise labor costs, biased problems, etc., achieve high accuracy and reduce manpower cost, effect of avoiding impact

Pending Publication Date: 2020-06-02
SHANGHAI SIIC LONGCHUANG SMARTER ENERGY TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0002] Sewage treatment enterprises currently use manual detection for foam identification in aeration tanks. Manual detection relies on the experience and industry knowledge of the staff. Talents are rare and will increase the labor cost of the enterprise; Situations tend to be biased and will lead to large deviations
In addition, staying on site for a long time will affect the health of workers

Method used

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  • Sewage aeration tank foam identification method based on GoogLeNet-SVM (Support Vector Machine)
  • Sewage aeration tank foam identification method based on GoogLeNet-SVM (Support Vector Machine)
  • Sewage aeration tank foam identification method based on GoogLeNet-SVM (Support Vector Machine)

Examples

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Embodiment

[0031] This embodiment provides a method for identifying foam in a sewage aeration tank based on GoogLeNet-SVM. The steps include:

[0032] 1) Collect pictures of the aeration tank.

[0033] 2) In view of the small number of pictures, the expansion method is used to expand the pictures to obtain expanded samples; the expansion methods mainly include:

[0034] a. The picture is flipped, and the positive picture is flipped left and right, and the data is doubled;

[0035] b. Add salt and pepper noise, add salt and pepper noise to the original positive image, and double the data;

[0036] c. Segment the picture, segment the target area, set the other areas to 0, and double the data;

[0037] d. Add lighting, rotate the picture 90°, 180°, 270°, and increase the data by 3 times.

[0038] 3) Using the openCV library (providing interfaces of Python, Ruby, MATLAB and other languages ​​to realize general algorithms in image processing and computer vision) to uniformly modify the siz...

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PUM

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Abstract

The invention relates to a sewage aeration tank foam identification method based on GoogLeNet-SVM. The method comprises the following steps: S1, obtaining a picture of an aeration tank; s2, obtainingan expanded sample based on the picture; s3, based on the sample and the openCV library, obtaining a sample with a unified size; s4, obtaining a training set and a test set based on the samples with the unified size; s5, constructing a sewage aeration tank foam recognition model based on the training set, the test set, the GoogLeNet and the SVM; and S6, based on the sewage aeration tank foam identification model, carrying out sewage aeration tank foam identification. Compared with the prior art, the method has the advantages that the foam recognition result can be obtained only by inputting the picture of the aeration tank to be detected, manual participation is not needed, the labor cost of an enterprise is reduced, the influence on the health of workers is avoided, and meanwhile the accuracy is higher.

Description

technical field [0001] The invention relates to the field of sewage treatment, in particular to a method for identifying foam in a sewage aeration tank based on GoogLeNet-SVM. Background technique [0002] Sewage treatment enterprises currently use manual detection for foam identification in aeration tanks. Manual detection relies on the experience and industry knowledge of the staff. Talents are rare and will increase the labor cost of the enterprise; Situations tend to be biased and can lead to large deviations. In addition, staying on site for a long time will affect the health of workers. Contents of the invention [0003] The purpose of the present invention is to provide a GoogLeNet-SVM-based sewage aeration tank foam recognition method that does not require manual participation and has higher accuracy in order to overcome the above-mentioned defects in the prior art. [0004] The purpose of the present invention can be achieved through the following technical solu...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N20/10G06N3/04G06N3/08
CPCG06N20/10G06N3/08G06N3/047G06N3/045G06F18/241G06F18/2411G06F18/2415
Inventor 杨志科蒋秋明
Owner SHANGHAI SIIC LONGCHUANG SMARTER ENERGY TECH CO LTD
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