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Measurement parameter prediction and sewage treatment control method based on deep learning

A technology for measuring parameters and sewage treatment, which is applied in the direction of electrical digital data processing, special data processing applications, and other database retrieval, etc. It can solve problems such as high measurement and maintenance costs, increased operating costs, and lagging control measures, so as to reduce the frequency of instrument measurement , reduce measurement costs and avoid the effect of control hysteresis

Pending Publication Date: 2021-03-26
SHANGHAI SIIC LONGCHUANG SMARTER ENERGY TECH CO LTD
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Problems solved by technology

[0003] However, the current sewage treatment online instrument has the characteristics of long measurement time and high measurement maintenance cost (measurement needs to consume measurement drugs, and measurement cost increases with the increase of measurement frequency), which not only increases operating costs, but also causes control measures due to long measurement time Relative hysteresis will cause unstable water outlet

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  • Measurement parameter prediction and sewage treatment control method based on deep learning
  • Measurement parameter prediction and sewage treatment control method based on deep learning
  • Measurement parameter prediction and sewage treatment control method based on deep learning

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specific Embodiment

[0053] Such as figure 1 Shown is a deep learning-based measurement parameter prediction and sewage treatment control method of the present invention, the method includes the following steps:

[0054] Step 1: Acquire real-time sewage treatment online meter measurement time series data;

[0055] Step 2: Perform data cleaning and wavelet transformation on the time series data to obtain high-frequency components at various levels;

[0056] Step 3: After processing the high-frequency components at each level, input them into the trained GRU neural network model, and then output the prediction results corresponding to the high-frequency components at each level;

[0057] Step 4: Reprocess the prediction results corresponding to the high-frequency components of each level and combine them for wavelet reconstruction to obtain the measurement parameters after prediction processing;

[0058] Step 5: Make corresponding control actions for the corresponding control links of sewage treat...

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Abstract

The invention relates to a measurement parameter prediction and sewage treatment control method based on deep learning. The method comprises the following steps: step 1, acquiring real-time sewage treatment online instrument measurement time sequence data; step 2, performing data cleaning and wavelet transform on the time series data to obtain high-frequency components of each level; step 3, respectively processing the high-frequency components of each level, inputting the processed high-frequency components into the trained GRU neural network model, and outputting prediction results corresponding to the high-frequency components of each level; step 4, processing the prediction results corresponding to the high-frequency components of all the levels again and then combining the results towavelet reconstruction to acquire measurement parameters subjected to prediction; and step 5, performing corresponding control action on the corresponding control link of the sewage treatment according to the measurement parameters subjected to the prediction treatment. Compared with the prior art, the invention has the advantages that parameters can be relatively accurately estimated in advance,control lag is avoided, meanwhile, the measurement frequency can be reduced, and the cost is reduced.

Description

technical field [0001] The invention relates to the technical field of sewage treatment measurement and control, in particular to a deep learning-based measurement parameter prediction and sewage treatment control method. Background technique [0002] With the wave of "standard upgrading and transformation" set off in recent years, in order to meet the increasingly stringent discharge standards, the sewage treatment with a relatively low degree of automation has gradually added online instruments. [0003] However, the current sewage treatment online instrument has the characteristics of long measurement time and high measurement maintenance cost (measurement needs to consume measurement drugs, and measurement cost increases with the increase of measurement frequency), which not only increases operating costs, but also causes control measures due to long measurement time The relative hysteresis will cause the water outlet to be unstable. Contents of the invention [0004]...

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Application Information

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IPC IPC(8): G06F16/9035G06F16/903
CPCG06F16/9035G06F16/90348
Inventor 杨志科蒋秋明王兴荣董孔益余俊陶乃峰黄健李国虎马峻青张元会
Owner SHANGHAI SIIC LONGCHUANG SMARTER ENERGY TECH CO LTD
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