Aeration control method and system based on open source gap

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By introducing open source Hongmeng operating system and neural network model into the sewage treatment system, and combining the deviation model to calculate the fan opening value, the problem of poor control accuracy and reliability in the existing aeration control solution is solved, and more efficient and reliable aeration control is achieved.

CN119739124APending Publication Date: 2025-04-01CHINA HYDROELECTRIC ENGINEERING CONSULTING GROUP CHENGDU RESEARCH HYDROELECTRIC INVESTIGATION DESIGN AND INSTITUTE

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Patent Information

Application Number: CN202411860428.3
Authority / Receiving Office: CN · China
Patent Type: Applications(China)
Current Assignee / Owner
Filing Date: 2024-12-17
Publication Date: 2025-04-01

Application Information

Patent Timeline

17 Dec 2024

Application

CN202411860428.3

01 Apr 2025

Publication

CN119739124A

IPC: G05B19/418; C02F7/00; G06N3/045; G06N3/0464; G06N3/0475; G06N3/094

CPC: Y02W10/10

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Application Domain

Water aeration Biological models

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AI Technical Summary

Technical Problem

The existing aeration control scheme has the problem of poor control accuracy and reliability, and it is difficult to effectively deal with complex water quality changes.

Method used

The open source Hongmeng aeration control method is adopted to obtain water quality parameters through the open source Hongmeng edge gateway, and the fan opening value is calculated using the pre-trained neural network model and deviation model to determine and adjust the final fan opening value.

Benefits of technology

It improves the accuracy and reliability of aeration control, solves the problem of difficult equipment protocol compatibility, and reduces the risk of model calculation abnormalities.

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Abstract

The invention relates to the technical field of sewage treatment, discloses an aeration control method and system based on an open source gap, and aims to solve the problem that an aeration control scheme is poor in control precision and reliability, and the scheme mainly comprises the following steps: obtaining water quality parameters by using a corresponding open source gap edge gateway; based on the water quality parameters, calculating a first fan opening value of the air blower by using a pre-trained neural network model and calculating a second fan opening value of the air blower by using a pre-established deviation model through a swan box; and determining a final fan opening value according to the first fan opening value and the second fan opening value, and sending the final fan opening value to a corresponding open source gap edge gateway, so that the fan is adjusted to the final fan opening value. The method improves the accuracy and reliability of aeration control, and is especially suitable for a sewage treatment plant.

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