Sewage treatment intelligent dosing method and system based on AI and Internet of Things
By combining the Internet of Things and AI technology, multi-source data of sewage is collected and processed in real time, water quality and image characteristics are extracted and integrated, and intelligent dosing for sewage treatment, the problem of insufficient dosing accuracy in the existing technology is solved, and more efficient sewage treatment and stable effluent water quality are achieved.
CN119723311APending Publication Date: 2025-03-28YIANNENG (ZHEJIANG) IOT TECHNOLOGY CO LTD
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Patent Information
- Application Number
- CN202411541327.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-31
- Publication Date
- 2025-03-28
AI Technical Summary
Technical Problem
The existing intelligent dosing method for sewage treatment fails to make full use of multi-source data such as water quality parameters and sewage images, resulting in limited dosing accuracy.
Method used
Water quality information and sewage images of sewage are collected in real time through IoT devices, multiple water quality characteristics and image characteristics are extracted, and fused, and input the dosage prediction model to determine the target dosage.
Benefits of technology
It improves the accuracy of dosing, improves the efficiency of sewage treatment and the stability of effluent water quality.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention discloses a sewage treatment intelligent dosing method and system based on AI and Internet of Things, and relates to the technical field of sewage treatment.The method comprises the steps that first water quality features and second water quality map features are extracted from water quality information; extracting a second water quality feature and a second water quality map feature from the sewage image; fusing the first water quality map feature and the second water quality map feature to obtain a first fused feature; fusing the first water quality feature with the second water quality feature to obtain a second fused feature; inputting the first fusion feature into a first dosing prediction model; inputting the second fusion feature into a second dosing prediction model; and determining a target dosing amount according to the model output, and executing dosing operation according to the target dosing amount. Multi-source data such as water quality parameters and sewage images are fully utilized, and the dosing precision is improved, so that the sewage treatment efficiency and the effluent quality stability are improved.
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