Sewage pipe network flow prediction algorithm based on rainfall and liquidometer data

By constructing a sewage pipe network flow prediction model based on rainfall and level gauge data, and using machine learning algorithms for accurate prediction and early warning, the problem of sewage pipe network flow prediction relying on human experience has been solved, improving the timeliness and effectiveness of responding to sudden flow events and reducing the risk of sewage overflow and urban flooding.

CN121787731APending Publication Date: 2026-04-03广东中拓华盛信息科技有限公司
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Patent Information

Application Number
CN202511988640.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-26
Publication Date
2026-04-03

AI Technical Summary

Technical Problem

In existing technologies, sewage pipe network flow prediction relies on human experience, which leads to lag and subjectivity, making it difficult to respond to sudden increases in flow in a timely and effective manner, and causing the risk of sewage overflow and urban flooding.

Method used

Based on rainfall and level gauge data, a sewage pipe network flow prediction model is constructed. Machine learning or deep learning algorithms are used to train and predict the model in combination with multi-dimensional data. The flow and level change curves are output in real time, and an early warning mechanism is activated when the predicted threshold is exceeded, generating suggestions for disposal measures.

Benefits of technology

It enables accurate prediction of sewage pipe network flow, reduces the lag and subjectivity of human experience, improves the timeliness and effectiveness of responding to sudden flow, and reduces the risk of sewage overflow and waterlogging.

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Abstract

The invention discloses a sewage pipe network flow prediction algorithm based on rainfall and liquidometer data, and belongs to the field of flow prediction.The sewage pipe network flow prediction algorithm based on rainfall and liquidometer data comprises the following steps that historical and real-time multi-dimensional data from multiple sources are continuously collected and integrated; performing cleaning, calibration and time alignment processing on the multi-dimensional data to obtain model training data based on historical data and model prediction input data based on real-time data; the method has the advantages that multi-dimensional data are collected and processed systematically, fuzzy manual observation is replaced, historical hydrological laws are converted into a quantifiable sewage pipe network flow prediction model, judgment depending on personal experience is replaced, accurate flow prediction in several hours in the future is carried out based on the model, and the flow prediction efficiency is improved. Passive emergency is converted into active early warning, and the hysteresis and subjective defects caused by dependence on artificial experience in the prior art are overcome fundamentally.
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