Tap water loss analysis method and system based on multi-source data

Through technical means such as multi-source data analysis and graph theory algorithm, the problem of difficulty in detecting abnormalities in the water distribution network in the existing technology is solved, and the rapid positioning and repair of pipeline leakage and other abnormal situations is achieved, and the management and safety of the water supply system are improved.

CN119691660AInactive Publication Date: 2025-03-25WUXI HUA YAN WATER
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Patent Information

Application Number
CN202510200924.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-02-24
Publication Date
2025-03-25
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

The prior art is difficult to detect abnormal situations in the water distribution network quickly and accurately, resulting in problems such as leakage of the pipeline network, damage to accessories and illegal water use, which are difficult to effectively monitor and locate.

Method used

The tap water loss analysis method based on multi-source data is adopted. By obtaining topological structure data, water pressure data, pipeline flow, user flow and daily freezing data, combined with graph theory algorithm, evidence theory and pre-trained ARIMA model, the water pressure ratio, flow ratio and theoretical leakage value are calculated, the abnormal ratio is analyzed and detected, and the abnormal level is divided to determine the abnormal pipe segment information and abnormal types.

Benefits of technology

It realizes rapid and accurate detection of abnormal pipe sections and abnormal types, improves the management level and service quality of water supply enterprises, reduces water resource waste and improves the safety of water supply systems.

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Abstract

The invention discloses a tap water loss analysis method and system based on multi-source data. The method comprises the steps that topological structure data, water pressure data, pipeline flow, user flow and daily freezing data of a target area are acquired; segmenting the target area through a graph theory algorithm to obtain a detection pipe section; calculating a water pressure ratio and a flow ratio according to the water pressure data and the pipeline flow; calculating a theoretical leakage value according to the leakage model; analyzing the water pressure data, the pipeline flow, the user flow and the theoretical leakage value through an evidence theory, and calculating a detection abnormal ratio; performing anomaly grade division according to the detection anomaly ratio to obtain a detection anomaly grade; and according to the water pressure data, the pipeline flow, the user flow, the daily freezing data, the detection abnormality level and the ARIMA model, analyzing the loss amount of each detection pipe section, and determining abnormal pipe section information and an abnormality type. According to the method, effective reference data is provided, so that the abnormal pipe section information and the abnormal type can be quickly found.
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