A water quality anomaly early warning system and method based on multi-source data fusion

By using multi-source data fusion to identify water quality sensitive areas and dynamically adjust early warning thresholds, the problem of delayed early warning and misjudgment in traditional water quality monitoring methods under gradual abnormal fluctuations has been solved, enabling early identification and accurate early warning of water quality anomalies.

CN121388930BActive Publication Date: 2026-06-09SUZHOU HUIZHI INTELLIGENT TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
SUZHOU HUIZHI INTELLIGENT TECH CO LTD
Filing Date
2025-10-23
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

Traditional water quality monitoring and early warning methods are unable to capture and accurately predict changes in water quality under gradual abnormal fluctuations in a timely manner. Especially under the combined effect of multiple factors, early warnings are prone to lag or misjudgment.

Method used

By using multi-source data fusion, sensitive areas of gradual water quality anomalies are identified. By combining the cumulative load characteristics of pollution sources and the trend of water quality changes, the early warning threshold is dynamically adjusted to form a closed-loop early warning mechanism.

Benefits of technology

It enables early identification and accurate warning of gradual water quality anomalies, reduces the risk of false alarms and missed alarms, and improves the scientific validity and reliability of the warning system.

✦ Generated by Eureka AI based on patent content.

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Abstract

This application provides a water quality anomaly early warning system and method based on multi-source data fusion, relating to the field of anomaly early warning technology. It identifies sensitive areas within a target water area exhibiting gradual water quality anomalies by utilizing the spatiotemporal correlation characteristics of various multi-source water quality data. Sensitivity analysis is performed on the response intensity of the gradual water quality anomalies within these sensitive areas to obtain a water quality sensitivity index. The early warning response period for gradual water quality anomalies at monitoring points is determined. A confidence correction is applied to the water quality anomaly fluctuation early warning threshold at the monitoring points using the water quality sensitivity index and the early warning response period to obtain an anomaly early warning confidence level at the monitoring points. When the anomaly early warning confidence level exceeds a preset confidence threshold, a water quality gradual anomaly verification command is sent to the intelligent sensor at the monitoring point, and a risk alarm information for the gradual water quality anomaly is generated based on the feedback verification data. This application can achieve risk early warning under conditions of gradual abnormal fluctuations in water quality.
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