River channel outlet pollution intelligent monitoring and tracing system and method

By predicting and generating dynamic risk profiles of river outfalls and performing active sampling, combined with matching of enterprise pollution characteristic spectra, the problems of passive response and low efficiency of manual source tracing in river outfall pollution monitoring have been solved, achieving efficient and flexible source tracing and monitoring optimization.

CN122175358APending Publication Date: 2026-06-09CHINA CONSTR WATER ENVIRONMENTAL PROTECTION CO LTD +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
CHINA CONSTR WATER ENVIRONMENTAL PROTECTION CO LTD
Filing Date
2026-02-27
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

In existing river outfall pollution monitoring, passive monitoring results in pollution plumes escaping sampling, manual source tracing is inefficient, and static monitoring strategies are difficult to cope with changing illegal discharge behaviors.

Method used

By predicting and generating dynamic risk profiles, active sampling is performed during high-risk periods. Abnormal water sample characteristics are matched with pre-built enterprise pollution characteristic spectra to generate abnormal enterprise sequences. The prediction process and characteristic spectra are then optimized after on-site verification.

Benefits of technology

This has enabled a shift from passive response to proactive sampling, improving the speed and accuracy of source tracing, enhancing the ability to respond to unauthorized emissions, and optimizing the flexibility and self-improvement capabilities of monitoring strategies.

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Abstract

This invention belongs to the field of environmental monitoring technology, specifically relating to an intelligent monitoring and source tracing system and method for sewage discharge from river outlets. This invention generates dynamic risk profiles based on predictions and performs proactive sampling during predicted high-risk periods, achieving a shift from passive response to proactive sampling. The sampling time is dynamically adjustable, improving flexibility. By intelligently matching abnormal water sample characteristics with pre-constructed enterprise pollution characteristic spectra, it directly generates a sequence of abnormal enterprises, transforming inefficient manual source tracing into efficient data comparison. It prioritizes enterprises with a high probability of illegal discharge, improving the speed and accuracy of source tracing. Furthermore, by utilizing on-site verification conclusions, iterative optimization of the prediction process and enterprise pollution characteristic spectra is achieved, realizing the optimization of monitoring and source tracing based on practical feedback. This enhances the ability to continuously learn and improve from real enforcement feedback, facilitating responses to changing illegal discharge behaviors.
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