Intelligent detection method and system for key risk object sample in water source

By obtaining and analyzing water source sample detection data in real time, identifying target risk parameters and predicting water quality change trends, the problem of lag in water quality monitoring reactions in the existing technology is solved, and more accurate risk assessment and early warning is achieved.

CN119903966AActive Publication Date: 2025-04-29SHENZHEN SHENGRUN ENG CO LTD +1
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Patent Information

Application Number
CN202510081485.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-01-20
Publication Date
2025-04-29
Estimated Expiration
2045-01-20

AI Technical Summary

Technical Problem

Existing water source detection technology is difficult to capture subtle changes in water quality parameters in a timely manner, and it is easy to lead to false alarms or missed alarms under extreme environmental conditions.

Method used

By obtaining water source sample detection data in real time, we can judge whether abnormal conditions are met, analyze and identify target risk parameters and sample locations, and generate risk data and send them to the management end; under non-abnormal conditions, we can extract target characteristics, predict water quality change trends, judge whether early warning conditions are met, and generate water quality treatment strategies and early warning information.

Benefits of technology

Real-time monitoring of water quality status and prediction of future trends are achieved, potential water quality problems are identified in advance, reaction lags are avoided, and more accurate risk assessment and early warning information are provided.

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Abstract

The invention relates to the field of water source detection, in particular to a water source region key risk object sample intelligent detection method and system. Judging whether the sample detection data of the water source meets abnormal conditions or not; when the water source meets the abnormal condition, analyzing the sample detection data, identifying a target risk parameter and a sample position according to the sample detection data, generating risk data based on the target risk parameter and the sample position, and sending the risk data to the management end; when the water source does not meet the abnormal condition, extracting target features of the sample detection data; predicting the water quality change trend of the water source according to the target characteristics; judging whether the water quality meets an early warning condition or not according to a prediction result; and when the water quality meets an early warning condition, generating a water quality treatment strategy and early warning information based on a prediction result, and sending the strategies and information to a management end. Through multi-dimensional risk assessment, errors caused by limitation of the sensor are reduced.
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