Water quality on-line monitoring method and system based on micro-flow sensor

CN122386702APending Publication Date: 2026-07-14SHANDONG LIZE ENVIRONMENTAL TECH SERVICE CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SHANDONG LIZE ENVIRONMENTAL TECH SERVICE CO LTD
Filing Date
2026-04-30
Publication Date
2026-07-14

AI Technical Summary

Technical Problem

In existing online water quality monitoring methods, the error of micro-flow sensors is difficult to control and their anti-interference ability is weak, resulting in insufficient monitoring accuracy and stability. In particular, in complex water samples with high turbidity and high salinity, sampling quantitative distortion and reagent ratio imbalance are prone to occur.

Method used

By acquiring water sample parameters and sensor characteristic data, the microfluidic operating parameters are extracted using the parameter inversion method, an error dynamic correction model is established, key error regions are identified, the sampling ratio parameters are adjusted using the zoning ratio optimization method, and a comprehensive control scheme is formulated to enhance the adaptive capability and monitoring accuracy.

Benefits of technology

It effectively eliminates baseline drift and signal distortion, significantly improves monitoring accuracy, broadens the scope of equipment application, enhances adaptability, achieves proactive error prevention, and improves the reliability and automation level of online water quality monitoring.

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Abstract

The present application belongs to the technical field of water quality monitoring, and specifically relates to a water quality online monitoring method and system based on a micro-flow sensor, which comprises the following steps: obtaining basic parameters of a target water sample, characteristic parameters of the micro-flow sensor, and sensing signals; extracting micro-flow path fluid operation parameters through a parameter inversion method, and setting micro-flow sampling ratio parameters in combination with the basic water sample and sensor working parameters; calculating the influence degree of sensor inherent errors and micro-flow path dynamic errors; establishing a micro-flow monitoring error dynamic correction model, generating a control strategy, and adjusting the sampling ratio to obtain optimized parameters; then using numerical iteration simulation to identify the key error area of the flow fluctuation threshold value; obtaining key area ratio parameters through a partition ratio optimization method, and formulating a water quality monitoring control scheme in combination with the optimized parameters; the present application can greatly improve the quantitative accuracy and operation stability of micro-flow water quality online monitoring, significantly reduce the relative error of water quality detection results, and improve the repeatability and reliability of monitoring data.
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