Water quality trend prediction system based on big data

By constructing the concentration sequence offset module and flow rate ratio analysis, the problem of insufficient dynamic modeling of water quality trend prediction in the prior art is solved, and efficient prediction of water quality trends and accurate identification of potential risks are achieved.

CN120470334AActive Publication Date: 2025-08-12HENAN WATER-CONSERVANCY EXPLORATING & SURVEYING CO LTD
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Patent Information

Application Number
CN202510554349.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-29
Publication Date
2025-08-12
Estimated Expiration
2045-04-29

AI Technical Summary

Technical Problem

The existing water quality trend prediction technology is difficult to model continuity dynamically during concentration propagation, lacks the correlation of the difference value of the concentration sequence within the time span, and it is difficult to reflect the response structure of water quality trends to hydrodynamic factors such as flow velocity in complex environments, resulting in prediction delays, misjudgment or missing key trend stages, reducing regulation efficiency and early warning reliability.

Method used

By constructing the concentration sequence offset module, a continuous time series of pollutant concentrations in each section in the water transport path is obtained, an error sliding window is established and the sliding difference is accumulated, the time window with the error level below the threshold is selected, the concentration conduction stability period is identified, and the concentration decreasing rate ratio is calculated in combination with the flow rate sensor to generate a concentration decay ratio change sequence, and a downstream predicted concentration trend curve is constructed.

Benefits of technology

It has improved the ability to identify the dynamic conduction path of pollution signals, enhanced the multi-stage prediction capability and scheduling strategy support effect for complex water quality fluctuations, and improved the early warning accuracy of potential risk signals.

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Abstract

The invention relates to the technical field of water quality analysis, in particular to a water quality trend prediction system based on big data, which comprises a concentration sequence offset module, a matching similarity extraction module, a lagging interval derivation module, a ratio trend construction module and a trend data output module. According to the method, by constructing a difference sliding mechanism between concentration sequences, time sequence offset feature extraction, pollution signal conduction path identification precision improvement, error sliding window and step frequency aggregation, offset interval and conduction window linkage, concentration change influence range determination and concentration decline rate and flow rate ratio combination are realized; the method comprises the following steps: constructing coupling expression of concentration evolution and hydrodynamic conditions, revealing structural characteristics of concentration attenuation, extracting a trend continuous section by specific value direction change, providing a structural basis for trend change identification, linking a trend increasing section with concentration value extension, forming a multi-condition screening prediction machine, and realizing multi-dimensional association, trend evolution and response time delay integration. And the water quality trend identification accuracy and prediction perspectiveness are improved.
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