A river water ecosystem health evaluation method, system, medium and product

By using a pre-defined indicator scoring neural network model and weight calculation, combined with subjective and objective weights, the problem of reliance on human experience in the health assessment of river aquatic ecosystems has been solved, resulting in a more accurate health assessment that adapts to the hydrological characteristics and ecological needs of different rivers.

CN120873873BActive Publication Date: 2025-12-09山西水资源研究所有限公司
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Patent Information

Application Number
CN202511385338.8
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-09-26
Publication Date
2025-12-09
Estimated Expiration
2045-09-26

AI Technical Summary

Technical Problem

In the current technology for assessing the health of river aquatic ecosystems, the selection of indicators and determination of weights rely too heavily on human experience, which leads to the assessment results deviating from the actual health status. Furthermore, it does not fully consider the hydrological characteristics and ecological needs of different rivers, resulting in low accuracy.

Method used

A pre-defined indicator scoring neural network model is adopted, which combines subjective and objective weight calculations. By identifying the characteristics of the criteria indicators and assigning them with exclusive scoring strategies, and combining scientific pre-defined indicator weights with criterion layer weights, a comprehensive score for river health is determined.

Benefits of technology

It improves the accuracy of river aquatic ecosystem health assessment, avoids interference from single factors, ensures the integrity and accuracy of the assessment perspective, and adapts to the hydrological characteristics and ecological needs of different rivers.

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Abstract

The application relates to the technical field of data analysis, in particular to a river water ecosystem health evaluation method and system, medium and product, which comprises the following steps: acquiring a plurality of criterion indexes and criterion index data corresponding to each criterion layer of a to-be-evaluated river; obtaining an index score value corresponding to each criterion index based on a preset index scoring neural network model and the criterion index data; acquiring preset index weights and preset criterion layer weights corresponding to the to-be-evaluated river; determining a river health comprehensive score corresponding to the to-be-evaluated river based on the preset index weights, the preset criterion layer weights and the index score values corresponding to the to-be-evaluated river; and determining the health condition grade of the to-be-evaluated river based on the river health comprehensive score. The application facilitates improving the accuracy of health evaluation of the river water ecosystem.
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