Recycled water wetland water quality risk factor probability screening method

By establishing a screening model and performing probability calculations, the problems of large amount of information and strong randomness in the water quality monitoring data are solved, and efficient screening and probability evaluation of water quality risk factors are achieved.

CN119646058AActive Publication Date: 2025-03-18JILIN INST OF WATER RESOURCES SCI
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Patent Information

Application Number
CN202411697587.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-11-26
Publication Date
2025-03-18
Estimated Expiration
2044-11-26

AI Technical Summary

Technical Problem

Due to the huge amount of information, large amount of information repetition and strong randomness of water quality monitoring data, it is difficult to effectively screen the impact of various random factors on water quality risk factors.

Method used

By establishing a screening model, calculating the matrix of the monitoring sample data, performing Bartret's normal distribution variance homogeneity test, and serially compute the parameters of the empirical normal distribution function, using these parameters for probability calculation, and screening the probability of water quality risk factors.

Benefits of technology

The probability evaluation of the impact of various random factors on water quality risk factors is achieved using existing monitoring data, which improves the efficiency and accuracy of water quality risk factor screening.

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Abstract

The invention relates to a reclaimed water wetland water quality risk factor probability screening method, and belongs to the technical field of water ecological water quality screening. Establishing a screening model calculation file, carrying out Butterfly normal distribution variance homogeneity test, carrying out sequence calculation on parameters of an empirical normal distribution function, carrying out probability calculation by adopting a sequence for calculating the empirical normal distribution function, and screening the probability of the water quality risk factor. The method has the advantages that aiming at the characteristics of large information amount, large information repetition amount, strong randomness and fixed monitoring data position of water quality monitoring data, the occurrence probability and probability and possible occurrence time of various screened water quality indexes are clarified by classifying and analyzing the characteristics of the information and comparing common characteristics. The algorithm model can assist related departments in effectively utilizing monitoring data to master possible occurrence conditions of wetland water quality risk factors, determining possible probabilities and possible occurrence time of various screened water quality indexes, and evaluating possible water quality risks.
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