A method of evaluating swimming pool water quality using backpropagation neural network model

A neural network model and back-propagation technology, which is applied in the field of evaluating swimming pool water quality using back-propagation neural network model, can solve the problems of few and imperfect research on comprehensive water quality evaluation.

Inactive Publication Date: 2016-01-06
上海市长宁区卫生局卫生监督所
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Problems solved by technology

However, the water quality evaluation method currently used in swimming places is mainly the analysis of the pass rate of individual indicators, and there are few studies on the comprehensive evaluation of water quality, and the methods are mainly close value method, fuzzy mathematics method and pass rate method, etc., all of which are early methodological studies. There are many imperfections

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  • A method of evaluating swimming pool water quality using backpropagation neural network model
  • A method of evaluating swimming pool water quality using backpropagation neural network model
  • A method of evaluating swimming pool water quality using backpropagation neural network model

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Embodiment 1

[0044] A method for evaluating the water quality of a swimming pool using a backpropagation neural network model, comprising a. index screening, b. standard selection, c. demarcation grading and d. model fitting, characterized in that: the following steps are followed in order:

[0045] a. Index screening:

[0046] Select the following 7 indicators, and the units in the brackets are the indicators used: ①free residual chlorine (mg / L), ②cyanuric acid (mg / L), ③oxidation-reduction potential (mV), ④pH value, ⑤total alkali degree (mg / L), ⑥ calcium hardness (mg / L) and ⑦ urea (mg / L);

[0047] b. Standard selection: The standard limits of the seven indicators selected in step a from ① to ⑦ are: [0.2,1.0], ≤150, ≥650, [7.0,7.8], [60,200], [200,450] and ≤ 3.5;

[0048] c. Classification: The grading standards for each indicator are shown in Table 1:

[0049] Table 1:

[0050]

[0051]

[0052] d. Model Fitting:

[0053] d.1 Establishment of training samples: the data in the g...

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Abstract

The invention relates to the field of methods for testing or analyzing materials by determining the chemical or physical properties of the materials, and concretely relates to a method for evaluating the water quality of a swimming pool by using a back-propagation neural network model. The method for evaluating the water quality of the swimming pool by using the back-propagation neural network model comprises the steps of index screening, standard selection, delimitation grading and model fitting. The method can reduce the artificial evaluation workload and improves the accuracy and the objectivity of an evaluation result.

Description

technical field [0001] The invention relates to the field of methods for testing or analyzing materials by means of measuring chemical or physical properties of materials, in particular to a method for evaluating swimming pool water quality by applying a backpropagation neural network model. Background technique [0002] Water quality evaluation is the process of selecting corresponding water quality parameters, water quality standards and calculation methods according to the evaluation objectives, and evaluating the water use value and water treatment requirements. For a long time, researchers at home and abroad have conducted a lot of research on water quality evaluation methods and proposed many methods, such as single index evaluation method, comprehensive pollution index evaluation method, gray evaluation method, fuzzy evaluation method, matter-element analysis method, etc. However, due to different research purposes and different emphases of water quality evaluation, c...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01N33/18G06N3/02
Inventor 黄丽红
Owner 上海市长宁区卫生局卫生监督所
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