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Water quality data deduction acquisition method and system based on machine learning

A technology of machine learning and acquisition methods, applied in chemical information database systems, machine learning, chemical machine learning and other directions, can solve problems such as risk factors, high comprehensive cost of acquiring water quality data, environmental pollution by water quality data, etc., to reduce the cost of acquisition. Effect

Active Publication Date: 2021-01-15
SHANDONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Due to the wide range of water areas, the acquisition of water quality data usually requires extensive distribution and periodic collection. At the same time, the acquisition of some water quality data also has environmental pollution and risk factors. Therefore, the comprehensive cost of obtaining water quality data has always been high.

Method used

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  • Water quality data deduction acquisition method and system based on machine learning
  • Water quality data deduction acquisition method and system based on machine learning

Examples

Experimental program
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Effect test

Embodiment 1

[0030] This embodiment provides a method for deriving and acquiring water quality data based on machine learning;

[0031] Such as figure 1 and figure 2 As shown, the water quality data deduction and acquisition method based on machine learning includes: model generation stage and data deduction stage;

[0032] In the model generation stage, machine learning methods are used to generate independent relevant feature sets and optimal deduction models of target water quality factors;

[0033] In the data deduction stage, the data sets of each water quality factor covered by the independent related feature set for a certain period of time are input into the optimal deduction model to calculate the target water quality factor data for the current period.

[0034] Further, the model generation phase includes the following steps:

[0035] S1-1: Determine the research water area E and the target water quality factor x, and obtain the water quality monitoring historical data set W ...

Embodiment 2

[0077] This embodiment provides a water quality data deduction and acquisition system based on machine learning;

[0078] A water quality data deduction and acquisition system based on machine learning, including: a model generation module and a data deduction module;

[0079] The model generation module is configured to: use a machine learning method to generate an independent correlation feature set and an optimal derivation model of the target water quality factor;

[0080] The data deduction module is configured to: input the data set of each water quality factor covered by the independent related feature set into the optimal deduction model for a certain period, and calculate the target water quality of the current period.

[0081] It should be noted here that the above-mentioned model generation module and data derivation module correspond to the model generation stage and data deduction stage in Embodiment 1, and the examples and application scenarios implemented by the...

Embodiment 3

[0085] This embodiment also provides an electronic device, including: one or more processors, one or more memory, and one or more computer programs; wherein, the processor is connected to the memory, and the above one or more computer programs and The data is stored in the memory, and when the electronic device is running, the processor executes one or more computer programs stored in the memory, so that the electronic device executes the method described in Embodiment 1 above.

[0086] It should be understood that in this embodiment, the processor can be a central processing unit CPU, and the processor can also be other general-purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate array FPGA or other programmable logic devices , discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional pro...

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Abstract

The invention discloses a water quality data deduction obtaining method and system based on machine learning. The method is divided into a model generation stage and a model deduction stage. In the model generation stage, historical data of various characteristics acquired by all monitoring stations of a researched water area is analyzed and processed to obtain an independent related characteristic set of a target water quality factor and a corresponding historical data set, and based on the data set, an optimal deduction model of the target water quality factor is obtained through a machine learning method; and in the model deduction stage, data of each characteristic factor covered by the independent related characteristic set in a certain period of time is collected, the data is input into the optimal deduction model, and data of the target water quality factor in the period of time is reckoned. The water quality data acquisition method is a novel water quality data acquisition method without traditional data acquisition modes such as chemical reagent and sensor detection, can reduce environmental pollution and hidden danger caused by the traditional water quality data acquisition modes, and has the advantages of low cost, safety, environmental protection, high technical added value and the like.

Description

technical field [0001] This application relates to the field of water quality monitoring data acquisition and data science and technology, in particular to a method and system for deriving and acquiring water quality data based on machine learning. Background technique [0002] The statements in this section merely mention the background art related to this application, and do not necessarily constitute the prior art. [0003] At present, water quality data, such as biological, chemical, hydrological and other characteristic data of seas, rivers, lakes and other waters, are mainly collected by sampling analysis and sensor detection. Sampling analysis, such as chemical reagents, optical methods, ion methods, etc., requires the input of instruments, reagents and manpower, and the cost of purchasing, deploying and maintaining water quality sensors is also high. Due to the wide range of water areas, the acquisition of water quality data usually requires extensive distribution a...

Claims

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Application Information

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IPC IPC(8): G16C20/70G16C20/90G01N33/18G06N20/00
CPCG16C20/70G16C20/90G01N33/18G06N20/00
Inventor 程杰
Owner SHANDONG UNIV
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