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Method for analyzing and predicating content of heavy metals in soil based on LIBS and stacked RBM deep learning technology

A deep learning and prediction method technology, applied in the field of soil analysis, can solve the problems of slow detection of soil heavy metal content, etc., and achieve the effect of improving generalization ability, high accuracy and fast analysis speed

Inactive Publication Date: 2017-08-15
HEFEI INSTITUTES OF PHYSICAL SCIENCE - CHINESE ACAD OF SCI
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Problems solved by technology

[0004] The purpose of the present invention is to solve the defect of slow detection speed of soil heavy metal content in the prior art, and provide a method for analyzing and predicting soil heavy metal content based on LIBS and stacked RBM deep learning technology to solve the above problems

Method used

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  • Method for analyzing and predicating content of heavy metals in soil based on LIBS and stacked RBM deep learning technology
  • Method for analyzing and predicating content of heavy metals in soil based on LIBS and stacked RBM deep learning technology
  • Method for analyzing and predicating content of heavy metals in soil based on LIBS and stacked RBM deep learning technology

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Embodiment Construction

[0044] In order to have a further understanding and understanding of the structural features of the present invention and the achieved effects, the preferred embodiments and accompanying drawings are used for a detailed description, as follows:

[0045] Such as figure 1 As shown, a method for analyzing and predicting soil heavy metal content based on LIBS and stacked RBM deep learning technology of the present invention comprises the following steps:

[0046] The first step is the acquisition and pretreatment of soil samples. Obtain soil samples and divide them into training samples and test samples, and the training samples and test samples are randomly divided according to actual needs. Use a spectrometer to obtain the laser-induced breakdown spectrum data of the training sample and the test sample, which can be processed as follows according to the prior art:

[0047] (1) The collected soil was air-dried, ground and sieved, and pressed into soil flakes with a powder table...

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Abstract

The invention relates to a method for analyzing and predicating the content of heavy metals in soil based on LIBS and a stacked RBM deep learning technology. The defect of slow detection speed of the content of the heavy metals in soil in the prior art is overcome in the invention. The method comprises the following steps: obtaining and preprocessing a soil sample; constructing a prediction model based on the stacked restricted Boltzmann machine deep learning technology; carrying out unsupervised training on the prediction model; carrying out supervised training on the predication model; and analyzing and predicating the content of the heavy metals in the soil. The content of the heavy metals in the soil is analyzed and predicated by using the mapping relationship between a laser induced breakdown spectrum and the content of the heavy metals in the soil and combining with the stacked RBM deep learning technology.

Description

technical field [0001] The present invention relates to the technical field of soil analysis, in particular to a method for analyzing and predicting soil heavy metal content based on LIBS and stacked RBM deep learning technology. Background technique [0002] Human industrial and agricultural production activities easily bring a large amount of heavy metal elements into the soil, causing soil heavy metal pollution, which in turn affects the quality of cultivated land and seriously endangers human health. At present, the detection methods of heavy metals in soil mainly include flame atomic absorption spectrophotometry, inductively coupled plasma emission spectrometry, etc., but the required instruments are relatively complex, and soil samples need to be digested and other pre-treatments, so it is impossible to achieve rapid detection of heavy metals in soil. Realizing the rapid detection of heavy metals in soil is of great significance to agricultural production and cultivate...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N21/71G06N3/08G06Q10/04G06Q50/02
CPCG01N21/71G06N3/08G06Q10/04G06Q50/02
Inventor 陈天娇王儒敬谢成军张洁李瑞陈红波
Owner HEFEI INSTITUTES OF PHYSICAL SCIENCE - CHINESE ACAD OF SCI
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