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
View PDF10 Cites 17 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

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

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • 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

Examples

Experimental program
Comparison scheme
Effect test

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...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

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...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
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
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap