Detection method, system, medium and device based on multi-spectral data fusion

Patent metadata

Field	Value
Publication number	US-12450316-B1
Application number	US-202519241593-A
Country	US
Kind code	B1
Filing date	Jun 18, 2025
Priority date	May 31, 2024
Publication date	Oct 21, 2025
Grant date	Oct 21, 2025

Abstract

A method, a system, a medium and a device based on multi-spectral data fusion are provided. The method includes: collecting spectrum: collecting NIR spectrum and MIR spectrum of a substance to be detected to obtain a NIR spectrum matrix and a MIR spectrum matrix of the substance to be detected; constructing a detection model; training the detection model; and predicting detection indexes.

First claim

What is claimed is:

1. A detection method based on multi-spectral data fusion, comprising:
collecting spectrum: collecting near infrared spectrum and mid infrared spectrum of a substance to be detected to obtain a near infrared spectrum matrix and a mid infrared spectrum matrix of the substance to be detected;
constructing a detection model: constructing the detection model by a following formula,
Y=XP T BQ

wherein X is an input matrix, Y is an output matrix, B is a coefficient matrix, and P and Q are load matrices of X and Y respectively; and T is a score matrix of an independent variable X;
training the detection model; and
predicting detection indexes;
wherein steps of training the detection model comprise:
collecting training samples to construct training sets, wherein the training sets comprise near infrared spectra and mid infrared spectra of a plurality of the training samples and index values of detection indexes of the plurality of the training samples;
dividing the training sets into a calibration set and a verification set to obtain a near infrared spectrum matrix, a mid infrared spectrum matrix and an index matrix of the calibration set and a near infrared spectrum matrix, a mid infrared spectrum matrix and an index matrix of the verification set;
performing spectrum preprocessing on near infrared spectra and mid infrared spectra of the calibration set and the verification set respectively to obtain a near infrared spectrum preprocessing matrix and a mid infrared spectrum preprocessing matrix of the calibration set, and a near infrared spectrum preprocessing matrix and a mid infrared spectrum preprocessing matrix of the verification set, wherein the spectrum preprocessing comprises derivative processing or/and vector normalization processing, and the derivative processing comprises first-order derivative processing or/and higher-order derivative processing;
performing spectrum variable screening processing on the near infrared spectra, the mid infrared spectra, near infrared spectra after the spectrum preprocessing and mid infrared spectra after the spectrum preprocessing of the calibration set and the verification set respectively to obtain a near infrared spectrum variable screening matrix, a mid infrared spectrum variable screening matrix, a preprocessing near infrared spectrum variable screening matrix and a preprocessing mid infrared spectrum variable screening matrix of the calibration set, and a near infrared spectrum variable screening matrix, a mid infrared spectrum variable screening matrix, a preprocessing near infrared spectrum variable screening matrix and a preprocessing mid infrared spectrum variable screening matrix of the verification set, wherein the spectrum variable screening processing comprises competitive adaptive reweighted sampling processing or/and variable importance projection processing;
inputting the near infrared spectrum matrix and the index matrix of the calibration set into the detection model, and training the detection model to obtain a first coefficient matrix;
inputting the mid infrared spectrum matrix and the index matrix of the calibration set into the detection model, and training the detection model to obtain a second coefficient matrix;
inputting the near infrared spectrum preprocessing matrix and the index matrix of the calibration set into the detection model, and training the detection model to obtain a third coefficient matrix;
inputting the mid infrared spectrum preprocessing matrix and the index matrix of the calibration set into the detection model, and training the detection model to obtain a fourth coefficient matrix;
inputting the near infrared spectrum matrix of the calibration set into the detection model corresponding to the first coefficient matrix to obtain a calibration set first index prediction matrix composed of predicted values of detection indexes of the calibration set;
inputting the mid infrared spectrum matrix of the calibration set into the detection model corresponding to the second coefficient matrix to obtain a calibration set second index prediction matrix composed of the predicted values of the detection indexes of the calibration set;
inputting the near infrared spectrum preprocessing matrix of the calibration set into the detection model corresponding to the third coefficient matrix to obtain a calibration set third index prediction matrix composed of the predicted values of the detection indexes of the calibration set;
inputting the mid infrared spectrum preprocessing matrix of the calibration set into the detection model corresponding to the fourth coefficient matrix to obtain a calibration set fourth index prediction matrix composed of the predicted values of the detection indexes of the calibration set;
inputting the near infrared spectrum matrix of the verification set into the detection model corresponding to the first coefficient matrix to obtain a verification set first index prediction matrix composed of predicted values of detection indexes of the verification set;
inputting the mid infrared spectrum matrix of the verification set into the detection model corresponding to the second coefficient matrix to obtain a verification set second index prediction matrix composed of the predicted values of the detection indexes of the verification set;
inputting the near infrared spectrum preprocessing matrix of the verification set into the detection model corresponding to the third coefficient matrix to obtain a verification set third index prediction matrix composed of the predicted values of the detection indexes of the verification set;
inputting the mid infrared spectrum preprocessing matrix of the verification set into the detection model corresponding to the fourth coefficient matrix to obtain a verification set fourth index prediction matrix composed of the predicted values of the detection indexes of the verification set;
obtaining a first verification value, a second verification value, a third verification value and a fourth verification value respectively according to verification indexes through the verification set first index prediction matrix, the verification set second index prediction matrix, the verification set third index prediction matrix, the verification set fourth index prediction matrix and the index matrix, wherein the verification indexes are used for representing prediction performance of the detection model;
taking an optimal value of the first verification value, the second verification value, the third verification value and the fourth verification value as an optimal verification value, taking the optimal verification value as a threshold value, and taking a range of the threshold value in a direction of improving the prediction performance of the detection model as a threshold value range;
performing data-level fusion on the near infrared spectrum matrix and the mid infrared spectrum matrix of the calibration set to form a calibration set first data-level fusion matrix, and inputting the calibration set first data-level fusion matrix and the index matrix of the calibration set into the detection model to train the detection model to obtain a fifth coefficient matrix;
performing the data-level fusion on the near infrared spectrum preprocessing matrix and the mid infrared spectrum preprocessing matrix of the calibration set to form a calibration set second data-level fusion matrix, and inputting the calibration set second data-level fusion matrix and the index matrix of the calibration set into the detection model to train the detection model to obtain a sixth coefficient matrix;
taking the calibration set first data-level fusion matrix as the input matrix, inputting the detection model corresponding to the fifth coefficient matrix, and obtaining a calibration set fifth index prediction matrix composed of the predicted values of the detection indexes

Assignees

• Nat Inst Metrology China

Inventors

• Kan Ying
• Li Ke
• Zhang Zhengdong
• Song Dan
• Zhang Xin
• Ding Chaomin
• Liu Fan
• Li Qi

Classifications

• G06N20/00

  Machine learning · CPC title

• G06F18/251 Primary

  of input or preprocessed data · CPC title