Hyperspectral-analysis-based copper quality detection method and system

Abstract

The invention discloses a hyperspectral-analysis-based copper quality detection method, which comprises the following steps of: (1) constructing a characteristic image cube of a copper material to be detected; (2) extracting contents and spectral information of ingredients of the copper material to be detected from the characteristic image cube; and (3) evaluating the quality of the copper material to be detected. According to the method disclosed by the invention, the concept of the characteristic image cube is introduced based on the traditional hyperspectral image processing technology, meanwhile, the image characteristics and the spectral characteristics of the copper material to be detected are acquired, and the copper material is subjected to characteristic extraction and quality detection by using a fused PSO-nsNMF (Particle Swarm Optimization-based non-smooth Nonnegative Matrix Factorization) algorithm, therefore, the accuracy in the detection of copper quality is ensured. The invention further discloses a hyperspectral-analysis-based copper quality detection system which comprises a hyperspectral imaging unit and an image processing unit; the hyperspectral-analysis-based copper quality detection system disclosed by the invention is used for carrying out real-time online acquisition and data processing on an image of a copper sample by utilizing a computer and combining with a hyperspectral imaging instrument, therefore, the timeliness in the detection of copper quality is ensured.

Description

technical field [0001] The invention belongs to the technical field of detection, and in particular relates to a copper quality detection method based on hyperspectral analysis and a detection system thereof. Background technique [0002] The copper processing process needs to be able to quickly and accurately detect the quality of copper materials. Online detection of copper quality is very important for timely and efficient subsequent copper processing. [0003] Under the existing technology, the copper quality detection generally adopts the detection method of off-line sampling inspection and sending to the laboratory for laboratory testing. Determine the quality of copper by measuring the content of the substance after the reaction; instrumental analysis, that is, use the atomic emission spectrometer to detect the atomic emission spectrum of the sample, and judge the copper quality index according to the corresponding detection standards; although the detection accuracy ...

AI Technical Summary

Problems solved by technology

[0003] Under the existing technology, the copper quality detection generally adopts the detection method of off-line sampling inspection and sending to the laboratory for laboratory testing. Determine the quality of copper by measuring the content of the substance after the reaction; instrumental analysis, that is, use the atomic emission spectrometer to detect the atomic emission spectrum of the sample, and judge the copper quality index according to the corresponding detection standards; although the detection accuracy of these two laboratory detection methods is high, However, the detection time is long and cannot meet the needs of online real-time control

[0004] At present, in many copper processing enterprises, operators often visually observe the color and other appearance characteristics of copper materials to estimate the copper quality. Although the real-time detection is satisfied to a certain extent, the accuracy of the estimation and judgment depends on the workers' operating experience and Fatigue state; therefore, the general detection accuracy is not high, and the reliability of the detection results is low

[0005] Therefore, domestic copper processing enterprises are currently unable to achieve accurate online real-time measurement of copper quality. Most of the accurate copper quality is obtained through offline laboratory analysis, but the time lag is several hours; and the field relies on the operator's experience estimation The quality of the copper produced, most of the data are not high in accuracy and low in reliability

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