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Coal ash content on-line detection system based on deep learning and detection method thereof

A deep learning and detection system technology, applied in neural learning methods, instruments, biological neural network models, etc., to achieve the effects of low operating costs, improved generalizability, and improved model accuracy

Active Publication Date: 2021-07-30
CHINA UNIV OF MINING & TECH (BEIJING)
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

However, there is still no application of this method in the field of coal ash measurement technology

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  • Coal ash content on-line detection system based on deep learning and detection method thereof
  • Coal ash content on-line detection system based on deep learning and detection method thereof
  • Coal ash content on-line detection system based on deep learning and detection method thereof

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Embodiment Construction

[0035] In order to make the purpose, technical solutions and points of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. The examples should not be construed as limiting the invention.

[0036] like figure 1 As shown, the main equipment of the supporting image acquisition device for online detection of coal ash content includes: light source 1, glass plate 2, test bench 3, camera height coarse quasi-focus spiral 4, computer 5, camera height fine quasi-focus spiral 6, industrial camera 7, Sample container 8, lens fixing button 9, microscope lens 10 with adjustable magnification, stage height adjuster 11. Two of the light sources are irradiated on the glass plate from left and right at 45 degrees, and the sample is evenly spread in...

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Abstract

The invention discloses a coal ash content on-line detection system based on deep learning and a detection method thereof, the system comprises a classification model and a regression model, the models are deployed to an embedded device after being trained, and distributed deployment of a coal ash content on-line detection function is realized; and real-time detection of the ash content is completed by a matched hardware image acquisition system. The on-line detection method comprises the following steps: acquiring a coal microscopic image and corresponding ash content through an image acquisition device, establishing a coal microscopic image database, constructing a feature extraction network based on a deep learning method to perform automatic feature extraction on the coal ash content image, designing a classification model and a regression model to complete a final decision, and obtaining an accurate coal ash content prediction result. Compared with other coal ash content detection methods, the method has the advantages of high detection precision, high speed and the like.

Description

technical field [0001] The invention relates to the technical field of coal quality detection in mineral processing, in particular to an online coal ash detection system based on deep learning and a detection method thereof. [0002] technical background [0003] my country has abundant coal reserves, and the rapid economic development has driven the energy consumption of all walks of life. In order to ensure the quality of coal products, it is of great significance to carry out online rapid detection of coal quality. In the process of coal preparation, the conventional coal quality measurement method requires manual sampling and sample preparation, many interference factors, long test cycle, lagging test results, and poor real-time performance. At the end of the 20th century, China began to introduce coal quality online detection equipment. Coal quality online detection technology includes dual-energy γ-ray projection method, natural ray method, neutron activation method, X-...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06N3/045G06F18/25G06F18/24G06F18/214
Inventor 王卫东张康辉吕子奇孙美洁涂亚楠徐志强
Owner CHINA UNIV OF MINING & TECH (BEIJING)
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