Structured sparse coding based coal rock identification method

Abstract

The invention discloses a structured sparse coding based coal rock identification method. According to the method, spatial structure features of coal rocks are captured, so that the method has very good discrimination capability and robustness to imaging environment change. Therefore, the method has very high identification stability and identification correctness and can provide reliable coal rock identification information for production processes of automated mining, automated coal discharge, automated waste rock selection and the like.

Description

technical field [0001] The invention relates to a coal rock identification method based on structured sparse coding, which belongs to the field of coal rock identification. Background technique [0002] Coal and rock identification is to use a method to automatically identify coal and rock objects as coal or rock. In the process of coal production, coal rock identification technology can be widely used in the production links such as drum coal mining, tunneling, caving coal mining, and raw coal gangue selection. It is of great significance to improve the working environment and realize the safe and efficient production of coal mines. [0003] Many methods have been applied to coal rock identification, such as natural gamma ray detection, radar detection, stress picks, infrared detection, active power monitoring, vibration detection, sound detection, dust detection, memory cutting, etc., but these methods have the following Problems: ① It is necessary to install various sen...

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Problems solved by technology

[0003] Many methods have been applied to coal rock identification, such as natural gamma ray detection, radar detection, stress picks, infrared detection, active power monitoring, vibration detection, sound detection, dust detection, memory cutting, etc., but these methods have the following Problems: ① It is necessary to install various sensors on the existing equipment to obtain information, resulting in complex structure and high cost of the device

② Shearer drums, roadheaders and other equipment are subjected to complex forces, severe vibrations, severe wear, and large dust during the production process. It is difficult to deploy sensors, which easily leads to damage to mechanical components, sensors, and electrical circuits, and poor device reliability.

③ For different types of mechanical equipment, there is a big difference in the optimal type of sensor and the selection of signal pickup points, which requires personalized customization and poor adaptability of the system

[0004] In order to solve the above problems, more and more attention has been paid to image technology, and some coal and rock identification methods based on image technology have been developed. However, the existing methods all use artificially designed image features or a combination of image features for coal and rock identification. Manually designed features often cannot accurately capture the essential structure of coal and rock images, so they are not robust to image data changes caused by changes in imaging conditions, so there are still great shortcomings in recognition stability and recognition accuracy

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