Coal and Rock Recognition Method Based on Similarity Measure Learning

Abstract

The invention discloses a coal-rock identification method based on similarity measurement learning. The method is directly oriented to the coal-rock identification task, and learns a measurement function capable of distinguishing the essential similarity of coal-rock from coal-rock data by supervising the correct rate of coal-rock identification. , has the ability to adapt to changes in coal and rock image data, so that the method has high recognition stability and recognition accuracy, and provides reliable coal and rock identification information for production processes such as automatic mining, automatic coal discharge, and automatic gangue selection.

Description

technical field [0001] The invention belongs to the field of coal rock identification methods, in particular to a coal rock identification method based on similarity measure learning. 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. The safe and efficient production of coal mines is of great significance. [0003] There are many coal rock identification methods, such as natural γ-ray detection method, radar detection method, stress pick method, infrared detection method, active power monitoring method, vibration detection method, sound detection method, dust detection method, memory cutting method etc., but these methods have the following problems: ① It is necessary to install vari...

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Problems solved by technology

[0003] There are many coal rock identification methods, such as natural γ-ray detection method, radar detection method, stress pick method, infrared detection method, active power monitoring method, vibration detection method, sound detection method, dust detection method, memory cutting method 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, image technology has been paid more and more attention and some coal and rock identification methods based on image technology have been developed. However, the existing methods need to manually select image features or a combination of image features, which often requires great efforts and attempts. , however, the obtained method is not always robust to changes in image data caused by changes in imaging conditions, resulting in a large deficiency in recognition stability and recognition accuracy

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