Training method of neural network for intelligent prediction of caving state

A neural network and intelligent prediction technology, applied in neural learning methods, biological neural network models, instruments, etc., can solve problems such as damage to vertical boards and rakes, affecting the service life of vertical boards and rakes, and achieve enhanced training speed and The effect of precision

Pending Publication Date: 2021-05-07
南京多金网络科技有限公司
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Problems solved by technology

However, if the rocks and sand that have fallen on the vertical plate are not cleaned up in time and new rocks and sands fall, the weight of the existing ore on the vertical plate plus the weig

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  • Training method of neural network for intelligent prediction of caving state
  • Training method of neural network for intelligent prediction of caving state
  • Training method of neural network for intelligent prediction of caving state

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Embodiment Construction

[0057] Hereinafter, exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited by the example embodiments described herein.

[0058] Scenario overview

[0059] As mentioned above, when underground mining technology is used in mining, it is usually necessary to excavate ore tunnels, and rock, sand, etc. on the ore wall are often encountered in the ore tunnel. Usually, the combination of vertical plate and rake is adopted in the mine tunnel, and the caving rock, sand, etc. fall on the vertical plate and are transported by the rake. However, if the rock and sand that has fallen on the riser is not cleaned in time and new rock and sand falls, due to the weight of the ore already...

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Abstract

The invention relates to intelligent prediction in the field of intelligent mining, and particularly discloses a training method of a neural network for intelligent prediction of a caving state, which is used for training the neural network for intelligent prediction of the caving state based on a triple loss function. Specifically, in a training process, partial randomness is introduced into a high-dimensional feature space of a surface image of a current mine wall to obtain a second feature map, and a distance loss function value between the second feature map and down-sampling of the high-dimensional space of the current image is calculated; a cross entropy loss function value is calculated with the second feature map by combining the weight of rock and sandy soil stacked on the current vertical plate to acquired a classification loss function value from the second feature map through a classifier, and a neural network is trained through triple loss function values, so that a classification result can be predicted to a certain extent. Meanwhile, the training speed and precision of the model can be enhanced.

Description

technical field [0001] The invention relates to intelligent prediction in the field of intelligent mining, and more particularly, to a neural network training method for intelligent prediction of caving state, a deep neural network-based intelligent prediction method for caving state, and a method for caving state intelligent prediction. A neural network training system for intelligent prediction, an intelligent prediction system for caving state based on a deep neural network, and electronic equipment. Background technique [0002] Mining technology is divided into two methods: open-pit mining and underground mining. Open-pit mining is used for parts close to the surface and shallower buried, and underground mining is used for deep parts. When using underground mining technology, it is usually necessary to excavate the mine tunnel, and the rock, sand, etc. on the mine wall are often caving in the mine tunnel. [0003] Usually, the combination of vertical plate and rake is ...

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Application Information

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IPC IPC(8): G06K9/62G06N3/08
CPCG06N3/08G06F18/2415G06F18/214
Inventor 李权
Owner 南京多金网络科技有限公司
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