Training method of neural network for intelligent prediction of caving state

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 ...

AI Technical Summary

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 weight of the fallen rocks and sand may cause the vertical plate and the rake to grab damage to

And if the cleaning is too frequent, it may affect the service life of the vertical board and the rake

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