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A blasting fragmentation prediction method based on cart tree regression algorithm

A regression algorithm and prediction method technology, applied in the field of engineering blasting, can solve problems such as affecting mine production cost and efficiency, affecting shovel production process, and increasing blasting cost, so as to prevent blasting fragmentation accidents, save blasting costs, reduce The effect of chunk rate

Inactive Publication Date: 2019-01-04
NORTH BLASTING TECH
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Problems solved by technology

The block size and composition of ore after blasting are not only the main information reflecting blasting design and operation level, but also affect subsequent production processes such as shovel loading, crushing, transportation, root removal, mineral processing, etc., thus affecting mine production costs and efficiency
If the blasting ore is large in size, secondary blasting is required; if the blasting ore is small in size, the cost of blasting will increase

Method used

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  • A blasting fragmentation prediction method based on cart tree regression algorithm
  • A blasting fragmentation prediction method based on cart tree regression algorithm
  • A blasting fragmentation prediction method based on cart tree regression algorithm

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Embodiment Construction

[0015] The present invention will be further described below in conjunction with specific embodiments. The exemplary embodiments and descriptions of the present invention are used to explain the present invention, but not as a limitation to the present invention.

[0016] Such as figure 1 , figure 2 As shown, a kind of blasting fragmentation prediction method based on the cart tree regression algorithm of the present embodiment comprises the following steps:

[0017] Step 1. Use the existing historical rock blockiness related parameters as sample attributes (see Table 1) to construct the CART decision tree model, specifically:

[0018] The decision tree algorithm model adopts the form of binary tree, and uses binary recursion to continuously divide the data space into different subsets. Similarly, each leaf node has classification rules associated with it, corresponding to different data set divisions;

[0019] In order to reduce the depth of the CART decision tree, when m...

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Abstract

The invention discloses a blasting fragmentation prediction method based on a cart tree regression algorithm. A CART decision tree model is constructed and trained by a CLS algorithm using the existing historical rock fragmentation related parameters as sample attributes. New rock fragmentation related parameters are collected and forecasted by the trained CART decision tree model. In accordance with that prior art, the invention firstly constructs a CART decision tree model and trains the CART decision tree model according to the related parameters of the historical rock fragmentation, then,the trained CART decision tree model can be used for predicting the blasting fragmentation. The invention prevents the occurrence of the blasting fragmentation accident from the root cause, reduces the large fragmentation rate generated in the blasting process, reduces the secondary fragmentation, improves the production efficiency and saves the blasting cost.

Description

technical field [0001] The invention relates to the technical field of engineering blasting, in particular to a blasting fragmentation prediction method based on a cart tree regression algorithm. Background technique [0002] Factors affecting the distribution of ore blasting fragmentation include: explosive parameters, blasting parameters, rock mass structure and its mechanical characteristics and other factors. The block size and composition of ore after blasting are not only the main information reflecting blasting design and operation level, but also affect subsequent production processes such as shovel loading, crushing, transportation, root removal, mineral processing, etc., thus affecting mine production costs and efficiency. If the blasting ore lumpiness is large, secondary blasting is required, and if the blasting ore lumpiness is small, the blasting cost will increase. Contents of the invention [0003] The purpose of the present invention is to provide a metho...

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Application Information

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IPC IPC(8): G06Q10/04G06F17/18
CPCG06F17/18G06Q10/04
Inventor 李泽华李顺波佟彦军王清华杨鹏飞赵强黄其冲张昭杨宁
Owner NORTH BLASTING TECH
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