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Blasting lumpiness prediction method and device based on random GA-BP neural network group, and medium

A BP neural network, GA-BP technology, applied in the field of blasting block degree prediction based on random GA-BP neural network group, can solve problems such as large error of blasting block degree and insufficient reliability

Pending Publication Date: 2020-06-09
NANHUA UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The present invention provides a method, device and medium for predicting blasting fragmentation based on random GA-BP neural network group to solve the problems in the prior art that the error of predicting blasting fragmentation based on BP neural network is too large and the reliability is insufficient

Method used

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  • Blasting lumpiness prediction method and device based on random GA-BP neural network group, and medium
  • Blasting lumpiness prediction method and device based on random GA-BP neural network group, and medium
  • Blasting lumpiness prediction method and device based on random GA-BP neural network group, and medium

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0062] Such as figure 1 As shown, this embodiment provides a method for predicting blasting fragmentation based on random GA-BP neural network group, including:

[0063] Step S01: Obtain the blasting parameters of the area to be blasted;

[0064] Step S02: extracting blasting characteristic data based on the blasting parameters;

[0065] Step S03: According to the blasting characteristic data and the preset random GA-BP neural network group blasting fragmentation prediction model, predict the average blasting fragmentation of the area to be blasted; wherein, the preset random GA-BP neural network group The blasting fragmentation prediction model is obtained after training a random GA-BP neural network group based on the historical blasting data of the blasted area.

[0066] Through the acquired blasting parameters of the area to be blasted and the trained random GA-BP neural network group blasting fragmentation prediction model, the average blasting fragmentation after blasting in the...

Embodiment 2

[0092] This embodiment provides a blasting fragmentation prediction device based on random GA-BP neural network group, including:

[0093] The first data acquisition module is used to acquire the blasting parameters of the area to be blasted;

[0094] The first data extraction module is configured to extract and obtain blasting characteristic data based on the blasting parameters;

[0095] The blasting average fragmentation prediction module is used to predict the blasting average fragmentation of the area to be blasted based on the blasting characteristic data and a preset random GA-BP neural network group blasting fragmentation prediction model; wherein the preset randomness The GA-BP neural network group blasting fragmentation prediction model is obtained after training the random GA-BP neural network group through the historical blasting data of the blasted area.

[0096] In this embodiment, it also includes:

[0097] The second data acquisition module is used to acquire the blasti...

Embodiment 3

[0112] This embodiment provides a computer-readable storage medium, the storage medium stores program instructions, and the program instructions are suitable for a processor to load and execute the blasting based on the random GA-BP neural network group as described in Embodiment 1. Lumpiness prediction method.

[0113] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, this application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, this application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

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Abstract

The invention discloses a blasting lumpiness prediction method and device based on a random GA-BP neural network group, and a medium. The method comprises the steps of acquiring blasting parameters ofa to-be-blasted area; blasting characteristic data are extracted based on the blasting parameters; according to the blasting characteristic data and a preset random GA-BP neural network group blasting lumpiness prediction model, predicting the blasting average lumpiness of the to-be-blasted area; wherein the preset random GA-BP neural network group blasting lumpiness prediction model is obtainedby training a random GA-BP neural network group through historical blasting data of a blasted area; predicting the blasting average lumpiness of the area to be blasted after blasting, and reasonably adjusting and designing blasting parameters to ensure that the blasting effect after blasting meets the working condition requirements; according to the method, the blasting average lumpiness is predicted by using the random GA-BP neural network group blasting lumpiness prediction model, so that the precision and reliability of the blasting average lumpiness prediction value are effectively improved.

Description

Technical field [0001] The invention relates to the technical field of geotechnical engineering, in particular to a method, device and medium for predicting blasting fragmentation based on a random GA-BP neural network group. Background technique [0002] The size of the rock fragmentation is an important indicator for evaluating the effect of blasting. It affects the subsequent shoveling, transportation and other processes, and is closely related to the benefit of the entire related production process, and different blasting operations have different target rock fragments after blasting. Therefore, many experts and scholars at home and abroad have made many efforts to study the prediction model of rock blasting fragmentation. [0003] The Kuz-Ram model is the first step blasting fragmentation prediction equation. It is an equation used to calculate the average fragment size and uniformity index n of the step blasting circle whose screening curve follows the RR function. In additi...

Claims

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Application Information

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IPC IPC(8): G06F30/27G06N3/04G06N3/08
CPCG06N3/084G06N3/086G06N3/045
Inventor 郭钦鹏杨仕教刘迎九相志斌陈然吴彪
Owner NANHUA UNIV
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