AU2022271759A1

AU2022271759A1 - Rock fragmentation analysis device and operation method of same

Info

Publication number: AU2022271759A1
Application number: AU2022271759A
Authority: AU
Inventors: Min Su Jeong; Geun Woo Jin; Seung Joong Lee
Original assignee: Hanwha Corp
Current assignee: Hanwha Corp
Priority date: 2021-05-11
Filing date: 2022-05-03
Publication date: 2023-02-16
Anticipated expiration: 2042-05-03
Also published as: CL2023000340A1; WO2022240044A1; KR102618878B1; AU2022271759B2; KR20220153298A

Abstract

A rock fragmentation analysis device according to an embodiment of the present invention comprises: a data loading unit that converts the format of point cloud data for fragmentation analysis according to a blast; a block region setting unit that generates a depth map by setting a block region, on the basis of the point cloud data; a block boundary extracting unit for extracting the block boundary of the block region on the basis of the point cloud data and the depth map; an individual block assigning unit that divides the point cloud data into a plurality of groups and designates same, according to the block boundary; and a fragment size analyzing unit for analyzing fragment sizes by calculating the volume of each of the plurality of groups, on the basis of the point cloud data.

Description

DESCRIPTION

Invention Title: ROCK FRAGMENTATION ANALYSIS DEVICE AND

OPERATION METHOD OF SAME

Technical Field

[1] An exemplary embodiment of the present disclosure relates

to a rock fragmentation analysis device and an operation

method of the same, wherein particle size distribution of a

pile of crushed rocks (i.e., a muck pile) generated after

blasting at a blasting site may be analyzed. More

particularly, an exemplary embodiment of the present

disclosure relates to a rock fragmentation analysis device and

an operation method of the same, wherein individual blocks may

be automatically extracted from three dimensional (3D) point

cloud data of a muck pile, the point cloud data being obtained

from image processing or a 3D scanner, and a particle size of

the entire muck pile may be analyzed by calculating volumes

and converted diameters of the individual blocks.

Background Art

[2] In conventional fragmentation analysis methods, a sieving

method for directly measuring a particle size, a two

dimensional analysis method for using a muck pile photograph

and an image processing method, and the like have been used.

[3] However, such a sieving method has many realistic

limitations related to equipment, manpower, cost, etc. during

on-site testing, and the two-dimensional image analysis method

has a problem that analysis results thereof provide low

reliability due to limitations of not being able to express a

three-dimensional effect.

[4] In particular, in conventional two-dimensional (2D)

fragmentation analysis devices, although various methods have

been applied to improve accuracy of extracting boundaries of

crushed rocks, there was a problem of low accuracy because an

area of a block should be calculated by using a reference

scale and the number of pixels of the block represented on a

2D image, and a representative particle size of the block

should be calculated by inversely calculating the area of the

block as a converted diameter of a circle.

Disclosure

Technical Problem

[5] An objective of the present disclosure is to provide a

rock fragmentation analysis device and an operation method of

the same, wherein particle size distribution of a pile of

crushed rocks (i.e., a muck pile) generated after blasting at

a blasting site may be analyzed.

[6] Another objective of the present disclosure is to provide

a rock fragmentation analysis device and an operation method of the same, wherein individual blocks may be automatically extracted from three dimensional (3D) point cloud data of a muck pile, the point cloud data being obtained from image processing or a 3D scanner, and a particle size of the entire muck pile may be analyzed by calculating volumes and converted diameters of the individual blocks.

[7] A yet another objective of the present disclosure is to

provide a rock fragmentation analysis device and an operation

method of the same, wherein input data may be usable for

fragmentation analysis as it is, without separate scale

conversion, by using real full-scale 3D point cloud data, and

a representative particle size of a block may be calculated

with volume calculation and a converted diameter of a sphere

for a three-dimensional shape of an individual crushed rock.

[8] A still another objective of the present disclosure is to

provide a rock fragmentation analysis device and an operation

method of the same, wherein reliability and accuracy of

analysis results are improved compared with those of 2D image

analysis method.

[9] A still another objective of the present disclosure is to

provide a rock fragmentation analysis device and an operation

method of the same, wherein a course of data processing is

simplified by automating processes from inputting point cloud

data to analyzing results.

Technical Solution

[10] According to the exemplary embodiment of the present

disclosure, a rock fragmentation analysis device includes: a

data loading unit configured to convert a format of point

cloud data in order to analyze fragmentation caused by

blasting; a block region setting unit configured to generate a

depth map by setting a block region on the basis of the point

cloud data; a block boundary extracting unit configured to

extract a block boundary of the block region on the basis of

the point cloud data and the depth map; an individual block

assigning unit configured to divide and specify the point

cloud data into a plurality of groups according to each block

boundary; and a fragmentation particle size analysis unit

configured to analyze a fragmentation particle size by

calculating a volume based on the point cloud data for each of

the plurality of groups.

[11] In the present disclosure, the point cloud data may

include at least one of 3D coordinate information and color

information.

[12] In the present disclosure, the block region setting unit

may correspond a coordinate value along a third axis to a

color on a reference plane defined by a first axis and a

second axis, extract a main shape of an individual block on

the basis of the corresponding color, and set the block region,

so as to generate the depth map.

[13] In the present disclosure, the block boundary extracting

unit may extract, as the block boundary, a closest boundary in

a range within a reference distance from a center point of the

block region.

[14] In the present disclosure, the individual block assigning

unit may specify the point cloud data included in an inner

region of the block boundary as a unit group and assign an

identification code to the unit group.

[15] In the present disclosure, the fragmentation particle

size analysis unit may include: a block volume calculation

unit configured to calculate a block volume for each of the

plurality of groups; a converted diameter calculation unit

configured to calculate a converted diameter on the basis of

the block volume; and a data analysis unit configured to

generate a particle size distribution curve on the basis of

each converted diameter.

[16] In the present disclosure, the particle size distribution

curve may be a Rosin-Rammler particle distribution curve

representing cumulative weight percent passing versus

fragmentation particle size.

[17] In the present disclosure, the rock fragmentation

analysis device may further include a data output unit

configured to output and store particle size analysis data and

the particle size distribution curve in respective preset data

formats.

[18] According to the exemplary embodiment of the present

disclosure, there is provided an operation method of a rock

fragmentation analysis device, the operation method including:

converting, by a data loading unit, a format of point cloud

data in order to analyze fragmentation caused by blasting;

generating, by a block region setting unit, a depth map by

setting a block region on the basis of the point cloud data;

extracting, by a block boundary extracting unit, a block

boundary for the block region on the basis of the point cloud

data and the depth map; dividing and specifying, by an

individual block assigning unit, the point cloud data into a

plurality of groups according to each block boundary;

analyzing, by a fragmentation particle size analysis unit, a

fragmentation particle size by calculating a volume based on

the point cloud data for each of the plurality of groups; and

outputting and storing the particle size analysis data and a

particle size distribution curve in respective preset data

formats.

[19] In the present disclosure, the analyzing of the

fragmentation particle size may include: calculating a block

volume for each of the plurality of groups; calculating a

converted diameter on the basis of the block volume; and

generating the particle size distribution curve on the basis

of each converted diameter.

Advantageous Effects

[20] The rock fragmentation analysis device and the operation

method of the same according to the present disclosure has an

effect that the particle size distribution of a pile of

crushed rocks (i.e., a muck pile) generated after blasting at

a blasting site may be analyzed.

[21] In addition, the rock fragmentation analysis device and

the operation method of the same has another effect that the

individual blocks may be automatically extracted from the

three dimensional (3D) point cloud data of a muck pile, the

point cloud data being obtained from the image processing or

the 3D scanner, and the particle size of the entire muck pile

may be analyzed by calculating the volumes and converted

diameters of the individual blocks.

[22] In addition, the rock fragmentation analysis device and

the operation method of the same has a yet another effect that

the input data may be usable for the fragmentation analysis as

it is, without the separate scale conversion, by using the

real full-scale 3D point cloud data, and the representative

particle size of a block may be calculated with the volume

calculation and the converted diameter of a sphere for the

three-dimensional shape of an individual crushed rock.

[23] In addition, the rock fragmentation analysis device and

the operation method of the same has a still another effect that the reliability and accuracy of analysis results are improved compared with those of 2D image analysis method.

[24] In addition, the rock fragmentation analysis device and

the operation method of the same has a still another effect

that the course of data processing is simplified by automating

processes from the inputting of point cloud data to the

analyzing of results.

Description of Drawings

[25] FIG. 1 is a view illustrating a rock fragmentation

analysis device according to an exemplary embodiment of the

present disclosure.

[26] FIG. 2 is a view illustrating an operation of a data

loading unit according to the exemplary embodiment of the

present disclosure.

[27] FIG. 3 is a view illustrating point cloud data according

to the exemplary embodiment of the present disclosure.

[28] FIG. 4 is a view illustrating an operation of a block

region setting unit according to the exemplary embodiment of

the present disclosure.

[29] FIG. 5 is a view illustrating an operation of a block

boundary extracting unit according to the exemplary embodiment

of the present disclosure.

[30] FIG. 6 is a view illustrating an operation of an

individual block assigning unit according to the exemplary

embodiment of the present disclosure.

[31] FIG. 7 is a view illustrating a fragmentation particle

size analysis unit according to the exemplary embodiment of

the present disclosure.

[32] FIG. 8 is a view illustrating an operation of a block

volume calculation unit according to the exemplary embodiment

of the present disclosure.

[33] FIG. 9 is a view illustrating an operation of a data

analysis unit according to the exemplary embodiment of the

present disclosure.

[34] FIG. 10 is a flowchart illustrating an operation of the

rock fragmentation analysis device according to the exemplary

embodiment of the present disclosure.

Best Mode

[35] The present disclosure will be described in more detail.

[36] Hereinafter, with reference to the accompanying drawings,

an exemplary embodiment of the present disclosure and other

subject matter required for those skilled in the art in order

to easily understand the content of the present disclosure

will be described in detail. However, since the present

disclosure may be implemented in many different forms within

the scope described in the claims, the exemplary embodiments described below are merely illustrative regardless of whether expressed or not.

[37] The same reference numerals indicate the same components.

In addition, in the drawings, the thickness, proportion, and

dimensions of the components are exaggerated for effective

description of the technical content. "And/or" includes all

combinations of one or more of which the associated

configurations may be defined.

[38] It will be understood that, although the terms first,

second, etc. may be used herein to describe various elements,

these elements should not be limited by these terms. These

terms are only used for the purpose of distinguishing one

component from another component. For example, the first

component may be referred to as a second component without

departing from the scope of the present disclosure, and

similarly, the second component may be referred to as a first

component. As used herein, the singular forms may include the

plural forms as well, unless the context clearly indicates

otherwise.

[39] In addition, the terms "below", "on a lower side",

"above", "on an upper side", etc. are used to describe the

association of the components shown in the drawings. The

terms are relative concepts and are explained based on the

directions indicated in the drawings.

[40] It will be further understood that the terms "comprise",

"include", "have", etc. when used in this specification,

specify the presence of stated features, integers, steps,

operations, elements, components, and/or combinations of them

but do not preclude the presence or addition of one or more

other features, integers, steps, operations, elements,

components, and/or combinations thereof.

[41] That is, the present disclosure is not limited to the

exemplary embodiment disclosed below and may be implemented in

various different forms. In the description below, an

expression such as "connected" is intended to include not only

"directly connected" but also "electrically connected" having

a different component in the middle therebetween. In addition,

it should be noted that the same reference numerals and

symbols refer to the same components in the drawings, even

when they are displayed on different drawings.

[42]

[43] FIG. 1 is a view illustrating a rock fragmentation

analysis device 10 according to the exemplary embodiment of

the present disclosure.

[44] Referring to FIG. 1, the rock fragmentation analysis

device 10 includes a data loading unit 100, a block region

setting unit 200, a block boundary extracting unit 300, an

individual block assigning unit 400, a fragmentation particle

size analysis unit 500, and a data output unit 600.

[45] The data loading unit 100 may convert a format of point

cloud data in order to analyze fragmentation caused by

blasting. In the present disclosure, the point cloud data is

characterized by including at least one of 3D coordinate

information and color information. The 3D coordinate

information may include coordinate values along a first axis,

a second axis, and a third axis for each point included in a

point cloud. In the present disclosure, the axes, which are

referred to as the first axis, the second axis, and the third

axis, may respectively correspond to an x-axis, a y-axis, and

a z-axis, which are orthogonal to each other. However, the

present disclosure is not limited thereto, and the first axis,

the second axis, and the third axis may correspond to

respective axes of various types of coordinate systems.

[46] The color information may include chromaticity values

according to a first color, a second color, and a third color.

In the present disclosure, the colors, which are referred to

as the first color, the second color, and the third color, may

correspond to respective colors of red, green, and blue of an

RGB model. However, the present disclosure is not limited

thereto, and the first color, second color, and third color

may correspond to respective elements of an HSV model that

uses hue, saturation, and value, or a YCbCr model that uses a

brightness component (Y), color difference (Cb), and color difference (Cr), or a CMYK color model that uses cyan, yellow, magenta, and black.

[47] According to the exemplary embodiment, the data loading

unit 100 may secure compatibility by integrating input formats

of point cloud data, so that all of 3D scan data measured by a

3D scanning device, image processing data, and the like are

able to be used.

[48] The block region setting unit 200 may generate a depth

map by setting a block region on the basis of point cloud data.

For example, the block region setting unit 200 may correspond

a coordinate value along a third axis to a color on a

reference plane defined by a first axis and a second axis,

extract a main shape of an individual block on the basis of

the corresponding color, and set a block region, thereby

generating the depth map. In this case, the depth map may

refer to a 2D image. According to the exemplary embodiment,

the block region setting unit 200 may generate the depth map

by using the Water-Shed algorithm to classify height

differences of height values into black and white levels.

[49] The block boundary extracting unit 300 may extract a

block boundary for a block region on the basis of the point

cloud data and the depth map. For example, the block boundary

extracting unit 300 may extract, as the block boundary, the

closest boundary in a range within a reference distance from a

center point of the block region. According to the exemplary embodiment, the block boundary extracting unit 300 may extract the block boundary by matching the depth map and the point cloud data. In this case, centered on the region that is set, the block boundary extracting unit 300 may extract the boundary of the block closest to the center point within the range of the reference distance (e.g., within 50 cm). Through this way, the block boundary extracting unit 300 according to the exemplary embodiment of the present disclosure may improve the accuracy of block division.

[50] The individual block assigning unit 400 may divide and

specify point cloud data into a plurality of groups according

to block boundaries. For example, the individual block

assigning unit 400 may specify point cloud data included in an

inner region of a block boundary as a unit group and assign an

identification code to the unit group. According to the

exemplary embodiment, the individual block assigning unit 400

may extract the point cloud data according to the block

boundary, specify the extracted point cloud data as the unit

group, and add the group identification code to the input data.

[51] The fragmented particle size analyzing unit 500 may

analyze a fragmented particle size by calculating a volume

based on the point group data for each of the plurality of

groups. Details of the fragmentation particle size analysis

unit 500 will be described in detail in FIG. 7.

[52] The data output unit 600 may output and store particle

size analysis data and a particle size distribution curve in

preset data formats. For example, the data output unit 600

may output the particle size analysis data in a table format

(i.e., a CSV file) and output the particle size distribution

curve in a picture format (i.e., a JPG file).

[53]

[54] FIG. 2 is a view illustrating an operation of the data

loading unit 100 according to the exemplary embodiment of the

present disclosure. FIG. 3 is a view illustrating the point

cloud data according to the exemplary embodiment of the

present disclosure.

[55] Referring to FIGS. 1 to 3, the data loading unit 100 may

convert the format of point cloud data in order to analyze

fragmentation according to blasting.

[56] For example, the data loading unit 100 may receive inputs

of the 3D image data of rocks after blasting shown in FIG. 2

and the point cloud data shown in FIG. 3.

[57] The data loading unit 100 may secure compatibility with

all types of bedrock data by extracting point cloud data on

the basis of 3D image data for rocks.

[58]

[59] FIG. 4 is a view illustrating an operation of the block

region setting unit 200 according to the exemplary embodiment

of the present disclosure.

[60] Referring to FIG. 4, the block region setting unit 200

may generate a depth map by setting a block region on the

basis of point cloud data. For example, the block region

setting unit 200 may correspond a coordinate value along a

third axis to a color on a reference plane defined by a first

axis and a second axis. In addition, the block region setting

unit 200 may extract a main shape of an individual block on

the basis of the corresponding color, and set the block region,

thereby generating the depth map. In this case, the depth map

may refer to a 2D image.

[61] As shown in FIG. 4, with respect to 3D point cloud data

generated by performing 3D scanning on a rock group fragmented

by blasting, the block region setting unit 200 may generate a

depth map in a 2D image format, which may have a pixel data

format.

[62]

[63] FIG. 5 is a view illustrating an operation of the block

boundary extracting unit 300 according to the exemplary

embodiment of the present disclosure.

[64] Referring to FIG. 5, the block boundary extracting unit

300 may extract a block boundary for a block region on the

basis of point cloud data and a depth map.

[65] For example, the block boundary extracting unit 300 may

extract, as a block boundary, the closest boundary in a range within a reference distance from a center point of the block region.

[66] According to the exemplary embodiment, the block boundary

extracting unit 300 may extract a block boundary by matching

the depth map and the point cloud data. In this case,

centered on the region that is set, the block boundary

extracting unit 300 may extract the boundary of a block

closest to the center point within the range of the reference

distance (e.g., within 50 cm) . Through this way, the block

boundary extracting unit 300 according to the exemplary

embodiment of the present disclosure may improve the accuracy

of the block division.

[67] As shown in FIG. 5, the block boundary extracting unit

300 may correspond colors to respective rocks fragmented by

blasting on the basis of the depth map and extract block

boundaries on the basis of the corresponding colors.

[68]

[69] FIG. 6 is a view illustrating an operation of the

individual block assigning unit 400 according to the exemplary

embodiment of the present disclosure.

[70] Referring to FIG. 6, the individual block assigning unit

400 may divide and specify point cloud data into a plurality

of groups according to block boundaries. For example, the

individual block assigning unit 400 may specify point cloud

data included in an inner region of a block boundary as a unit group and assign an identification code to the unit group.

According to the exemplary embodiment, the individual block

assigning unit 400 may extract the point cloud data according

to the block boundary, specify the extracted point cloud data

as the unit group, and add the group identification code to

the input data.

[71] As shown in FIG. 6, the individual block assigning unit

400 may extract and group the point cloud data for individual

blocks by matching block images divided on the basis of depth

map images with 3D point cloud data. Through this way, the

individual block assigning unit 400 may specify groups

corresponding to respective rocks.

[72]

[73] FIG. 7 is a view illustrating the fragmentation particle

size analysis unit 500 according to the exemplary embodiment

of the present disclosure.

[74] Referring to FIG. 7, the fragmentation particle size

analysis unit 500 may include a block volume calculation unit

510, a converted diameter calculation unit 520, and a data

analysis unit 530.

[75] The block volume calculation unit 510 may calculate a

block volume for each of a plurality of groups. For example,

the block volume calculation unit 510 may set a block lowest

point reference plane on the basis of the point cloud data of

the extracted individual blocks, use a base area and a height to determine a volume of a unit figure (e.g., a rectangular parallelepiped, a cylinder, a triangular prism, etc.), and perform the same process on the entire point cloud, thereby calculating a volume for the entire block.

[76] The converted diameter calculation unit 520 may calculate

a converted diameter on the basis of a block volume. For

example, the converted diameter calculation unit 520 may

calculate the converted diameter by assuming the block volume

as a volume of a sphere and inversely calculating the formula

for the volume of a sphere. In this case, the converted

diameter may be calculated through Equation 1 below.

[77] [Equation 1]

[78] D=2 3V/4,

[79] where, D denotes converted diameter and V denotes block

volume.

[80] The data analysis unit 530 may generate a particle size

distribution curve on the basis of each converted diameter.

For example, the data analysis unit 530 may generate a graph

showing cumulative particle size distribution according to

each converted diameter.

[81]

[82] FIG. 8 is a view illustrating an operation of the block

volume calculation unit 510 according to the exemplary

embodiment of the present disclosure.

[83] Referring to FIGS. 7 and 8, the block volume calculation

unit 510 may set a block lowest-point reference plane on the

basis of an extracted point cloud data of each individual

block. For example, the block volume calculation unit 510 may

set an arbitrary depth point (e.g., a lowest point) as a point

on a reference plane on the basis of the point cloud data.

Accordingly, a height value for the point cloud data in a

block is specified.

[84] The block volume calculation unit 510 may obtain a volume

of a unit figure (e.g., a rectangular parallelepiped, cylinder,

triangular prism, etc.) by using a base area and a height of

the reference plane.

[85] In addition, the block volume calculation unit 510 may

calculate the volume of the entire block by performing the

calculation for each unit figure on the entire point cloud

within the block.

[86]

[87] FIG. 9 is a view illustrating an operation of the data

analysis unit 530 according to the exemplary embodiment of the

present disclosure.

[88] Referring to FIG. 9, the data analysis unit 530 may

generate a particle size distribution curve on the basis of

each converted diameter.

[89] As shown in FIG. 9, the present disclosure is

characterized in that the particle size distribution curve is a Rosin-Rammler particle distribution curve representing cumulative weight percent passing versus fragmentation particle size.

[90] However, the present disclosure is not limited thereto,

and according to the exemplary embodiment, the data analysis

unit 530 may generate particle size distribution curves

through various types of distribution graphs.

[91]

[92] FIG. 10 is a flowchart illustrating an operation of the

rock fragmentation analysis device according to the exemplary

embodiment of the present disclosure. With reference to FIGS.

1 to 10, an operation method of a rock fragmentation analysis

device according to the present disclosure will be described

in detail below.

[93] In step S10, a data loading unit 100 may convert a format

of point cloud data in order to analyze fragmentation caused

by blasting. That is, the data loading unit 100 may convert

the format of real full-scale 3D point cloud data.

[94] In step S20, a block region setting unit 200 may generate

a depth map by setting a block region on the basis of the

point cloud data. That is, the block region setting unit 200

may set the block region for each rock on the basis of the

point cloud data without separate scale conversion. In

addition, the block region setting unit 200 may generate the

depth map for each point cloud.

[95] In step S30, a block boundary extracting unit 300 may

extract a block boundary for the block region on the basis of

the point cloud data and the depth map. That is, the block

boundary extracting unit 300 may set the block boundary by

clearly setting the boundary for the block, which is set by

the block region setting unit 200.

[96] In step S40, an individual block assigning unit 400 may

divide and specify the point cloud data into a plurality of

groups according to each block boundary. That is, the

individual block assigning unit 400 may divide the entire

region into the plurality of groups according to each block

boundary set by the block boundary extracting unit 300 and

assign an identification code for each group.

[97] In step S50, a fragmentation particle size analysis unit

500 may analyze a fragmentation particle size by calculating a

volume based on the point group data for each of the plurality

of groups. Specifically, a step of analyzing the

fragmentation particle size may include: calculating a block

volume for each of the plurality of groups; calculating a

converted diameter on the basis of the block volume; and

generating a particle size distribution curve on the basis of

each converted diameter. In this regard, details are

described in FIG. 7.

[98] In step S60, a data output unit 600 may output and store

particle size analysis data and a particle size distribution curve in respective preset data formats. That is, the data output unit 600 may output the particle size analysis data and the particle size distribution curve according to formats that may be used in the existing system in order to improve user convenience. In addition, the data output unit 600 may automatically store the output data in an external storage device or a database server.

[99] Through the above described method, the rock

fragmentation analysis device and the operation method of the

same according to the present disclosure has the effect that

the particle size distribution of a pile of crushed rocks

(i.e., a muck pile) generated after blasting at a blasting

site may be analyzed.

[100]In addition, the rock fragmentation analysis device and

the operation method of the same has another effect that the

individual blocks may be automatically extracted from the

three dimensional (3D) point cloud data of a muck pile, the

point cloud data being obtained from the image processing or

the 3D scanner, and the particle size of the entire muck pile

may be analyzed by calculating the volumes and converted

diameters of the individual blocks.

[101]In addition, the rock fragmentation analysis device and

the operation method of the same has a yet another effect that

the input data may be usable for the fragmentation analysis as it is, without the separate scale conversion, by using the real full-scale 3D point cloud data, and the representative particle size of a block may be calculated with the volume calculation and the converted diameter of a sphere for the three-dimensional shape of an individual crushed rock.

[102]In addition, the rock fragmentation analysis device and

the operation method of the same has a still another effect

that the reliability and accuracy of analysis results are

improved compared with those of 2D image analysis method.

[103]In addition, the rock fragmentation analysis device and

the operation method of the same has a still another effect

that the course of data processing is simplified by automating

processes from the inputting of point cloud data to the

analyzing of results.

[104]

[105]As described above, the functional operation and the

embodiments related to the present subject matter, which are

described in the present specification, may be implemented in

a digital electronic circuit or computer software, firmware,

hardware, or a combination of one or more thereof, including

the structures and structural equivalents thereof, which are

disclosed herein.

[106] The embodiments of the subject matter described herein

may be implemented as one or more computer program products,

i.e., one or more modules related to computer program instructions encoded on a tangible program medium for execution by or for controlling the operation of a data processing device. The tangible program medium may be a radio signal or a computer-readable medium. The radio signal is an artificially generated signal generated for encoding information to be transmitted to an appropriate reception device and executed by a computer, e.g., a machine generated electrical, optical, or electromagnetic signal. The computer readable medium may be a machine-readable storage device, a machine-readable storage substrate, a memory device, a combination of materials that affect a machine-readable radio signal, or a combination of one or more thereof.

[107] The computer program (also known as a program, software,

software application, script, or code) may be written in any

form of programming language, including a compiled or

interpreted language or an empirical or procedural language,

and may be deployed in any form including stand-alone programs

or modules, components, subroutines or other units suitable

for use in a computer environment.

[108]The computer program does not necessarily correspond to a

file in a file device. The program may be stored in a single

file provided to a requested program, or in multiple

interactive files (e.g., files that store one or more modules,

subprograms, or a piece of code), or in a part of a file that maintains other programs or data (e.g., one or more scripts stored within a markup language document).

[109]The computer program may be deployed to be executed on

one computer or multiple computers located at one site or

distributed over a plurality of sites and interconnected by a

communication network.

[110]Additionally, the logic flows and structural block

diagrams described in the present patent document are intended

to describe corresponding acts and/or specific methods

supported by corresponding functions and steps supported by

the disclosed structural means, and may also be used to

implement corresponding software structures and algorithms and

their equivalents.

[111]The processes and logic flows described herein may be

performed by one or more programmable processors executing one

or more computer programs in order to perform functions by

operating on input data and generating output.

[112] Processors suitable for the execution of the computer

programs include, for example, both general and special

purpose microprocessors and any one or more processors of any

form of digital computer. In general, a processor will

receive instructions and data from either read-only memory or

random access memory, or both.

[113] A key component of a computer is one or more memory

devices for storing instructions and data and a processor for executing the instructions. In addition, generally, the computer may include or be operably coupled with one or more mass storage devices for storing data and including disks such as magneto-optical disks or optical disks in order to receive or transfer data from or to the mass storage devices, or to perform such operations of both receiving and transferring the data. However, computers are not required to own such devices.

[114]The present description presents the best mode of the

present disclosure, and provides examples for describing the

present disclosure and for enabling those skilled in the art

to make and use the present disclosure. The specification

thus prepared does not limit the present disclosure to the

specific terms presented therein.

[115]As described above, the present disclosure has been

described with reference to the preferred exemplary

embodiments. However, those skilled in the art or those

having ordinary knowledge in the relevant technical field will

appreciate that various modifications and amendments are

possible, without departing from the scope and spirit of the

present disclosure as disclosed in the accompanying claims to

be described below.

[116]Therefore, the technical scope of the present disclosure

is not limited to the content described in the detailed

description of the specification, but should be determined by

the scope of the claims.

Claims

1. A rock fragmentation analysis device comprising:

a data loading unit configured to convert a format of

point cloud data in order to analyze fragmentation caused by

blasting;

a block region setting unit configured to generate a depth

map by setting a block region on the basis of the point cloud

data;

a block boundary extracting unit configured to extract a

block boundary of the block region on the basis of the point

cloud data and the depth map;

an individual block assigning unit configured to divide

and specify the point cloud data into a plurality of groups

according to each block boundary; and

a fragmentation particle size analysis unit configured to

analyze a fragmentation particle size by calculating a volume

based on the point cloud data for each of the plurality of

groups.

2. The rock fragmentation analysis device of claim 1,

wherein the point cloud data comprises at least one of 3D

coordinate information and color information.

3. The rock fragmentation analysis device of claim 2, wherein the block region setting unit corresponds a coordinate value along a third axis to a color on a reference plane defined by a first axis and a second axis, extracts a main shape of an individual block on the basis of the corresponding color, and sets the block region, so as to generate the depth map.

4. The rock fragmentation analysis device of claim 3,

wherein the block boundary extracting unit extracts, as the

block boundary, a closest boundary in a range within a

reference distance from a center point of the block region.

5. The rock fragmentation analysis device of claim 4,

wherein the individual block assigning unit specifies the point

cloud data included in an inner region of the block boundary as

a unit group and assign an identification code to the unit

group.

6. The rock fragmentation analysis device of claim 5,

wherein the fragmentation particle size analysis unit

comprises:

a block volume calculation unit configured to calculate a

block volume for each of the plurality of groups;

a converted diameter calculation unit configured to

calculate a converted diameter on the basis of the block volume; and a data analysis unit configured to generate a particle size distribution curve on the basis of each converted diameter.

7. The rock fragmentation analysis device of claim 6,

wherein the particle size distribution curve is a Rosin-Rammler

particle distribution curve representing cumulative weight

percent passing versus fragmentation particle size.

8. The rock fragmentation analysis device of claim 7,

further comprising:

a data output unit configured to output and store particle

size analysis data and the particle size distribution curve in

respective preset data formats.

9. An operation method of a rock fragmentation analysis

device, the operation method comprising:

converting, by a data loading unit, a format of point

cloud data in order to analyze fragmentation caused by

blasting;

generating, by a block region setting unit, a depth map by

setting a block region on the basis of the point cloud data;

extracting, by a block boundary extracting unit, a block

boundary for the block region on the basis of the point cloud data and the depth map; dividing and specifying, by an individual block assigning unit, the point cloud data into a plurality of groups according to each block boundary; analyzing, by a fragmentation particle size analysis unit, a fragmentation particle size by calculating a volume based on the point cloud data for each of the plurality of groups; and outputting and storing, by a data output unit, the particle size analysis data and a particle size distribution curve in respective preset data formats.

10. The operation method of claim 9, wherein the analyzing

of the fragmentation particle size comprises:

calculating a block volume for each of the plurality of

groups;

calculating a converted diameter on the basis of the block

volume; and

generating the particle size distribution curve on the

basis of each converted diameter.