AU2022271759B2

AU2022271759B2 - Rock fragmentation analysis device and operation method of same

Info

Publication number: AU2022271759B2
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: 2024-04-04
Anticipated expiration: 2042-05-03
Also published as: CL2023000340A1; AU2022271759A1; WO2022240044A1; KR102618878B1; 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] Each document, reference, patent application or patent

cited in this text is expressly incorporated herein in their

entirety by reference, which means that it should be read and

considered by the reader as part of this text. That the document, reference, patent application or patent cited in this text is not repeated in this text is merely for reasons of conciseness.

[3] The following discussion of the background to the invention

is intended to facilitate an understanding of the present

invention only. It should be appreciated that the discussion is

not an acknowledgement or admission that any of the material

referred to was published, known or part of the common general

knowledge of the person skilled in the art in any jurisdiction

as at the priority date of the invention.

[4] 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.

[5] 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.

[6] 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 And Summary

[7] An embodiment of the present disclosure seeks 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.

[8] Another embodiment of the present disclosure seeks 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.

[9] A yet another embodiment of the present disclosure seeks

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.

[10] A still another embodiment of the present disclosure seeks

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.

[11] A still another embodiment of the present disclosure seeks

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.

[12] According to a first principal aspect, there is provided 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, wherein the point cloud data comprises at least one of 3D coordinate information and color information, 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, 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.

[13] Optionally, 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.

[14] Optionally, 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.

[15] Optionally, the particle size distribution curve is a Rosin

Rammler particle distribution curve representing cumulative

weight percent passing versus fragmentation particle size.

[16] Optionally, the rock fragmentation analysis further

comprises:

a data output unit configured to output and store particle

size analysis data and the particle size distribution curve

in respective preset data formats.

[17] According to a second principal aspect, there is provided

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, wherein the point cloud data comprises at least one of 3D coordinate information and color information, 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, 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.

[18] Optionally, 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.

[19] According to an aspect and 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.

[20] In the present disclosure, the point cloud data may include

at least one of 3D coordinate information and color information.

[21] 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.

[22] 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.

[23] 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.

[24] 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.

[25] 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.

[26] 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.

[27] 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.

[28] 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 of Embodiments

[29] The rock fragmentation analysis device and the operation

method of the same according to an embodiment of 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.

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

the operation method of the same, in embodiments, has another

effect that the individual blocks may be automatically extracted

from 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.

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

the operation method of the same, in embodiments, 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.

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

the operation method of the same, in embodiments, has a still

another effect that the reliability and accuracy of analysis

results are improved compared with those of 2D image analysis

method.

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

the operation method of the same, in embodiments, 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

[34] FIG. 1 is a view illustrating a rock fragmentation analysis

device according to an exemplary embodiment of the present

disclosure.

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

unit according to the exemplary embodiment of the present

disclosure.

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

to the exemplary embodiment of the present disclosure.

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

region setting unit according to the exemplary embodiment of

the present disclosure.

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

boundary extracting unit according to the exemplary embodiment

of the present disclosure.

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

block assigning unit according to the exemplary embodiment of

the present disclosure.

[40] FIG. 7 is a view illustrating a fragmentation particle size

analysis unit according to the exemplary embodiment of the

present disclosure.

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

volume calculation unit according to the exemplary embodiment

of the present disclosure.

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

analysis unit according to the exemplary embodiment of the

present disclosure.

[43] 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

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

[45] 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.

[46] 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.

[47] 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.

[48] 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.

[49] 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. Throughout the specification, unless the context requires otherwise, the word "comprise" or variations such as "comprises" or "comprising", will be understood to imply the inclusion of a stated integer or group of integers but not the exclusion of any other integer or group of integers.

Furthermore, throughout the specification, unless the context

requires otherwise, the word "include" or variations such as

"includes" or "including", will be understood to imply the

inclusion of a stated integer or group of integers but not the

exclusion of any other integer or group of integers.

[50] 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.FIG. 1 is a view

illustrating a rock fragmentation analysis device 10 according

to the exemplary embodiment of the present disclosure.

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

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.

[52] 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, in embodiments, 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.

[53] 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.

[54] 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.

[55] 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.

[56] 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.

[57] 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.

[58] 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.

[59] 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).

[60] 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.

[61] 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.

[62] 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.

[63] 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.

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

region setting unit 200 according to the exemplary embodiment

of the present disclosure.

[65] 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.

[66] 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.

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

boundary extracting unit 300 according to the exemplary

embodiment of the present disclosure.

[68] 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.

[69] 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.

[70] 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 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.

[71] 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.

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

individual block assigning unit 400 according to the exemplary

embodiment of the present disclosure.

[73] 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.

[74] 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.

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

size analysis unit 500 according to the exemplary embodiment of

the present disclosure.

[76] 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.

[77] 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.

[78] 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.

[79] [Equation 1]

[80] D=2 _ 3V/4,

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

volume.

[82] 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.

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

volume calculation unit 510 according to the exemplary

embodiment of the present disclosure.

[84] 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.

[85] 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.

[86] 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.

[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] 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 an embodiment of the present disclosure will

be described in detail below.

[92] 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.

[93] 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.

[94] 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.

[95] 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.

[96] 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.

[97] 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.

[98] Through the above described method, the rock fragmentation

analysis device and the operation method of the same according

to the present disclosure, in embodiments, 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.

[99] 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

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.

[100]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.

[101]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.

[102]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.

[103]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.

[104]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.

[105] 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.

[106]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).

[107]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.

[108]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.

[109]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.

[110]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.

[111] 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.

[112]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.

[113]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.

[114]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.

[115]Modifications and variations such as would be apparent to

a skilled addressee are deemed to be within the scope of the

present invention.

Claims

CLAIMS The claims defining the invention are as follows:

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,

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

coordinate information and color information,

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, 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.

2. The rock fragmentation analysis device of claim 1,

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.

3. The rock fragmentation analysis device of claim 2,

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.

4. The rock fragmentation analysis device of claim 3,

wherein the particle size distribution curve is a Rosin-Rammler

particle distribution curve representing cumulative weight

percent passing versus fragmentation particle size.

5. The rock fragmentation analysis device of claim 4,

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.

6. 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, wherein the point cloud data comprises at least one of 3D coordinate information and color information, 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, 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.

7. The operation method of claim 6, 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.