KR102618878B1 - Rock fragmentation analysis device and operation method of the same - Google Patents

Abstract

A rock fracture degree analysis device according to an embodiment of the present invention includes a data load unit for converting the format of point cloud data to analyze the degree of fracture due to blasting; a block area setting unit for generating a depth map by setting a block area based on the point cloud data; a block boundary extraction unit for extracting a block boundary for the block area based on the point cloud data and the depth map; an individual block allocation unit for dividing and assigning the point cloud data into a plurality of groups according to the block boundary; and a crushed particle size analysis unit for analyzing the crushed particle size by calculating a volume for each of the plurality of groups based on the point cloud data.

Description

Rock fractility analysis device and method of operation thereof {ROCK FRAGMENTATION ANALYSIS DEVICE AND OPERATION METHOD OF THE SAME}

An embodiment of the present invention is a rock crushability analysis device capable of analyzing the particle size distribution of a muck pile generated after a blasting operation at a blast site and a method of operating the same, especially crushed rock obtained from image processing or a 3D scanner. A rock crushability analysis device that automatically extracts individual blocks from the 3D point cloud data of the muck pile and analyzes the particle size of the entire crushed stone pile by calculating the volume and converted diameter. and a method of operation thereof.

Conventional methods for analyzing the degree of fracture have been used, such as a sieving method that directly measures particle size, and a two-dimensional analysis method using photographs of the crushed stone and image processing methods.

However, this sieving method has many practical limitations such as equipment, manpower, and cost during field testing, and the two-dimensional image analysis method has the problem of low reliability of analysis results due to the limitation of not being able to express three-dimensional effect.

In particular, existing 2D fracture analysis devices have applied various methods to improve the accuracy of fracture boundary extraction, but the number of pixels and reference scale of the block expressed in the 2D image are used to determine the block size. There was a problem of low accuracy because the representative particle size of the block had to be calculated by calculating the area of and inverting it to the converted diameter of the circle.

US registered patent US 7020307 ‘Rock fragmentation analysis system’ (published on March 28, 2006) US published patent US 2020-0089823 ‘3D BLOCK MODELLING OF A RESOURCE BOUNDARY IN A POST-BLAST MUCKPILE TO OPTIMIZE DESTINATION DELINEATION’ (published March 19, 2020)

The purpose of the present invention is to provide a rock crushability analysis device and a method of operating the same that can analyze the particle size distribution of a muck pile generated after blasting at a blast site.

Another object of the present invention is to automatically extract individual blocks from 3D point cloud data of a muck pile obtained from image processing or a 3D scanner, and calculate the volume and converted diameter for this. The object is to provide a rock crushability analysis device that can analyze the particle size of an entire crushed rock pile and a method of operating the same.

Another purpose of the present invention is to use full-scale 3D point cloud data, so that the input data can be used for fracture degree analysis without separate scale conversion, and volume calculation and sphere conversion for the 3D shape of individual crushed stones. The aim is to provide a rock fracture analysis device that can calculate the representative particle size of a block based on its diameter and a method of operating the same.

Another object of the present invention is to provide a rock fracture analysis device and a method of operating the same with improved reliability and accuracy of analysis results compared to the two-dimensional image analysis method.

Another purpose of the present invention is to provide a rock fracture analysis device and a method of operating the same, which simplifies the data processing process by automating everything from point cloud data input to result analysis.

A rock fracture degree analysis device according to an embodiment of the present invention includes a data load unit for converting the format of point cloud data to analyze the degree of fracture due to blasting; a block area setting unit for generating a depth map by setting a block area based on the point cloud data; a block boundary extraction unit for extracting a block boundary for the block area based on the point cloud data and the depth map; an individual block allocation unit for dividing and assigning the point cloud data into a plurality of groups according to the block boundary; and a crushed particle size analysis unit for analyzing the crushed particle size by calculating a volume for each of the plurality of groups based on the point cloud data.

In the present invention, the point cloud data includes at least one of three-dimensional coordinate information and color information.

In the present invention, the block area setting unit corresponds coordinate values along the third axis to colors on a reference plane defined by the first axis and the second axis, extracts the main shape of the individual block based on the corresponding color, and The depth map is generated by setting a block area.

In the present invention, the block boundary extraction unit extracts the closest boundary within a standard distance from the center point of the block area as the block boundary.

In the present invention, the individual block allocation unit is characterized in that it designates point cloud data included in the inner area of the block boundary as a unit group and assigns a recognition code.

In the present invention, the crushed particle size analysis unit includes a block volume calculation unit for calculating a block volume for each of the plurality of groups; a conversion diameter calculation unit for calculating a conversion diameter based on the block volume; and a data analysis unit for generating a particle size distribution curve based on the converted diameter.

In the present invention, the particle size distribution curve is characterized as a Rosin-Rammler particle distribution curve representing the cumulative weight passage rate with respect to the crushed particle size.

In the present invention, the apparatus further includes a data output unit for outputting and storing particle size analysis data and the particle size distribution curve in a preset data format.

A method of operating a rock fracture degree analysis device according to an embodiment of the present invention includes the steps of: a data load unit converting the format of point cloud data to analyze the degree of fracture according to blasting; A block area setting unit generating a depth map by setting a block area based on the point cloud data; A block boundary extraction unit extracting a block boundary for the block area based on the point cloud data and the depth map; An individual block allocator dividing the point cloud data into a plurality of groups according to the block boundary and designating them; A crushed particle size analysis unit analyzing the crushed 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 the particle size distribution curve in a preset data format.

In the present invention, analyzing the crushed particle size includes calculating a block volume for each of the plurality of groups; calculating a converted diameter based on the block volume; and generating a particle size distribution curve based on the converted diameter.

The rock fracture analysis device and its operating method of the present invention are effective in analyzing the particle size distribution of a muck pile generated after blasting work at a blast site.

In addition, the rock fracture analysis device and its operating method of the present invention automatically extract individual blocks from the 3D point cloud data of the muck pile obtained from image processing or a 3D scanner, This has the effect of analyzing the particle size of the entire crushed stone pile by calculating the volume and converted diameter.

In addition, the rock fracture degree analysis device and its operating method of the present invention use full-scale 3D point cloud data, allowing the input data to be used as is for fracture degree analysis without separate scale conversion, and the 3D analysis of individual crushed stones. It has the effect of being able to calculate the representative particle size of a block by calculating the volume of the shape and the converted diameter of the sphere.

In addition, the rock fracture analysis device and its operating method of the present invention have the effect of improving the reliability and accuracy of the analysis results compared to the two-dimensional image analysis method.

In addition, the rock fracture analysis device and its operating method of the present invention have the effect of simplifying the data processing process by automating everything from point cloud data input to result analysis.

1 is a diagram showing a rock fracture analysis device according to an embodiment of the present invention.
Figure 2 is a diagram showing the operation of a data load unit according to an embodiment of the present invention.
Figure 3 is a diagram showing point cloud data according to an embodiment of the present invention.
Figure 4 is a diagram showing the operation of a block area setting unit according to an embodiment of the present invention.
Figure 5 is a diagram showing the operation of a block boundary extraction unit according to an embodiment of the present invention.
Figure 6 is a diagram showing the operation of an individual block allocation unit according to an embodiment of the present invention.
Figure 7 is a diagram showing a crushed particle size analysis unit according to an embodiment of the present invention.
Figure 8 is a diagram showing the operation of the block volume calculation unit according to an embodiment of the present invention.
Figure 9 is a diagram showing the operation of the data analysis unit according to an embodiment of the present invention.
Figure 10 is a flow diagram showing the operation of the rock fracture analysis device according to an embodiment of the present invention.

The present invention will be described in more detail.

Hereinafter, with reference to the attached drawings, embodiments of the present invention and other matters necessary for those skilled in the art to easily understand the contents of the present invention will be described in detail. However, since the present invention can be implemented in various different forms within the scope set forth in the claims, the embodiments described below are merely illustrative, regardless of whether they are expressed or not.

Like reference numerals refer to like elements. Additionally, in the drawings, the thickness, proportions, and dimensions of components are exaggerated for effective explanation of technical content. “And/or” may include any one or more combinations that the associated configurations may define.

Terms such as first, second, etc. may be used to describe various components, but the components should not be limited by the terms. The above terms are used only for the purpose of distinguishing one component from another. For example, a first component may be named a second component, and similarly, the second component may also be named a first component without departing from the scope of the present invention. Singular expressions may include plural expressions, unless the context clearly indicates otherwise.

Additionally, terms such as “below,” “on the lower side,” “above,” and “on the upper side” are used to describe the relationship between the components shown in the drawings. The above terms are relative concepts and are explained based on the direction indicated in the drawings.

Terms such as “include” or “have” are intended to designate the presence of features, numbers, steps, operations, components, parts, or combinations thereof described in the specification, but do not include one or more other features, numbers, or steps. , it should be understood that it does not exclude in advance the possibility of the existence or addition of operations, components, parts, or combinations thereof.

In other words, the present invention is not limited to the embodiments disclosed below, but can be implemented in various different forms. In the description below, when a part is connected to another part, it is directly connected. In addition, it may also include cases where they are electrically connected with another element in between. In addition, it should be noted that the same components in the drawings are indicated with the same reference numbers and symbols as much as possible, even if they are shown in different drawings.

Figure 1 is a diagram showing a rock fracture analysis device 10 according to an embodiment of the present invention.

Referring to FIG. 1, the rock crushability analysis device 10 includes a data load unit 100, a block area setting unit 200, a block boundary extraction unit 300, an individual block allocation unit 400, and a crush particle size analysis unit. It includes a unit 500 and a data output unit 600.

The data load unit 100 may convert the format of the point cloud data to analyze the degree of fragmentation caused by blasting. In the present invention, point cloud data is characterized in that it includes at least one of three-dimensional coordinate information and color information. The 3D coordinate information may include coordinate values along the first, second, and third axes for each point included in the point cloud. In the present invention, each axis, referred to as the first axis, the second axis, and the third axis, may correspond to the x-axis, y-axis, and z-axis, respectively, which are orthogonal to each other. However, the present invention is not limited to this, and the first, second, and third axes may correspond to axes of various coordinate systems.

Color information may include chromaticity values according to the first color, second color, and third color. Each color, referred to as the first color, second color, and third color in the present invention, may correspond to each color of the RGB model of red, green, and blue. However, the present invention is not limited to this, and the first color, second color, and third color are an HSV model using Hue, Saturation, and Value, brightness component (Y), and color difference. It can correspond to the YCbCr model using information (Cb, Cr), and the CMYK color model using cyan, yellow, magenta, and black.

Depending on the embodiment, the data load unit 100 may secure compatibility by integrating input point cloud data formats so that both 3D scan data and image processing data measured by a 3D scanning device can be used.

The block area setting unit 200 may generate a depth map by setting a block area based on point cloud data. For example, the block area setting unit 200 corresponds coordinate values along the third axis to colors on a reference plane defined by the first axis and the second axis, and extracts the main shape of the individual block based on the corresponding color. By setting the block area, you can create a depth map. At this time, the depth map may refer to a two-dimensional image. Depending on the embodiment, the block area setting unit 200 may use the Water-Shed algorithm to generate a depth map by dividing the difference in height values into black and white levels.

The block boundary extraction unit 300 may extract a block boundary for a block area based on point cloud data and a depth map. For example, the block boundary extractor 300 may extract the closest boundary within a standard distance from the center point of the block area as the block boundary. Depending on the embodiment, the block boundary extractor 300 may extract a block boundary by matching depth map and point cloud data. At this time, the block boundary extractor 300 may extract the boundary of the block closest to the center within a standard distance (eg, within 50 cm) around the set area. Through this, the block boundary extraction unit 300 according to an embodiment of the present invention can improve the accuracy of block division.

The individual block allocator 400 may divide the point cloud data into a plurality of groups according to block boundaries. For example, the individual block allocator 400 may designate the point cloud data included in the inner area of the block boundary as a unit group and assign a recognition code. Depending on the embodiment, the individual block allocation unit 400 may extract point cloud data according to the block boundary, designate it as a unit group, and add a group recognition code to the input data.

The crushed particle size analysis unit 500 may analyze the crushed particle size by calculating the volume based on point cloud data for each of the plurality of groups. Details about the crushed particle size analysis unit 500 are described in detail in FIG. 7.

The data output unit 600 can output and store particle size analysis data and particle size distribution curves in a preset data format. For example, the data output unit 600 may output particle size analysis data in table format (CSV file) and particle size analysis curve in graphic format (JPG file).

Figure 2 is a diagram showing the operation of the data load unit 100 according to an embodiment of the present invention. Figure 3 is a diagram showing point cloud data according to an embodiment of the present invention.

Referring to FIGS. 1 to 3 , the data load unit 100 may convert the format of point cloud data to analyze the degree of fracture due to blasting.

For example, the data load unit 100 may receive 3D image data for the rock after blasting shown in FIG. 2 and point cloud data shown in FIG. 3 .

The data load unit 100 extracts point cloud data based on 3D image data for rocks, thereby ensuring compatibility with all types of rock data.

Figure 4 is a diagram showing the operation of the block area setting unit 200 according to an embodiment of the present invention.

Referring to FIG. 4, the block area setting unit 200 may generate a depth map by setting a block area based on point cloud data. For example, the block area setting unit 200 may correspond to a color with a coordinate value along the third axis on a reference plane defined by the first and second axes. Additionally, the block area setting unit 200 may generate a depth map by extracting the main shape of each individual block based on the corresponding color and setting the block area. At this time, the depth map may refer to a two-dimensional image.

As shown in FIG. 4, for 3D point cloud data generated by performing 3D scanning on a group of rocks crushed by blasting, the block area setting unit 200 generates a depth map in a 2D image format. It can have a pixel data format.

Figure 5 is a diagram showing the operation of the block boundary extraction unit 300 according to an embodiment of the present invention.

Referring to FIG. 5, the block boundary extraction unit 300 may extract a block boundary for a block area based on point cloud data and a depth map.

For example, the block boundary extractor 300 may extract the closest boundary within a standard distance from the center point of the block area as the block boundary.

Depending on the embodiment, the block boundary extractor 300 may extract a block boundary by matching depth map and point cloud data. At this time, the block boundary extractor 300 may extract the boundary of the block closest to the center within a standard distance (eg, within 50 cm) around the set area. Through this, the block boundary extraction unit 300 according to an embodiment of the present invention can improve the accuracy of block division.

As shown in FIG. 5, colors can be matched to each rock shattered by blasting based on the depth map, and block boundaries can be extracted based on the matched colors.

Figure 6 is a diagram showing the operation of the individual block allocation unit 400 according to an embodiment of the present invention.

Referring to FIG. 6, the individual block allocation unit 400 may divide the point cloud data into a plurality of groups according to block boundaries and designate them. For example, the individual block allocator 400 may designate the point cloud data included in the inner area of the block boundary as a unit group and assign a recognition code. Depending on the embodiment, the individual block allocation unit 400 may extract point cloud data according to the block boundary, designate it as a unit group, and add a group recognition code to the input data.

As shown in FIG. 6, the individual block allocator 400 can extract and group point cloud data for individual blocks by matching block images classified based on the depth map image and 3D point cloud data. Through this, the individual block allocator 400 can specify groups corresponding to each rock.

Figure 7 is a diagram showing the crushed particle size analysis unit 500 according to an embodiment of the present invention.

Referring to FIG. 7 , the crushed 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.

The block volume calculation unit 510 may calculate the block volume for each of the plurality of groups. For example, the block volume calculation unit 510 sets the reference surface of the lowest point of the block based on the point cloud data of the extracted individual blocks, and calculates the volume for the unit figure (e.g., rectangular parallelepiped, cylinder, triangular prism, etc.) using the base area and height. By obtaining and performing this on the entire point cloud, the volume can be calculated for the entire block.

The converted diameter calculation unit 520 may calculate the converted diameter based on the block volume. For example, the converted diameter calculation unit 520 can calculate the converted diameter by assuming that the volume of the block is the volume of a sphere and inverting the volume formula for the sphere. At this time, the converted diameter can be calculated through Equation 1 below.

[Equation 1]

Here, D is the converted diameter and V is the volume of the block.

The data analysis unit 530 may generate a particle size distribution curve based on the converted diameter. For example, the data analysis unit 530 may generate a graph showing the cumulative particle size distribution according to the converted diameter.

Figure 8 is a diagram showing the operation of the block volume calculation unit 510 according to an embodiment of the present invention.

Referring to FIGS. 7 and 8 , the block volume calculation unit 510 may set the block lowest point reference surface based on the point cloud data of the extracted individual blocks. For example, the block volume calculation unit 510 may set an arbitrary depth point (eg, lowest point) as the reference surface based on point cloud data. Accordingly, the height value for the point cloud data within the block is specified.

The block volume calculation unit 510 may obtain the volume for a unit figure (eg, a rectangular parallelepiped, a cylinder, a triangular prism, etc.) using the base area and height of the reference surface.

Additionally, the block volume calculation unit 510 can calculate the volume for the entire block by performing the calculation for the unit shape on the entire point group within the block.

Figure 9 is a diagram showing the operation of the data analysis unit 530 according to an embodiment of the present invention.

Referring to FIG. 9, the data analysis unit 530 may generate a particle size distribution curve based on the converted diameter.

As shown in Figure 9, in the present invention, the particle size distribution curve is characterized as a Rosin-Rammler particle distribution curve representing the cumulative weight passage rate with respect to the crushed particle size.

However, the present invention is not limited to this, and depending on the embodiment, the data analysis unit 530 may generate a particle size distribution curve through various types of distribution graphs.

Figure 10 is a flow diagram showing the operation of the rock fracture analysis device according to an embodiment of the present invention. With reference to FIGS. 1 to 10, the operation method of the rock fracture analysis device of the present invention will be described in detail below.

The data load unit 100 may convert the format of the point cloud data to analyze the degree of fragmentation due to blasting (S10). That is, the data load unit 100 can convert the format of full-scale 3D point cloud data.

The block area setting unit 200 may generate a depth map by setting a block area based on point cloud data (S20). That is, the block area setting unit 200 can set the block area for each rock based on point cloud data without separate scale conversion. Additionally, the block area setting unit 200 may generate a depth map for each point cloud.

The block boundary extraction unit 300 may extract the block boundary for the block area based on the point cloud data and the depth map (S30). That is, the block boundary extraction unit 300 can set a block boundary by clearly setting the boundary for the block set by the block area setting unit 200.

The individual block allocation unit 400 may divide the point cloud data into a plurality of groups according to block boundaries and specify them (S40). That is, the individual block allocation unit 400 divides the entire area into a plurality of groups according to the block boundary set by the block boundary extraction unit 300, and can specify an identification code for each group. .

The crushed particle size analysis unit 500 may analyze the crushed particle size by calculating the volume based on the point cloud data for each of the plurality of groups (S50). Specifically, analyzing the crushed particle size includes calculating a block volume for each of a plurality of groups; Calculating a converted diameter based on the block volume; And it may include generating a particle size distribution curve based on the converted diameter. Details related to this are explained in FIG. 7 .

The data output unit 600 may output and store particle size analysis data and particle size distribution curves in a preset data format (S60). That is, the data output unit 600 can output particle size analysis data and particle size analysis curves according to a format that can be used in the existing system in order to improve user convenience. Additionally, the data output unit 600 can automatically store the output data in an external storage device or database server.

Through the above-described method, the rock crushability analysis device and its operating method of the present invention are effective in analyzing the particle size distribution of the crushed stone pile (Muck Pile) generated after blasting work at the blasting site.

In addition, the rock fracture analysis device and its operating method of the present invention automatically extract individual blocks from the 3D point cloud data of the muck pile obtained from image processing or a 3D scanner, This has the effect of analyzing the particle size of the entire crushed stone pile by calculating the volume and converted diameter.

In addition, the rock fracture degree analysis device and its operating method of the present invention use full-scale 3D point cloud data, allowing the input data to be used as is for fracture degree analysis without separate scale conversion, and the 3D analysis of individual crushed stones. It has the effect of being able to calculate the representative particle size of a block by calculating the volume of the shape and the converted diameter of the sphere.

In addition, the rock fracture analysis device and its operating method of the present invention have the effect of improving the reliability and accuracy of the analysis results compared to the two-dimensional image analysis method.

In addition, the rock fracture analysis device and its operating method of the present invention have the effect of simplifying the data processing process by automating everything from point cloud data input to result analysis.

The functional operations described herein and embodiments of the subject matter described above may be implemented in digital electronic circuits, computer software, firmware or hardware, including the structures disclosed herein and their structural equivalents, or in a combination of one or more of these. possible.

Embodiments of the subject matter described herein may relate to one or more computer program products, that is, computer program instructions encoded on a tangible program medium for execution by or to control the operation of a data processing device. It can be implemented as a module. The tangible program medium may be a radio signal or a computer-readable medium. A radio signal is an artificially generated signal, such as a machine-generated electrical, optical or electromagnetic signal, that is generated to encode information for transmission to a suitable receiver device for execution by a computer. 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 wave signal, or a combination of one or more of these.

A computer program (also known as a program, software, software application, script, or code) may be written in any form of a programming language, including a compiled or interpreted language, or an a priori or procedural language, as a stand-alone program or module; It can be deployed in any form, including components, subroutines, or other units suitable for use in a computer environment.

Computer programs do not necessarily correspond to files on a file device. A program may be stored within a single file that serves the requested program, or within multiple interacting files (e.g., more than one file storing a module, subprogram, or portion of code), or within a file holding other programs or data. Some (e.g., one or more scripts stored within a markup language document) may be stored.

The computer program may be deployed to run on a single computer or multiple computers located at one site or distributed across multiple sites and interconnected by a communications network.

Additionally, the logic flow and structural block diagram described in this patent document describe corresponding actions and/or specific methods supported by corresponding functions and steps supported by the disclosed structural means, It can also be used to build software structures and algorithms and their equivalents.

The processes and logic flows described herein can be performed by one or more programmable processors, each of which executes one or more computer programs to perform functions by operating on input data and generating output.

Processors suitable for executing computer programs include, for example, any one or more processors of both general-purpose and special-purpose microprocessors and any type of digital computer. Typically, the processor will receive instructions and data from read-only memory, random access memory, or both.

The core elements of a computer are one or more memory devices for storing instructions and data and a processor for executing instructions. Additionally, a computer generally includes one or more mass storage devices for storing data, such as magnetic, magneto-optical or optical disks, operable to receive data from, transfer data to, or perform both such operations. It may be combined with or include these. However, a computer does not need to have such a device.

The present description sets forth the best mode of the invention and provides examples to illustrate the invention and to enable any person skilled in the art to make or use the invention. The specification prepared in this way does not limit the present invention to the specific terms presented.

Although the present invention has been described above with reference to preferred embodiments, those skilled in the art or have ordinary knowledge in the relevant technical field should not deviate from the spirit and technical scope of the present invention as set forth in the claims to be described later. It will be understood that the present invention can be modified and changed in various ways within the scope of the present invention.

Therefore, the technical scope of the present invention should not be limited to what is described in the detailed description of the specification, but should be defined by the scope of the patent claims.

10: Rock fracture analysis device
100: data loading unit
200: Block area setting unit
300: Block boundary extraction unit
400: Individual block allocation unit
500: Crushing particle size analysis unit
510: Block volume calculation unit
520: Conversion diameter calculation unit
530: Data analysis department
600: data output unit

Claims (10)

A data load unit for converting the format of point cloud data to analyze the degree of fracture caused by blasting;
a block area setting unit for generating a depth map by setting a block area based on the point cloud data;
a block boundary extraction unit for extracting a block boundary for the block area based on the point cloud data and the depth map;
an individual block allocation unit for dividing and assigning the point cloud data into a plurality of groups according to the block boundary; and
A crushed particle size analysis unit for analyzing the crushed particle size by calculating the volume for each of the plurality of groups based on the point cloud data,
The point cloud data includes at least one of three-dimensional coordinate information and color information,
The block area setting unit corresponds coordinate values along the third axis to colors on a reference plane defined by the first and second axes, extracts the main shape of the individual block based on the corresponding color, and sets the block area. By doing so, generating the depth map,
The block boundary extraction unit extracts the closest boundary within a standard distance from the center point of the block area as the block boundary,
Rock fracture analysis device. delete delete delete According to paragraph 1,
The individual block allocation unit specifies point cloud data included in the inner area of the block boundary as a unit group and assigns a recognition code,
Rock fracture analysis device. According to clause 5,
The crushed particle size analysis unit,
a block volume calculation unit for calculating a block volume for each of the plurality of groups;
a conversion diameter calculation unit for calculating a conversion diameter based on the block volume; and
Characterized by comprising a data analysis unit for generating a particle size distribution curve based on the converted diameter,
Rock fracture analysis device. According to clause 6,
The particle size distribution curve is a Rosin-Rammler particle distribution curve representing the cumulative weight passage rate for crushed particle size,
Rock fracture analysis device. In clause 7,
Characterized in that it further comprises a data output unit for outputting and storing particle size analysis data and the particle size distribution curve in a preset data format,
Rock fracture analysis device. A data loading unit converting the format of the point cloud data to analyze the degree of fragmentation caused by blasting;
A block area setting unit generating a depth map by setting a block area based on the point cloud data;
A block boundary extraction unit extracting a block boundary for the block area based on the point cloud data and the depth map;
An individual block allocator dividing the point cloud data into a plurality of groups according to the block boundary and designating them;
A crushed particle size analysis unit analyzing the crushed particle size by calculating a volume based on the point cloud data for each of the plurality of groups; and
A data output unit includes a step of outputting and storing particle size analysis data and particle size distribution curves in a preset data format,
In the step of converting the format of the point cloud data,
The point cloud data includes at least one of three-dimensional coordinate information and color information,
The step of generating the depth map is,
The block area setting unit corresponds coordinate values along the third axis to colors on a reference plane defined by the first and second axes, extracts the main shape of the individual block based on the corresponding color, and sets the block area. , generate the depth map,
The step of extracting the block boundary is,
Characterized in that the block boundary extraction unit extracts the closest boundary within a standard distance from the center point of the block area as the block boundary,
How the rock fracture analysis device operates. According to clause 9,
The step of analyzing the crushed particle size is,
calculating a block volume for each of the plurality of groups;
calculating a converted diameter based on the block volume; and
Characterized in that it includes the step of generating a particle size distribution curve based on the converted diameter,
How the rock fracture analysis device operates.

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