Deep hole blasting construction method based on three-dimensional scanning

Abstract

The invention relates to a deep hole blasting construction method based on three-dimensional scanning, which comprises the following steps: S1, scanning to obtain spatial information and image information in a roadway to be blasted, and obtaining a three-dimensional model of the roadway fused with image data; s2, performing deep hole blasting design on the three-dimensional model to obtain a plurality of sections of pre-deep hole blasting; s3, for each section, dividing the section into grid units, fitting each grid unit into a plane rock stratum surface, identifying rock stratum classification of the plane rock stratum surface through the spatial information and the image information, and obtaining the thickness of each rock stratum and the position of each rock stratum in each section through the rock stratum classification; s4, according to the thickness of each rock stratum in each section and the position of each rock stratum, the change condition of the rock stratum in the roadway is obtained; and S5, adjusting the deep hole parameter of each section according to the rock stratum change condition to obtain a deep hole design result.

Description

Technical Field

[0001] The present invention relates to the field of deep-hole blasting, and specifically refers to a deep-hole blasting construction method based on three-dimensional scanning. Background Technique

[0002] Deep-hole blasting refers to the blasting technology of blast holes with a drilling diameter greater than 50 mm and a hole depth greater than 5 m. During the process of deep-hole blasting, generally, a roadway needs to be excavated first, and then a cross-section is taken every 5-7 m in the roadway to design the hole depth, hole spacing, etc. of the deep-hole blasting, and then drilling, filling explosives, and blasting are carried out according to this design.

[0003] With the continuous in-depth promotion of the construction of digital mines, the application of medium-deep hole blasting design for mining methods such as open stoping method and sublevel caving method is relatively extensive using mine three-dimensional mapping software. As a new surveying and mapping technology, three-dimensional laser scanning technology has been widely used in topographic surveying. Three-dimensional laser scanning collects the coordinate information of all points of the measured roadway, so the formed solid model can truly restore the original appearance of the roadway, improve the accuracy of blast hole design, and greatly reduce safety accidents caused by inaccurate measurement data.

[0004] However, after obtaining the three-dimensional data of the roadway in the prior art, generally, any cross-section is analyzed independently. For example, the rock formation information of the current cross-section is obtained through drilling or image analysis, etc., and then the drilling is designed according to the rock formation information of the current cross-section. Due to the large variety and complex situation of the rock formation data, when plastic extrusion flow occurs during the formation of the rock formation, its hardness will increase, and when the rock formation is subjected to the extrusion force of plate movement, tensile stress acts on the top of the rock formation, and tensile cracks or thinning may occur. The prior art does not optimize the design of the hole depth, hole spacing, etc. of the blast holes for deep-hole blasting according to the changes in the strike, thickness, etc. of the rock formation based on the three-dimensional scanning data.

[0005] Designing a deep-hole blasting construction method based on three-dimensional scanning for the problems existing in the above-mentioned prior art is the purpose of the research of the present invention. Summary of the Invention

[0006] Aiming at the problems existing in the above-mentioned prior art, the present invention provides a deep-hole blasting construction method based on three-dimensional scanning, which can effectively solve at least one of the problems existing in the above-mentioned prior art.

[0007] The technical solution of the present invention is as follows: A deep-hole blasting construction method based on three-dimensional scanning includes the following steps: S1. Scan to obtain the spatial information and image information in the roadway to be blasted, and obtain a three-dimensional model of the roadway integrated with image data; S2. Carry out deep-hole blasting design on the three-dimensional model to obtain multiple cross-sections of pre-deep-hole blasting; S3. For each cross-section, perform the following: divide the cross-section into grid cells, fit each grid cell to a planar rock formation surface, identify the rock formation classification of the planar rock formation surface through the spatial information and the image information, and obtain the thickness of each rock formation and the location of each rock formation in each cross-section based on the rock formation classification; S4. Based on the thickness of each rock formation and the location of each rock formation in each cross-section, obtain the rock formation change situation in the roadway; S5. Adjust the deep hole parameters of each cross-section according to the rock formation change situation to obtain the deep hole design result.

[0008] Further, fitting each grid cell to a planar rock formation surface includes: Obtain the normal direction of each point in each point cloud in each grid cell, and fit the continuous points with similar normal directions to the same planar rock formation.

[0009] Further, identifying the rock formation classification of the planar rock formation surface through the spatial information and the image information includes: Fuse the point cloud coordinates, RGB colors, and reflection intensities of the planar rock formation surface according to preset weights to construct a multi-dimensional data set; Identify the rock formation classification of the planar rock formation surface from the multi-dimensional data set through a pre-trained rock formation recognition learning algorithm.

[0010] Further, the preset weight w1 of the point cloud coordinates is 1 to 1.2, the preset weight w2 of the RGB colors is 0.6 to 0.8, and the preset weight w3 of the reflection intensity is 0.2 to 0.4; .

[0011] Further, the rock formation recognition learning algorithm is obtained through the following steps: Use the multi-dimensional data set of the planar rock formation surface as the input and the rock formation classification of the planar rock formation surface as the output to train a neural network to obtain the rock formation recognition learning algorithm.

[0012] Further, S4. Based on the thickness of each rock formation and the location of each rock formation in each cross-section, obtaining the rock formation change situation in the roadway includes: Extract the horizontal height of the same rock formation in the cross-section, and fit multiple horizontal heights to obtain the strike of the rock formation in the roadway; If the dip angle of the strike of the rock formation in the adjacent cross-section is less than 30°, then: if each rock formation increases in thickness according to the roadway path and the strike of the rock formation is upward, it is determined that the strata where the roadway is located is the anticlinal part; if each rock formation decreases in thickness according to the roadway path and the strike of the rock formation is downward, it is determined that the strata where the roadway is located is the synclinal part; if otherwise, it is determined that the strata where the roadway is located is the planar layer; If the dip angle of the strike of the rock formation in the adjacent cross-section is greater than 30°, it is determined that there is a fault plane in the cross-section where it is located.

[0013] Furthermore, adjusting the deep hole parameters of each cross-section according to the change of the rock formation includes: Carrying out deep hole blasting design in 3Dmin software according to the 3D model to obtain the hole position distribution spacing and hole depth of deep hole blasting for each cross-section; If there is a fault plane in the cross-section where it is located, reduce the hole position distribution spacing and reduce the hole depth; If it is determined that the strata where the roadway is located is the anticlinal part, gradually reduce the hole position distribution spacing according to the path direction of the roadway and increase the hole depth; If it is determined that the strata where the roadway is located is the synclinal part, gradually increase the hole position distribution spacing according to the path direction of the roadway and reduce the hole depth.

[0014] Furthermore, if there is a fault plane in the cross-section where it is located, reduce the hole position distribution spacing by 10% and reduce the hole depth by 15%; If it is determined that the strata where the roadway is located is the anticlinal part or the synclinal part, the difference in hole position distribution spacing between the starting point and the ending point according to the path direction of the roadway is 10%, and the difference in hole depth is 10%.

[0015] Furthermore, after scanning and obtaining the spatial information and image information in the roadway to be blasted, execute: Perform noise reduction processing on the spatial information and image information.

[0016] Furthermore, through the rock formation classification, the thickness of each rock formation and the location of each rock formation in each cross-section include: Fill the grid cells with colors according to the rock formation classification, and the filling colors for different rock formation classifications are different to obtain a cross-section rock formation distribution map; Perform edge calculation on the rock formation distribution map to obtain the edges of each rock formation; Perform linear fitting on the edges of each rock formation to obtain the edge lines of each rock formation; Calculate the distance between the edge lines of each rock formation as the thickness of each rock formation, and calculate the height where the edge lines of each rock formation are located as the location of each rock formation.

[0017] Therefore, the present invention provides the following effects and / or advantages: This application composes a three-dimensional model by scanning the spatial information and image information in the roadway, and then processes the corresponding information in the three-dimensional model to identify the rock formation classification. According to the changes in the rock formation classification in each cross-section, the parameters of the blasting hole position design are adjusted, thereby optimizing the blasting.

[0018] This application fits continuous points with similar normal directions into the same planar rock formation, avoiding situations such as rough rock formation surfaces, fissures, and damage, and extracting relevant information that can be used for subsequent processing.

[0019] This application can identify the category of the rock formation through the point cloud coordinates, RGB colors, reflection intensity on the surface of the planar rock formation, and by setting corresponding weights, and accurately obtain the rock formation category through a learning algorithm.

[0020] This application determines the stratigraphic structure where the rock formation is located based on the strike of the rock formation and its changes, identifies the changes in the rock formation in the roadway path direction, and thereby increases or decreases parameters such as the distribution spacing and hole depth of the blasting holes to adapt to the changes in the rock formation.

[0021] Other features and advantages of the present invention will be described in the subsequent specification, and part of them will become obvious from the specification or be understood by implementing the present invention. The objectives and other advantages of the present invention are achieved and obtained through the structures specifically pointed out in the specification and the drawings.

[0022] It should be understood that the above summary and the following detailed description of the present invention are exemplary and explanatory, and are intended to provide further explanation of the present invention as claimed. Brief Description of the Drawings

[0023] Figure 1 It is a schematic flow chart provided for one embodiment of the present invention.

[0024] Figure 2 It is a schematic diagram of the three-dimensional model of the roadway.

[0025] Figure 3 It is a schematic diagram of a perspective view within the three-dimensional model.

[0026] Figure 4 It is a schematic diagram of the blasting design drawn by the 3Dmin three-dimensional software platform.

[0027] Figure 5 It is a schematic diagram after one cross-section is divided into grid cells. Detailed Description of the Specific Embodiment

[0028] For the convenience of those skilled in the art to understand, the embodiments will now further describe the present invention in detail: Reference Figure 1, A deep-hole blasting construction method based on 3D scanning, comprising the following steps: S1, Scan the spatial information and image information in the roadway to be blasted to obtain a 3D model of the roadway integrated with image data; In this step, the acquisition of the spatial information and image information in the roadway to be blasted can be achieved through existing technologies, so as to obtain a 3D model with color information, which can truly reflect the 3D situation in the roadway. For example, the wing-eye god HM100 radar laser scanner can be used to actually measure the mining roadway to establish a roadway entity model, as Figures 2-3 shown.

[0029] S2, Conduct deep-hole blasting design on the 3D model to obtain multiple cross-sections of pre-deep-hole blasting; In this step, by inputting the 3D model of the roadway into the 3Dmin 3D software platform for medium-deep hole blasting design, relevant parameters are preset first, such as: drilling parameters, drill rig parameters, mining parameters, cutting parameters of the stoping entry, and charging parameters, etc. Then set the center of the machine support point, create the machine support roadway, and create the stoping boundary; arrange fan-shaped holes or parallel holes, set the charging of the blast holes, and delineate the stoping blasting range; draw the medium-deep holes row by row with this program to obtain the blasting design as Figure 4 shown. At this time, this design is initially automatically generated according to the parameters preset by the software.

[0030] S3, Execute for each cross-section: Divide the cross-section into grid cells, fit each grid cell into a planar rock formation surface, identify the rock formation classification of the planar rock formation surface through the spatial information and the image information, and obtain the thickness of each rock formation and the location of each rock formation in each cross-section through the rock formation classification; In this step, divide the cross-section into 0.3m * 0.3m grid cells to obtain the grid cells as Figure 5 shown. At this time, due to the unevenness, defects, cracks, textures, etc. on the inner wall of the roadway, it is difficult to identify the rock formation category and other calculations in the subsequent algorithms. Therefore, in this step, each grid cell is fitted into a planar rock formation surface to make the rock formation strike planar and the strike of the same rock formation continuously change within the cross-section, reducing the influence of defects, faults, distortions, etc. on the subsequent recognition algorithms.

[0031] Then, through the rock formation classification of the planar rock formation surface, the thickness and location of each rock formation can be obtained, so that for the thickness change and position change of multiple rock formations in multiple cross-sections in the roadway, information such as whether the thickness of the rock formation becomes larger or smaller in the path of the roadway and whether the rock formation strike is downward or upward can be obtained. The intelligent conversion from point cloud data to rock formations can be realized, providing basic data for deep-hole blasting design.

[0032] S4. Based on the thicknesses of various rock layers and their positions in each cross-section, obtain the variation of rock layers in the roadway; In step S3, the rock layer conditions of each cross-section are obtained. Next, in this step, the rock layer conditions of multiple cross-sections are correlated to obtain the variation of rock layers. For example, in the first cross-section, sedimentary rock is present from 0 to 1.5 meters, igneous rock from 1.5 to 4 meters, and metamorphic rock from 4 to 6 meters; in the second cross-section, sedimentary rock is present from 0 to 1.6 meters, igneous rock from 1.6 to 4.13 meters, and metamorphic rock from 4.13 to 6 meters; in the third cross-section, sedimentary rock is present from 0 to 1.66 meters, igneous rock from 1.66 to 4.25 meters, and metamorphic rock from 4.25 to 6 meters. It can be seen that in multiple cross-sections, the starting height of the igneous rock layer develops from 1.5 meters in the first cross-section to 1.66 meters in the third cross-section, and the thickness of the igneous rock layer increases from 2.5 meters in the first cross-section to 2.59 meters in the third cross-section. Overall, the position of the igneous rock layer rises and the thickness increases.

[0033] By analyzing the variations such as the height and thickness of each rock layer, it serves as the variation of the rock layers.

[0034] S5. Adjust the deep hole parameters of each cross-section according to the variation of the rock layers to obtain the deep hole design result.

[0035] In this step, the deep hole parameters of each cross-section are adjusted according to the obtained variation of the rock layers. For example, if the position rises and the thickness increases as mentioned above, it indicates that there is an influence of interplate extrusion in the rock layer at that location, resulting in a uplifted terrain, and the space within the rock layer is further compressed under the influence of the plates. Under the stress, the rock layers are repeatedly stacked to produce a thicker thickness. In this case, the rock layer generally has greater hardness and requires more powerful blasting.

[0036] In this embodiment, the variation of the rock layers in the roadway is identified through the variations of the thicknesses and heights of the rock layers in multiple cross-sections, thereby providing more accurate basic data for the design process of deep hole blasting. Deep hole blasting can identify the conditions of the rock layers based on this basic data and adjust parameters such as the spacing or depth of the hole positions of the deep hole blasting, so as to obtain an optimized deep hole design result.

[0037] Further, fitting each grid unit to a planar rock layer surface includes: Obtain the normal directions of each point in each point cloud in each grid unit, and fit the continuous points with similar normal directions to the same planar rock layer.

[0038] Due to the presence of depressions, deformations, cracks, etc. on the inner wall of the roadway, in this step, continuous points with similar normal directions are extracted. In this case, through the similarity of the normal directions and spatial continuity, the points belonging to the same plane are gathered together and fitted into a plane, and this plane serves as the planar rock layer in the grid unit.

[0039] Furthermore, identifying the rock formation classification on the surface of the planar rock formation through the spatial information and the image information includes: Fusing the point cloud coordinates, RGB colors, and reflection intensities on the surface of the planar rock formation according to preset weights to construct a multi-dimensional data set; Identifying the rock formation classification on the surface of the planar rock formation from the multi-dimensional data set through a pre-trained rock formation recognition learning algorithm.

[0040] In this step, the point cloud data is usually a set composed of a large number of point coordinates (x, y, z) obtained by a 3D scanner. At the same time, image information is also combined in step S1. Therefore, the point cloud data also contains information such as colors and reflection intensities. Different rock formations have different hardnesses, colors, textures, etc. For example, there are color differences in the rock formations in the RGB image. For example, limestone is grayish-white and has a smooth surface, while shale is dark gray and has dense bedding planes. Also, for example, the laser reflection intensity reflects the hardness of the rock formation. For example, quartz sandstone has a high reflection intensity, while mudstone has a low reflection intensity. Through these parameters, the situation of the rock formation can be identified.

[0041] At the same time, since different rock formations have different properties and hardnesses, colors are relatively easy to distinguish limestone and shale. Because limestone usually has a lighter color, such as grayish-white, while shale has a darker color, such as dark gray or black. And the reflection intensity is related to the surface roughness and mineral composition. Hard rocks such as quartz sandstone have a high reflection intensity, while soft rocks such as mudstone have a lower one. However, the reflection intensity may be affected by factors such as the scanning angle and surface humidity, and its stability may be inferior to that of colors. And the reflection intensity can help distinguish hard rocks and cooperate with colors to prevent misjudgment. Therefore, it is necessary to adjust the point cloud coordinates, RGB colors, and reflection intensities according to preset weights to obtain a more accurate fused multi-dimensional data set.

[0042] Furthermore, the preset weight w1 of the point cloud coordinates is 1 to 1.2, the preset weight w2 of the RGB colors is 0.6 to 0.8, and the preset weight w3 of the reflection intensity is 0.2 to 0.4; .

[0043] In this embodiment, it is set that the preset weight w1 of the point cloud coordinates is 1, the preset weight w2 of the RGB colors is 0.7, and the preset weight w3 of the reflection intensity is 0.3.

[0044] As mentioned above, RGB colors can easily and intuitively distinguish the properties of rock layers, while the reflection intensity can assist in analyzing parameters such as the hardness of rock layers. Through different preset weights, the two are fused with the point cloud coordinates, so that among the three principal components, the contribution rate of the point cloud coordinates is 100%, the contribution rate of the color variance is 70%, and the reflection intensity is 30%. This weight setting can balance the empirical values of distinguishability, stability, and practicality, and is especially applicable to scenarios dominated by limestone / shale.

[0045] Further, the rock layer identification learning algorithm is obtained through the following steps: Taking the multi-dimensional data set on the surface of the planar rock layer as the input and the rock layer classification on the surface of the planar rock layer as the output, training the neural network to obtain the rock layer identification learning algorithm.

[0046] Further, in S4, according to the thickness of each rock layer and the location of each rock layer in each cross-section, the rock layer changes in the roadway are obtained, including: Extracting the horizontal height of the same rock layer in the cross-section and fitting multiple horizontal heights to obtain the strike of the rock layer in the roadway; If the dip angle of the strike of the rock layer in adjacent cross-sections is less than 30°, then: if each rock layer increases in thickness according to the roadway path and the strike of the rock layer is upward, it is determined that the strata where the roadway is located is the anticlinal part; if each rock layer decreases in thickness according to the roadway path and the strike of the rock layer is downward, it is determined that the strata where the roadway is located is the synclinal part; if otherwise, it is determined that the strata where the roadway is located is the planar layer; If the dip angle of the strike of the rock layer in adjacent cross-sections is greater than 30°, it is determined that there is a fault plane in the cross-section.

[0047] The lowest point of the horizontal height of the same rock layer can be used as the horizontal height of the rock layer. After combining the multiple horizontal heights of multiple rock layers in multiple cross-sections in the roadway, a straight line that best represents the strike of the rock layer can be obtained through linear fitting. Generally, the strike of the rock layer is a continuous and gently changing curve, and the roadway only intercepts a small segment of the strike of the rock layer. Therefore, the strike of the rock layer in the roadway can be approximated as a straight line. In the case where there is no sudden deviation or dislocation in the strike of the rock layer, the dip angle of the rock layer is less than 30°. And the rock layer may become thinner due to plate stretching or thicker due to extrusion. Further, the anticlinal part of the rock layer is subjected to extrusion, causing the rock layer to arch upward, and the rock layer in the core part may undergo plastic flow or overlapping due to strong compression, resulting in a significant increase in thickness, while the synclinal part is the opposite. According to this property, combined with the specific situation of the strike of the rock layer obtained above, the strata situation can be inferred.

[0048] Subsequently, the deep hole blasting can be optimized and adjusted according to the strata situation.

[0049] Further, adjusting the deep hole parameters of each section according to the changes in the rock strata includes: Carry out deep hole blasting design in 3Dmin software according to the 3D model to obtain the hole position distribution spacing and hole depth of deep hole blasting for each section; The hole position distribution spacing and hole depth of the deep hole blasting designed in this step are basic data.

[0050] If there is a fault plane in the section, reduce the hole position distribution spacing and reduce the hole depth; If it is determined that the strata where the roadway is located is the anticline part, gradually reduce the hole position distribution spacing according to the path direction of the roadway and increase the hole depth; If it is determined that the strata where the roadway is located is the syncline part, gradually increase the hole position distribution spacing according to the path direction of the roadway and reduce the hole depth.

[0051] Specifically, a fault is a fracture zone in the rock strata, and the rock strata on both sides may be displaced due to compression, tension or shear. During the blasting process, the rocks near the fault zone are usually more fragmented and may contain a large number of fissures and joints, which will change the propagation path and energy distribution of the blasting wave. In addition, the fault zone may contain soft interlayers or moisture, and these factors will affect the blasting effect and the stability of the surrounding rock mass. Encrypting the blast holes can increase the energy coverage density, ensure that the fractured rock mass is fully broken, and reducing the charge amount can avoid excessive fragmentation and suppress flying rocks, etc.

[0052] When the strata where the roadway is located is the anticline part, the rock strata have high density, high hardness, etc. Therefore, it is necessary to increase the blasting effect, thereby increasing the blast hole density and increasing the blast hole depth; the situation where the strata where the roadway is located is the syncline part is the opposite.

[0053] Further, if there is a fault plane in the section, reduce the hole position distribution spacing by 10% and reduce the hole depth by 15%; If it is determined that the strata where the roadway is located is the anticline part or the syncline part, the difference in the hole position distribution spacing between the starting point and the ending point of the path direction of the roadway is 10%, and the difference in the hole depth is 10%.

[0054] For this design, the deep hole blasting design in 3Dmin software according to the 3D model can be used as basic data. For example, the data generated by 3Dmin software for the last fault plane (corresponding to the end of the roadway) is that the hole depth of hole No. 1 is 6.3 meters, the hole depth of hole No. 2 is 5.3 meters, and the spacing between hole No. 1 and hole No. 2 is 80 cm. If the strata where the roadway is located is the anticline part, after optimization, the hole depth of hole No. 1 increases by 10% to 6.93 meters, the hole depth of hole No. 2 increases by 10% to 5.83 meters, and the spacing between hole No. 1 and hole No. 2 decreases by 10% to 72 cm.

[0055] Further, after scanning the spatial information and image information in the roadway to be blasted, the following steps are performed: Perform noise reduction processing on the spatial information and image information.

[0056] Point cloud data usually has noise and may require filtering, such as statistical filtering or radius filtering to remove outliers.

[0057] Further, through the rock formation classification, the thicknesses of each rock formation and the positions of each rock formation in each cross-section are obtained, including: Fill the grid cells with colors according to the rock formation classification. Different rock formation classifications have different filling colors, and a cross-section rock formation distribution map is obtained. Perform edge calculation on the rock formation distribution map to obtain the edges of each rock formation. Perform linear fitting on the edges of each rock formation to obtain the edge lines of each rock formation. Calculate the distance between the midpoints of the edge lines of each rock formation as the thickness of each rock formation, and calculate the height at which the midpoints of the edge lines of each rock formation are located as the position of each rock formation.

[0058] In this step, the grid cells are filled with colors according to different rock formations. For example, the grid cells corresponding to metamorphic rocks are filled with black, the grid cells corresponding to igneous rocks are filled with red, and the grid cells corresponding to sedimentary rocks are filled with yellow, etc., to form a map with color distribution.

[0059] Since there may be fluctuations in rock formation classification due to unclear rock formation classification at the rock formation junction during the recognition process, in this embodiment, edge detection is used for recognition, such as calculating with the Sobel operator, and then performing linear fitting based on the obtained edges.

[0060] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0061] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be realized by computer program instructions. These computer program instructions can be provided to the processors of general purpose computers, special purpose computers, embedded processors, or other programmable data processing devices to produce a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for realizing the functions specified in the flow Figure 1 one flow or multiple flows and / or blocks Figure 1 means for realizing the functions specified in one block or multiple blocks.

[0062] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that realize the functions specified in the flow Figure 1 one flow or multiple flows and / or blocks Figure 1 means for realizing the functions specified in one block or multiple blocks.

[0063] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made by those skilled in the art once they learn the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0064] In the description of this specification, the descriptions with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms should not be understood as necessarily referring to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

Claims

1. A deep hole blasting construction method based on three-dimensional scanning, characterized in that: The following steps are involved: S1, scanning to obtain spatial information and image information in the tunnel to be blasted, and obtaining a three-dimensional model of the tunnel fused with image data; S2, performing deep hole blasting design on the three-dimensional model to obtain multiple sections of pre-deep hole blasting; S3, for each section, the following steps are performed: dividing the section into grid units, fitting each grid unit into a plane rock layer surface, identifying the rock layer classification of the plane rock layer surface through the spatial information and the image information, and obtaining the thickness and location of each rock layer in each section through the rock layer classification; S4, obtaining the change of rock layers in the tunnel according to the thickness and location of each rock layer in each section; S5, adjusting the deep hole parameters of each section according to the change of the rock formation to obtain a deep hole design result.

2. The deep hole blasting construction method based on three-dimensional scanning according to claim 1 is characterized in that: Fitting each grid cell to a planar rock surface involves: The normal direction of each point in each point cloud in each grid unit is obtained, and continuous points with similar normal directions are fitted into the same plane rock layer.

3. The deep hole blasting construction method based on three-dimensional scanning according to claim 1 is characterized in that: Identifying the rock formation classification of the plane rock formation surface by using the spatial information and the image information includes: The point cloud coordinates, RGB colors, and reflection intensity of the plane rock surface are integrated according to preset weights to construct a multidimensional data set; The multi-dimensional data set is used to identify the rock formation classification of the planar rock formation surface through a pre-trained rock formation identification learning algorithm.

4. A deep hole blasting construction method based on three-dimensional scanning according to claim 3, characterized in that: The preset weight w1 of the point cloud coordinates is 1-1.2, the preset weight w2 of the RGB color is 0.6-0.8, and the preset weight w3 of the reflection intensity is 0.2-0.4; 。 5. The deep hole blasting construction method based on three-dimensional scanning according to claim 4 is characterized in that: The rock formation identification learning algorithm is obtained by the following steps: The multi-dimensional data set of the plane rock formation surface is used as input, the rock formation classification of the plane rock formation surface is used as output, and a neural network is trained to obtain a rock formation recognition learning algorithm.

6. The deep hole blasting construction method based on three-dimensional scanning according to claim 1 is characterized in that: S4, according to the thickness and location of each rock layer in each section, the changes of rock layers in the tunnel are obtained, including: Extracting the horizontal height of the same rock layer in the cross section, and fitting multiple horizontal heights to obtain the direction of the rock layer in the tunnel; If the inclination angle of the strike of the rock layers of the adjacent sections is less than 30°, then: if the thickness of each rock layer increases according to the roadway path and the rock layer strikes upward, the stratum where the roadway is located is determined to be an anticline; if the thickness of each rock layer decreases according to the roadway path and the rock layer strikes downward, the stratum where the roadway is located is determined to be a syncline; otherwise, the stratum where the roadway is located is determined to be a planar layer; If the inclination angle of the strike of the rock strata in the adjacent sections is greater than 30°, it is determined that a fault plane exists in the section.

7. A deep hole blasting construction method based on three-dimensional scanning according to claim 6, characterized in that: Adjusting the deep hole parameters of each section according to the change of the rock formation includes: Perform deep hole blasting design in 3Dmin software according to the three-dimensional model to obtain the hole position distribution spacing and hole depth of the deep hole blasting of each section; If there is a fault plane in the section, reduce the hole distribution spacing and reduce the hole depth; If the stratum where the tunnel is located is determined to be anticline, the hole distribution spacing is gradually reduced and the hole depth is increased according to the path direction of the tunnel; If the stratum where the tunnel is located is determined to be a syncline, the hole distribution spacing is gradually increased according to the path direction of the tunnel, and the hole depth is reduced.

8. The deep hole blasting construction method based on three-dimensional scanning according to claim 7 is characterized in that: If there is a fault plane in the section, reduce the hole spacing by 10% and the hole depth by 15%; If the stratum where the tunnel is located is determined to be an anticline or syncline, the hole position distribution spacing difference between the starting point and the end point of the tunnel path direction is 10%, and the hole depth difference is 10%.

9. The deep hole blasting construction method based on three-dimensional scanning according to claim 1, characterized in that: After scanning and obtaining the spatial information and image information of the tunnel to be blasted, execute: Perform noise reduction on spatial information and image information.

10. The deep hole blasting construction method based on three-dimensional scanning according to claim 1, characterized in that: The thickness of each rock layer in each section and the location of each rock layer obtained by the rock layer classification include: Fill the grid cells with colors according to rock formation classification, with different rock formation classifications having different filling colors, to obtain a cross-sectional rock formation distribution map; Performing edge calculation on the rock layer distribution map to obtain the edge of each rock layer; Perform straight line fitting on the edge of each rock layer to obtain the edge straight line of each rock layer; The distance between the midpoints of the edge straight lines of each rock layer is calculated as the thickness of each rock layer, and the height of the midpoint of the edge straight line of each rock layer is calculated as the position of each rock layer.

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