Method and system for representing the topography of a rock surface after rock breaking by a rotary cutter

Abstract

Embodiments of the present application relate to the field of irregular surface metrology, and in particular to a method and system for characterizing the topographic features of a rock surface after rock breaking by a cutter. The method and system extract a plurality of two-dimensional elevation profiles from the processed three-dimensional point cloud data, and determine the three-dimensional roughness of the rock surface in combination with the three-dimensional point cloud data, which can reduce the influence of profile direction and profile line length difference on the evaluation result of three-dimensional roughness, and improve the accuracy of characterization of the three-dimensional spatial roughness features of the rock surface. By combining the overall undulation degree of the rock surface and the three-dimensional roughness to characterize the topographic features of the rock surface, the overall spatial undulation features and the local undulation features of the rock surface are cooperatively characterized, which can more comprehensively reflect the three-dimensional spatial topographic features of the rock surface after rock breaking by the cutter compared to the traditional two-dimensional profile analysis method, and improve the consistency and comparability of the rock surface topography evaluation results under different cutter wear states and different test conditions.

Description

Technical Field

[0001] The embodiments of this application relate to the field of metrology for irregular surfaces, and particularly to a method and system for characterizing the morphological features of a rock surface after a rock-breaking cutter. Background Technology

[0002] The statements herein are provided merely as background information in connection with this application and do not necessarily constitute prior art.

[0003] Under hard rock conditions, the cutterheads of a tunnel boring machine (TBM) induce crushing zones, radial cracks, and lateral cracks in the rock mass through normal pressing and rolling cutting actions. When cracks induced by adjacent cutters propagate and connect with each other, the rock mass between the cutters becomes unstable and spalls, thus achieving effective rock breaking. Therefore, the rock surface morphology after cutter action can reflect the characteristics of the rock breaking process, such as rock mass damage, crack propagation, and block spalling. Characterizing the rock surface morphology after rock breaking is helpful for analyzing the rock breaking state and effect of the cutterheads, optimizing tunneling parameters, and analyzing the tunnel face. Summary of the Invention

[0004] A brief overview of this application is provided below to offer a basic understanding of certain aspects thereof. It should be understood that this overview is not an exhaustive summary of the application. It is not intended to identify key or essential parts of the application, nor is it intended to limit its scope. Its purpose is merely to present certain concepts in a simplified form as a prelude to the more detailed description that follows.

[0005] This application provides a method for characterizing the morphological features of a rock surface after rock breaking by a roller cutter, comprising the following steps: S10: acquiring three-dimensional point cloud data of the rock surface; S20: processing the three-dimensional point cloud data to unify the spatial position and elevation of the three-dimensional point cloud data; S30: determining the elevation data of the rock surface based on the three-dimensional point cloud data processed in step S20; S40: determining the overall undulation of the rock surface based on the three-dimensional point cloud data and the elevation data; S50: extracting multiple two-dimensional elevation contour lines from the three-dimensional point cloud data processed in step S20; S60: determining the three-dimensional roughness of the rock surface based on the three-dimensional point cloud data processed in step S20 and the multiple two-dimensional elevation contour lines, wherein the three-dimensional roughness is used to characterize the local undulation of the rock surface; S70: determining the morphological features of the rock surface after rock breaking by a roller cutter based on the overall undulation of the rock surface and the three-dimensional roughness.

[0006] The method for characterizing the morphological features of the rock surface after roller cutting provided in this application preprocesses the three-dimensional point cloud data of the rock surface to unify the spatial position and elevation, thereby eliminating coordinate deviations that are prone to occur during the scanning process. This ensures that the overall undulation degree determined subsequently truly reflects the amplitude changes at the macroscopic scale of the rock surface. By extracting multiple two-dimensional elevation contour lines and combining them with the three-dimensional point cloud data to determine the three-dimensional roughness of the rock surface, the influence of the difference between the contour direction and the contour line length on the three-dimensional roughness evaluation results can be reduced, improving the accuracy of characterizing the three-dimensional spatial roughness features of the rock surface. By combining the overall undulation degree of the rock surface and the three-dimensional roughness to characterize the morphological features of the rock surface, the method achieves the coordinated characterization of the overall spatial undulation features and local undulation features of the rock surface. Compared with the traditional two-dimensional contour analysis method, this method can more comprehensively reflect the three-dimensional spatial morphological features of the rock surface after roller cutting, improving the consistency and comparability of the rock surface morphological evaluation results under different roller wear states and different test conditions.

[0007] This application also provides a characterization system for the morphological features of a rock surface after rock breaking by a roller cutter, comprising: a laser scanner configured to scan the rock surface to obtain three-dimensional point cloud data of the rock surface; and a processor configured to: process the three-dimensional point cloud data to unify the spatial position and elevation of the three-dimensional point cloud data; determine the elevation data of the rock surface based on the processed three-dimensional point cloud data; determine the overall undulation of the rock surface based on the three-dimensional point cloud data and the elevation data; extract multiple two-dimensional elevation contour lines from the processed three-dimensional point cloud data; determine the three-dimensional roughness of the rock surface based on the three-dimensional point cloud data and the multiple two-dimensional elevation contour lines, wherein the three-dimensional roughness is used to characterize the local undulation of the rock surface; and determine the morphological features of the rock surface after rock breaking by a roller cutter based on the overall undulation of the rock surface and the three-dimensional roughness.

[0008] The system for characterizing the morphology of rock surface after cutter rock breaking, provided in the embodiments of this application, preprocesses the three-dimensional point cloud data of the rock surface to unify the spatial position and elevation, thereby eliminating coordinate deviations that are prone to occur during the scanning process. This ensures that the overall undulation degree determined subsequently truly reflects the amplitude changes at the macroscopic scale of the rock surface. By extracting multiple two-dimensional elevation contour lines and combining them with three-dimensional point cloud data to determine the three-dimensional roughness of the rock surface, the influence of the difference between the contour direction and the contour line length on the three-dimensional roughness evaluation results can be reduced, improving the accuracy of characterizing the three-dimensional spatial roughness features of the rock surface. By combining the overall undulation degree of the rock surface and the three-dimensional roughness to characterize the morphology features of the rock surface, the system achieves the coordinated characterization of the overall spatial undulation features and local undulation features of the rock surface. Compared with the traditional two-dimensional contour analysis method, it can more comprehensively reflect the three-dimensional spatial morphology features of the rock surface after cutter rock breaking, improving the consistency and comparability of the rock surface morphology evaluation results under different cutter wear states and different test conditions. Attached Figure Description

[0009] To further illustrate the above and other advantages and features of this application, the specific embodiments of this application will be described in more detail below with reference to the accompanying drawings. The drawings, together with the following detailed description, are included in and form a part of this specification. Elements having the same function and structure are indicated by the same reference numerals. It should be understood that these drawings only depict typical examples of this application and should not be considered as limiting the scope of this application.

[0010] Figure 1 These are schematic diagrams of the three-dimensional roughness of the rock surface determined by the method provided in the embodiments of this application and by existing methods, respectively. Figure 2 This is a schematic diagram of the elevation distribution of the rock surface in the method provided in the embodiments of this application; Figure 3 This is a schematic side view of the rock surface under one working condition in the method provided in the embodiments of this application; Figure 4 This is a side view of the rock surface under another working condition in the method provided in the embodiments of this application; Figure 5 This is a schematic diagram of a three-dimensional point cloud model obtained after preprocessing by the method provided in the embodiments of this application; Figure 6 This is a schematic diagram illustrating the operation of acquiring three-dimensional point cloud data of a rock surface in the method provided in the embodiments of this application. Detailed Implementation

[0011] Exemplary embodiments of this application will be described below with reference to the accompanying drawings. For clarity and brevity, not all features of actual implementations are described in the specification. However, it should be understood that many implementation-specific decisions must be made in the development of any such actual embodiment to achieve the developer's specific goals, such as complying with constraints related to the system and business, and these constraints may vary depending on the implementation. Furthermore, it should be understood that while development work can be very complex and time-consuming, such development work is merely a routine task for those skilled in the art who benefit from the content of this application.

[0012] It should also be noted that, in order to avoid obscuring this application with unnecessary details, only the equipment structure and / or processing steps closely related to the solution according to this application are shown in the accompanying drawings, while other details that are not closely related to this application are omitted.

[0013] The following disclosure provides several different implementations or examples for carrying out this application. To simplify the disclosure of this application, specific examples of components and methods are described below. Of course, these are merely examples and are not intended to limit this application. In the description of the embodiments of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0014] In indoor linear cutting tests of roller cutters, the rock-breaking effect of roller cutters is usually evaluated by indicators such as triaxial force, specific energy, and rock debris sieving. However, the above indicators mainly characterize the rock-breaking process from the perspective of mechanical response and crushing products, and are difficult to directly reflect the changes in the spatial morphology of the rock surface after rock breaking.

[0015] When evaluating the rock-breaking effect of roller cutters from the perspective of rock surface roughness, it often relies on two-dimensional profile contours or single surface statistical parameters. Such methods can reflect the undulation characteristics on a specified profile, but the evaluation results are closely related to the profile selection direction, survey line position and sampling interval. When the rock surface has obvious spatial undulations or anisotropic characteristics, a single direction or a small number of profiles are difficult to fully characterize the three-dimensional morphological characteristics of the rock surface, which can easily lead to localization and directional deviation in the roughness evaluation results.

[0016] To address the aforementioned problems, one embodiment of this application provides a method for characterizing the morphological features of a rock surface after rock breaking by a roller cutter, comprising the following steps: S10: acquiring three-dimensional point cloud data of the rock surface; S20: processing the three-dimensional point cloud data to unify the spatial position and elevation of the three-dimensional point cloud data; S30: determining the elevation data of the rock surface based on the three-dimensional point cloud data processed in step S20; S40: determining the overall undulation of the rock surface based on the three-dimensional point cloud data and the elevation data; S50: extracting multiple two-dimensional elevation contour lines from the three-dimensional point cloud data processed in step S20; S60: determining the three-dimensional roughness of the rock surface based on the three-dimensional point cloud data processed in step S20 and the multiple two-dimensional elevation contour lines, wherein the three-dimensional roughness is used to characterize the local undulation of the rock surface; S70: determining the morphological features of the rock surface after rock breaking by a roller cutter based on the overall undulation of the rock surface and the three-dimensional roughness.

[0017] The method for characterizing the morphological features of the rock surface after roller cutting provided in this application preprocesses the three-dimensional point cloud data of the rock surface to unify the spatial position and elevation, thereby eliminating coordinate deviations that are prone to occur during the scanning process. This ensures that the overall undulation degree determined subsequently truly reflects the amplitude changes at the macroscopic scale of the rock surface. By extracting multiple two-dimensional elevation contour lines and combining them with the three-dimensional point cloud data to determine the three-dimensional roughness of the rock surface, the influence of the difference between the contour direction and the contour line length on the three-dimensional roughness evaluation results can be reduced, improving the accuracy of characterizing the three-dimensional spatial roughness features of the rock surface. By combining the overall undulation degree of the rock surface and the three-dimensional roughness to characterize the morphological features of the rock surface, the method achieves the coordinated characterization of the overall spatial undulation features and local undulation features of the rock surface. Compared with the traditional two-dimensional contour analysis method, this method can more comprehensively reflect the three-dimensional spatial morphological features of the rock surface after roller cutting, improving the consistency and comparability of the rock surface morphological evaluation results under different roller wear states and different test conditions.

[0018] In some embodiments, step S60 specifically includes the following steps: S61: Determine the root mean square slope parameter of the two-dimensional elevation profile line based on the elevation data of the rock surface and the two-dimensional elevation profile line; S62: Determine the two-dimensional profile roughness coefficient of the two-dimensional elevation profile line based on the root mean square slope parameter determined in step S61; S63: Determine the three-dimensional roughness of the rock surface based on multiple two-dimensional elevation profile lines and the two-dimensional profile roughness coefficient.

[0019] The above settings can convert the discrete variation of the elevation of each point on the two-dimensional elevation contour line into a dimensionless index under a unified scale, so that contour lines of different lengths or directions are on the same comparison benchmark, avoiding the systematic deviation caused by directly using the original slope or elevation difference; by combining multiple two-dimensional elevation contour lines and their respective two-dimensional contour roughness coefficients to determine the three-dimensional roughness of the rock surface, the limitations caused by a single contour direction or limited coverage range are avoided, so that the three-dimensional roughness includes both the texture strength of each profile to which each two-dimensional elevation contour line belongs, as well as the spacing density and directional distribution between profiles, which can realize a comprehensive quantitative characterization of the spatial roughness of the rock surface after the roller cutter breaks the rock.

[0020] In some embodiments, in step S63, the three-dimensional roughness of the rock surface is determined as follows: ;in, The value represents the three-dimensional roughness of the rock surface; m represents the number of two-dimensional elevation contour lines extracted in the X direction of the three-dimensional point cloud data; n represents the number of two-dimensional elevation contour lines extracted in the Y direction of the three-dimensional point cloud data. This represents the contour length of the j-th two-dimensional elevation contour line extracted in the X direction of the 3D point cloud data. This represents the contour length of the kth two-dimensional elevation contour line extracted in the Y direction of the 3D point cloud data. This represents the two-dimensional profile roughness coefficient of the j-th two-dimensional elevation profile extracted in the X direction of the three-dimensional point cloud data. It represents the two-dimensional profile roughness coefficient of the k-th two-dimensional elevation profile extracted in the Y direction of the three-dimensional point cloud data.

[0021] Since the roughness of rock surfaces has significant spatial differences, it is difficult to fully reflect the true spatial roughness characteristics of rock surfaces by using only a single-direction contour line or a simple averaging method. By considering the length of multiple two-dimensional elevation contour lines in the process of determining the three-dimensional roughness of rock surfaces, contour information of different directions and length scales can be used together to determine the three-dimensional roughness, thereby improving the accuracy of the representation of the spatial roughness characteristics of rock surfaces.

[0022] The existing indices for evaluating three-dimensional roughness are determined as follows: ;in, Let represent the two-dimensional profile roughness coefficient of the i-th two-dimensional elevation profile, where n is the number of two-dimensional elevation profiles. This method obtains an index for evaluating three-dimensional roughness by determining the two-dimensional profile roughness coefficient of each two-dimensional elevation profile separately and averaging them. .

[0023] The methods obtained by the existing methods and the methods provided in the embodiments of this application are determined in the following manner. and The relative error E between them: . Figure 1 These are schematic diagrams of the three-dimensional roughness of the rock surface determined by the method provided in the embodiments of this application and by existing methods, respectively. Figure 1 As shown, the gray bars represent the results obtained using the traditional averaging method under different penetration depths. The red bars represent the penetration depths obtained using the methods provided in the embodiments of this application. The three-dimensional roughness values ​​obtained by the two methods above have a consistent variation law, and the relative error E of both is less than 5%, indicating that the method provided by the embodiments of this application can maintain the effective characterization of rock surface roughness by the traditional two-dimensional contour evaluation method. Moreover, the three-dimensional roughness determined by the method provided by the embodiments of this application is greater than the three-dimensional roughness of the rock surface obtained by the traditional averaging method under different penetration degrees, indicating that the traditional averaging method underestimates the contribution of local significant fluctuations. In contrast, the method of this application considers the length of multiple two-dimensional elevation contour lines, so that contour information of different directions and different length scales participates in determining the three-dimensional roughness, making the three-dimensional roughness value more reflective of the true morphological characteristics of the rock surface.

[0024] Understandably, the outline length of a two-dimensional elevation profile is the actual path length of the two-dimensional elevation profile.

[0025] In some embodiments, step S61 specifically includes the following steps: S611: Determine the number of sampling points included in the two-dimensional elevation contour line based on the three-dimensional point cloud data; S612: Determine the elevation values ​​of the sampling points of the two-dimensional elevation contour line; S613: Determine the root mean square slope parameter of the two-dimensional elevation contour line based on the number of sampling points and the elevation values. The elevation values ​​of each sampling point can reflect the vertical fluctuation of the rock surface. By combining the number of sampling points and the elevation values ​​to determine the root mean square slope parameter, the degree of discrete change in the elevation of each point on a single two-dimensional elevation contour line can be quantitatively characterized.

[0026] In some embodiments, in step S613, the root mean square slope parameter of the two-dimensional elevation profile is determined as follows: ;in, The root mean square slope parameter represents a single two-dimensional elevation profile; M represents the number of intervals between adjacent sampling points within a single two-dimensional elevation profile; Δx represents the distance between adjacent sampling points. , This represents the elevation values ​​of the i-th and (i+1)-th sampling points.

[0027] The number of sampling intervals can reflect the coverage density and spatial resolution of the two-dimensional elevation profile on the rock surface. By setting a fixed sampling interval Δx, it can be ensured that the slope value of each interval is determined only by the elevation difference, avoiding additional scale interference introduced due to different interval lengths. By summing and averaging the slope changes of all intervals, the undulations of each interval within a single two-dimensional elevation profile can participate in determining the root mean square slope parameter, enabling two-dimensional elevation profiles of different locations and lengths to compare their undulations on a uniform scale.

[0028] In some embodiments, in step S62, the two-dimensional profile roughness coefficient of the two-dimensional elevation profile is determined as follows: ;in, The two-dimensional profile roughness coefficient representing a single two-dimensional elevation profile line; This represents the root mean square slope parameter of a single two-dimensional elevation profile; the two constant parameters, 32.2 and 32.47, are obtained empirically. The above settings can obtain the two-dimensional profile roughness coefficient values ​​corresponding to two-dimensional elevation profiles in different directions, providing a basis for determining the three-dimensional rock surface roughness.

[0029] In some embodiments, in step S50, before extracting multiple two-dimensional elevation contour lines from the three-dimensional point cloud data processed in step S20, the three-dimensional point cloud data can be resampled at a fixed interval Δx. After resampling, the projection distribution of the point cloud in the horizontal direction is regularized into an equally spaced grid, and the horizontal step size between adjacent points is uniformly Δx. These points with a fixed horizontal interval are the sampling points on the two-dimensional elevation contour lines.

[0030] In some embodiments, in step S50, multiple two-dimensional elevation contour lines can be extracted along different spatial directions of the resampled 3D point cloud data. Specifically, when performing indoor linear cutting on a rock surface, the rock surface destruction is a unidirectional straight line. Based on a predetermined sampling interval, the 3D point cloud data can be truncated along the X and Y directions respectively to obtain multiple two-dimensional elevation contour lines. Each two-dimensional elevation contour line can be represented as a set of multiple sampling points, and each sampling point is represented as: ; This represents the i-th sampling point on the two-dimensional elevation profile line; This represents the horizontal coordinate of the i-th sampling point; This represents the elevation value of the i-th sampling point.

[0031] In some embodiments, step S40 specifically includes the following steps: S41: Determine the average elevation of the rock surface based on the elevation data; S42: Determine the arithmetic mean height parameter and root mean square height parameter of the rock surface based on the elevation data and the average elevation, so as to characterize the overall undulation of the rock surface.

[0032] Determining the average elevation allows for the establishment of a unified elevation datum, enabling the determination of elevation deviations at each sampling point. The arithmetic mean height reflects the average deviation level of the rock surface. The root mean square height is more sensitive to areas with significant local undulations and can be used to analyze differences in rock surface morphology under different rock-breaking conditions. These two parameters simultaneously express the central tendency and discrete characteristics of fluctuations, thus providing a complete characterization of the overall undulation of the rock surface.

[0033] In some embodiments, in step S42, the arithmetic mean height parameter of the rock surface is determined as follows: The root mean square height parameter of the rock surface is determined as follows: ;in, The parameter representing the arithmetic mean height of the rock surface; The root mean square height parameter representing the rock surface; Indicates the average elevation of the rock surface; The elevation value represents the k-th point in the 3D point cloud data; N represents the number of points in the preprocessed 3D point cloud data.

[0034] Figure 2 This is a schematic diagram of the elevation distribution of the rock surface in the method provided by the embodiments of this application. In some embodiments, such as... Figure 2 As shown, in step S40, the 3D point cloud data processed in step S20 can be further processed into a regular grid to obtain the rock surface elevation matrix, and a schematic diagram of the rock surface elevation distribution can be drawn based on the elevation matrix. Figure 2 The change from green to blue indicates the degree of deviation of the rock surface from the elevation datum, from small to large.

[0035] Figure 3 This is a side view of the rock surface under one working condition in the method provided in the embodiments of this application. Figure 4 This is a side view of the rock surface under another working condition in the method provided by the embodiments of this application. In some embodiments, such as... Figure 3 and Figure 4 As shown, in step S40, the three-dimensional point cloud data can be converted into a two-dimensional elevation profile based on the spatial coordinate information of the three-dimensional point cloud data, so as to intuitively represent the spatial undulation characteristics of the rock surface after the rock is broken by the roller cutter. Specifically, a representative predetermined direction can be selected from the three-dimensional point cloud data of the rock surface, and the three-dimensional point cloud data can be projected according to the spatial position to extract the point cloud elevation data in the same direction, so as to obtain the side view elevation profile of the rock surface. Figure 3 and Figure 4 In the diagram, the blue curve and dots represent the lateral elevation change of the rock surface at a penetration depth of 0.5 mm, the yellow curve and dots represent the lateral elevation change at a penetration depth of 1.0 mm, the orange curve and dots represent the lateral elevation change at a penetration depth of 1.5 mm, and the purple curve and dots represent the lateral elevation change at a penetration depth of 1.7 mm. Since the elevation coordinates are negative, it indicates that the elevation reference surface selected in this embodiment is above the rock surface. Therefore, the greater the penetration depth, the smaller the elevation coordinate value. Figure 3 and Figure 4 They represent different working conditions, and the changes in side-view elevation under the same penetration depth are also significantly different.

[0036] like Figures 2-4 As shown, by determining the elevation distribution diagram of the rock surface and the side view contours of the rock surface under different working conditions, the overall undulation, groove distribution, local depressions, rock ridge remnants, and block spalling areas of the rock surface after roller cutting can be intuitively characterized. The elevation distribution diagram can be used to compare the differences in rock surface morphology under different penetration depths and different wear combinations, providing morphological basis for subsequent analysis of the roller cutting mechanism. Based on this, combined with the overall undulation parameters of the rock surface... , The overall degree of undulation can be verified by cross-validation of qualitative observation and quantitative characterization, thus achieving a unified qualitative observation and quantitative evaluation of the spatial morphology of the rock surface.

[0037] In some embodiments, step S70 specifically includes the following steps: S71: Determine the amplitude saturation threshold of the arithmetic mean height parameter and the root mean square height parameter of the rock surface in step S42; S72: Normalize the arithmetic mean height parameter and the root mean square height parameter according to the amplitude saturation threshold; S73: Determine the rock surface morphology index of the roller cutter rock breaking based on the normalization result of step S72 and the three-dimensional roughness; S74: Characterize the morphological features of the rock surface based on the rock surface morphology index of the roller cutter rock breaking. Determining the amplitude saturation threshold provides a fixed benchmark for the arithmetic mean height parameter and the root mean square height parameter, thus limiting the true potential fluctuation level of the rock surface. Normalizing these two parameters based on their amplitude saturation thresholds eliminates dimensional interference caused by regional scale differences. By combining the normalization result with three-dimensional roughness, a rock surface morphology index for roller cutting is obtained, which simultaneously reflects the overall fluctuation of the rock surface and the local texture roughness. Ultimately, when characterizing the rock surface morphology based on this index, the index value directly corresponds to the actual morphological differences of the rock surface after roller cutting, making the rock-breaking effects under different wear states or different penetration depths intuitively comparable.

[0038] In some embodiments, in step S73, the rock surface morphology index of the roller cutter is determined in the following manner: Among them, RSMI represents the rock surface morphology index of the roller cutter rock breaking process; Represents the three-dimensional roughness of the rock surface; This represents the arithmetic mean height parameter. This represents the root mean square height parameter; This represents the amplitude saturation threshold of the arithmetic mean height parameter; This represents the amplitude saturation threshold of the root mean square height parameter.

[0039] By directly amplifying or reducing RSMI using three-dimensional roughness, the contribution of local texture to the overall morphology of the rock surface can be reflected. Furthermore, a lithology-specific amplitude saturation threshold can be set as an upper limit to ensure that the measured values ​​are accurate. , After exceeding their respective amplitude saturation thresholds, the values ​​are truncated to 1, which avoids local texture information being obscured by large local anomalies. This allows RSMI to respond to changes in both rock surface texture and macroscopic undulations simultaneously, achieving a comprehensive quantitative characterization of the rock surface morphology after rock breaking by a roller cutter.

[0040] In some embodiments, in step S74, if RSMI < 36.7, the overall rock surface undulation is small, and crushing is the main form of damage; if RSMI ≤ 36.7 < 64.35, the overall rock surface undulation increases, and local blocks begin to peel off; if RSMI ≤ 64.35 < 72.8, the overall rock surface undulation further increases, and block peeling intensifies; if RSMI ≥ 72.8, both the overall rock surface undulation and three-dimensional roughness reach a high level, indicating a high degree of rock surface fragmentation. RSMI represents the rock surface morphology index of the roller cutter. By determining the numerical classification of RSMI, the quantitative characterization and grading of the rock surface morphology after roller cutting can be achieved. The larger the RSMI value, the more obvious the overall rock surface undulation, the higher the degree of local undulation, and the more sufficient the roller cutting action. Compared with a single roughness index, the above setting organically combines the overall rock surface undulation characteristics with the local roughness characteristics, which can more comprehensively reflect the three-dimensional morphology characteristics after roller cutting.

[0041] In some embodiments, in step S74, the same method can be used to redetermine the RSMI value range corresponding to each level of rock breaking action for different lithologies, cutter sizes and test conditions.

[0042] In some embodiments, in step S71, the amplitude saturation threshold can be determined based on the target lithology and the roller cutter rock breaking test database. When the overall undulation of the rock surface tends to stabilize with the increase of the rock breaking degree, the corresponding... and As an amplitude saturation threshold, the amplitude saturation threshold can be determined to characterize the limit of the rock surface undulation amplitude that a specific rock can reach under the action of a roller cutter, and to provide a comparable quantitative basis for the rock breaking effect under different lithologies and working conditions.

[0043] Preferably, they can be taken separately. =3.06mm, =3.52mm.

[0044] In some embodiments, step S20 specifically includes the following steps: S21: Decentralize the three-dimensional point cloud data to determine the spatial location reference of the three-dimensional point cloud data; S22: Determine the minimum feature direction of the spatial distribution of the three-dimensional point cloud data based on the three-dimensional point cloud data obtained by step S21; S23: Correct the three-dimensional point cloud data obtained by step S22 based on the minimum feature direction to determine the elevation reference of the three-dimensional point cloud data. Figure 5 This is a schematic diagram of a 3D point cloud model obtained after preprocessing according to the method provided in the embodiments of this application, as shown below. Figure 5As shown, decentralization can eliminate the overall spatial positional differences of the point cloud, obtaining centralized 3D point cloud data with the centroid of the point cloud as the reference. Based on this, the minimum characteristic direction of the spatial distribution of the point cloud can be determined, which can further determine the true normal of the rock surface to reflect the actual tilt posture of the rock mass in space. The point cloud is corrected according to the minimum characteristic direction, so that the elevation value of the processed point cloud only reflects the undulation of the rock surface itself, eliminating the errors caused by the scanning angle or the natural tilt of the rock mass, and providing an accurate elevation reference for subsequent processing.

[0045] In some embodiments, in step S22, a covariance matrix can be established based on the three-dimensional point cloud data obtained in step S21, and eigenvalues ​​can be solved to obtain the characteristic directions of the spatial distribution of the point cloud, and the minimum characteristic direction can be determined from the characteristic directions. Those skilled in the art can implement the above operations using principal component analysis.

[0046] In some embodiments, in step S23, the feature vector corresponding to the smallest feature direction is selected as the normal vector of the rock surface point cloud. By adjusting the angle between this normal vector and the Z-axis of the spatial coordinate system, the point cloud is rotated to make the direction of the rock surface normal vector consistent with the direction of the Z-axis of the coordinate system. The Z-axis represents the elevation data of the rock surface. Figure 5 The Z-axis direction value and Figure 3 The color distribution in the diagrams reflects the degree of deviation of the rock surface from the elevation reference surface on the rock surface.

[0047] In some embodiments, in step S30, the three-dimensional point cloud data processed in step S23 can be interpolated into a regular grid to determine the rock surface elevation function, so that the rock surface elevation data distribution is continuous. The specific interpolation method is something that can be implemented by those skilled in the art.

[0048] Figure 6 This is a schematic diagram illustrating the operation of acquiring three-dimensional point cloud data of a rock surface in the method provided in the embodiments of this application. In some embodiments, such as... Figure 6 As shown, in step S10, after the rock breaking test, the residue on the surface of the rock sample can be cleaned, and a three-dimensional laser scanning device can be used to scan the surface of the rock sample after rock breaking to obtain three-dimensional point cloud data of the rock surface. The three-dimensional point cloud data is a set of spatial coordinates of each point on the rock surface.

[0049] In some embodiments, in step S10, the three-dimensional point cloud data obtained by scanning can be regionally cropped to focus on the actual working area of ​​the roller cutter, and the three-dimensional point cloud data can be denoised and outlier removed to remove invalid points, duplicate points and abnormal points that deviate from the real rock surface area generated during the scanning process, thereby improving the quality of the point cloud data.

[0050] Another embodiment of this application provides a characterization system for the morphological features of a rock surface after rock breaking by a roller cutter, comprising: a laser scanner configured to scan the rock surface to obtain three-dimensional point cloud data of the rock surface; and a processor configured to: process the three-dimensional point cloud data to unify the spatial position and elevation of the three-dimensional point cloud data; determine the elevation data of the rock surface based on the processed three-dimensional point cloud data; determine the overall undulation of the rock surface based on the three-dimensional point cloud data and the elevation data; extract multiple two-dimensional elevation contour lines from the processed three-dimensional point cloud data; determine the three-dimensional roughness of the rock surface based on the three-dimensional point cloud data and the multiple two-dimensional elevation contour lines, wherein the three-dimensional roughness is used to characterize the local undulation of the rock surface; and determine the morphological features of the rock surface after rock breaking by a roller cutter based on the overall undulation of the rock surface and the three-dimensional roughness.

[0051] The system for characterizing the morphology of rock surface after cutter rock breaking, provided in the embodiments of this application, preprocesses the three-dimensional point cloud data of the rock surface to unify the spatial position and elevation, thereby eliminating coordinate deviations that are prone to occur during the scanning process. This ensures that the overall undulation degree determined subsequently truly reflects the amplitude changes at the macroscopic scale of the rock surface. By extracting multiple two-dimensional elevation contour lines and combining them with three-dimensional point cloud data to determine the three-dimensional roughness of the rock surface, the influence of the difference between the contour direction and the contour line length on the three-dimensional roughness evaluation results can be reduced, improving the accuracy of characterizing the three-dimensional spatial roughness features of the rock surface. By combining the overall undulation degree of the rock surface and the three-dimensional roughness to characterize the morphology features of the rock surface, the system achieves the coordinated characterization of the overall spatial undulation features and local undulation features of the rock surface. Compared with the traditional two-dimensional contour analysis method, it can more comprehensively reflect the three-dimensional spatial morphology features of the rock surface after cutter rock breaking, improving the consistency and comparability of the rock surface morphology evaluation results under different cutter wear states and different test conditions.

[0052] Regarding the embodiments of this application, it should also be noted that, without conflict, the embodiments of this application and the features in the embodiments can be combined with each other to obtain new embodiments.

[0053] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. The scope of protection of this application shall be determined by the scope of the claims.

Claims

1. A method for characterizing the morphological features of a rock surface after a rock-breaking cutter, characterized in that, It includes the following steps: S10: Collect the three-dimensional point cloud data of the rock surface; S20: Process the three-dimensional point cloud data to unify the spatial position and elevation of the three-dimensional point cloud data; S30: Determine the elevation data of the rock surface based on the three-dimensional point cloud data processed in step S20; S40: Determine the overall undulation of the rock surface based on the three-dimensional point cloud data and the elevation data; S50: Extract multiple two-dimensional elevation contour lines from the three-dimensional point cloud data processed in step S20; S60: Based on the three-dimensional point cloud data processed in step S20 and the multiple two-dimensional elevation contour lines, determine the three-dimensional roughness of the rock surface. The three-dimensional roughness is used to characterize the local undulation of the rock surface. S70: Determine the morphological characteristics of the rock surface after the rock is broken by the roller cutter based on the overall undulation of the rock surface and the three-dimensional roughness.

2. The method according to claim 1, characterized in that, Step S60 specifically includes the following steps: S61: Determine the root mean square slope parameter of the two-dimensional elevation profile line based on the elevation data of the rock surface and the two-dimensional elevation profile line. S62: Determine the two-dimensional profile roughness coefficient of the two-dimensional elevation profile line based on the root mean square slope parameter determined in step S61. S63: Determine the three-dimensional roughness of the rock surface based on the multiple two-dimensional elevation contour lines and the two-dimensional contour roughness coefficient.

3. The method according to claim 2, characterized in that, In step S63, the three-dimensional roughness of the rock surface is determined as follows: ； in, The value represents the three-dimensional roughness of the rock surface; m represents the number of two-dimensional elevation contour lines extracted in the X direction of the three-dimensional point cloud data; n represents the number of two-dimensional elevation contour lines extracted in the Y direction of the three-dimensional point cloud data. This represents the contour length of the j-th two-dimensional elevation contour line extracted in the X direction of the three-dimensional point cloud data. This represents the contour length of the kth two-dimensional elevation contour line extracted in the Y direction of the three-dimensional point cloud data. This represents the two-dimensional profile roughness coefficient of the j-th two-dimensional elevation profile extracted in the X direction of the three-dimensional point cloud data. It represents the two-dimensional profile roughness coefficient of the k-th two-dimensional elevation profile extracted in the Y direction of the three-dimensional point cloud data.

4. The method according to claim 2, characterized in that, Step S61 specifically includes the following steps: S611: Determine the number of sampling points contained in the two-dimensional elevation contour line based on the three-dimensional point cloud data; S612: Determine the elevation values ​​of the sampling points of the two-dimensional elevation contour line; S613: Determine the root mean square slope parameter of the two-dimensional elevation profile based on the number of sampling points and the elevation value.

5. The method according to claim 2, characterized in that, In step S613, the root mean square slope parameter of the two-dimensional elevation profile is determined as follows: ； in, The root mean square slope parameter represents a single two-dimensional elevation profile; M represents the number of intervals between adjacent sampling points within a single two-dimensional elevation profile; Δx represents the distance between adjacent sampling points. , This represents the elevation values ​​of the i-th and (i+1)-th sampling points.

6. The method according to claim 2, characterized in that, In step S62, the two-dimensional profile roughness coefficient of the two-dimensional elevation profile is determined as follows: ； in, The two-dimensional profile roughness coefficient representing a single two-dimensional elevation profile line; This represents the root mean square slope parameter of a single two-dimensional elevation profile.

7. The method according to any one of claims 1-6, characterized in that, Step S40 specifically includes the following steps: S41: Determine the average elevation of the rock surface based on the elevation data; S42: Based on the elevation data and the average elevation, determine the arithmetic mean height parameter and root mean square height parameter of the rock surface to characterize the overall undulation of the rock surface.

8. The method according to claim 7, characterized in that, Step S70 specifically includes the following steps: S71: Determine the amplitude saturation threshold of the arithmetic mean height parameter of the rock surface and the root mean square height parameter mentioned in step S42. S72: Normalize the arithmetic mean height parameter and the root mean square height parameter according to the amplitude saturation threshold; S73: Based on the normalization result of step S72 and the three-dimensional roughness, determine the rock surface morphology index of the roller cutter rock breaking. S74: Characterize the morphological features of the rock surface based on the rock surface morphology index of the rolling cutter.

9. The method according to claim 8, characterized in that, In step S73, the rock surface morphology index of the roller cutter is determined in the following manner: ； Wherein, RSMI represents the rock surface morphology index of the roller cutter rock breaker; This represents the three-dimensional roughness of the rock surface; This represents the arithmetic mean height parameter; This represents the root mean square height parameter; This represents the amplitude saturation threshold of the arithmetic mean height parameter; This represents the amplitude saturation threshold of the root mean square height parameter.

10. The method according to claim 9, characterized in that, In step S74, The RSMI was determined to be <36.7, indicating that the overall rock surface had small undulations and was mainly subjected to crushing damage. If 36.7 ≤ RSMI < 64.35, it is determined that the overall undulation of the rock surface has increased, and local blocks have begun to peel off; It was determined that 64.35≤RSMI<72.8, indicating that the overall undulation of the rock surface further increased and the block spalling intensified; The RSMI was determined to be ≥72.8, indicating that the overall undulation and three-dimensional roughness of the rock surface were both at a high level, and that the rock surface was highly fragmented. RSMI represents the rock surface morphology index of the roller cutter.

11. The method according to any one of claims 1-10, characterized in that, Step S20 specifically includes the following steps: S21: Decentralize the three-dimensional point cloud data to determine the spatial location reference of the three-dimensional point cloud data; S22: Based on the three-dimensional point cloud data obtained in step S21, determine the minimum feature direction of the spatial distribution of the three-dimensional point cloud data; S23: Correct the three-dimensional point cloud data obtained in step S22 according to the minimum feature direction to determine the elevation benchmark of the three-dimensional point cloud data.

12. A characterization system for the morphological features of a rock surface after rock breaking by a roller cutter, characterized in that, It includes: A laser scanner is configured to scan the rock surface to obtain three-dimensional point cloud data of the rock surface; The processor is configured to: The three-dimensional point cloud data is processed to unify the spatial position and elevation of the three-dimensional point cloud data; Based on the processed three-dimensional point cloud data, the elevation data of the rock surface is determined; Based on the three-dimensional point cloud data and the elevation data, the overall undulation of the rock surface is determined; Multiple two-dimensional elevation contour lines are extracted from the processed three-dimensional point cloud data; Based on the three-dimensional point cloud data and multiple two-dimensional elevation contour lines, the three-dimensional roughness of the rock surface is determined, and the three-dimensional roughness is used to characterize the local undulation of the rock surface. Based on the overall undulation of the rock surface and the three-dimensional roughness, the morphological characteristics of the rock surface after the rock is broken by the roller cutter are determined.

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