Method and system for identifying microscopic morphology of micro-convex body on rough surface and related device

Abstract

The invention provides a method, system and related device for identifying the microscopic morphology of a micro-convex body on a rough surface, and the method comprises the following steps: carrying out the three-dimensional contour scanning of a to-be-measured rough surface, and obtaining three-dimensional point cloud data; cutting, aligning and denoising the obtained three-dimensional point cloud data in sequence to obtain a denoised upper surface and a denoised lower surface; all local peak points on the denoised upper surface and the denoised lower surface are searched respectively; based on all local peak points of the upper surface and the lower surface, obtaining point cloud data of all the micro-convexes; visualizing the identified micro-convex bodies one by one according to the obtained point cloud data; and obtaining the reference surface of the identified micro-convex body, generalizing the part above the reference surface into a semi-ellipsoid, and calculating the morphology parameter of each micro-convex body. According to the method, the interference of micro data noise is reduced, the defects of over-segmentation and under-segmentation of traditional methods such as watershed are overcome, and simple, efficient and refined identification of the rough surface micro-convex body (micron-mm level) is realized.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of contact mechanics, and particularly relates to a rough surface micro-convex body micro-morphology identification method and system and related device. BACKGROUND

[0002] Quantifying the morphology characteristics of different micro-convex bodies at the microscale on a rough contact surface is crucial for evaluating the stress and deformation characteristics of materials at a long timescale, which plays a key role in evaluating the durability of materials and the safety of engineering in the fields of material science, aerospace and engineering geology.

[0003] The current methods for evaluating the roughness of a material surface include a watershed algorithm, a statistical method and a fractal theory method. The traditional watershed algorithm is only suitable for rapid evaluation of initial roughness, is prone to misjudging micro-noise as a micro-convex body to cause over-segmentation, or to combine or ignore multiple micro-convex bodies to cause under-segmentation. The statistical method ignores the spatial distribution and geometric diversity of micro-convex bodies. The fractal theory method has the defects of high complexity and large amount of calculation. SUMMARY

[0004] The present application aims to provide a rough surface micro-convex body micro-morphology identification method, system and related device, which solves the above-mentioned deficiencies in the prior art.

[0005] To achieve the above-mentioned purpose, the technical solution adopted by the present application is as follows: In a first aspect, the present application provides a rough surface micro-convex body micro-morphology identification method, comprising the following steps: performing three-dimensional profile scanning on the rough surface to be measured to obtain three-dimensional point cloud data; sequentially performing cutting, alignment and noise reduction processing on the obtained three-dimensional point cloud data to obtain an upper surface after noise reduction processing and a lower surface after noise reduction processing; respectively searching for all local peak points on the upper and lower surfaces after noise reduction processing; based on all the local peak points on the upper and lower surfaces, obtaining point cloud data of all micro-convex bodies; visually identifying the micro-convex bodies one by one according to the obtained point cloud data; drawing a reference surface for the identified micro-convex bodies, generalizing the part above the reference surface into a semi-ellipsoid, and calculating the morphology parameters of each micro-convex body based thereon.

[0006] Preferably, wavelet analysis is used for noise reduction processing to obtain the upper surface after noise reduction processing and the lower surface after noise reduction processing.

[0007] Preferably, according to all local peak points of the upper and lower surfaces, in combination with a region growing algorithm, search around from the peak points until the height gradient reverses, stop searching, and obtain the point cloud data of each microconvex body one by one, and then obtain the point cloud data of all microconvex bodies.

[0008] Preferably, a reference surface of the identified microconvex body is obtained, the part above the reference surface is approximated to a semi-ellipsoid, and the morphology parameters of each microconvex body are calculated, and the specific method is as follows: The highest point in the edge point of each microconvex body is obtained. A reference point is taken as the highest point in the edge, and a horizontal plane is made through the reference point as a reference surface. Each microconvex body is cut based on the reference surface, and the part above the reference surface is retained, so as to realize the identification of the microconvex body microscopic morphology characteristics.

[0009] Preferably, the identification method further comprises calculating the morphology parameters of each microconvex body and visualizing, and the specific method is as follows: The area surrounded by the bottom surface of the cut microconvex body is regarded as a convex hull, the area of the convex hull is taken as the bottom area of the corresponding microconvex body, and the convex hull is approximated to a circle. The height-diameter ratio, bottom area, curvature radius and height difference of the semi-ellipsoid are calculated, so as to obtain the morphology parameters of different microconvex bodies of the rough surface. The cut microconvex body is approximated to a semi-ellipsoid, and the center of gravity of the semi-ellipsoid is placed at the center of gravity position of the cut microconvex body.

[0010] In a second aspect, the present application provides a rough surface microconvex body microscopic morphology identification system, comprising: A three-dimensional scanning unit is used to perform three-dimensional profile scanning on the rough surface to be measured, and three-dimensional point cloud data is obtained. A data preprocessing unit is used to perform cutting, alignment and noise reduction processing on the obtained three-dimensional point cloud data in sequence, and an upper surface after noise reduction and a lower surface after noise reduction are obtained. A peak point searching unit is used to search all local peak points on the upper and lower surfaces after noise reduction, respectively. A microconvex body point cloud data acquisition unit is used to obtain the point cloud data of all microconvex bodies based on all local peak points of the upper and lower surfaces. A microconvex body visualization unit is used to visualize the identified microconvex bodies one by one according to the obtained point cloud data. A morphology parameter acquisition unit is used to obtain a reference surface of the identified microconvex body, approximate the part above the reference surface to a semi-ellipsoid, and calculate the morphology parameters of each microconvex body.

[0011] In a third aspect, the present application provides an electronic device, comprising a processor and a memory, wherein the memory stores computer instructions, and when the computer instructions are executed by the processor, the electronic device performs the method.

[0012] In a fourth aspect, the present application provides a computing device cluster, comprising at least one computing device, and each computing device comprises a processor and a memory. The processor of the at least one computing device is configured to execute instructions stored in the memory of the at least one computing device, so that the computing device cluster performs the method.

[0013] In a fifth aspect, the present application provides a computer program product, wherein the computer program product comprises computer executable instructions, and when the computer executable instructions are executed, the method is implemented.

[0014] In a sixth aspect, the present application provides a computer readable storage medium, wherein the computer readable storage medium stores computer executable instructions, and when the computer executable instructions are executed by a processor, the method is implemented.

[0015] Compared with the prior art, the present application has the following beneficial effects: The rough surface microconvex body micro-morphology identification method provided by the present application can obtain high-precision point cloud data through three-dimensional contour scanning, and after cutting, aligning and noise reduction processing, the accuracy and cleanliness of the data are significantly improved, noise and irrelevant data interference are effectively eliminated, and a reliable foundation is laid for microconvex body identification and visualization; by searching for local peak points on the upper and lower surfaces, the highest points of all microconvex bodies can be automatically and efficiently located, subjective errors and inefficiency problems of traditional manual identification are avoided, the comprehensiveness and consistency of microconvex body detection are ensured, the range and boundary of the microconvex body are effectively defined by using the regional growth algorithm and the feature that the height gradient reverses at the "valley", the reference surface of each microconvex body is respectively demarcated, the part above the reference surface represents the micro-morphology characteristics of the microconvex body, and the analysis and quantization of the micro-morphology characteristics of a single microconvex body, such as height, volume, curvature and spatial distribution, and other key parameters are realized, the automatic process of the method not only greatly improves the identification efficiency and shortens the processing time, but also the microconvex body height after fine identification is in the order of μm~mm, and the actual error is within 10 -4 mm.

[0016] Further, the method combines wavelet analysis method, region growing algorithm and reference surface method, can simply and efficiently identify the micro convex body on the rough surface, filters out the micro data noise and discontinuous points by using the wavelet analysis method, makes the surface more smooth and continuous, reduces the interference of data noise, makes up the defects of the traditional watershed method of dividing the unified reference surface for all micro convex bodies, thus causing over-segmentation and under-segmentation, and finally can visualize the morphology of all identified micro convex bodies, obtains the morphology parameters such as height, bottom area, height-diameter ratio and curvature radius of the micro convex body, and provides an effective means for analyzing the compression characteristics and friction characteristics of two rough surfaces in contact from the micro convex body scale. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 For the three-dimensional point cloud data graph involved in the embodiment of the application, (a) is the upper surface, (b) is the lower surface, and (c) is the crack spacing; Figure 2 For the image after noise reduction involved in the embodiment of the application; Figure 3 For the peak point visualization graph involved in the embodiment of the application; Figure 4 For the effect graph of the identified micro convex body after visualization involved in the embodiment of the application, (a) and (b) are two identified micro convex bodies, and the micro convex body after being cut by the reference surface; Figure 5 For the effect graph of the micro convex body after cutting involved in the embodiment of the application; Figure 6 For the visualization graph of the micro convex body after cutting involved in the embodiment of the application; Figure 7 For the area and range of different rock crack micro convex bodies (in turn, red sandstone, limestone and dolomite) involved in the embodiment of the application; Figure 8 For the semi-ellipsoidal micro convex body sample with different height-diameter ratios involved in the embodiment of the application; Figure 9 For the comparison graph of the method involved in the embodiment of the application and the traditional watershed method, (a) is the method involved in the embodiment of the application; and (b) is the traditional watershed method. DETAILED DESCRIPTION

[0018] In the following description, specific details are set forth such as particular system configurations, techniques, etc., in order to provide a thorough understanding of the embodiments of the application. However, persons skilled in the art will understand that the application can be practiced without these specific details. In other instances, well-known structures, devices, circuits, and methods have not been described in detail in order to avoid obscuring the application.

[0019] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0020] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0021] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0022] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0023] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0024] Example 1 This embodiment provides a method for identifying the microstructure of micro-protrusions on a rough fractured surface, comprising the following steps: The rough surface to be measured is subjected to three-dimensional contour scanning to obtain three-dimensional point cloud data; The obtained 3D point cloud data is cropped, aligned and denoised in sequence to obtain the denoised upper surface and the denoised lower surface. Search for all local peak points on the denoised upper and lower surfaces respectively; Based on all local peak points on the upper and lower surfaces, obtain the point cloud data of all micro-convexities; The identified micro-convexities are visualized one by one based on the obtained point cloud data; Obtain the reference surface of the identified micro-protrusions, generalize the part above the reference surface into a semi-ellipsoid, and calculate the morphological parameters of each micro-protrusion.

[0025] In this embodiment, Example 2 This embodiment provides a method for identifying the microstructure of micro-protrusions on a rough surface, taking natural rock fissures as an example, including the following steps: Step 1: Using a 3D profilometer, perform a 3D profile scan of the rock fracture surface with a resolution of ~0.01 mm to obtain 3D point cloud data, such as... Figure 1 As shown.

[0026] Since the sample is not perfectly flat and symmetrical, before running the code, the four edges of the upper and lower fracture surfaces are trimmed using Geomagic Wrap to make them standard rectangles, resulting in the trimmed upper and lower fracture surfaces.

[0027] After importing both the upper and lower crack surfaces into Matlab, a unit mesh length of 0.1 mm (dx) was used. Considering that two arbitrarily contacting rough surfaces are not perfectly matched, this embodiment takes into account the case of misalignment between the upper and lower surfaces (shear); the upper and lower crack surfaces are then aligned. Establish a coordinate system and use ndgrid to mesh the upper and lower fracture surfaces, i.e., 'xx_lower' and 'yy_lower'. Use a figure to display images of the upper and lower surfaces zz_lower, zz_upper, and the fracture spacing aperture, with units uniformly set to ×10. -3 Set it to mm.

[0028] Finally, the variables 'xx_lower', 'yy_lower', 'zz_upper', 'zz_lower', and 'aperture' are saved to the geo_'num2str(shear)'_mm.mat file.

[0029] Step 2: Due to the fractal characteristics of rock fracture surfaces, smaller micro-protrusions are often distributed on larger protrusions. Therefore, wavelet analysis is used to denoise the upper and lower fracture surfaces separately. The aim is to treat the micro-protrusions with different fractal characteristics as a superposition of different waveforms, resulting in the denoised upper and lower fracture surfaces. Specifically: First, data preprocessing is performed: the coordinates and height data of the upper and lower fracture surfaces are separated, and then the separated coordinates are normalized. The wavelet basis function [c,l] = wavedec2(x,8,wave) is specified, and different waveforms, i.e., waves, such as db1~db8, can be selected.

[0030] Then, the approximate components and detail components of each layer are extracted: a8=wrcoef2('a',c,l,wave,8); a7=wrcoef2('a',c,l,wave,7), where a1~a8 represent the approximate components of the two-dimensional wavelet decomposition. The smaller the value, the higher the amount of information retained, and the larger the value, the lower the amount of information retained; d8=wrcoef2('d',c,l,wave,8); h8=wrcoef2('h',c,l,wave,8); v8=wrcoef2('v',c,l,wave,8); d represents diagonal detail, h represents horizontal detail, v represents vertical detail, and dhv1~dhv8 are multi-scale feature matrices generated by combining the three detail components (d, h, v).

[0031] Calculate the variance contribution rate of each layer component: var0=var(reshape(x,1,numel(x))); var1=var(reshape(a1,1,numel(a1))); ly=[var0 var1 var2 var3 var4 var5 var6 var7 var8]; Normalized variance contribution rate: ly=(ly). / var0.

[0032] Finally, surface images with different levels of noise reduction are plotted and the data is output, such as... Figure 2 As shown, these are A1 to A8, with larger values ​​indicating more significant noise reduction. Depending on the specific situation, a4 or a5 is generally chosen as the surface after noise reduction.

[0033] Step 3: Taking surface a4 processed using wavelet analysis as an example, the imregionalmax method is used to search for the peak points of each micro-protrusion on surface a4, finding all peak points on the upper and lower fracture surfaces, marking them with solid red dots, and numbering them. Simultaneously, the coordinates of the peak points on the upper and lower surfaces, peakLocations_upper and peakLocations_lower, as well as their numbers peakIds_upper and peakIds_lower, are recorded and saved in all_peak_locations.mat. The identification results are then visualized as a figure, such as... Figure 3 As shown.

[0034] Step 4: Load the peak point file all_peak_locations.mat for each micro-protrusion. Taking the denoised upper crack surface as an example, first create a cell array asperities_point_upper for each micro-protrusion. The length of the cell array is the same as the number of micro-protrusions on the denoised upper crack surface. Record the point cloud coordinates of the micro-protrusions.

[0035] Set up a queue, place the peak point in the queue, and use it as the initial point; Using the region growing algorithm, we search downwards from the peak point for grid points in the neighborhood with elevations lower than the center point. Grid points that meet the conditions are used as seed points. The seed points are placed in a queue, and their coordinates are merged into the cell array of the corresponding micro-convexity. Traverse the micro-convex body until the height gradient is reversed, i.e., current_z (z-coordinate of the current point) > center_z (z-coordinate of the center point), then stop the search; This process identifies all micro-protrusions on the fracture surface one by one. `visited` is a marker matrix cell array with the same number of micro-protrusions. It marks the range of each identified micro-protrusion. Points on the identified micro-protrusions are marked as `true` in `visited` to prevent them from being used as new seed points for identification in the next loop.

[0036] The point cloud coordinates (asperities_point_upper) of each micro-convexity, the number of micro-convexity point clouds (point_count_upper), and the marker matrix (visited) are saved to asperities_point_upper.mat.

[0037] Step 5: Taking the above surface as an example, based on the point cloud data of each identified micro-protrusion, draw these micro-protrusions sequentially. For the completed micro-protrusions, you can choose whether to display peak points, numbers, and height colorlists. The recognition effect is as follows: Figure 4 All units are in mm.

[0038] Step 6: In this embodiment, the reference surface method is used to divide each identified micro-protrusion into reference surfaces one by one. The portion above the reference surface of each identified micro-protrusion is then trimmed to obtain the trimmed micro-protrusion, thereby realizing the identification of the micro-protrusion morphology features. Specifically, taking the above crack surface as an example: First, load the peak point file all_peak_locations.mat for each micro-protrusion and the point cloud coordinate file asperities_point_upper.mat for the micro-protrusions on the upper fracture surface. Then, set the cell arrays, such as the clipped point cloud array asperities_point_upper_cropped, the reference surface elevation array reference_z_upper, the clipped micro-protrusion number array peakIds_upper_cropped, the height-to-diameter ratio array H_R_ratios, the valid point array point_cropped (i.e., points above the reference surface), the convex hull array K (i.e., the plane enclosed by the orthographic projection of the micro-protrusions), and the projected area array project_area. Their lengths are all consistent with the number of protrusions on the upper surface.

[0039] The edge point of the kth micro-protrusion is found by using the bwperim function, and then the point with the highest elevation among the edge points is found by using the max method. A horizontal plane is defined with the point with the highest elevation among the edge points. A horizontal plane is drawn through this reference point as a reference plane. The part above the reference plane of each identified micro-protrusion is clipped to obtain the clipped micro-protrusion, thus realizing the identification of the morphological features of the micro-protrusion.

[0040] The area enclosed by the bottom surface of the trimmed micro-protrusion is regarded as a convex hull, and the area of ​​the convex hull is taken as the bottom area of ​​the corresponding micro-protrusion. The convex hull is generalized as a circle. Calculate the height-to-diameter ratio, radius of curvature, and height difference of the semi-ellipsoid, and generalize the trimmed micro-convex body into a semi-ellipsoid. The height-to-diameter ratio parameter H / R (i.e., height to base radius) is used to facilitate quantitative analysis of the deformation and stress characteristics of the micro-convex body. Figure 5 , Figure 6 As shown.

[0041] The specific method for calculating the height-to-diameter ratio of this semi-ellipsoid is as follows: The iteration loop is set to 100,000 times. The height of the reference surface is increased through iteration until the height-to-diameter ratio of the micro-protrusion is calculated. When the number of points is less than or equal to 2 (condition 1), the projected area is zero (condition 2), the height-to-diameter ratio is less than 0.1 (condition 3), or the reference surface exceeds the vertex (condition 4), it indicates that the micro-protrusion is extremely small or very flat and can be ignored, and the iteration is terminated.

[0042] The convex hull function is used to draw the convex hull, and the area enclosed by the convex hull is the base area of ​​the micro-convex body.

[0043] After each micro-cropped body is clipped, the clipped point cloud data is saved. The parameters for each clipped micro-cropped body, including its peakIds_upper_cropped, reference elevation_z_upper, projected area_projected_area, height H, base radius R, and height-to-diameter ratio (upper_H_R_ratios), are all stored in the asperities_point_upper_cropped.mat file.

[0044] Step 7: Simultaneously, the region and extent of each micro-protrusion were obtained, such as... Figure 7 As shown, comparative verification was conducted using three types of rocks: red sandstone, limestone, and dolomite. The effect was significant, with a decrease in the number of micro-protrusions as the smoothness and evenness of the fracture surface increased.

[0045] In addition, this application fabricated four semi-ellipsoidal micro-convex specimens with different aspect ratios, A-1 to A-4, with heights of 2.5 mm, 5.0 mm, 7.5 mm, and 10.0 mm, respectively. All specimens had a base radius of 5 mm and exhibited good recognition performance with an error within 1 × 10⁻⁶ mm. -4 Within mm, such as Figure 8 .

[0046] Example 3 This embodiment provides a system for identifying the microstructure of micro-protrusions on rough surfaces, comprising: The three-dimensional scanning unit is used to perform three-dimensional contour scanning on the rough surface to be measured to obtain three-dimensional point cloud data; The data preprocessing unit is used to perform cropping, alignment and noise reduction on the obtained 3D point cloud data in sequence to obtain the noise-reduced upper surface and the noise-reduced lower surface. The peak point search unit is used to search for all local peak points on the denoised upper surface and the denoised lower surface, respectively. The micro-protrusion point cloud data acquisition unit is used to acquire point cloud data of all micro-protrusions based on all local peak points on the upper and lower surfaces; The micro-protrusion visualization unit is used to visualize the identified micro-protrusions one by one based on the obtained point cloud data; The morphology parameter acquisition unit is used to obtain the reference surface of the identified micro-protrusions, generalize the part above the reference surface into a semi-ellipsoid, and calculate the morphology parameters of each micro-protrusion.

[0047] Example 4 This embodiment also provides a computing device. The computing device includes a bus, a processor, a memory, and a communication interface. The processor, memory, and communication interface communicate with each other via the bus. The computing device can be a server or a terminal device. It should be understood that this application does not limit the number of processors and memory in the computing device.

[0048] A bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, a bus can include a path for transmitting information between various components of a computing device (e.g., memory, processor, communication interfaces).

[0049] The processor may include any one or more of the following: central processing unit (CPU), graphics processing unit (GPU), tensor processing unit (TPU), application specific integrated circuit (ASIC), field-programmable gate array (FPGA), microprocessor (MP), or digital signal processor (DSP).

[0050] Memory can include volatile memory, such as random access memory (RAM). Processors can also include non-volatile memory. volatile memory, such as read-only memory (ROM). ROM (memory only), flash memory, hard disk drive (HDD), or solid state drive (SSD).

[0051] The memory stores executable program code, which the processor executes to implement the functions of the aforementioned units, thereby achieving, for example, the method described in Embodiment 1. That is, the memory may store instructions for the methods and functions relating to the computing device in any of the above embodiments.

[0052] The communication interface uses transceiver modules such as, but not limited to, network interface cards and transceivers to enable communication between computing devices and other devices or communication networks.

[0053] Example 5 This embodiment also provides a computing device cluster. The computing device cluster includes at least one computing device. The computing device can be a server, such as a central server, an edge server, or a local server in a local data center. In some embodiments, the computing device can also be a terminal device such as a desktop computer, a laptop computer, or a smartphone.

[0054] The computing device cluster includes at least one computing device. The memory of one or more computing devices in the computing device cluster may store the same instructions for performing the methods and functions related to the computing devices in any of the above embodiments.

[0055] In some possible implementations, the memories of one or more computing devices in the computing device cluster may also each store partial instructions for executing the methods and functions of the computing devices involved in any of the above embodiments. In other words, a combination of one or more computing devices can jointly execute the instructions for performing the methods and functions of the computing devices.

[0056] It should be noted that the memory in different computing devices within a computing device cluster can store different instructions, which are used to execute parts of the device's functions.

[0057] In some possible implementations, one or more computing devices in a computing device cluster can be connected via a network. This network can be a wide area network (WAN) or a local area network (LAN), etc. Two computing devices are connected to each other via the network. Specifically, they connect to the network through communication interfaces in each computing device.

[0058] Embodiments of this disclosure also provide a computer program product containing instructions that, when run on a computer, cause the computer to perform the methods and functions related to a computing device in any of the above embodiments.

[0059] Example 6 This embodiment also provides a computer-readable storage medium storing computer instructions that, when executed by a processor, cause the processor to perform the methods and functions of the computing device involved in any of the above embodiments.

[0060] Generally, the various embodiments of this disclosure can be implemented in hardware or dedicated circuitry, software, logic, or any combination thereof. Some aspects can be implemented in hardware, while others can be implemented in firmware or software, which can be executed by a controller, microprocessor, or other computing device. Although various aspects of the embodiments of this disclosure are shown and described as block diagrams, flowcharts, or represented using some other illustration, it should be understood that the blocks, apparatuses, systems, techniques, or methods described herein can be implemented as, as non-limiting examples, in hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0061] Example 7 This embodiment provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions included in program modules, which execute in a device on a target real or virtual processor to perform the processes / methods as described above with reference to the accompanying drawings. Typically, program modules include routines, programs, libraries, objects, classes, components, data structures, etc., that perform specific tasks or implement specific abstract data types. In various embodiments, the functionality of program modules can be combined or divided among program modules as needed. The machine-executable instructions for the program modules can execute within a local or distributed device. In a distributed device, the program modules can reside in both local and remote storage media.

[0062] Computer program code used to implement the methods of this disclosure may be written in one or more programming languages. This computer program code may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus, such that when executed by the computer or other programmable data processing apparatus, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be performed. The program code may be executed entirely on a computer, partially on a computer, as a stand-alone software package, partially on a computer and partially on a remote computer, or entirely on a remote computer or server.

[0063] In the context of this disclosure, computer program code or related data may be carried on any suitable carrier to enable a device, apparatus, or processor to perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, and so on. Examples of signals may include electrical, optical, radio, sound, or other forms of propagation signals, such as carrier waves, infrared signals, etc.

[0064] Computer-readable media can be any tangible medium that contains or stores programs for or relating to an instruction execution system, apparatus, or device, or a data storage device such as a data center containing one or more available media. Computer-readable media can be computer-readable signal media or computer-readable storage media. Computer-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. More detailed examples of computer-readable storage media include electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0065] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for identifying the microstructure of micro-protrusions on a rough surface, characterized in that, Includes the following steps: The rough surface to be measured is subjected to three-dimensional contour scanning to obtain three-dimensional point cloud data; The obtained 3D point cloud data is cropped, aligned and denoised in sequence to obtain the denoised upper surface and the denoised lower surface. Search for all local peak points on the denoised upper and lower surfaces respectively; Based on all local peak points on the upper and lower surfaces, obtain the point cloud data of all micro-convexities; The identified micro-convexities are visualized one by one based on the obtained point cloud data; Obtain the reference surface of the identified micro-protrusions, generalize the part above the reference surface into a semi-ellipsoid, and use this to calculate the morphological parameters of each micro-protrusion.

2. The method for identifying the microstructure of micro-protrusions on a rough surface according to claim 1, characterized in that, Denoising is performed using wavelet analysis to obtain the denoised upper and lower surfaces.

3. The method for identifying the microstructure of micro-protrusions on a rough surface according to claim 1, characterized in that, Based on all local peak points on the upper and lower surfaces, and combined with a region growing algorithm, the point cloud data of each micro-convexity is obtained one by one, thus obtaining the point cloud data of all micro-convexities and visualizing them.

4. The method for identifying the microstructure of micro-protrusions on a rough surface according to claim 1, characterized in that, Obtain the reference surface of the identified micro-protrusions, and use the portion above the reference surface to represent the morphological features of each micro-protrusion and visualize it. The specific method is as follows: Obtain the point with the highest elevation among the edge points of each identified micro-protrusion; Take the point with the highest elevation in the edge as the reference point, and draw a horizontal plane through the reference point as the reference surface; Using the reference plane as the datum plane, all the identified micro-protrusions are cropped one by one, retaining only the part above the reference plane, to obtain the cropped micro-protrusions and visualize them.

5. The method for identifying the microstructure of a rough cracked surface micro-protrusion according to claim 1, characterized in that, The identification method further includes calculating the morphological feature parameters of each micro-protrusion, specifically: The area enclosed by the bottom surface of the trimmed micro-protrusion is regarded as a convex hull, and the area of ​​the convex hull is taken as the bottom area of ​​the corresponding micro-protrusion. The convex hull is generalized as a circle. The trimmed micro-convex body is generalized into a semi-ellipsoid; Calculate the aspect ratio, radius of curvature, base area, and height difference of the semi-ellipsoid, and obtain the morphological parameters of different micro-protrusions on the rough surface one by one.

6. A system for identifying the microstructure of micro-protrusions on a rough surface, characterized in that, include: The three-dimensional contour scanning unit is used to perform three-dimensional contour scanning on the rough surface to be measured to obtain three-dimensional point cloud data; The data preprocessing unit is used to perform cropping, alignment and noise reduction on the obtained 3D point cloud data in sequence to obtain the noise-reduced upper and lower surfaces; The peak point search unit is used to search for all local peak points on the upper and lower surfaces after noise reduction. The micro-protrusion point cloud data acquisition unit is used to acquire point cloud data of all micro-protrusions based on all local peak points on the upper and lower surfaces; The micro-protrusion visualization unit is used to visualize the identified micro-protrusions one by one based on the obtained point cloud data; The morphology parameter acquisition unit is used to obtain the reference surface of the identified micro-protrusions, take the part above the reference surface as the micro-morphological features of each micro-protrusion, and calculate the morphology parameters.

7. An electronic device, characterized in that, It includes a processor and a memory, the memory storing computer instructions that, when executed by the processor, cause the electronic device to perform the method of any one of claims 1 to 5.

8. A computing device cluster, characterized in that, It includes at least one computing device, each computing device including a processor and memory; The processor of the at least one computing device is configured to execute instructions stored in the memory of the at least one computing device to cause the cluster of computing devices to perform the method according to any one of claims 1 to 5.

9. A computer program product, characterized in that, The computer program product includes computer-executable instructions that, when executed, implement the method of any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions that, when executed by a processor, implement the method of any one of claims 1 to 5.

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