Method, system, medium and equipment for measuring characteristics of drilling rock sample based on image processing

Abstract

The invention provides a method, a system, a medium and equipment for measuring characteristics of a drilled rock sample based on image processing. The method comprises the following steps: acquiring an original image of the drilled rock sample; performing preprocessing and contour extraction on the original image of the drilled rock sample to obtain a background-free image of the drilled rock sample; performing image gray analysis on the drilled rock sample background-free image to obtain a drilled rock sample gray segmentation image; constructing a particle reconstruction image of the drilled rock sample based on the gray segmentation image of the drilled rock sample; and calculating rock sample characteristic parameters based on the drilled rock sample gray segmentation map and the drilled rock sample particle reconstruction map. According to the drilling rock sample characteristic measurement method and system based on image processing, the medium and the equipment, automatic acquisition of rock sample characteristic data of the on-site drilling original sample can be realized, and the on-site recording efficiency and the accuracy of rock sample characteristic monitoring are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of engineering geological exploration, and particularly relates to a method, system, medium and device for measuring the characteristics of borehole rock samples based on image processing. Background Art

[0002] Geological logging refers to the method and process of correctly recording or systematically representing the directly observed geological phenomena or geological data obtained by other means (drilling, geophysical exploration, testing and chemical analysis, etc.) in the form of words and charts, which is an important data source for engineering geological exploration. However, in the traditional on-site logging process, manual observation and recording of borehole rock samples are carried out, which is inefficient and prone to problems such as low statistical accuracy. Manual observation is highly subjective and is easily affected by individual experience and subjective consciousness, resulting in inaccurate statistical results. In addition, manual logging requires a large amount of time and human resources, restricting the improvement of work efficiency and becoming an important problem in engineering practice.

[0003] The advent of the digital and information age has brought new research means to various fields of geotechnical engineering. The particle tracking method represented by digital image technology can determine the displacement of spots or particles in an image and is applied to the research of non-visible fields. Digital image technology has been widely applied in the micro-particle detection, high-speed dynamics and acoustic imaging in the non-visible fields of geotechnical engineering. Therefore, how to apply digital image technology in the automatic acquisition of borehole rock sample data has become an urgent problem to be solved in this field. Summary of the Invention

[0004] In view of the above-mentioned disadvantages of the prior art, the purpose of the present invention is to provide a method, system, medium and device for measuring the characteristics of borehole rock samples based on image processing, which can realize the automatic acquisition of the rock sample characteristic data of on-site borehole original samples, and improve the on-site logging efficiency and the accuracy of rock sample characteristic monitoring.

[0005] In a first aspect, the present invention provides a method for measuring the characteristics of borehole rock samples based on image processing, and the method includes the following steps:

[0006] Obtain the original image of the borehole rock sample;

[0007] Perform preprocessing and contour extraction on the original image of the borehole rock sample to obtain a background-free image of the borehole rock sample;

[0008] Perform image gray-scale analysis on the background-free image of the borehole rock sample to obtain a gray-scale segmentation map of the borehole rock sample;

[0009] Based on the gray-scale segmentation map of the borehole rock sample, construct a particle reconstruction map of the borehole rock sample;

[0010] Calculate the characteristic parameters of the rock sample based on the grayscale segmentation map of the drilled rock sample and the particle reconstruction map of the drilled rock sample.

[0011] In one implementation of the first aspect, it further includes extracting the boundary profiles of different rock layers based on the original images of continuous drilled rock samples to obtain the thicknesses of different rock layers in the drilled rock samples.

[0012] In one implementation of the first aspect, the preprocessing and contour extraction of the original image of the drilled rock sample to obtain the background-free image of the drilled rock sample include:

[0013] Preprocess the original image of the drilled rock sample, including denoising, smoothing, enhancing the contrast, and removing background interference from the original image of the drilled rock sample;

[0014] Perform contour extraction on the preprocessed original image of the drilled rock sample. The contour extraction includes using an edge detection algorithm to extract the contour boundary of the drilled rock sample, and obtaining the shape and boundary information of the drilled rock sample through binarization to obtain the background-free image of the drilled rock sample.

[0015] In one implementation of the first aspect, the image grayscale analysis of the background-free image of the drilled rock sample includes:

[0016] Set the global binarization threshold and the adaptive binarization threshold;

[0017] Perform grayscale processing on the background-free image of the drilled rock sample based on the global binarization threshold;

[0018] Or, perform grayscale processing on the background-free image of the drilled rock sample based on the adaptive binarization threshold.

[0019] In one implementation of the first aspect, constructing the particle reconstruction map of the drilled rock sample based on the grayscale segmentation map of the drilled rock sample includes: taking the contour geometric center of each boulder in the grayscale segmentation map of the drilled rock sample as the origin, and constructing the contour feature map of the rock sample particles according to the pixel point coordinates to obtain the boulder particle feature information.

[0020] In one implementation of the first aspect, the characteristic parameters of the rock sample at least include: boulder particle diameter, boulder particle size, roundness, boulder content, area envelope degree, particle size distribution.

[0021] In one implementation of the first aspect, the boulder particle diameter is obtained through image processing domain analysis. The boulder particle size includes the image length of the boulder particle size and the real length of the boulder particle size, where the real length of the boulder particle size needs to be obtained through camera calibration conversion of the image length of the boulder particle size and the real length of the boulder particle size.

[0022] In a second aspect, the present invention provides a drilling rock sample feature measurement system based on image processing. The corresponding system includes an acquisition module, a processing module, an analysis module, a reconstruction module, and a calculation module;

[0023] The acquisition module is used to acquire the original image of the drilling rock sample;

[0024] The processing module is used to preprocess and extract the contour of the original image of the drilling rock sample to obtain a background-free image of the drilling rock sample;

[0025] The analysis module is used to perform image gray-scale analysis on the background-free image of the drilling rock sample to obtain a gray-scale segmentation map of the drilling rock sample;

[0026] The reconstruction module is used to construct a particle reconstruction map of the drilling rock sample based on the gray-scale segmentation map of the drilling rock sample;

[0027] The calculation module is used to calculate the rock sample feature parameters based on the gray-scale segmentation map of the drilling rock sample and the particle reconstruction map of the drilling rock sample.

[0028] In a third aspect, the present invention provides an electronic device, which includes: a processor and a memory;

[0029] The memory is used to store computer programs;

[0030] The processor is used to execute the computer programs stored in the memory, so that the electronic device executes the above-mentioned drilling rock sample feature measurement method based on image processing.

[0031] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored. It is characterized in that when the program is executed by an electronic device, the above-mentioned drilling rock sample feature measurement method based on image processing is realized.

[0032] As described above, the drilling rock sample feature measurement method, system, medium, and device based on image processing according to the present invention have the following beneficial effects:

[0033] The drilling rock sample feature measurement method, system, medium, and device based on image processing according to the present invention can measure the size, shape, and geometric features of each block stone particle, and obtain quantitative feature information about the rock sample. Characterize the particle composition, particle distribution, and rock structure and other features of the rock sample, and provide a basis for rock type and lithology identification. Realize the automatic collection of rock sample feature data of on-site drilling original samples, improve the on-site cataloging efficiency and the accuracy of rock sample feature detection, and are suitable for popularization and use. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 Shown is a flowchart of the drilling rock sample feature measurement method based on image processing according to the present invention in an embodiment;

[0035] Figure 2 Shown is a schematic diagram of measuring the actual length of block particles in an embodiment of the method for measuring the characteristics of a drilled rock sample based on image processing according to the present invention;

[0036] Figure 3 Shown is a schematic diagram of measuring the envelope degree of block particles in an embodiment of the method for measuring the characteristics of a drilled rock sample based on image processing according to the present invention;

[0037] Figure 4 Shown is a schematic structural diagram of the system for measuring the characteristics of a drilled rock sample based on image processing according to the present invention in an embodiment;

[0038] Figure 5 Shown is a schematic structural diagram of the electronic device according to the present invention in an embodiment. Detailed implementation manners

[0039] The following uses specific specific examples to illustrate the implementation manners of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation manners. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0040] It should be noted that the drawings provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner. Therefore, only the components related to the present invention are shown in the drawings, rather than being drawn according to the number, shape, and size of the components in actual implementation. The types, quantities, and proportions of the components in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0041] Next, the technical solutions in the embodiments of the present invention will be described in detail with reference to the accompanying drawings in the embodiments of the present invention.

[0042] As Figure 1 shown, in an embodiment, the method for measuring the characteristics of a drilled rock sample based on image processing according to the present invention includes step S11-step S14.

[0043] Step S11, obtain the original image of the drilled rock sample.

[0044] Specifically, obtain the original image of the rock sample as the data input for image processing. The original image should be taken of the borehole rock sample using a high-quality digital camera or other professional image acquisition equipment to ensure clear and accurate images are captured, facilitating subsequent image processing and analysis work. During the acquisition process, pay attention to factors such as lighting conditions, shooting angles, and distances to obtain as realistic and accurate an image of the rock sample as possible. Additionally, relevant information about the image, including image resolution, camera focal length, and shooting distance, should be recorded.

[0045] Step S12: Preprocess the original image of the borehole rock sample and extract the contour to obtain a background-free image of the borehole rock sample.

[0046] Specifically, preprocess the original image of the borehole rock sample, including denoising, smoothing, enhancing contrast, and removing background interference from the original image of the borehole rock sample; perform contour extraction on the preprocessed original image of the borehole rock sample. The contour extraction includes using an edge detection algorithm to extract the contour boundary of the borehole rock sample, determining the gray-scale demarcation threshold between the borehole rock sample and the background, and obtaining the shape and boundary information of the borehole rock sample through binarization to obtain the background-free image of the borehole rock sample.

[0047] Borehole rock samples are usually cylindrical in shape, and rectangular images usually contain background interference, such as equipment, gloves, or other objects. To remove this interference, image preprocessing is required. Preprocessing and contour extraction are mainly implemented through steps such as denoising, smoothing, enhancing contrast, and removing background interference.

[0048] Image denoising is achieved by applying methods such as filters and Fourier transforms. Replace the pixel point values in the rock sample image with the median values of the points in a certain neighborhood of this point, making the pixels with relatively large differences in the gray-scale values of the surrounding pixels take values closer to the surrounding pixel values to eliminate isolated noise points. Further smoothing is performed by using the Gaussian filtering method to reduce the noise points in the image. Contrast is enhanced through methods such as histogram equalization and adaptive histogram equalization to highlight the characteristic boundaries of the rock sample.

[0049] Contour extraction is to distinguish the rock sample from the background. Use an edge detection algorithm to extract the contour boundary of the borehole rock sample, determine the gray-scale demarcation threshold between the rock sample and the background, and obtain the accurate shape and boundary information of the borehole rock sample through binarization. After accurately extracting the rock sample, remove the interfering background and superimpose it on the original image for image gray-scale analysis and processing to extract the boulders in the rock sample.

[0050] Step S13: Perform image gray-scale analysis on the background-free image of the borehole rock sample to obtain a gray-scale segmentation image of the borehole rock sample.

[0051] Specifically, the image grayscale analysis of the drill core sample without background image includes: setting a global binary threshold and an adaptive binary threshold; performing grayscale processing on the drill core sample without background image based on the global binary threshold; or, performing grayscale processing on the drill core sample without background image based on the adaptive binary threshold.

[0052] Since the drill core sample image is usually a color image, performing grayscale analysis and processing first is more conducive to subsequent feature extraction and analysis. Determining the demarcation grayscale threshold for block stone contour extraction includes setting a global binary threshold and an adaptive binary threshold. Different binary threshold methods are determined through grayscale analysis of the drill core sample without background image. The grayscale values of the pixel points of the image are set to 0 or 255 through an appropriate threshold, and the size of the threshold needs to be adjusted according to the illumination intensity.

[0053] For images with clear demarcations between block stones, rock debris, soil, etc., and strong contrast between elements, the rock sample image is segmented through the global threshold to obtain a binary image, enhancing the contrast and edge information of the image to obtain the grayscale segmentation map of the drill core sample. This grayscale segmentation map of the drill core sample can highlight the boundary between rock particles and rock layers, making subsequent feature extraction more accurate and reliable.

[0054] For images with unclear demarcations between block stones, rock debris, soil, etc., weak contrast between elements, and uneven local light distribution, the rock sample image is divided into many small blocks through the adaptive threshold, the threshold is calculated separately for each small block, and then the corresponding small block is segmented by this threshold to obtain a binary image, enhancing the contrast and edge information of the image, and further obtaining the grayscale segmentation map of the drill core sample.

[0055] Step S14: Based on the grayscale segmentation map of the drill core sample, construct a reconstructed map of the drill core sample particles.

[0056] Specifically, taking the geometric center of the contour of each block stone in the grayscale segmentation map of the drill core sample as the origin, the contour feature map of the rock particles can be reconstructed according to the pixel point coordinates. By calculating the shape parameters (such as area, perimeter, aspect ratio, etc.) of each particle, detailed particle feature information can be obtained, including the particle distribution and morphological characteristics of the rock.

[0057] Step S15: Calculate the rock sample characteristic parameters based on the grayscale segmentation map of the drill core sample and the reconstructed map of the drill core sample particles.

[0058] Specifically, the rock sample characteristic parameters at least include: block stone particle diameter, block stone particle size, roundness, block stone content, area envelope degree, particle size distribution.

[0059] Among them, the diameter of the block stone particles is obtained through image processing domain analysis. The particle size of the block stone particles includes the image length of the block stone particle size and the true length of the block stone particle size. Among them, the true length of the block stone particle size needs to be obtained by converting the image length of the block stone particle size and the true length of the block stone particle size through camera calibration.

[0060] Specifically, to obtain the true particle size length of the block stone, the length in the captured image of the block stone particle size is calibrated with the true length and converted into the true length of the block stone particle size. See Figure 2 , Figure 2 which shows a schematic diagram of the measurement of the true length of block stone particles in an embodiment of the method for measuring the characteristics of drilled rock samples based on image processing according to the present invention. The calibration process is as Figure 2 shown. The true length of the block stone particle size is calculated by a formula and recorded:

[0061]

[0062] Among them, the true length of the block stone particle size is X, the focal length of the lens is f, the direct distance between the lens and the object is Z, the pixel width of the image frame is W, the width of the target surface is w, and the diameter length of the block stone in the image is x.

[0063] As Figure 3 shown, Figure 3 which shows a schematic diagram of the measurement of the block stone envelope degree in an embodiment of the method for measuring the characteristics of drilled rock samples based on image processing according to the present invention. The roundness of the block stone is calculated by the following formula:

[0064]

[0065] Among them, in the binary image of the rock sample, the area of the pixel points covered by the block stone is set as S, the perimeter of the block stone is set as L, and the roundness of 1 means a perfect circle; the more complex the shape, the smaller the value of the roundness is compared to 1.

[0066] The block stone content is calculated by the following formula:

[0067]

[0068] Among them, in the binary image of the rock sample, the area of the pixel points covered by the block stone is set as S, and the area of the pixel points covered by the whole rock sample is S 总 .

[0069] The area envelope degree is calculated by the following formula:

[0070]

[0071] Among them, in the binary image of the rock sample, the actual pixel area covered by the boulder particles is set as S1, and the pixel area of the boulder envelope perimeter is set as S2. The smoother the particle contour, the closer the values of the envelope degree and the area envelope degree are to 1.

[0072] Furthermore, the method for measuring the characteristics of the drilled rock sample based on image processing further includes extracting the boundary profiles of different rock layers from the original images of the continuous drilled rock samples to obtain the thicknesses of different rock layers in the drilled rock sample. That is, the thickness of the rock layer is obtained by extracting the boundary profiles of different rock layers from the continuous drilled rock sample images, and the thicknesses of different rock layers in the drilled rock sample are obtained. The above calculations are implemented by programming relevant programs in a programming language, measuring the sizes, shapes, and geometric characteristics of each boulder particle, and obtaining quantitative characteristic information about the rock sample. Characterize the characteristics such as the particle composition, particle distribution, and rock structure of the rock sample, providing a basis for rock type and lithology identification.

[0073] The protection scope of the method for measuring the characteristics of the drilled rock sample based on image processing described in the embodiments of the present invention is not limited to the execution order of the steps listed in this embodiment. Any solution achieved by adding or subtracting steps of the prior art and replacing steps according to the principles of the present invention is included in the protection scope of the present invention.

[0074] The embodiments of the present invention also provide a system for measuring the characteristics of the drilled rock sample based on image processing. The system for measuring the characteristics of the drilled rock sample based on image processing can implement the method for measuring the characteristics of the drilled rock sample based on image processing described in the present invention. However, the implementation devices of the system for measuring the characteristics of the drilled rock sample based on image processing described in the present invention include, but are not limited to, the structure of the system for measuring the characteristics of the drilled rock sample based on image processing listed in this embodiment. Any structural deformation and replacement of the prior art made according to the principles of the present invention are included in the protection scope of the present invention.

[0075] As Figure 4 shown, in one embodiment, the system for measuring the characteristics of the drilled rock sample based on image processing of the present invention includes an acquisition module 41, a processing module 42, an analysis module 43, a reconstruction module 44, and a calculation module 45.

[0076] The acquisition module 41 is used to acquire the original image of the drilled rock sample.

[0077] The processing module 42 is connected to the acquisition module 41 and is used to preprocess and extract the contour of the original image of the drilled rock sample to obtain a background-free image of the drilled rock sample.

[0078] The analysis module 43 is connected to the processing module 42 and is used to perform image gray-scale analysis on the background-free image of the drilled rock sample to obtain a gray-scale segmentation image of the drilled rock sample.

[0079] The reconstruction module 44 is connected to the analysis module 43 and is used to construct a particle reconstruction diagram of the borehole rock sample based on the grayscale segmentation diagram of the borehole rock sample.

[0080] The calculation module 45 is connected to the analysis module 43 and the reconstruction module 44 and is used to calculate the characteristic parameters of the rock sample based on the grayscale segmentation diagram of the borehole rock sample and the particle reconstruction diagram of the borehole rock sample.

[0081] In several embodiments provided by the present invention, it should be understood that the disclosed system, device or method can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of modules / units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of devices or modules or units can be in electrical, mechanical or other forms.

[0082] The modules / units described as separate components may or may not be physically separated. The components shown as modules / units may or may not be physical modules, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules / units can be selected according to actual needs to achieve the purpose of the embodiments of the present invention. For example, in each embodiment of the present invention, the functional modules / units can be integrated in a processing module, or each module / unit can exist physically alone, or two or more modules / units can be integrated in one module / unit.

[0083] Those of ordinary skill in the art should also further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0084] The embodiments of the present invention also provide a computer-readable storage medium. Those of ordinary skill in the art can understand that all or part of the steps in the methods of the above embodiments can be completed by instructing a processor through a program. The program can be stored in a computer-readable storage medium. The storage medium is a non-transitory medium, such as random access memory, read-only memory, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disc, and any combination thereof. The above storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center integrating one or more available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, or magnetic tape), an optical medium (such as a digital video disc (DVD)), or a semiconductor medium (such as a solid-state disk (SSD)).

[0085] The embodiments of the present invention also provide an electronic device. The electronic device includes a processor and a memory.

[0086] The memory is used to store a computer program.

[0087] The memory includes various media that can store program codes, such as ROM, RAM, magnetic disk, USB flash drive, memory card, or optical disc.

[0088] The processor is connected to the memory and is used to execute the computer program stored in the memory, so that the electronic device executes the above-mentioned method for measuring the characteristics of a drilled rock sample based on image processing.

[0089] Preferably, the processor can be a general-purpose processor, including a central processing unit (CPU for short), a network processor (NP for short), etc.; it can also be a digital signal processor (DSP for short), an application-specific integrated circuit (ASIC for short), a field-programmable gate array (FPGA for short), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0090] Such as Figure 5As shown, the electronic device of the present invention is presented in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: one or more processors or processing units 51, a memory 52, and a bus 53 that connects different system components (including the memory 52 and the processing unit 51).

[0091] The bus 53 represents one or more of several types of bus architectures, including a memory bus or a memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. By way of example, these architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

[0092] The electronic device typically includes a variety of computer system-readable media. These media can be any available media that can be accessed by the electronic device, including volatile and non-volatile media, removable and non-removable media.

[0093] The memory 52 may include computer system-readable media in the form of volatile memory, such as random access memory (RAM) 521 and / or cache memory 522. The electronic device may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, a storage system 523 may be used for reading and writing on non-removable, non-volatile magnetic media ( Figure 5 not shown, commonly referred to as a "hard disk drive"). Although Figure 5 not shown in the figure, a disk drive for reading and writing on a removable non-volatile disk (such as a "floppy disk") and an optical disk drive for reading and writing on a removable non-volatile optical disk (such as a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 53 through one or more data media interfaces. The memory 52 may include at least one program product having a set (such as at least one) of program modules that are configured to perform the functions of the various embodiments of the present invention.

[0094] A program / utility 524 having a set (at least one) of program modules 5241 may be stored, for example, in the memory 52. Such program modules 5241 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment. The program modules 5241 generally perform the functions and / or methods described in the embodiments of the present invention.

[0095] The electronic device can also communicate with one or more external devices (such as a keyboard, a pointing device, a display, etc.), and can also communicate with one or more devices that enable a user to interact with the electronic device, and / or communicate with any device that enables the electronic device to communicate with one or more other computing devices (such as a network card, a modem, etc.). Such communication can be carried out through the input / output (I / O) interface 54. Moreover, the electronic device can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through the network adapter 55. As Figure 5 shown, the network adapter 55 communicates with other modules of the electronic device through the bus 53. It should be understood that although not shown in the figure, other hardware and / or software modules can be used in combination with the electronic device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0096] The above embodiments are only illustrative of the principles and effects of the present invention, rather than limiting the present invention. Any person familiar with this technology can modify or change the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or changes made by those with ordinary knowledge in the technical field without departing from the spirit and technical idea disclosed by the present invention should still be covered by the claims of the present invention.

Claims

1. A method for measuring the characteristics of a drilled rock sample based on image processing, characterized in that, The method includes the following steps: Obtain the original image of the borehole rock sample; Perform preprocessing and contour extraction on the original image of the borehole rock sample to obtain a background-free image of the borehole rock sample; Perform image gray-scale analysis on the background-free image of the borehole rock sample to obtain a gray-scale segmentation map of the borehole rock sample; Based on the gray-scale segmentation map of the borehole rock sample, construct a particle reconstruction map of the borehole rock sample; Calculate the characteristic parameters of the rock sample based on the gray-scale segmentation map of the borehole rock sample and the particle reconstruction map of the borehole rock sample.

2. The method for measuring the characteristics of a drilled rock sample based on image processing according to claim 1, wherein: It also includes extracting the boundary profile of different rock layers based on the original images of continuous borehole rock samples to obtain the thickness of different rock layers in the borehole rock sample.

3. The method for measuring the characteristics of a drilled rock sample based on image processing according to claim 1, wherein: Performing preprocessing and contour extraction on the original image of the borehole rock sample to obtain a background-free image of the borehole rock sample includes: Perform preprocessing on the original image of the borehole rock sample, including denoising, smoothing, enhancing contrast, and removing background interference on the original image of the borehole rock sample; Perform contour extraction on the preprocessed original image of the borehole rock sample. The contour extraction includes using an edge detection algorithm to extract the contour boundary of the borehole rock sample, and obtaining the shape and boundary information of the borehole rock sample through binarization to obtain the background-free image of the borehole rock sample.

4. The method for measuring the characteristics of a drilled rock sample based on image processing according to claim 1, wherein: Performing image gray-scale analysis on the background-free image of the borehole rock sample includes: Set the global binarization threshold and the adaptive binarization threshold; Perform gray-scale processing on the background-free image of the borehole rock sample based on the global binarization threshold; Or, perform gray-scale processing on the background-free image of the borehole rock sample based on the adaptive binarization threshold.

5. The method for measuring the characteristics of a drilled rock sample based on image processing according to claim 1, wherein: Based on the gray-scale segmentation map of the borehole rock sample, constructing a particle reconstruction map of the borehole rock sample includes: taking the contour geometric center of each block stone in the gray-scale segmentation map of the borehole rock sample as the origin, and constructing a contour feature map of the rock sample particles according to the pixel coordinates to obtain the block stone particle feature information.

6. The method for measuring the characteristics of a drilled rock sample based on image processing according to claim 1, wherein: The characteristic parameters of the rock sample at least include: block stone particle diameter, block stone particle size, roundness, block stone content, area envelope degree, particle size distribution.

7. The method for measuring the characteristics of a drilled rock sample based on image processing according to claim 6, wherein: The block stone particle diameter is obtained through analysis in the image processing domain. The block stone particle size includes the image length of the block stone particle size and the real length of the block stone particle size. Among them, the real length of the block stone particle size needs to be obtained by converting the image length of the block stone particle size and the real length of the block stone particle size through camera calibration.

8. A drilling rock sample feature measurement system based on image processing, characterized in that, The system includes an acquisition module, a processing module, an analysis module, a reconstruction module, and a calculation module; The acquisition module is used to obtain the original image of the borehole rock sample; The processing module is used to perform preprocessing and contour extraction on the original image of the borehole rock sample to obtain a background-free image of the borehole rock sample; The analysis module is used to perform image gray-scale analysis on the background-free image of the borehole rock sample to obtain a gray-scale segmentation map of the borehole rock sample; The reconstruction module is used to construct a particle reconstruction map of the borehole rock sample based on the gray-scale segmentation map of the borehole rock sample; The calculation module is used to calculate the characteristic parameters of the rock sample based on the gray-scale segmentation map of the borehole rock sample and the particle reconstruction map of the borehole rock sample.

9. An electronic device, characterized in that, The electronic device includes: a processor and a memory; The memory is used to store computer programs; The processor is used to execute the computer program stored in the memory, so that the electronic device executes the method for measuring the characteristics of a drilled rock sample based on image processing according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the electronic device, it implements the method for measuring the characteristics of a drilled rock sample based on image processing according to any one of claims 1 to 7.

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