Laser point load experiment system and method for acquiring multi-scale rock mechanics parameters

Abstract

The application discloses a laser point load experiment system and method for obtaining multi-scale rock mechanics parameters. The experiment system comprises a rotating scanning platform, a three-dimensional scanning device, a laser irradiator, a camera device and a data processing system, etc. The rotating scanning platform is used for placing a rock sample; the three-dimensional scanning device obtains point cloud data; the laser irradiator irradiates the sample surface to make it break; the camera device collects surface images; and the data processing system constructs a three-dimensional numerical model and determines a laser irradiation point. The method steps comprise: preparing a rock sample and calibrating parameters; constructing a model through three-dimensional scanning; irradiating a laser and recording a failure time; constructing a laser point load discrete numerical model, iteratively adjusting input parameters to make the simulation error less than 5% compared with the experimental failure time; and inputting the inversion parameters into a numerical model of a Brazilian disk to predict the tensile strength. The application realizes non-contact, automation and high-efficiency multi-scale rock mechanics parameter acquisition.

Description

Technical Field

[0001] This invention relates to the fields of geotechnical engineering and geological exploration, and in particular to a laser point load experimental system and method for obtaining multi-scale rock mechanical parameters. Background Technology

[0002] Currently, in cutting-edge fields such as deep earth resource exploration, deep space exploration (e.g., lunar and Martian exploration), and underground space development, rapidly, in-situ, and accurately obtaining the mechanical parameters of rock masses (such as tensile strength and elastic modulus) is a crucial prerequisite for engineering stability assessment, scientific research, and construction design. However, traditional rock mechanics testing methods (such as uniaxial compression, Brazilian splitting, and point load tests) heavily rely on large, cumbersome hydraulic loading equipment and require precise cutting and grinding of rock samples to prepare standard specimens. This process is extremely difficult to achieve in extreme environments such as deep earth and deep space, and suffers from numerous bottlenecks, including long processing times, high energy consumption, transportation difficulties, and the inability to achieve in-situ multi-scale measurements.

[0003] While existing technologies utilize lasers for rock breaking or drilling, most studies are limited to the technological level and fail to quantitatively correlate laser action with rock mechanical parameters. In particular, how to quickly deduce key mechanical parameters from a simple laser irradiation experiment remains a pressing technical challenge. Summary of the Invention

[0004] This invention provides a laser point load experimental system and method for obtaining multi-scale rock mechanical parameters to solve the technical problems existing in the prior art.

[0005] The technical solution adopted by this invention to solve the technical problems existing in the prior art is as follows:

[0006] A laser point load experimental system for obtaining multi-scale rock mechanical parameters is characterized in that the system includes: a rotating scanning platform, a three-dimensional scanning device, a laser irradiator, a first camera device, a rotation switching device, and a data processing system; the laser irradiator and the first camera device are located above the rotating scanning platform, and the three-dimensional scanning device is located diagonally above the rotating scanning platform.

[0007] The rotating scanning platform is used to place rock samples and rotate the rock samples placed on it 360°.

[0008] The 3D scanning equipment is used to scan rock samples and acquire point cloud data; it then sends the acquired point cloud data to a data processing system.

[0009] A laser irradiator is used to irradiate the surface of a rock sample to cause it to crack.

[0010] The first camera device is used to acquire images of a preset central area on the surface of a rock sample; it then sends the acquired images to a data processing system.

[0011] The rotation switching device is used to rotate and switch the laser irradiator and the first camera device to align with the rock sample on the rotating scanning platform.

[0012] The data processing system is used to process the point cloud data acquired by the 3D scanning equipment, construct a 3D numerical geometric model of the rock sample, process the image acquired by the first camera device, and determine the laser irradiation point in the 3D numerical geometric model of the rock sample.

[0013] Furthermore, it also includes a robotic arm used to flip rock samples and pick up and place rock samples on a rotating scanning platform.

[0014] Furthermore, the surface of the rotating scanning platform is provided with a heat-insulating layer made of a laser-resistant etching material.

[0015] Furthermore, it also includes a second camera device, which is used to acquire images of the irradiated area on the surface of the rock sample when irradiated by the laser irradiator; it sends the acquired images to the data processing system; the data processing system performs crack identification on the images acquired by the second camera device, and when the size of the identified crack reaches a set value, it sends a signal to stop the laser irradiator from irradiating.

[0016] Furthermore, the rotation switching device includes a rotating arm located above the rotating scanning platform, the axis of the rotating arm being perpendicular to the horizontal plane, a horizontal support fixed to the lower part of the rotating arm, and the horizontal support rotating around the axis of the rotating arm; a first camera device and a laser irradiator are installed at both ends of the horizontal support, so that the horizontal support rotates 180° each time, and when the horizontal support is stationary, the optical axis of the lens of the first camera device or the laser optical axis output by the laser irradiator coincides with the rotation axis of the rotating scanning platform.

[0017] Furthermore, the rotary switching device includes an indexing plate; the first camera device and the laser illuminator are set at different indexing positions on the indexing plate; the indexing plate is rotated, and when the indexing plate stops rotating, the optical axis of the lens of the first camera device or the laser optical axis output by the laser illuminator coincides with the rotation axis of the rotary scanning platform.

[0018] The present invention also provides a laser point load test method for obtaining multi-scale rock mechanical parameters using the above-described laser point load test system for obtaining multi-scale rock mechanical parameters, the method comprising the following steps:

[0019] Step 1: Using the same type of rock from the same origin, prepare rock samples for the prediction experiment and calibration samples respectively; the calibration samples are used to calibrate the coefficient of thermal expansion in the discrete numerical model of laser point load.

[0020] Step 2: Place the experimental rock sample on the rotating scanning platform and align the 3D scanning device with the rock sample; rotate the rotating scanning platform 360° while the 3D scanning device scans the rock sample; and enable the data processing system to construct a 3D numerical geometric model based on the point cloud data obtained from the scan.

[0021] Step 3: The first camera device acquires an image of the preset central area on the surface of the rock sample and sends the acquired image to the data processing system; the rotation switching device is rotated so that the laser irradiator is aimed at the preset central area on the surface of the rock sample and a constant power laser beam is used to continuously irradiate the preset central area on the surface of the rock sample; when the rock sample is detected to be cracked, the laser irradiation is stopped and the time elapsed from the start of irradiation to the cracking of the rock sample is recorded.

[0022] Step 4: Based on the three-dimensional numerical geometric model of the rock sample, construct a discrete numerical model of laser point load;

[0023] Let the tensile strength input to the discrete numerical model of laser point load be... The elastic modulus input to the discrete numerical model of laser point load is: The failure time output by the discrete numerical model of laser point load is: The failure time measured by the laser point load experiment was... ;

[0024] The following input parameters in the discrete numerical model of laser point load are iteratively adjusted using a polynomial interpolation method: , until and The relative error is less than 5%;

[0025] Step 5, to meet the error requirements and The data are combined and input into a preset Brazilian splitting numerical model for rock samples. The Brazilian splitting numerical model then outputs the predicted tensile strength of the rock. .

[0026] Furthermore, a robotic arm is also provided, which is used to flip the rock sample and pick up and place the rock sample on the rotating scanning platform; so that the 3D scanning device is positioned above the rock sample, the rotating scanning platform rotates 360° to complete the upper half scanning of the rock sample; the robotic arm grabs the rock sample and flips the rock sample 180° up and down before placing it on the rotating scanning platform, and the rotating scanning platform rotates 360° again to complete the lower half scanning of the rock sample.

[0027] Furthermore, a second camera device is also provided, which is used to acquire images of the irradiated area on the surface of the rock sample when irradiated by the laser irradiator; the acquired images are sent to the data processing system; the data processing system performs crack identification on the images acquired by the second camera device, and when the size of the identified crack reaches a set value, it sends a signal to stop the laser irradiator from irradiating.

[0028] Furthermore, in step 1, when preparing calibration samples, multiple sets of calibration samples with different mechanical parameters are prepared; then, Brazilian splitting test, uniaxial compression test, and laser point load test are performed on each set of calibration samples. The Brazilian splitting test is used to measure the tensile strength of the calibration sample, the uniaxial compression test is used to measure the compressive strength and elastic modulus of the calibration sample, and the laser point load test is used to measure the experimental failure time of the calibration sample. By conducting the above tests on the calibration samples, the thermal expansion coefficient parameters of experimental rock samples prepared from the same type of rock from the same origin are calibrated based on the measurement results.

[0029] The advantages and positive effects of this invention are:

[0030] This invention proposes a laser point loading experimental system for acquiring multi-scale rock mechanical parameters, achieving full automation from scanning and loading to inversion without human intervention. It is particularly suitable for extreme environments such as deep space and deep earth, capable of long-term autonomous operation, and enables rapid and accurate prediction of parameters such as tensile strength of rocks by constructing digital twins of rocks.

[0031] The present invention proposes a laser point load experimental method for obtaining multi-scale rock mechanical parameters, which has the following advantages compared with traditional mechanical experiments: high efficiency and precision, fast experimental speed and high inversion accuracy; non-contact loading, using laser loading, avoiding mechanical wear and extending equipment life; strong adaptability to rock samples, with no special requirements on the shape of rock samples, low processing precision requirements, and wide applicability. Attached Figure Description

[0032] Figure 1 This is a schematic diagram of a laser point load experimental system for obtaining multi-scale rock mechanical parameters according to the present invention.

[0033] Figure 2 This is a flowchart of an embodiment of the laser point load experimental method for obtaining multi-scale rock mechanical parameters according to the present invention.

[0034] Figure 3 A schematic diagram of multi-scale rocks involved in obtaining multi-scale rock parameters using the method of the present invention.

[0035] Figure 1In the middle, 1- data analysis and control platform, 2- robotic arm, 3- first camera device, 4- laser irradiator light source; 5- rotating arm, 6- laser irradiator body, 7- 3D scanner, 8- rotating scanning platform, 9- rock sample, 10- horizontal support. Detailed Implementation

[0036] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0037] In the description of this invention, the terms "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," and "bottom," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and do not require the invention to be constructed and operated in a specific orientation; therefore, they should not be construed as limitations on the invention. The terms "connected" and "linked" used in this invention should be interpreted broadly. For example, they can refer to a fixed connection or a detachable connection; a direct connection or an indirect connection through intermediate components; or an electrical connection or signal transmission. Those skilled in the art can understand the specific meaning of the above terms according to the specific circumstances.

[0038] Please see Figures 1 to 3 A laser point load experimental system for obtaining multi-scale rock mechanical parameters includes: a rotating scanning platform 8, a three-dimensional scanning device, a laser irradiator, a first camera device 3, a rotation switching device, and a data processing system; the laser irradiator and the first camera device 3 are located above the rotating scanning platform 8, and the three-dimensional scanning device is located diagonally above the rotating scanning platform 8.

[0039] The rotating scanning platform 8 is used to place the rock sample 9 and rotate the rock sample 9 placed on it by 360°.

[0040] The 3D scanning equipment is used to scan rock sample 9 and acquire point cloud data; it then sends the acquired point cloud data to the data processing system.

[0041] A laser irradiator was used to irradiate the surface of rock sample 9 to cause it to crack.

[0042] The first camera device 3 is used to acquire images of a preset central area on the surface of the rock sample 9; it then sends the acquired images to the data processing system.

[0043] The rotation switching device is used to rotate and switch the laser irradiator and the first camera device 3 to align with the rock sample 9 on the rotating scanning platform 8.

[0044] The data processing system is used to process the point cloud data acquired by the 3D scanning device, construct a 3D numerical geometric model of rock sample 9, process the images acquired by the first camera device 3, and determine the laser irradiation points in the 3D numerical geometric model of rock sample 9.

[0045] The laser irradiator includes a laser irradiator light source 4 and a laser irradiator body 6. The laser irradiator body 6 is equipped with a collimation device, such as a laser collimator. The three-dimensional scanning device can be a three-dimensional scanner 7.

[0046] The data processing system may be equipped with a first image processing neural network to process the images acquired by the first camera device 3, identify the marked laser irradiation points of the rock sample 9, and perform region selection and positioning. It matches the processed image features with the three-dimensional surface of the rock sample 9 formed by the point cloud, and selects the laser irradiation points in the three-dimensional numerical geometric model of the rock sample 9 constructed based on the point cloud map.

[0047] Preferably, it also includes a robotic arm 2, which is used to flip the rock sample 9 and pick up and place the rock sample 9 on the rotating scanning platform 8.

[0048] Preferably, the surface of the rotary scanning platform 8 is provided with a heat insulation layer made of a laser-resistant etching material.

[0049] Preferably, it may also include a second camera device, which is used to acquire images of the irradiated area on the surface of the rock sample 9 when irradiated by the laser irradiator; it sends the acquired images to the data processing system; the data processing system performs crack identification on the images acquired by the second camera device, and when the size of the identified crack reaches a set value, it sends a signal to stop the laser irradiator from irradiating.

[0050] The second camera device can be positioned above the irradiated rock sample 9, or it can be installed together with the laser irradiator body 6.

[0051] Other methods can also be used to confirm that the crack size has reached the set value, such as a machine learning-based intelligent acoustic feature recognition method, the specific implementation of which is as follows:

[0052] First, laser destruction experiments were conducted in a laboratory environment using a large number of standard cubic specimens (2 cm × 2 cm × 2 cm). The destruction time point of each specimen was initially and accurately determined using a dichotomy method. Then, experiments were conducted with lasers slightly exceeding this time, and the sound signal was recorded throughout the process at a high sampling rate of 12000 Hz. Sound segments before and after the destruction time, as well as during the laser's initial phase, were extracted and segmented according to fixed time windows (e.g., 5 ms). Only the sound pressure amplitude of each segment was extracted and converted into an amplitude array to form a data sample.

[0053] Secondly, unsupervised clustering algorithms (such as K-means clustering, DBSCAN clustering, or Gaussian mixture model) are used to train the large amount of amplitude array data obtained from the above preprocessing. 70% of the data is used as the training set, allowing the algorithm to learn the temporal pattern characteristics of the sound signal, thereby clustering the sound data into several typical states such as "background noise", "stable ablation" and "critical destruction".

[0054] Finally, the remaining 30% of the data is used as a validation set. A complete, unsegmented audio data segment is input into the trained clustering model, which classifies it in real time using a sliding time window. The system determines the moment of failure in the experiment by identifying the first consecutive "critical failure" state cluster. The time determined by this model is compared with the "true value" determined by the previous binary classification method. If the error is within a preset tolerance (e.g., ±5%), it indicates that the acoustic recognition model is effective and reliable, and can be deployed in a real-time device for real-time judgment.

[0055] The data processing system can be equipped with a second image processing neural network to process the images acquired by the second camera device, identify the crack shape characteristics and size of the rock sample 9, match them with historical crack shapes and sizes, and after identifying and judging the crack as meeting the requirements, send a signal to the laser irradiator light source 4 to immediately stop emitting laser.

[0056] The first and second image processing neural networks can include types such as convolutional neural networks (CNN) and graph neural networks (GNN), which process image data or related structures through different architectural designs.

[0057] Convolutional Neural Networks (CNNs) are the most fundamental and widely used models in image processing. Their core components include:

[0058] Convolutional layers: These layers extract local features of an image, such as edges, textures, and annotations, through learnable filters like convolutional kernels. They also reduce the number of parameters and enhance invariance to transformations such as translation and scaling by utilizing local connectivity and weight sharing mechanisms.

[0059] Pooling layers: Downsample the feature map, such as max pooling or average pooling, to reduce data dimensionality, compress features and improve computational efficiency, while also increasing translation invariance.

[0060] Fully connected layer: Flattens the features after convolution and pooling, and performs the final classification or regression output.

[0061] CNNs (Convolutional Neural Networks) construct hierarchical feature representations from local to global levels by stacking multiple layers of convolution and pooling operations. For example, the classic LeNet-5 model has demonstrated its effectiveness in handwritten digit recognition and is widely used in tasks such as image classification and object detection.

[0062] Graph Neural Networks (GNNs) are primarily used to process graph-structured data, but they also have specific applications in image processing, such as scene graph generation or image segmentation. Their core principle lies in aggregating node neighbor information through message passing mechanisms to capture complex relationships between pixels or objects. Typical variations include:

[0063] Graph Convolutional Networks (GCN): Borrowing the convolutional concept from CNNs, GCNs achieve local perception and weight sharing on graph structures.

[0064] Graph Attention Network (GAT): Introduces an attention mechanism to dynamically weight important node information.

[0065] GNNs can be used in image processing to model the relationships between objects.

[0066] Preferably, the rotation switching device may include a rotating arm 5 located above the rotating scanning platform 8, the axis of the rotating arm 5 being perpendicular to the horizontal plane, and a horizontal support 10 being fixedly connected to the lower part of the rotating arm 5. The horizontal support 10 rotates around the axis of the rotating arm 5. The first camera device 3 and the laser irradiator are installed at both ends of the horizontal support 10, so that the horizontal support 10 rotates 180° each time. When the horizontal support 10 is stationary, the optical axis of the lens of the first camera device 3 or the laser optical axis output by the laser irradiator coincides with the rotation axis of the rotating scanning platform 8.

[0067] Preferably, the rotation switching device may include an indexing plate; the first camera device 3 and the laser irradiator may be set at different indexing positions on the indexing plate; the indexing plate is rotated, and when the indexing plate stops rotating, the optical axis of the lens of the first camera device 3 or the laser optical axis output by the laser irradiator coincides with the rotation axis of the rotating scanning platform 8.

[0068] The present invention also provides a laser point load test method for obtaining multi-scale rock mechanical parameters using the above-described laser point load test system for obtaining multi-scale rock mechanical parameters, the method comprising the following steps:

[0069] Step 1: Using the same type of rock from the same origin, prepare rock sample 9 for the prediction experiment and calibration sample respectively; the calibration sample is used to calibrate the coefficient of thermal expansion in the discrete numerical model of laser point load.

[0070] Step 2: Place the experimental rock sample 9 on the rotating scanning platform 8 and align the 3D scanning device with the rock sample 9; rotate the rotating scanning platform 8 360° while the 3D scanning device scans the rock sample 9; and enable the data processing system to construct a 3D numerical geometric model based on the point cloud data obtained from the scan.

[0071] Step 3: The first camera device 3 acquires an image of the preset central area on the surface of the rock sample 9 and sends the acquired image to the data processing system; the rotation switching device is rotated so that the laser irradiator is aimed at the preset central area on the surface of the rock sample 9 and a constant power laser beam is used to continuously irradiate the preset central area on the surface of the rock sample 9; when the rock sample 9 is detected to be cracked, the laser irradiation is stopped and the time elapsed from the start of irradiation to the cracking of the rock sample 9 is recorded.

[0072] Step 4: Based on the three-dimensional numerical geometric model of the rock sample, construct a discrete numerical model of laser point load;

[0073] Let the tensile strength input to the discrete numerical model of laser point load be... The elastic modulus input to the discrete numerical model of laser point load is: The failure time output by the discrete numerical model of laser point load is: The failure time measured by the laser point load experiment was... ;

[0074] The following input parameters in the discrete numerical model of laser point load are iteratively adjusted using a polynomial interpolation method: , until and The relative error is less than 5%;

[0075] Step 5, to meet the error requirements and The data are combined and input into a preset Brazilian splitting numerical model for rock samples. The Brazilian splitting numerical model then outputs the predicted tensile strength of the rock. .

[0076] A three-dimensional numerical geometric model of a rock sample refers to a digital three-dimensional geometric representation reconstructed from point cloud data of the rock surface acquired by a three-dimensional scanning device and processed by a data processing system. This model accurately reflects the shape, size, and surface characteristics of the rock sample and serves as the fundamental geometric basis for subsequent numerical simulation and mechanical analysis.

[0077] The laser point load discrete numerical model is a computational model based on the four-dimensional discrete lattice method (4D-LSM) used to simulate the fracture process of rock samples irradiated by lasers. Based on a three-dimensional numerical geometric model of the rock, the model calculates the stress response, crack initiation and propagation process of the rock under laser thermo-mechanical coupling by inputting material mechanical parameters (such as tensile strength and elastic modulus) and laser loading conditions, and outputs the simulated failure time.

[0078] The Brazilian splitting numerical model for rock samples is a virtual simulation model based on the four-dimensional discrete lattice method (4D-LSM) used to simulate the standard Brazilian splitting experiment. The model takes a standard Brazilian disk three-dimensional geometric model as input, applies splitting loads, calculates the stress distribution and failure behavior of the rock under tension, and outputs the predicted tensile strength.

[0079] When preparing the experimental rock sample 9, rock samples 9 of different sizes and shapes were selected. Laser point load experiments were conducted on multiple rock samples 9 of different sizes and shapes using a laser point load experimental system to obtain multi-scale rock mechanical parameters.

[0080] Preferably, a robotic arm 2 can also be provided. The robotic arm 2 is used to flip the rock sample 9 and pick up and place the rock sample 9 on the rotating scanning platform 8; so that the three-dimensional scanning device is positioned above the rock sample 9, the rotating scanning platform 8 rotates 360° to complete the upper half scanning of the rock sample 9; the robotic arm 2 grabs the rock sample 9 and flips the rock sample 9 up and down 180° before placing it on the rotating scanning platform 8, and the rotating scanning platform 8 rotates 360° again to complete the lower half scanning of the rock sample 9.

[0081] Preferably, a second camera device may also be provided, which is used to acquire images of the irradiated area on the surface of the rock sample 9 when irradiated by the laser irradiator; the acquired images are sent to the data processing system; the data processing system performs crack identification on the images acquired by the second camera device, and when the size of the identified crack reaches a set value, it sends a signal to stop the laser irradiator from irradiating.

[0082] Preferably, in step 1, when preparing calibration samples, multiple sets of calibration samples with different mechanical parameters can be prepared; then, Brazilian splitting test, uniaxial compression test and laser point load test are performed on each set of calibration samples respectively. The Brazilian splitting test is used to measure the tensile strength of the calibration sample, the uniaxial compression test is used to measure the compressive strength and elastic modulus of the calibration sample, and the laser point load test is used to measure the experimental failure time of the calibration sample; by performing the above tests on the calibration samples, the thermal expansion coefficient parameters of experimental rock samples prepared from the same type of rock from the same origin are calibrated based on the measurement results.

[0083] Please refer to Figure 2In this embodiment of the laser point load test method for obtaining multi-scale rock mechanical parameters of the present invention, the same type of rock from the same origin is used to prepare rock samples for prediction experiments and calibration specimens. The calibration specimens include cylindrical specimens with a diameter of 50 mm and a height of 100 mm for uniaxial compression tests; disk specimens with a diameter of 50 mm and a height of 25 mm for Brazilian splitting tests; and cube specimens with a side length of 20 mm for laser point load tests. Multiple sets of calibration specimens are prepared, and each set includes three types of calibration specimens corresponding to the experiments.

[0084] Multiple sets of calibration samples were heat-treated at different temperatures to make their mechanical parameters different.

[0085] Multiple sets of calibration samples were subjected to Brazilian splitting test, uniaxial compression test, and laser point load test.

[0086] Experimental data A consists of experimental data from the Brazilian splitting test, uniaxial compression test, and laser point load test of the calibration specimens; experimental data B consists of experimental data on the laser point load failure time of rock samples of different sizes under laser point load; numerical model B is a discrete numerical model of laser point load reflecting the laser point load failure time of rock samples of different sizes under laser point load.

[0087] The rock sample 9 used in the laser point load prediction experiment is the same type of rock from the same origin, but its size and shape may differ from the laser point load calibration sample in the calibration experiment.

[0088] The workflow and working principle of the present invention will be further described below with reference to a preferred embodiment:

[0089] To enable those skilled in the art to better understand the technical solution of the present invention, a detailed description is provided below with reference to a specific embodiment simulating deep in-situ exploration. This embodiment aims to demonstrate how to use the method of the present invention to accurately predict the standard Brazilian splitting tensile strength of a small-sized rock sample 9 based on a laser point load experiment.

[0090] The same batch of rocks was used to process and calibrate the samples. Five groups of samples with different mechanical parameters were prepared by heat treatment at different temperatures. Each group included the samples required for the three experiments involved in the calibration process.

[0091] Five sets of calibration samples were subjected to Brazilian splitting test, uniaxial compression test, and laser point load test.

[0092] The coefficient of thermal expansion in the numerical model is calibrated based on experimental results.

[0093] After the model was calibrated, laser point load experiments were conducted on similar rocks from the same origin. The prediction experiment used three specimens of different sizes. The first cubic prediction specimen was designated G1, the second prediction specimen was designated G2, and the third prediction specimen was designated G3.

[0094] G1 dimensions: approximately 2.5cm × 2.5cm × 2.5cm; G2 dimensions: approximately 2cm × 2cm × 2cm; G3 dimensions: approximately 1.5cm × 1.5cm × 1.5cm. Test G1, G2, and G3 separately and verify the accuracy of their predicted tensile strength.

[0095] Experimental equipment:

[0096] A laser point load experimental system for acquiring multi-scale rock mechanical parameters includes: a horizontal support 10, a rotating scanning platform 8, a three-dimensional scanning device 7, a laser irradiator body 6, a rotating arm 5, a laser irradiator light source 4, a first camera device 3, a robotic arm 2, a second camera device, a rotation switching device, and a data analysis and control platform 1. The data analysis and control platform 1 includes a data processing system and a database. The laser irradiator light source 4 and the first camera device 3 are located above the rotating scanning platform 8, and the three-dimensional scanning device and the second camera device are located diagonally above the rotating scanning platform 8.

[0097] The rotating scanning platform 8 is used to place the rock sample 9 and rotate the rock sample 9 placed on it by 360°.

[0098] The 3D scanning equipment is used to scan rock sample 9 and acquire point cloud data; it then sends the acquired point cloud data to the data processing system.

[0099] A laser irradiator was used to irradiate the surface of rock sample 9 to cause it to crack.

[0100] The robotic arm 2 is used to flip the rock sample 9 and pick up and place the rock sample 9 on the rotating scanning platform 8.

[0101] The first camera device 3 is used to acquire images of a preset central area on the surface of the rock sample 9; it then sends the acquired images to the data processing system.

[0102] The second camera device is used to acquire images of the irradiated area on the surface of rock sample 9 when irradiated by the laser irradiator; it sends the acquired images to the data processing system; the data processing system performs crack identification on the images acquired by the second camera device, and when the size of the identified crack reaches a set value, it sends a signal to stop the laser irradiator from irradiating.

[0103] The second camera device can be a specialized camera suitable for strong light and high temperature environments, such as a welding monitoring camera. Welding monitoring cameras use high-resolution cameras and advanced image processing technology, enabling them to operate stably in harsh environments such as strong light and high temperatures, capturing the welding arc, molten pool dynamics, and weld formation process in real time.

[0104] These cameras have a variety of practical functions: for example, they support high-definition real-time monitoring, helping operators or systems to accurately observe minute changes on the surface of objects irradiated by laser irradiators; they can be used with image processing software to automatically detect cracks and provide feedback.

[0105] The rotation switching device is used to rotate and switch the laser irradiator and the first camera device 3 to align with the rock sample 9 on the rotating scanning platform 8.

[0106] The data analysis and control platform 1 is used to control the operation of the rotating scanning platform 8, the three-dimensional scanning device, the laser irradiator, the robotic arm 2, the first camera device 3, the second camera device, and the rotation switching device, and to analyze and process the data collected by the three-dimensional scanning device, the first camera device 3, and the second camera device.

[0107] The data processing system is used to process the point cloud data acquired by the 3D scanning device, construct a 3D numerical geometric model of the rock sample 9, process the image acquired by the first camera device 3, and determine the laser irradiation point in the 3D numerical geometric model of the rock sample 9.

[0108] The surface of the rotating scanning platform 8 is a heat-insulating layer made of a laser-resistant etching material.

[0109] The 3D scanning equipment uses a 3D scanner 7.

[0110] The rotation switching device includes a rotating arm 5 located above the rotating scanning platform 8. The axis of the rotating arm 5 is perpendicular to the horizontal plane. A horizontal support 10 is fixed to the lower part of the rotating arm 5. The horizontal support 10 rotates around the axis of the rotating arm 5. The first camera device 3 and the laser irradiator are installed at both ends of the horizontal support 10, so that the horizontal support 10 rotates 180° each time. When the horizontal support 10 is stationary, the optical axis of the lens of the first camera device 3 or the laser optical axis output by the laser irradiator coincides with the rotation axis of the rotating scanning platform 8.

[0111] The laser power emitted by the laser irradiator is fixed at 1000 W, and the spot diameter is 2 mm; the accuracy of the 3D scanner 7 is 0.1 mm.

[0112] Pre-stored calibration relationships: The database has pre-stored calibration relationships for common rocks. The applicable relationship in this example is:

[0113] ;

[0114] In the formula:

[0115] The elastic modulus input to the discrete numerical model of laser point load:

[0116] The tensile strength is the input for the discrete numerical model of laser point load.

[0117] A laser point load test method for obtaining multi-scale rock mechanical parameters using the aforementioned laser point load test system for obtaining multi-scale rock mechanical parameters, the method comprising the following steps:

[0118] S1. Using the same type of rock from the same origin, experimental rock samples and calibration samples were prepared separately; the calibration samples were used to calibrate the coefficient of thermal expansion in the discrete numerical model of laser point load.

[0119] During the preparation of calibration samples, heat treatment at different temperatures was performed to change the internal stress parameters of the rock, thereby preparing multiple sets of calibration samples with different mechanical parameters. Then, each set of calibration samples was subjected to Brazilian splitting test, uniaxial compression test, and laser point load test. The Brazilian splitting test was used to measure the tensile strength of the calibration samples, the uniaxial compression test was used to measure the compressive strength and elastic modulus of the calibration samples, and the laser point load test was used to measure the experimental failure time of the calibration samples. By conducting the above tests on the calibration samples, the thermal expansion coefficient parameters of experimental rock samples prepared from the same type of rock from the same origin were calibrated based on the measurement results.

[0120] Then, in the Brazilian splitting numerical model (twin), the input tensile strength parameters are adjusted so that the error between the output tensile strength value and the experimental tensile strength value is less than 5%. The calibrated tensile strength and the experimentally measured elastic modulus are then input into the laser point load discrete numerical model.

[0121] Because the relative error between the input and output elastic modulus in the uniaxial compression simulation is always less than 5%, the experimentally measured elastic modulus is directly used as the input to the discrete numerical model of the laser point load. The coefficient of thermal expansion is adjusted to ensure that the average error between the failure time of multiple sets of calibrated specimens in the experiment and simulation is less than 5%. Based on multiple sets of calibration data, an empirical relationship between the tensile strength and elastic modulus of this type of rock can also be established.

[0122] S2. Place the experimental rock sample 9 sequentially on the rotating scanning platform 8 and start the automated scanning process. The rotating scanning platform 8 rotates 360°, and the 3D scanner 7 scans the upper half of the rock sample 9. After the robotic arm 2 automatically flips the rock sample 9, the rotating scanning platform 8 rotates again, and the 3D scanner 7 scans the lower half of the rock sample 9. After obtaining the high-precision 3D point cloud of the rock sample 9, it is automatically reconstructed into a discrete element numerical model with a particle diameter of 0.5 mm in the data processing system.

[0123] S3. Laser Point Load Test. The rotating switching device causes the first camera device 3 to take a picture of the top of the rock sample 9, and the laser irradiation point is automatically determined through image recognition. Then, the rotating switching device is rotated to switch the laser irradiator to be aimed at the top of the rock sample 9, so that the laser irradiator irradiates the laser irradiation point of the rock sample 9 with a power of 1000W.

[0124] Laser point load experiments were conducted on G1, G2, and G3 respectively, and the failure times of G1, G2, and G3 were recorded as follows:

[0125] G1: Laser point load failure time = 3.5s. G2: Laser point load failure time = 1.907s. G3: Laser point load failure time = 1.094s.

[0126] S4. The three-dimensional numerical geometric model of rock sample 9 used in this step has been calibrated through a series of physical experiments on similar rocks. Based on the calibrated three-dimensional numerical geometric model, corresponding discrete numerical models of laser point loads are established for G1, G2, and G3, respectively. Polynomial interpolation is used to iteratively adjust the tensile strength input to the model. and elastic modulus ,tensile strength and elastic modulus The numerical ranges of both are constrained by a preset relationship until the simulated failure time. With experimental destruction time The relative error is less than 5%.

[0127] The reason for using the tensile strength output from the Brazilian splitting numerical model as the predicted value for S4, instead of directly using the tensile strength input from the numerical simulation in the laser point load discrete numerical model, is as follows:

[0128] Instead of directly inputting the simulated tensile strength value from the discrete numerical model of laser point load, this design uses the simulated tensile strength output from the Brazilian splitting numerical model as the input value for the simulated tensile strength of the discrete numerical model of laser point load, embodying the concept of digital twins. The Brazilian splitting twin, as a calibrated, high-fidelity virtual experimental machine, provides more reliable predictions that better conform to standard testing concepts. This design enhances the flexibility and scalability of the method; the calibrated numerical model (twin) can also be used to simulate other mechanical behaviors or experiments in the future.

[0129] S5. Rapid inversion and verification of multi-scale mechanical parameters. The tensile strength of the three sets of inversion parameters obtained in S4... and elastic modulus The values ​​are input into the calibrated Brazilian splitting numerical model digital twin, the simulation is run, and the predicted tensile strength is directly output. The relative errors between the predicted tensile strength and the measured values ​​for all three groups were within 10%.

[0130] Therefore, it can be seen that the method of the present invention can accurately predict the results of large-scale Brazilian splitting experiments in the laboratory using small-scale laser point load experiments.

[0131] The aforementioned rotating scanning platform 8, three-dimensional scanning device, laser irradiator, robotic arm 2, first camera device 3, second camera device, rotation switching device, data analysis and control platform 1, data processing system, rotating arm 5, horizontal support 10, indexing plate, three-dimensional scanner 7, three-dimensional numerical geometric model and other devices, models and systems can all adopt applicable devices, models and systems in the prior art, or adopt devices, models and systems in the prior art and construct them using conventional technical means.

[0132] The embodiments described above are only used to illustrate the technical ideas and features of the present invention. Their purpose is to enable those skilled in the art to understand the content of the present invention and implement it accordingly. The patent scope of the present invention should not be limited by these embodiments. That is, any equivalent changes or modifications made in accordance with the spirit disclosed in the present invention still fall within the patent scope of the present invention.

Claims

1. A laser point load experimental system for obtaining multi-scale rock mechanical parameters, characterized in that, The system includes: a rotating scanning platform, a 3D scanning device, a laser irradiator, a first camera device, a rotation switching device, and a data processing system; the laser irradiator and the first camera device are located above the rotating scanning platform, and the 3D scanning device is located diagonally above the rotating scanning platform; The rotating scanning platform is used to place rock samples and rotate the rock samples placed on it 360°. The 3D scanning equipment is used to scan rock samples and acquire point cloud data; it then sends the acquired point cloud data to a data processing system. A laser irradiator is used to irradiate the surface of a rock sample to cause it to crack. The first camera device is used to acquire images of a preset central area on the surface of a rock sample; it then sends the acquired images to a data processing system. The rotation switching device is used to rotate and switch the laser irradiator and the first camera device to align with the rock sample on the rotating scanning platform. The data processing system is used to process the point cloud data acquired by the 3D scanning equipment, construct a 3D numerical geometric model of the rock sample, process the image acquired by the first camera device, and determine the laser irradiation point in the 3D numerical geometric model of the rock sample.

2. The laser point load experimental system for obtaining multi-scale rock mechanical parameters according to claim 1, characterized in that, It also includes a robotic arm, which is used to flip rock samples and pick up and place rock samples on a rotating scanning platform.

3. The laser point load experimental system for obtaining multi-scale rock mechanical parameters according to claim 1, characterized in that, The surface of the rotating scanning platform is covered with a heat-insulating layer made of a laser-resistant etching material.

4. The laser point load experimental system for obtaining multi-scale rock mechanical parameters according to claim 1, characterized in that, It also includes a second camera device, which is used to acquire images of the irradiated area on the surface of the rock sample when irradiated by the laser irradiator; it sends the acquired images to the data processing system; the data processing system performs crack identification on the images acquired by the second camera device, and when the size of the identified crack reaches a set value, it sends a signal to stop the laser irradiator from irradiating.

5. The laser point load experimental system for obtaining multi-scale rock mechanical parameters according to claim 1, characterized in that, The rotation switching device includes a rotating arm located above the rotating scanning platform. The axis of the rotating arm is perpendicular to the horizontal plane. A horizontal support is fixed to the lower part of the rotating arm. The horizontal support rotates around the axis of the rotating arm. A first camera device and a laser irradiator are installed at both ends of the horizontal support, so that the horizontal support rotates 180° each time. When the horizontal support is stationary, the optical axis of the lens of the first camera device or the laser optical axis output by the laser irradiator coincides with the rotation axis of the rotating scanning platform.

6. The laser point load experimental system for obtaining multi-scale rock mechanical parameters according to claim 4, characterized in that, The rotary switching device includes an indexing plate; a first camera device and a laser irradiator are set at different indexing positions on the indexing plate; the indexing plate is rotated, and when the indexing plate stops rotating, the optical axis of the lens of the first camera device or the laser optical axis output by the laser irradiator coincides with the rotation axis of the rotary scanning platform.

7. A laser point load test method for obtaining multi-scale rock mechanical parameters using the laser point load test system for obtaining multi-scale rock mechanical parameters as described in claim 1, characterized in that, The method includes the following steps: Step 1: Using the same type of rock from the same origin, prepare rock samples for the prediction experiment and calibration samples respectively; the calibration samples are used to calibrate the coefficient of thermal expansion in the discrete numerical model of laser point load. Step 2: Place the experimental rock sample on the rotating scanning platform and align the 3D scanning device with the rock sample; rotate the rotating scanning platform 360° while the 3D scanning device scans the rock sample; and enable the data processing system to construct a 3D numerical geometric model based on the point cloud data obtained from the scan. Step 3: The first camera device acquires an image of the preset central area on the surface of the rock sample and sends the acquired image to the data processing system; the rotation switching device is rotated so that the laser irradiator is aimed at the preset central area on the surface of the rock sample and a constant power laser beam is used to continuously irradiate the preset central area on the surface of the rock sample; when the rock sample is detected to be cracked, the laser irradiation is stopped and the time elapsed from the start of irradiation to the cracking of the rock sample is recorded. Step 4: Based on the three-dimensional numerical geometric model of the rock sample, construct a discrete numerical model of laser point load; Let the tensile strength input to the discrete numerical model of laser point load be... The elastic modulus input to the discrete numerical model of laser point load is: The failure time output by the discrete numerical model of laser point load is: The failure time measured by the laser point load experiment was... ; The following input parameters in the discrete numerical model of laser point load are iteratively adjusted using a polynomial interpolation method: , until and The relative error is less than 5%; Step 5, to meet the error requirements and The data are combined and input into a preset Brazilian splitting numerical model for rock samples. The Brazilian splitting numerical model then outputs the predicted tensile strength of the rock. .

8. The laser point load experimental method for obtaining multi-scale rock mechanical parameters according to claim 7, characterized in that, A robotic arm is also provided. The robotic arm is used to flip the rock sample and pick up and place the rock sample on the rotating scanning platform. The 3D scanning device is positioned above the rock sample. The rotating scanning platform rotates 360° to complete the upper half scanning of the rock sample. The robotic arm grabs the rock sample and flips it 180° up and down before placing it on the rotating scanning platform. The rotating scanning platform rotates 360° again to complete the lower half scanning of the rock sample.

9. The laser point load experimental method for obtaining multi-scale rock mechanical parameters according to claim 7, characterized in that, A second camera device is also provided, which is used to collect images of the irradiated area on the surface of the rock sample when irradiated by the laser irradiator; the collected images are sent to the data processing system; the data processing system performs crack identification on the images collected by the second camera device, and when the size of the identified crack reaches a set value, it sends a signal to stop the laser irradiator from irradiating.

10. The laser point load experimental method for obtaining multi-scale rock mechanical parameters according to claim 7, characterized in that, In step 1, multiple sets of calibration specimens with different mechanical parameters are prepared. Then, Brazilian splitting test, uniaxial compression test, and laser point load test are performed on each set of calibration specimens. The Brazilian splitting test is used to measure the tensile strength of the calibration specimens, the uniaxial compression test is used to measure the compressive strength and elastic modulus of the calibration specimens, and the laser point load test is used to measure the experimental failure time of the calibration specimens. By conducting the above tests on the calibration specimens, the thermal expansion coefficient parameters of experimental rock samples prepared from the same type of rock from the same origin are calibrated based on the measurement results.

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