Rock crack change real-time detection method, system, device and platform

Abstract

The invention discloses a rock crack change real-time detection method, system, device and platform. According to the scheme, first data corresponding to rock sample crack changes are generated and obtained in real time through the method; wherein the first data is optical signal data reflected by the light surface of the rock sample; creating at least one crack feature change data model corresponding to the first data according to the first data in combination with a neural network; generating second data corresponding to the first data through the crack characteristic change data model; wherein the second data is crack characteristic change data corresponding to the rock sample, and the system, the device and the platform corresponding to the method effectively solve the problem that the damage form change in the whole process of the rock mechanics experiment cannot be measured, and ensure that the physical data of the rock sample is recorded. The measured data can be used for rock damage constitutive model research, rock damage mechanical behavior research and numerical simulation fitting.

Description

Technical Field

[0001] The present invention belongs to the technical field of rock test crack determination, and in particular relates to a method, system, device and platform for real-time detection of rock crack changes. Background Art

[0002] At present, rock mechanics tests are an important means to study the mechanical properties of rocks, including uniaxial tests, triaxial tests and triaxial creep tests. These test methods can help understand the damage, deformation and failure behavior of rocks under different stress conditions.

[0003] Among them, the uniaxial test of rock is one of the most commonly used test methods. In this test, the rock sample is placed under a stress load perpendicular to its axial direction. By applying pressure perpendicular to the axial direction, the stress-strain relationship of the rock sample can be observed and its mechanical properties can be inferred. The triaxial test is a more complex test method that can simulate a stress state closer to actual geological conditions. In this test, radial and axial stresses are applied to the rock sample at the same time to simulate the multi-directional stress state in the earth's crust. Through this test, the strength, deformation and failure characteristics of the rock can be more fully understood. The triaxial creep test is a deformation test conducted on the basis of the triaxial test. It is mainly used to study the deformation and failure behavior of rocks under long-term loads. In this test, continuous stress and deformation are applied to the rock sample to simulate the creep phenomenon in actual engineering.

[0004] The above existing triaxial test devices cannot observe the changes of cracks in real time during these tests. Researchers can only speculate the damage mechanism through the damage morphology of rock samples after the test, and study the damage process by studying the crack morphology after damage through CT scanning and electron microscope scanning. In addition, the existing computer numerical simulation of the damage process cannot be fitted, resulting in a large deviation between the final result and the actual damage situation. These factors will affect the research on rock damage mechanism.

[0005] Therefore, in view of the technical problem that rock damage morphology is difficult to determine during the above rock mechanics experiments, it is urgent to design and develop a real-time detection method, system, device and platform for rock crack changes. Summary of the invention

[0006] In order to overcome the shortcomings and difficulties of the above-mentioned prior art, the purpose of the present invention is to provide a method, system, device and platform for real-time detection of rock crack changes in view of the above-mentioned technical problems and defects, which can effectively solve the problem that the damage morphology changes in the whole process of rock mechanics experiments cannot be measured, and ensure that the physical data of the rock sample is recorded. The measured data can be used for the study of rock damage constitutive model, rock damage mechanical behavior and numerical simulation fitting.

[0007] The first purpose of the present invention is to provide a method for real-time detection of rock crack changes; the second purpose of the present invention is to provide a system for real-time detection of rock crack changes; the third purpose of the present invention is to provide a device for real-time detection of rock crack changes; the fourth purpose of the present invention is to provide a platform for real-time detection of rock crack changes.

[0008] The first object of the present invention is achieved in that the method comprises the following steps:

[0009] Generate and acquire first data corresponding to the crack changes of the rock sample in real time; wherein the first data is light signal data reflected from the light surface of the rock sample;

[0010] Based on the first data and in combination with a neural network, create at least one crack characteristic change data model corresponding to the first data;

[0011] The second data corresponding to the first data is generated through the fracture characteristic change data model; wherein the second data is the fracture characteristic change data corresponding to the rock sample.

[0012] Furthermore, the real-time generation and acquisition of first data corresponding to the crack changes of the rock sample further includes: generating and acquiring loading pressure data corresponding to the rock sample, and generating first control data according to the loading pressure data; wherein the loading pressure data includes loading confining pressure data, loading axial pressure data and loading permeability pressure data; the first control data is control data for starting the optical signal projection;

[0013] Generate and obtain light column incident angle data corresponding to the rock sample, and generate second control data based on the light column incident angle data; wherein the second control data is control data for adjusting the light column incident angle.

[0014] Furthermore, the creating at least one crack characteristic change data model corresponding to the first data based on the first data and in combination with a neural network also includes:

[0015] Generate third data corresponding to the first data, and establish a corresponding relationship between the first data and the third data; wherein the third data is photoresistance value change data corresponding to the light signal data;

[0016] According to the third data, real-time status data corresponding to the change of the rock sample crack is created, and a change curve corresponding to the real-time status data is generated.

[0017] Furthermore, the generating second data corresponding to the first data through the crack characteristic change data model further includes:

[0018] Creating at least one photoresistor array corresponding to the first data, and generating crack characteristic change data corresponding to the resistance change in the photoresistor array; wherein the crack characteristic change data includes crack width change data, crack length change data and crack change position data;

[0019] According to the crack characteristic change data, crack width or length estimation data corresponding to the crack characteristic change data is generated in real time.

[0020] Furthermore, the step of creating at least one photoresistor array corresponding to the first data and generating crack characteristic change data corresponding to the resistance change in the photoresistor array further includes:

[0021] Generate and obtain resistance change data in the photoresistor array, and generate current value change data corresponding to the resistance change data;

[0022] The resistance value change data is fused and processed, and combined with the corresponding current value change data to generate damage change data corresponding to the rock sample.

[0023] Furthermore, the generating second data corresponding to the first data through the crack characteristic change data model further includes:

[0024] generating two-dimensional shape change data corresponding to the second data according to the second data;

[0025] Based on the two-dimensional shape change data and in combination with the photosensitive element parameter change data, shape mapping relationship data corresponding to the rock sample cracks are created; wherein the photosensitive element parameter change data includes emission light parameter data, current parameter data and resistance parameter data;

[0026] A calibration curve corresponding to the crack characteristic change data is generated according to the shape mapping relationship data.

[0027] The second object of the present invention is achieved in this way: the system is used to implement the real-time detection method of rock crack changes; the system comprises:

[0028] A first data generating unit is used to generate and obtain first data corresponding to the crack changes of the rock sample in real time; wherein the first data is light signal data reflected from the light surface of the rock sample;

[0029] A data model creation unit, configured to create at least one crack characteristic change data model corresponding to the first data based on the first data and in combination with a neural network;

[0030] The second data generating unit is used to generate second data corresponding to the first data through the fracture characteristic change data model; wherein the second data is fracture characteristic change data corresponding to the rock sample.

[0031] Furthermore, the first data generating unit further includes:

[0032] A first data generation module is used to generate and obtain loading pressure data corresponding to the rock sample, and generate first control data according to the loading pressure data; wherein the loading pressure data includes loading confining pressure data, loading axial pressure data and loading osmotic pressure data; the first control data is control data for starting the optical signal projection;

[0033] A second data generation module is used to generate and obtain light column incident angle data corresponding to the rock sample, and generate second control data according to the light column incident angle data; wherein the second control data is control data for adjusting the light column incident angle;

[0034] And / or, the data model creation unit further includes:

[0035] A third data generating module, used to generate third data corresponding to the first data, and establish a corresponding relationship between the first data and the third data; wherein the third data is photoresistance value change data corresponding to the light signal data;

[0036] A first data creation module, used to create real-time status data corresponding to the change of the rock sample cracks according to the third data, and generate a change curve corresponding to the real-time status data;

[0037] And / or, the second data generating unit further includes:

[0038] A second data creation module is used to create at least one photoresistor array corresponding to the first data, and generate crack characteristic change data corresponding to the resistance change in the photoresistor array; wherein the crack characteristic change data includes crack width change data, crack length change data and crack change position data;

[0039] A fourth data generating module, configured to generate crack width or length estimation data corresponding to the crack characteristic change data in real time according to the crack characteristic change data;

[0040] And / or, the second data creation module further includes:

[0041] A fifth data generating module, used to generate and obtain resistance value change data in the photoresistor array, and generate current value change data corresponding to the resistance value change data;

[0042] A data fusion processing module, used for fusing and processing the resistance value change data, and combining it with the corresponding current value change data, to generate damage change data corresponding to the rock sample;

[0043] And / or, the second data generating unit further includes:

[0044] a sixth data generating module, configured to generate two-dimensional shape change data corresponding to the second data according to the second data;

[0045] A third data creation module is used to create shape mapping relationship data corresponding to the cracks of the rock sample based on the two-dimensional shape change data and in combination with the photosensitive element parameter change data; wherein the photosensitive element parameter change data includes emission light parameter data, current parameter data and resistance parameter data;

[0046] The seventh data generating module is used to generate a calibration curve corresponding to the crack characteristic change data according to the shape mapping relationship data.

[0047] The third object of the present invention is achieved in this way: the device is used to implement the real-time detection method of rock crack changes; the device comprises:

[0048] An axial loading rod for applying axial pressure to the rock sample through a top seat; a seepage loading system for applying osmotic pressure to the bottom of the rock sample through a reserved channel; a confining pressure loading system for applying confining pressure to the surrounding of the rock sample through a reserved channel; and a triaxial loading system for applying axial pressure, osmotic pressure, and confining pressure to the rock sample;

[0049] The device is also provided with a light-sensing receiving screen for recording light signal changes caused by the generation and development of cracks when the rock is damaged; the rock sample is a rock sample with coating on the surface.

[0050] The fourth object of the present invention is achieved as follows: it includes a processor, a memory and a rock crack change real-time detection platform control program; wherein the rock crack change real-time detection platform control program is executed by the processor, the rock crack change real-time detection platform control program is stored in the memory, and the rock crack change real-time detection platform control program implements the rock crack change real-time detection method.

[0051] The present invention generates and obtains first data corresponding to the crack changes of rock samples in real time through a method; wherein the first data is the light signal data reflected from the light surface of the rock sample; based on the first data and in combination with a neural network, at least one crack characteristic change data model corresponding to the first data is created; through the crack characteristic change data model, second data corresponding to the first data is generated; wherein the second data is the crack characteristic change data corresponding to the rock sample, and the system, device, and platform corresponding to the method can effectively solve the problem that the damage morphology changes during the whole process of rock mechanics experiments cannot be measured, and ensure that the physical data of the rock sample is recorded. The measured data can be used for the study of rock damage constitutive models, the study of rock damage mechanical behavior, and the fitting of numerical simulations.

[0052] That is to say, the triaxial experimental device of the present invention can record the real-time change process of the crack change of the rock sample, and the set light-sensitive receiving screen can be installed in the existing common uniaxial test, which is simple and easy to operate. In addition, the crack change data recorded by the scheme of the present invention can be used for numerical model fitting. That is, the change process of the crack of the rock sample can be measured in real time through the scheme of the present invention, and the relevant data can be recorded; these data can be used for the study of rock damage constitutive model, rock damage mechanical behavior and numerical simulation fitting. Compared with the existing technology, the scheme of the present invention has the advantages of real-time determination of crack changes, simplicity and ease of operation, and can improve the accuracy and reliability of rock mechanics experiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0054] Figure 1 A schematic diagram of a flow chart of a method for real-time detection of rock crack changes according to the present invention;

[0055] Figure 2 This is a three-dimensional schematic diagram of the structure of a real-time detection device for rock crack changes of the present invention;

[0056] Figure 3 This is a schematic diagram of the test work of a real-time detection device for rock crack changes of the present invention;

[0057] Figure 4 This is a schematic diagram of a light-sensing receiving screen of a real-time detection device for rock crack changes of the present invention;

[0058] Figure 5This is a schematic diagram of the reflection principle of real-time detection of rock crack changes in the present invention;

[0059] Figure 6 This is a schematic diagram of the architecture of a real-time detection system for rock crack changes according to the present invention;

[0060] Figure 7 This is a schematic diagram of the architecture of a real-time detection platform for rock crack changes of the present invention;

[0061] In the figure: 1-axial pressure rod; 2-pressure pump oil inlet valve; 3-three-axis oil inlet valve; 4-three-axis oil return valve; 5-pressure pump oil return valve; 6-oil pressure pump pressure key; 7-oil pressure pump stop key; 8-return oil negative pressure key; 9-water pressure pump water outlet valve; 10-three-axis water inlet valve; 11-three-axis return valve; 12-water pressure pump return valve; 13-base; 14-base water channel; 15-base water inlet; 16-top seat; 17-top seat water outlet; 18- Upper water channel; 19-high-strength glass cover; 20-hydraulic pump pressurization button; 21-hydraulic pump stop button; 22-return water negative pressure button; 23-osmotic pressure loading system; 24-projection light source area; 25-light-sensing receiving curtain; 26-surface coating rock sample; 27-optical signal sensing belt; 28-side adhesive belt of receiving curtain; 29-opaque layer of receiving curtain; 30-optical signal acquisition channel; 31-optical signal receiver; 32-confining pressure loading system; 33-triaxial loading system;

[0062] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0063] In order to better understand the purpose, technical solutions and advantages of the present invention, the present invention is further described below in conjunction with the accompanying drawings and specific implementation methods. Those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification.

[0064] The present invention may also be implemented or applied through other different specific examples, and the details in this specification may also be modified and changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention.

[0065] It should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0066] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. Secondly, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0067] Preferably, the real-time detection method of rock fracture changes of the present invention is applied in one or more terminals or servers. The terminal is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), digital signal processors (DSP), embedded devices, etc.

[0068] The terminal can be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal can interact with the client through a keyboard, a mouse, a remote control, a touch pad, or a voice control device.

[0069] The present invention is to realize a method, system and platform for real-time detection of rock crack changes. Figure 1 , which is a flow chart of a method for real-time detection of rock fracture changes provided by an embodiment of the present invention.

[0070] In this embodiment, the real-time detection method of rock crack changes can be applied to a terminal or a fixed terminal with a display function. The terminal is not limited to a personal computer, a smart phone, a tablet computer, a desktop computer or an all-in-one computer equipped with a camera, etc.

[0071] The rock crack change real-time detection method can also be applied to a hardware environment consisting of a terminal and a server connected to the terminal via a network. The network includes but is not limited to: a wide area network, a metropolitan area network or a local area network. The rock crack change real-time detection method of the embodiment of the present invention can be executed by a server, or by a terminal, or by both a server and a terminal.

[0072] For example, for a terminal that needs to perform real-time detection of rock crack changes, the real-time detection function of rock crack changes provided by the method of the present invention can be directly integrated on the terminal, or a client for implementing the method of the present invention can be installed. For another example, the method provided by the present invention can also be run on a server or other device in the form of a software development kit (SDK), and an interface for the real-time detection function of rock crack changes is provided in the form of SDK. The terminal or other device can implement the real-time detection function of rock crack changes through the provided interface. The present invention is further described below in conjunction with the accompanying drawings.

[0073] like Figure 1 As shown, the present invention provides a method for real-time detection of rock crack changes, the method comprising the following steps:

[0074] S1. Generate and acquire first data corresponding to changes in cracks in a rock sample in real time; wherein the first data is light signal data reflected from a light surface of the rock sample;

[0075] S2. Creating at least one crack characteristic change data model corresponding to the first data based on the first data and in combination with a neural network;

[0076] S3. Generate second data corresponding to the first data through the fracture characteristic change data model; wherein the second data is fracture characteristic change data corresponding to the rock sample.

[0077] The real-time generation and acquisition of first data corresponding to the changes in the cracks of the rock sample also includes:

[0078] S11, generating and acquiring loading pressure data corresponding to the rock sample, and generating first control data according to the loading pressure data; wherein the loading pressure data includes loading confining pressure data, loading axial pressure data and loading osmotic pressure data; the first control data is control data for starting the optical signal projection;

[0079] S12, generating and acquiring light column incident angle data corresponding to the rock sample, and generating second control data according to the light column incident angle data; wherein the second control data is control data for adjusting the light column incident angle.

[0080] The method of creating at least one crack characteristic change data model corresponding to the first data based on the first data and in combination with a neural network also includes:

[0081] S21, generating third data corresponding to the first data, and establishing a corresponding relationship between the first data and the third data; wherein the third data is photoresistance value change data corresponding to the light signal data;

[0082] S22. Create real-time status data corresponding to the change of the rock sample cracks according to the third data, and generate a change curve corresponding to the real-time status data.

[0083] The step of generating second data corresponding to the first data by using the crack characteristic change data model further includes:

[0084] S31, creating at least one photoresistor array corresponding to the first data, and generating crack characteristic change data corresponding to the resistance change in the photoresistor array; wherein the crack characteristic change data includes crack width change data, crack length change data and crack change position data;

[0085] S32. Based on the crack characteristic change data, generate crack width or length estimation data corresponding to the crack characteristic change data in real time.

[0086] The step of creating at least one photoresistor array corresponding to the first data and generating crack characteristic change data corresponding to the resistance change in the photoresistor array further includes:

[0087] S311, generating and acquiring resistance value change data in the photoresistor array, and generating current value change data corresponding to the resistance value change data;

[0088] S312, fusing the resistance value change data and combining it with the corresponding current value change data to generate damage change data corresponding to the rock sample.

[0089] The step of generating second data corresponding to the first data by using the crack characteristic change data model further includes:

[0090] S33, generating two-dimensional shape change data corresponding to the second data according to the second data;

[0091] S34, creating shape mapping relationship data corresponding to the cracks in the rock sample based on the two-dimensional shape change data and in combination with the photosensitive element parameter change data; wherein the photosensitive element parameter change data includes emitted light parameter data, current parameter data and resistance parameter data;

[0092] S35. Generate a calibration curve corresponding to the crack characteristic change data according to the shape mapping relationship data.

[0093] Specifically, in the embodiment of the present invention, Figure 2-Figure 5As shown, a light surface reflection triaxial test device and a measurement method for real-time determination of crack changes are specifically provided, which are used to solve the problem that crack changes cannot be measured in real time during rock mechanics experiments. Traditional rock mechanics test methods cannot directly observe crack changes, and can only infer the damage mechanism through rock samples after the test. The scheme of the present invention designs a device for real-time determination of crack changes, which can record the real-time change process of cracks in rock samples. The main device of the device is a light-sensitive receiving curtain. Before the test, the surface of the rock sample is evenly coated with light-reflecting material, and then confining pressure loading, axial pressure loading and osmotic pressure loading are carried out. During the test, the light-sensitive receiving curtain is surrounded and attached to the high-strength glass cover, and the light source projection area projects the light column on the rock sample. When cracks are generated by rock damage, the cracks cannot reflect light because there is no coating. The light reflection changes, and the data of crack changes can be recorded in real time through the optical signal acquisition channel and the optical signal receiver.

[0094] Preferably, the smooth surface reflection triaxial test device for real-time determination of crack changes includes the following functional structures: an axial loading rod, which is made of high-strength steel and can apply axial pressure to the rock sample through the top seat. A seepage loading system, which is composed of water, a hydraulic pump, a valve, and a hose, and can load the seepage pressure to the bottom of the rock sample through a reserved channel. A confining pressure loading system, which is composed of hydraulic oil, an oil pressure pump, a valve, and a hose, and can load the confining pressure to the surrounding of the rock sample through a reserved channel. A triaxial loading system, which can provide axial pressure, seepage pressure, and confining pressure on the rock sample. A light-sensitive receiving screen, which can record the changes in light signals caused by the generation and development of cracks when the rock is damaged. A surface-coated rock sample, which can reflect the projected light beam.

[0095] The three-axis loading system has a high-strength glass cover on the side, which can project light onto the rock sample through the light source. The main material of the light-sensing receiving screen is dark rubber to isolate the interference of external light sources, which is convenient for surrounding the glass cover and installing the projection light source area and the light signal sensor belt.

[0096] The light receiving screen has a projection light source area composed of many neatly arranged small lamp tubes, the angle of which can be adjusted in advance, thereby adjusting the incident angle of the light column to cover the entire rock sample surface.

[0097] The light-sensitive receiving screen is composed of a photoresistor. When the light source changes or disappears, its resistance value changes, thereby affecting the change in the current. When cracks appear on the rock surface, since the cracks have no paint and cannot reflect the light source, the light signal sensor belt at the corresponding position records the changes in rock sample damage.

[0098] The photosensitive receiving screen has side adhesive tapes at both ends to fix the photosensitive receiving screen. The surface coating is made of silver, aluminum and other glossy reflective coatings to reflect the incident light beam to the light signal sensor belt according to the reflection angle.

[0099] That is to say, the present invention proposes a light surface reflection triaxial test device and a test method for real-time measurement of crack changes, the main features of which include: an osmotic pressure loading system 23, a light-sensitive receiving curtain 25, a confining pressure loading system 32, and a triaxial loading system 33.

[0100] The axial loading rod 1 is made of high-strength steel and is used to perform axial stress loading on the rock sample.

[0101] The booster pump oil inlet valve 2 belongs to the confining pressure loading system 32 and adopts a spiral switch to control the entry of hydraulic oil into the confining pressure loading system 32 .

[0102] The three-axis oil inlet valve 3 belongs to the three-axis loading system 33, is connected to the pressure pump oil inlet valve 2 through a rubber tube, and is sealed by a nut, and is opened for a long time to allow the hydraulic oil in the three-axis loading system 33 to enter.

[0103] The three-axis oil return valve 4 belongs to the three-axis loading system 33, is connected to the pressure pump oil return valve 5 through a rubber tube, and is fixed with a nut to ensure sealing. It is opened for a long time to allow the hydraulic oil in the three-axis loading system 33 to return.

[0104] The booster pump oil return valve 5 belongs to the confining pressure loading system 32 and adopts a spiral switch to control the return of hydraulic oil in the confining pressure loading system 32 .

[0105] The hydraulic pump pressurizing key 6 can generate positive pressure on the hydraulic oil, thereby increasing the confining pressure of the three-axis loading system 33 .

[0106] The hydraulic pump stop button 7 can stop the hydraulic pump from working, thereby stopping pressurization or stopping oil return.

[0107] The oil return negative pressure key 8 can generate negative pressure on the hydraulic oil, thereby drawing the hydraulic oil in the axial pressure loading system 33 back to the confining pressure loading system 32 to achieve the recycling of the hydraulic oil.

[0108] The water outlet valve 9 of the water pressure pump belongs to the osmotic pressure loading system 23 and adopts a spiral switch to control the entry of the osmotic pressure water flow in the confining pressure loading system 32 .

[0109] The three-axis water inlet valve 10 belongs to the three-axis loading system 33, is connected to the water pressure pump outlet valve 9 through a rubber tube, and is sealed by a nut. It can be opened and closed by a knob to allow the osmotic pressure water flow in the three-axis loading system 33 to enter or unload the osmotic pressure.

[0110] The three-axis return valve 11 belongs to the three-axis loading system 33, is connected to the water pressure pump outlet valve 12 through a rubber tube, and is fixed with a nut to ensure sealing. It is opened for a long time to allow the osmotic pressure water in the three-axis loading system 33 to flow back.

[0111] The water pressure pump return valve 12 belongs to the osmotic pressure loading system 23 and adopts a spiral switch to control the osmotic pressure water flow backflow in the confining pressure loading system 32. The base 13 is made of high-strength steel and plays a role in fixing the bottom of the rock sample and bearing. The base water channel 14 is a hollow iron pipe, connecting the three-axis water inlet valve 10 and the base water inlet 15, so that the osmotic pressure water flow can seep upward from the bottom of the rock sample.

[0112] The water inlet 15 of the base is a hollow hole through which water flows, so that the bottom of the rock sample is subjected to osmotic pressure. The top seat 16 is made of high-strength steel and plays a role in fixing the top of the rock sample and transmitting axial pressure. The water outlet 17 of the top seat is a hollow circular hole, which can flow out the pore water flow that seeps from the bottom of the rock to the top. The upper water pipe 18 is a hollow iron pipe, which connects the water outlet 17 of the top seat with the three-axis return valve 12, and can lead out the pore water. The high-strength glass cover 19 is made of glass with high compressive strength. The light-sensing receiving screen 25 is installed around the high-strength glass cover. The projection light source area 24 can project the light column at a certain angle through the high-strength glass cover 19 onto the surface coating rock sample 26. The water pressure pump pressure key 20 can load positive water pressure when started, so that the water in the seepage loading system 23 is loaded to the bottom of the rock sample. During the startup, the osmotic pressure gradually increases until the preset osmotic pressure is reached, and the water pressure pump stop key 21 is pressed.

[0113] The hydraulic pump stop button 21 can stop the hydraulic pump from positively pressurizing and negatively refluxing. The water return negative pressure button 22 can start the hydraulic pump to increase the negative pressure, thereby adjusting the osmotic pressure at the top of the rock, or pumping the water flow in the pipe back for recycling at the end of the test. The seepage pressurization system 23 is composed of a hydraulic pump outlet valve 9, a hydraulic pump return valve 11, a hydraulic pump pressurization button 20, a hydraulic pump stop button 21, and a water return negative pressure button 22, which can apply osmotic pressure at the bottom of the rock and adjust the osmotic pressure at the top of the rock. The projection light source area 24 is the upper structure of the light-sensing receiving screen 25, which is composed of many small lamps. When the small lamps are started, they can project light beams, and the light beams projected by many lamps can cover the rock sample. The light-sensing receiving screen 25 is composed of a projection light source area 24, a light signal sensor belt 27, a receiving screen side adhesive belt 28, a receiving screen opaque layer 29, and a light signal collection channel 30. It can project a beam of light onto the surface coating rock sample 26 through the projection light source area 24, and reflect it to the optical signal sensor belt 27 at a certain reflection angle. When the photosensitive receiving screen 25 is installed, it is wrapped around the high-strength glass cover 19 and tightly fixed by the side adhesive tapes 28 of the receiving screen on both sides. The surface coating rock sample 26 adopts a mirror-like silver or aluminum light-reflecting paint, and the paint is evenly covered on the surface of the rock sample to reflect the light beam. The optical signal sensor belt 27 is composed of photoresistors. The increase or disappearance of light may cause a change in resistance value, thereby changing the current size, and the current size can be further converted into crack morphology data; when cracks appear in the rock sample, since the cracks cannot reflect light, the photoresistor of the corresponding reflection area changes, and the shape of the cracks is recorded; the side adhesive tapes 28 of the receiving screen are present at both ends of the photosensitive receiving screen 25, and the tightness can be adjusted by the adhesive width, so that the photosensitive receiving screen 25 is firmly installed on the high-strength glass cover 19.

[0114] The light-proof layer 29 of the receiving screen is mainly composed of dark rubber such as black, which can avoid interference from external light sources on the one hand, and facilitate the installation of the projection light source area 24 and the optical signal sensor belt 27 on the other hand.

[0115] The optical signal collection channel 30 is a preset circuit signal transmission channel, connecting the optical signal sensor belt 27 and the optical signal receiving instrument 31. The optical signal receiving instrument 31 can convert the electrical signal recorded by the optical signal sensor belt 27 into crack morphology data.

[0116] The triaxial loading system 33 is composed of an axial loading rod 1, a triaxial oil inlet valve 3, a triaxial oil return valve 4, a triaxial water inlet valve 10, a triaxial water return valve 12, a base 13, a base water channel 14, a base water inlet 15, a top seat 16, a top seat water outlet 17, an upper water pipe 18, and a high-strength glass cover 19, and can load axial pressure, penetration pressure, and circumferential pressure on the rock sample.

[0117] In order to realize the above device functions, the embodiment of the present invention further provides a method for measuring crack changes in a light surface reflection triaxial test device, which includes the following steps:

[0118] S01. Evenly coat the surface of the rock sample with light-reflecting materials; among them, light-reflecting coatings usually include metal coatings (such as aluminum, silver), mirror coatings, high-gloss coatings (such as high-gloss polymer coatings), and some specially designed optical coatings. These coatings can effectively reflect light and produce a shiny effect. S02. Seal and fix the bottom of the surface-coated rock sample to the upper part of the base with a waterstop, and seal and fix the top of the rock sample to the lower part of the top seat with a waterstop; S03. Load confining pressure: open the booster pump oil inlet valve and the booster pump oil return valve, start the hydraulic pump pressurization key, and the hydraulic oil enters the three-axis chamber through the three-axis oil inlet valve; when hydraulic oil flows out of the three-axis oil return valve, the confining pressure chamber is filled with oil, and the booster pump oil return valve is closed; the confining pressure continues to increase, and when the preset confining pressure is reached, the booster pump oil inlet valve is closed, and then the 7 hydraulic pump stop button is opened; S04. Load axial pressure to drive the axial loading rod to move downward, and the rock sample obtains initial Step 1: Stop after preloading pressure; S05: Load osmotic pressure, open the three-axis water inlet valve and the water pressure pump outlet valve, then open the water pressure pump return valve and the water pressure pump pressurization button, the osmotic pressure continues to increase, and the water pressure pump stop button is started after reaching the preset size; S06: Surround the light-sensitive receiving screen and fit it on the high-strength glass cover, and connect the side adhesive tapes of the receiving screen at both ends to make them closely connected, and connect the optical signal acquisition channel with the optical signal receiver; S07: Through the axial loading rod, continuously load downward at a fixed displacement rate until the rock sample breaks; S08: Remove the light-sensitive receiving screen and untie the side adhesive tapes of the receiving screen at both ends. Among them, a photoresistor tape is sewn on the receiving screen; a photoresistor, also known as a photoresistor or a photo-dependent resistor, is a photosensitive element whose resistance value changes with changes in light intensity. They are widely used in light detection, automatic brightness adjustment, security systems and other fields. There are many brands of photoresistors on the market, including but not limited to Vishay, ON Semiconductor, ROHM, Panasonic, etc. Product specifications and performance vary to meet the needs of different application scenarios.

[0119] Photoresistors are mainly divided into two categories: cadmium sulfide (CdS) and zinc sulfide (ZnS) photoresistors. CdS photoresistors are more sensitive to visible light, while ZnS photoresistors are more sensitive to ultraviolet rays. Its working principle is based on the internal photoelectric effect. The stronger the light, the lower the resistance. As the light intensity increases, the resistance value decreases rapidly and the resistance value can be as small as 1KQ or less. The photoresistor value is as small as 1KO or less. Photoresistors are very sensitive to light. When there is no light, the dark resistance can generally reach 1.5MQ. This instrument is used for indoor light detection, and products that are more sensitive to artificial light sources can be selected.

[0120] When choosing a photoresistor, you need to consider the following key parameters: Photoresistance value: The resistance value in the absence of light, usually ranging from a few thousand ohms to several megohms. Photosensitivity: The degree to which the resistance value changes with light intensity, usually expressed as the rate of change of the resistance value when the light intensity changes. Response time: The time required for the light intensity to change until the resistance value stabilizes, including the rise time (from dark to light) and the fall time (from light to dark). Spectral response: The sensitivity of the photoresistor to different wavelengths of light, which determines its performance under different light sources. Operating temperature: The temperature range in which the photoresistor can operate normally.

[0121] Among them, product parameters: product model: 5528; maximum pressure (VDC): 150; maximum power consumption (MW): 100; ambient temperature (℃): -30~+70; spectrum peak (Nm): 540; bright resistance (kiloohm): 10-20; dark resistance (megaohm): 1; response time (ms): rise: 20 fall: 30; illumination resistance characteristics: 3.

[0122] S06, Unloading osmotic pressure: Close the triaxial water inlet valve. S010, Unloading axial pressure: Lift the axial loading rod upward at a fixed rate to unload the load. S011, Unloading confining pressure: Open the booster pump oil return valve, then open the booster pump oil inlet valve, and start the oil return negative pressure key until the triaxial oil return valve no longer discharges oil.

[0123] In the solution of the present invention, the light intensity at different positions on a surface can be detected by using an array of photosensitive elements, each of which can independently respond to the light intensity on it. The following are several technologies used:

[0124] Photodiode Array: A photodiode array consists of multiple photodiodes, each of which can independently detect the light intensity on it. These arrays are often used in spectrometers and image sensors. The current output of a photodiode is proportional to the light intensity on it and can be used to measure the distribution of light intensity.

[0125] CMOS image sensor: CMOS image sensors contain an array of many individual pixels, each of which contains one or more light-sensitive elements. These sensors can capture images and can also be used to measure the distribution of light intensity. The output of each pixel can be used to determine the light intensity at that pixel location.

[0126] CCD Image Sensor: Similar to CMOS sensors, CCD (charge coupled device) image sensors are also composed of an array, but their working principle is slightly different. CCD sensors can also be used to capture images and measure light intensity distribution.

[0127] Photoresistor array: Make an array of multiple photoresistors, each of which responds independently to the light intensity on it. Such an array can be used to measure the distribution of light intensity on a surface, but may be limited in resolution and sensitivity.

[0128] The above technology can provide detailed information about the light intensity distribution, measuring the changes in photocurrent or voltage during the test, which can then be converted into changes in resistance value, which can further determine the development of cracks. Combining these sensors with appropriate circuits and data processing systems can interpret and record the changes in resistance value at different locations.

[0129] Two-dimensional shape change: When the photosensitive element is located at the place where the crack occurs, the reflected light becomes weaker, the current becomes smaller, and the resistance value becomes larger. Each element can independently capture the light intensity; the position of each element corresponds to a certain position relationship of the rock; for the element corresponding to the crack part, the current becomes smaller, and the crack shape can be mapped by changing the position of some elements; as the crack width or length increases, the number of elements with corresponding current changes increases and their positions also expand.

[0130] Assuming that the resistance value of a normal reflection component is x, the crack width increases, the light source is absorbed, the reflected light becomes weaker, the light intensity at the component position becomes weaker, the current becomes smaller, and the resistance value increases. The larger the crack width, the larger the range of components affected.

[0131] The change in the photoresistor value as a function of crack width depends on a variety of factors, including the characteristics of the light source, the sensitivity of the photoresistor, and the reflection, refraction, and scattering properties of light. An increase in crack width may cause more light to be absorbed or scattered by the crack, thereby reducing the amount of light reflected back to the photoresistor, resulting in an increase in resistance value.

[0132] Determining this relationship requires calibration through experiments. Under conditions where the crack width and length are known, the response of the photoresistor can be measured by reflected light, and a large amount of mapping data can be analyzed through a neural network to establish a calibration curve of two parameters, crack width, length and resistance value change.

[0133] The resistance in the center of the crack may be 1W, and the surrounding components may have increasing resistance. This relationship can be corrected to determine the response of the photoresistor under known crack width, and a calibration curve of crack width and length and resistance change can be established. Components with maximum resistance (crack formation area); crack width increases in the resistance change area (crack formation area), and the range of affected components increases. For components in the development position, cracks develop slightly, and the resistance decreases with a changing pattern. For example, the increase in resistance acceleration can tell the development of the crack width in this area.

[0134] If the relationship between crack width, length and photoresistance value change has been determined through experiments and machine learning training, then in practical applications, the crack width and length can be estimated by measuring the resistance value of the photoresistor. By monitoring which photoresistors have changed resistance, the approximate location and length of the crack can be determined.

[0135] To improve accuracy, it may be necessary to use multiple photoresistor arrays to monitor different parts of the crack and use data fusion techniques to combine this information to obtain a more accurate estimate of the crack width.

[0136] In summary, using photoresistor arrays to monitor crack width and length does require a series of preset experimental data for machine learning to establish the relationship between the change in photoresistance value and crack characteristics. There may be problems with the actual length of the crack that cannot be accurately measured, especially when the crack shape is complex or branched. The only way is to increase the array density of the photoresistor to improve the resolution from the measurement or to use machine learning data to approximate numerically.

[0137] Although machine learning such as convolutional neural networks (CNN) are very powerful in processing image data, their results are usually black boxes and it is not easy to directly extract a clear mathematical formula. If a clear mathematical formula is required, traditional methods such as linear regression or polynomial regression need to be used to fit the formula.

[0138] This method can extract an approximate mathematical formula, that is, by training a simple linear model or polynomial model to fit the output of the CNN. This method can help understand the relationship between the change in photoresistance value and the width and length of the crack.

[0139] In order to fit the relationship between the change of photoresistance value and the mathematical model of crack width and length, a method combining convolutional neural network (CNN) and linear regression was used. The specific steps are as follows:

[0140] Data preparation: First, read the data containing the position of the photosensitive element, the change in resistance value, and the change in crack width and length. According to the position of the photosensitive element and the change in resistance value, create a two-dimensional array to represent the photosensitive element array.

[0141] Features and target variables: The two-dimensional array is used as the feature, and the crack width and length changes are used as the target variables. The dataset is then divided into training and test sets, and a dimension is added to fit the input format of CNN.

[0142] Build and train CNN models: Use Keras to build a simple CNN model, including convolutional layers, pooling layers, and fully connected layers. Train two CNN models, one for predicting crack width changes and the other for predicting crack length changes.

[0143] Prediction using CNN model: Use the trained CNN model to predict the training set and test set to obtain the predicted crack width and length changes.

[0144] Use linear regression to fit the output of CNN: In order to extract an approximate mathematical formula, linear regression is used to fit the output of CNN. Through the coefficients obtained by linear regression fitting, an approximate mathematical formula can be obtained to describe the relationship between the change in photoresistance value and the width and length of the crack.

[0145] Print mathematical formula: Print the mathematical formula for predicting the change in crack width and length based on the coefficients obtained from linear regression fitting.

[0146] Evaluate the linear regression model: Use the test data to make predictions and calculate the mean squared error (MSE) to evaluate the performance of the linear regression model.

[0147] Through the above method, not only the powerful feature extraction ability of machine learning is utilized, but also a clear mathematical formula is extracted through linear regression to better fit the relationship between the change of photoresistance value and the width and length of the crack.

[0148] In the solution of the present invention, a machine learning (CNN convolutional neural network) model is used for prediction: first, the CNN model is used to predict the data to obtain the predicted crack width and length changes.

[0149] Fitting the output of CNN using linear regression: Linear regression or polynomial regression is then used to fit the output of CNN to obtain an approximate mathematical formula.

[0150] Code implementation: We will use the previously trained CNN model and use linear regression to fit the output of CNN.

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[0152]

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[0156] Code explanation: Data reading: Use Pandas to read the data in the CSV file. Create a two-dimensional array: According to the position of the photosensitive element and the change in resistance value, create a two-dimensional array to represent the array of photosensitive elements. Features and target variables: Use the two-dimensional array as a feature, and the change in crack width and length as the target variable. Split the dataset: Divide the dataset into a training set and a test set. Add a dimension: Add a dimension to adapt to the input format of CNN. Build a CNN model: Use Keras to build a simple CNN model, including convolutional layers, pooling layers, and fully connected layers. Train the model: Train the models for predicting crack width and length changes respectively. Use the CNN model for prediction: Use the trained CNN model to predict the training set and test set. Use linear regression to fit the output of CNN: Use linear regression to fit the output of CNN to obtain an approximate mathematical formula. Print the mathematical formula: According to the coefficients obtained by linear regression fitting, print out the mathematical formula for predicting crack width and length changes. Evaluate the linear regression model: Use the test data for prediction, and calculate the mean square error (MSE) to evaluate the performance of the linear regression model.

[0157] To achieve the above object, the present invention also provides a rock crack change real-time detection system, such as Figure 6 As shown, the system is used to implement the real-time detection method for rock crack changes; the system specifically includes: a first data generating unit, used to generate and obtain first data corresponding to the crack changes of rock samples in real time; wherein the first data is light signal data reflected from the light surface of the rock sample; a data model creating unit, used to create at least one crack characteristic change data model corresponding to the first data based on the first data and in combination with a neural network; a second data generating unit, used to generate second data corresponding to the first data through the crack characteristic change data model; wherein the second data is crack characteristic change data corresponding to the rock sample.

[0158] The first data generation unit further includes: a first data generation module, used to generate and obtain loading pressure data corresponding to the rock sample, and generate first control data according to the loading pressure data; wherein the loading pressure data includes loading confining pressure data, loading axial pressure data and loading osmotic pressure data; the first control data is control data for starting the light signal projection; a second data generation module, used to generate and obtain light column incident angle data corresponding to the rock sample, and generate second control data according to the light column incident angle data; wherein the second control data is control data for adjusting the light column incident angle;

[0159] And / or, the data model creation unit further includes: a third data generation module, used to generate third data corresponding to the first data, and establish a corresponding relationship between the first data and the third data; wherein the third data is photoresistor value change data corresponding to the optical signal data; a first data creation module, used to create real-time status data corresponding to the rock sample crack change according to the third data, and generate a change curve corresponding to the real-time status data;

[0160] And / or, the second data generating unit further includes: a second data creating module, used to create at least one photoresistor array corresponding to the first data, and generate crack characteristic change data corresponding to the resistance change in the photoresistor array; wherein the crack characteristic change data includes crack width change data, crack length change data and crack change position data; a fourth data generating module, used to generate crack width or length estimation data corresponding to the crack characteristic change data in real time according to the crack characteristic change data;

[0161] And / or, the second data creation module further includes: a fifth data generation module, used to generate and obtain resistance change data in the photoresistor array, and generate current value change data corresponding to the resistance change data; a data fusion processing module, used to fuse and process the resistance change data, and generate damage change data corresponding to the rock sample in combination with the corresponding current value change data;

[0162] And / or, the second data generating unit further includes: a sixth data generating module, used to generate two-dimensional shape change data corresponding to the second data based on the second data; a third data creating module, used to create shape mapping relationship data corresponding to the rock sample cracks based on the two-dimensional shape change data and in combination with the photosensitive element parameter change data; wherein the photosensitive element parameter change data includes emitted light parameter data, current parameter data and resistance parameter data; and a seventh data generating module, used to generate a calibration curve corresponding to the crack characteristic change data based on the shape mapping relationship data.

[0163] In the system solution embodiment of the present invention, the method steps involved in the real-time detection of rock fracture changes have been described above in detail. That is to say, the functional modules in the system are used to implement the steps or sub-steps in the above method embodiment, which have been described above and will not be repeated here.

[0164] To achieve the above object, the present invention also provides a rock crack change real-time detection device, the device is used to implement the rock crack change real-time detection method;

[0165] The device comprises: an axial loading rod for applying axial pressure to the rock sample through a top seat; a seepage loading system for applying osmotic pressure to the bottom of the rock sample through a reserved channel; a confining pressure loading system for applying confining pressure to the surrounding of the rock sample through a reserved channel; and a triaxial loading system for applying axial pressure, osmotic pressure and confining pressure to the rock sample;

[0166] The device is also provided with a light-sensing receiving screen for recording light signal changes caused by the generation and development of cracks when the rock is damaged; the rock sample is a rock sample with coating on the surface.

[0167] In the embodiment of the device scheme of the present invention, the method steps involved in the real-time detection of rock fracture changes have been described above in detail. That is to say, the functional structure in the device is used to implement the steps or sub-steps in the above method embodiment, which have been described above and will not be repeated here.

[0168] To achieve the above objectives, the present invention also provides a real-time detection platform for rock crack changes, such as Figure 7 As shown, it includes a processor, a memory, and a rock crack change real-time detection platform control program; wherein, the rock crack change real-time detection platform control program is executed by the processor, the rock crack change real-time detection platform control program is stored in the memory, and the rock crack change real-time detection platform control program implements the rock crack change real-time detection method steps. For example:

[0169] S1. Generate and acquire first data corresponding to the crack changes of rock samples in real time; wherein, the first data is light signal data reflected from the light surface of the rock sample; S2. Create at least one crack characteristic change data model corresponding to the first data based on the first data and in combination with a neural network; S3. Generate second data corresponding to the first data through the crack characteristic change data model; wherein, the second data is crack characteristic change data corresponding to the rock sample.

[0170] The specific details of the steps have been explained above and will not be repeated here.

[0171] In the embodiment of the present invention, the built-in processor of the rock crack change real-time detection platform can be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of integrated circuits with the same or different functions, including one or more central processing units (CPU), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor uses various interfaces and lines to connect various components, and executes various functions of rock crack change real-time detection and processes data by running or executing programs or units stored in the memory, and calling data stored in the memory;

[0172] The memory is used to store program codes and various data. It is installed in the real-time detection platform for rock fracture changes and can automatically access programs or data at high speed during operation.

[0173] The memory includes read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electronically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0174] The present invention generates and obtains first data corresponding to the crack changes of rock samples in real time through a method; wherein the first data is the light signal data reflected from the light surface of the rock sample; based on the first data and in combination with a neural network, at least one crack characteristic change data model corresponding to the first data is created; through the crack characteristic change data model, second data corresponding to the first data is generated; wherein the second data is the crack characteristic change data corresponding to the rock sample, and the system, device, and platform corresponding to the method can effectively solve the problem that the damage morphology changes during the whole process of rock mechanics experiments cannot be measured, and ensure that the physical data of the rock sample is recorded. The measured data can be used for the study of rock damage constitutive models, the study of rock damage mechanical behavior, and the fitting of numerical simulations.

[0175] That is to say, the triaxial experimental device of the present invention can record the real-time change process of the crack change of the rock sample, and the set light-sensitive receiving screen can be installed in the existing common uniaxial test, which is simple and easy to operate. In addition, the crack change data recorded by the scheme of the present invention can be used for numerical model fitting. That is, the change process of the crack of the rock sample can be measured in real time through the scheme of the present invention, and the relevant data can be recorded; these data can be used for the study of rock damage constitutive model, rock damage mechanical behavior and numerical simulation fitting. Compared with the existing technology, the scheme of the present invention has the advantages of real-time determination of crack changes, simplicity and ease of operation, and can improve the accuracy and reliability of rock mechanics experiments.

[0176] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for real-time detection of rock crack changes, characterized in that: The method comprises the following steps: Generate and acquire first data corresponding to the crack changes of the rock sample in real time; wherein the first data is light signal data reflected from the light surface of the rock sample; Based on the first data and in combination with a neural network, create at least one crack characteristic change data model corresponding to the first data; The second data corresponding to the first data is generated through the fracture characteristic change data model; wherein the second data is the fracture characteristic change data corresponding to the rock sample.

2. A method for real-time detection of rock crack changes according to claim 1, characterized in that: The real-time generation and acquisition of first data corresponding to the changes in the cracks of the rock sample also includes: Generate and obtain loading pressure data corresponding to the rock sample, and generate first control data according to the loading pressure data; wherein the loading pressure data includes loading confining pressure data, loading axial pressure data and loading osmotic pressure data; the first control data is control data for starting the optical signal projection; Generate and obtain light column incident angle data corresponding to the rock sample, and generate second control data based on the light column incident angle data; wherein the second control data is control data for adjusting the light column incident angle.

3. A method for real-time detection of rock crack changes according to claim 1, characterized in that: The method of creating at least one crack characteristic change data model corresponding to the first data based on the first data and in combination with a neural network also includes: Generate third data corresponding to the first data, and establish a corresponding relationship between the first data and the third data; wherein the third data is photoresistance value change data corresponding to the light signal data; According to the third data, real-time status data corresponding to the change of the rock sample crack is created, and a change curve corresponding to the real-time status data is generated.

4. A method for real-time detection of rock crack changes according to claim 1, characterized in that: The step of generating second data corresponding to the first data by using the crack characteristic change data model further includes: Creating at least one photoresistor array corresponding to the first data, and generating crack characteristic change data corresponding to the resistance change in the photoresistor array; wherein the crack characteristic change data includes crack width change data, crack length change data and crack change position data; According to the crack characteristic change data, crack width or length estimation data corresponding to the crack characteristic change data is generated in real time.

5. A method for real-time detection of rock crack changes according to claim 4, characterized in that: The step of creating at least one photoresistor array corresponding to the first data and generating crack characteristic change data corresponding to the resistance change in the photoresistor array further includes: Generate and obtain resistance change data in the photoresistor array, and generate current value change data corresponding to the resistance change data; The resistance value change data is fused and processed, and combined with the corresponding current value change data, to generate damage change data corresponding to the rock sample.

6. A method for real-time detection of rock crack changes according to claim 1, 4 or 5, characterized in that: The step of generating second data corresponding to the first data by using the crack characteristic change data model further includes: generating two-dimensional shape change data corresponding to the second data according to the second data; Based on the two-dimensional shape change data and in combination with the photosensitive element parameter change data, shape mapping relationship data corresponding to the rock sample cracks are created; wherein the photosensitive element parameter change data includes emission light parameter data, current parameter data and resistance parameter data; A calibration curve corresponding to the crack characteristic change data is generated according to the shape mapping relationship data.

7. A real-time detection system for rock crack changes, characterized in that: The system is used to implement the real-time detection method for rock crack changes according to any one of claims 1 to 6; the system comprises: A first data generating unit is used to generate and obtain first data corresponding to the crack changes of the rock sample in real time; wherein the first data is light signal data reflected from the light surface of the rock sample; A data model creation unit, configured to create at least one crack characteristic change data model corresponding to the first data based on the first data and in combination with a neural network; The second data generating unit is used to generate second data corresponding to the first data through the fracture characteristic change data model; wherein the second data is fracture characteristic change data corresponding to the rock sample.

8. A rock crack change real-time detection system according to claim 7, characterized in that: The first data generating unit further includes: A first data generation module is used to generate and obtain loading pressure data corresponding to the rock sample, and generate first control data according to the loading pressure data; wherein the loading pressure data includes loading confining pressure data, loading axial pressure data and loading osmotic pressure data; the first control data is control data for starting the optical signal projection; A second data generation module is used to generate and obtain light column incident angle data corresponding to the rock sample, and generate second control data according to the light column incident angle data; wherein the second control data is control data for adjusting the light column incident angle; And / or, the data model creation unit further includes: A third data generating module, used to generate third data corresponding to the first data, and establish a corresponding relationship between the first data and the third data; wherein the third data is photoresistance value change data corresponding to the light signal data; A first data creation module, used to create real-time status data corresponding to the change of the rock sample cracks according to the third data, and generate a change curve corresponding to the real-time status data; And / or, the second data generating unit further includes: A second data creation module, used to create at least one photoresistor array corresponding to the first data, and generate crack characteristic change data corresponding to the resistance change in the photoresistor array; wherein the crack characteristic change data includes crack width change data, crack length change data and crack change position data; A fourth data generating module, configured to generate crack width or length estimation data corresponding to the crack characteristic change data in real time according to the crack characteristic change data; And / or, the second data creation module further includes: A fifth data generating module, used to generate and obtain resistance value change data in the photoresistor array, and generate current value change data corresponding to the resistance value change data; A data fusion processing module, used for fusing and processing the resistance value change data, and combining it with the corresponding current value change data, to generate damage change data corresponding to the rock sample; And / or, the second data generating unit further includes: a sixth data generating module, configured to generate two-dimensional shape change data corresponding to the second data according to the second data; A third data creation module is used to create shape mapping relationship data corresponding to the cracks of the rock sample based on the two-dimensional shape change data and in combination with the photosensitive element parameter change data; wherein the photosensitive element parameter change data includes emission light parameter data, current parameter data and resistance parameter data; The seventh data generating module is used to generate a calibration curve corresponding to the crack characteristic change data according to the shape mapping relationship data.

9. A real-time detection device for rock crack changes, characterized in that: The device is used to implement the real-time detection method of rock crack changes according to any one of claims 1 to 6; The device comprises: an axial loading rod for applying axial pressure to the rock sample through a top seat; a seepage loading system for applying osmotic pressure to the bottom of the rock sample through a reserved channel; a confining pressure loading system for applying confining pressure to the surrounding of the rock sample through a reserved channel; and a triaxial loading system for applying axial pressure, osmotic pressure and confining pressure to the rock sample; The device is also provided with a light-sensing receiving screen for recording light signal changes caused by the generation and development of cracks when the rock is damaged; the rock sample is a rock sample with coating on the surface.

10. A real-time detection platform for rock crack changes, characterized in that: The method comprises a processor, a memory and a rock crack change real-time detection platform control program; wherein the rock crack change real-time detection platform control program is executed by the processor, the rock crack change real-time detection platform control program is stored in the memory, and the rock crack change real-time detection platform control program implements the rock crack change real-time detection method according to any one of claims 1 to 6.

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