Detection Method and System for Embedded Depth of Internal Defects in Non-adiabatic Materials

Abstract

The present invention provides a method and system for detecting the buried depth of internal defects in non-adiabatic materials. The method includes: collecting and storing infrared thermal images on the surface of non-adiabatic materials; analyzing the surface temperature field of non-adiabatic materials to identify the positions corresponding to internal defects on the material surface; extracting the surface temperature value T1 of the defect area and the surface temperature value T2 of the defect-free area, and obtaining the temperature difference ΔT = T1 - T2 at different times t; solving the calculated values of the buried depth of the defect corresponding to different temperature differences at each time; analyzing the optimal time to obtain the finally determined buried depth of the defect. The method for detecting the buried depth of internal defects in non-adiabatic materials provided by this application realizes non-destructive, fast, non-contact, non-magnetic and convenient detection of internal defects in non-adiabatic materials. On the one hand, its detection depth is deeper, and on the other hand, it is less affected by the properties of the measured materials, and can be applied to the buried depth detection of defects such as delamination, holes or cracks in various rock masses, metals or concretes.

Description

Technical Field

[0001] The present invention relates to the technical field of identification and information extraction, and more particularly to a method and system for detecting the buried depth of internal defects in non-adiabatic materials. Background Art

[0002] During the manufacturing or natural formation process of materials, it is inevitable to generate various buried defects such as cavities, cracks, delaminations, and pores, which make the materials discontinuous and anisotropic, greatly reducing the engineering stability. These buried defects do not expose on the material surface. Using advanced technologies to accurately identify the defect locations and predict the buried depth of the structural plane in advance is extremely important for the stability evaluation and reinforcement repair plan design of projects such as aviation engineering, construction, and cultural relics protection.

[0003] In the process of implementing the present invention, the inventors found that there are at least the following problems in the prior art:

[0004] Currently, the method for obtaining the buried depth of material defects mostly uses the drilling method for direct acquisition, which has the characteristics of strong observability and high accuracy, but it is a destructive detection method that will further damage the integrity of the tested material. With the development of technology, various new non-destructive or micro-destructive detection means have gradually emerged, such as ultrasonic method, terahertz method, infrared thermal imaging method, etc. However, the ultrasonic method is severely affected by the environment and is a contact measurement method, making it difficult to meet the requirements of material detection in complex environments such as high-risk and high-steep areas; the terahertz method, as a non-destructive, non-ionizing, and highly sensitive detection technology, has the characteristics of strong penetration ability and strong imaging ability, but there are problems such as a small measurable depth and difficulty in field application; while the infrared thermal imaging detection technology, as a method for testing and characterizing non-adiabatic material defects, has good application value in the identification and buried depth acquisition of non-adiabatic material defects.

[0005] For defective materials, their thermophysical parameters will change, resulting in differences in the thermal reactions on the material surface. Under the excitation of an external heat source, more infrared thermal radiation will be generated on the surface of the defective area due to the higher temperature, causing an obvious high-temperature area to appear in the infrared thermal image. Therefore, the use of infrared thermal imaging technology can meet the requirements of less intervention, non-contact, and non-destructive detection of internal defects in non-adiabatic materials. However, there are two deficiencies in the existing infrared defect depth detection methods: on the one hand, most detection methods use the cooling process of the material for data extraction and analysis. For the long-time thermal excitation process, the cooling stage takes a long time, and the temperature field of the material is easily disturbed by the on-site environment during the cooling process; on the other hand, most detection methods are based on instantaneous heating and use a pulsed heat source for thermal excitation. Such excitation equipment is not only expensive and inconvenient to carry, but also mostly used for indoor testing. It should be noted that the biggest disadvantage of the infrared non-destructive detection technology based on instantaneous heating is that the detection depth is relatively shallow, and it can only detect defects with a depth of mm level, and it is difficult to detect deeper defects. For example, the detection depth of the defects in the Chinese patent "Quantitative measurement method based on integral average in pulsed infrared thermal wave technology" with the application number 201611035269.9 is 1-6 mm. Therefore, a detection method and system for the buried depth of internal defects in non-adiabatic materials are needed to at least partially solve the above technical problems. Summary of the Invention

[0006] In order to at least solve one of the problems in the prior art, according to the first aspect of the present application, a method for detecting the buried depth of internal defects in non-adiabatic materials is provided, and the method includes:

[0007] Collect and save the real-time infrared thermal image of the surface of the non-adiabatic material to be measured; based on the infrared thermal image, analyze the surface temperature field of the non-adiabatic material and identify the position corresponding to the internal defect on the material surface; extract the surface temperature value T1 of the defect area and the surface temperature value T2 of the defect-free area at different times t, and obtain the temperature difference ΔT = T1 - T2 at different times t; based on the heat transfer principle, establish a mathematical model of the surface temperature difference ΔT between the defect area and the defect-free area of the non-adiabatic material: In the formula, ierfc(u) is the first integral of the Gaussian error complementary function, and u is the independent variable of the Gaussian error complementary function; ρ is the density of the non-adiabatic material, λ is the thermal conductivity of the non-adiabatic material, c is the specific heat capacity of the non-adiabatic material, q w is the heat flux density on the surface of the non-adiabatic material, and h is the buried depth of the defect; substitute the density ρ, thermal conductivity λ, specific heat capacity c, time t, the temperature difference ΔT corresponding to the time t, and the heat flux density q on the surface of the non-adiabatic material w into the mathematical model of the temperature difference ΔT, and solve the first integral value of the Gaussian error complementary function: On the premise that ierfc(u) is known, the independent variable is obtained according to the table of the first integral value of the Gaussian error complementary function: On the premise that u is known, the calculation formula for the buried depth of the defect is: Solve the change rate of the buried depth h of the defect calculated at different times: In the formula: h(t n ) is the buried depth h of the defect calculated at time t, n is the sequence value of sampling when collecting the infrared thermal image, and n is a positive integer; Δt is the time difference between two adjacent infrared thermal image acquisition sequences; the time at which the minimum value of the change rate D is selected is the best calculation time for the buried depth h of the defect, and the buried depth h at this time is obtained, which is the finally determined buried depth of the defect. n

[0008] In an embodiment of the present application, the method further includes: continuously heating the surface of the non-adiabatic material to be measured. On the one hand, this can deepen the detection depth, and on the other hand, it can make the difference between different colors representing different temperatures on the collected real-time infrared thermal image more obvious, so as to more quickly and accurately identify the position corresponding to the internal defect on the surface of the material.

[0009] In an embodiment of the present application, the surface of the non-adiabatic material to be measured is continuously heated by a constant heat flux heating device. Preferably, the constant heat flux heating device is a far-infrared heating plate to make the non-adiabatic material to be measured heated evenly.

[0010] In an embodiment of the present application, the heat flux density q on the surface of the non-adiabatic material to be measured is adjusted by changing the power of the far-infrared heating plate and / or the distance between the far-infrared heating plate and the non-adiabatic material to be measured w magnitude.

[0011] In an embodiment of the present application, the method further includes: measuring the density ρ, thermal conductivity λ, specific heat capacity c of the non-adiabatic material and the heat flux density q on the surface of the non-adiabatic material w .

[0012] In an embodiment of the present application, the real-time infrared thermal image of the surface of the non-adiabatic material to be measured is collected and saved by an infrared thermal imager. Preferably, the infrared thermal resolution of the infrared thermal imager is 300,000, the temperature measurement range is -10°C to 1000°C, the temperature measurement accuracy is 2%, and the thermal sensitivity ≤ 0.05°C.

[0013] In an embodiment of the present application, the thermal conductivity λ and the specific heat capacity c are measured by a thermal constant analyzer. And / or the heat flux density q is measured by an infrared radiometer and a heat flux meter w .

[0014] According to a second aspect of the present application, a detection system for the buried depth of internal defects in a non-adiabatic material is provided. The system includes: an infrared thermal imager for collecting and storing real-time infrared thermal images of the surface of the non-adiabatic material to be measured; a memory for storing executable programs; and a processor for executing the executable programs stored in the memory, such that the processor performs the following actions, including: analyzing the surface temperature field of the non-adiabatic material based on the infrared thermal image to identify the position on the material surface corresponding to the internal defect; extracting the temperature value T1 of the surface of the defect projection area and the temperature value T2 of the surface of the complete non-adiabatic material at different times t, and obtaining the temperature difference ΔT = T1 - T2 at different times t; based on the heat transfer principle, establishing a mathematical model of the surface temperature difference ΔT between the defective area and the non-defective area of the non-adiabatic material: where ierfc(u) is the first integral of the Gaussian error complementary function, and u is the independent variable of the Gaussian error complementary function; ρ is the density of the non-adiabatic material, λ is the thermal conductivity of the non-adiabatic material, c is the specific heat capacity of the non-adiabatic material, q w is the heat flux density on the surface of the non-adiabatic material, and h is the buried depth of the defect; substituting the density ρ, thermal conductivity λ, specific heat capacity c, time t, the temperature difference ΔT corresponding to time t, and the heat flux density q on the surface of the non-adiabatic material w into the mathematical model of the temperature difference ΔT to solve the value of the first integral of the Gaussian error complementary function: On the premise that ierfc(u) is known, find the independent variable according to the table of the first integral values of the Gaussian error complementary function: On the premise that u is known, the calculation formula for the buried depth of the defect is: Solve the change rate of the buried depth h of the defect calculated at different times: where: h(t n ) is the buried depth h of the defect calculated at time t n , n is the sampling sequence value when collecting infrared thermal images, and n is a positive integer; Δt is the time difference between adjacent two infrared thermal image acquisition sequences; select the time at which the minimum value of the change rate D is located as the optimal calculation time of the buried depth h of the defect, and obtain the buried depth h at this time, which is the finally determined buried depth of the defect.

[0015] In an embodiment of the present application, the system further includes: a constant heat flux heating device for continuously heating the surface of the non-adiabatic material to be measured; preferably, the constant heat flux heating device is a far-infrared heating plate.

[0016] According to a third aspect of the present application, a storage medium is provided. A computer program is stored on the storage medium, and when the computer program is executed by a processor, the steps of the detection method for the buried depth of internal defects in the non-adiabatic material as described above are implemented.

[0017] The detection method and system for the buried depth of internal defects in non - adiabatic materials according to the embodiments of the present application are based on infrared thermal imaging detection technology. The temperature field on the surface of the non - adiabatic material is obtained, the approximate area corresponding to the internal defects on the material surface is determined, and the buried depth of the defects is calculated through the temperature difference on the surface of the non - adiabatic material, realizing non - destructive, fast, non - contact, non - magnetic, and convenient detection of the buried depth of internal defects in non - adiabatic materials. In addition, the detection method and system for the buried depth of internal defects in non - adiabatic materials according to the embodiments of the present application can achieve deeper detection, are less affected by the properties of the measured materials, and can also be applied to the detection of the buried depth of defects such as stratification, holes, or cracks in various rock masses, metals, or concretes. Thus, it can provide basic data for the establishment of three - dimensional defect network models of more non - adiabatic materials and provide reference materials for the stability reinforcement of non - adiabatic materials.

[0018] It should be understood that the foregoing general description and the following detailed description are both exemplary and explanatory and should not be construed as limiting the content claimed in the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] By describing the embodiments of the present application in more detail in conjunction with the drawings, the above - mentioned and other objects, features, and advantages of the present application will become more apparent. The drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation to the present application. In the drawings, the same reference numerals generally represent the same components or steps.

[0020] Figure 1 The flowchart showing the detection method for the buried depth of internal defects in non - adiabatic materials according to an embodiment of the present invention;

[0021] Figure 2 The partial flowchart showing the detection method for the buried depth of internal defects in non - adiabatic materials according to another embodiment of the present invention;

[0022] Figure 3 The geometric schematic diagram of a rock mass model with pre - fabricated cracks in the detection method for the buried depth of internal defects in non - adiabatic materials according to an embodiment of the present invention;

[0023] Figure 4 The temperature difference curve graph of cracks with different depths in the detection method for the buried depth of internal defects in non - adiabatic materials according to an embodiment of the present invention;

[0024] Figure 5 The calculated depth curve graph of cracks at different times in the detection method for the buried depth of internal defects in non - adiabatic materials according to an embodiment of the present invention;

[0025] Figure 6Shows the D-curve of the change rate at different times in the detection method of the buried depth of internal defects in non-adiabatic materials according to an embodiment of the present invention;

[0026] Figure 7 Shows a schematic block diagram of a detection system for the buried depth of internal defects in non-adiabatic materials according to an embodiment of the present invention;

[0027] Figure 8 Shows a schematic diagram when the detection system for the buried depth of internal defects in non-adiabatic materials according to another embodiment of the present invention is applied to the detection of the buried depth of internal fractures in rock masses. Detailed implementation manners

[0028] In order to make the objectives, technical solutions and advantages of the present application more apparent, the exemplary embodiments according to the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. It should be understood that the present application is not limited by the exemplary embodiments described herein. Based on the embodiments of the present application described in the present application, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the protection scope of the present application.

[0029] Adiabatic materials refer to materials that can block the transfer of heat flow, also known as thermal insulation materials. Traditional adiabatic materials, such as glass fiber, asbestos, rock wool, silicate, etc., and new adiabatic materials, such as aerogel felt, vacuum panel, etc. They are used for building envelopes or thermal equipment, materials or material composites that impede the transfer of heat flow, including both thermal insulation materials and cold insulation materials. Adiabatic materials satisfy the thermal environment of building spaces or thermal equipment on the one hand, and save energy on the other hand.

[0030] The non-adiabatic materials in the present application refer to materials with opposite properties to adiabatic materials, that is, they cannot block the transfer of heat flow. The non-adiabatic materials in the present application may include rock masses, metals or concretes. The defects in the present application may refer to delamination, holes or fractures. Therefore, the internal defects in the non-adiabatic materials referred to in the present application may be internal delamination in rock masses, internal holes in rock masses or internal fractures in rock masses, or internal delamination in metals, internal holes in metals or internal fractures in metals, or internal delamination in concretes, internal holes in concretes or internal fractures in concretes, but are not limited to the above. For the convenience of explanation and illustration, the detection method and system for the buried depth of internal defects in non-adiabatic materials provided by the present invention will be described by taking the detection of internal fractures in rock masses as an example, but it does not mean that the detection method and system for the buried depth of internal defects in non-adiabatic materials provided by the present invention are only limited to the detection of internal fractures in rock masses.

[0031] First, reference will be made to Figure 1 Describe the detection method 200 for the buried depth of internal fractures in rock masses according to an embodiment of the present application. As Figure 1As shown, the detection method 200 for the buried depth of internal fissures in a rock mass may include the following steps:

[0032] In step S210, collect and save the real-time infrared thermal image of the surface of the rock mass to be measured.

[0033] In step S220, based on the infrared thermal image, analyze the surface temperature field of the rock mass, identify the positions on the rock mass surface corresponding to the internal fissures, extract the surface temperature values T1 of the fissure regions and the surface temperature values T2 of the non-fissure regions at different times t, and obtain the temperature difference ΔT = T1 - T2 at different times t.

[0034] In step S230, based on the heat transfer principle, establish a mathematical model for the temperature difference ΔT between the fissure region and the non-fissure region on the surface of the rock mass: In the formula, ierfc(u) is the first integral of the Gaussian error complementary function, and u is the independent variable of the Gaussian error complementary function; ρ is the density of the rock mass, λ is the thermal conductivity of the rock mass, c is the specific heat capacity of the rock mass, q w is the heat flux density on the surface of the rock mass, and h is the buried depth of the fissure.

[0035] In step S240, substitute the density ρ, thermal conductivity λ, specific heat capacity c, time t, the temperature difference ΔT corresponding to time t, and the heat flux density q on the surface of the rock mass w into the temperature difference ΔT mathematical model to solve for the value of the first integral of the Gaussian error complementary function:

[0036] In step S250, on the premise that ierfc(u) is known, find the independent variable according to the table of the first integral values of the Gaussian error complementary function:

[0037] In step S260, on the premise that u is known, the calculation formula for the buried depth of the fissure is:

[0038] In step S270, solve for the change rate of the buried depth h of the fissure calculated at different times: In the formula: h(t n ) is the buried depth h of the fissure calculated at time t n , n is the sequence value of sampling when collecting infrared thermal images, and n is a positive integer; Δt is the time difference between adjacent two infrared thermal image collection sequences.

[0039] In step S280, select the time at which the minimum value of the change rate D is located as the best calculation time for the buried depth h of the fissure, and obtain the buried depth h at this time, which is the finally determined buried depth of the fissure.

[0040] In an embodiment of the present application, based on the infrared thermal imaging detection technology, by collecting and saving the real-time infrared thermal images of the surface of the rock mass to be measured, analyzing the surface temperature field of the rock mass, identifying the positions corresponding to the internal fissures on the surface of the rock mass, extracting the surface temperature value T1 of the fissure area and the surface temperature value T2 of the non-fissure area, obtaining the temperature difference ΔT = T1 - T2 at different heating times t, based on the heat transfer principle, establishing a mathematical model of the surface temperature difference ΔT between the fissure area and the non-fissure area of the rock mass, and solving the calculated values of the fissure burial depths corresponding to different temperature differences at each moment; considering that the differences in the fissure burial depths h calculated at different moments are relatively large and the errors generated with the actual burial depth are different, solving the change rate of the fissure burial depths h calculated at different moments, and selecting the moment at which the minimum value of the change rate D is located as the best calculation time for the fissure burial depth h, obtaining the fissure burial depth h at this moment, and the h value corresponding to the best calculation time has a relatively high accuracy, which is the finally determined fissure burial depth.

[0041] As can be seen from the description of the above process, according to the detection method 200 for the internal fissure burial depth of the rock mass in the embodiment of the present application, based on the infrared thermal imaging detection technology, the temperature field of the rock mass surface is obtained, the approximate area of its internal fissures is determined, and the burial depth of the fissures is calculated through the surface temperature difference of the rock mass, so as to realize the non-destructive, fast, non-contact, non-magnetic and convenient detection of the internal fissure burial depth of the rock mass.

[0042] Next, the content of each of the above steps of the detection method 200 for the internal fissure burial depth of the rock mass in the embodiment of the present application will be specifically described.

[0043] First, in order to implement the detection method 200 for the internal fissure burial depth of the rock mass in the embodiment of the present application, the implementation object is a physical model of a rock mass with a single fissure printed by 3D printing (hereinafter referred to as the rock mass model), whose geometric size is 40 cm × 15 cm × 30 cm (length × width × height), and the material property is sandstone. A total of five rock mass models are printed in this embodiment, and each model contains a disc-shaped buried fissure, and the designed burial depths of the fissures are 1 cm, 2 cm, 3 cm, 4 cm and 5 cm respectively, and the geometric schematic diagram is as Figure 3 shown. And as Figure 8 shown, the infrared thermal imager is erected according to the requirements, and the elevation angle and horizontal angle of the infrared thermal imager are adjusted to obtain the best shooting range.

[0044] In the embodiments of the present application, in step S210, by collecting and saving the real-time infrared thermal images of the surface of the rock mass model to be measured, the real-time infrared thermal images of the surface of the non-adiabatic material to be measured can be collected and saved by an infrared thermal imager. For example, the Fluke Ti 480pro infrared thermal imager can be used. Preferably, the infrared thermal resolution of the infrared thermal imager can be 300,000, the temperature measurement range can be from -10°C to 1000°C, the temperature measurement accuracy can be 2%, and the thermal sensitivity ≤ 0.05°C. Among them, the specific process of using the infrared thermal imager to collect the real-time infrared thermal images of the surface of the rock mass model to be measured can be: the first step is to select the shooting mode. Here, it is recommended to select the picture-in-picture mode, and the palette color is selected as the rainbow high-saturation tone; the second step is to select the image storage location, which can be selected for storage in the infrared thermal imager camera and the SD card. It is recommended to select SD card storage; the third step is to start shooting. Select the timed shooting function, and the time interval is set to 18s for the infrared thermal images of the rock mass model at different stages.

[0045] In order to deepen the detection depth and make the differences between different colors representing different temperatures on the collected real-time infrared thermal images more obvious, so as to more quickly and accurately identify the positions corresponding to the internal fractures on the surface of the rock mass model. Refer to Figure 2 , before step S210, step S110 can also be included: continuously heating the surface of the rock mass model to be measured. In order to continuously heat the surface of the rock mass model to be measured, the surface of the rock mass model to be measured can be continuously heated by a constant heat flux heating device. Preferably, the constant heat flux heating device is a far-infrared heating plate to make its heating uniform. As Figure 8 shown, a tripod can be erected at a distance of about 1m from the rock mass model, and the far-infrared heating plate is placed on the tripod. Adjust the height of the tripod so that the far-infrared heating plate and the rock mass model are at the same height and parallel to each other. And it is erected in front of the infrared thermal imager (that is, between the infrared thermal imager and the rock mass model), and the erection height is lower than the infrared thermal imager. The surface of the rock mass model is irradiated by the far-infrared heating plate to increase its surface temperature. The heating time can be set according to the detection requirements, and here it can be set to 1200s.

[0046] In an embodiment of the present application, after the infrared thermal imager has acquired the infrared thermal image, in step S220, the temperature field on the surface of the rock mass model is analyzed based on the infrared thermal image to identify the positions on the surface of the rock mass model corresponding to the internal fissures, and the surface temperature value T1 of the fissure region and the surface temperature value T2 of the non-fissure region are extracted at different times t. The temperature difference ΔT = T1 - T2 at different times t is obtained. The infrared thermal image data can be read using a portable computer, and the Smartview 4.3 software developed by Fluke Corporation can be used to determine the range where the internal fissures are located. If there are fissures inside the rock mass model, when the uniform heat flow generated by the far-infrared heating plate is transmitted to the fissures, since the thermal conductivity of the air at the fissure part is different from that of the rock mass, the heat flow is blocked by the fissures. Therefore, heat accumulation occurs at the fissure part, and part of the heat flow returns to the surface of the rock mass model after a time delay, resulting in the formation of a high-temperature zone on the surface of the fissure region and thus generating hot spots. The Smartview 4.3 software is used to obtain T1 and T2 in the rock mass model. Calculate the temperature difference ΔT = T1 - T2 between the surface of the fissure region and the non-fissure region in the rock mass model at each moment. The temperature difference - time curve graph is shown in the appendix Figure 4 in.

[0047] In an embodiment of the present application, after obtaining the above temperature difference ΔT data in step S220, a mathematical model of the temperature difference ΔT between the surface of the fissure region and the non-fissure region can be established based on the heat transfer principle in step S230. The relevant calculation steps are as follows:

[0048] Considering that the heat flow generated by the far-infrared heating plate is transmitted perpendicularly to the surface of the rock mass model and into the interior, the one-dimensional unsteady heat conduction differential equation is adopted:

[0049]

[0050] where: T is the temperature, ρ is the density, λ is the thermal conductivity, c is the specific heat capacity, and q w is the heat flux density on the surface of the rock mass model.

[0051] Using the method of separation of variables, the mathematical model of the surface temperature of the non-fissure projection region in the rock mass model can be obtained:

[0052]

[0053] where: ierfc(u) is the first integral of the Gaussian error complementary function.

[0054] If there is a fissure parallel to the heating surface inside the rock mass model, assuming that there is no heat exchange between the air at the fissure part and the rock mass model, the boundary conditions of the temperature field at the fissure part can be processed using the heat source temperature field superposition method, and the mathematical model of the surface temperature of the fissure region can be obtained:

[0055]

[0056] In the formula: h is the buried depth of the crack;

[0057] Subtracting equation (3) from equation (2), the mathematical model of the surface temperature difference between the cracked area and the crack-free area is:

[0058]

[0059] To obtain the density ρ, thermal conductivity λ, specific heat capacity c of the rock mass model and the heat flux density q on the surface of the rock mass model w , referring to Figure 2 , according to the detection method 200 of the buried depth of internal cracks in the rock mass in the embodiment of the present application, step S120 may further be included: by measuring the density ρ, thermal conductivity λ, specific heat capacity c of the rock mass model and the heat flux density q on the surface of the rock mass model w . Among them, the measurement method may be: based on the principle of the transient plane heat source method, a thermal constant analyzer (for example, Hot Disk TPS2500S can be used) can be used to test the thermal conductivity λ = 2 W·(m·℃) of the rock mass -1 , specific heat capacity c = 970 J·(kg·℃) -1 . At the same time, calculate the density ρ = 2350 kg·m of the rock mass -3 , and an infrared radiometer and a heat flux meter can be used to measure the heat flux density q on the surface of the rock mass model w = 2000 W·m -2 . Among them, the heat flux density q on the surface of the rock mass model w can be adjusted by changing the power of the far-infrared heating plate and / or the distance between the far-infrared heating plate and the rock mass model.

[0060] In Figure 2 , although the order of step S120 is shown between step S110 and step S210, it is only an example. It can be understood that step S120 can also be located at any position between step S210 and step S240.

[0061] In the embodiment of the present application, after obtaining the mathematical model of the surface temperature difference between the cracked part and the intact area of the rock mass, in step S240, substitute the density ρ, thermal conductivity λ, specific heat capacity c, time t, the temperature difference ΔT corresponding to time t and the heat flux density q on the surface of the rock mass model w into formula (4), then the first integral value of the Gaussian error complementary function is:

[0062]

[0063] In the embodiment of the present application, in step S250, on the premise that ierfc(u) is known, according to Table 1 of the first integral value of the Gaussian error complementary function, determine value.

[0064]

[0065] Table 1 - Table of the First Integral Values of the Gaussian Error Complementary Function

[0066] In the embodiments of the present application, in step S260, on the premise that u is known, the calculation formula for the fracture burial depth is:

[0067]

[0068] The calculation results of the fracture depth h in each rock mass model are shown in the appendix Figure 5 . As Figure 5 can be seen, the differences in the fracture burial depth h calculated at different times are relatively large, and the errors from the designed burial depth are different. This is mainly affected by the three-dimensional thermal expansion effect, resulting in deviations in the temperature difference values at different times. Therefore, it is necessary to determine an appropriate time t to make it have a high accuracy.

[0069] In the embodiments of the present application, in step S270, the change rate of the fracture burial depth h calculated at different times is solved:

[0070]

[0071] In the formula: h(t n ) is the fracture burial depth h calculated at time t n , n is the sequence value sampled when the infrared thermal imager collects infrared thermal images, and n is a positive integer; Δt is the time difference between two adjacent infrared thermal image acquisition sequences.

[0072] In the embodiments of the present application, in step S280, the time at which the minimum value (greater than zero) of the change rate D is located is selected as the best calculation time for the fracture burial depth h (such as Figure 6 N1, N2, N3, N4, and N5 in), and the fracture burial depth h at this time is obtained, which is the finally determined fracture burial depth. Referring to Figure 6 , the time at which the minimum value (greater than zero) of the change rate D is located is selected as the best calculation time for the fracture burial depth h (N1, N2, N3, N4, and N5). Substituting the temperature data at this time into formulas (5)-(6), the fracture burial depths h of the five models can be calculated to be 0.93 cm, 2.09 cm, 3.26 cm, 4.3 cm, and 5.47 cm respectively. Compared with the designed burial depths (1 cm, 2 cm, 3 cm, 4 cm, 5 cm), the errors are 7%, 4.5%, 8.6%, 7.5%, and 9.4% respectively.

[0073] From the above calculation results, it can be seen that for the test object in this experiment, 2000 W·m -2The heat flux density can detect fractures within a depth of 5 cm after heating for 20 minutes. Compared with the detection depth of 5 mm in the Chinese patent "An Infrared Thermal Wave Pulse Phase Nondestructive Detection Method with a Fixed Field of View" with the application number 201410619630.7 and the detection depth of 6.4 mm in the Chinese patent "Reconstruction Method of Infrared Thermal Wave Detection Tomographic Images" with the application number 200510077750.X, etc., the detection depth has been significantly improved.

[0074] Based on the above description, the detection method 200 for the buried depth of internal fractures in rock masses according to the embodiments of the present application can quickly identify the areas where internal fractures in rock masses are located. The detection depth of the fractures is relatively large, and the calculation error of the buried depth of the fractures is relatively small, fully meeting the requirements of engineering construction. At the same time, the detection process has characteristics such as on-line, real-time, and non-contact, and has excellent effects on the detection of internal fractures in rock masses. The present invention applies the infrared thermal imaging detection technology to obtain the depth information of internal fractures in rock masses, and has great application value for rock mass projects such as stone cultural relic protection, nuclear waste storage, and shale gas development, which are highly sensitive to the rock mass structure and have special usage requirements.

[0075] It should be added that only fractures within a depth of 5 cm were detected in the experiment, but this does not represent the maximum detection depth that can be achieved by this invention patent. To increase the detection depth of fractures, the power of the far-infrared heating plate can be increased and the heating time can be extended. At the same time, it should be noted that the size of the experimental rock mass model is small. To eliminate the influence brought by the size effect of the object to be measured, when carrying out the detection work of on-site rock mass fractures, the irradiation area of the far-infrared heating plate can be increased, the lateral diffusion effect of the heat of the rock mass in the detection area can be reduced, and the average temperature of the detection area can be selected as the temperature parameter.

[0076] The above exemplarily shows the detection method 200 for the buried depth of internal fractures in rock masses according to the embodiments of the present application. The following will describe the detection system 300 for the buried depth of internal fractures in rock masses provided by another aspect of the present application in combination with Figure 7 、 Figure 8 Describe the detection system 300 for the buried depth of internal fractures in rock masses provided by another aspect of the present application.

[0077] Refer to Figure 7 To describe an example detection system 300 for implementing the detection method for the buried depth of internal fractures in rock masses of the embodiments of the present invention. The system 300 includes at least one infrared thermal imager 310 and at least one example electronic device 320. The connection relationship between the infrared thermal imager 310 and the electronic device 320 can be such that only the infrared thermal imager 310 transmits the collected infrared thermal images to the electronic device 320, or the electronic device 320 controls the start and stop of the infrared thermal imager 310, controls the time and frequency of the infrared thermal imager 310 to collect infrared thermal images, and controls the infrared thermal imager 310 to transmit the collected infrared thermal images to the electronic device 320 at a set time, etc.

[0078] Such asFigure 7 As shown, an infrared thermal imager 310 is used to collect and save real-time infrared thermal images of the surface of the rock mass model to be measured.

[0079] Reference Figure 8 , in a preferred embodiment, in order to deepen the detection depth and make the difference between different colors representing different temperatures on the collected real-time infrared thermal images more obvious, so as to more quickly and accurately identify the position corresponding to the internal fissure on the surface of the rock mass model. The detection system 300 for the buried depth of internal fissures in a rock mass according to an embodiment of the present application may include a constant heat flux heating device for continuously heating the surface of the non-adiabatic material to be measured. Preferably, the constant heat flux heating device is a far-infrared heating plate 330.

[0080] Among them, although Figure 8 there is no connection relationship shown between the far-infrared heating plate and the electronic device, it can be understood that the electronic device 320 can also control the start and stop of the far-infrared heating plate, and control the heating time and heating power of the far-infrared heating plate, etc.

[0081] As Figure 7 shown, the electronic device 100 may include one or more processors 321, one or more memories 322, an input device 323, and an output device 324, and these components are interconnected through a bus system 325 and / or other forms of connection mechanisms (not shown). It should be noted that Figure 7 the components and structures of the electronic device 320 shown are only exemplary and not restrictive. According to needs, the electronic device may also have other components and structures.

[0082] The processor 321 may be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 320 to perform desired functions.

[0083] The memory 322 may include one or more computer program products, and the computer program products may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 321 may run the program instructions to implement the client functions (implemented by the processor) in the embodiments of the present invention described herein and / or other desired functions. Various application programs and various data may also be stored in the computer-readable storage medium, such as various data used and / or generated by the application programs, etc.

[0084] The input device 323 can be a device used by a user to input instructions, and can include one or more of a keyboard, a mouse, a microphone, a touch screen, etc. In addition, the input device 323 can also be any interface for receiving information.

[0085] The output device 324 can output various information (such as images or sounds) to the outside (such as a user), and can include one or more of a display, a speaker, etc. In addition, the output device 324 can also be any other device with an output function.

[0086] Exemplarily, the exemplary electronic device 320 for implementing the detection method of the internal defect burial depth of the non-adiabatic material according to the embodiment of the present invention can be applied to electronic devices such as terminal devices (such as mobile phones), tablet computers, laptop computers, ultra-mobile personal computers (UMPCs), handheld computers, netbooks, personal digital assistants (PDAs), wearable devices (such as smart watches, smart glasses or smart helmets, etc.), augmented reality (AR) / virtual reality (VR) devices, smart home devices, in-vehicle computers, etc. The embodiments of the present application do not make any restrictions on this.

[0087] Reference Figure 7 , the electronic device 320 in the detection system 300 of the internal fissure burial depth of the rock mass according to the embodiment of the present application includes a processor 321 and a memory 322. The memory 322 stores an executable program run by the processor 321. When the executable program is run by the processor 321, the processor 321 is caused to execute the detection method 200 of the internal fissure burial depth of the rock mass according to the embodiment of the present application described above. Those skilled in the art can understand the specific operations of the detection system of the internal fissure burial depth of the rock mass according to the embodiment of the present application in combination with the content described above. For the sake of brevity, the specific details are not described here, and only some main operations of the processor 321 are described.

[0088] In an embodiment of the present application, when the executable program is run by the processor 321, the processor 321 is caused to execute the following steps: collecting and saving a real-time infrared thermal image of the surface of the measured rock mass model; based on the infrared thermal image, analyzing the surface temperature field of the rock mass model, identifying the position corresponding to the internal defect on the model surface, extracting the surface temperature value T1 of the fissure area and the surface temperature value T2 of the non-fissure area at different times t, and obtaining the temperature difference ΔT = T1 - T2 at different times t; based on the heat transfer principle, establishing a mathematical model of the surface temperature difference ΔT between the fissure area and the non-fissure area: Where ierfc(u) is the first integral of the Gaussian error complementary function, and u is the independent variable of the Gaussian error complementary function; ρ is the density, λ is the thermal conductivity, c is the specific heat capacity, q w is the heat flux density on the surface of the rock mass model, and h is the buried depth of the defect; Substitute the density ρ, thermal conductivity λ, specific heat capacity c, time t, the temperature difference ΔT corresponding to the moment t, and the heat flux density q on the surface of the rock mass model w into the mathematical model of the temperature difference ΔT, and solve the first integral value of the Gaussian error complementary function: On the premise that ierfc(u) is known, find the independent variable according to the first integral value table of the Gaussian error complementary function: On the premise that u is known, the calculation formula for the buried depth of the crack is: Solve the change rate of the buried depth h of the crack calculated at different times: Where: h(t n ) is the buried depth h of the crack calculated at the moment t n , n is the sampling sequence value when collecting the infrared thermal image, and n is a positive integer; Δt is the time difference between adjacent two infrared thermal image acquisition sequences; Select the moment when the minimum value of the change rate D is located as the best calculation time of the buried depth h of the crack, and obtain the buried depth h at this moment, which is the finally determined buried depth of the crack.

[0089] In an embodiment of the present application, when the executable program is run by the processor 321, the processor 321 is further caused to perform the following steps: continuously heating the surface of the non-adiabatic material to be measured.

[0090] In addition, according to an embodiment of the present application, a storage medium is further provided. A computer program is stored on the storage medium and is used to execute the corresponding steps of the method for detecting the buried depth of internal cracks in a rock mass according to an embodiment of the present application when the computer program is run by a processor. The storage medium may include, for example, a memory card of a smart phone, a storage component of a tablet computer, a hard disk of a personal computer, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disc read-only memory (CD-ROM), a USB memory, or any combination of the above storage media. The computer-readable storage medium may be any combination of one or more computer-readable storage media.

[0091] Based on the above description, the detection method and system for the buried depth of internal defects in rock masses according to the embodiments of the present application, on the one hand, improve the detection depth of the infrared thermal imaging detection technology. Existing infrared thermal imaging detection technologies are mostly for the detection of defects in metal materials (with excellent thermal conductivity). They mostly use instantaneous heating and solve using temperature parameters at the moments of the contrast peak value and the logarithmic second derivative peak value. The detectable crack depth is mostly at the millimeter level. Relatively speaking, the thermal conductivity of rock masses is poor. Therefore, the present invention additionally proposes a method for calculating the crack depth of rock masses based on continuous heating by a constant heat flux heating device, and selects the moment when the minimum value of the change rate of the calculated crack depth value is the best time for calculating the crack depth, which corrects the error caused by the three-dimensional thermal expansion effect to a certain extent. The maximum detectable depth mainly depends on the power of the far-infrared heating plate and the heating duration, which greatly improves the detection depth and can easily achieve depth detection of centimeter level and above. On the other hand, the present invention is less affected by the properties of the measured material and is applicable to the detection of the buried depth of shallow surface cracks in various rock masses. Compared with the traditional drilling detection method and ground penetrating radar method, it has the characteristics of non-destructive, non-contact, fast and real-time, and anti-interference. Compared with the emerging terahertz detection method, it has the advantages of high precision, deep detection, and convenient field application. It has good application value for rock masses with special usage requirements such as stone cultural relic protection, nuclear waste storage, and shale gas development, where destructive detection is not suitable. In addition, this method can also be used for the detection of internal defects in other non-adiabatic materials such as metals and concrete, providing a new research means for the analysis of depth information of defects such as stratification, holes, and cracks inside materials.

[0092] Although example embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above example embodiments are merely exemplary and are not intended to limit the scope of the present application. Those of ordinary skill in the art can make various changes and modifications therein without departing from the scope and spirit of the present application. All such changes and modifications are intended to be included within the scope of the present application as claimed in the appended claims.

[0093] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present application.

[0094] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed.

[0095] In the specification provided herein, a large number of specific details are set forth. However, it can be understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known methods, structures, and technologies have not been shown in detail so as not to obscure the understanding of this specification.

[0096] Similarly, it should be understood that, in order to streamline the present application and assist in understanding one or more of the various inventive aspects, in the description of the exemplary embodiments of the present application, the various features of the present application are sometimes grouped together into a single embodiment, figure, or description thereof. However, the methods of the present application should not be construed as reflecting the intention that the claimed present application requires more features than are expressly recited in each claim. Rather, as reflected by the corresponding claims, the inventive point lies in that the corresponding technical problems can be solved by features less than all the features of a single disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into the detailed description, where each claim itself serves as a separate embodiment of the present application.

[0097] Those skilled in the art can understand that, except for features that are mutually exclusive, any combination can be used for all the features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all the processes or units of any method or device so disclosed. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) can be replaced by an alternative feature that provides the same, equivalent, or similar purpose.

[0098] In addition, those skilled in the art can understand that, although some of the embodiments described herein include certain features included in other embodiments rather than other features, the combination of the features of different embodiments means that it is within the scope of the present application and forms different embodiments. For example, in the claims, any one of the claimed embodiments can be used in any combination.

[0099] Each component embodiment of the present application can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art should understand that a microprocessor or a digital signal processor (DSP) can be used in practice to implement some or all of the functions of some modules according to the embodiments of the present application. The present application can also be implemented as a device program (e.g., a computer program and a computer program product) for executing part or all of the methods described herein. Such a program implementing the present application can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, or provided on a carrier signal, or in any other form.

[0100] It should be noted that the above embodiments illustrate the present application rather than limit the present application, and those skilled in the art can design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in the claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present application can be implemented by means of hardware including several different elements and by means of a suitably programmed computer. In the unit claims listing several devices, several of these devices can be embodied by the same item of hardware. The use of the words first, second, and third, etc. does not denote any order. These words can be interpreted as names.

[0101] As described above, it is only the specific implementation manner of the present application or the description of the specific implementation manner, and the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present application, and all of them should be covered by the protection scope of the present application. The protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for detecting the buried depth of internal defects in a non-adiabatic material, characterized in that, The method includes: Collecting and saving the real-time infrared thermal image of the surface of the non-adiabatic material to be measured; Based on the infrared thermal image, analyzing the surface temperature field of the non-adiabatic material, identifying the position corresponding to the internal defect on the material surface, extracting the surface temperature value T1 of the defect area and the surface temperature value T2 of the defect-free area at different times t, and obtaining the temperature difference ΔT = T1 - T2 at different times t; Based on the principle of heat transfer, a mathematical model for the surface temperature difference ΔT between the defective area and the defect-free area of non-adiabatic materials is established: In the formula, ierfc(u) is the first integral of the Gaussian error complementary function, and u is the independent variable of the Gaussian error complementary function; ρ is the density of the non-adiabatic material, λ is the thermal conductivity of the non-adiabatic material, c is the specific heat capacity of the non-adiabatic material, q w is the heat flux density on the surface of the non-adiabatic material, and h is the depth of the defect; Substitute the density ρ, thermal conductivity λ, specific heat capacity c, time t, temperature difference ΔT corresponding to time t, and heat flux density q on the surface of the non-adiabatic material w into the mathematical model of the temperature difference ΔT, and solve for the first integral value of the Gaussian error complementary function: On the premise that ierfc(u) is known, find the independent variable according to the table of the first integral value of the Gaussian error complementary function: On the premise that u is known, the calculation formula for the defect burial depth is as follows: Solve for the rate of change of the defect burial depth h calculated at different times: Where: h(t n ) is t n The defect depth h calculated at the moment, n is the sequence value sampled when collecting infrared thermal images, n is a positive integer; Δt is the time difference between two adjacent infrared thermal image collection sequences; Selecting the moment when the minimum value of the change rate D is located as the best calculation time for the defect depth h, and obtaining the defect depth h at this moment, which is the finally determined defect depth.

2. The method for detecting the buried depth of internal defects in a non-adiabatic material according to claim 1, characterized in that, The method further includes: Continuously heating the surface of the non-adiabatic material to be measured.

3. The method for detecting the buried depth of internal defects in a non-adiabatic material according to claim 2, characterized in that, Continuously heating the surface of the non-adiabatic material to be measured through a constant heat flux heating device, and the constant heat flux heating device is a far-infrared heating plate to make the non-adiabatic material to be measured heated evenly.

4. The method for detecting the buried depth of internal defects in a non-adiabatic material according to claim 3, characterized in that, Adjust the heat flux density q on the surface of the non-adiabatic material to be measured by changing the power of the far-infrared heating plate and / or the distance between the far-infrared heating plate and the non-adiabatic material to be measured. w of the size.

5. The method for detecting the buried depth of internal defects in a non-adiabatic material according to claim 1, characterized in that, The method further includes: By measuring the density ρ, thermal conductivity λ, specific heat capacity c of the non-adiabatic material, and the heat flux density q on the surface of the non-adiabatic material w .

6. The method for detecting the buried depth of internal defects in a non-adiabatic material according to claim 1, characterized in that, Collecting and saving the real-time infrared thermal image of the surface of the non-adiabatic material to be measured through an infrared thermal imager, and the infrared thermal resolution of the infrared thermal imager is 300,000, the temperature measurement range is -10°C to 1000°C, the temperature measurement accuracy is 2%, and the thermal sensitivity ≤ 0.05°C.

7. The method for detecting the buried depth of internal defects in a non-adiabatic material according to claim 5, characterized in that, Measuring the thermal conductivity λ and the specific heat capacity c through a thermal constant analyzer; and / or Measure the heat flux density q by an infrared radiometer and a heat flow meter w .

8. A detection system for the buried depth of internal defects in a non-adiabatic material, characterized in that, The system includes: An infrared thermal imager for collecting and saving the real-time infrared thermal image of the surface of the non-adiabatic material to be measured; A memory for storing executable programs; A processor for executing the executable programs stored in the memory, so that the processor performs the following actions, including: Based on the infrared thermal image, analyzing the temperature field of the non-adiabatic material, identifying the position corresponding to the internal defect on the material surface, extracting the surface temperature value T1 of the defect area and the surface temperature value T2 of the defect-free area at different times t, and obtaining the temperature difference ΔT = T1 - T2 at different times t; Based on the principle of heat transfer, a mathematical model of the temperature difference ΔT between the surface of the defective area and the defect-free area of the non-adiabatic material is established: In the formula, ierfc(u) is the first integral of the Gaussian error complementary function, and u is the independent variable of the Gaussian error complementary function; ρ is the density of the non-adiabatic material, λ is the thermal conductivity of the non-adiabatic material, c is the specific heat capacity of the non-adiabatic material, q w is the heat flux density on the surface of the non-adiabatic material, and h is the depth of the defect; Substitute the density ρ, thermal conductivity λ, specific heat capacity c, time t, temperature difference ΔT corresponding to time t, and heat flux density q on the surface of the non-adiabatic material w into the mathematical model of the temperature difference ΔT to solve the first integral value of the Gaussian error complementary function: On the premise that ierfc(u) is known, find the independent variable according to the table of the first integral value of the Gaussian error complementary function: On the premise that u is known, the calculation formula for the buried depth of the defect is as follows: Solve for the rate of change of the defect burial depth h calculated at different times: Where: h(t n ) is t n The defect depth h calculated at the moment, n is the sequence value sampled when collecting infrared thermal images, n is a positive integer; Δt is the time difference between two adjacent infrared thermal image collection sequences; Selecting the moment when the minimum value of the change rate D is located as the best calculation time for the defect depth h, and obtaining the defect depth h at this moment, which is the finally determined defect depth.

9. The detection system for the buried depth of internal defects of non-adiabatic materials according to claim 8, characterized in that, The system further includes: A constant heat flux heating device for continuously heating the surface of the non-adiabatic material to be measured; the constant heat flux heating device is a far-infrared heating plate.

10. A storage medium, characterized in that, A computer program is stored on the storage medium, and when the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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