Rock internal temperature calculation method and system based on multi-view infrared image

Abstract

The invention provides a rock internal temperature calculation method and system based on a multi-view infrared image, and belongs to the technical field of mining engineering, and the method comprises the steps: collecting rock infrared images through four orthogonal and asymmetric view angles, and obtaining the rock surface temperature data of each image through a temperature inversion technology; a temperature measurement result is approached through a Newton iteration algorithm, surface temperature estimation values of multiple visual angles are obtained, and a temperature data matrix is constructed. And carrying out joint inversion and weighted fusion on the temperature matrixes of the multiple visual angles to obtain a local rock surface temperature field, and then carrying out elastic splicing compensation to obtain a rock surface global temperature field. A thermal diffusion coefficient is calculated according to rock thermophysical parameters, and heat transfer characteristic time is determined by combining the heat transfer direction distance. The heat transfer state is judged through the actual observation time and the characteristic time, and the temperature distribution in the rock is calculated according to the global temperature field. The method realizes accurate calculation of the internal temperature of the rock.

Description

Technical Field

[0001] This invention relates to the field of mining engineering technology, specifically to a method, system, and medium for calculating the internal temperature of rocks based on multi-view infrared images. Background Technology

[0002] To ensure the efficient development of geological exploration and mining industries in my country, understanding the internal temperature distribution of rocks is of great significance. The aforementioned research must be based on accurate prediction models of rock surface and internal temperatures. Only with accurate prediction models of rock surface and internal temperatures can the internal temperature distribution of rocks be accurately predicted, thereby enabling the formulation of practical solutions.

[0003] Currently, the main methods for determining the internal temperature distribution of rocks are thermocouple temperature measurement, thermistor temperature measurement, and infrared thermometers. These methods have many limitations, such as thermocouple temperature measurement damaging the rock structure and poor long-term stability, thermistor temperature measurement having a limited temperature range and slow response speed, and infrared thermometers being only suitable for measuring surface temperature and greatly affected by the environment. Patent CN113375815A discloses a method for measuring the surface temperature of an object by combining a CCD and an infrared thermal imager in a single-view imaging method. It combines a CCD, an infrared thermal imager, and a ceramic heating furnace, and constructs an infrared temperature measurement model after emissivity correction based on the three primary color temperature measurement method and the dual background radiation principle to obtain the true surface temperature of the object. However, this method has observation blind spots, a cumbersome calibration process, poor real-time performance, and expensive equipment. Summary of the Invention

[0004] To address the aforementioned problems, this invention provides a method for calculating the internal temperature of rocks based on multi-view infrared images. This method uses multi-view infrared images of rocks and the principle of heat conduction to establish a mathematical model of the surface temperature and internal temperature of rocks, thereby achieving accurate calculation of the internal temperature of rocks.

[0005] To achieve the above objectives, the present invention provides the following technical solution.

[0006] A method for calculating the internal temperature of rocks based on multi-view infrared images includes the following steps: Infrared images of rocks were acquired from four orthogonal and asymmetric perspectives, and the rock emissivity, ambient reflectance temperature, and atmospheric transmittance at the time of acquisition were determined. Based on rock emissivity, atmospheric transmittance, ambient reflectance temperature, target true temperature, and atmospheric temperature, temperature inversion technology is used to obtain rock surface temperature data corresponding to each infrared image, and the temperature measurement results are approximated by Newton's iterative algorithm to obtain surface temperature estimates from multiple perspectives. A temperature data matrix from multiple perspectives is constructed based on surface temperature estimates from multiple perspectives; the temperature data matrices from multiple perspectives are jointly inverted and weighted to obtain a local rock surface temperature field; the local rock surface temperature field is elastically spliced ​​and compensated to obtain a global rock surface temperature field. The thermal diffusivity is determined based on the thermal properties of the rock, and the characteristic time of heat transfer is determined based on the thermal diffusivity and the distance in the heat transfer direction of the rock. Based on the actual observation time and characteristic time, the current heat transfer state is determined to be transient or steady state, and the temperature distribution inside the rock is obtained according to the global temperature field on the rock surface at the current actual observation time.

[0007] Preferably, the step of obtaining rock surface temperature data corresponding to each infrared image using temperature inversion technology, and approximating the temperature measurement results using Newton's iterative algorithm to obtain surface temperature estimates from multiple perspectives includes the following steps: Obtain the current measurement distance and the relative humidity of the environment. RH Calculate atmospheric transmittance: ; In the formula: For measuring distance; Relative humidity; Humidity influence coefficient; The basic attenuation coefficient; Atmospheric transmittance; Atmospheric attenuation coefficient; Based on rock emissivity, atmospheric transmittance, ambient reflectance temperature, target true temperature, and atmospheric temperature, the temperature inversion core formula is used to calibrate the surface temperature data of each rock image in the preprocessed local infrared image: ; In the formula: The effective radiation value received by the sensor; The emissivity of the rock; Atmospheric transmittance; The target is the true temperature; Atmospheric temperature; The ambient temperature is reflected. Numerical iteration is performed to approximate high-precision temperature measurement results, while correcting errors caused by factors such as emissivity, environmental radiation, and optical transmittance. ; In the formula: This is the temperature estimate for the nth iteration.

[0008] Preferably, the step of constructing a temperature data matrix from multiple perspectives based on surface temperature estimates from multiple perspectives, performing joint inversion and weighted fusion of the temperature data matrices from multiple perspectives to obtain the local rock surface temperature field includes the following steps: Local rock surface temperature was obtained by multi-view joint inversion: ; In the formula: Let be the temperature estimate for the nth iteration, where It is the median temperature from all viewpoints; This is the temperature update value for the (n+1)th iteration; Let be the surface emissivity of the k-th camera; Let be the atmospheric transmittance at the k-th viewpoint; The theoretical radiation value calculated according to Planck's law of blackbody radiation; This represents the voltage signal measured by the k-th camera. This is the derivative of blackbody radiation with respect to temperature; A weighted fusion of multi-view temperature data matrices is used to form a local rock surface temperature field. ; In the formula: The thermal diffusivity; For characteristic observation time; This represents the offset of the projected coordinates of the k-th viewpoint at coordinates (x, y); For the first k Temperature matrix for each perspective conversion; This is the weighted and fused local temperature field matrix; For the first k The weight of each viewpoint at coordinates (x, y).

[0009] Preferably, the step of elastically splicing and compensating the local rock surface temperature field to obtain the global rock surface temperature field includes the following steps: Edge detection is performed on infrared images using the Canny operator to extract the pixel coordinates of cracks and edges, and the curvature of the edges is calculated by quadratic polynomial fitting. ; In the formula: Representing coordinates The curvature of the rock surface; An adaptive curvature weighting function is introduced into the local rock surface temperature field for elastic splicing compensation to form a global rock surface temperature field. ; In the formula: This is the normalized value of curvature; This is a curvature sensitivity parameter; Minimum weight for high curvature regions; The maximum weight is applied to the low curvature region. To minimize the total deformation energy; These are the weighting coefficients for the temperature gradient smoothing term; For curvature adaptive weighting function; For temperature gradient fields; The weighting coefficient for the multi-view temperature difference penalty term; The local temperature field after weighted fusion; This represents the global temperature field.

[0010] Preferably, the step of determining the thermal diffusivity based on the rock's thermal properties and determining the characteristic time of heat transfer based on the thermal diffusivity and the distance in the heat transfer direction of the rock includes the following steps: Record the thermal properties of the rock and solve for the thermal diffusivity. ; In the formula: Thermal conductivity; Density; Specific heat capacity at constant pressure; The thermal diffusivity; Calculate the characteristic time; ; In the formula: Characteristic dimensions of the rock mass; The surface of the rock mass; Let the k-th viewpoint be the optical center; Characteristic time; is the thermal diffusivity.

[0011] Preferably, the step of determining whether the current heat transfer state is transient or steady-state based on the actual observation time and characteristic time, and obtaining the temperature distribution inside the rock at the current actual observation time according to the global temperature field of the rock surface, includes the following steps: The temperature distribution inside the rock is obtained by comparing the actual observation time with the characteristic time, and by using the analytical solution of the error function or numerical methods to solve for the internal temperature field. ; In the formula: For temperature; This refers to the actual observation time; For a three-dimensional Laplacian operator; Q is the heat source intensity per unit volume; Density of the rock; The specific heat capacity of the rock at constant pressure; The thermal diffusivity; Thermal conductivity; The boundary of the rock surface; The global temperature field on the rock surface; It is a three-dimensional divergence operator.

[0012] The present invention also provides a system for calculating the internal temperature of rocks based on multi-view infrared images, the system comprising: The data acquisition module is used to acquire infrared images of rocks from four orthogonal and asymmetric perspectives, and to determine the rock emissivity, ambient reflectance temperature, and atmospheric transmittance at the time of acquisition. The temperature estimation module is used to obtain the rock surface temperature data corresponding to each infrared image based on rock emissivity, atmospheric transmittance, ambient reflectance temperature, target true temperature and atmospheric temperature, using temperature inversion technology, and approximating the temperature measurement results through Newton's iterative algorithm to obtain surface temperature estimates from multiple perspectives. The global temperature field estimation module is used to construct a temperature data matrix from multiple perspectives based on the surface temperature estimates from multiple perspectives; perform joint inversion and weighted fusion on the temperature data matrices from multiple perspectives to obtain the local rock surface temperature field; and perform elastic splicing compensation on the local rock surface temperature field to obtain the global temperature field of the rock surface. The module for solving the temperature distribution inside the rock is used to determine the thermal diffusivity coefficient based on the rock's thermal properties and to determine the characteristic time of heat transfer based on the thermal diffusivity coefficient and the distance in the heat transfer direction of the rock. Based on the actual observation time and the characteristic time, it determines whether the current heat transfer state is transient or steady state, and obtains the temperature distribution inside the rock at the current actual observation time according to the global temperature field on the rock surface.

[0013] The present invention also provides a computer-readable storage medium storing a data processing program, which, when executed by a processor, implements the steps of the method for calculating the internal temperature of rocks based on multi-view infrared images.

[0014] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method for calculating the internal temperature of rocks based on multi-view infrared images.

[0015] The beneficial effects of this invention are: This invention proposes a method for calculating the internal temperature of rocks based on multi-view infrared images. The method uses four orthogonally asymmetrically placed cameras to simultaneously acquire infrared images of rocks, extracts multi-view surface temperatures from the infrared images, converts the infrared images into temperature data matrices, fuses and stitches the multi-view temperature data matrices to obtain the rock surface temperature field, and calculates the internal temperature of rocks using the principle of heat conduction. This establishes a method for calculating the internal temperature of rocks based on multi-view infrared images, which can accurately calculate the internal temperature of rocks. Attached Figure Description

[0016] Figure 1 This is a flowchart of a method according to an embodiment of the present invention; Figure 2 This is a two-dimensional radial internal temperature cloud map according to an embodiment of the present invention; Figure 3 These are internal temperature graphs at different monitoring points at different times, according to embodiments of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0018] Example 1 In this embodiment, to verify the accuracy of the method for calculating the internal temperature of rocks, granite samples taken from the Late Indosinian strata of the Longcaigou Basin in Gonghe County, Qinghai Province, were analyzed. Following international rock mechanics standards, the rock samples were prepared into Φ50mm×100mm specimens. The specimens were subjected to transient high-temperature thermal shock to change their surface temperature, and infrared images of the rock surface were simultaneously captured using an infrared thermal imager. Specific steps are as follows... Figure 1 As shown, it includes:

[0019] S1: Acquire infrared images of rocks from four orthogonal and asymmetrical perspectives, and determine the rock emissivity, ambient reflectance temperature, and atmospheric transmittance at the time of acquisition.

[0020] Specifically, atmospheric transmittance is calculated using the following formula; ; In the formula: To measure distance, ; Relative humidity, ; Humidity influence coefficient ; Based on the attenuation coefficient, ; Atmospheric transmittance, dimensionless; Atmospheric attenuation coefficient, .

[0021] S2: Based on rock emissivity, atmospheric transmittance, ambient reflectance temperature, target true temperature, and atmospheric temperature, the surface temperature data of the rock corresponding to each infrared image is obtained using temperature inversion technology. The surface temperature estimates from multiple perspectives are obtained by approximating the temperature measurement results using the Newton-Raphson iterative algorithm.

[0022] Specifically, the temperature inversion core formula is used to calibrate the surface temperature data of each rock image in the preprocessed local infrared image: ; In the formula: The effective radiation value received by the sensor. ; Let be the emissivity of the rock, dimensionless; Atmospheric transmittance, dimensionless; To achieve the target true temperature, ; Atmospheric temperature, ; The ambient reflected temperature, .

[0023] The following formula is used to perform numerical iterations to approximate the high-precision temperature measurement results, while correcting for errors caused by factors such as emissivity, environmental radiation, and optical transmittance: ; In the formula: This is the temperature estimate for the nth iteration. .

[0024] S3: Construct a temperature data matrix from multiple perspectives based on the surface temperature estimates from multiple perspectives; perform joint inversion and weighted fusion on the temperature data matrices from multiple perspectives to obtain the local rock surface temperature field; perform elastic splicing compensation on the local rock surface temperature field to obtain the global rock surface temperature field.

[0025] Specifically, the local rock surface temperature is calculated using a multi-view joint inversion method based on the following formula: ; In the formula: Let be the temperature estimate for the nth iteration, where It is the median temperature from all viewpoints. ; This is the temperature update value for the (n+1)th iteration. ; Let be the surface emissivity of the k-th camera, which is dimensionless; Let be the atmospheric transmittance at the k-th viewpoint, which is dimensionless; The theoretical radiation value calculated according to Planck's law of blackbody radiation. ; The voltage signal measured by the k-th camera. ; Let be the derivative of blackbody radiation with respect to temperature. .

[0026] A weighted fusion of multi-view temperature data matrices is used to form a local rock surface temperature field. ; In the formula: Where is the thermal diffusivity, ; For characteristic observation time, ; This represents the offset of the projected coordinates of the k-th viewpoint at coordinates (x, y); The temperature matrix for the k-th viewpoint transformation. ; This is the weighted and fused local temperature field matrix. ; The weight of the k-th viewpoint at coordinates (x, y) is dimensionless. The weights in the weighted fusion of multi-view temperature data matrix: these weights measure which viewpoint's temperature data is more reliable; the smaller the projection offset, the more reliable the data, and the larger the weight. This invention uses weighted averaging to fuse multi-view temperature data, suppressing noise from unreliable viewpoints such as occlusion and lens distortion, and generating a spatially continuous and more accurate local temperature field.

[0027] Edge detection is performed on infrared images using the Canny operator to extract the pixel coordinates of cracks and edges, and the curvature of the edges is calculated by quadratic polynomial fitting. ; In the formula: Representing coordinates The curvature of the rock surface, ; An adaptive weighting function is introduced for elastic splicing compensation to form a global temperature field on the rock surface: ; In the formula: The curvature normalization value is dimensionless; This is a curvature sensitivity parameter, dimensionless; It represents the minimum weight for high curvature regions and is dimensionless. The maximum weight is given in the low curvature region; it is dimensionless. To minimize the total deformation energy, ; is the weighting coefficient for the temperature gradient smoothing term, which is dimensionless; For curvature adaptive weighting function, dimensionless; For temperature gradient field, ; The weighting coefficient for the multi-view temperature difference penalty term is dimensionless. The local temperature field after weighted fusion. ; For the global temperature field, .

[0028] This invention proposes a curvature adaptive weighting method to address the problem of temperature field distortion caused by geometric discontinuities on the rock surface when splicing local temperature fields into a global temperature field. It adaptively adjusts the smoothness of the splicing process based on the geometric characteristics of the rock surface (high curvature areas retain the geometric features of the rock surface, such as cracks, edges, holes, etc., while areas with low curvature are smoothed to ensure global consistency and avoid splicing discontinuities). Furthermore, by adjusting... T global This minimizes the total deformation energy. When the total deformation energy is minimized, the... T global It is about finding the optimal global temperature field that balances smoothness and data consistency, and then using E to inversely deduce the optimal value. T global .

[0029] S4: Determine the thermal diffusivity based on the rock's thermal properties, and determine the characteristic time of heat transfer based on the thermal diffusivity and the distance in the heat transfer direction of the rock.

[0030] Specifically, record the thermal properties of the rock and solve for the thermal diffusivity: ; In the formula: Thermal conductivity, ; For density, ; For isobaric specific heat capacity, ; Where is the thermal diffusivity, .

[0031] Calculate the characteristic time: ; In the formula: Characteristic dimensions of the rock mass ; The surface of the rock mass; Let the k-th viewpoint be the optical center. ; For characteristic time, ; Where is the thermal diffusivity, .

[0032] S5: Determine whether the current heat transfer state is transient or steady state based on the actual observation time and characteristic time, and obtain the temperature distribution inside the rock at the current actual observation time according to the global temperature field of the rock surface.

[0033] By comparing actual observation time with characteristic time, a mathematical model is constructed, and the internal temperature field is solved using analytical solutions of error functions or numerical methods to obtain the temperature distribution inside the rock. ; In the formula: For temperature; This refers to the actual observation time; For a three-dimensional Laplacian operator; Q is the heat source intensity per unit volume; Density of the rock; The specific heat capacity of the rock at constant pressure; The thermal diffusivity; Thermal conductivity; The boundary of the rock surface; The global temperature field on the rock surface; It is a three-dimensional divergence operator.

[0034] The calculation results are as follows Figure 2 As shown, Figure 2 This is a two-dimensional radial internal temperature contour map. Figure 3 Statistical results of internal temperature maps at different monitoring points at different times. In this embodiment, fitting software was used to fit the calculated values ​​and the measured values. It can be found that the fitting curves of the calculated values ​​and the measured values ​​have a good degree of agreement, indicating that the method for calculating the internal temperature of rocks based on infrared images has accuracy and applicability.

[0035] The above is one embodiment of the method for calculating the internal temperature of rocks based on multi-view infrared images. Based on the same idea, this embodiment also provides a corresponding system for calculating the internal temperature of rocks based on multi-view infrared images. Specific limitations of the system for calculating the internal temperature of rocks based on multi-view infrared images can be found in the limitations of the method for calculating the internal temperature of rocks based on multi-view infrared images described above, and will not be repeated here. Each module in the above-described system for calculating the internal temperature of rocks based on multi-view infrared images can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0036] This embodiment also provides a computer-readable storage medium storing a computer program that can be used to execute the above-described... Figure 1A method for calculating the internal temperature of rocks based on multi-view infrared images is provided.

[0037] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0038] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for calculating the internal temperature of rocks based on multi-view infrared images, characterized in that, Includes the following steps: Infrared images of rocks were acquired from four orthogonal and asymmetric perspectives, and the rock emissivity, ambient reflectance temperature, and atmospheric transmittance at the time of acquisition were determined. Based on rock emissivity, atmospheric transmittance, ambient reflectance temperature, target true temperature, and atmospheric temperature, temperature inversion technology is used to obtain rock surface temperature data corresponding to each infrared image, and the temperature measurement results are approximated by Newton's iterative algorithm to obtain surface temperature estimates from multiple perspectives. A temperature data matrix with multiple perspectives is constructed based on surface temperature estimates from multiple perspectives; the temperature data matrices with multiple perspectives are jointly inverted and weighted and fused to obtain the local rock surface temperature field. The local rock surface temperature field is elastically spliced ​​and compensated to obtain the global rock surface temperature field. The thermal diffusivity is determined based on the thermal properties of the rock, and the characteristic time of heat transfer is determined based on the thermal diffusivity and the distance in the heat transfer direction of the rock. Based on the actual observation time and characteristic time, the current heat transfer state is determined to be transient or steady state, and the temperature distribution inside the rock is obtained according to the global temperature field on the rock surface at the current actual observation time.

2. The method for calculating the internal temperature of rocks based on multi-view infrared images according to claim 1, characterized in that, The process involves obtaining rock surface temperature data corresponding to each infrared image using temperature inversion technology, and then approximating the temperature measurement results using a Newton-Raphson iterative algorithm to obtain surface temperature estimates from multiple perspectives. This includes the following steps: Obtain the current measurement distance and the relative humidity of the environment. RH Calculate atmospheric transmittance: ； In the formula: For measuring distance; Relative humidity; Humidity influence coefficient; The basic attenuation coefficient; Atmospheric transmittance; Atmospheric attenuation coefficient; Based on rock emissivity, atmospheric transmittance, ambient reflectance temperature, target true temperature, and atmospheric temperature, the temperature inversion core formula is used to calibrate the surface temperature data of each rock image in the preprocessed local infrared image: ； In the formula: The effective radiation value received by the sensor; The emissivity of the rock; Atmospheric transmittance; The target is the true temperature; Atmospheric temperature; The ambient reflected temperature; Numerical iteration is performed to approximate high-precision temperature measurement results, while correcting errors caused by factors such as emissivity, environmental radiation, and optical transmittance. ； In the formula: This is the temperature estimate for the nth iteration.

3. The method for calculating the internal temperature of rocks based on multi-view infrared images according to claim 1, characterized in that, The process of constructing a temperature data matrix from multiple perspectives based on surface temperature estimates from multiple perspectives, performing joint inversion and weighted fusion of the temperature data matrices from multiple perspectives to obtain the local rock surface temperature field includes the following steps: Local rock surface temperature was obtained by multi-view joint inversion: ； In the formula: Let be the temperature estimate for the nth iteration, where It is the median temperature from all viewpoints; This is the temperature update value for the (n+1)th iteration; Let be the surface emissivity of the k-th camera; Let be the atmospheric transmittance at the k-th viewpoint; The theoretical radiation value calculated according to Planck's law of blackbody radiation; This represents the voltage signal measured by the k-th camera. This is the derivative of blackbody radiation with respect to temperature; A weighted fusion of multi-view temperature data matrices is used to form a local rock surface temperature field. ； In the formula: The thermal diffusivity; For characteristic observation time; For the first k The offset of the projected coordinates of a viewpoint at coordinates (x, y); For the first k Temperature matrix for each perspective conversion; This is the weighted and fused local temperature field matrix; For the first k The weight of each viewpoint at coordinates (x, y).

4. The method for calculating the internal temperature of rocks based on multi-view infrared images according to claim 3, characterized in that, The process of elastically splicing and compensating the local rock surface temperature field to obtain the global rock surface temperature field includes the following steps: Edge detection is performed on infrared images using the Canny operator to extract the pixel coordinates of cracks and edges, and the curvature of the edges is calculated by quadratic polynomial fitting. ； In the formula: Representing coordinates The curvature of the rock surface; An adaptive curvature weighting function is introduced into the local rock surface temperature field for elastic splicing compensation to form a global rock surface temperature field. ； In the formula: This is the normalized value of curvature; This is a curvature sensitivity parameter; Minimum weight for high curvature regions; The maximum weight is applied to the low curvature region. To minimize the total deformation energy; These are the weighting coefficients for the temperature gradient smoothing term; For curvature adaptive weighting function; For temperature gradient fields; The weighting coefficient for the multi-view temperature difference penalty term; The local temperature field after weighted fusion; This represents the global temperature field.

5. The method for calculating the internal temperature of rocks based on multi-view infrared images according to claim 1, characterized in that, The process of determining the thermal diffusivity based on rock thermal properties and determining the characteristic time of heat transfer based on the thermal diffusivity and the distance in the heat transfer direction of the rock includes the following steps: Record the thermal properties of the rock and solve for the thermal diffusivity. ； In the formula: Thermal conductivity; Density; Specific heat capacity at constant pressure; The thermal diffusivity; Calculate the characteristic time; ； In the formula: Characteristic dimensions of the rock mass; The surface of the rock mass; Let the k-th viewpoint be the optical center; Characteristic time; is the thermal diffusivity.

6. The method for calculating the internal temperature of rocks based on multi-view infrared images according to claim 5, characterized in that, The process of determining whether the current heat transfer state is transient or steady-state based on the actual observation time and characteristic time, and obtaining the temperature distribution inside the rock at the current actual observation time according to the global temperature field of the rock surface, includes the following steps: The temperature distribution inside the rock is obtained by comparing the actual observation time with the characteristic time, and by using the analytical solution of the error function or numerical methods to solve for the internal temperature field. ； In the formula: For temperature; This refers to the actual observation time; For a three-dimensional Laplace operator; Q is the heat source intensity per unit volume; Density of the rock; The specific heat capacity of the rock at constant pressure; The thermal diffusivity; Thermal conductivity; The boundary of the rock surface; The global temperature field on the rock surface; It is a three-dimensional divergence operator.

7. A system for calculating the internal temperature of rocks based on multi-view infrared images, characterized in that, include: The data acquisition module is used to acquire infrared images of rocks from four orthogonal and asymmetric perspectives, and to determine the rock emissivity, ambient reflectance temperature, and atmospheric transmittance at the time of acquisition. The temperature estimation module is used to obtain the rock surface temperature data corresponding to each infrared image based on rock emissivity, atmospheric transmittance, ambient reflectance temperature, target true temperature and atmospheric temperature, using temperature inversion technology, and approximating the temperature measurement results through Newton's iterative algorithm to obtain surface temperature estimates from multiple perspectives. The global temperature field estimation module is used to construct a temperature data matrix from multiple perspectives based on surface temperature estimates from multiple perspectives; it performs joint inversion and weighted fusion on the temperature data matrices from multiple perspectives to obtain the local rock surface temperature field. The local rock surface temperature field is elastically spliced ​​and compensated to obtain the global rock surface temperature field. The module for solving the temperature distribution inside the rock is used to determine the thermal diffusivity based on the rock's thermal properties and to determine the characteristic time of heat transfer based on the thermal diffusivity and the distance in the heat transfer direction of the rock. Based on the actual observation time and characteristic time, the current heat transfer state is determined to be transient or steady state, and the temperature distribution inside the rock is obtained according to the global temperature field on the rock surface at the current actual observation time.

8. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the method for calculating the internal temperature of rocks based on multi-view infrared images as described in any one of claims 1 to 6.

9. A computer device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the method for calculating the internal temperature of rocks based on multi-view infrared images as described in any one of claims 1 to 6.

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