Graphene Thermal Conductivity Detection System Based on Data Analysis

Abstract

The present invention belongs to the technical field of graphene thermal conductivity detection, specifically a graphene thermal conductivity detection system based on data analysis, including a thickness zoning module, a laser irradiation module, a thermal conductivity analysis module, and a comprehensive evaluation module. It realizes thickness self-adaptive zoning through multi-level connected domain analysis, and the regions cover defect-free types, internal wrinkle defect types, and internal pollution defect types. Combining laser energy intelligent matching and defect thermal resistance compensation technology, it directly performs in-situ detection on graphene films with wrinkles, pollution, and thickness fluctuations without relying on pretreatment, and further eliminates the influence of substrate heat conduction on the basis of in-situ detection to achieve the analysis of the intrinsic thermal conductivity of graphene films, effectively avoiding the secondary damage error caused by pretreatment such as planarization in the prior art, providing a high-precision and non-destructive solution for industrial on-line detection, and also providing a reliable on-line quality monitoring means for the large-scale production of graphene electronic devices.

Description

Technical Field

[0001] The present invention belongs to the technical field of graphene thermal conductivity detection, and specifically relates to a graphene thermal conductivity detection system based on data analysis. Background Art

[0002] As a two-dimensional material with atomic-level thickness, graphene film has become a core material for heat dissipation of electronic devices and high-end thermal management due to its excellent in-plane thermal conductivity. However, its actual thermal conductivity is significantly affected by factors such as layer uniformity, grain boundary defect concentration, and interface bonding state, and there can be significant differences in thermal conductivity performance under different preparation processes and application scenarios. Therefore, accurately detecting the thermal conductivity of graphene film is the core prerequisite for ensuring the stability of material performance and supporting the reliable operation of devices.

[0003] In the prior art, there are also some solutions related to the detection of the thermal conductivity of graphene film. For example, a thermal conductivity detection method applied to a graphene / graphite thermal conduction module with the Chinese patent publication number CN113533415A measures the length and width dimensions of the module, analyzes the materials of each stacked layer and accurately measures the thickness after verifying compliance requirements, and calculates the thermal conductivity parameters based on the material characteristics and thickness data. Innovatively, it simplifies the complex thermal conductivity detection to thickness measurement, providing an efficient solution for industrial on-line detection.

[0004] Another Chinese patent with the publication number CN119147581B discloses a graphene thermal conductivity detection system and method based on data analysis. By systematically analyzing key parameters such as boundary effect, crystal orientation, and heat flow path change, it accurately quantifies the attenuation law of the thermal conductivity of graphene materials under complex conditions such as structural defects, deformation, and substrate thermal resistance. Based on the essential characteristics that the material can still maintain excellent thermal conductivity under various interference factors, a comprehensive evaluation system is established, which not only reflects the thermal conductivity performance under actual working conditions, but also reveals its intrinsic heat transport potential, providing a scientific and reliable performance evaluation basis for the application of graphene in the fields of electronic heat dissipation, energy devices, etc.

[0005] Although the above two solutions propose some solutions for the graphene film thermal conductivity detection technology, there are still certain limitations. Specifically, in the existing graphene film thermal conductivity detection, when the film is flatly placed on the substrate, problems such as uneven thickness, wrinkles, and surface contamination existing in the film itself are often exposed. Although the prior art can use pretreatment methods such as mechanical compaction or chemical treatment to try to eliminate these problems, it is difficult to ensure complete elimination and new defects may be introduced or the microscopic structure of graphene may be changed during the treatment process, resulting in the distortion of the intrinsic heat conduction performance data of the graphene film and being unable to accurately reflect the true thermal conductivity characteristics of the film. Summary of the Invention

[0006] To overcome the drawbacks in the background art, the embodiments of the present invention provide a graphene thermal conductivity detection system based on data analysis, which can effectively solve the problems involved in the above-mentioned background art.

[0007] The objective of the present invention can be achieved through the following technical solutions: A graphene thermal conductivity detection system based on data analysis, comprising: a thickness zoning module, a laser irradiation module, a thermal conductivity analysis module, and a comprehensive evaluation module.

[0008] The thickness zoning module is connected to the laser irradiation module, the laser irradiation module is connected to the thermal conductivity analysis module, and the thermal conductivity analysis module is connected to the comprehensive evaluation module.

[0009] The thickness zoning module obtains the thickness data of each detection point on the surface of the graphene film, and divides the film into several equal-thickness regions through multi-level connected domain analysis. The equal-thickness regions cover defect-free types, built-in fold defect types, and built-in contamination defect types.

[0010] The laser irradiation module plans the regional laser energy parameters based on the seed point thickness data, and performs laser irradiation in regions according to the preset time-space sequence.

[0011] The thermal conductivity analysis module quantifies the thermal conductivity performance index according to the regional laser irradiation response. Among them, for the defect-free region, the thermal conductivity performance index is solved based on the temperature response time and thickness data according to the heat conduction theory, and for the defect region, the measured thermal conductivity performance index is compensated inversely based on the thermal resistance increment of its built-in defect.

[0012] The comprehensive evaluation module performs weighted fusion on the thermal conductivity performance index according to the area ratio of each region to output the comprehensive thermal conductivity performance evaluation result of the graphene film.

[0013] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: The present invention realizes thickness self-adaptive zoning through multi-level connected domain analysis, combines laser energy intelligent matching and defect thermal resistance compensation technology, and directly performs in-situ detection on graphene films with folds, contamination, and thickness fluctuations without relying on preprocessing, eliminating the secondary damage error caused by preprocessing such as flattening in the prior art, realizing the analysis of the intrinsic thermal conductivity of graphene films, and providing a high-precision and non-destructive solution for industrial on-line detection. Description of the Drawings

[0014] The present invention is further described with the accompanying drawings, but the embodiments in the drawings do not constitute any limitation to the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to the following drawings without creative efforts.

[0015] Figure 1 It is a schematic diagram of the module connection of the present invention.

[0016] Figure 2 It is a schematic diagram of the detection logic for the wrinkled sub-region in the remaining area of the present invention.

[0017] Figure 3 It is a schematic diagram of the detection logic for the contaminated sub-region in the remaining area of the present invention. Detailed implementation manners

[0018] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0019] Refer to Figure 1 As shown, the present invention provides a graphene thermal conductivity detection system based on data analysis, including: a thickness partitioning module, a laser irradiation module, a thermal conductivity analysis module, and a comprehensive evaluation module.

[0020] The thickness partitioning module is connected to the laser irradiation module, the laser irradiation module is connected to the thermal conductivity analysis module, and the thermal conductivity analysis module is connected to the comprehensive evaluation module.

[0021] The thickness partitioning module obtains the thickness data of each detection point on the surface of the graphene film, and divides the film into several equal-thickness regions through multi-level connected component analysis. The equal-thickness regions cover non-defect types, built-in wrinkle defect types, and built-in contamination defect types.

[0022] In a preferred embodiment of the present invention, the division of several equal-thickness regions in the film is as follows: After discretizing the thickness data of each detection point on the film surface, select the high-frequency thickness as the seed point according to the frequency, and expand and merge the detection points with thickness differences within the preset tolerance range in the eight-neighborhood to form an initial equal-thickness region.

[0023] Identify the remaining areas that do not meet the connectivity condition, detect and separate the wrinkled sub-region and the contaminated sub-region in the remaining areas to generate an exclusion mask.

[0024] Re-execute the connected component analysis in the remaining areas excluding the wrinkled sub-region and the contaminated sub-region, and merge the remaining detection points to form a secondary equal-thickness region.

[0025] Classify the wrinkled sub-region and the contaminated sub-region into the adjacent largest connected region, so as to obtain several finally divided equal-thickness regions.

[0026] Refer to Figure 2As shown, in a preferred embodiment of the present invention, the detection of the wrinkled sub-region in the remaining region is as follows: Based on the thickness gradient field of the remaining region, candidate sub-regions whose gradient magnitude satisfies the first preset condition and whose gradient direction distribution satisfies the direction consistency condition are screened.

[0027] It should be added that the thickness gradient field of the remaining region specifically refers to the thickness gradient magnitude and gradient direction angle calculated by applying the Sobel operator to each detection point in the remaining region. The first preset condition specifically refers to that the gradient magnitude of each detection point in the candidate sub-region is greater than the preset gradient magnitude threshold requirement. The gradient direction distribution satisfying the direction consistency condition specifically refers to that the standard deviation of the gradient direction angles of the detection points in the candidate sub-region is less than the preset gradient direction angle standard deviation threshold requirement. Among them, the preset gradient magnitude threshold requirement is determined according to the yield strength of the thin film material and the thickness measurement error. For example, the 3-fold standard deviation principle is set by statistically analyzing the gradient distribution of the initial equal-thickness region. The preset gradient direction angle standard deviation threshold requirement is set based on the maximum allowable deviation angle of the wrinkling extension direction and can be obtained through finite element simulation or training with known wrinkled samples.

[0028] It should also be added that the screening basis for the above candidate sub-regions is: Wrinkles exhibit a directional thickness mutation caused by mechanical stress, and the gradient direction of this mutation is continuous in spatial distribution.

[0029] Perform surface curvature analysis on the candidate sub-regions, and extract continuous regions where the curvature extreme value and curvature change rate satisfy the second preset condition as potential wrinkled core regions.

[0030] It should be added that the surface curvature analysis process of the above candidate sub-regions specifically includes: constructing the surface height function of the candidate sub-region through bicubic spline interpolation, and substituting the thickness data of each detection point in the candidate sub-region to output the surface curvature analysis value.

[0031] The second preset condition specifically refers to that both the curvature extreme value and the curvature change rate are greater than their corresponding preset thresholds. Among them, the preset curvature extreme value threshold is the critical buckling curvature of the thin film, and the preset curvature change rate threshold is set according to the curvature derivative range of the wrinkled peak / trough transition region.

[0032] It should also be added that the basis for the above potential wrinkled core region is: When wrinkles are formed, high-curvature regions are generated due to surface buckling, and the curvature change rate is related to the geometric characteristics of the wrinkled peak / trough.

[0033] Perform periodic feature analysis on the potential wrinkled core region, and determine the wrinkled sub-region according to the significance comparison result between the characteristic frequency components and the noise baseline in the spectrum.

[0034] It should be added that the periodic feature analysis process of the above potential wrinkled core region is: performing fast Fourier transform along the main gradient direction to calculate the power spectral density.

[0035] The definition of the significance of the comparison result is that the power spectral density value of the characteristic frequency component is greater than three times the standard deviation of the noise baseline, and the noise baseline is determined by the spectral analysis of the non-folded area.

[0036] It should also be added that the final determination basis of the above-mentioned folded sub-region is that the mechanical stress fold has a periodic distribution characteristic related to the stress frequency.

[0037] Refer to Figure 3 As shown, in a preferred embodiment of the present invention, the detection of the contaminated sub-region in the remaining area is as follows: The remaining area is divided into several analysis windows, and candidate windows that meet the thickness anomaly standard are screened based on the thickness fluctuation characteristics within the windows.

[0038] It should be added that the above-mentioned analysis window size is set by considering the minimum size of the pollutant and the thickness detection resolution to ensure that at least one complete window is covered by a single pollutant, and it can be exemplified as 20×20 pixels.

[0039] The above-mentioned thickness anomaly standard specifically means that the thickness range within the window is greater than the preset range standard.

[0040] The basis for screening candidate windows is that the local thickness mutation caused by the physical attachment of pollutants on the film surface has a significantly higher fluctuation intensity than the natural defects of the film.

[0041] Extract the texture features of the candidate windows, and identify the abnormal regions that meet the texture anomaly standard through the joint analysis of the contrast and disorder metrics.

[0042] It should be noted that the above-mentioned texture anomaly standard specifically refers to that both the texture contrast and the texture entropy value are greater than their corresponding preset index standards.

[0043] The basis for identifying abnormal regions is that the pollutant boundary has a high contrast due to the difference in the optical properties of the materials, and the disordered deposition structure leads to an increase in the texture entropy value.

[0044] Perform morphological verification on the abnormal regions, and determine the attribution of the contaminated sub-region according to the matching degree between its structural closing characteristics and the pollution characteristics.

[0045] It should be noted that the specific process of the above-mentioned morphological verification is as follows: Perform an erosion operation on the abnormal region using a circular structuring element, and then perform a dilation operation with the same structuring element. If the ratio of the dilated recovery area to the eroded area reaches the preset area recovery rate threshold, and the overlap rate between the dilated region and the original region reaches the preset shape retention rate threshold, then it is determined that the abnormal region belongs to the contaminated sub-region. The determination basis is that real pollutants have a dense and continuous structure, and both the area and shape can be highly maintained after morphological operations.

[0046] The laser irradiation module plans the laser energy parameters for regions based on the thickness data of the seed points, and performs laser irradiation in regions in accordance with a preset spatio-temporal sequence.

[0047] In a preferred embodiment of the present invention, the planning of the laser energy parameters for regions based on the thickness data of the seed points includes: querying the laser energy parameters corresponding to the thickness data of the seed points from a pre-stored thickness-laser energy parameter mapping table, where the mapping table is generated through experimental calibration and includes discrete thickness values and their corresponding optimal laser energy parameters.

[0048] When the thickness data of the seed points does not match the discrete thickness values recorded in the mapping table, the linear interpolation method is used to calculate the laser energy parameters.

[0049] It should be noted that the calculation process of the above linear interpolation method is as follows: retrieve two discrete thickness values adjacent to the thickness data of the seed points in the pre-stored thickness-laser energy parameter mapping table, and obtain the corresponding laser energy parameters.

[0050] Calculate the linear distribution ratio of the thickness of the seed point relative to the interval formed by its two adjacent discrete thicknesses, that is, take the difference between the thickness of the seed point and the lower limit discrete thickness of the interval as the denominator, and the span of the interval formed by the two adjacent discrete thicknesses as the numerator, and expand the ratio analysis.

[0051] Based on the linear distribution ratio, linearly superimpose the laser energy parameters corresponding to the lower limit discrete thickness of the interval to obtain the laser energy parameters corresponding to the thickness data of the seed points.

[0052] Special note: If the thickness of the seed point is less than the minimum discrete thickness of the mapping table, directly adopt the optimal laser energy parameter corresponding to the minimum discrete thickness. Similarly, if the target thickness is greater than the maximum discrete thickness of the mapping table, adopt the optimal laser energy parameter corresponding to the maximum discrete thickness.

[0053] In a preferred embodiment of the present invention, the process of performing laser irradiation in regions further includes: inserting a forced cooling period between adjacent regions, and the laser energy parameters of each region are dynamically adjusted according to the residual temperature field distribution of this region detected by an infrared thermal imager within a preset period before the execution time of its laser irradiation, and the adjustment value is determined by the thermal history correction coefficient jointly inverted from the residual temperature field and the thickness data of the seed points.

[0054] The above thermal history correction coefficient is defined as , where , are the regional average temperature value and temperature gradient obtained according to the residual temperature field of this region within the preset period, is the preset equilibrium temperature without thermal history interference, which depends on the graphene film thermal conductivity detection environment and can be artificially calibrated at the initial stage of system development, is the thickness of the regional seed point, is the gradient attenuation function, related to the temperature gradient and the seed point thickness shows a negative correlation, reflecting the inhibitory effect of the heat diffusion ability, and can be exemplarily expressed as an exponential attenuation function.

[0055] For the area with the residual temperature field of the previous laser irradiation, the thermal history correction coefficient usually shows a positive deviation from the reference value due to the thermal accumulation effect. The determination logic of the adjustment value is as follows: calculate the deviation between the thermal history correction coefficient and the reference value, and perform cumulative operation on it with the process sensitivity coefficient and the originally planned laser energy parameters of this area to generate a dynamic adjustment value dominated by energy reduction. Among them, the process sensitivity coefficient is a dimensionless proportional factor, and the refined control of the correction amplitude is realized through experimental calibration to ensure that the energy adjustment can effectively suppress the thermal history interference and avoid process instability caused by overcompensation. The reference value is 1.

[0056] The thermal conductivity analysis module quantifies the thermal conductivity performance index according to the regional laser irradiation response. Among them, for the defect-free area, the thermal conductivity performance index is solved based on the temperature response time and thickness data according to the heat conduction theory, and for the defect area, the measured thermal conductivity performance index is reversely compensated based on the thermal resistance increment of its built-in defect.

[0057] In a preferred embodiment of the present invention, the quantification process of the thermal conductivity performance index of the defect-free area includes: real-time collecting the temperature change curve on the back of the thin film in the defect-free area under laser irradiation through an infrared thermal imager, recording the time span corresponding to rising to the half-peak temperature, combining the seed point thickness data of the defect-free area, and importing it into the heat conduction equation to solve the thermal diffusivity of the defect-free area under laser irradiation performance. Perform cumulative operation on the thermal diffusivity with the preset density calibration value and specific heat capacity calibration value of the graphene thin film to obtain the thermal conductivity, and use the thermal conductivity as the thermal conductivity performance index.

[0058] It should be noted that the above heat conduction equation specifically refers to the standard Parker formula used for calculating the thermal diffusivity by the laser flash method.

[0059] In a preferred embodiment of the present invention, the quantification process of the thermal conductivity performance index of the area with the built-in fold defect type includes: the same as the analysis method of the thermal conductivity of the defect-free area, obtaining the thermal conductivity of the area with the built-in fold defect type, and further marking it as the measured thermal conductivity.

[0060] The geometric characteristic parameters of the pleat sub-area are obtained, including the curvature radius, pleat height and the loss rate of the effective contact area between layers. The pleat sub-area is converted into a multi-layer equivalent structure with a preset inclination angle. The preset inclination angle is determined by the geometric relationship between the pleat height and the curvature radius. The contact thermal resistance increment caused by the increase in the effective spacing between layers and the inclination angle is calculated, and the lateral conduction thermal resistance caused by the extension of the heat flow path is calculated simultaneously.

[0061] It should be noted that the above-mentioned preset inclination angle is equal to the inverse tangent value of the fold height divided by the curvature radius, which reflects the quantitative influence of the fold deformation on the interlayer arrangement direction.

[0062] The above contact thermal resistance increment is proportional to the area loss rate and the effective spacing increment, and inversely proportional to the intrinsic thermal conductivity between graphene layers and the area of the wrinkle region. The higher the feedback loss rate and the larger the spacing, the more significant the increase in contact thermal resistance.

[0063] The above-mentioned lateral conduction thermal resistance is proportional to the path extension amount and the sine value of the inclination angle, and inversely proportional to the intrinsic thermal conductivity between graphene layers and the area of the wrinkle region, so as to reflect the characteristic that the larger the inclination angle, the more significant the extension of the heat flow path, and the higher the lateral thermal resistance. The calculation process can be illustrated as follows: the product of the path extension amount and the sine value of the inclination angle is used as the denominator, the product of the thermal conductivity between graphene layers and the area of the wrinkle region is used as the numerator, and the ratio calculation is expanded to obtain the lateral conduction thermal resistance.

[0064] According to the area ratio of the wrinkle sub-region to the equal-thickness region, the measured thermal conductivity is reversely compensated for the superposition effect of the contact thermal resistance increment and the lateral thermal resistance, and the intrinsic thermal conductivity of the built-in wrinkle defect type region is obtained as the thermal conductivity performance index of the region.

[0065] In a preferred embodiment of the present invention, the quantification process of the thermal conductivity performance index of the built-in contamination defect type area includes: dynamically capturing the extension of the lateral heat diffusion path generated when the heat flow bypasses the contaminated sub-area based on an infrared thermal imager, and analyzing the induced lateral conduction thermal resistance increment.

[0066] The additional contact thermal resistance caused by thermal mismatch between the contaminated sub-region and the substrate interface is quantified by combining the vertical thickness of the contaminated sub-region and the interface thermal conductivity parameter.

[0067] The additional contact thermal resistance is superimposed on the lateral conduction thermal resistance increment to construct the equivalent composite thermal resistance of the contaminated sub-area. According to the area ratio weight of the contaminated sub-area in the equal thickness area, the thermal resistance effect is reversely compensated for the measured thermal conductivity to obtain the intrinsic thermal conductivity of the built-in contamination defect type area as the thermal conductivity performance indicator of the area.

[0068] The comprehensive evaluation module performs weighted fusion of thermal conductivity indicators according to the area proportion of each region to output a comprehensive thermal conductivity evaluation result of the graphene film.

[0069] In a preferred embodiment of the present invention, the output of the comprehensive thermal conductivity evaluation result includes the peeling calculation of the influence of the substrate thermal conductivity: the same laser irradiation conditions are applied to the blank substrate area without graphene film coverage, the substrate temperature change data is recorded by an infrared thermal imager, and the substrate's own thermal conductivity index is calculated based on the heat conduction theory.

[0070] For each equal-thickness region, according to its seed point thickness data and the quantification result of the thermal conductivity index, combined with the substrate's own thermal conductivity index, the equivalent thermal conductivity index of the film-substrate composite system is analyzed, and the thermal conductivity index contributed only by the film itself is separated therefrom.

[0071] It should be noted that the equivalent thermal conductivity index of the above film-substrate composite system can be exemplarily referred to the following formula: , where respectively represent the thickness of the film seed point in the region, the substrate thickness, and the total thickness of their superposition, respectively represent the thermal conductivity index contributed by the film itself in the region and the substrate's own thermal conductivity index, represents the cross-sectional area of the region, represents the thermal resistance of the superposition of the film and the substrate in the region, which can be calculated based on the Fourier heat conduction law by monitoring the temperature difference and heat flux density at both ends of the film-substrate composite system during the laser irradiation process in the region. Specifically, it is defined as the ratio of the product of the heat flux density and the heat transfer area to the temperature difference in the denominator state.

[0072] The thermal conductivity indexes of the graphene film in the pure film state of each region are weighted and averaged according to the area ratio, and the comprehensive thermal conductivity evaluation result of the graphene film after peeling off the influence of the substrate is output.

[0073] In the embodiment of the present invention, thickness self-adaptive zoning is realized through multi-level connected domain analysis, combined with laser energy intelligent matching and defect thermal resistance compensation technology. Without relying on preprocessing, in-situ detection of graphene films with wrinkles, contamination, and thickness fluctuations is directly carried out, eliminating the secondary damage error caused by preprocessing such as flattening in the prior art, realizing the analysis of the intrinsic thermal conductivity of graphene films, and providing a high-precision and non-destructive solution for industrial on-line detection.

[0074] The above formulas are all dimensionless and take their numerical calculations. The formulas are obtained by collecting a large amount of data for software simulation to obtain a formula closest to the real situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0075] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product.

[0076] Those of ordinary skill in the art can realize that the modules 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 to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0077] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0078] As mentioned above, the above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed in this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claimed rights.

[0079] Finally, the above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A graphene thermal conductivity detection system based on data analysis, characterized in that, Including: A thickness zoning module that obtains the thickness data of each detection point on the surface of the graphene film and divides the film into several equal-thickness regions through multi-level connected component analysis. The equal-thickness regions cover defect-free types, built-in wrinkle defect types, and built-in contamination defect types; A laser irradiation module that plans the regional laser energy parameters based on the seed point thickness data and performs laser irradiation in regions in a preset time-space sequence; A thermal conductivity analysis module that quantifies the thermal conductivity performance index according to the regional laser irradiation response. Among them, for the defect-free region, the thermal conductivity performance index is solved based on the temperature response time and thickness data according to the heat conduction theory, and for the defect region, the measured thermal conductivity performance index is inversely compensated based on the thermal resistance increment of its built-in defect; A comprehensive evaluation module that performs weighted fusion on the thermal conductivity performance index according to the area ratio of each region to output the comprehensive thermal conductivity performance evaluation result of the graphene film.

2. The graphene thermal conductivity detection system based on data analysis according to claim 1, wherein: The division of several equal-thickness regions in the film is as follows: After discretizing the thickness data of each detection point on the film surface, sort them by frequency and select the high-frequency thickness as the seed point. Expand the eight-neighborhood to merge the detection points with thickness differences within the preset tolerance range to form the initial equal-thickness region; Identify the remaining regions that do not meet the connectivity condition, detect and separate the wrinkle sub-regions and contamination sub-regions in the remaining regions to generate an exclusion mask; Re-perform connected component analysis in the remaining regions after excluding the wrinkle sub-regions and contamination sub-regions, and merge the remaining detection points to form the secondary equal-thickness region; Assign the wrinkle sub-regions and contamination sub-regions to the adjacent largest connected region to obtain several finally divided equal-thickness regions.

3. The graphene thermal conductivity detection system based on data analysis according to claim 2, wherein: The detection of the wrinkle sub-regions in the remaining regions is as follows: Based on the thickness gradient field of the remaining region, screen the candidate sub-regions whose gradient modulus values meet the first preset condition and the gradient direction distribution meets the direction consistency condition; Perform surface curvature analysis on the candidate sub-regions, and extract the continuous region whose curvature extreme value and curvature change rate meet the second preset condition as the potential wrinkle core region; Perform periodic feature analysis on the potential wrinkle core region, and determine the wrinkle sub-region according to the significance comparison result between the characteristic frequency components and the noise baseline in the spectrum.

4. The graphene thermal conductivity detection system based on data analysis according to claim 2, characterized in that: The detection of the contamination sub-regions in the remaining regions is as follows: Divide the remaining region into several analysis windows, and screen the candidate windows that meet the thickness anomaly standard based on the thickness fluctuation characteristics within the window; Extract the texture features of the candidate windows, and identify the abnormal regions that meet the texture anomaly standard through the joint analysis of the contrast and disorder degree indicators; Perform morphological verification on the abnormal regions, and determine the attribution of the contamination sub-regions according to the matching degree between their structural closure characteristics and contamination characteristics.

5. The graphene thermal conductivity detection system based on data analysis according to claim 1, wherein: The planning of the regional laser energy parameters based on the seed point thickness data includes: querying the laser energy parameters corresponding to the seed point thickness data from the pre-stored thickness-laser energy parameter mapping table, where the mapping table is generated through experimental calibration and contains discrete thickness values and their corresponding optimal laser energy parameters; When the seed point thickness data does not match the discrete thickness values recorded in the mapping table, the linear interpolation method is used to calculate the laser energy parameters.

6. The graphene thermal conductivity detection system based on data analysis according to claim 1, characterized in that: The process of performing laser irradiation in sub-regions further includes: inserting a forced cooling period between adjacent regions, and dynamically adjusting the laser energy parameters of each region according to the residual temperature field distribution of this region detected by an infrared thermal imager within a preset period before the execution moment of its laser irradiation. The adjustment value is determined by the thermal history correction coefficient jointly inverted from the residual temperature field and the seed point thickness data.

7. The graphene thermal conductivity detection system based on data analysis according to claim 1, characterized in that: The quantification process of the thermal conductivity index of the defect-free region includes: real-time collecting the temperature change curve on the back of the thin film in the defect-free region under laser irradiation by an infrared thermal imager, recording the time span corresponding to rising to the half-peak temperature, combining the seed point thickness data of the defect-free region, and introducing the heat conduction equation to solve the thermal diffusivity of the defect-free region under laser irradiation performance. Cumulatively operating the thermal diffusivity with the preset density calibration value and specific heat capacity calibration value of the graphene film to obtain the thermal conductivity, and using the thermal conductivity as the thermal conductivity index.

8. The graphene thermal conductivity detection system based on data analysis according to claim 7, wherein: The quantification process of the thermal conductivity index of the region with the built-in wrinkling defect type includes: in the same way as the analysis method of the thermal conductivity of the defect-free region, obtaining the thermal conductivity of the region with the built-in wrinkling defect type, and further marking it as the measured thermal conductivity; Obtaining the geometric characteristic parameters of the wrinkled sub-region, including the radius of curvature, the wrinkling height, and the loss rate of the effective contact area between layers, converting the wrinkled sub-region into a multi-layer equivalent structure with a preset inclination angle, where the preset inclination angle is determined by the geometric relationship between the wrinkling height and the radius of curvature, calculating the increment of the contact thermal resistance caused by the increase in the effective spacing between layers and the inclination angle, and synchronously calculating the lateral conduction thermal resistance caused by the extension of the heat flow path; According to the area ratio of the wrinkled sub-region in the isopachous region, reversely compensating the superposition effect of the contact thermal resistance increment and the lateral thermal resistance on the measured thermal conductivity to obtain the intrinsic thermal conductivity of the region with the built-in wrinkling defect type, which is used as the thermal conductivity index of this region.

9. The graphene thermal conductivity detection system based on data analysis according to claim 1, characterized in that: The quantification process of the thermal conductivity index of the region with the built-in contamination defect type includes: based on the infrared thermal imager, dynamically capturing the extension amount of the lateral heat diffusion path generated when the heat flow bypasses the contaminated sub-region, and analyzing the induced increment of the lateral conduction thermal resistance; Combining the vertical thickness of the contaminated sub-region and the interface thermal conductivity parameter, quantifying the additional contact thermal resistance formed at the interface between the contaminated sub-region and the substrate due to thermal mismatch; Superposing the additional contact thermal resistance and the increment of the lateral conduction thermal resistance to construct the equivalent composite thermal resistance of the contaminated sub-region. According to the area ratio weight of the contaminated sub-region in the isopachous region, reversely compensating the thermal resistance effect on the measured thermal conductivity to obtain the intrinsic thermal conductivity of the region with the built-in contamination defect type, which is used as the thermal conductivity index of this region.

10. The graphene thermal conductivity detection system based on data analysis according to claim 1, characterized in that: The output of the comprehensive thermal conductivity evaluation result includes the peeling calculation of the influence of the substrate thermal conductivity: applying the same laser irradiation conditions to the blank substrate region without graphene film coverage, recording the substrate temperature change data by an infrared thermal imager, and calculating the substrate's own thermal conductivity index based on the heat conduction theory; For each isopachous region, according to its seed point thickness data and the quantification result of the thermal conductivity index, combining the substrate's own thermal conductivity index, analyzing the equivalent thermal conductivity index of the film-substrate composite system, and separating the thermal conductivity index contributed only by the film itself from it; The thermal conductivity indexes of each region in the pure thin film state are weighted and averaged according to the area ratio, and the comprehensive thermal conductivity evaluation results of the graphene film after the influence of the peeling substrate are output.

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