A graphene thermal conductivity testing method and system based on multi-dimensional analysis

Abstract

The present invention discloses a method and system for testing the thermal conductivity of graphene based on multi-dimensional analysis, which relates to the technical field of graphene. By supplementing the thermal information of the substrate based on the principle of heat conduction and preprocessing the thermal information set, different processed data are divided. This process can not only improve the data information, but also help to quickly and accurately discover the relationships and laws between the data. By analyzing the differences between the processed data, a set of test process parameters including sensing signals and influencing signals is generated. When the sensing signal is associated, a test result generation instruction can be generated in a timely manner, triggering an early warning and generating detailed result information, including influence location description information, influence range and report list, which enables the influence of the substrate thermal resistance and the change of graphene thickness on the thermal conductivity of graphene to be quickly and accurately judged. On the contrary, result verification information is generated to verify the differences between the test and the experiment and generate test feedback information.

Description

Technical Field

[0001] The present invention relates to the technical field of graphene, and particularly to a method and system for testing the thermal conductivity of graphene based on multi-dimensional analysis. Background Art

[0002] In the field of materials science, as a new type of carbon nanomaterial, graphene has become a research hotspot due to its excellent mechanical, electrical, and thermal properties. In the research direction of thermal properties, the ultra-high thermal conductivity of graphene shows great application potential in the fields of thermal management, heat dissipation materials, etc. And the accurate testing and analysis of the thermal conductivity of graphene is a key link to explore its application value.

[0003] Currently, traditional methods for testing the thermal conductivity of graphene often only consider from a single dimension. For example, only the influence of the thickness of graphene itself on its thermal conductivity is concerned, and it is difficult to reveal the heat conduction mechanism of the graphene-substrate system in multiple aspects. During the testing process, the analysis of the differences in heat conduction characteristics in different directions is also lacking, resulting in difficulty in accurately obtaining the combined influence of the substrate thermal resistance and the graphene thickness on the thermal conductivity. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides a method and system for testing the thermal conductivity of graphene based on multi-dimensional analysis, which solves the problems in the above background art.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: A method for testing the thermal conductivity of graphene based on multi-dimensional analysis includes the following steps.

[0006] S1: Select multiple groups of graphene samples with different thicknesses, and configure substrate samples. Use measuring equipment to obtain a set of thermal information of the samples under different test conditions. The set of thermal information includes graphene thermal information and substrate thermal information.

[0007] S2: Based on the heat conduction principle and graphene thermal information, supplement the substrate thermal information, and preprocess the set of thermal information to obtain first processed data and second processed data.

[0008] S3: Analyze the difference situation between the first processed data and the second processed data to obtain a set of test process parameters. The set of test process parameters includes a sensing signal and an influencing signal.

[0009] S4: When the sensing signal in the set of test process parameters is associated, generate a test result generation instruction. The test result generation instruction is used to trigger an alarm and generate result information. The result information includes influence location description information, influence range, and a report list.

[0010] S5: When the sensed signal in the test process parameters is uncorrelated, result verification information is generated. The result verification information is used to verify the difference between the test and the experiment, and test feedback information is generated.

[0011] S6: According to the test feedback information, repeat steps S1 to S5 until no test feedback information is generated.

[0012] Preferably, multiple graphene samples with different thicknesses are selected, and a substrate sample is configured, including:

[0013] A series of graphene samples with different thicknesses are prepared in advance. At the same time, substrate samples with the same specifications are configured for the graphene samples, and the x-direction and y-direction of the graphene samples in the plane are determined. According to the determined x-direction and y-direction of the graphene samples in the plane, the x-direction and y-direction of the substrate in the plane are obtained to obtain direction information. The direction information includes the determined x-direction, y-direction, and the z-direction perpendicular to the graphene plane.

[0014] Preferably, using a measuring device, a set of thermal information of the samples under different test conditions is obtained. The set of thermal information includes graphene thermal information and substrate thermal information, including:

[0015] The measuring device is used to sequentially monitor the thermal conductivity of graphene samples with different thicknesses under the conditions of with and without a substrate according to the directions in the direction information to obtain graphene thermal conductivity sub-information. When the graphene samples with different thicknesses are transferred to the same substrate, the fitting state is maintained. Then, the measuring device is used again to sequentially monitor the thermal conductivity of the substrate sample alone according to the directions in the direction information to obtain substrate thermal conductivity sub-information. Combining the relationship between thermal resistance and thermal conductivity, the thermal resistance of graphene samples with different thicknesses in different directions under the condition of without a substrate and the thermal resistance of the substrate sample are determined to generate graphene thermal resistance sub-information and substrate thermal resistance sub-information. The graphene thermal resistance sub-information and the graphene thermal conductivity sub-information are combined to obtain graphene thermal information, and the substrate thermal resistance sub-information and the substrate thermal conductivity sub-information are combined to obtain substrate thermal information.

[0016] Preferably, based on the heat conduction principle and the graphene thermal information, the substrate thermal information is supplemented, and the set of thermal information is preprocessed to obtain first processed data and second processed data, including:

[0017] According to the fitting state between the graphene sample and the substrate sample, the circuit state is determined, and combined with the heat conduction principle, the relationship between thermal resistance and thermal conductivity, and the set of thermal information, the corresponding substrate thermal resistance in different directions is calculated to supplement the substrate thermal information;

[0018] Identify invalid information in the thermal information set and fill in outliers, and then classify the information in the preprocessed thermal information set into direction data, thickness data, information with substrate conditions, and information without substrate conditions. Combine the information with substrate conditions and information without substrate conditions to generate the first processed data, and combine the direction data and thickness data to generate the second processed data.

[0019] Preferably, analyze the differences between the first processed data and the second processed data to obtain a set of test process parameters, which includes sensing signals and influencing signals, including:

[0020] According to the information with substrate conditions and information without substrate conditions in the first processed data, analyze the change of the thermal conductivity of graphene samples with different thicknesses when there is a substrate and when there is no substrate by means of a ratio. Specifically:

[0021] ; where is the change rate of the thermal conductivity of the graphene sample with thickness n when there is or is not a substrate, is the thermal conductivity of graphene with thickness n when there is no substrate, is the thermal conductivity of graphene with thickness n when there is a substrate;

[0022] According to the thickness data in the second processed data, in the way of obtaining the change rate of thermal conductivity , respectively obtain the change rates of thermal conductivity of graphene samples with different thicknesses when there is or is not a substrate, and combine with the direction data to determine the change rates of thermal conductivity of graphene samples with different thicknesses when there is or is not a substrate under different direction conditions. Calculate the mean value of the change rates of thermal conductivity of graphene samples with different thicknesses when there is or is not a substrate under different direction conditions to obtain verification data. The verification data is used to avoid analysis deviation caused by analysis only in a single direction. The verification data refers to the average change rate of thermal conductivity of graphene samples with different thicknesses when there is or is not a substrate;

[0023] Compare the average change rate of thermal conductivity with a preset change threshold to generate a sensing signal;

[0024] If the average change rate of thermal conductivity exceeds the preset change threshold, the sensing signal is associated; otherwise, the sensing signal is not associated;

[0025] According to the sensing signal, analyze the influence of the change of the substrate thermal resistance with the thickness of the graphene sample on the thermal conductivity of graphene to generate an influencing signal;

[0026] Combine the sensing signal and the influencing signal to obtain a set of test process parameters.

[0027] Preferably, according to the sensing signal, analyze the influence of the change of the substrate thermal resistance with the thickness of the graphene sample on the thermal conductivity of graphene to generate an influence signal, including:

[0028] Group the thermal information set again according to the direction type data to obtain data groups in different directions;

[0029] According to the thickness type data, determine the thicknesses of graphene, and use the thickness of the graphene sample as the abscissa and the substrate thermal resistance in different directions as the ordinate. For each data group in each direction, plot the corresponding points in the coordinate system, and connect the adjacent points with a smooth curve to obtain the influence distribution change curve of the substrate thermal resistance with the thickness of the graphene sample on the thermal conductivity of graphene in different directions. According to the distribution of adjacent points within the influence distribution change curve, obtain the influence signal.

[0030] Preferably, when the sensing signal in the test process parameter set is associated, generate a test result generation instruction, and the test result generation instruction is used to trigger an alarm and generate result information. The result information includes influence location description information, influence range, and report list, including:

[0031] When the sensing signal in the test process parameter set is associated, based on the distribution of adjacent points within the influence distribution change curve in the influence signal, construct a fitting function, and the specific expression is:

[0032] ;

[0033] In the formula, is the thermal conductivity of graphene with a substrate in the j-th direction, is the amplitude in the j-th direction; is the thermal conductivity attenuation coefficient of graphene thickness in the j-th direction; is the graphene thickness, is the substrate thermal resistance influence coefficient in the j-th direction; is the substrate thermal resistance thermal conductivity attenuation coefficient in the j-th direction; is the substrate thermal resistance, is the exponential function with e as the base;

[0034] Trigger the test result generation instruction, and the non-linear least squares method will be used to calculate 、 、 and in the fitting function. The goal of the non-linear least squares method is to minimize the sum of the squares of the errors between the monitored values and the predicted values of the fitting function;

[0035] Based on the perception signal in the test process parameters being correlated, it is determined that the substrate thermal resistance will affect the thermal conductivity of graphene with the change of the graphene sample thickness, so as to obtain the influence location description information;

[0036] Obtain the substrate thermal resistance and thermal conductance attenuation coefficient d in all directions to determine the influence range;

[0037] Compare the substrate thermal resistance and thermal conductance attenuation coefficient d in all directions with the preset threshold respectively to generate a report list. If the substrate thermal resistance and thermal conductance attenuation coefficient d in the corresponding direction exceeds the preset threshold, obtain the first level, otherwise obtain the second level; According to the first level, extract the thickness of the corresponding graphene to generate the first thickness group, and according to the second level, extract the thickness of the corresponding graphene to generate the second thickness group. Present the first thickness group and the second thickness group on the operation platform to generate a report list;

[0038] Combine the influence location description information, influence range and report list to obtain the result information.

[0039] Preferably, when the perception signal in the test process parameters is uncorrelated, result verification information is generated. The result verification information is used to verify the difference between the test and the experiment, and test feedback information is generated, including:

[0040] When the perception signal in the test process parameters is uncorrelated, according to the root mean square error algorithm, calculate the difference between the actual measurement value and the predicted value of the fitting function, specifically:

[0041] ;

[0042] In the formula, is the root mean square error, N is the number of test conditions, i = 1, 2,..., N, is the thermal conductivity change rate of the graphene sample with thickness n with and without the substrate under the i-th test condition; is the predicted thermal conductivity change rate of the graphene sample with thickness n with and without the substrate under the i-th test condition according to the fitting function;

[0043] If the root mean square error is less than the preset error threshold, a qualified instruction is generated, otherwise an unqualified instruction is generated. Combine the qualified instruction and the unqualified instruction to obtain the result verification information. When a qualified instruction is generated, use the unqualified instruction as the test feedback information.

[0044] A graphene thermal conductivity test system based on multi-dimensional analysis includes:

[0045] The preparation module is used to select multiple groups of graphene samples with different thicknesses, configure substrate samples, and use measuring equipment to obtain a set of thermal property information of the samples under different test conditions. The set of thermal property information includes graphene thermal property information and substrate thermal property information;

[0046] The processing module is used to supplement the substrate thermal property information based on the heat conduction principle and the graphene thermal property information, and preprocess the set of thermal property information to obtain first processed data and second processed data;

[0047] The testing module is used to analyze the difference between the first processed data and the second processed data to obtain a set of test process parameters. The set of test process parameters includes sensing signals and influencing signals;

[0048] The information presentation module is used to generate a test result generation instruction when the sensing signal in the set of test process parameters is associated. The test result generation instruction is used to trigger an alarm and generate result information. The result information includes influence location description information, influence range, and report list;

[0049] The verification module is used to generate result verification information when the sensing signal in the set of test process parameters is not associated. The result verification information is used to verify the difference between the test and the experiment and generate test feedback information;

[0050] The closed-loop control module is used to repeat the operation process from the preparation module to the verification module according to the test feedback information until no test feedback information is generated.

[0051] The present invention provides a graphene thermal conductivity testing method and system based on multi-dimensional analysis, which has the following beneficial effects:

[0052] (1) By selecting multiple groups of graphene samples with different thicknesses and configuring substrate samples, a comprehensive set of thermal information is obtained under different test conditions, covering the thermal information of both graphene and the substrate. This multi-dimensional data collection method can more realistically reflect the thermal conductivity of graphene when interacting with the substrate in actual application scenarios. Compared with single-dimensional tests, it can obtain richer and more accurate information, providing a solid data foundation for in-depth analysis. Based on the principle of heat conduction, the thermal information of the substrate is supplemented, and the set of thermal information is preprocessed to divide it into first-processed data and second-processed data. This process not only improves the data information but also helps to quickly and accurately discover the relationships and laws between the data, improving the analysis efficiency and accuracy. By analyzing the differences between the first-processed data and the second-processed data, a set of test process parameters including sensing signals and influencing signals is generated. When the sensing signal is associated, a test result generation instruction can be generated in a timely manner, triggering an alarm and generating detailed result information, including influence positioning description information, influence range, and report list. This enables the quick and accurate judgment of the influence of substrate thermal resistance and graphene thickness change on graphene thermal conductivity, and issues an alarm in a timely manner, providing strong guidance for subsequent material optimization and application. When the sensing signal is not associated, result verification information is generated to verify the differences between the test and the experiment and generate test feedback information. According to the test feedback information, the test steps are repeated until no test feedback information is generated, ensuring the reliability and accuracy of the test results. This continuous verification and optimization process can gradually improve the accuracy of the test method, reduce errors, and make the test results more in line with the actual situation.

[0053] (2) Calculate the thermal conductivity change rate of graphene with different thicknesses with and without a substrate using the first-processed data to intuitively present the influence of substrate thermal resistance on graphene thermal conductivity with a quantitative index. For example, when the thermal conductivity of a graphene sample with a certain thickness decreases by 15% with a substrate, this data can directly reflect the degree of inhibition of heat conduction by the substrate, providing a key reference for material optimization. Combining the direction-type and thickness-type information in the second-processed data, calculate the average thermal conductivity change rate, effectively avoiding the one-sidedness of single-direction data. Comparing the average change rate with a preset threshold to generate a sensing signal can quickly judge the correlation between substrate thermal resistance and graphene thermal conductivity, clarifying the direction for subsequent analysis. By grouping the data by direction and plotting the influence distribution curve of substrate thermal resistance with the change of graphene thickness, the anisotropic characteristics of heat conduction can be visually revealed. This visual analysis helps to deeply understand the heat conduction mechanism in different directions. Finally, integrating the sensing signal and the influencing signal into a set of test process parameters provides a systematic and scientific basis for the accurate evaluation and optimized design of graphene thermal conductivity. Brief Description of the Drawings

[0054] Figure 1 Schematic flowchart of a method for testing the thermal conductivity of graphene based on multi-dimensional analysis according to the present invention;

[0055] Figure 2 This is the overall logic diagram of a method for testing the thermal conductivity of graphene based on multi-dimensional analysis according to the present invention;

[0056] Figure 3 This is the partial logic diagram of a method for testing the thermal conductivity of graphene based on multi-dimensional analysis according to the present invention;

[0057] Figure 4 This is the block diagram of a system for testing the thermal conductivity of graphene based on multi-dimensional analysis according to the present invention. Detailed implementation manner

[0058] 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 of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0059] Embodiment 1

[0060] Please refer to Figures 1 to 3 , the present invention provides a method for testing the thermal conductivity of graphene based on multi-dimensional analysis, including the following steps,

[0061] S1: Select multiple groups of graphene samples with different thicknesses, and configure substrate samples. Use measuring equipment to obtain a set of thermal property information of the samples under different test conditions. The set of thermal property information includes graphene thermal property information and substrate thermal property information;

[0062] S2: Based on the heat conduction principle and graphene thermal property information, supplement the substrate thermal property information, and preprocess the set of thermal property information to obtain the first processed data and the second processed data;

[0063] S3: Analyze the difference between the first processed data and the second processed data to obtain a set of test process parameters. The set of test process parameters includes a sensing signal and an influencing signal;

[0064] S4: When the sensing signal in the set of test process parameters is correlated, generate a test result generation instruction. The test result generation instruction is used to trigger an alarm and generate result information. The result information includes impact location description information, impact range, and a report list;

[0065] S5: When the sensing signal in the set of test process parameters is uncorrelated, generate result verification information. The result verification information is used to verify the difference between the test and the experiment and generate test feedback information;

[0066] S6: According to the test feedback information, repeat steps S1 to S5 until no test feedback information is generated.

[0067] In this embodiment, by selecting multiple groups of graphene samples with different thicknesses and configuring substrate samples, the thermal information sets of graphene and the substrate are comprehensively covered in multiple aspects, providing a rich data basis for subsequent analysis. For example, when studying the thermal conductivity of graphene with different thicknesses on a silicon substrate, a large amount of original data can be obtained. After supplementing and preprocessing according to the principle of heat conduction, different processed data generated can effectively integrate and classify the data, facilitating further analysis.

[0068] By comparing the differences in different processed data, the parameter set of the test process is obtained, and the influence relationship between the substrate thermal resistance and the graphene thickness on the thermal conductivity is accurately judged. When the sensing signal is associated, the influencing factors can be quickly located, the influencing range can be determined, and a report list can be generated. For example, in an experiment, it is found that the substrate thermal resistance has a significant influence on the thermal conductivity of thin graphene, and an early warning is triggered in a timely manner, generating a report including the specific influence area and degree, providing a basis for material optimization.

[0069] When the sensing signal is not associated, the result verification information is used to verify the differences between the test and the experiment to ensure the reliability of the results. By continuously repeating the test process and continuously optimizing the test results, this method can accurately and comprehensively analyze the thermal conductivity of graphene, effectively solving the limitations of the single-dimensional analysis of traditional methods, and providing reliable data support and technical guarantee for the application of graphene in the fields of thermal management, heat dissipation materials, etc.

[0070] Embodiment 2

[0071] Please refer to Figure 1 , specifically: Select multiple groups of graphene samples with different thicknesses and configure substrate samples, including:

[0072] Prepare a series of graphene samples with different thicknesses in advance. At the same time, configure substrate samples with the same specifications for the graphene samples, and determine the x-direction and y-direction of the graphene samples in the plane. According to the determined x-direction and y-direction of the graphene samples in the plane, obtain the x-direction and y-direction of the substrate in the plane to obtain direction information, where the direction information includes the determined x-direction, y-direction, and the z-direction perpendicular to the graphene plane;

[0073] Test the thermal conductivity of graphene or its substrate by the 3ω method; and the measuring devices used in the test process include a signal generator, a lock-in amplifier, a current source / voltage source, or a dual-channel source meter;

[0074] Using the measuring device, obtain the thermal information set of the sample under different test conditions, where the thermal information set includes graphene thermal information and substrate thermal information, including:

[0075] The measurement device is used to monitor the thermal conductivity of graphene samples with different thicknesses in the presence and absence of a substrate in sequence according to the directions in the direction information, so as to obtain graphene thermal phonon information. When transferring graphene samples with different thicknesses onto the same substrate, the fitting state is maintained. The graphene thermal phonon information includes the thermal conductivity of graphene samples with different thicknesses in different directions without a substrate and with a substrate. Then, the measurement device is used again to separately monitor the thermal conductivity of the substrate sample in sequence according to the directions in the direction information to obtain substrate thermal phonon information. Combining the relationship between thermal resistance and thermal conductivity, the thermal resistance of graphene samples with different thicknesses in different directions without a substrate and the thermal resistance of the substrate sample are determined to generate graphene thermal resistance phonon information and substrate thermal resistance phonon information. The graphene thermal resistance phonon information and the graphene thermal phonon information are combined to obtain graphene thermal property information, and the substrate thermal resistance phonon information and the substrate thermal phonon information are combined to obtain substrate thermal property information.

[0076] In practical applications, graphene-based composites are usually used in close combination with a substrate. For example, in the field of heat dissipation of electronic devices, a graphene heat dissipation film needs to be closely attached to the substrate of the heating element to effectively dissipate heat. Conducting tests while maintaining the fitting state can more realistically simulate the working environment of graphene under actual working conditions, making the test data more valuable for practical reference, and the test results can be directly applied to guide the design and optimization of graphene-based composites. If the graphene sample is not closely attached to the substrate, there are gaps or poor contacts in the middle, an additional thermal resistance will be formed, resulting in an unstable heat conduction path and difficult to accurately evaluate. Maintaining the fitting state can ensure that heat can be stably transferred from graphene to the substrate or conducted between the two.

[0077] Among them, the relationship between thermal resistance and thermal conductivity is: ; where R is the thermal resistance, m is the sample thickness, K is the thermal conductivity, and A is the heat transfer area.

[0078] In this embodiment, in the sample preparation stage, graphene samples with different thicknesses are prepared in advance and a substrate with a unified specification is configured. At the same time, the x, y directions in the plane and the z direction perpendicular to the plane are accurately determined to ensure the consistency and directionality of the test conditions. For example, when preparing two types of graphene samples with ultra-thin (such as single-layer) and relatively thick (such as 10-layer) thicknesses, by standardizing the substrate configuration, interference with the test results caused by substrate differences is avoided, laying a reliable foundation for subsequent analysis.

[0079] In the data acquisition stage, a measurement method of dividing by direction and condition is adopted. The thermal conductivities of graphene in different directions under the conditions of with and without a substrate are respectively recorded. At the same time, the thermal conductivity of the substrate is monitored separately, and the data is improved by combining the relationship between thermal resistance and thermal conductivity. Taking the application scenario of an electronic device heat sink as an example, in actual work, there are directional differences in the heat conduction within the graphene material. Through the monitoring of the thermal conductivities in the x, y, and z directions, this method can accurately capture the heat conduction efficiency in different directions. At the same time, by comparing the changes in thermal conductivity with and without a substrate, the influence of the substrate on the thermal conductivity of graphene can be clearly quantified. Through this systematic data collection method, not only can the thermal conductivity characteristics of graphene in a complex actual environment be reflected in multiple aspects, but also detailed data support can be provided for optimizing the design of graphene-based thermal management materials, facilitating their efficient application in the fields of electronic heat dissipation, new energy batteries, etc.

[0080] This data collection and processing method has significant beneficial effects. In the sample selection link, multiple groups of graphene samples with different thicknesses are prepared and substrates with the same specifications are configured, which can comprehensively cover the test requirements under different thickness conditions, avoid the one-sidedness of the results caused by samples of a single thickness, and ensure that the data can reflect the influence of thickness variables on the thermal conductivity of graphene. The x and y directions in the sample plane and the perpendicular z direction are defined, and a three-dimensional direction information system is constructed, so that the measurement covers the full-space thermal conductivity characteristics of graphene, captures the anisotropic differences, and improves the comprehensiveness of the test. During the measurement process, the thermal conductivities of graphene with different thicknesses under the conditions of with and without a substrate are sequentially monitored in the direction, multi-dimensional thermal conductivity sub-information is obtained, and by comparing the thermal conductivities with and without a substrate, the influence mechanism of the substrate on the thermal conductivity of graphene is accurately analyzed. The graphene and the substrate are kept in a bonded state to simulate the actual application scenario and enhance the authenticity of the data. At the same time, the thermal conductivity of the substrate is monitored separately, and the thermal resistance of each sample is determined by combining the relationship between thermal resistance and thermal conductivity, and a complete thermal property information set is constructed. The thermal resistance and thermal conductivity information are combined to form the thermal property information of graphene and the substrate respectively, providing a rich and systematic data basis for subsequent in-depth analysis, helping to accurately reveal the heat conduction law of the graphene-substrate system, and providing a reliable basis for the research and application optimization of the thermal conductivity of graphene.

[0081] Example 3

[0082] Please refer to Figure 1 , specifically: Based on the heat conduction principle and the thermal property information of graphene, the thermal property information of the substrate is supplemented, and the thermal property information set is preprocessed to obtain the first processed data and the second processed data, including:

[0083] According to the bonding state between the graphene sample and the substrate sample, the circuit state is determined, and combined with the heat conduction principle, the relationship between thermal resistance and thermal conductivity, and the thermal property information set, the substrate thermal resistance corresponding to different directions is calculated to supplement the thermal property information of the substrate;

[0084] When graphene and the substrate are in a series relationship, according to the basic principle of heat conduction, the total thermal resistance is equal to the sum of the thermal resistances of each part. This is because in the series model, heat passes through graphene and the substrate in sequence, just like current passes through each resistor in a series circuit. During the heat conduction process, the temperature difference is the driving force for heat transfer, while the thermal resistance hinders heat transfer. Graphene and the substrate respectively impede heat transfer, and the overall hindering effect is the sum of their thermal resistances.

[0085] If graphene and the substrate are in a parallel relationship, it means that heat can be transferred simultaneously through two paths: graphene and the substrate. At this time, the total thermal conductance (the reciprocal of thermal resistance) is equal to the sum of the thermal conductances of each part. This situation is similar to the current shunt in a parallel circuit, where heat is distributed among different paths, and the sum of the heat fluxes of each path is equal to the total heat flux.

[0086] For the sake of simplicity in the analysis process, it can be assumed that graphene and the substrate are in a series relationship first;

[0087] Identify the invalid information in the thermal information set and fill in the outliers, and then classify the information in the preprocessed thermal information set into different types, which are respectively classified into direction - type data, thickness - type data, information with substrate conditions, and information without substrate conditions. Combine the information with substrate conditions and information without substrate conditions to generate the first - type processed data, and combine the direction - type data and thickness - type data to generate the second - type processed data.

[0088] Specifically, identify the invalid information in the thermal information set and fill in the outliers, and then classify the information in the preprocessed thermal information set into different types, which are respectively classified into direction - type data, thickness - type data, information with substrate conditions, and information without substrate conditions, specifically including:

[0089] Direction - type data refers to the information extracted from the preprocessed thermal information set in different directions;

[0090] Thickness - type data refers to the information extracted from the preprocessed thermal information set about the graphene samples with different thicknesses;

[0091] Information with substrate conditions refers to the information extracted from the preprocessed thermal information set about the graphene samples when there is a substrate;

[0092] Information without substrate conditions refers to the information extracted from the preprocessed thermal information set about the graphene samples when there is no substrate;

[0093] In this embodiment, the substrate thermal resistance is calculated based on the heat conduction principle and the thermal property information of graphene, effectively supplementing the thermal property information of the substrate and improving the data system. For example, in actual tests, if only the thermal conductivity of graphene is known, it is difficult to judge the influence of the substrate on the overall heat conduction. By calculating the substrate thermal resistance, the role of the substrate in the heat conduction process can be clearly presented. Preprocessing the thermal property information set to identify invalid information and fill in outliers can avoid the interference of incorrect data on the analysis results. For example, abnormal thermal conductivity data generated due to a short-term equipment failure during a certain measurement was corrected after processing, ensuring the authenticity and reliability of the data. The information is carefully divided into direction type, thickness type, substrate-present condition type, and substrate-absent condition type, and the first and second processed data are respectively generated by combination, laying a foundation for subsequent targeted analysis. For example, when exploring the influence of the substrate on the thermal conductivity of graphene, the first processed data can directly compare the thermal conductivity differences with and without the substrate; the second processed data can be used to analyze the heat conduction characteristics in different directions and thicknesses, improving the efficiency and accuracy of the analysis.

[0094] Determine the circuit state based on the fitting state of graphene and the substrate, calculate the substrate thermal resistance in different directions in combination with the heat conduction principle, improve the thermal property information of the substrate, and provide key data for a comprehensive analysis of the heat conduction characteristics.

[0095] Identify invalid information in the thermal property information set, fill in outliers, ensure data quality, and avoid analysis deviation caused by data errors.

[0096] Example 4

[0097] Please refer to Figure 1 , specifically: Analyze the difference between the first processed data and the second processed data to obtain the test process parameter set. The test process parameter set includes the sensing signal and the influencing signal, including:

[0098] According to the substrate-present condition type information and the substrate-absent condition type information in the first processed data, analyze the change in the thermal conductivity of graphene samples with different thicknesses with and without the substrate by means of a ratio. Specifically:

[0099] ; where is the change rate of the thermal conductivity of the graphene sample with thickness n with and without the substrate, is the thermal conductivity of graphene with thickness n without the substrate, is the thermal conductivity of graphene with thickness n with the substrate;

[0100] Calculate the change rate of the thermal conductivity of graphene samples with different thicknesses with and without the substrate through the formula to quantify the influence of the substrate on the thermal conductivity, which is the basis for subsequent analysis. This change represents the relative change degree of the thermal conductivity with and without the substrate, reflecting the role of the substrate thermal resistance in the heat conduction of graphene.

[0101] According to the thickness data in the second - processed data, with the acquisition method of the thermal conductivity change rate respectively obtain the thermal conductivity change rates of graphene samples with different thicknesses with and without a substrate , and in combination with the direction - type data, determine the thermal conductivity change rates of graphene samples with different thicknesses with and without a substrate under different direction conditions . Calculate the mean value of the thermal conductivity change rates of graphene samples with different thicknesses with and without a substrate under different direction conditions to obtain verification data. The verification data is used to avoid the analysis deviation caused by analysis only in a single direction. The verification data refers to the average thermal conductivity change rate of graphene samples with different thicknesses with and without a substrate;

[0102] Calculate the average thermal conductivity change rate in combination with the direction - type data to eliminate the data deviation in a single direction, thereby improving the comprehensiveness of the analysis. The verification data, that is, the average thermal conductivity change rate, is used to verify the reliability of the analysis results.

[0103] Compare the average thermal conductivity change rate with a pre - set change threshold to generate a perception signal;

[0104] If the average thermal conductivity change rate exceeds the pre - set change threshold, the perception signal is "associated", otherwise the perception signal is "not associated";

[0105] According to the perception signal, analyze the influence of the change of the substrate thermal resistance with the thickness of the graphene sample on the thermal conductivity of graphene to generate an influence signal;

[0106] Although graphene itself is a material with unique physical properties, when it is placed on a substrate, the properties of the substrate, including the substrate thermal resistance, will affect the thermal transport and other properties of graphene. For example, the substrate thermal resistance may affect the heat exchange efficiency between graphene and the substrate, and thus affect the measurement of the thermal conductivity of graphene and thermal management in practical applications. Even if the thermal conductivity of graphene itself is high, if the substrate thermal resistance is large, it may limit the heat transfer from graphene to the substrate, resulting in different thermal behaviors from the ideal state at the macroscopic level. Therefore, in some related research, it is necessary to consider the influence of the substrate thermal resistance on the thermal properties of graphene.

[0107] By observing the change trend of the substrate thermal resistance with the thickness of graphene, it can help us understand the internal mechanism of the influence of the substrate thermal resistance on the thermal conductivity of graphene. For example, if it is found that the substrate thermal resistance increases with the increase of the graphene thickness, it may mean that as the number of graphene layers increases, the intermolecular van der Waals force enhances, causing the interaction between the substrate and graphene to change, thus affecting the heat transfer at the interface between the two, resulting in an increase in the substrate thermal resistance. The analysis of this change trend helps to deeply understand the action mode of the substrate thermal resistance in graphene with different thicknesses.

[0108] Combine the sensing signal and the influencing signal to obtain a set of test process parameters.

[0109] According to the sensing signal, analyze the influence of the change of the substrate thermal resistance with the thickness of the graphene sample on the thermal conductivity of graphene to generate an influencing signal, including:

[0110] Group the thermal property information set again according to the direction - type data to obtain data groups in different directions, so as to distinguish the differences in the heat conduction characteristics in different directions;

[0111] Group the data according to the directions (x, y, z directions). Because the heat conduction characteristics may be different in different directions, fitting needs to be carried out separately. In this way, three data sets can be obtained, corresponding to the x, y, and z directions respectively.

[0112] According to the thickness - type data, determine the thickness of graphene. Taking the thickness of the graphene sample as the abscissa and the substrate thermal resistance in different directions as the ordinate, for each data group in each direction, plot the corresponding points in the coordinate system, and connect the adjacent points with a smooth curve to obtain the influence distribution change curve of the substrate thermal resistance with the change of the graphene sample thickness on the thermal conductivity of graphene. According to the distribution of adjacent points within the influence distribution change curve, obtain the influencing signal.

[0113] Group the data by direction, plot the curve of the substrate thermal resistance changing with the graphene thickness, analyze the influence distribution law, and generate an influencing signal. Among them, the influencing signal contains the specific trend and characteristic information of the influence of the substrate thermal resistance on the thermal conductivity of graphene.

[0114] In this embodiment, by calculating the change rate of the thermal conductivity of graphene with different thicknesses with and without a substrate, the influence degree of the substrate on the thermal conductivity of graphene can be quantified. For example, when the thermal conductivity of a graphene sample with a certain thickness is 500 W / (m·K) without a substrate and drops to 400 W / (m·K) with a substrate, the change rate calculated by the formula is 20%, intuitively showing that the substrate significantly reduces the thermal conductivity of this thickness of graphene.

[0115] Combining the direction - type data to calculate the average change rate of thermal conductivity effectively avoids the analysis deviation caused by single - direction data. For example, the change rate of thermal conductivity in the x - direction shows that the influence of the substrate is weak, but the influence in the y - and z - directions is significant. Through the mean value calculation, the overall influence can be comprehensively evaluated to ensure the reliability of the conclusion. Comparing the average change rate with a threshold value to generate a sensing signal can quickly judge the correlation between the two. If the change rate exceeds the threshold value, it is determined that there is a correlation between the substrate thermal resistance and the thermal conductivity of graphene, pointing out the direction for subsequent analysis.

[0116] Plot the influence distribution curves of the substrate thermal resistance in different directions with the change of graphene thickness, which can visually show the anisotropy of the heat conduction characteristics. For example, it is found in the curve that the influence of the substrate thermal resistance on the graphene thermal conductivity in the x direction decreases linearly with the increase of thickness, while in the y direction, it shows non-linear fluctuations, providing a visual basis for in-depth understanding of the heat conduction mechanism. This multi-dimensional and multi-level analysis method provides scientific and accurate decision-making support for the optimization and application of the thermal properties of graphene.

[0117] Example 5

[0118] Please refer to Figure 1 , specifically: when the sensed signal in the test process parameters is associated, a test result generation instruction is generated. The test result generation instruction is used to trigger an early warning and generate result information. The result information includes influence location description information, influence range, and report list, including:

[0119] When the sensed signal in the test process parameters is associated, a fitting function is constructed based on the distribution of adjacent points in the influence distribution change curve in the influence signal. The specific expression is:

[0120] ;

[0121] In the formula, is the graphene thermal conductivity with a substrate in the j-th direction, is the amplitude in the j-th direction; is the graphene thickness thermal conductivity attenuation coefficient in the j-th direction; is the graphene thickness, is the substrate thermal resistance influence coefficient in the j-th direction; is the substrate thermal resistance thermal conductivity attenuation coefficient in the j-th direction; is the substrate thermal resistance, is the exponential function with base e, where e is the Euler number, and its value is approximately 2.71828;

[0122] The amplitude in the j-th direction mainly affects the amplitude of the change rate of the thermal conductivity;

[0123] The graphene thickness thermal conductivity attenuation coefficient in the j-th direction , reflecting the influence degree of the graphene thickness on the thermal conductivity. The larger its value, the faster the thermal conductivity decreases with the increase of the graphene thickness, indicating the hindrance effect of the thickness on the heat conduction process. For example, for thinner graphene, when the thickness increases by a certain amount, the decrease in the thermal conductivity is relatively small; while for thicker graphene, the same increase in thickness may lead to a more obvious decrease in the thermal conductivity, indicating that the inhibitory effect of the thickness on the thermal conductivity is enhanced.

[0124] The substrate thermal resistance influence coefficient in the j-th direction It mainly reflects the overall level of the influence of the substrate thermal resistance on the thermal conductivity, and includes the contributions of other factors besides the thickness influence to the thermal conductivity;

[0125] The substrate thermal resistance thermal conductivity attenuation coefficient in the j direction Characterizes the influence degree of the substrate thermal resistance on the thermal conductivity of graphene. The larger its value, the more significant the influence of the substrate thermal resistance on the thermal conductivity, reflecting the importance of the substrate thermal resistance in the heat conduction process.

[0126] Trigger the test result generation instruction, and the non - linear least - squares method will be used to calculate the 、 、 and in the fitting function. The goal of the non - linear least - squares method is to minimize the sum of the squares of the errors between the monitored values and the predicted values of the fitting function. By minimizing the sum of the squares of the errors between the monitored values and the predicted values, the function can accurately fit the data and improve the analysis accuracy.

[0127] Based on the association of the perception signals in the test process parameter set, it is determined that the substrate thermal resistance will affect the thermal conductivity of graphene with the change of the thickness of the graphene sample, so as to obtain the influence location description information;

[0128] Obtain the substrate thermal resistance thermal conductivity attenuation coefficients d in all directions to determine the influence range; the influence range refers to the distribution of the numerical magnitudes of the substrate thermal resistance thermal conductivity attenuation coefficients d in all directions;

[0129] Compare the substrate thermal resistance thermal conductivity attenuation coefficients d in all directions with the pre - set thresholds respectively to generate a report list. If the substrate thermal resistance thermal conductivity attenuation coefficient d in the corresponding direction exceeds the pre - set threshold, obtain the first level, otherwise obtain the second level; according to the first level, extract the thickness of the corresponding graphene to generate the first thickness group, and according to the second level, extract the thickness of the corresponding graphene to generate the second thickness group. By presenting the first thickness group and the second thickness group on the operation platform, a report list is generated;

[0130] Combine the influence location description information, the influence range and the report list to obtain the result information.

[0131] In this embodiment, when associating the perception signals, through constructing a fitting function and data grading processing, the accurate analysis and intuitive presentation of the influencing factors of the thermal conductivity of graphene are realized. Taking a graphene - substrate test as an example, when the perception signal determines that there is an association between the substrate thermal resistance and the thermal conductivity of graphene, using the fitting function, the complex relationship between different thicknesses h, the substrate thermal resistance and the thermal conductivity is quantified. By optimizing the parameters with the non - linear least - squares method, the error between the predicted value and the measured value is extremely small, thus revealing the law of the change of the thermal conductivity.

[0132] Determine the influencing positioning description information, which can clearly indicate how the substrate thermal resistance affects the thermal conductivity with the change of graphene thickness, provide a basis for tracing the problem source, and assist in understanding the influencing degree.

[0133] Obtain the influencing range, that is, the distribution of the thermal resistance and thermal conductivity attenuation coefficients of the substrate in each direction, which can visually show the differences in the affected degrees in different directions. For example, if the numerical value of the thermal resistance and thermal conductivity attenuation coefficient of the substrate in the x direction is large, it indicates that the thermal conductivity in this direction is sensitive to the change of the substrate thermal resistance; if the numerical value in the y direction is small, the sensitivity is low.

[0134] When generating a report list, classify the levels by comparing with the threshold value to clearly distinguish graphene samples under different conditions. For example, classify the graphene thickness with the substrate thermal resistance and thermal conductivity attenuation coefficient exceeding the threshold value into the first level, indicating to the R & D personnel that the substrate has a significant influence on the thermal conductivity at these thicknesses and needs to be focused on, providing a direct reference for material design and performance optimization, effectively improving the R & D efficiency and the ability to control material performance, classifying the graphene thickness groups according to the levels, visually showing the sample classification under different influencing degrees, and guiding the decision-making of practical applications.

[0135] Example 6

[0136] Please refer to Figure 1 , specifically: when the sensed signal in the test process parameter set is uncorrelated, generate result verification information, which is used to verify the difference between the test and the experiment, and generate test feedback information, including:

[0137] When the sensed signal in the test process parameter set is uncorrelated, according to the root mean square error (RMSE) algorithm, calculate the difference between the actual measured value and the predicted value of the fitting function, specifically:

[0138] ;

[0139] In the formula, is the root mean square error, N is the number of test conditions, i = 1, 2,..., N, is the thermal conductivity change rate of the graphene sample with thickness n under the condition of with or without substrate in the i-th test condition; is the thermal conductivity change rate of the graphene sample with thickness n under the condition of with or without substrate predicted according to the fitting function in the i-th test condition;

[0140] The root mean square error value comprehensively reflects the deviation degree between the predicted value and the actual value, and is the key basis for judging the test accuracy;

[0141] If the root mean square error is less than the pre-set error threshold, generate a qualified instruction, otherwise generate an unqualified instruction, combine the qualified instruction and the unqualified instruction to obtain the result verification information, and when generating a qualified instruction, use the unqualified instruction as the test feedback information.

[0142] According to the comparison results, if the RMSE is less than the error threshold, a qualified instruction is generated; otherwise, an unqualified instruction is generated. The qualified instruction indicates that the test results are reliable and can be used as test feedback information for subsequent analysis; the unqualified instruction indicates that the test process needs to be improved, and intuitively reflects that there is a large deviation between the actual measured value and the predicted value of the fitting function, which means that the data obtained by the current test is not accurate enough. For example, when testing the thermal conductivity of graphene, if key parameters such as sample thickness and substrate thermal resistance are not accurately measured, or there are accuracy problems with the measuring equipment, the data error will be large. Prompting the improvement of the test process can prompt researchers to check for loopholes in the data collection process or further check the accuracy of the experimental data, correct the measurement errors, and obtain more accurate and reliable data to ensure the rigor of the subsequent analysis of the thermal conductivity of graphene. The test operation can be repeated by changing different x-directions and y-directions.

[0143] In this embodiment, the method verifies the test results through the root mean square error algorithm, effectively ensuring the reliability and accuracy of the graphene thermal conductivity test. When the sensed signal is uncorrelated, the RMSE algorithm is used to calculate the difference between the actual measured value and the predicted value of the fitting function, which can scientifically evaluate the effectiveness of the test model.

[0144] For example, in a certain test, for a graphene sample with a thickness of 5nm, the actual measured thermal conductivity change rate was 12%, while the fitting function predicted value was 10%. The difference between the two was calculated by the RMSE algorithm, and the applicability of the fitting function to the sample could be quantitatively determined. Comparing the RMSE result with the preset error threshold can quickly determine whether the test result is qualified. If the error threshold is set to 5%, when the calculated RMSE value is 3%, a qualified instruction is generated, indicating that the test model has a high degree of fit with the actual measurement data and the test results are credible; conversely, if the RMSE value is 7%, an unqualified instruction is generated, indicating that the test process or model needs to be corrected. This verification mechanism is like a quality detector, ensuring the credibility of the test results and avoiding wrong decisions due to data deviation.

[0145] Example 7

[0146] Please refer to Figure 4 , specifically: A graphene thermal conductivity testing system based on multi-dimensional analysis, comprising:

[0147] The preparation module is used to select multiple groups of graphene samples with different thicknesses, configure substrate samples, and use measuring equipment to obtain a thermal information set of the samples under different test conditions, wherein the thermal information set includes graphene thermal information and substrate thermal information;

[0148] The processing module is used to supplement the thermal property information of the substrate based on the principle of heat conduction and the thermal property information of graphene, and preprocess the thermal property information set to obtain the first processed data and the second processed data;

[0149] The testing module is used to analyze the difference between the first processed data and the second processed data to obtain a set of test process parameters, which includes a sensing signal and an influencing signal;

[0150] The information presentation module is used to generate a test result generation instruction when the sensing signal in the set of test process parameters is associated. The test result generation instruction is used to trigger an alarm and generate result information, and the result information includes influencing location description information, influencing range, and a report list;

[0151] The verification module is used to generate result verification information when the sensing signal in the set of test process parameters is not associated. The result verification information is used to verify the difference between the test and the experiment and generate test feedback information;

[0152] The closed-loop control module is used to repeat the operation process from the preparation module to the verification module according to the test feedback information until no test feedback information is generated.

[0153] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for testing the thermal conductivity of graphene based on multi-dimensional analysis, characterized in that: including the following steps, S1: Select multiple groups of graphene samples with different thicknesses, and configure substrate samples. Use measuring equipment to obtain the thermal information set of the samples under different test conditions. The thermal information set includes graphene thermal information and substrate thermal information; S2: Based on the heat conduction principle and graphene thermal information, supplement the substrate thermal information, and preprocess the thermal information set to obtain the first processed data and the second processed data. Specifically, according to the fitting state between the graphene sample and the substrate sample, determine the circuit state, and combine the heat conduction principle, the relationship between thermal resistance and thermal conductivity, and the thermal information set to calculate and obtain the corresponding substrate thermal resistance in different directions to supplement the substrate thermal information; Identify invalid information in the thermal information set and fill in outliers, and then classify the information in the preprocessed thermal information set, and classify it into direction type data, thickness type data, substrate condition type information, and non-substrate condition type information respectively. Combine the substrate condition type information and the non-substrate condition type information to generate the first processed data, and combine the direction type data and the thickness type data to generate the second processed data; S3: Analyze the difference between the first processed data and the second processed data to obtain the test process parameter set. The test process parameter set includes a sensing signal and an influencing signal, including: According to the substrate condition type information and the non-substrate condition type information in the first processed data, analyze the change of the thermal conductivity of graphene samples with different thicknesses when there is a substrate and when there is no substrate by means of a ratio. Specifically: ; where, is the thermal conductivity change rate of the graphene sample with a thickness of n with and without a substrate, is the thermal conductivity of graphene with a thickness of n without a substrate, is the thermal conductivity of graphene with a thickness of n with a substrate; According to the thickness data in the second processed data, with the acquisition method of the thermal conductivity change rate respectively obtain the thermal conductivity change rates of graphene samples with different thicknesses with and without substrates , and combine the direction data to determine the thermal conductivity change rates of graphene samples with different thicknesses with and without substrates under different direction conditions . Calculate the mean value of the thermal conductivity change rates of graphene samples with different thicknesses with and without substrates under different direction conditions to obtain verification data. The verification data is used to avoid analysis deviations caused by analysis only in a single direction. The verification data refers to the average thermal conductivity change rates of graphene samples with different thicknesses with and without substrates; Compare the average thermal conductivity change rate with a preset change threshold to generate a sensing signal; If the average thermal conductivity change rate exceeds the preset change threshold, the sensing signal is associated, otherwise the sensing signal is not associated; According to the sensing signal, analyze the influence of the change of the substrate thermal resistance with the thickness of the graphene sample on the thermal conductivity of graphene to generate an influencing signal; Combine the sensing signal and the influencing signal to obtain the test process parameter set; S4: When the sensing signal in the test process parameter set is associated, generate a test result generation instruction. The test result generation instruction is used to trigger an alarm and generate result information. The result information includes influence location description information, influence range, and report list; S5: When the sensing signal in the test process parameter set is not associated, generate result verification information. The result verification information is used to verify the difference between the test and the experiment and generate test feedback information; S6: According to the test feedback information, repeat steps S1 to S5 until no test feedback information is generated.

2. The method for testing the thermal conductivity of graphene based on multi-dimensional analysis according to claim 1, wherein: Select multiple groups of graphene samples with different thicknesses, and configure substrate samples, including: Prepare a series of graphene samples with different thicknesses in advance, and at the same time configure substrate samples with the same specifications for the graphene samples. Determine the x direction and y direction of the graphene samples in the plane. According to the determined x direction and y direction of the graphene samples in the plane, obtain the x direction and y direction of the substrate in the plane to obtain direction information. The direction information includes the determined x direction, y direction, and the z direction perpendicular to the graphene plane.

3. The graphene thermal conductivity testing method based on multi-dimensional analysis according to claim 2, wherein: Using a measuring device, a set of thermal property information of a sample under different test conditions is obtained. The set of thermal property information includes graphene thermal property information and substrate thermal property information, including: The measuring device is used to sequentially monitor the thermal conductivity of graphene samples with different thicknesses under the conditions of with and without a substrate according to the directions in the direction information to obtain graphene thermal conductivity sub-information. When the graphene samples with different thicknesses are transferred to the same substrate, the fitting state is maintained. Then, the measuring device is used again to sequentially monitor the thermal conductivity of the substrate samples separately according to the directions in the direction information to obtain substrate thermal conductivity sub-information. Combining the relationship between thermal resistance and thermal conductivity, the thermal resistance of graphene samples with different thicknesses in different directions under the condition of without a substrate and the thermal resistance of the substrate samples are determined to generate graphene thermal resistance sub-information and substrate thermal resistance sub-information. The graphene thermal resistance sub-information and the graphene thermal conductivity sub-information are combined to obtain graphene thermal property information, and the substrate thermal resistance sub-information and the substrate thermal conductivity sub-information are combined to obtain substrate thermal property information.

4. A method for testing the thermal conductivity of graphene based on multi-dimensional analysis according to claim 3, characterized in that: According to the sensed signal, analyze the influence of the change of the substrate thermal resistance with the thickness of the graphene sample on the graphene thermal conductivity to generate an influence signal, including: Group the set of thermal property information again according to the direction type data to obtain data groups in different directions; According to the thickness type data, determine the thicknesses of the graphene. Taking the thickness of the graphene sample as the abscissa and the substrate thermal resistance in different directions as the ordinate, for each data group in a direction, corresponding points are plotted in the coordinate system, and adjacent points are connected by a smooth curve to obtain the influence distribution change curve of the substrate thermal resistance with the thickness of the graphene sample on the graphene thermal conductivity in different directions. According to the distribution of adjacent points in the influence distribution change curve, the influence signal is obtained.

5. The graphene thermal conductivity testing method based on multi-dimensional analysis according to claim 4, characterized in that: When the sensed signal in the test process parameter set is associated, a test result generation instruction is generated. The test result generation instruction is used to trigger an alarm and generate result information. The result information includes influence location description information, influence range, and report list, including: When the sensed signal in the test process parameter set is associated, a fitting function is constructed based on the distribution of adjacent points in the influence distribution change curve in the influence signal. The specific expression is: ； In the formula, is the graphene thermal conductivity with a substrate in the j - direction, is the amplitude in the j - direction; is the graphene thickness thermal conductivity attenuation coefficient in the j - direction; is the graphene thickness, is the substrate thermal resistance influence coefficient in the j - direction; is the substrate thermal resistance thermal conductivity attenuation coefficient in the j - direction; is the substrate thermal resistance, is the exponential function with base e; Trigger the test result generation instruction, and the non-linear least squares method will be used to calculate the , , and in the fitting function. The goal of the non-linear least squares method is to minimize the sum of the squares of the errors between the monitored values and the predicted values of the fitting function; Based on the fact that the sensed signal in the test process parameter set is associated, it is determined that the substrate thermal resistance will affect the graphene thermal conductivity with the change of the thickness of the graphene sample to obtain the influence location description information; Obtain the substrate thermal resistance and thermal conductivity attenuation coefficient d in all directions to determine the influence range; Compare the substrate thermal resistance and thermal conductivity attenuation coefficient d in all directions with a preset threshold respectively to generate a report list. If the substrate thermal resistance and thermal conductivity attenuation coefficient d in the corresponding direction exceeds the preset threshold, the first level is obtained, otherwise the second level is obtained. According to the first level, the thickness of the corresponding graphene is extracted to generate a first thickness group. According to the second level, the thickness of the corresponding graphene is extracted to generate a second thickness group. The first thickness group and the second thickness group are presented on the operation platform to generate a report list; Combine the influence location description information, influence range, and report list to obtain the result information.

6. The method for testing the thermal conductivity of graphene based on multi-dimensional analysis according to claim 5, wherein: When the sensed signal in the test process parameter set is uncorrelated, result verification information is generated. The result verification information is used to verify the differences between the test and the experiment, and test feedback information is generated, including: When the sensed signal in the test process parameter set is uncorrelated, according to the root mean square error algorithm, calculate the difference between the actual measurement value and the predicted value of the fitting function, specifically: ； In the formula, is the root mean square error, N is the number of test conditions, and i = 1, 2,..., N. is the change rate of the thermal conductivity of the graphene sample with a thickness of n with and without a substrate under the i-th test condition; is the predicted change rate of the thermal conductivity of the graphene sample with a thickness of n with and without a substrate under the i-th test condition according to the fitting function. If the root mean square error is less than a pre-set error threshold, a qualified instruction is generated; otherwise, an unqualified instruction is generated. The qualified and unqualified instructions are combined to obtain result verification information. When a qualified instruction is generated, the unqualified instruction is used as test feedback information.

7. A graphene thermal conductivity testing system based on multi-dimensional analysis, which is used to implement the graphene thermal conductivity testing method based on multi-dimensional analysis according to any one of claims 1 to 6 above, and is characterized in that: Including: The preparation module is used to select multiple graphene samples with different thicknesses, configure the substrate samples, and use the measuring device to obtain the thermal property information set of the samples under different test conditions. The thermal property information set includes graphene thermal property information and substrate thermal property information; The processing module is used to supplement the substrate thermal property information based on the heat conduction principle and the graphene thermal property information, and preprocess the thermal property information set to obtain the first processed data and the second processed data; The test module is used to analyze the differences between the first processed data and the second processed data to obtain the test process parameter set. The test process parameter set includes the sensed signal and the influencing signal; The information presentation module is used to generate a test result generation instruction when the sensed signal in the test process parameter set is correlated. The test result generation instruction is used to trigger an alarm and generate result information. The result information includes influence location description information, influence range, and report list; The verification module is used to generate result verification information when the sensed signal in the test process parameter set is uncorrelated. The result verification information is used to verify the differences between the test and the experiment, and generate test feedback information; The closed-loop control module is used to repeat the operation process from the preparation module to the verification module according to the test feedback information until no test feedback information is generated.

Images