Method and system for testing heat-conducting property of graphene based on multi-dimensional analysis

Abstract

The invention discloses a graphene heat-conducting property testing method and system based on multi-dimensional analysis, and relates to the technical field of graphene, substrate thermal information is supplemented based on a heat conduction principle, a thermal information set is preprocessed, different processing data are divided, the data information can be perfected in the process, and the heat-conducting property of the graphene can be further improved. And the relationship and rule between the data can be quickly and accurately found. And generating a test process parameter set containing a sensing signal and an influence signal by analyzing and processing the difference condition between the data. When the sensing signals are associated, a test result generation instruction can be generated in time, early warning can be triggered, and detailed result information including influence positioning description information, an influence range and a report list can be generated, so that the influence condition of the substrate thermal resistance and the graphene thickness change on the graphene thermal conductivity can be quickly and accurately judged; on the contrary, result verification information is generated and used for verifying the difference between the test and the experiment and generating test feedback information.

Description

Technical Field

[0001] The present invention relates to the technical field of graphene, and specifically provides 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 and heat dissipation materials. The accurate testing and analysis of the thermal conductivity of graphene are the key links 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 existing technology, 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. S1: Select multiple groups of graphene samples with different thicknesses, and configure substrate samples. Use measuring equipment 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. S2: Based on the heat conduction principle and the graphene thermal property information, supplement the substrate thermal property information, and preprocess the thermal property information set to obtain the first processed data and the second processed data. S3: Analyze the difference situation 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. 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.

[0006] Preferably, multiple groups of graphene samples with different thicknesses are selected, and substrate samples are configured, including: 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 in the plane of the graphene samples are determined. According to the determined x-direction and y-direction in the plane of the graphene samples, the x-direction and y-direction in the plane of the substrate are obtained to obtain direction information, where the direction information includes the determined x-direction, y-direction, and the z-direction perpendicular to the graphene plane.

[0007] 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: The measuring device is used to monitor the thermal conductivity of graphene samples with different thicknesses under the conditions of with and without substrates in sequence according to the directions in the direction information to obtain graphene thermal conductivity sub-information. When transferring graphene samples with different thicknesses to the same substrate, the fitting state is maintained; and then the measuring device is used to separately monitor the thermal conductivity of the substrate samples in sequence according to the directions in the direction information again 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 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 information, and the substrate thermal resistance sub-information and the substrate thermal conductivity sub-information are combined to obtain substrate thermal information.

[0008] 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: 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; Invalid information in the set of thermal information is identified and outliers are filled. Then, the information in the preprocessed set of thermal information is classified by type, and is respectively classified into direction type data, thickness type data, with-substrate condition type information, and without-substrate condition type information. The with-substrate condition type information and the without-substrate condition type information are combined to generate first processed data, and the direction type data and the thickness type data are combined to generate second processed data.

[0009] Preferably, analyze the difference between the first processed data and the second processed data to obtain a set of test process parameters, where the set of test process parameters includes sensing signals and influencing signals, including: According to the information of the substrate condition class and the non-substrate condition class 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: In the formula, is the change rate of the thermal conductivity of the graphene sample with thickness n when there is and 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; According to the thickness data in the second processed data, in the way of obtaining the thermal conductivity change rate respectively obtain the thermal conductivity change rates of graphene samples with different thicknesses when there is and is not a substrate, and combine with the direction data to determine the thermal conductivity change rates of graphene samples with different thicknesses when there is and is not a substrate under different direction conditions Perform a mean calculation on the thermal conductivity change rates of graphene samples with different thicknesses when there is and is not a substrate under different direction conditions to obtain verification data. The verification data is used to avoid the analysis deviation caused by the analysis only in a single direction. The verification data refers to the average thermal conductivity change rate of graphene samples with different thicknesses when there is and is not a substrate; 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 a set of test process parameters.

[0010] 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 influencing signal, including: Group the thermal property information set again according to the direction data to obtain data groups in different directions; According to the thickness data, determine the thicknesses of graphene. Using the thickness of the graphene samples as the abscissa and the substrate thermal resistance in different directions as the ordinate, for each set of data in each direction, plot the corresponding points in the coordinate system, and use a smooth curve to connect adjacent points to obtain the distribution change curve of the influence of the substrate thermal resistance on the graphene thermal conductivity with the change of the graphene sample thickness. According to the distribution of adjacent points within the influence distribution change curve, obtain the influence signal.

[0011] Preferably, 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 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 parameters is associated, a fitting function is constructed based on the distribution of adjacent points within 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 -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; Triggering the test result generation instruction will use the non - linear least - squares method to calculate 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 sensed signal in the test process parameters being associated, it is determined that the substrate thermal resistance will affect the graphene thermal conductivity with the change of the graphene sample thickness to obtain the influence location description information; Obtain the substrate thermal resistance thermal conductivity attenuation coefficient d in all directions to determine the influence range; Compare the substrate thermal resistance thermal conductivity attenuation coefficient d in all directions with a preset threshold to generate a report list. If the substrate thermal resistance thermal conductivity 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; Combine the influence positioning description information, the influence range, and the report list to obtain the result information.

[0012] Preferably, 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 difference 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 measured 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, is the change rate of the thermal conductivity of the graphene sample with a thickness of n under the condition of the presence or absence of 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 under the condition of the presence or absence of a substrate under the i-th test condition according to the fitting function; If the root mean square error is less than the pre-set 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, the unqualified instruction is used as the test feedback information.

[0013] A graphene thermal conductivity test system based on multi-dimensional analysis, comprising: The preparation module is used to select multiple groups of 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 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 sensed signal and the influence 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, and the result information includes influence positioning 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 difference 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.

[0014] The present invention provides a method and system for testing the thermal conductivity of graphene based on multi-dimensional analysis, having the following beneficial effects: 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 graphene and the substrate. This multi-dimensional data acquisition method can more truly reflect the thermal conductivity of graphene when interacting with the substrate in actual application scenarios. Compared with single-dimensional testing, 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 can not only improve the data information but also help 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 a sensing signal and an influencing signal 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 determination of the influence of substrate thermal resistance and graphene thickness change on graphene thermal conductivity, and timely warning, providing strong guidance for subsequent material optimization and application. When the sensing signal is not associated, result verification information is generated to verify the difference 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 again, 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.

[0015] The change rate of thermal conductivity of graphene with different thicknesses with and without a substrate is calculated using the first processed data to visually 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 and thickness information in the second processed data, the average change rate of thermal conductivity is calculated, 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 determine 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 the thermal conductivity of graphene. Description of the Drawings

[0016] Figure 1 Schematic flow chart of a method for testing the thermal conductivity of graphene based on multi-dimensional analysis according to the present invention; Figure 2 Overall logic diagram of a method for testing the thermal conductivity of graphene based on multi-dimensional analysis according to the present invention; Figure 3 Partial logic diagram of a method for testing the thermal conductivity of graphene based on multi-dimensional analysis according to the present invention; Figure 4 Block diagram of a system for testing the thermal conductivity of graphene based on multi-dimensional analysis according to the present invention. Specific implementation manner

[0017] 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. Embodiment 1

[0018] 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, S1: Select multiple groups of graphene samples with different thicknesses, and configure substrate samples. Using measuring equipment, 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; 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 first processed data and second processed data; 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 sensing signals and influencing signals; 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 influence location description information, influence range, and report list; 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; S6: According to the test feedback information, repeat steps S1 to S5 until no test feedback information is generated.

[0019] 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 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 heat conduction principle, different processed data can be generated, which can effectively integrate and classify the data for further analysis.

[0020] By comparing the differences in different processed data, a set of test process parameters is obtained, and the influence relationship between the substrate thermal resistance and the graphene thickness on the thermal conductivity is accurately judged. When the sensed signal is correlated, the influencing factors can be quickly located, the influence 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.

[0021] When the sensed signal is uncorrelated, 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. Example 2

[0022] Please refer to Figure 1 , specifically: 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. 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; Test the thermal conductivity of graphene or its substrate by the 3ω method; and the measuring equipment used in the test process includes a signal generator, a lock-in amplifier, a current source / voltage source, or a dual-channel source meter; Using the measuring equipment, obtain the thermal information set of the samples under different test conditions, where the thermal information set includes graphene thermal information and substrate thermal information, including: The measuring device monitors 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 to obtain graphene heat carrier information. When transferring graphene samples with different thicknesses onto the same substrate, the samples are kept in a conforming state. The graphene heat carrier information includes the thermal conductivities of graphene samples with different thicknesses in different directions in the absence of a substrate and in the presence of a substrate. Then, the measuring device separately monitors the thermal conductivity of the substrate sample in sequence according to the directions in the direction information again to obtain substrate heat carrier information. Combining the relationship between thermal resistance and thermal conductivity, the thermal resistances of graphene samples with different thicknesses in different directions in the absence of 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 heat carrier information are combined to obtain graphene thermal property information, and the substrate thermal resistance sub-information and the substrate heat carrier information are combined to obtain substrate thermal property information.

[0023] 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 a heating element to effectively dissipate heat. Conducting tests while keeping the conforming state can more realistically simulate the working environment of graphene under actual working conditions, making the test data more practically valuable, 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, with gaps or poor contact in between, additional thermal resistance will be formed, resulting in an unstable heat conduction path and difficulty in accurate evaluation. Keeping the conforming state can ensure that heat can be stably transferred from graphene to the substrate or conducted between the two.

[0024] Among them, the relationship between thermal resistance and thermal conductivity is: Among them, is the thermal resistance, m is the sample thickness, K is the thermal conductivity, and A is the heat transfer area; 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, an ultra-thin (such as a single layer) and a relatively thick (such as 10 layers), by standardizing the substrate configuration, interference with the test results caused by substrate differences is avoided, laying a reliable foundation for subsequent analysis.

[0025] In the data acquisition stage, a measurement method of dividing directions and conditions is adopted to record the thermal conductivity of graphene in each direction under the conditions of with and without a substrate respectively. 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 conductivity 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 from multiple aspects, but also detailed data support can be provided for optimizing the design of graphene-based thermal management materials, helping its efficient application in the fields of electronic heat dissipation, new energy batteries, etc.

[0026] 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 with a single thickness, and ensure that the data can reflect the influence of the thickness variable on the thermal conductivity of graphene. The x and y directions in the sample plane and the perpendicular z direction are defined to construct a three-dimensional direction information system, 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 conductivity of graphene with different thicknesses under the conditions of with and without a substrate is monitored sequentially in each direction to obtain multi-dimensional thermal conductivity sub-information. By comparing the thermal conductivity with and without a substrate, the influence mechanism of the substrate on the thermal conductivity of graphene can be accurately analyzed. The state of fitting between graphene and the substrate is maintained 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. Example 3

[0027] 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: According to the fitting state between the graphene sample and the substrate sample, the circuit state is determined, and in combination 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; 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 the heat transfer. Graphene and the substrate respectively have an obstructive effect on heat transfer, and the total obstructive effect is the addition of their thermal resistances.

[0028] If graphene and the substrate are in a parallel relationship, it means that heat can be transferred through two paths, graphene and the substrate, simultaneously. 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.

[0029] Considering the simplicity of the analysis process, it is preferable to assume that graphene and the substrate are in a series relationship; 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. Specifically, it is 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 - processed data, and combine the direction - type data and thickness - type data to generate the second - processed data.

[0030] 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. Specifically, it is classified into direction - type data, thickness - type data, information with substrate conditions, and information without substrate conditions, which specifically includes: Direction - type data refers to the information extracted from the preprocessed thermal information set in different directions; Thickness - type data refers to the information extracted from the preprocessed thermal information set about the graphene samples at different thicknesses; Information with substrate conditions refers to the information extracted from the preprocessed thermal information set about the graphene samples when there is a substrate; Information without substrate conditions refers to the information extracted from the preprocessed thermal information set about the graphene samples when there is no substrate; In this embodiment, the substrate thermal resistance is calculated based on the heat conduction principle and the thermal information of graphene, effectively supplementing the thermal 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 information set, identifying invalid information and filling 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 types, thickness types, substrate-present condition types, and substrate-absent condition types, 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; using the second processed data can analyze the heat conduction characteristics in different directions and thicknesses, improving the efficiency and accuracy of the analysis.

[0031] Determine the circuit state based on the bonding state of graphene and the substrate, calculate the substrate thermal resistance in different directions in combination with the heat conduction principle, improve the thermal information of the substrate, and provide key data for a comprehensive analysis of the heat conduction characteristics.

[0032] Identify invalid information in the thermal information set, fill in outliers, ensure data quality, and avoid analysis deviations caused by data errors. Example 4

[0033] 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: 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: 100%; In the formula, is the thermal conductivity change rate 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; Calculate the thermal conductivity change rate of graphene samples with different thicknesses with and without the substrate through the formula, quantify the influence of the substrate on the thermal conductivity, and it 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 graphene heat conduction.

[0034] According to the thickness type data in the second processed data, with the thermal conductivity change rate The acquisition method is used to obtain the change rates of the thermal conductivity of graphene samples with different thicknesses with and without a substrate respectively. Combined with the direction-related data, the change rates of the thermal conductivity of graphene samples with different thicknesses with and without a substrate under different direction conditions are determined. The change rates of the thermal conductivity of graphene samples with different thicknesses with and without a substrate under different direction conditions Are averaged to obtain verification data. The verification data is used to avoid analysis biases caused by analysis only in a single direction. The verification data refers to the average change rate of the thermal conductivity of graphene samples with different thicknesses with and without a substrate. The average change rate of the thermal conductivity is calculated by combining the direction-related data to eliminate the data bias in a single direction, thereby improving the comprehensiveness of the analysis. The verification data is the average change rate of the thermal conductivity and is used to verify the reliability of the analysis results.

[0035] The average change rate of the thermal conductivity is compared with a pre-set change threshold to generate a sensing signal. If the average change rate of the thermal conductivity exceeds the pre-set change threshold, the sensing signal is associated; otherwise, the sensing signal is not associated. According to the sensing signal, the influence of the change of the substrate thermal resistance with the thickness of the graphene sample on the thermal conductivity of graphene is analyzed to generate an influence signal. 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, thereby affecting 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 macroscopically. Therefore, in some related studies, it is necessary to consider the influence of the substrate thermal resistance on the thermal properties of graphene.

[0036] 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 interlayer van der Waals force increases, causing a change in the interaction between the substrate and graphene, thereby 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.

[0037] The sensing signal and the influence signal are combined to obtain the test process parameter set.

[0038] Analyze the influence of the change of the substrate thermal resistance with the thickness of the graphene sample on the thermal conductivity of graphene according to the sensing signal to generate an influence signal, including: Group the thermal 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; Group the data according to the directions (x, y, z directions). Since the heat conduction characteristics may be different in different directions, separate fittings are required. In this way, three data sets can be obtained, corresponding to the x, y, and z directions respectively.

[0039] 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 each direction, plot the corresponding points in the coordinate system, and use a smooth curve to connect adjacent points 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. According to the distribution of adjacent points within the influence distribution change curve, obtain the influence signal.

[0040] 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 influence signal. Among them, the influence signal includes the specific trend and characteristic information of the influence of the substrate thermal resistance on the thermal conductivity of graphene.

[0041] 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.

[0042] Calculating the average thermal conductivity change rate in combination with the direction - type data can effectively avoid the analysis deviation caused by single - direction data. For example, the change rate of the 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 the threshold to generate a sensing signal can quickly judge the correlation between the two. If the change rate exceeds the threshold, it is determined that there is a correlation between the substrate thermal resistance and the thermal conductivity of graphene, pointing the way for subsequent analysis.

[0043] Drawing the influence distribution curves of the substrate thermal resistance changing with the graphene thickness in different directions can visually display 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 thermal conductivity of graphene in the x - direction decreases linearly with the increase of the thickness, while the y - direction 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. Example 5

[0044] Please refer to Figure 1 , specifically: When the sensed signal in the test process parameters is correlated, 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 impact location description information, impact range, and report list, including: When the sensed signal in the test process parameters is correlated, a fitting function is constructed based on the distribution of adjacent points within the impact distribution change curve in the impact signal. The specific expression is: ; 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, with a value approximately equal to 2.71828; The amplitude in the j-th direction mainly affects the amplitude of the thermal conductivity change rate; The graphene thickness thermal conductivity attenuation coefficient in the j-th direction reflects the degree of influence of graphene thickness on thermal conductivity. The larger its value, the faster the thermal conductivity decreases with the increase of graphene thickness, indicating the hindering effect of thickness on the heat conduction process. For example, for thinner graphene, when the thickness increases by a certain amount, the decrease in thermal conductivity is relatively small; while for thicker graphene, the same increase in thickness may lead to a more significant decrease in thermal conductivity, indicating that the inhibitory effect of thickness on thermal conductivity is increasing.

[0045] The substrate thermal resistance influence coefficient in the j-th direction mainly reflects the overall level of the influence of substrate thermal resistance on thermal conductivity, including the contribution of other factors besides thickness influence to thermal conductivity; The substrate thermal resistance thermal conductivity attenuation coefficient in the j-th direction characterizes the degree of influence of substrate thermal resistance on graphene thermal conductivity. The larger its value, the more significant the influence of substrate thermal resistance on thermal conductivity, reflecting the importance of substrate thermal resistance in the heat conduction process.

[0046] Trigger the test result generation instruction, and the non-linear least squares method will be used to calculate the 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.

[0047] Based on the association of the sensed signal 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 graphene sample thickness, so as to obtain the impact - location description information. Obtain the substrate thermal - resistance thermal - conductivity attenuation coefficient d in all directions to determine the influence range. The influence range refers to the distribution of the numerical values of the substrate thermal - resistance thermal - conductivity attenuation coefficient d in all directions. Compare the substrate thermal - resistance thermal - conductivity attenuation coefficient d in all directions with a pre - set threshold 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. 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. Combine the impact - location description information, the influence range and the report list to obtain the result information.

[0048] In this embodiment, when the method associates the sensed signals, through constructing a fitting function and data classification processing, it realizes the accurate analysis and intuitive presentation of the influencing factors of the thermal conductivity of graphene. Taking a graphene - substrate test as an example, when the sensed 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 change law of the thermal conductivity.

[0049] Determining the impact - location description information can clearly point out how the substrate thermal resistance affects the thermal conductivity with the change of the graphene thickness, provide a basis for problem tracing, and assist in understanding the degree of influence.

[0050] Obtaining the influence range, that is, the distribution of the substrate thermal - resistance thermal - conductivity attenuation coefficients in each direction, can intuitively show the differences in the affected degrees in different directions. For example, if the numerical value of the substrate thermal - resistance thermal - conductivity attenuation coefficient 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.

[0051] When generating a report list, grades are divided by comparing with a threshold value to clearly distinguish graphene samples under different conditions. For example, the graphene thickness with a substrate thermal resistance and thermal conductivity attenuation coefficient exceeding the threshold is classified into the first grade, prompting researchers that the substrate has a significant impact 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. The graphene thickness groups are divided by grade to intuitively display the sample classification under different influence degrees and guide the decision-making of practical applications. Example 6

[0052] Please refer to Figure 1 , specifically: 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 difference 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 (RMSE) algorithm, calculate the difference between the actual measured 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, is the change rate of the thermal conductivity of the graphene sample with a thickness of n under the condition of the presence or absence of 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 under the condition of the presence or absence of a substrate under the i-th test condition according to the fitting function; 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; If the root mean square error is less than the pre-set error threshold, a qualified instruction is generated. Otherwise, an unqualified instruction is generated. The qualified instruction and the unqualified instruction are combined to obtain the result verification information. When a qualified instruction is generated, the unqualified instruction is used as the test feedback information.

[0053] According to the comparison result, if Less than the error threshold, a qualified instruction is generated; otherwise, an unqualified instruction is generated. The qualified instruction indicates that the test result is reliable and can be used as test feedback information for subsequent analysis; the unqualified instruction prompts the need to improve the test process and intuitively reflects a large deviation between the actual measured value and the predicted value of the fitting function, which means that the accuracy of the data obtained from the current test is insufficient. For example, when testing the thermal conductivity of graphene, if key parameters such as the sample thickness and substrate thermal resistance are not accurately measured, or there are accuracy problems with the measurement equipment, it will lead to a large data error. Prompting to improve the test process can prompt researchers to check for loopholes in the data collection link or further check the accuracy of the experimental data, correct the measurement error, so as to obtain more accurate and reliable data, and ensure the rigor of the subsequent analysis of the thermal conductivity of graphene. The test operation can be restarted by changing different x and y directions.

[0054] In this embodiment, the method verifies the test result 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 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.

[0055] For example, in a certain test, for a graphene sample with a thickness of 5 nm, the actual measured thermal conductivity change rate is 12%, while the predicted value of the fitting function is 10%. By calculating the difference between the two using the algorithm, the applicability of the fitting function to this sample can be quantitatively judged. Comparing the result with the pre-set error threshold can quickly determine whether the test result is qualified. If the set error threshold is 5%, when the calculated value is 3%, a qualified instruction is generated, indicating that the test model fits well with the actual measured data and the test result is credible; otherwise, if the value is 7%, an unqualified instruction is generated, prompting the need to correct the test process or model. This verification mechanism is like a quality detector, ensuring the credibility of the test result and avoiding wrong decisions due to data deviation. Embodiment 7

[0056] Please refer to Figure 4 , specifically: A graphene thermal conductivity test system based on multi-dimensional analysis, including: The preparation module is used to select multiple groups of graphene samples with different thicknesses, configure the substrate samples, and use the measurement equipment 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, and obtain a set of test process parameters, where the set of test process parameters includes sensing signals and influencing signals; 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, where the result information includes influencing location description information, influencing scope, and report list; 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 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.

[0057] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles 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: The following steps are included: S1: selecting multiple groups of graphene samples with different thicknesses and configuring substrate samples, and using 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; S2: Based on the heat conduction principle and graphene thermal information, the substrate thermal information is supplemented, and the thermal information set is preprocessed to obtain the first processing data and the second processing data; S3: Analyze the difference between the first processing data and the second processing data to obtain a test process parameter set, where the test process parameter set includes a perception signal and an impact signal; S4: When the sensing signal in the test process parameter set is associated, a test result generation instruction is generated, and the test result generation instruction is used to trigger an early warning and generate result information, and the result information includes impact location description information, impact range and report list; S5: When the perceived signal in the test process parameter set is not associated, result verification information is generated, and the result verification information is used to verify the difference between the test and the experiment, and generate test feedback information; S6: Repeat steps S1 to S5 according to the test feedback information 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, characterized in that: Select multiple groups of graphene samples with different thicknesses and configure substrate samples, including: A series of graphene samples with different thicknesses are prepared in advance, and substrate samples of the same specifications are configured for the graphene samples. The x-direction and y-direction of the graphene samples in the plane are determined, and the x-direction and y-direction of the substrate in the plane are obtained according to the determined x-direction and y-direction of the graphene samples in the plane to obtain direction information, which includes the determined x-direction, y-direction and z-direction perpendicular to the graphene plane.

3. The method for testing the thermal conductivity of graphene based on multi-dimensional analysis according to claim 2, characterized in that: Using measuring equipment, obtain a set of thermal information of samples under different test conditions. The thermal information set includes graphene thermal information and substrate thermal information, including: The measuring device is used to monitor the thermal conductivity of graphene samples of different thicknesses in the presence or absence of a substrate in the direction of the directional information to obtain the graphene thermal conductivity information, and the graphene samples of different thicknesses are kept in a fitted state when being transferred to the same substrate; and the thermal conductivity of the substrate sample is separately monitored again in the direction of the directional information by the measuring device to obtain the substrate thermal conductivity information, and the thermal resistance of graphene samples of different thicknesses in the absence of a substrate in different directions and the thermal resistance of the substrate sample are determined in combination with the relationship between thermal resistance and thermal conductivity to generate graphene thermal resistance information and substrate thermal resistance information; the graphene thermal resistance information and graphene thermal conductivity information are combined to obtain graphene thermal information, substrate thermal resistance information and substrate thermal conductivity information to obtain substrate thermal information.

4. The method for testing thermal conductivity of graphene based on multi-dimensional analysis according to claim 3, characterized in that: Based on the principle of heat conduction and graphene thermal information, the substrate thermal information is supplemented, and the thermal information set is pre-processed to obtain No. 1 processing data and No. 2 processing data, including: According to the bonding state between the graphene sample and the substrate sample, the circuit state is determined, and the thermal resistance of the substrate corresponding to different directions is calculated and obtained by combining the heat conduction principle, the relationship between thermal resistance and thermal conductivity, and the thermal information set to supplement the thermal information of the substrate; Identify invalid information in the thermal information set and fill in abnormal values, then classify the information in the preprocessed thermal information set into direction data, thickness data, information with base conditions and information without base conditions, combine the information with base conditions and information without base conditions to generate processed data No. 1, combine the direction data and thickness data to generate processed data No.

2.

5. The method for testing thermal conductivity of graphene based on multi-dimensional analysis according to claim 4, characterized in that: Analyze the differences between the first processing data and the second processing data to obtain the test process parameter set, which includes the perception signal and the impact signal, including: According to the information of substrate conditions and no substrate conditions in the No. 1 processed data, the changes in thermal conductivity of graphene samples with different thicknesses with and without substrate are analyzed by ratio, specifically: 100%; where is the thermal conductivity change rate of the graphene sample with or without substrate when the thickness is n, is the thermal conductivity of graphene with thickness n without substrate, is the thermal conductivity of graphene with thickness n when there is a substrate; According to the thickness data in the second processing data, the thermal conductivity change rate The thermal conductivity change rate of graphene samples with different thicknesses with or without substrate is obtained by Combined with the directional data, the thermal conductivity change rate of graphene samples with different thicknesses under different directional conditions with or without a substrate is determined. The thermal conductivity change rate of graphene samples with different thicknesses under different directions with or without substrate Perform mean calculation to obtain verification data. The verification data is used to avoid analysis deviation caused by analysis in a single direction. The verification data refers to the average thermal conductivity change rate of graphene samples with different thicknesses with or without a substrate. The average thermal conductivity change rate is compared with a preset change threshold to generate a perception signal; If the average thermal conductivity change rate exceeds a preset change threshold, the sensing signal is associated, otherwise the sensing signal is not associated; According to the sensed signal, the influence of the change of the substrate thermal resistance with the thickness of the graphene sample on the thermal conductivity of the graphene is analyzed to generate an influence signal; The perception signal and the impact signal are combined to obtain a test process parameter set.

6. The method for testing thermal conductivity of graphene based on multi-dimensional analysis according to claim 5, characterized in that: According to the sensed signal, the influence of the change of substrate thermal resistance with the thickness of graphene sample on the thermal conductivity of graphene is analyzed to generate an influence signal, including: The thermal information set is grouped again according to the direction data to obtain data groups in different directions; According to the thickness data, the thickness of each graphene is determined, and the thickness of the graphene sample is used as the horizontal coordinate, and the substrate thermal resistance in different directions is used as the vertical coordinate. For the data group in each direction, the corresponding points are drawn in the coordinate system, and the adjacent points are connected by a sliding curve to obtain the distribution change curve of the influence of the substrate thermal resistance in different directions on the thermal conductivity of graphene as the thickness of the graphene sample changes. According to the distribution of adjacent points in the influence distribution change curve, the influence signal is obtained.

7. The method for testing thermal conductivity of graphene based on multi-dimensional analysis according to claim 6, characterized in that: When the sensing signal in the test process parameter set is related, 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 the impact location description information, the impact range and the report list, including: When the perception 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 thermal conductivity of graphene with substrate in the jth direction, is the amplitude in the jth direction; is the thermal conductivity attenuation coefficient of graphene thickness in the jth direction; is the graphene thickness, is the thermal resistance influence coefficient of the substrate in the jth direction; is the thermal resistance and thermal conductivity attenuation coefficient of the substrate in the jth direction; is the base thermal resistance, is an exponential function with base e; Triggering the test result generation instruction will use the nonlinear least squares method to calculate the fitting function The goal of the nonlinear least squares method is to minimize the sum of squared errors between the monitored values ​​and the values ​​predicted by the fitted function; Based on the correlation of the sensing signals in the test process parameters, it is determined that the substrate thermal resistance will affect the thermal conductivity of graphene as the thickness of the graphene sample changes, so as to obtain the impact location description information; Obtain the thermal resistance and thermal conductivity attenuation coefficient d of the substrate in all directions to determine the impact range; The substrate thermal resistance and thermal conductivity attenuation coefficient d in all directions are compared with the preset threshold value to generate a report list. If the substrate thermal resistance and thermal conductivity attenuation coefficient d in the corresponding direction exceeds the preset threshold value, 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 operating platform to generate a report list; Combine the impact location description information, impact scope and report list to obtain result information.

8. The method for testing thermal conductivity of graphene based on multi-dimensional analysis according to claim 7, characterized in that: When the perceived signal in the test process parameter set is not associated, result verification information is generated. The result verification information is used to verify the difference between the test and the experiment and generate test feedback information, including: When the sensing signal in the test process parameter set is uncorrelated, the difference between the actual measured value and the predicted value of the fitting function is calculated according to the root mean square error algorithm, specifically: ； In the formula, is the root mean square error, N is the number of test conditions, is the change rate of thermal conductivity of the graphene sample with or without substrate when the thickness is n under the i-th test condition; To predict the rate of change of thermal conductivity of graphene sample with or without substrate when thickness is n under the i-th test condition according to the fitting function; If the root mean square error If the error is less than a preset threshold, a qualified instruction is generated; otherwise, an unqualified instruction is generated. The qualified instruction and the unqualified instruction are combined to obtain result verification information. When a qualified instruction is generated, the unqualified instruction is used as test feedback information.

9. A graphene thermal conductivity testing system based on multi-dimensional analysis, used to implement a graphene thermal conductivity testing method based on multi-dimensional analysis as described in any one of claims 1 to 8, characterized in that: include: 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; The processing module is used to supplement the thermal information of the substrate based on the heat conduction principle and the thermal information of graphene, and pre-process the thermal information set to obtain the first processing data and the second processing data; The test module is used to analyze the difference between the first processing data and the second processing data, and obtain the test process parameter set, which includes the perception signal and the influence signal; The information presentation module is used to generate a test result generation instruction when the test process parameter set senses that the signal is associated. The test result generation instruction is used to trigger an early warning and generate result information. The result information includes the impact location description information, the impact range and the report list; The verification module is used to generate result verification information when the sensing signal in the test process parameter set is not associated. The result verification information is used to verify the difference 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.

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