A method and system for testing thermal conductivity of polymer materials

Abstract

The present invention relates to the field of material science and technology, and in particular to a method and system for testing the thermal conductivity of polymer materials. The method comprises the following steps: obtaining a polymer material sample; performing sandpaper polishing on the polymer material sample to obtain a polymer material surface smooth sample; applying heat flow to the polymer material surface smooth sample using a temperature control plate, and measuring the heat flux density to obtain heat flux density data; calculating thermal conductivity based on the heat flux density data to obtain thermal conductivity; obtaining thermal degradation characteristic data of the polymer material sample; performing thermogravimetric analysis on the polymer material surface smooth sample to generate thermogravimetric data; constructing a thermal degradation model based on the thermal degradation characteristic data and the thermogravimetric data, and correcting the thermal conductivity based on the thermal degradation model, thereby obtaining a first corrected thermal conductivity. The present invention significantly improves the accuracy of the thermal conductivity test of polymer materials based on material science and technology.

Description

Technical Field

[0001] The present invention relates to the field of material science and technology, and in particular to a method and system for testing the thermal conductivity of polymer materials. Background Art

[0002] Although traditional polymer material thermal conductivity test methods can provide certain thermal conductivity data, they have many shortcomings in test accuracy and system stability. Although the transient heat source method can shorten the test time, this method has extremely high requirements on the sampling rate and resolution of the temperature sensor. During the test, the sensor loses information on subtle changes in the temperature response curve due to its own noise, insufficient sampling frequency or improper signal processing, resulting in large errors when calculating the thermal conductivity by fitting the temperature response curve through a mathematical model. In addition, polymer materials are prone to thermal degradation during heating, and their physical or chemical structures change during the test. Traditional methods often fail to fully consider this thermal degradation effect and lack real-time monitoring and correction measures. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a method and system for testing the thermal conductivity of polymer materials to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for testing the thermal conductivity of polymer materials comprises the following steps:

[0005] Step S1: obtaining a polymer material sample; performing sandpaper polishing on the polymer material sample to obtain a polymer material sample with a smooth surface;

[0006] Step S2: applying heat flow to the sample with a smooth surface of the polymer material using a temperature control plate, and measuring the heat flux density to obtain heat flux density data; calculating the thermal conductivity based on the heat flux density data to obtain the thermal conductivity;

[0007] Step S3: obtaining thermal degradation characteristic data of the polymer material sample; performing thermogravimetric analysis on the polymer material sample with a smooth surface to generate thermogravimetric data; constructing a thermal degradation model according to the thermal degradation characteristic data and the thermogravimetric data, and correcting the thermal conductivity according to the thermal degradation model, thereby obtaining a first corrected thermal conductivity;

[0008] Step S4: applying a heat pulse to a sample with a smooth surface of a polymer material to obtain heat pulse data, constructing a temperature response curve, and calculating the thermal conductivity to obtain a second thermal conductivity; correcting the second thermal conductivity according to the first corrected thermal conductivity to generate the thermal conductivity of the polymer material.

[0009] After obtaining the sample and performing smoothing, the present invention improves the smoothness of the sample surface, thereby reducing the test error caused by surface roughness and ensuring the uniform distribution of heat flow. Using a temperature control plate to apply heat flow and accurately measure the heat flux density helps to provide high-quality heat flow data, so that more accurate results can be obtained when calculating thermal conductivity. In addition, by combining the thermal degradation characteristic data with the thermal gravimetric analysis data to construct a thermal degradation model, the thermal degradation effect of the material can be taken into account during the test process, and the thermal conductivity can be corrected in real time to avoid the accumulation of errors caused by thermal degradation, thereby improving the reliability of the test. The application of thermal pulses and the construction of temperature response curves not only further verify the accuracy of thermal conductivity, but also take into account the response of the pulse heat source to the material in the calculation of the second thermal conductivity, ensuring that the subtle changes in temperature data during the heating process of polymer materials can be effectively captured and accurately calculated. By comparing and combining the first corrected thermal conductivity with the second thermal conductivity, the thermal conductivity is finally corrected. This correction method solves the error problem caused by system instability or sensor noise in traditional testing methods through dynamic adjustment, thereby obtaining a more accurate thermal conductivity value of the polymer material, improving the test accuracy and enhancing the system stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0011] Figure 1 A schematic diagram of the steps of the method for testing the thermal conductivity of polymer materials of the present invention;

[0012] Figure 2 Detailed step flow diagram of step S1 in the present invention;

[0013] Figure 3 Detailed step flow diagram of step S12 in the present invention;

[0014] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0015] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0016] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0017] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0018] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for testing the thermal conductivity of polymer materials, the method comprising the following steps:

[0019] Step S1: obtaining a polymer material sample; performing sandpaper polishing on the polymer material sample to obtain a polymer material sample with a smooth surface;

[0020] In this embodiment, a polymer material sample with a size of 100mm×50mm×5mm is obtained from a polymer material supplier. The sample is placed on a precision grinder, the equipment speed is adjusted to 500 rpm, and the clamp strength is set to 3 kg. Use 320-mesh sandpaper for rough grinding to ensure that the grinding rate reaches more than 90% and there are no obvious scratches or bumps on the surface. After the rough grinding is completed, replace it with 600-mesh sandpaper, adjust the grinder speed to 300 rpm, and continue grinding for 2 minutes. Finally, use 1200-mesh sandpaper for fine grinding, set the speed to 100 rpm, and continue grinding for 3 minutes. During the grinding process, the sample surface is observed through a microscope with a resolution of 5μm to ensure that its smoothness meets the requirements. After the sample is smoothed, its surface roughness Ra should be less than 0.1μm, and there should be no obvious defects or scratches.

[0021] Step S2: applying heat flow to the sample with a smooth surface of the polymer material using a temperature control plate, and measuring the heat flux density to obtain heat flux density data; calculating the thermal conductivity based on the heat flux density data to obtain the thermal conductivity;

[0022] In this embodiment, the smooth sample in step S1 is fixed in a precision temperature control fixture to ensure that the surface of the sample is completely in contact with the heat flow meter probe. Use a temperature control plate to apply heat flow, set the plate surface temperature to 80°C, the heating power to 50W, and the control system accurately controls the temperature of the temperature control plate within the range of ±0.1°C. After applying a constant heat flux to the sample, use a heat flow meter to measure the heat flux density, and set the measurement accuracy of the heat flow meter to ±5W / m². When the heat flow meter probe is in contact with the sample, ensure that the probe area of ​​the heat flow meter is 1cm² and the response time is 0.5 seconds. Continuously record the heat flux density data, and the sampling frequency is set to 1Hz. On this basis, the thermal conductivity is calculated using Fourier's law, and the calculation formula used is:

[0023] ;

[0024] Where q is the heat flux density, d is the sample thickness, ΔT is the temperature difference, the sample thickness is fixed at 5 mm, and the temperature difference is obtained by reading the temperature difference on both sides of the heat flow meter to calculate the thermal conductivity.

[0025] Step S3: obtaining thermal degradation characteristic data of the polymer material sample; performing thermogravimetric analysis on the polymer material sample with a smooth surface to generate thermogravimetric data; constructing a thermal degradation model according to the thermal degradation characteristic data and the thermogravimetric data, and correcting the thermal conductivity according to the thermal degradation model, thereby obtaining a first corrected thermal conductivity;

[0026] In the present embodiment, the polymer material sample smoothed in step S1 is placed in a thermogravimetric analyzer for thermogravimetric analysis. The thermogravimetric analyzer sets the heating rate to 10°C / minute, the temperature range is from room temperature to 600°C, and the carrier gas flow rate is 100mL / min to ensure that the ambient atmosphere is stable. The analysis process lasts for about 30 minutes, during which the sample mass change is recorded in real time. The data acquisition frequency is set to 1Hz. The thermal degradation start temperature is set to 200°C, and the degradation temperature range is calculated based on the mass loss, and the mass loss data of the sample at a specific temperature is recorded. According to the thermogravimetric data, a thermal degradation model is constructed. According to the known relationship between degradation temperature and mass loss, a thermal degradation function is constructed using regression analysis. The function is applied to the thermal conductivity data in step S2 for correction. By comparing with the mass loss data obtained in the thermal degradation model, the thermal conductivity is corrected to obtain the first corrected thermal conductivity.

[0027] Step S4: applying a heat pulse to a sample with a smooth surface of a polymer material to obtain heat pulse data, constructing a temperature response curve, and calculating the thermal conductivity to obtain a second thermal conductivity; correcting the second thermal conductivity according to the first corrected thermal conductivity to generate the thermal conductivity of the polymer material.

[0028] In this embodiment, a heat pulse is applied to the surface smooth sample obtained in step S1, and a pulse heating device is used, whose pulse power is set to 100W, the pulse duration is 50ms, and the pulse interval is 500ms. The temperature control system ensures precise control of the heating time. During the heating process, a temperature sensor with a response time of 0.1 seconds is used to monitor the surface temperature changes of the sample in real time. The temperature sampling frequency is set to 1kHz, and the temperature change data is recorded. Through the data acquisition system, the temperature response data after each pulse is collected in real time, and a temperature response curve is constructed. Using the known sample thickness (5mm) and pulse heat flow data, combined with Fourier's law, the second thermal conductivity is calculated. According to the first corrected thermal conductivity and the second thermal conductivity obtained in step S3, the weighted average method is used for correction. The correction formula is:

[0029] ;

[0030] in, is the first corrected thermal conductivity, is the second thermal conductivity, and the final thermal conductivity is taken as the thermal conductivity of the polymer material.

[0031] It is particularly important that step S4 comprises the following steps:

[0032] Step S41: applying a heat pulse to a polymer material sample with a smooth surface to obtain preliminary heat pulse data;

[0033] In this embodiment, appropriate application conditions are set in the heat pulse application device. The heating rate is set to 5°C / min, the duration is set to 15 minutes, and the pulse intensity is 10W / cm². The polymer material sample is fixed on the heating platform of the heat pulse application equipment, and the heating device is started to apply a pulse heat flow through an infrared heating source. During the application process, the temperature change on the surface of the material is continuously monitored, and the temperature distribution is measured using a thermocouple sensor to ensure that the entire sample surface is heated evenly. After the application is completed, the temperature and heat flow data are collected to obtain preliminary heat pulse data. These data include time-temperature change curves, which record the temperature rise, peak value and cooling rate during the application of the heat pulse.

[0034] Step S42: Based on the preliminary heat pulse data, temperature distribution identification is performed on the sample with a smooth surface of the polymer material to obtain temperature distribution data and generate a temperature distribution diagram;

[0035] In this embodiment, based on the preliminary heat pulse data, the temperature distribution of the smooth surface sample of the polymer material is identified by the data processing software. A high-precision infrared thermal imager is used to capture the temperature distribution map of the sample surface. The resolution of the infrared thermal imager is 0.1°C, which can capture the slight temperature difference on the surface of the material. The temperature distribution data includes the temperature conditions of each point on the sample surface during the application of the heat pulse. The data processing software analyzes the temperature information of each pixel and generates a temperature distribution map. The temperature distribution conditions of each area are marked in the figure, and the high temperature and low temperature areas on the sample surface are identified. The temperature distribution map is displayed in the form of a color code, with higher temperature areas in red and lower temperature areas in blue.

[0036] Step S43: performing uneven identification on the temperature distribution diagram to obtain uneven temperature distribution position data;

[0037] In this embodiment, the generated temperature distribution map is subjected to uneven identification, and the basis for identification is that the temperature difference between different areas on the sample surface exceeds a set threshold. The threshold is set to 1°C, that is, when the temperature difference is greater than 1°C, the temperature distribution in the area is considered to be uneven. Using image processing technology, the temperature distribution map is analyzed to identify areas with uneven temperatures. Through an algorithm, the areas where the temperature changes most dramatically on the sample surface are detected, and the coordinate positions of these areas are marked to obtain uneven temperature distribution position data. This data provides specific location information about uneven temperature distribution, which can help with subsequent temperature compensation and heat pulse application adjustment.

[0038] Step S44: performing temperature compensation on the polymer material surface smooth sample according to the uneven temperature distribution position data to obtain temperature compensation data;

[0039] In this embodiment, additional heat pulse compensation is applied to the non-uniform area, and a heater or cooling device is used to apply a supplementary heat flow to the area with lower temperature to ensure the uniformity of the surface temperature. During the compensation process, a thermocouple is used to accurately measure the temperature of the compensation area, and the compensation intensity is adjusted in real time to ensure that the temperature difference in each area does not exceed the set threshold. After the compensation is completed, temperature compensation data is obtained, which includes the heat applied to each non-uniform area and the final temperature balance result.

[0040] Step S45: adjusting the heat pulse application based on the temperature compensation data and the preliminary heat pulse data to obtain the heat pulse data;

[0041] In this embodiment, the operating parameters of the heating device are optimized using temperature compensation data and preliminary heat pulse data. The temperature compensation data includes the temperature deviation of each area on the sample surface, while the preliminary heat pulse data provides the temperature change during the heat pulse application process. Combining these two data sets, the control system analyzes the temperature requirements of each area in real time and adjusts the power of the heating device. The power regulation of the heating device is achieved through the control system, which receives temperature sensor data (such as thermocouple or infrared sensor data) and calculates the temperature requirements of different areas on the sample surface. For areas with low temperatures, the control system increases the heating power; for areas with high temperatures, the control system reduces the heating power. In this process, the specific power adjustment is based on the set power adjustment rate, which is set to a maximum of 5% per minute to ensure that the temperature change is stable and controllable. Continue to use an infrared thermal imager or other high-precision temperature sensor to continuously monitor the temperature distribution on the sample surface and provide real-time feedback to the control system. If the temperature of a certain area still does not meet the set standard, the system will immediately adjust the power output of the heating device to further optimize the heat distribution in that area. The set temperature uniformity standard is that the temperature difference between each area does not exceed 1°C. If the temperature difference is detected to be more than 1°C, the system automatically makes a secondary adjustment to ensure uniform temperature distribution across the sample surface. Data from the adjustment process is continuously recorded for subsequent analysis. Ultimately, the heat distribution applied by the heat pulse is more uniform through feedback and optimization during the adjustment process. The optimization of heat distribution during the heating process improves the heat pulse data. The improved heat pulse data reflects the uniformity of heat distribution during the application process, which helps to more accurately analyze the thermal response characteristics of the material.

[0042] Step S46: constructing a temperature response curve according to the heat pulse data, and calculating the thermal conductivity to obtain a second thermal conductivity;

[0043] In this embodiment, by analyzing the temperature data at different time points, the least square method is used to curve fit the data to obtain a response curve of temperature change over time. On this basis, the thermal conductivity is calculated. Using the known material thickness and heat flow data, according to the heat conduction theory, the thermal conductivity of the sample at different temperatures is calculated. Finally, the second thermal conductivity is obtained, which reflects the thermal conductivity of the polymer material under specific conditions.

[0044] Step S47: Correcting the second thermal conductivity according to the first corrected thermal conductivity to generate the thermal conductivity of the polymer material.

[0045] In this embodiment, the relationship between the first corrected thermal conductivity and the second thermal conductivity is analyzed using a linear regression method, and a correction relationship between the two is established through a regression equation. In the regression analysis, multiple experimental data points are collected and linear fitting is performed to obtain a regression equation k2=1.1*k1+0.05, where k1 is the first corrected thermal conductivity and k2 is the second thermal conductivity. Through this formula, the second thermal conductivity can be corrected according to the known first corrected thermal conductivity. In the specific operation, the first corrected thermal conductivity value 0.2W / (m·K) is substituted into the regression equation, and the corrected second thermal conductivity is calculated to be 0.25W / (m·K), which verifies that this result is consistent with the actual measured second thermal conductivity value. The final thermal conductivity of the polymer material is calculated by the weighted average method, and the final thermal conductivity is determined by the average value of the first corrected thermal conductivity and the corrected second thermal conductivity. In this process, the first thermal conductivity of 0.2 W / (m·K) and the second thermal conductivity of 0.25 W / (m·K) are added and divided by 2, and the final thermal conductivity of the polymer material is 0.225 W / (m·K). Through this series of steps, the second thermal conductivity is corrected according to the first corrected thermal conductivity, and the final thermal conductivity of the polymer material is generated.

[0046] Preferably, step S1 specifically comprises:

[0047] Step S11: obtaining a polymer material sample;

[0048] In this embodiment, the operation of obtaining polymer material samples is carried out by purchasing materials that meet the technical requirements from suppliers. The size requirement of each sample is 100mm×50mm×5mm, and the thickness deviation is controlled within the range of ±0.2mm to ensure that the selected samples have no obvious cracks, bubbles or other quality defects. Use a precision caliper to measure the length, width and thickness of each sample and ensure that it meets the standard specifications. The surface of the sample needs to be cleaned and gently wiped with a fiber-free cleaning cloth or dust-free paper towel to remove all oil and impurities. After cleaning, use an optical microscope to check the surface for scratches, blemishes or other defects that may affect subsequent processes. Only polymer material samples that fully meet the requirements can enter the next step of processing. During sample processing, the ambient temperature must be maintained at 25°C and the humidity must be controlled below 60% to avoid the influence of environmental factors on material properties.

[0049] Step S12: Identify the texture direction of the polymer material sample and obtain texture direction data;

[0050] In this embodiment, the texture direction of the sample surface is identified by using a high-resolution texture analyzer. The cleaned polymer material sample is fixed in a precision fixture to ensure that the sample surface is parallel to the sensor of the analyzer. The laser scanning function of the analyzer will obtain texture data based on the light signal reflected by the surface microstructure, and the collection accuracy of the texture data is 0.1mm. During the scanning process, the instrument is set to multi-angle scanning, and the scanning depth is 0.5mm to ensure that all important texture features on the sample surface are scanned. By analyzing the fluctuations of the reflected light signal, the analyzer calculates the main direction of the texture with a data accuracy of 1°. The texture direction data will serve as an important reference in the subsequent polishing process, used to adjust the polishing direction, and ensure that each step of processing conforms to the material texture characteristics.

[0051] Step S13: using 80-120 mesh coarse-grained sandpaper to preliminarily grind the polymer material sample according to the texture direction data, wherein the grinding direction is set to be along the texture direction, the grinding angle is set to be 10°-30°, and the grinding force is set to be 0.5-1.5 kg, to obtain a coarsely ground material sample;

[0052] In this embodiment, under the guidance of the texture direction data obtained in step S12, the polymer material sample is preliminarily polished using 80-120 mesh coarse-grained sandpaper. The selection of sandpaper and the grinding intensity are the key to ensuring the effect of the preliminary grinding. The speed of the grinder is set to 300 rpm to ensure a stable grinding effect. The grinder is fixed on the workbench, and the sample is firmly clamped to prevent movement during the grinding process. The grinding direction is strictly set according to the texture direction data in step S12 to ensure that the grinding direction is consistent with the texture direction of the sample, and the grinding angle is set to 20°. The grinding intensity is set to 1 kg and is precisely controlled by measuring the pressure application system of the grinder. The time for preliminary grinding is 5 minutes. During the whole process, the roughness of the sample surface is regularly checked by a roughness meter to ensure that the roughness meets the requirement of Ra 0.5μm.

[0053] Step S14: using 180-220 mesh medium-grained sandpaper to perform medium-grinding on the coarse-grinded material sample, wherein the grinding direction is set to 45° along the grain direction, the grinding angle is 10°-20°, and the grinding force is 0.3-1.0 kg to obtain a medium-grinded material sample;

[0054] In this embodiment, the rough-ground material sample is moderately polished using medium-grained sandpaper of 180-220 mesh. First, make sure that the medium-grit sandpaper has been correctly installed on the grinder, and adjust the speed of the grinder to 250 rpm to ensure sufficient stability during the grinding process. The grinding direction of the grinder is set to 45° along the texture direction to avoid uneven texture wear during the grinding process. The grinding angle is set to 15° to make the pressure applied to the sample surface more uniform. The grinding force is adjusted to 0.8 kg, and the force is accurately controlled by a measuring tool to ensure the uniformity and fineness of the material surface during the grinding process. The operation lasts for 4 minutes. During the grinding process, the sample surface is checked every 2 minutes using a roughness meter to ensure that the final surface roughness meets the standard of Ra 0.3μm.

[0055] Step S15: Use 400-600 mesh fine-grained sandpaper to fine-grind the medium-grinding material sample, wherein the grinding direction is set to 30° along the texture direction, the grinding angle is 10°-15°, and the grinding force is 0.1-0.5 kg to obtain a sample of the polymer material with a smooth surface.

[0056] In this embodiment, the material sample after intermediate grinding is finely polished using 400-600 mesh fine-grained sandpaper. During fine grinding, the grinding machine speed is set to 200 rpm to ensure a fine grinding effect on the sample surface. The grinding direction is set at an angle of 30° to the texture direction, and the grinding angle is set to 12° to reduce unnecessary wear on the material surface and ensure a smooth surface. The grinding force is controlled at 0.3 kg, and consistency is maintained by applying precise pressure to avoid local excessive wear. The fine grinding process lasts for 6 minutes, and the surface smoothness is monitored every 1 minute using a roughness meter to ensure that the surface roughness reaches the standard of Ra 0.1μm. During the entire fine grinding process, detailed adjustments need to be made based on real-time measurement data to ensure that the final sample surface smoothness meets high-demand industrial standards.

[0057] Preferably, step S12 is specifically:

[0058] Step S121: photographing the polymer material sample using a digital camera, wherein the resolution is set to at least 30 million pixels, the focal length range can be set to 50-100 mm, and the distance between the camera lens and the sample surface should be maintained at 20-50 cm, to obtain an image of the polymer material sample;

[0059] In this embodiment, a digital camera with a resolution of at least 30 million pixels is selected, and the focal length range of the camera is adjusted to 50-100mm to ensure that the camera can provide sufficient image clarity during the shooting process. The distance between the camera lens and the sample surface is kept within the range of 20-50cm, and a tripod is used to stabilize the camera to avoid any shaking during the shooting process. When shooting, it should be ensured that the ambient light is sufficient and uniform to avoid strong light sources or shadows affecting the image quality. The camera should be set to manual mode, the exposure time is 1 / 500 seconds, and the ISO value is set to 100 to reduce the interference of image noise. During the shooting process, the autofocus function of the camera is used to ensure that every detail in the image is clearly visible, and the image size captured is set to 5000×3000 pixels to ensure that the image has enough details for subsequent processing.

[0060] Step S122: performing high-pass filtering on the polymer material sample image to generate a noise-reduced polymer material sample image;

[0061] In this embodiment, the original image is high-pass filtered using image processing software to remove low-frequency noise and enhance the high-frequency information in the image. First, the cutoff frequency of the high-pass filter is set to 0.1 Hz, which can effectively remove the low-frequency noise in the image while retaining the texture details. The filtering operation is performed using the standard Fourier transform method. In the converted spectrum data, the high-frequency part is enhanced and the low-frequency part is suppressed. The filtered image is back-transformed back to the spatial domain to generate a denoised polymer sample image. During the image processing process, the filter window size is set to 5x5 pixels to ensure smoothness during processing, and the software's built-in smoothing algorithm is used to remove unnecessary high-frequency noise.

[0062] Step S123: grayscale conversion is performed on the noise reduction polymer material sample image to obtain a grayscale image;

[0063] In this embodiment, an image processing tool is used to convert a color image into a grayscale image. The grayscale value is calculated using the standard weighted average method: the grayscale value calculation formula for each pixel is Gray=0.299×R+0.587×G+0.114×B, where R, G, and B represent the values ​​of the red, green, and blue channels of each pixel in the image, respectively. During the conversion process, the color information of all color images is removed, and only the brightness information is retained. The processed grayscale image can clearly present the texture changes on the surface of the material. The image size is maintained at the original resolution when it was shot, that is, 5000×3000 pixels, to ensure that the converted image does not lose details.

[0064] Step S124: performing gradient calculation based on the grayscale image to generate gradient data;

[0065] In this embodiment, the Sobel operator is used to perform gradient calculation on the grayscale image to obtain the gradient value of each pixel in the image. During the gradient calculation process, the gradient of the image in the x-axis and y-axis directions are calculated respectively, the kernel size of the Sobel operator is set to 3×3 pixels, and the image is operated using the standard convolution method. The calculated gradient value represents the rate of change of the texture in the image. The gradient data will serve as the basis for subsequent texture direction analysis. During the calculation process, the threshold is set to 20 to filter out smaller gradient changes and ensure that only the more significant texture features in the image are retained. The gradient value of each pixel is calculated by Calculate, where Gx and Gy are the gradient values ​​of the x-axis and y-axis respectively.

[0066] Step S125: performing direction analysis on the gradient data to obtain texture direction data.

[0067] In this embodiment, the gradient value of each pixel in the gradient data is used in combination with its gradient direction to calculate the texture direction of each pixel. The texture direction is calculated using the inverse tangent algorithm: θ=arctan(Gy / Gx), where θ represents the direction angle of the texture, and Gy and Gx represent the gradient values ​​in the y-axis and x-axis directions, respectively. During the calculation process, for each pixel, the result angle range is limited to -90° to +90° to ensure the validity of the texture direction data. By scanning the entire image, a data matrix containing the texture direction of each pixel is obtained, and the size of the matrix is ​​consistent with the original image. The final result of this step is a texture direction data map, in which the value of each pixel represents the texture direction of that position.

[0068] Preferably, step S125 is specifically as follows:

[0069] Select a 5×5 convolution kernel to perform gradient direction weighted calculation on the gradient data to obtain preliminary gradient direction data;

[0070] In this embodiment, a 5×5 convolution kernel is selected, and the weight distribution of the convolution kernel is designed so that it can be weighted according to the direction of the gradient data. The size of the convolution kernel is 5×5, containing 25 elements. The core idea is to adjust the weight of each pixel in the calculation according to the distance and relationship between each pixel and its neighboring pixels. In order to achieve this, the weight of the central pixel of the convolution kernel is set to 1, and the weight value of the surrounding pixels is gradually reduced according to the distance from the central pixel. For example, the farther the pixel is from the central pixel, the smaller its weight value should be, and the weight is allocated by using a Gaussian function or the like. The Gaussian function can ensure that the pixels closer to the center have a greater impact on the calculation, while the pixels farther away have a gradually reduced impact on the calculation. The 5×5 convolution kernel is used to perform a convolution operation on the gradient data. The goal of the convolution operation is to obtain the preliminary gradient direction data of the area by weighted calculation of the gradient direction value in each local area. The specific steps are: first, traverse each pixel in the image to determine its local neighborhood, and the neighborhood range is a 5×5 pixel area. For each pixel, calculate the gradient direction value of the pixel and the 24 pixels around it. During the calculation, multiply the gradient direction value of each pixel by the weight value in the convolution kernel, and then sum the weighted results to finally obtain the weighted gradient direction value of the pixel. When calculating the weighted gradient direction, the weight value should be adjusted according to its position in the convolution kernel. For the pixel at the center, the corresponding weight is 1; while for the pixel far from the center pixel, the weight value gradually decreases, using linear attenuation or Gaussian attenuation. For example, the pixel one grid away from the center can be assigned a weight value of 0.8, the pixel two grids away can be assigned a weight value of 0.6, and so on. Through this weighting method, it can be ensured that the pixels closer to the center have a greater impact on the calculation results, and the pixels far from the center have a smaller impact on the calculation, so as to accurately capture the dominant direction of the local texture. The weighted calculation in this process requires high-precision calculation of the gradient direction of each pixel and its neighborhood pixels, and ensures that the weight distribution during the convolution operation is reasonable, which can fully reflect the dominant direction of the texture in the local area. In particular, during the convolution operation, attention should be paid to the gradient direction data type and value range of each pixel and its neighborhood to avoid inaccurate final results due to data overflow or error. The weighted gradient direction value of each pixel obtained after convolution is the preliminary gradient direction data of the area. After this process, the original gradient data will be further processed and optimized for subsequent direction analysis and texture feature extraction.

[0071] Reconstructing the local gradient vector field based on the preliminary gradient direction data, thereby obtaining local gradient vector field data;

[0072] In this embodiment, based on the preliminary gradient direction data, a local gradient vector is constructed at each pixel position in the image using a vector field reconstruction method, the direction of which is the gradient direction of the pixel, and the size is determined by its gradient value. First, the gradient direction of each pixel is extracted based on the preliminary gradient direction data calculated in step S126. The gradient direction data of each pixel can be regarded as a two-dimensional vector, the direction of which is the gradient direction of the pixel, and the size is determined by the gradient value of the pixel (i.e., the intensity or amplitude of the gradient). For example, if the gradient amplitude of a certain pixel is 10, the size of the gradient vector of the pixel is 10, and the direction is the corresponding angle in the preliminary gradient direction data. Using the vector field reconstruction method, each pixel and its adjacent pixels are processed to construct a local gradient vector field. Specifically, for each pixel in the image, the pixel gradient vectors in its neighborhood will be taken into consideration, and the local gradient vector of the pixel will be calculated based on the gradient direction and size of each pixel. At this time, each pixel in the image has a gradient vector corresponding to its gradient value and gradient direction. These gradient vectors are distributed in the image according to their directions and sizes to form a local gradient vector field. In order to more accurately reflect the details of the image texture, each local gradient vector needs to be weighted during the reconstruction process. The purpose of weighting is to smooth the changes of the local gradient vector, avoid the single isolated gradient information from having too much impact on the overall result, and ensure the continuity and consistency of the overall texture features. At this time, a weighted average method based on the local area is used to process each gradient vector and combine it with the gradient vectors of the surrounding adjacent pixels. In the weighting process, a weight window of 3×3 pixels is first set. This window determines the neighborhood range considered in each weighted calculation. During the calculation, the gradient vectors of the pixel and its adjacent pixels are considered. The weight of the gradient vector in each neighborhood is determined by its distance from the central pixel. The closer the distance, the greater the weight of the pixel, and the farther the distance, the smaller the weight of the pixel. In this way, it can be ensured that the correlation between each local gradient vector and its surrounding gradient vectors is fully considered in the calculation. For example, suppose there are 9 pixels in a 3×3 neighborhood, and each pixel has a gradient direction and gradient magnitude. In the weighting process, for the central pixel, the pixels closer to the center in the neighborhood will be given higher weights, and vice versa, the pixels farther away will be given smaller weights. This weighted average method can effectively reduce the noise or isolated, unreliable data in the local gradient information, thereby enhancing the stability and reliability of the overall gradient vector field. The gradient vectors after weighted average processing will be reintegrated to form a smoother and more accurate local gradient vector field. This local gradient vector field contains the gradient direction and gradient magnitude of each pixel in the image, and can accurately reflect the local details and overall structure of the image texture.

[0073] A pixel gradient direction angle histogram is constructed based on the local gradient vector field data, wherein the angle range is set to [0°, 180°] and the angle resolution of the histogram is set to 5°-10°, thereby obtaining a pixel gradient direction angle histogram;

[0074] In this embodiment, the range of the gradient direction angle is set to [0°, 180°], and the angular resolution of the histogram is set to 5°. Specifically, the histogram will be divided into multiple angle intervals, and each interval (or "barrel") represents a directional range of 5°. That is, the intervals with angles of [0°, 5°), [5°, 10°), ..., [175°, 180°) correspond to a histogram bucket respectively, and each bucket represents the gradient distribution range of a specific direction. According to the local gradient vector field data obtained in step S127, the gradient direction data of each pixel in the image is processed. For each pixel, its gradient direction data represents the dominant gradient direction of the pixel. By calculating the gradient direction of the pixel and comparing it with the angle interval in the histogram, it is determined which angle bucket the gradient direction of the pixel should be classified into. For example, assuming that the gradient direction of a pixel is 45°, then the pixel will be classified into the bucket of the interval [42.5°, 47.5°] to ensure that its gradient direction matches the corresponding angle interval. For each pixel, the gradient amplitude of the pixel also needs to be considered when classifying. In order to improve the accuracy of the histogram, a weighted statistical method is used, that is, the contribution of each pixel is adjusted according to the size of its gradient amplitude. Specifically, when calculating the histogram, the pixel with a larger gradient amplitude is given a larger weight, which has a greater impact on the count value of the corresponding bucket in the histogram. Through this weighted method, it can be ensured that the histogram reflects the distribution of the main texture directions in the image and avoids the influence of small gradients on the statistics of the dominant direction. For example, for a pixel, its gradient direction is 45° and the gradient amplitude is 8. After weighting by the gradient amplitude, it will increase the count of 8 at the corresponding position in the histogram bucket. In this way, for each pixel, its gradient direction and the corresponding gradient amplitude can contribute to the angular distribution of the histogram, ensuring that the statistics of the dominant texture direction in the histogram can be more accurate. In the processing process, in order to ensure that the statistics of the histogram are more detailed, a window range of ±2.5° is adopted to classify the gradient direction of each pixel. That is to say, if the gradient direction of a pixel is 45°, the pixel will be classified into the bucket of the interval [42.5°, 47.5°] instead of just the exact value of 45°. This method can smooth the calculation of the gradient direction and avoid the deviation of too high or too low precision affecting the final result. The calculation of the entire histogram will traverse all pixels in the image, and the weighted contribution of the gradient direction and gradient amplitude of each pixel will be counted according to this rule, and finally a complete gradient direction angle histogram will be obtained. This histogram shows the gradient distribution in various directions in the image, reflecting the dominant direction of the local texture of the image. Each bucket of the histogram represents the distribution area of ​​a gradient direction, and the count value of the bucket represents the gradient strength of that direction. The higher the count value, the more dominant the texture in that direction is in the image.Ultimately, the obtained gradient direction angle histogram will serve as the basis for subsequent texture analysis, image pattern recognition, and machine learning tasks, providing accurate texture direction information for subsequent image processing.

[0075] Direction peaks are identified based on the pixel gradient direction angle histogram, where 10°-15° is set as the screening direction distribution area to obtain direction consistency evaluation data;

[0076] In this embodiment, after calculating the gradient direction angle histogram of the image, each angle interval in the histogram is traversed. Each interval represents the texture intensity in a certain direction in the image. According to the set screening conditions, when the count value of a certain direction (i.e., a certain angle interval) is greater than 10 and less than 15 degrees, the direction is considered to be a peak. The screening conditions are used to identify areas with relatively consistent texture directions in the image and eliminate those insignificant direction distributions. Specifically, each angle interval in the histogram corresponds to a bucket, and the count value in the bucket represents the intensity of the texture in the direction. If the count value of the bucket is greater than 10 and the angle corresponding to the direction is between 10°-15°, the direction is considered to be a valid texture direction peak. The histogram is traversed. Assume that the histogram has 36 buckets, each bucket corresponds to an angle interval of 5° (such as [0°, 5°), [5°, 10°), [10°, 15°), etc.). For each bucket, its count value is calculated, that is, the weighted statistical value of the pixel gradient amplitude in the angle interval. Then, by setting a threshold (such as a count value greater than 10), the angle intervals with strong texture directions are screened out. Furthermore, by setting the range of the angle interval (such as the 10°-15° interval), those insignificant direction distributions are excluded. If the count value of a bucket meets these conditions, the direction is considered to be a peak, indicating that the texture of the image in this direction is relatively consistent and can be used as a representative of the texture direction. For example, assuming that in a bucket with an angle range of [45°, 50°], the count value is 12, and the angle range is within the range of 10°-15°, then this angle is considered to be a texture direction peak. Similarly, if the count value of other angle ranges (such as [80°, 85°]) is 6, the screening condition is not met, so the direction will not be identified as a texture direction peak. By screening and analyzing all angle intervals in the histogram, the main texture directions in the image can be finally identified. In order to better reflect the consistency of the texture direction, the peak density of the texture direction can also be calculated, that is, the number of peaks and their corresponding angle distribution range are identified. For example, if the count value of the gradient direction of the image in the range of [40°, 50°] is high and meets the screening conditions, it is considered that the main texture direction in the image is between 40° and 50°, indicating that the texture in the image has a strong directionality. According to the identified texture direction peak, the direction consistency evaluation data is generated. This data represents the consistency and importance of the texture direction in the image by statistics on the distribution range of the identified peak angles. If the texture direction peaks in multiple angle ranges are relatively concentrated, it means that the texture direction in the image is relatively consistent and the direction consistency is high. On the contrary, if the texture direction peak distribution is relatively scattered, it means that the texture direction consistency in the image is low. This consistency evaluation data will be used for subsequent tasks such as texture analysis and pattern recognition.

[0077] The global direction of the gradient data is extracted according to the direction consistency evaluation data, wherein the non-main direction components with an energy share of 80%-85% are filtered to obtain the global gradient direction data;

[0078] In this embodiment, based on the direction consistency evaluation data, a suitable energy proportion range is selected and set to 80%-85%. The selection of this range is intended to screen out the main directions with the most significant energy proportion in the texture direction distribution in the image, thereby removing those texture direction components with smaller contributions. By calculating the energy proportion of each direction in the image, the energy proportion reflects the proportion of the gradient amplitude of all pixels in the image in that direction. Specifically, the gradient amplitude of each pixel is calculated by the square root of the square sum of the horizontal and vertical gradients of the pixel. The gradient direction of each pixel is classified into the corresponding direction bucket according to the previous angle histogram, and the gradient amplitude in each direction bucket is accumulated to calculate the energy of the direction. Then, the energy of all directions is summed up to obtain the total energy of the image. Then, for each direction, its energy proportion is calculated by the ratio of the energy of the direction to the total energy of the image. According to the set energy proportion range of 80%-85%, starting from the direction with the highest energy proportion, the energy proportion of each direction is gradually accumulated until the cumulative energy proportion of these directions reaches 80%-85%. The directions selected in this way are the main texture directions in the image, which can effectively represent the main texture features in the image, while those directions with a smaller energy share are considered to be secondary texture components and are eliminated. The final generated global texture direction data includes the main directions with energy shares higher than the set threshold. These directions represent the dominant texture features of the image and provide an accurate basis for subsequent image processing and texture analysis.

[0079] The global gradient direction data is normalized in texture direction to generate texture direction data.

[0080] In this embodiment, each direction angle in the global gradient direction data is standardized so that the angle range of all direction data is limited to [0°, 180°]. The normalization process ensures that the data is within a uniform range by subtracting the minimum value from the angle value in the gradient direction data and dividing it by the difference between the maximum value and the minimum value. The standardized data can be processed uniformly to avoid data deviations caused by different original angle ranges. In addition, in order to further standardize the data so that it can be suitable for subsequent texture analysis or template recognition, the standardized texture direction data will be unitized, that is, mapped to the same coordinate system for further analysis and calculation. This step provides a unified standard data input for texture analysis and recognition in subsequent industrial robot welding methods.

[0081] Preferably, step S2 specifically comprises:

[0082] Step S21: applying heat flow to the polymer material sample with a smooth surface by using a temperature control plate, wherein the temperature control plate is set to a temperature control accuracy of ±0.1°C to adjust the heating work and obtain heat flow data;

[0083] In this embodiment, a precise temperature control plate is selected, and its temperature control accuracy is set to ±0.1°C to ensure that the heating temperature is controlled very accurately. The temperature control plate is brought into close contact with the smooth surface sample of the polymer material, and the heat flow is applied by heating the plate surface. The heating power of the temperature control plate should be accurately adjusted according to the thermal conductivity characteristics of the sample to ensure a stable heating process. In the process of applying heat flow, the set temperature value of the temperature control plate should be consistent with the surface temperature of the sample, and the temperature change should be finely controlled according to the set heating power. Through the heating process, the heat flow changes of the sample on the surface of the polymer material are recorded in real time to generate heat flow data. During the heating process of the heat flow, thermocouples or infrared temperature measuring instruments can be used to collect surface temperature data at different positions to ensure temperature uniformity and stability during the temperature control process.

[0084] Step S22: using a heat flow meter to measure the heat flux density of the heat flux data, wherein the measurement range is -2000 W / m² to +2000 W / m², and the heat flux density change data is recorded every 0.1 s to obtain the heat flux density data;

[0085] In this embodiment, based on the heat flux data obtained in step S21, a heat flux meter with a measurement range of -2000W / m² to +2000W / m² is used to measure the heat flux density. The heat flux meter should be able to collect heat flux data per unit area of ​​the sample surface in real time to ensure data accuracy. The changes in heat flux density are recorded and stored every 0.1 seconds. The heat flux meter needs to collect data according to a preset sampling frequency to ensure that the recorded heat flux density data has a high time resolution. During the measurement process, the heat flux meter will calculate the heat flux density based on the temperature change on the surface of the polymer material, and finally generate detailed heat flux density data by measuring the heat flux intensity and its fluctuation trend in the local area of ​​its surface. During the data recording process, the calibration of the heat flux meter must ensure that its error is less than 0.5% to ensure the reliability of the results.

[0086] Step S23: performing abnormal analysis on the heat flux density data to obtain abnormal heat flux density data;

[0087] In this embodiment, abnormal heat flux values ​​are identified by analyzing the peak values ​​or fluctuations that appear in the heat flux data. The screening of abnormal data is based on a set threshold range. For example, when the change in heat flux exceeds ±20%, the point is considered to be an abnormal point. By setting a filter or using standard deviation analysis, abnormal points in the data can be automatically detected and removed. In specific operations, a sliding window method can be used to analyze the changing trend of each data point and the data points before and after it, and data with sudden changes or unreasonable fluctuations are found, and marked as abnormal heat flux data. Furthermore, a mathematical model (such as a mean-variance model or trend analysis) can be used to identify abnormalities in the data to ensure that the removal of abnormal data does not affect the accuracy of the overall data trend.

[0088] Step S24: calibrating the heat flux data based on the abnormal heat flux data to generate calibrated heat flux data;

[0089] In this embodiment, the remaining heat flux density data is fitted by regression analysis method to obtain an ideal heat flux density distribution model. According to the model, the abnormal data part is interpolated to restore the heat flux density value covered by the abnormal data. The interpolation algorithm selects linear interpolation or cubic interpolation to ensure that the calibrated data is smooth and continuous. The key parameters in the calibration process include the accuracy of the fitting model, the selection of the interpolation algorithm and the setting of the interpolation interval. The heat flux density data after interpolation should be close to the actual measured data, and the accuracy of the calibration result is verified by calculating the mean square error (MSE). After the calibration process is completed, a set of corrected heat flux density data is obtained with high accuracy and consistency.

[0090] Step S25: Calculate thermal conductivity according to the calibrated heat flux density data to obtain thermal conductivity.

[0091] In this embodiment, the thermal conductivity is calculated by applying Fourier's heat conduction law based on the heat flux density and the temperature difference on both sides of the sample. The thermal conductivity (k) can be calculated by the formula Calculate, where Q is heat flow, L is material thickness, A is cross-sectional area, and ΔT is temperature difference. After setting the material thickness, cross-sectional area, and temperature difference, the thermal conductivity is calculated using the above formula based on the calibrated heat flux density data obtained in the previous step. During the calculation process, the accuracy of each parameter is guaranteed. For example, the thickness and temperature difference of the sample need to be accurately obtained through high-precision measuring equipment to reduce the impact of system errors on the thermal conductivity calculation results. According to the calculation formula, the thermal conductivity of the polymer material is obtained.

[0092] Preferably, step S23 is specifically:

[0093] Step S231: When any of the following situations occurs, it is determined that the heat flux measurement is abnormal, and the heat flux measurement abnormal data is obtained: the heat flux meter measurement signal noise exceeds ±10% of the reference signal amplitude, the measurement data fluctuates more than ±15% of the mean value in less than 5 seconds, and the measuring instrument operating temperature deviates from 10°C-50°C;

[0094] In this embodiment, the amplitude of the noise of the heat flux meter measurement signal needs to be compared with the reference signal amplitude. If the noise amplitude of the measurement signal exceeds ±10% of the reference signal amplitude, it is determined that the heat flux density measurement is abnormal. The calculation of the noise amplitude is based on the signal strength collected by the heat flux meter each time it is measured, and the noise amplitude is determined by calculating the standard deviation of the signal. When the standard deviation exceeds the set ±10% range, it can be determined as an abnormal signal. In addition, if the fluctuation amplitude of the measurement data exceeds ±15% of the mean within 5 seconds, it is also regarded as abnormal data. The calculation of data fluctuation is performed by performing time series analysis on the measurement data, taking the mean within a certain time period, and if it exceeds the set fluctuation threshold of ±15%, it is an abnormal fluctuation. Finally, if the operating temperature of the measuring instrument deviates from the set range of 10°C-50°C, and the temperature fluctuation continues to exceed the specified time threshold, it is determined as a measurement abnormality caused by temperature deviation. In actual operation, the temperature of the working environment of the heat flux meter can be monitored in real time by an integrated temperature sensor to ensure that the measuring equipment is measured within the specified operating temperature range.

[0095] Step S232: When the following conditions occur at the same time, it is determined that the heat flux data collection is abnormal, and the heat flux collection abnormal data is obtained: the data drift rate exceeds 5% within three consecutive measurement cycles, and the data recording system loses data or the timestamp is disordered;

[0096] In this embodiment, the calculation of the data drift rate is performed by analyzing the difference between the heat flux data in three consecutive measurement cycles and the data in the previous cycle. If the drift rate exceeds 5%, it is determined to be an abnormal drift. The drift rate exceeds the threshold due to factors such as equipment aging and sensor instability, so it is necessary to screen according to the set standards. In addition, the accuracy of the data recording system needs to be monitored. If data loss or timestamp confusion occurs in multiple consecutive measurement cycles (for example, the recorded timestamp interval is greater than the set maximum value or less than the minimum value, or jumpy data appears), it is an abnormality caused by data loss or system failure. During the implementation process, the data recording system needs to be configured with time synchronization and integrity check functions to ensure that each data point has a valid timestamp and can promptly alarm in case of loss or confusion.

[0097] Step S233: Integrate the abnormal heat flux measurement data and the abnormal heat flux collection data to obtain abnormal heat flux data.

[0098] In this embodiment, first, the two groups of abnormal data obtained from step S231 and step S232 need to be unified and integrated. Each group of abnormal data contains specific abnormal judgment criteria. In this step, it is necessary to ensure that each abnormal data is clearly classified and marked for subsequent data analysis and correction. For data from abnormal heat flow meter measurements, the noise amplitude abnormality must first be screened. When the noise amplitude exceeds ±10% of the reference signal, these abnormal points must be marked and stored separately. This operation can be programmed to set a threshold condition to detect the standard deviation of each heat flow meter signal in real time. If it exceeds the set range, it can be considered as noise amplitude abnormal data. Secondly, if the measured data fluctuates beyond ±15% of the mean in less than 5 seconds, these data need to be screened, and the screening condition is set to a fluctuation amplitude exceeding ±15%, and an identifier is set for these abnormal data. Finally, when the operating temperature of the measuring device deviates from 10°C to 50°C, the operating temperature data recorded by the integrated temperature sensor is compared with the set temperature range of the device. If it exceeds this range, it can be determined as a temperature deviation abnormality, and these data need to be marked and recorded. When dealing with abnormal heat flux acquisition data, the first thing to do is to identify the situation where the data drift exceeds the standard. By calculating the data drift rate between every three consecutive measurement cycles, if the drift rate exceeds 5%, the data will be classified as acquisition anomalies and marked. The drift rate calculation is performed by comparing the percentage change of data in adjacent measurement cycles. For example, if the data of a certain cycle changes by more than 5% from the previous cycle, it is considered that the data has a drift problem and needs to be eliminated or corrected. Next, check the data loss and timestamp confusion in the data recording system. If data loss is found in multiple consecutive cycles, or there is confusion in the timestamp (such as too long interval time or disordered sequence), these problem data will also be classified as acquisition anomalies. The timestamp check can detect the time synchronization of the recording system. If the timestamp jumps or records are missed, it will be immediately marked as a data acquisition anomaly. After completing these screening and marking, the measurement abnormal data and the acquisition abnormal data are integrated to form a complete abnormal data set. During the integration process, each abnormal data needs to be classified and stored according to its source and abnormality type. For example, abnormal noise amplitude data, abnormal fluctuation data, and abnormal temperature deviation data can be classified into one category, and data with excessive drift, data loss, and disordered timestamps can be classified into another category. This ensures that different types of abnormal data can be processed and analyzed separately during subsequent processing. Database management systems (such as SQL databases) or special abnormal data storage systems can be used to classify and store abnormal data. Each abnormal data record will contain detailed information such as abnormal type, occurrence time, abnormal amplitude, and related measurement values ​​for subsequent tracking, correction, or elimination.At the same time, by setting the identifier of abnormal data, the abnormal records in the entire data set can be clearly distinguished, so as to ensure that the subsequent data processing is not interfered by the abnormal data. In this process, it is necessary to make full use of the performance of data storage tools to ensure that when processing large amounts of data, the management and tracking of abnormal data can be completed efficiently, with high reliability and transparency.

[0099] Preferably, step S3 specifically comprises:

[0100] Step S31: obtaining thermal degradation characteristic data of a polymer material sample;

[0101] In this embodiment, a batch of polymer material samples are selected, and the sizes of these samples should be uniform to ensure the comparability of experimental data. The thermal degradation characteristics of the samples are analyzed using a thermogravimetric analyzer (TGA), and the temperature rise rate is set to 10°C / min, and the temperature rise range starts from room temperature and rises to 500°C. During the analysis process, the thermogravimetric analyzer monitors the mass change of the sample in real time, and records it synchronously with the temperature change to obtain a thermal degradation curve. The temperature accuracy of the thermogravimetric analyzer should be ±0.1°C, and the accuracy of the mass change should be 0.01mg. In this way, the mass change of the sample at different temperatures is obtained, and then the thermal degradation characteristic data such as the thermal degradation starting temperature, thermal degradation rate and degradation end temperature of the sample are calculated.

[0102] Step S32: Performing mass change analysis on the polymer material sample with a smooth surface to obtain mass change data;

[0103] In this embodiment, the surface of the polymer material sample needs to be treated to be smooth to ensure the accuracy of the data. At this time, use a precision electronic balance to weigh the initial mass of the sample and record the initial mass M_0 of the sample. Subsequently, the sample is placed in a thermogravimetric analyzer, and appropriate environmental conditions are set and the temperature is gradually increased. The mass value is recorded at a certain temperature interval (such as 5°C) until the sample reaches the set maximum temperature. Through these data, the mass change data of the sample in the entire temperature range can be obtained, which will be used to analyze the mass loss rate of the material and provide basic data for subsequent analysis.

[0104] Step S33: analyzing the weight loss rate of the polymer material sample with a smooth surface to obtain weight loss rate data;

[0105] In this embodiment, in addition to recording the mass change of the sample during the thermogravimetric analysis, the weight loss rate needs to be calculated. The weight loss rate is the amount of change in the mass of the sample per unit time, which is usually calculated by comparing each mass change value with the corresponding time difference. Set a temperature change rate, such as 10°C / min, and calculate the mass change of the sample per unit time in real time to obtain the weight loss rate data. This process requires precise control of temperature changes to avoid the influence of temperature fluctuations on the weight loss rate. Through this analysis, the weight loss rate of the polymer material at each temperature point is obtained, and then the thermal degradation process of the material and its thermal stability can be revealed.

[0106] Step S34: integrating the mass change data and the weight loss rate data to obtain thermogravimetric data;

[0107] In this embodiment, the mass change data obtained in step S32 is first compared with the weight loss rate data obtained in step S33, and integrated into a comprehensive thermogravimetric data set. The specific method is to combine the mass change value of each temperature interval with the corresponding weight loss rate to form a two-dimensional data table containing mass change and weight loss rate. Each row in the data table represents a specific temperature point and includes the mass change value, weight loss rate and other physical parameters (such as temperature change rate, etc.) at that point. Through this integration, the thermal degradation process and thermal stability of the material can be clearly displayed, providing data support for subsequent model construction.

[0108] Step S35: constructing a thermal degradation model according to the thermal degradation characteristic data and the thermogravimetric data, and correcting the thermal conductivity according to the thermal degradation model, thereby obtaining a first corrected thermal conductivity.

[0109] In this embodiment, it is first necessary to construct a thermal degradation model of polymer materials through thermal degradation characteristic data and thermogravimetric data. The model is intended to reflect the thermal degradation behavior of materials under different temperature conditions, especially the key data such as the thermal degradation starting temperature, thermal degradation rate, and mass loss of the material. In order to accurately describe this thermal degradation behavior, it is crucial to select a suitable thermodynamic model. For example, the Arrhenius model is a common model used to describe the relationship between temperature and reaction rate. This model can help predict the rate of material degradation at different temperatures and the mass loss it produces. When constructing this model, sufficient experimental data must be collected first, especially the degradation rate, starting degradation temperature, and mass loss of the material during degradation at a specific temperature. These experimental data provide the necessary input parameters for the fitting process. When constructing the model, it is necessary to fit according to the experimental data to determine the relevant parameters, which usually include parameters such as degradation rate constant and activation energy. By fitting the experimental data, a mathematical expression describing the degradation behavior of the material at different temperatures can be obtained, which can be used to predict the degradation characteristics of the material at a specific temperature. After the model is completed, the next step is to use this thermal degradation model to correct thermal conductivity. In the thermal performance analysis of polymer materials, thermal conductivity is an important physical parameter that changes at different temperatures. Therefore, it is necessary to correct the thermal conductivity in combination with the thermal degradation model, especially the effect of temperature on the thermal degradation behavior of the material. The correction process is usually completed by comparing the thermal conductivity calculated by the theoretical model with the thermal conductivity data obtained in the experiment. If there is a deviation between the theoretical value and the experimental value, it is necessary to adjust the relevant parameters in the thermal degradation model (such as degradation rate, mass loss rate, etc.) until the predicted results of the model are close to the actual experimental data. Specifically, the correction of thermal conductivity needs to consider the thermal degradation behavior of the material at different temperatures. For example, when the temperature rises, the thermal degradation rate of the material increases and the mass loss gradually increases. These factors will affect the thermal conductivity of the material. Therefore, by predicting the thermal degradation characteristics of the material at a certain temperature through the thermal degradation model and comparing it with the actual thermal conductivity data at that temperature, the error of the model parameters can be calculated and adjusted. The adjusted thermal degradation model can more accurately reflect the changes in the thermal conductivity of the material under different temperature conditions, thereby obtaining a more accurate thermal conductivity data. Finally, through this series of steps, the first corrected thermal conductivity can be obtained. This thermal conductivity not only takes into account the thermal degradation characteristics of the material, but also, through comparison and correction with experimental data, makes the obtained thermal conductivity better reflect the thermal performance of the material under actual use conditions, providing accurate data support for further thermal management analysis.

[0110] Preferably, step S32 is specifically:

[0111] Step S321: cutting the high analytical material sample with a smooth surface into thin slices to obtain a thin slice high analytical material sample;

[0112] In this embodiment, a high-analysis material sample with uniform surface smoothness is selected, and the sample should have sufficient flatness and stability. During the cutting process, a precision cutting machine is used to ensure that the thickness of the cut slices is uniform, and the cutting thickness is strictly controlled between 50 and 200 microns to ensure that the sample can fully reflect the thermal degradation characteristics of the material in subsequent thermogravimetric experiments. When cutting, ensure that excessive temperature or stress is avoided during the cutting process to avoid unnecessary thermal effects or physical damage to the sample. In order to further improve the cutting accuracy, laser cutting technology or water jet cutting technology can be used to ensure that the edges of the cut samples are smooth and free of impurities to ensure the quality of the samples. After cutting, each thin slice sample is cleaned to remove contaminants on the surface, and dried to ensure that its surface is clean and free of moisture.

[0113] Step S322: using a thermogravimetric analyzer to perform a thermogravimetric experiment on the thin slice high analytical material sample, wherein the heating rate is set to 5-20°C / min and the carrier gas flow rate is set to 50-100mL / min, to obtain thermogravimetric experiment data;

[0114] In the present embodiment, a thermogravimetric experiment was performed on a thin slice high analysis material sample using a thermogravimetric analyzer. In the experiment, the heating rate of the thermogravimetric analyzer was set to 10°C / min, and the carrier gas flow rate was 80mL / min. The selection of the heating rate controls the heating rate of the material during the experiment, ensuring that the thermal degradation reaction of the material during the heating process can be accurately measured. The carrier gas flow rate is set to 80mL / min, in order to ensure the stability of the gas flow rate and avoid experimental errors caused by too fast or too slow gas flow. The temperature range of the thermogravimetric analyzer is set to room temperature to 600°C to ensure that the entire thermal degradation process of the material can be covered. During the experiment, the change in sample mass is monitored, and the mass loss in each temperature interval is recorded to obtain detailed thermogravimetric experimental data.

[0115] Step S323: identifying the initial degradation temperature according to the thermogravimetric experimental data to obtain initial degradation temperature data, and measuring the mass of the high-analysis material smooth surface sample according to the initial degradation temperature data to obtain initial degradation sample mass data;

[0116] In this embodiment, the temperature range at which the material begins to degrade is determined by analyzing the mass change in the thermogravimetric curve. Specifically, in the thermogravimetric experimental data, the initial degradation temperature usually corresponds to the temperature point at which the sample mass curve first shows a significant decrease. The thermogravimetric curve is smoothed by data processing software, the point at which the mass loss begins is identified, and the temperature value corresponding to the point is determined. This temperature is the initial degradation temperature of the material. According to the temperature data, the mass measurement of the smooth surface sample of the high-analysis material is performed. During the measurement process, an accurate electronic balance is used to record the sample mass at the initial degradation temperature, and the corresponding data is saved to obtain the initial degradation sample mass data.

[0117] Step S324: performing 5% mass loss temperature identification according to the thermogravimetric experimental data to obtain 5% mass loss temperature data, and performing mass measurement on the high-analysis material smooth surface sample according to the 5% mass loss temperature data to obtain 5% mass loss temperature material mass data;

[0118] In this embodiment, when analyzing the thermogravimetric curve, first find the temperature point at which the mass loss of the material during the heating process reaches 5%. By observing the thermogravimetric curve, it can be seen that the mass gradually decreases, and at a certain temperature, the mass loss of the material reaches 5%. The temperature value is obtained by calculation, and it is ensured that this value is verified by multiple experimental data to reduce the error. The 5% mass loss temperature point can be accurately located by interpolation to ensure the accuracy of the temperature point. According to the 5% mass loss temperature data, the electronic balance is used again to measure the mass of the smooth surface sample of the high analysis material, the sample mass at this temperature point is recorded, and the data is saved to obtain the 5% mass loss temperature material mass data.

[0119] Step S325: performing maximum decomposition rate temperature identification according to the thermogravimetric experimental data to obtain maximum decomposition rate temperature data, and performing mass measurement on the high-analysis material smooth surface sample according to the maximum decomposition rate temperature data to obtain maximum decomposition rate temperature sample mass data;

[0120] In the present embodiment, in the thermogravimetric experiment, the maximum decomposition rate of the material usually occurs in the most violent stage of material degradation. On the thermogravimetric curve, this stage corresponds to the maximum value of the mass loss rate. By performing first-order derivative processing on the thermogravimetric curve, the mass loss rate is calculated, and the temperature point at which the maximum decomposition rate occurs is found. This temperature point represents the maximum decomposition rate of the material during thermal degradation, which usually corresponds to the extreme point of temperature. The temperature value is calculated by data processing software and recorded. At this temperature point, the sample is mass measured again using an electronic balance, and the sample mass data at the maximum decomposition rate temperature is recorded to obtain the maximum decomposition rate temperature sample mass data.

[0121] Step S326: Integrate the initial degradation sample mass data, the 5% mass loss temperature sample mass data and the maximum decomposition rate temperature sample mass data to obtain mass change data.

[0122] In the present embodiment, the initial degradation sample mass data, 5% mass loss temperature sample mass data and maximum decomposition rate temperature sample mass data are integrated to obtain mass change data. The mass data obtained by the above steps are integrated into a comprehensive data set. In the data integration process, first ensure that the units of all data are consistent, and the mass data need to be converted into the same mass standard, for example, in grams. Then, the initial degradation temperature, 5% mass loss temperature, maximum decomposition rate temperature and corresponding sample mass data are matched using data analysis software to generate a complete mass change curve data set. These data reflect the mass change of the material at different temperatures, and further provide detailed basic data support for subsequent thermal degradation analysis.

[0123] Preferably, step S33 is specifically:

[0124] Step S331: performing thermogravimetric heating on the polymer material sample with a smooth surface, wherein a heating rate of 5°C / min-10°C / min is set to obtain heating data;

[0125] In the present embodiment, the sample with smooth surface of polymer material is subjected to thermogravimetric heating. When performing thermogravimetric experiment, the sample with smooth surface of polymer material is selected and placed in the sample slot of thermogravimetric analyzer. The heating rate is set to 8°C / min to ensure that the heating rate meets the experimental requirements and the thermal degradation behavior of the material is not affected by heating too fast or too slowly. The temperature control system in the thermogravimetric analyzer will stably heat the sample according to the set heating rate. During the heating process, the temperature change is recorded in real time and synchronously monitored with the mass change of the sample. The temperature range is set to room temperature to 600°C to ensure that the thermal degradation process of polymer material can be covered. During the experiment, the temperature change of the sample during the heating process is monitored, and the mass change data at different temperatures are recorded to finally obtain the heating data. During the data acquisition process, it is ensured that the record is accurate to two decimal places and the temperature can be accurately controlled.

[0126] Step S332: performing preliminary measurement of the weight loss rate of the polymer material sample with a smooth surface according to the temperature rise data to obtain preliminary weight loss rate data;

[0127] In this embodiment, during the heating process, a thermogravimetric analyzer is used to monitor the mass change of the sample in real time, and the mass change is associated with the temperature change to obtain the mass loss at each temperature point. When calculating the weight loss rate, a preliminary measurement is performed based on the ratio of the mass loss at each temperature point to the heating rate, and the unit of the weight loss rate is mg / min. During the experiment, the air flow rate during the heating process is ensured to be 80 mL / min to avoid the accumulation or excessive influence of the reaction gas on the sample surface, thereby ensuring the accuracy of the data. The preliminary weight loss rate data is recorded in a table of temperature and mass loss association, providing a basis for subsequent data analysis.

[0128] Step S333: plotting the preliminary weight loss rate data to obtain a preliminary weight loss rate curve;

[0129] In this embodiment, after obtaining the preliminary weight loss rate data, the data processing software is used to organize it, and the temperature and the corresponding weight loss rate data are plotted into a curve graph. In order to ensure the accuracy of the data, a linear interpolation method is used when drawing the curve to fill the gaps between the data points to obtain a smooth weight loss rate curve. The curve will show the weight loss rate change trend of the polymer material at different temperatures. When drawing, the horizontal axis represents the temperature, the vertical axis represents the weight loss rate, and each point on the curve corresponds to the weight loss rate at the corresponding temperature during the heating process. Ensure that the interpolation calculation accuracy of the data at each temperature point reaches 0.01 mg / min, and save the generated curve as an image and data file for subsequent analysis.

[0130] Step S334: using the airflow sensor to obtain airflow data;

[0131] In the present embodiment, in the thermogravimetric experiment, the airflow sensor is installed in the airflow channel of the thermogravimetric analyzer for real-time monitoring of the change of the carrier gas flow rate. Ensure that the measurement accuracy of the airflow sensor is ± 0.5mL / min, and set the airflow rate to 100mL / min to ensure the stability of the airflow during the experiment. The data obtained by the airflow sensor will be fed back to the control system in real time to keep the airflow rate within the set range to avoid airflow fluctuations from interfering with the experimental results. During the data acquisition process, the airflow data is recorded regularly and synchronized with the temperature and mass change data in the thermogravimetric experiment to provide a reference for further analysis.

[0132] Step S335: performing stagnation identification on the preliminary weight loss rate curve based on the airflow data to obtain stagnation data;

[0133] In this embodiment, airflow data is used to determine whether there is a stagnation phenomenon in the experiment, that is, the impact of airflow interruption or instability on the weight loss rate curve. By analyzing the airflow data, it is identified that the airflow rate is not maintained within the set value range during certain time periods. If the airflow rate fluctuates or deviates from the set value by more than ±5%, it is considered that there is a stagnation phenomenon during this period. Based on these fluctuations in the airflow data, the preliminary weight loss rate curve is corrected using a data analysis algorithm, and the data of the affected part is removed or smoothed to obtain a more realistic weight loss rate curve. In this way, the curve error caused by abnormal airflow can be effectively eliminated.

[0134] It is particularly important that step S335 includes the following steps:

[0135] Perform statistics of unstable periods based on airflow data to obtain unstable airflow period data;

[0136] In this embodiment, the airflow sensor installed in the experimental device is used to continuously monitor the flow rate, pressure, temperature and other data of the airflow to ensure the timeliness and integrity of the collected data. The frequency of airflow data collection is set to once per second to ensure that the data can reflect real-time airflow fluctuations. Next, the instability of the airflow is identified by calculating the standard deviation and coefficient of variation of the airflow. If the standard deviation of the airflow is greater than the set threshold (for example, 0.05 m / s) within a certain time period, the airflow in this period is considered to be unstable. The statistical process involves calculating the fluctuation amplitude of the airflow data for each time period, and judging whether each time period belongs to an unstable period according to the set threshold. Finally, the time period that meets the instability standard is marked as unstable period data, and it is recorded in the form of a time series.

[0137] Perform nonlinear period identification on the preliminary weight loss rate curve to obtain nonlinear period data;

[0138] In this embodiment, the data of the preliminary weight loss rate curve is smoothed to remove the influence of noise, and the acceleration of the weight loss rate is calculated using the second-order difference method. If the acceleration increases or decreases significantly in certain time periods, and the rate of change exceeds a set threshold (for example, a 5% change), the time period is determined to be a nonlinear time period. During the implementation process, a continuous time window is first set, such as 5 seconds, and the acceleration of the weight loss rate within the window is calculated. If the change in the acceleration value is greater than 5%, the time period is considered to be a nonlinear time period. All time periods that meet the conditions are recorded to generate nonlinear time period data. These nonlinear time period data reflect the violent fluctuation area of ​​the preliminary weight loss rate curve, which can help identify specific nonlinear dynamic processes related to airflow changes.

[0139] Perform a sluggish period intersection operation based on the unstable airflow period data and the nonlinear period data to obtain the sluggish period data;

[0140] In this embodiment, the unstable airflow period data and the nonlinear period data are intersected to find the time period where both occur simultaneously. The specific operation of the intersection operation is to match the timestamps of the two data sets. If a certain moment belongs to both the unstable airflow period and the nonlinear period, the period is identified as a sluggish period. In the implementation, the unstable period data and the nonlinear period data are first sorted according to the time series, and then the start and end time of each period are compared one by one. If it is found that the two have overlapping parts, the period is recorded as a sluggish period, and finally the sluggish period data set is generated.

[0141] The hysteresis features of the preliminary weight loss rate curve are extracted according to the hysteresis period data to obtain the hysteresis data.

[0142] In this embodiment, the characteristic changes of the weight loss rate curve in the stagnation interval are identified by screening the stagnation period data. First, based on the stagnation period data obtained above, the corresponding time period data is extracted from the preliminary weight loss rate curve. Then, these data are analyzed in detail, focusing on the fluctuation characteristics of the weight loss rate curve in the stagnation period, and the frequency domain characteristics of the signal are analyzed using Fourier transform to identify the typical characteristics of the stagnation process. In implementation, a significant fluctuation area is screened out by setting a threshold (for example, a rate fluctuation amplitude of 0.01 m / s). Finally, based on these characteristic information, a complete stagnation data set is generated, the characteristic values ​​of each stagnation period are recorded, and saved as a data file for subsequent analysis. These stagnation data help to further understand the complex relationship between airflow and weight loss rate, and provide a reference for subsequent optimization processes.

[0143] Step S336: Optimize the preliminary weight loss rate curve according to the stagnation data to obtain weight loss rate data.

[0144] In this embodiment, the preliminary weight loss rate curve is optimized based on the stagnation data. The stagnation data obtained in step S335 is used to identify the interference part in the preliminary weight loss rate curve. For the identified stagnation part, a smoothing filtering algorithm is used to correct the data. Specifically, in the temperature range where there is airflow interference, a Gaussian smoothing filter or a moving average filter method is used to reduce the impact of fluctuations or mutations to obtain optimized weight loss rate data. The filter parameters in the optimization process should be set to a window of 5 data points to ensure that the corrected curve is smooth and consistent with the actual situation. After optimization, the obtained weight loss rate data is more accurate and can reflect the actual thermal degradation behavior of polymer materials during the heating process. The optimized data will be saved and used for subsequent thermal degradation analysis.

[0145] Preferably, the present specification also provides a polymer material thermal conductivity testing system, which is used to perform the polymer material thermal conductivity testing method as described above, and the polymer material thermal conductivity testing system comprises:

[0146] A smoothing processing module is used to obtain polymer material samples; the polymer material samples are sanded to obtain polymer material samples with smooth surfaces;

[0147] The heat flow application module is used to apply heat flow to the smooth surface sample of the polymer material using the temperature control plate, and measure the heat flow density to obtain the heat flow density data; the thermal conductivity is calculated based on the heat flow density data to obtain the thermal conductivity;

[0148] A thermal degradation model building module is used to obtain thermal degradation characteristic data of polymer material samples; perform thermogravimetric analysis on polymer material samples with smooth surfaces to generate thermogravimetric data; build a thermal degradation model based on the thermal degradation characteristic data and the thermogravimetric data, and correct the thermal conductivity based on the thermal degradation model to obtain a first corrected thermal conductivity;

[0149] The thermal conductivity correction module is used to apply thermal pulses to samples with smooth surfaces of polymer materials, obtain thermal pulse data, construct a temperature response curve, and calculate the thermal conductivity to obtain a second thermal conductivity; the second thermal conductivity is corrected according to the first corrected thermal conductivity to generate the thermal conductivity of the polymer material.

[0150] The polymer material thermal conductivity testing system of the present invention can implement the thermal conductivity testing method of any polymer material of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete the polymer material thermal conductivity testing method. The internal modules of the system cooperate with each other, which significantly improves the accuracy of the thermal conductivity testing of the polymer material.

[0151] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0152] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for testing the thermal conductivity of polymer materials, characterized in that: The following steps are involved: Step S1: obtaining a polymer material sample; performing sandpaper polishing on the polymer material sample to obtain a polymer material sample with a smooth surface; Step S2: applying heat flow to the sample with a smooth surface of the polymer material using a temperature control plate, and measuring the heat flux density to obtain heat flux density data; calculating the thermal conductivity based on the heat flux density data to obtain the thermal conductivity; Step S3: obtaining thermal degradation characteristic data of the polymer material sample; performing thermogravimetric analysis on the polymer material sample with a smooth surface to generate thermogravimetric data; constructing a thermal degradation model according to the thermal degradation characteristic data and the thermogravimetric data, and correcting the thermal conductivity according to the thermal degradation model, thereby obtaining a first corrected thermal conductivity; Step S3 is specifically: Step S31: obtaining thermal degradation characteristic data of a polymer material sample; Step S32: Performing mass change analysis on the polymer material sample with a smooth surface to obtain mass change data; Step S33: analyzing the weight loss rate of the polymer material sample with a smooth surface to obtain weight loss rate data; Step S34: integrating the mass change data and the weight loss rate data to obtain thermogravimetric data; Step S35: constructing a thermal degradation model according to the thermal degradation characteristic data and the thermogravimetric data, and correcting the thermal conductivity according to the thermal degradation model, thereby obtaining a first corrected thermal conductivity; Step S4: applying a heat pulse to a sample with a smooth surface of a polymer material to obtain heat pulse data, constructing a temperature response curve, and calculating the thermal conductivity to obtain a second thermal conductivity; correcting the second thermal conductivity according to the first corrected thermal conductivity to generate the thermal conductivity of the polymer material.

2. The method for testing the thermal conductivity of polymer materials according to claim 1, characterized in that: Step S1 is specifically as follows: Step S11: obtaining a polymer material sample; Step S12: Identify the texture direction of the polymer material sample and obtain texture direction data; Step S13: using 80-120 mesh coarse-grained sandpaper to preliminarily grind the polymer material sample according to the texture direction data, wherein the grinding direction is set to be along the texture direction, the grinding angle is set to be 10°-30°, and the grinding force is set to be 0.5-1.5 kg, to obtain a coarsely ground material sample; Step S14: using 180-220 mesh medium-grained sandpaper to perform medium-grinding on the coarse-grinded material sample, wherein the grinding direction is set to 45° along the grain direction, the grinding angle is 10°-20°, and the grinding force is 0.3-1.0 kg to obtain a medium-grinded material sample; Step S15: Use 400-600 mesh fine-grained sandpaper to fine-grind the medium-grinding material sample, wherein the grinding direction is set to 30° along the texture direction, the grinding angle is 10°-15°, and the grinding force is 0.1-0.5 kg to obtain a sample of the polymer material with a smooth surface.

3. The method for testing the thermal conductivity of polymer materials according to claim 2, characterized in that: Step S12 is specifically as follows: Step S121: photographing the polymer material sample using a digital camera, wherein the resolution is set to at least 30 million pixels, the focal length range can be set to 50-100 mm, and the distance between the camera lens and the sample surface should be maintained at 20-50 cm, to obtain an image of the polymer material sample; Step S122: performing high-pass filtering on the polymer material sample image to generate a noise-reduced polymer material sample image; Step S123: grayscale conversion is performed on the noise reduction polymer material sample image to obtain a grayscale image; Step S124: performing gradient calculation based on the grayscale image to generate gradient data; Step S125: performing direction analysis on the gradient data to obtain texture direction data.

4. The method for testing the thermal conductivity of polymer materials according to claim 2, characterized in that: Step S125 is specifically as follows: Select a 5×5 convolution kernel to perform gradient direction weighted calculation on the gradient data to obtain preliminary gradient direction data; Reconstructing the local gradient vector field based on the preliminary gradient direction data, thereby obtaining local gradient vector field data; A pixel gradient direction angle histogram is constructed based on the local gradient vector field data, wherein the angle range is set to [0°, 180°] and the angle resolution of the histogram is set to 5°-10°, thereby obtaining a pixel gradient direction angle histogram; Direction peaks are identified based on the pixel gradient direction angle histogram, where 10°-15° is set as the screening direction distribution area to obtain direction consistency evaluation data; The global direction of the gradient data is extracted according to the direction consistency evaluation data, wherein the non-main direction components with an energy share of 80%-85% are filtered to obtain the global gradient direction data; The global gradient direction data is normalized in texture direction to generate texture direction data.

5. The method for testing the thermal conductivity of polymer materials according to claim 1, characterized in that: Step S2 is specifically as follows: Step S21: applying heat flow to the polymer material sample with a smooth surface by using a temperature control plate, wherein the temperature control plate is set to a temperature control accuracy of ±0.1°C to adjust the heating work and obtain heat flow data; Step S22: using a heat flow meter to measure the heat flux density of the heat flux data, wherein the measurement range is -2000 W / m² to +2000 W / m², and the heat flux density change data is recorded every 0.1 s to obtain the heat flux density data; Step S23: performing abnormal analysis on the heat flux density data to obtain abnormal heat flux density data; Step S24: calibrating the heat flux data based on the abnormal heat flux data to generate calibrated heat flux data; Step S25: Calculate thermal conductivity according to the calibrated heat flux density data to obtain thermal conductivity.

6. The method for testing the thermal conductivity of polymer materials according to claim 5, characterized in that: Step S23 is specifically as follows: Step S231: When any of the following situations occurs, it is determined that the heat flux measurement is abnormal, and the heat flux measurement abnormal data is obtained: the heat flux meter measurement signal noise exceeds ±10% of the reference signal amplitude, the measurement data fluctuates more than ±15% of the mean value in less than 5 seconds, and the measuring instrument operating temperature deviates from 10°C-50°C; Step S232: When the following conditions occur at the same time, it is determined that the heat flux data collection is abnormal, and the heat flux collection abnormal data is obtained: the data drift rate exceeds 5% within three consecutive measurement cycles, and the data recording system loses data or the timestamp is disordered; Step S233: Integrate the abnormal heat flux measurement data and the abnormal heat flux collection data to obtain abnormal heat flux data.

7. The method for testing the thermal conductivity of polymer materials according to claim 1, characterized in that: Step S32 is specifically as follows: Step S321: cutting the high analytical material sample with a smooth surface into thin slices to obtain a thin slice high analytical material sample; Step S322: using a thermogravimetric analyzer to perform a thermogravimetric experiment on the thin slice high analytical material sample, wherein the heating rate is set to 5-20°C / min and the carrier gas flow rate is set to 50-100mL / min, to obtain thermogravimetric experiment data; Step S323: identifying the initial degradation temperature according to the thermogravimetric experimental data to obtain initial degradation temperature data, and measuring the mass of the high-analysis material smooth surface sample according to the initial degradation temperature data to obtain initial degradation sample mass data; Step S324: performing 5% mass loss temperature identification according to the thermogravimetric experimental data to obtain 5% mass loss temperature data, and performing mass measurement on the high-analysis material smooth surface sample according to the 5% mass loss temperature data to obtain 5% mass loss temperature material mass data; Step S325: performing maximum decomposition rate temperature identification according to the thermogravimetric experimental data to obtain maximum decomposition rate temperature data, and performing mass measurement on the high-analysis material smooth surface sample according to the maximum decomposition rate temperature data to obtain maximum decomposition rate temperature sample mass data; Step S326: Integrate the initial degradation sample mass data, the 5% mass loss temperature sample mass data and the maximum decomposition rate temperature sample mass data to obtain mass change data.

8. The method for testing the thermal conductivity of polymer materials according to claim 1, characterized in that: Step S33 is specifically as follows: Step S331: performing thermogravimetric heating on the polymer material sample with a smooth surface, wherein a heating rate of 5°C / min-10°C / min is set to obtain heating data; Step S332: performing preliminary measurement of the weight loss rate of the polymer material sample with a smooth surface according to the temperature rise data to obtain preliminary weight loss rate data; Step S333: plotting the preliminary weight loss rate data to obtain a preliminary weight loss rate curve; Step S334: using the airflow sensor to obtain airflow data; Step S335: performing stagnation identification on the preliminary weight loss rate curve based on the airflow data to obtain stagnation data; Step S336: Optimize the preliminary weight loss rate curve according to the stagnation data to obtain weight loss rate data.

9. A polymer material thermal conductivity testing system, characterized in that: Used to perform the polymer material thermal conductivity testing method as claimed in claim 1, the polymer material thermal conductivity testing system comprises: A smoothing processing module is used to obtain polymer material samples; the polymer material samples are sanded to obtain polymer material samples with smooth surfaces; The heat flow application module is used to apply heat flow to the smooth surface sample of the polymer material using the temperature control plate, and measure the heat flow density to obtain the heat flow density data; the thermal conductivity is calculated based on the heat flow density data to obtain the thermal conductivity; A thermal degradation model building module is used to obtain thermal degradation characteristic data of polymer material samples; perform thermogravimetric analysis on polymer material samples with smooth surfaces to generate thermogravimetric data; build a thermal degradation model based on the thermal degradation characteristic data and the thermogravimetric data, and correct the thermal conductivity based on the thermal degradation model to obtain a first corrected thermal conductivity; The thermal conductivity correction module is used to apply thermal pulses to samples with smooth surfaces of polymer materials to obtain thermal pulse data, construct a temperature response curve, and calculate the thermal conductivity to obtain a second thermal conductivity; the second thermal conductivity is corrected according to the first corrected thermal conductivity to generate the thermal conductivity of the polymer material.

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