A silicon carbide thermal performance prediction method and system based on thermal conductivity testing

Abstract

The present application relates to the technical field of biomass energy, and discloses a silicon carbide thermal performance prediction method and system based on thermal conductivity testing, which comprises the following steps: applying a periodic thermal excitation signal to a silicon carbide detection surface to determine a thermal excitation area, then collecting a dynamic thermal response signal, and separating a spatial gradient parameter of a temperature change characteristic vector. A defect density value is obtained based on the proportional relationship between the gradient parameter and the lattice defect density, different frequency phonons are identified, the contribution of the phonons to heat conduction is quantified, and the heat energy transmission amount of each frequency band is obtained. After dynamic scaling, the thermal conductivity prediction value is obtained by combining the phonon group velocity weighting, and finally a thermal performance evaluation report is generated. The present application can improve the accuracy of thermal conductivity prediction in silicon carbide thermal performance prediction based on thermal conductivity testing.

Description

Technical Field

[0001] This invention relates to the field of biomass energy technology, and in particular to a method and system for predicting the thermal performance of silicon carbide based on thermal conductivity testing. Background Technology

[0002] The thermal properties of silicon carbide are a core consideration for its application in many industrial scenarios, and accurate prediction of these properties is a crucial step in materials research and development and application optimization. Currently, most methods for predicting the thermal properties of silicon carbide are based on the direct measurement and derivation of macroscopic thermal parameters, failing to delve into the relationship between the material's internal microstructure and thermal conductivity. This results in an inability to accurately capture the impact of defects on the heat transfer process, leading to significant discrepancies between predicted thermal properties and actual conditions, making it difficult to meet the demands of high-precision applications.

[0003] Existing technologies lack effective separation and analysis of the spatial gradient of the characteristic vector of temperature change when processing dynamic response signals under thermal excitation. They also fail to accurately quantify and reasonably weight the contribution of phonons at different frequencies to thermal conduction. As a result, the predicted thermal conductivity value cannot truly reflect the actual situation of heat energy transfer in each frequency band. Consequently, the thermal performance evaluation based on this predicted value lacks pertinence and reliability, making it difficult to effectively guide the defect control and performance optimization of silicon carbide materials. Summary of the Invention

[0004] This invention provides a method and system for predicting the thermal properties of silicon carbide based on thermal conductivity testing. Its main purpose is to solve the problems of low accuracy of thermal conductivity prediction and insufficient comprehensiveness of thermal performance evaluation when predicting the thermal properties of silicon carbide based on thermal conductivity testing.

[0005] To achieve the above objectives, the present invention provides a method for predicting the thermal properties of silicon carbide based on thermal conductivity testing, comprising:

[0006] A periodic thermal excitation signal is applied to the detection surface of silicon carbide to obtain the thermal excitation region of the silicon carbide;

[0007] Collect the dynamic thermal response signal of the thermally excited region, and separate the gradient parameter of the temperature change feature vector in the dynamic response signal in space;

[0008] Based on the proportional relationship between the gradient parameter and the silicon carbide lattice defect density, the defect density value of the silicon carbide is obtained;

[0009] Based on the defect density value, different frequency phonons of the silicon carbide are identified, and the contribution of the different frequency phonons to heat conduction is quantified and stacked to obtain the heat transfer amount of the silicon carbide in each frequency band.

[0010] The thermal energy transfer amount is dynamically scaled, and the scaled thermal energy transfer amount is weighted by group velocity based on the group velocity value corresponding to the phonon frequency in the phonons of different frequencies to obtain the predicted value of the thermal conductivity of silicon carbide.

[0011] A thermal performance evaluation report for the silicon carbide is generated based on the predicted thermal conductivity value.

[0012] In a preferred embodiment, applying a periodic thermal excitation signal to the detection surface of the silicon carbide to obtain the thermal excitation region of the silicon carbide includes:

[0013] The laser beam is split and modulated to obtain a periodic thermal pulse sequence;

[0014] The periodic thermal pulse sequence is focused onto a preset coordinate point on the silicon carbide detection surface to obtain the thermal excitation region of the silicon carbide.

[0015] In a preferred embodiment, the step of acquiring the dynamic thermal response signal of the thermally excited region and separating the gradient parameter of the temperature change feature vector in the dynamic response signal in space includes:

[0016] The temperature distribution information of the thermally excited region is reconstructed to obtain the dynamic response signal of the silicon carbide;

[0017] A three-dimensional wavelet transform is performed on the dynamic response signal to obtain the spatiotemporal two-dimensional vector of the silicon carbide;

[0018] The feature vectors in the spatiotemporal dual-dimensional vectors whose temperature change rate exceeds a preset threshold are extracted. The second derivative of the feature vectors is obtained along the thermal diffusion direction with the thermal excitation center point as the origin, and used as the gradient parameter of the silicon carbide.

[0019] In a preferred embodiment, reconstructing the temperature distribution information of the thermally excited region to obtain the dynamic response signal of the silicon carbide includes:

[0020] Capture the temperature field distribution of the thermal excitation region, and extract the temperature data of the concentric annular region centered on the thermal excitation point from the temperature field distribution;

[0021] The temperature data of the concentric ring region are aligned over time to obtain the dynamic thermal response signal of the silicon carbide.

[0022] In a preferred embodiment, obtaining the defect density value of silicon carbide based on the proportional relationship between the gradient parameter and the silicon carbide lattice defect density includes:

[0023] Extract the spatial distribution amplitude features from the gradient parameters;

[0024] Based on the suppression effect of defects in the silicon carbide crystal structure on heat conduction, the correlation characteristics between the spatial distribution amplitude and the silicon carbide lattice defect density are established.

[0025] The intrinsic parameter index of the silicon carbide is invoked, and the coefficients of the correlation characteristics are calibrated according to the lattice distortion characteristics in the intrinsic parameter index, so as to convert the spatial distribution amplitude into the defect density value of the silicon carbide.

[0026] In a preferred embodiment, the step of identifying different frequency phonons of the silicon carbide based on the defect density value, quantifying and stacking the contribution of the different frequency phonons to heat conduction, and obtaining the heat transfer amount of the silicon carbide in each frequency band includes:

[0027] The phonon spectrum of the silicon carbide is divided, and the distribution weight of the phonon spectrum of each frequency band is adjusted according to the defect density value;

[0028] The carrying ratio of each phonon in the total heat capacity is determined according to the distribution weight, and the heat capacity contribution of each phonon is obtained.

[0029] The phonon dispersion relation data of the silicon carbide is loaded, and the transmission parameters of each frequency band feature in the phonon dispersion relation data are extracted. By coupling the heat capacity contribution with the transmission parameters, the heat energy transfer amount of each frequency band of the silicon carbide is obtained.

[0030] In a preferred embodiment, the dynamic scaling of the heat transfer amount, and the group velocity weighting of the scaled heat transfer amount based on the group velocity values ​​corresponding to the phonon frequencies in the different frequency phonons, to obtain the predicted thermal conductivity value of silicon carbide, includes:

[0031] The nonlinear enhancement effect of phonon scattering is excited based on the defect density value, and the heat transfer is suppressed according to the nonlinear enhancement effect to obtain the scattering reset transfer amount of silicon carbide.

[0032] By associating the phonon frequency with the characteristic momentum coordinates of the silicon carbide, the group velocity values ​​corresponding to the phonon frequencies of the phonons at different frequencies are obtained.

[0033] The predicted thermal conductivity of silicon carbide is obtained by aggregating the scattering reset transmission amount based on the group velocity value.

[0034] In a preferred embodiment, the predicted thermal conductivity of silicon carbide is calculated using the following formula:

[0035]

[0036] κ ′ The values ​​represent predicted thermal conductivity; f=1 represents the low-frequency phonon index, f=2 represents the mid-frequency phonon index, and f=3 represents the high-frequency phonon index.g,f Let κ be the phonon group velocity in the f-th frequency band. f This represents the scattering reset transmission amount of the phonons in the f-th frequency band.

[0037] In a preferred embodiment, generating a thermal performance evaluation report for the silicon carbide based on the predicted thermal conductivity value includes:

[0038] Compare the deviation of the predicted thermal conductivity value from the defect-free baseline value of silicon carbide;

[0039] A defect control optimization evaluation report for the silicon carbide is generated based on the degree of deviation.

[0040] To address the above problems, the present invention also provides a silicon carbide thermal performance prediction system based on thermal conductivity testing, the system comprising:

[0041] The thermal excitation region module applies a periodic thermal excitation signal to the detection surface of silicon carbide to obtain the thermal excitation region of the silicon carbide.

[0042] The spatial gradient parameter module collects the dynamic thermal response signal of the thermally excited region and separates the gradient parameter of the temperature change feature vector in the dynamic response signal in space.

[0043] The defect density value module obtains the defect density value of silicon carbide based on the proportional relationship between the gradient parameter and the silicon carbide lattice defect density.

[0044] The heat transfer module identifies different frequency phonons of the silicon carbide based on the defect density value, quantifies and stacks the contribution of the different frequency phonons to heat conduction, and obtains the heat transfer amount of the silicon carbide in each frequency band.

[0045] The thermal conductivity prediction module dynamically scales the amount of heat transfer and, based on the group velocity value corresponding to the phonon frequency in the phonons of different frequencies, performs group velocity weighting on the scaled amount of heat transfer to obtain the predicted value of the thermal conductivity of silicon carbide.

[0046] The thermal performance evaluation report module generates a thermal performance evaluation report for the silicon carbide based on the predicted thermal conductivity value.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] 1. By applying periodic thermal excitation and collecting dynamic thermal responses, and combining three-dimensional wavelet transform to extract gradient parameters in the spatiotemporal dual-dimensional vector, the proportional relationship between the gradient parameters and the lattice defect density is accurately established, thus accurately obtaining the defect density value. Based on the defect density, phonons of different frequencies are identified, the contribution of each frequency band of phonons to heat conduction is quantified, and then through dynamic scaling and group velocity weighting, the predicted thermal conductivity value is made to better fit the actual heat conduction characteristics, thereby improving the accuracy of thermal conductivity prediction.

[0049] 2. From the thermal excitation region to the determination of the thermal performance evaluation report, the reliability and correlation of each parameter are ensured through the refined processing of multiple aspects such as temperature change characteristics, defect density, and phonon contribution. The final thermal performance evaluation report can comprehensively reflect the thermal performance characteristics of silicon carbide and provide a more effective basis for its thermal performance evaluation. Attached Figure Description

[0050] Figure 1 This is a flowchart illustrating a method for predicting the thermal properties of silicon carbide based on thermal conductivity testing, provided in an embodiment of the present invention.

[0051] Figure 2 A functional block diagram of a silicon carbide thermal performance prediction system based on thermal conductivity testing provided in an embodiment of the present invention;

[0052] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0053] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0054] This application provides a method for predicting the thermal performance of silicon carbide based on thermal conductivity testing. The execution entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for predicting the thermal performance of silicon carbide based on thermal conductivity testing can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cluster of cloud servers. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0055] Reference Figure 1The diagram shown is a flowchart illustrating a method for predicting the thermal performance of silicon carbide based on thermal conductivity testing, according to an embodiment of the present invention. In this embodiment, the reference information generation method based on artificial intelligence and smart home technology includes:

[0056] In this embodiment of the invention, when applying a periodic thermal excitation signal to the detection surface of silicon carbide to obtain the thermal excitation region of silicon carbide, it is specifically used for:

[0057] The laser beam is split and modulated to obtain a periodic thermal pulse sequence;

[0058] The periodic thermal pulse sequence is focused onto a preset coordinate point on the silicon carbide detection surface to obtain the thermal excitation region of the silicon carbide.

[0059] Specifically, a continuous laser beam with a stable wavelength and power is emitted and guided to the center of a semi-transparent and semi-reflective beam splitter. After the laser beam is incident on the surface of the beam splitter, part of the laser energy is transmitted through the beam splitter and the other part of the laser energy is reflected by the beam splitter, thereby splitting the single laser beam into two sub-laser beams with different propagation directions but uniform intensity.

[0060] Specifically, the periodic thermal pulse sequence is guided to the reflector group, and the periodic thermal pulse sequence, whose direction has been adjusted by the reflector group, is sent to the focusing device composed of the objective lens, so that the beam diameter gradually decreases and is concentrated towards the silicon carbide detection surface.

[0061] Furthermore, the two sub-laser beams obtained from the splitting are respectively introduced into two independent acousto-optic modulators. Each acousto-optic modulator is connected to the same periodic electrical signal generator, which outputs a periodic square wave signal with a fixed frequency. When the square wave signal is at a high level, the acousto-optic modulator allows the sub-laser beams to pass through. When the square wave signal is at a low level, the acousto-optic modulator blocks the propagation of the sub-laser beams, so that both sub-laser beams are converted into pulses with the same period.

[0062] Furthermore, a positioning mark is installed on the worktable where the silicon carbide detection surface is located. The position of the mark corresponds to the position of the preset coordinate point. The overlap between the spot of the periodic thermal pulse sequence and the positioning mark is observed through the optical alignment system. The relative position of the focusing device and the worktable is finely adjusted to ensure that the convergence center of the periodic thermal pulse sequence completely coincides with the preset coordinate point.

[0063] In summary, two modulated pulse sub-laser beams are guided to a beam combiner. The beam combiner adjusts the propagation paths of the two pulse sub-laser beams to make them coincide in space, while controlling the time difference between the two pulse sub-laser beams to ensure that the next pulse sub-laser beam starts immediately after the previous one ends, thereby forming a continuous and periodic thermal pulse sequence with a fixed period.

[0064] In general, when a periodic thermal pulse sequence is accurately focused on a preset coordinate point on the silicon carbide detection surface, the coordinate point and the surrounding area within a certain range will experience continuous temperature fluctuations due to the absorption of energy from the periodic thermal pulse sequence. This specific area affected by the temperature fluctuations is the thermal excitation region of the silicon carbide.

[0065] In this embodiment of the invention, the step of acquiring the dynamic thermal response signal of the thermally excited region and separating the gradient parameter of the temperature change feature vector in the dynamic response signal in space is specifically used for:

[0066] The temperature distribution information of the thermally excited region is reconstructed to obtain the dynamic response signal of the silicon carbide;

[0067] A three-dimensional wavelet transform is performed on the dynamic response signal to obtain the spatiotemporal two-dimensional vector of the silicon carbide;

[0068] The feature vectors in the spatiotemporal dual-dimensional vectors whose temperature change rate exceeds a preset threshold are extracted. The second derivative of the feature vectors is obtained along the thermal diffusion direction with the thermal excitation center point as the origin, and used as the gradient parameter of the silicon carbide.

[0069] Specifically, an infrared thermal imager is used to target the thermally excited area of ​​the silicon carbide. The focal length of the infrared thermal imager lens is adjusted to clearly cover the entire thermally excited area. By activating the continuous acquisition mode of the infrared thermal imager, the temperature values ​​of each point in the thermally excited area are captured at fixed time intervals. After continuous acquisition for a preset duration, the temperature field distribution containing temperature information of all locations in the thermally excited area is obtained.

[0070] Specifically, the dynamic response signal is converted into a three-dimensional data array, where the first dimension is the time axis, corresponding to the sequence of the dynamic response signal changing over time, and the second and third dimensions are the spatial axes, corresponding to the radial distance and circumferential angle centered on the thermal excitation point within the thermal excitation region, respectively. The value of each data point is the temperature value at the corresponding time and spatial location, thereby constructing a three-dimensional data structure containing time and spatial information.

[0071] Specifically, the preset threshold is pre-set based on the inherent thermal response characteristics of silicon carbide material and the intensity of the thermal excitation signal. This threshold is determined by conducting multiple thermal excitation experiments on defect-free silicon carbide samples and statistically analyzing the typical range of their temperature change rate. For each feature vector in the spatiotemporal dual-dimensional vector, the temperature values ​​at two adjacent moments in the time dimension are extracted, the difference between the two temperature values ​​is calculated, and divided by the corresponding time interval to obtain the temperature change rate of that feature vector.

[0072] Furthermore, taking the thermal excitation point in the thermal excitation region as the center, multiple concentric rings with different radii are delineated in the obtained temperature field distribution. Each concentric ring is uniformly divided into several sector sub-regions along the circumference. The average temperature value of each sector sub-region is extracted one by one. The average temperature values ​​of all sector sub-regions under the same radius ring are summarized to form the temperature data of the concentric ring of that radius. The temperature data extraction of all concentric rings is completed in sequence to obtain the temperature data of the concentric ring region.

[0073] Furthermore, a wavelet basis function suitable for processing the spatiotemporal variation characteristics of the temperature field is selected. This wavelet basis function must possess good temporal and spatial locality, capable of simultaneously capturing the temporal impulse variations and spatial gradient variations of the dynamic response signal. By matching and verifying this wavelet basis function with the three-dimensional data array, and confirming that its decomposition effect on the dynamic response signal meets the preset standards, this wavelet basis function is determined as the fundamental function of the three-dimensional wavelet transform. The three-dimensional wavelet transform is performed on the constructed three-dimensional data array. First, the selected wavelet basis function is applied along the time axis to decompose the data, obtaining the time component coefficients at different time scales. Then, the same wavelet basis function is applied along the radial distance and circumferential angle directions in space to decompose the data, obtaining the radial component coefficients and angular component coefficients at different spatial scales. By integrating these component coefficients, a transformation result reflecting the multi-scale characteristics of the dynamic response signal in time and space is obtained.

[0074] Furthermore, the calculated temperature change rate of each eigenvector is compared with a preset threshold, and all eigenvectors with a temperature change rate greater than the preset threshold are selected. These eigenvectors are those whose temperature change rate exceeds the preset threshold. Taking the thermal excitation center point as the origin, the dynamic evolution of the temperature distribution in the spatiotemporal dual-dimensional vector over time is analyzed to observe the path of temperature diffusion from the origin to the surrounding area. The direction pointed to by the path with the fastest temperature diffusion speed and the widest range is the direction of thermal diffusion.

[0075] In summary, the extracted temperature data of each concentric ring region are arranged according to the time sequence of acquisition by the infrared thermal imager. The temperature data sequence of each concentric ring is calibrated to ensure that the temperature data corresponding to each time point accurately matches the period of the thermal excitation pulse, so that the temperature data of each concentric ring forms a sequence that changes continuously over time. This time-varying temperature sequence is the dynamic thermal response signal of the silicon carbide.

[0076] In summary, all time component coefficients, radial component coefficients, and angular component coefficients are extracted from the results of the three-dimensional wavelet transform. These coefficients are then arranged in order of increasing time scale and increasing spatial scale to form a multidimensional array containing the time and spatial characteristics of the dynamic response signal. This multidimensional array is the spatiotemporal two-dimensional vector of the silicon carbide.

[0077] In summary, for feature vectors whose temperature change rate exceeds a preset threshold, along a determined thermal diffusion direction, the feature vector components corresponding to three consecutive spatial sampling points in that direction are selected. The difference between the components of the first and second sampling points is calculated, and then the difference between the components of the second and third sampling points is calculated. Subtracting these two differences and dividing by the square of the spatial distance between the two sampling points yields the second derivative of the feature vector in the thermal diffusion direction. The second derivatives of all feature vectors meeting the conditions in the thermal diffusion direction are integrated to form a set reflecting the spatial change rate of temperature change feature vectors; this set constitutes the gradient parameter of the silicon carbide.

[0078] In this embodiment of the invention, when reconstructing the temperature distribution information of the thermally excited region to obtain the dynamic response signal of the silicon carbide, it is specifically used for:

[0079] Capture the temperature field distribution of the thermal excitation region, and extract the temperature data of the concentric annular region centered on the thermal excitation point from the temperature field distribution;

[0080] The temperature data of the concentric ring region are aligned over time to obtain the dynamic thermal response signal of the silicon carbide.

[0081] Specifically, the lens of the infrared thermal imager is aimed at the thermally excited area, the lens focal length is adjusted until the thermally excited area is fully and clearly imaged, and the real-time acquisition function of the infrared thermal imager is activated, so that the infrared thermal imager can continuously capture the temperature information of each spatial point in the thermally excited area. These temperature information together constitute the temperature field distribution of the thermally excited area.

[0082] Specifically, a corresponding acquisition time marker is added to each temperature value in the temperature data of the concentric ring region. This time marker is consistent with the time record when the infrared thermal imager captures the temperature field distribution, ensuring that the temperature data of each concentric ring can correspond to a specific acquisition time.

[0083] Furthermore, in the acquired temperature field distribution, the position coordinates of the thermal excitation point are determined. These coordinates are the preset coordinate points where the periodic thermal pulse sequence is focused on the silicon carbide detection surface. Using the position coordinates of the thermal excitation point as the center, multiple circular trajectories with different radii are set in the temperature field distribution. Each circular trajectory forms a concentric ring, and these concentric rings are all centered on the thermal excitation point and do not overlap with each other.

[0084] Furthermore, a unified time axis is established with the moment when the periodic thermal pulse sequence begins to be applied to the silicon carbide detection surface as the time origin. The scale interval of this time axis is the same as the acquisition time interval of the infrared thermal imager, which can completely cover the acquisition period of temperature data for all concentric ring regions. The temperature data of each concentric ring is matched to the corresponding position on the unified time axis according to its corresponding acquisition time mark. For positions on the time axis where a certain concentric ring does not acquire temperature data at that moment, the temperature value at that position is determined based on the temperature data of that concentric ring at adjacent moments, so that the temperature data of each concentric ring forms a continuous sequence on the unified time axis.

[0085] In summary, for each concentric ring, the temperature values ​​of all spatial points on the ring are extracted. These temperature values ​​are then filtered to remove outliers that significantly deviate from the overall temperature distribution trend of the ring. The average value of the remaining temperature values ​​is then calculated and used as the temperature data for the corresponding concentric ring. The temperature data of all concentric rings together constitute the temperature data of the concentric ring region centered on the thermal excitation point.

[0086] In summary, when the temperature data of all concentric rings are matched on a unified time axis to form multiple temperature sequences that change continuously over time, these sequences together constitute a signal that can reflect the dynamic change of temperature in the thermally excited region of silicon carbide over time. This signal is the dynamic thermal response signal of the silicon carbide.

[0087] In this embodiment of the invention, when obtaining the defect density value of silicon carbide based on the proportional relationship between the gradient parameter and the silicon carbide lattice defect density, it is specifically used for:

[0088] Extract the spatial distribution amplitude features from the gradient parameters;

[0089] Based on the suppression effect of defects in the silicon carbide crystal structure on heat conduction, the correlation characteristics between the spatial distribution amplitude and the silicon carbide lattice defect density are established.

[0090] The intrinsic parameter index of the silicon carbide is invoked, and the coefficients of the correlation characteristics are calibrated according to the lattice distortion characteristics in the intrinsic parameter index, so as to convert the spatial distribution amplitude into the defect density value of the silicon carbide.

[0091] Specifically, a one-dimensional spatial coordinate system is established along the direction of thermal diffusion, with the thermal excitation center point as the origin. The scale of this coordinate system directly corresponds to the straight-line distance from the origin to each spatial point, so that each component of the gradient parameter can correspond to a specific position in this coordinate system. For each component of the gradient parameter, its value at the corresponding spatial position is read, and the absolute value of the value is taken as the magnitude of the gradient parameter at that spatial position, thereby obtaining the gradient magnitude corresponding to each spatial point.

[0092] Specifically, this study elucidates the mechanism by which defects in the silicon carbide crystal structure suppress thermal conduction. Defects in the silicon carbide lattice reduce the mean free path of phonons, the primary carriers of thermal conduction, thus weakening thermal conductivity. This suppression effect alters the temperature gradient distribution in the thermally excited region, thereby affecting the magnitude and trend of its spatial distribution amplitude. A series of standard silicon carbide samples with different known lattice defect densities were selected. The lattice defect densities of these samples were pre-determined using established detection methods such as transmission electron microscopy and X-ray diffraction analysis. The samples maintained consistency in crystal orientation, doping concentration, and other characteristics, differing only in lattice defect density.

[0093] Specifically, the intrinsic parameter index of silicon carbide is a pre-established database that stores the inherent crystal structure parameters of the silicon carbide material, including lattice distortion characteristics, such as the deviation range of the lattice constant and the irregularity of the atomic arrangement. These characteristics are pre-determined and stored through detection methods such as X-ray diffraction and high-resolution transmission electron microscopy. Calling the index means retrieving the lattice distortion characteristic data corresponding to the currently tested silicon carbide from the database.

[0094] Furthermore, the gradient magnitudes of all spatial points are arranged sequentially according to their positions in the one-dimensional spatial coordinate system, forming a sequence that can intuitively reflect the changes in gradient magnitude with spatial position. This sequence is the spatial distribution pattern of the gradient parameter.

[0095] Furthermore, for each silicon carbide standard sample, periodic thermal excitation is applied, dynamic thermal response signals are collected, gradient parameters are extracted, and the spatial distribution amplitude corresponding to each sample is obtained in accordance with the aforementioned steps to ensure that the test environment of all samples is exactly the same, so as to eliminate the interference of external factors on the spatial distribution amplitude.

[0096] Furthermore, the correlation characteristic is the variation law between the spatial distribution amplitude and the defect density of silicon carbide lattice. Its coefficient reflects the strength of this law. According to the extracted lattice distortion characteristics, when the degree of lattice distortion is small, it indicates that the crystal structure is relatively regular and the inhibition effect of defects on heat conduction is weak. At this time, the coefficient of the correlation characteristic is reduced to decrease the influence of the spatial distribution amplitude on the defect density. When the degree of lattice distortion is large, it indicates that the crystal structure is more disordered and the inhibition effect of defects is stronger. At this time, the coefficient of the correlation characteristic is increased to enhance the influence of the spatial distribution amplitude on the defect density. The correlation characteristic coefficient is calibrated in this way.

[0097] In summary, from the above spatial distribution patterns, the maximum value of the gradient magnitude and its spatial location are identified, the distance range covered by spatial points with gradient magnitudes greater than zero is statistically analyzed, and the trend of gradient magnitude gradually changing from the origin to the distance is analyzed. These maximum values, coverage ranges, and trends together constitute a set that can characterize the spatial distribution intensity features of the gradient parameter. This set is the spatial distribution magnitude feature of the gradient parameter.

[0098] In summary, a one-to-one correspondence is established between the known lattice defect density of each standard sample and its corresponding spatial distribution amplitude. A relationship curve is plotted, and the changing trend of the curve is observed. For example, the spatial distribution amplitude shows an increasing or decreasing pattern as the lattice defect density increases. Simultaneously, the specific characteristics of the spatial distribution amplitude change within different defect density ranges are recorded. Based on the above relationship curves and changing characteristics, a deterministic law governing the change of spatial distribution amplitude with silicon carbide lattice defect density is summarized. This law clearly reflects how the spatial distribution amplitude changes accordingly when the lattice defect density changes; this law is the correlation characteristic between the spatial distribution amplitude and the silicon carbide lattice defect density.

[0099] In summary, by substituting the extracted spatial distribution amplitude into the correlation characteristics after coefficient calibration, the specific value of the spatial distribution amplitude will correspond to a unique lattice defect density value based on the variation pattern reflected by the correlation characteristics. This value is the defect density value of the silicon carbide.

[0100] In this embodiment of the invention, the step of identifying different frequency phonons of the silicon carbide based on the defect density value, quantifying and stacking the contribution of the different frequency phonons to heat conduction, and obtaining the heat transfer amount of the silicon carbide in each frequency band is specifically used for:

[0101] The phonon spectrum of the silicon carbide is divided, and the distribution weight of the phonon spectrum of each frequency band is adjusted according to the defect density value;

[0102] The carrying ratio of each phonon in the total heat capacity is determined according to the distribution weight, and the heat capacity contribution of each phonon is obtained.

[0103] The phonon dispersion relation data of the silicon carbide is loaded, and the transmission parameters of each frequency band feature in the phonon dispersion relation data are extracted. By coupling the heat capacity contribution with the transmission parameters, the heat energy transfer amount of each frequency band of the silicon carbide is obtained.

[0104] Specifically, a phonon spectrometer is used to measure the phonon spectrum of the silicon carbide. The phonon spectrometer acquires complete spectral data covering the frequency range of phonon vibrations in silicon carbide by detecting energy transition signals generated by the vibrations of the silicon carbide lattice. This data includes phonon vibration intensity information at different frequencies, forming the phonon spectrum of the silicon carbide. Based on the frequency characteristics of the silicon carbide phonon vibrations, the acquired phonon spectrum is divided into multiple continuous and non-overlapping frequency bands in ascending order of frequency. The frequency range of each band is determined by analyzing the nodes of vibration intensity change in the phonon spectrum. These nodes correspond to the transition points of phonon vibration modes, ensuring the consistency of phonon vibration characteristics within each band, thereby completing the division of the silicon carbide phonon spectrum.

[0105] Specifically, a differential scanning calorimeter is used to measure the total heat capacity of the silicon carbide. The differential scanning calorimeter controls the heat change of the silicon carbide sample during the programmed temperature rise process, records the relationship between the heat absorbed or released by the sample and the temperature, and after calibration and calculation, obtains the total heat capacity value of the silicon carbide within the test temperature range. This total heat capacity value reflects the sum of the heat capacity contributed by all phonons in the silicon carbide.

[0106] Specifically, the phonon dispersion relation data of silicon carbide is data that is pre-determined by neutron scattering experiments and stored in a material property database. This data records the correspondence between the vibration frequency and wave vector of phonons of different frequencies in silicon carbide. The loading process retrieves phonon dispersion relation data from the database that perfectly matches the crystal structure and doping type of the silicon carbide sample being tested, ensuring that the loaded data can accurately reflect the phonon propagation characteristics of the silicon carbide.

[0107] Furthermore, defect-free silicon carbide standard samples were selected, and their phonon spectra were measured using the same phonon spectrometer and divided into the same frequency bands. The proportion of each frequency band in the total phonon vibration intensity of the standard samples was statistically analyzed, and this proportion was used as the initial distribution weight of the phonon spectrum of each frequency band. The initial distribution weight reflects the contribution of each frequency band of phonons to heat conduction in the defect-free state. The correlation between the defect density value and phonon scattering was analyzed. It is known that the higher the defect density in silicon carbide, the stronger the scattering effect on high-frequency phonons, resulting in a more significant attenuation of the vibration intensity of high-frequency phonons, while the scattering effect on low-frequency phonons is relatively weak; conversely, the lower the defect density, the slighter the attenuation of the vibration intensity of high-frequency phonons.

[0108] Furthermore, the distribution weights are correlated with the contribution of each frequency band phonon to the total heat capacity. It is known that the distribution weights of the phonon spectrum of each frequency band already reflect the proportion of phonons of different frequencies in the total phonon vibration. The heat capacity contribution of phonons is directly related to their vibration proportion. Therefore, the carrying ratio of phonons in the total heat capacity of each frequency band is equal to the distribution weight of the corresponding frequency band. That is, the carrying ratio of a certain frequency band is the same as the distribution weight of that frequency band. This is used to determine the carrying ratio of phonons in the total heat capacity of each frequency band.

[0109] Furthermore, the loaded phonon dispersion relation data is matched to the previously divided phonon spectrum bands. For each band, the vibration mode and propagation characteristics of the phonons in that band are analyzed, and physical quantities that can reflect the transmission capability of phonons in that band are extracted. These physical quantities are the transmission parameters of each band. Each band corresponds to a transmission parameter, and its magnitude is directly related to the propagation efficiency of phonons in that band.

[0110] In summary, when the defect density value is high, the distribution weight of the high-frequency phonon spectrum is reduced and the distribution weight of the low-frequency phonon spectrum is increased; when the defect density value is low, the distribution weight of the high-frequency phonon spectrum is increased and the distribution weight of the low-frequency phonon spectrum is decreased. The adjustment range is proportional to the magnitude of the defect density value, so that the adjusted distribution weights of each frequency band can accurately reflect the influence of the defect on the vibration intensity of phonons at different frequencies, and finally obtain the distribution weights of the phonon spectrum of each frequency band adjusted according to the defect density value.

[0111] In summary, based on the carrying capacity ratio of phonons in each frequency band and the total heat capacity of silicon carbide, the heat capacity value corresponding to each phonon in each frequency band is calculated. Specifically, the total heat capacity value is allocated according to the carrying capacity ratio of each frequency band. The heat capacity contribution of a phonon in a certain frequency band is equal to the product of the carrying capacity ratio of that frequency band and the total heat capacity value. Through this allocation method, the heat capacity part corresponding to each phonon in each frequency band is obtained, and these heat capacity parts are the heat capacity contribution of each phonon.

[0112] In summary, for each frequency band, the thermal capacity contribution of that band is correlated with the corresponding transmission parameters. The thermal capacity contribution reflects the ability of phonons in that band to carry thermal energy, while the transmission parameters reflect the efficiency of phonons in transmitting thermal energy. By combining the physical characteristics of the two, i.e., when the thermal capacity contribution is greater and the transmission parameters are higher, the amount of thermal energy transferred in that frequency band is greater, the thermal energy transfer value corresponding to each frequency band is finally obtained. These values ​​together constitute the amount of thermal energy transferred in each frequency band of the silicon carbide.

[0113] In this embodiment of the invention, the dynamic scaling of the heat transfer amount and the group velocity weighting of the scaled heat transfer amount based on the group velocity values ​​corresponding to the phonon frequencies in the different frequency phonons to obtain the predicted value of the thermal conductivity of silicon carbide are specifically used for:

[0114] The nonlinear enhancement effect of phonon scattering is excited based on the defect density value, and the heat transfer is suppressed according to the nonlinear enhancement effect to obtain the scattering reset transfer amount of silicon carbide.

[0115] By associating the phonon frequency with the characteristic momentum coordinates of the silicon carbide, the group velocity values ​​corresponding to the phonon frequencies of the phonons at different frequencies are obtained.

[0116] The predicted thermal conductivity of silicon carbide is obtained by aggregating the scattering reset transmission amount based on the group velocity value.

[0117] Specifically, the manifestation of the nonlinear enhancement effect of phonon scattering is clarified. When the defect density value of silicon carbide exceeds a certain range, the defects will interact with each other, so that the probability of phonons being scattered no longer increases linearly with the increase of defect density, but shows a faster growth trend. This faster growth trend is the nonlinear enhancement effect, and its intensity increases with the increase of defect density value.

[0118] Specifically, the characteristic momentum coordinates of silicon carbide are the coordinates of high symmetry points in the Brillouin zone determined by its crystal structure. These coordinates reflect the key momentum positions for phonon propagation in the silicon carbide lattice. The phonon dispersion relation data of the silicon carbide is loaded, which records the phonon frequencies corresponding to different momentum coordinates, presenting a continuous curve of phonon frequency changing with momentum coordinates. Each curve corresponds to a phonon vibration mode, covering all previously divided phonon frequency bands.

[0119] Specifically, the group velocity values ​​corresponding to phonons of different frequencies are classified according to the frequency bands of the phonon spectrum, so that each frequency band corresponds to a unique group velocity value. The group velocity value is the average level of the group velocity values ​​corresponding to all phonon frequencies in the frequency band. At the same time, it is ensured that the group velocity value of each frequency band is associated with the scattering reset transmission amount of the frequency band.

[0120] Furthermore, based on the specific magnitude of the defect density value, the intensity level of the corresponding phonon scattering nonlinear enhancement effect is determined. The larger the defect density value, the higher the corresponding intensity level, indicating that the probability of phonons being scattered is increased more significantly, and the stronger the hindering effect on heat energy transfer.

[0121] Furthermore, for each phonon frequency among the different frequency phonons, a point with the same value as that frequency is found in the phonon dispersion relation data, and the momentum coordinate corresponding to that point is read. If the frequency corresponds to multiple momentum coordinates, the momentum coordinate at or near the characteristic momentum coordinate is selected as the association result.

[0122] Furthermore, for each frequency band, the scattering reset transmission amount of that frequency band is combined with the corresponding group velocity value. The combination method is to use the group velocity value to reflect the efficiency of phonon thermal energy transmission in that frequency band, so that the scattering reset transmission amount can reflect the difference in thermal conductivity caused by the difference in phonon propagation speed, forming a separate contribution of each frequency band to thermal conductivity.

[0123] In summary, the determined nonlinear enhancement effect intensity level is applied to the heat transfer amount. For the heat transfer amount of each frequency band, suppression adjustments are made according to its corresponding phonon frequency and intensity level. High-frequency phonons are more easily scattered by defects, and therefore experience greater suppression than low-frequency phonons at the same intensity level. The suppression magnitude increases with the increase of the intensity level, thereby achieving directional suppression of heat transfer amount based on the nonlinear enhancement effect. After the above suppression adjustments, the heat transfer amount of each frequency band reflects the nonlinear effect of defect scattering. These adjusted heat transfer amounts of each frequency band are the scattering reset transfer amount of the silicon carbide.

[0124] In summary, for each associated characteristic momentum coordinate phonon frequency, the frequency variation near that characteristic momentum coordinate in the phonon dispersion relation data is analyzed. Specifically, two adjacent momentum points on both sides of the characteristic momentum coordinate are selected, the phonon frequencies corresponding to these two momentum points are read, the difference between these two frequencies is calculated, and then divided by the momentum difference between the two momentum points. The result is the rate of change of the phonon frequency at that characteristic momentum coordinate, which is the group velocity value corresponding to the phonon frequency in the phonons of different frequencies.

[0125] In summary, the individual contributions of all frequency bands are summed up, while maintaining the independence of each band's contribution during the summation process and without changing its original value. Only a comprehensive value is obtained by summing, which fully reflects the thermal conductivity under the combined action of phonons in all frequency bands, and is the predicted thermal conductivity value of silicon carbide.

[0126] In this embodiment of the invention, the formula for calculating the predicted thermal conductivity of silicon carbide is as follows:

[0127]

[0128] κ ′ The values ​​represent predicted thermal conductivity; f=1 represents the low-frequency phonon index, f=2 represents the mid-frequency phonon index, and f=3 represents the high-frequency phonon index. g,f Let κ be the phonon group velocity in the f-th frequency band. f This represents the scattering reset transmission amount of the phonons in the f-th frequency band.

[0129] Specifically, the phonon band index is derived from the continuous and non-overlapping frequency bands determined when dividing the phonon spectrum of the silicon carbide, with each index corresponding to a specific phonon frequency range. The phonon group velocity of the i-th frequency band is a value calculated by analyzing the frequency changes near the characteristic momentum coordinate in the phonon dispersion relation data after associating the phonon frequency of the corresponding frequency band with the characteristic momentum coordinate of the silicon carbide, reflecting the propagation speed of phonons in that frequency band. The scattering reset transmission amount of the i-th frequency band is a value obtained by suppressing and adjusting the thermal energy transmission amount of each frequency band based on the nonlinear enhancement effect of phonon scattering excited by the defect density value, reflecting the thermal energy transmission capability of that frequency band after being affected by defect scattering.

[0130] Furthermore, by summing the combined contributions of phonon group velocity and corresponding scattering reset transmission in each frequency band, and integrating the effects of phonons in all frequency bands on heat conduction, a predicted thermal conductivity value that reflects the overall thermal conductivity of the silicon carbide is obtained.

[0131] In general, when the phonon group velocity of a certain frequency band increases, the contribution of that frequency band to the predicted thermal conductivity increases, which may lead to an increase in the predicted total thermal conductivity. When the scattering reset transmission of a certain frequency band increases, the contribution of that frequency band also increases, and the predicted total thermal conductivity may increase. Conversely, if the phonon group velocity or scattering reset transmission of a certain frequency band decreases, the contribution of that frequency band decreases, and the predicted total thermal conductivity may decrease. The changes in parameters of each frequency band directly affect the overall magnitude of the predicted thermal conductivity through accumulation.

[0132] In this embodiment of the invention, the step of generating the thermal performance evaluation report of silicon carbide based on the predicted thermal conductivity value is specifically used for:

[0133] Compare the deviation of the predicted thermal conductivity value from the defect-free baseline value of silicon carbide;

[0134] A defect control optimization evaluation report for the silicon carbide is generated based on the degree of deviation.

[0135] Specifically, the defect-free benchmark value of silicon carbide is obtained by testing a standard sample of silicon carbide known to be free of lattice defects using the same thermal conductivity testing method. This benchmark value is pre-stored in a material property database, and its acquisition process is consistent with the testing process of the predicted thermal conductivity value, ensuring that the testing conditions and calculation methods are exactly the same.

[0136] Specifically, a standard for classifying the degree of deviation is determined. This standard is based on the deviation ratio between the predicted thermal conductivity value and the defect-free baseline value. Combined with the defect density value and deviation level of the silicon carbide, the defect-related causes leading to the deviation are analyzed. For example, significant deviation usually corresponds to a higher defect density. These defects reduce thermal conductivity by enhancing phonon scattering. The performance of the defect on the heat conduction suppression effect needs to be explained in detail, such as the correlation between the degree of reduction in high-frequency phonon transmission and the defect density.

[0137] Furthermore, the defect-free baseline value of the silicon carbide is retrieved from the material property database, and the predicted thermal conductivity value of the silicon carbide obtained through the aforementioned steps is extracted. These two values ​​are placed in the same comparison system to ensure that their units and testing environment parameters are completely matched, eliminating comparison errors caused by parameter inconsistencies. The absolute difference between the predicted thermal conductivity value and the defect-free baseline value is calculated, i.e., subtracting the defect-free baseline value from the predicted thermal conductivity value. If the result is positive, it indicates that the predicted thermal conductivity value is higher than the defect-free baseline value; if the result is negative, it indicates that the predicted thermal conductivity value is lower than the defect-free baseline value. This absolute difference directly reflects the magnitude of the numerical difference between the two values.

[0138] Furthermore, based on the analyzed causes and deviation levels of defects, defect control optimization measures should be formulated. For slight deviations, it is recommended to fine-tune the temperature gradient during crystal growth to reduce the generation of local defects. For moderate deviations, it is necessary to strengthen the control of raw material purity to reduce defects caused by impurities. For significant deviations, the growth atmosphere pressure should be optimized to suppress the generation of serious defects such as dislocations. All measures should clearly define the operation direction and the expected effect on reducing defect density.

[0139] In summary, using the defect-free benchmark value as a reference, the ratio between the absolute difference and the defect-free benchmark value is calculated. This ratio reflects the degree of deviation of the predicted thermal conductivity value from the defect-free benchmark value. The larger the ratio, the more significant the deviation. In this way, the comparison of the degree of deviation between the predicted thermal conductivity value and the defect-free benchmark value of silicon carbide is completed.

[0140] In summary, by integrating deviation levels, defect cause analysis, and optimization measures, a structured document is formed that includes basic information about the evaluation object, thermal conductivity deviation, defect impact mechanism, and specific optimization suggestions. This document is the defect control optimization evaluation report for silicon carbide. The report must ensure that all parts are consistent with each other and provide actionable guidance for defect control.

[0141] Compared with the prior art, the present invention has the following beneficial effects:

[0142] 1. By applying periodic thermal excitation and collecting dynamic thermal responses, and combining three-dimensional wavelet transform to extract gradient parameters in the spatiotemporal dual-dimensional vector, the proportional relationship between the gradient parameters and the lattice defect density is accurately established, thus accurately obtaining the defect density value. Based on the defect density, phonons of different frequencies are identified, the contribution of each frequency band of phonons to heat conduction is quantified, and then through dynamic scaling and group velocity weighting, the predicted thermal conductivity value is made to better fit the actual heat conduction characteristics, thereby improving the accuracy of thermal conductivity prediction.

[0143] 2. From the thermal excitation region to the determination of the thermal performance evaluation report, the reliability and correlation of each parameter are ensured through the refined processing of multiple aspects such as temperature change characteristics, defect density, and phonon contribution. The final thermal performance evaluation report can comprehensively reflect the thermal performance characteristics of silicon carbide and provide a more effective basis for its thermal performance evaluation.

[0144] like Figure 2 The diagram shown is a functional block diagram of a reference information generation system based on artificial intelligence and smart home provided in an embodiment of the present invention.

[0145] The silicon carbide thermal performance prediction system 100 based on thermal conductivity testing described in this invention can be installed in an electronic device. Depending on the functions implemented, the silicon carbide thermal performance prediction system 100 may include a thermal excitation region module 101, a spatial gradient parameter module 102, a defect density value module 103, a heat transfer quantity module 104, a thermal conductivity prediction value module 105, and a thermal performance evaluation report module 106. The module described in this invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and are stored in the memory of the electronic device.

[0146] In this embodiment, the functions of each module / unit are as follows:

[0147] The thermal excitation region module applies a periodic thermal excitation signal to the detection surface of silicon carbide to obtain the thermal excitation region of the silicon carbide.

[0148] The spatial gradient parameter module collects the dynamic thermal response signal of the thermally excited region and separates the gradient parameter of the temperature change feature vector in the dynamic response signal in space.

[0149] The defect density value module obtains the defect density value of silicon carbide based on the proportional relationship between the gradient parameter and the silicon carbide lattice defect density.

[0150] The heat transfer module identifies different frequency phonons of the silicon carbide based on the defect density value, quantifies and stacks the contribution of the different frequency phonons to heat conduction, and obtains the heat transfer amount of the silicon carbide in each frequency band.

[0151] The thermal conductivity prediction module dynamically scales the amount of heat transfer and, based on the group velocity value corresponding to the phonon frequency in the phonons of different frequencies, performs group velocity weighting on the scaled amount of heat transfer to obtain the predicted value of the thermal conductivity of silicon carbide.

[0152] The thermal performance evaluation report module generates a thermal performance evaluation report for the silicon carbide based on the predicted thermal conductivity value.

[0153] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0154] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0155] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0156] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0157] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0158] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for predicting the thermal properties of silicon carbide based on thermal conductivity testing, characterized in that, The method includes: A periodic thermal excitation signal is applied to the detection surface of silicon carbide to obtain the thermal excitation region of the silicon carbide; The dynamic thermal response signal of the thermally excited region is acquired, and the gradient parameter of the temperature change feature vector in the dynamic thermal response signal in space is separated. This includes: reconstructing the temperature distribution information of the thermally excited region to obtain the dynamic thermal response signal of the silicon carbide; A three-dimensional wavelet transform is performed on the dynamic thermal response signal to obtain the spatiotemporal two-dimensional vector of the silicon carbide. The feature vectors in the spatiotemporal dual-dimensional vectors whose temperature change rate exceeds a preset threshold are extracted. The second derivative of the feature vectors is obtained along the thermal diffusion direction with the thermal excitation center point as the origin, and used as the gradient parameter of the silicon carbide. Based on the proportional relationship between the gradient parameter and the silicon carbide lattice defect density, the defect density value of the silicon carbide is obtained, including: Extract the spatial distribution amplitude features from the gradient parameters; Based on the suppression effect of defects in the silicon carbide crystal structure on heat conduction, the correlation characteristics between the spatial distribution amplitude and the silicon carbide lattice defect density are established. The intrinsic parameter index of the silicon carbide is called, and the coefficients of the correlation characteristics are calibrated according to the lattice distortion characteristics in the intrinsic parameter index, and the spatial distribution amplitude is converted into the defect density value of the silicon carbide. Based on the defect density value, different frequency phonons of the silicon carbide are identified, and the contribution of the different frequency phonons to heat conduction is quantified and stacked to obtain the heat transfer amount of the silicon carbide in each frequency band. include: The phonon spectrum of the silicon carbide is divided, the distribution weight of the phonon spectrum of each frequency band is adjusted according to the defect density value, and the carrying ratio of each phonon in the total heat capacity is determined according to the distribution weight to obtain the heat capacity contribution of each phonon. Load the phonon dispersion relation data of the silicon carbide, extract the transmission parameters of each frequency band feature in the phonon dispersion relation data, and obtain the heat energy transfer amount of each frequency band of the silicon carbide by coupling the heat capacity contribution with the transmission parameters. The thermal energy transfer quantity is dynamically scaled, and based on the group velocity values ​​corresponding to the phonon frequencies in the phonons of different frequencies, the scaled thermal energy transfer quantity is weighted by group velocity to obtain the predicted value of the thermal conductivity of silicon carbide, including: The nonlinear enhancement effect of phonon scattering is excited based on the defect density value, and the heat transfer is suppressed according to the nonlinear enhancement effect to obtain the scattering reset transfer amount of silicon carbide. By associating the phonon frequency with the characteristic momentum coordinates of the silicon carbide, the group velocity values ​​corresponding to the phonon frequencies of the phonons at different frequencies are obtained. Based on the group velocity value, the scattering reset transmission amount is aggregated to obtain the predicted value of the thermal conductivity of silicon carbide. A thermal performance evaluation report for the silicon carbide is generated based on the predicted thermal conductivity value.

2. The method for predicting the thermal properties of silicon carbide based on thermal conductivity testing as described in claim 1, characterized in that, The step of applying a periodic thermal excitation signal to the detection surface of silicon carbide to obtain the thermal excitation region of silicon carbide includes: The laser beam is split and modulated to obtain a periodic thermal pulse sequence; The periodic thermal pulse sequence is focused onto a preset coordinate point on the silicon carbide detection surface to obtain the thermal excitation region of the silicon carbide.

3. The method for predicting the thermal properties of silicon carbide based on thermal conductivity testing as described in claim 1, characterized in that, The process of reconstructing the temperature distribution information of the thermally excited region to obtain the dynamic thermal response signal of the silicon carbide includes: Capture the temperature field distribution of the thermal excitation region, and extract the temperature data of the concentric annular region centered on the thermal excitation point from the temperature field distribution; The temperature data of the concentric ring region are aligned over time to obtain the dynamic thermal response signal of the silicon carbide.

4. The method for predicting the thermal properties of silicon carbide based on thermal conductivity testing as described in claim 1, characterized in that, The formula for calculating the predicted thermal conductivity of silicon carbide is as follows: This is the predicted value for thermal conductivity. The time is for low-frequency phonon indexing. The time is the mid-frequency phonon index. High-frequency phonon index, For the first Frequency band phonon group velocity, For the first The scattering of phonons in the frequency band resets the transmission volume.

5. The method for predicting the thermal properties of silicon carbide based on thermal conductivity testing as described in claim 1, characterized in that, The step of generating a thermal performance evaluation report for silicon carbide based on the predicted thermal conductivity value includes: Compare the deviation of the predicted thermal conductivity value from the defect-free baseline value of silicon carbide; A defect control optimization evaluation report for the silicon carbide is generated based on the degree of deviation.

6. The silicon carbide thermal performance prediction system according to any one of claims 1-5, characterized in that, The system includes: The thermal excitation region module applies a periodic thermal excitation signal to the detection surface of silicon carbide to obtain the thermal excitation region of the silicon carbide. The spatial gradient parameter module collects the dynamic thermal response signal of the thermally excited region and separates the gradient parameter of the temperature change feature vector in the dynamic thermal response signal in space. The defect density value module obtains the defect density value of silicon carbide based on the proportional relationship between the gradient parameter and the silicon carbide lattice defect density. The heat transfer module identifies different frequency phonons of the silicon carbide based on the defect density value, quantifies and stacks the contribution of the different frequency phonons to heat conduction, and obtains the heat transfer amount of the silicon carbide in each frequency band. The thermal conductivity prediction module dynamically scales the amount of heat transfer and, based on the group velocity value corresponding to the phonon frequency in the phonons of different frequencies, performs group velocity weighting on the scaled amount of heat transfer to obtain the predicted value of the thermal conductivity of silicon carbide. The thermal performance evaluation report module generates a thermal performance evaluation report for the silicon carbide based on the predicted thermal conductivity value.

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