Method, apparatus, equipment and storage medium for predicting temperature-varying electric fields based on multiphase heterogeneous interfaces

Abstract

This application discloses a method, apparatus, device, and storage medium for predicting temperature-varying electric fields based on multiphase heterogeneous interfaces. This application acquires a dielectric property characterization dataset; calculates the electric field distribution based on the dielectric property characterization dataset to obtain the temperature-varying electric field calculation results; constructs a model based on the temperature-varying electric field calculation results to obtain a temperature-varying electric field prediction model; and inputs the multiphase heterogeneous interface material data into the temperature-varying electric field prediction model for fitting calculation to obtain the temperature-varying electric field prediction results. This application first constructs a temperature-varying electric field prediction model based on the dielectric property characterization dataset, and then performs fitting calculation through the temperature-varying electric field prediction model to obtain the temperature-varying electric field prediction results, achieving high-precision electric field prediction and improving the insulation reliability of the equipment.

Description

Technical Field

[0001] This application relates to the technical field of high-temperature and high-frequency insulation of multiphase heterogeneous interfaces, and in particular to a method, apparatus, equipment and storage medium for predicting temperature-varying electric fields based on multiphase heterogeneous interfaces. Background Technology

[0002] Currently, under harsh operating conditions of high temperature, high frequency, and positive square wave voltage, the electric field distortion and local concentration problems caused by the temperature-dependent electrical parameter mismatch between materials at multiphase heterogeneous interfaces in power electronic equipment seriously threaten the insulation reliability of these devices. Furthermore, while existing electric field calculation methods based on frequency-variable dielectric constants can effectively characterize the dielectric response under high-frequency conditions, they struggle to accurately describe the electric field distribution at multiphase heterogeneous interfaces under temperature variations, exhibiting shortcomings such as poor universality and low accuracy.

[0003] To overcome the above limitations, there is an urgent need for a temperature-varying electric field prediction method for multiphase heterogeneous interfaces that has both wide applicability to materials and high precision characteristics, and can reflect the conductivity mechanism of materials, so as to improve the electric field prediction capability and insulation design reliability of multiphase heterogeneous interfaces under harsh working conditions. Summary of the Invention

[0004] This application provides a method, apparatus, device, and storage medium for predicting temperature-varying electric fields based on multiphase heterogeneous interfaces to solve the aforementioned technical problems.

[0005] The first aspect of this application provides a method for predicting temperature-varying electric fields based on multiphase heterogeneous interfaces, including:

[0006] Obtain a set of dielectric property characterization data;

[0007] Electric field distribution was calculated based on dielectric property characterization dataset, and temperature-varying electric field calculation results were obtained.

[0008] A model is constructed based on the calculation results of the temperature-varying electric field to obtain the temperature-varying electric field prediction model;

[0009] The data of multiphase heterogeneous interface materials are input into the temperature-varying electric field prediction model for fitting calculation to obtain the temperature-varying electric field prediction results.

[0010] In some embodiments, the electric field distribution is calculated based on a dielectric property characterization dataset to obtain the temperature-varying electric field calculation results, including:

[0011] The time-domain voltage excitation is decomposed to obtain the set of frequency-domain voltage components;

[0012] Model fitting was performed based on the dielectric property characterization dataset to obtain the fitting results of frequency-varying dielectric parameters.

[0013] The electric field distribution is calculated based on the fitting results of the frequency domain component voltage set and the frequency-varying dielectric parameters, and the temperature-varying electric field calculation results are obtained.

[0014] In some embodiments, a model is constructed based on the calculated results of the temperature-varying electric field to obtain a temperature-varying electric field prediction model, including:

[0015] Correlation analysis was performed based on the calculated results of the temperature-varying electric field and the temperature-varying conductivity data to obtain the correlation analysis results;

[0016] A model for predicting temperature-varying electric fields was constructed based on the results of correlation analysis.

[0017] In some embodiments, the multiphase heterogeneous interface has a three-binding point structure formed by a ceramic substrate, a silicon elastomer, and a metal electrode.

[0018] In some embodiments, a model is constructed based on the correlation analysis results to obtain a temperature-varying electric field prediction model, including:

[0019] Read the correlation analysis results. The correlation analysis results can be expressed by the formula:

[0020]

[0021] In the formula, E0 is the first fitting parameter, A is the second fitting parameter, and σ a σ is the DC conductivity of the ceramic substrate. b The DC conductivity of silicon elastomer;

[0022] Based on the correlation analysis results, a model for predicting temperature-varying electric fields was constructed, which can be expressed by the formula:

[0023]

[0024] In the formula, E is the temperature-varying electric field strength, E0 is the first fitting parameter, A is the second fitting parameter, T is the thermodynamic temperature, and E a E represents the activation energy corresponding to the ceramic substrate. a ' is the activation energy corresponding to the silicon elastomer, k B Let T0 be the Boltzmann function, and T0 be a quantity related to the glass transition temperature in the fitting of the conductivity of silicon elastomers.

[0025] In some embodiments, a model is constructed based on the correlation analysis results to obtain a temperature-varying electric field prediction model, including:

[0026] Based on the correlation analysis results, a temperature change model of the heterogeneous interface was constructed, and temperature change models of ceramic substrate and silicon elastomer were obtained respectively.

[0027] Based on the correlation analysis results, the temperature change model of the ceramic substrate, and the temperature change model of the silicon elastomer, a model for predicting the temperature-changing electric field was constructed.

[0028] In some embodiments, a heterogeneous interface temperature change model is constructed based on the correlation analysis results, resulting in a ceramic substrate temperature change model and a silicon elastomer temperature change model, respectively, including:

[0029] Based on the correlation analysis results, the relationship between the conductivity of the ceramic substrate and temperature change is described, resulting in a temperature change model for the ceramic substrate. This model can be expressed by the formula:

[0030]

[0031] In the formula, σ1 is the temperature change model of the ceramic substrate, σ is a constant, and E a It is the activation energy, k B Boltzmann's constant is denoted as T, and T is the thermodynamic temperature.

[0032] Based on the correlation analysis results, the relationship between the conductivity of silicon elastomers and temperature changes is described, resulting in a temperature-dependent model for silicon elastomers. This model can be expressed by the formula:

[0033]

[0034] In the formula, σ2 is the temperature change model of silicon elastomer, σ is a constant, Ea' is the activation energy of the corresponding silicon elastomer, and k B is Boltzmann constant, T is thermodynamic temperature, and T0 is a quantity related to the glass transition temperature in the fitting of the conductivity of silicon elastomers.

[0035] The second aspect of this application proposes a temperature-varying electric field prediction device based on a multiphase heterogeneous interface, comprising a data acquisition module, a data calculation module, a model building module, and a calculation and prediction module, wherein...

[0036] The data acquisition module is used to acquire a set of dielectric performance characterization data.

[0037] The data calculation module is used to calculate the electric field distribution based on the dielectric property characterization data set and obtain the temperature-varying electric field calculation results.

[0038] The model building module is used to build a model based on the temperature-varying electric field calculation results, and obtain a temperature-varying electric field prediction model.

[0039] The calculation and prediction module is used to input multiphase heterogeneous interface material data into the temperature-varying electric field prediction model for fitting calculation and to obtain the temperature-varying electric field prediction results.

[0040] A third aspect of this application provides an electronic device comprising: a memory; a processor; and one or more computer programs stored in the memory, the one or more computer programs including instructions that, when executed by the processor, enable the implementation of the temperature-varying electric field prediction method based on a multiphase heterogeneous interface as described above.

[0041] A fourth aspect of this application provides a computer-readable storage medium storing a computer program, wherein the storage medium stores a temperature-varying electric field prediction program based on a multiphase heterogeneous interface, and when the temperature-varying electric field prediction program based on a multiphase heterogeneous interface is executed by a processor, it implements the steps of the temperature-varying electric field prediction method based on a multiphase heterogeneous interface as described above.

[0042] This application provides a method, apparatus, device, and storage medium for predicting temperature-varying electric fields based on multiphase heterogeneous interfaces. The method involves acquiring a set of dielectric property characterization data; calculating the electric field distribution based on the dielectric property characterization data set to obtain temperature-varying electric field calculation results; constructing a model based on the temperature-varying electric field calculation results to obtain a temperature-varying electric field prediction model; and inputting multiphase heterogeneous interface material data into the temperature-varying electric field prediction model for fitting calculation to obtain the temperature-varying electric field prediction results. This application first constructs a temperature-varying electric field prediction model based on the dielectric property characterization data set, and then performs fitting calculations using the temperature-varying electric field prediction model to obtain the temperature-varying electric field prediction results. This achieves high-precision electric field prediction and improves the insulation reliability of the equipment. Attached Figure Description

[0043] Figure 1 This is a schematic diagram of the structure of the temperature-varying electric field prediction device based on a multiphase heterogeneous interface provided in the embodiments of this application.

[0044] Figure 2 This is a flowchart illustrating the temperature-varying electric field prediction method based on a multiphase heterogeneous interface provided in this application embodiment.

[0045] Figure 3 yes Figure 2 A schematic diagram of the sub-process of step S20.

[0046] Figure 4 yes Figure 2 A schematic diagram of the sub-process of step S30.

[0047] Figure 5 yes Figure 4 A schematic diagram of the sub-process of step S32.

[0048] Figure 6 yes Figure 5 A schematic diagram of the sub-process of step S322.

[0049] Figure 7This is a structural block diagram of the temperature-varying electric field prediction device based on a multiphase heterogeneous interface provided in the embodiments of this application.

[0050] Figure 8 This is another structural block diagram of the temperature-varying electric field prediction device based on a multiphase heterogeneous interface provided in the embodiments of this application. Detailed Implementation

[0051] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of this application.

[0052] Currently, under harsh operating conditions such as high temperatures (100-200℃), high frequencies (100Hz-10kHz), and positive square wave voltages, the multiphase heterogeneous interfaces commonly found in power electronic equipment—typically the three-way junctions consisting of a ceramic substrate, silicon elastomer, and metal electrode—are prone to interfacial electric field distortion and localized concentration phenomena due to significant mismatches in the electrical parameters (conductivity, dielectric constant) of the different materials as they change with temperature. This problem severely restricts the long-term stability of the insulation system and has become a core bottleneck threatening the insulation reliability of power electronic equipment.

[0053] In existing technologies, while electric field calculation methods based on frequency-varying dielectric constants can effectively characterize dielectric response under high-frequency conditions, they struggle to accurately describe the electric field distribution at multiphase heterogeneous interfaces under temperature variations. These methods suffer from the following limitations: First, their model universality is severely insufficient. Existing solutions are mostly designed for specific material combinations, such as silicon elastomer interfaces, performing parameter fitting. However, their adaptability to other common material systems like silicon elastomers is extremely poor, with goodness-of-fit often below 0.01, failing to meet the diverse material combination requirements of practical engineering. Second, their computational accuracy is limited. Because they do not fully consider the differences in conductivity mechanisms among different materials—for example, silicon elastomers rely on chain segment transitions for conductivity, while ceramic materials exhibit ionothermal activated conductivity—and fail to establish a physical correlation between key parameters such as electric field and conductivity, the models cannot accurately reflect the distribution characteristics of the interfacial electric field under temperature variations, thus hindering their effective application in insulation optimization design.

[0054] Therefore, embodiments of this application provide a method, apparatus, device, and storage medium for predicting temperature-varying electric fields based on multiphase heterogeneous interfaces. This involves acquiring a dielectric property characterization dataset; calculating the electric field distribution based on the dielectric property characterization dataset to obtain temperature-varying electric field calculation results; constructing a model based on the temperature-varying electric field calculation results to obtain a temperature-varying electric field prediction model; and inputting multiphase heterogeneous interface material data into the temperature-varying electric field prediction model for fitting calculations to obtain temperature-varying electric field prediction results. This application first constructs a temperature-varying electric field prediction model based on the dielectric property characterization dataset, and then performs fitting calculations using the temperature-varying electric field prediction model to obtain temperature-varying electric field prediction results, achieving high-precision electric field prediction and improving the insulation reliability of the equipment.

[0055] Reference Figure 1 , Figure 1 This is a schematic diagram of the electronic device structure according to an embodiment of this application. Figure 1 As shown, the electronic device 1000 may include: a processor 1001, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The processor 1001 may be, for example, a Central Processing Unit (CPU). The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard. Optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wireless-Fidelity (Wi-Fi) interface). The memory 1005 may be high-speed random access memory (RAM) or stable non-volatile memory (NVM), such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0056] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the electronic device 1000, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0057] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and a temperature-varying electric field prediction program based on a multiphase heterogeneous interface.

[0058] Understandable, Figure 1 In the illustrated electronic device 1000, the network interface 1004 is mainly used for data communication with a network server. The user interface 1003 is mainly used for data interaction with the user. In this application, the electronic device 1000 calls the control program stored in the memory 1005 through the processor 1001 to execute the temperature-varying electric field prediction method based on a multiphase heterogeneous interface provided in this application embodiment.

[0059] Please refer to Figure 2 , Figure 2 This is a flowchart illustrating the temperature-varying electric field prediction method based on a multiphase heterogeneous interface provided in an embodiment of this application. In some embodiments, the temperature-varying electric field prediction method based on a multiphase heterogeneous interface can be derived from... Figure 1The electronic device 1000 in the system performs this operation. Specifically, for example... Figure 2 As shown, the method for predicting temperature-varying electric fields based on multiphase heterogeneous interfaces includes the following steps:

[0060] Step S10: Obtain the dielectric performance characterization data set.

[0061] It is understood that, in some embodiments, the dielectric performance characterization dataset is a comprehensive dataset used to fully describe the polarization and loss behavior of dielectric materials in alternating electromagnetic fields. This dataset includes, but is not limited to, frequency-varying dielectric constant data and DC conductivity data of ceramic substrates and silicon elastomers in multiphase heterojunctions within a certain temperature range, such as 100–200°C. The ceramic substrate material includes, but is not limited to, one or more of alumina, aluminum nitride, or silicon nitride, and the silicon elastomer material includes, but is not limited to, type 1533 silicon elastomer.

[0062] Step S20: Calculate the electric field distribution based on the dielectric property characterization data set to obtain the temperature-varying electric field calculation results.

[0063] It is understood that, in some embodiments, the process of calculating the electric field distribution based on the dielectric property characterization dataset is implemented using a frequency-time domain hybrid solution algorithm. This process includes, but is not limited to, first decomposing the irregular voltage in the time domain into frequency domain components using Fourier transform, and then testing the broadband dielectric spectrum of the material accordingly; next, fitting the spectrum using multiple dielectric models to obtain the frequency-varying relationship of the complex dielectric constant; finally, calculating the electric field distribution at each frequency point based on this relationship, and reconstructing the complete electric field distribution under time-domain excitation through linear superposition and inverse Fourier transform. The dielectric models include, but are not limited to, conductivity models, universal relaxation models, Debye models, Cole-Cole models, Davidson-Cole models, Havriliak-Negami models, Dissado-Hill models, and high-frequency dielectric constants.

[0064] Please refer to this as well. Figure 3 , Figure 3 yes Figure 2 A schematic diagram of the sub-process of step S20. In some embodiments, the temperature-varying electric field calculation results can be obtained based on steps S21 to S23.

[0065] Step S21: Decompose the time-domain voltage excitation to obtain the set of frequency-domain component voltages.

[0066] It is understood that in some embodiments, the process of decomposing the time-domain voltage excitation includes, but is not limited to, decomposing the irregular time-domain voltage excitation into a superposition of multiple frequency-domain sinusoidal voltage components through Fourier transform, thereby obtaining a set of frequency-domain component voltages.

[0067] Step S22: Perform model fitting based on the dielectric performance characterization dataset to obtain the fitting results of frequency-varying dielectric parameters.

[0068] It is understood that in some embodiments, the process of model fitting based on dielectric property characterization data sets includes, but is not limited to, first determining the key frequency test range based on the frequency domain component voltage set, then measuring the broadband dielectric spectrum of the multiphase heterogeneous interface material within the key frequency range based on the dielectric property characterization data set, and using physical models, such as the Debye model, Cole-Cole model, etc., to perform model fitting, and finally obtaining the functional relationship describing the dielectric behavior of the material, i.e., the frequency-varying dielectric parameter fitting result.

[0069] Step S23: Calculate the electric field distribution based on the frequency domain component voltage set and the fitting results of the frequency-varying dielectric parameters to obtain the temperature-varying electric field calculation results.

[0070] It is understood that, in some embodiments, the process of calculating the electric field distribution based on the set of frequency-domain component voltages and the fitting results of frequency-varying dielectric parameters includes, but is not limited to, first calculating the frequency-domain voltage response of each frequency-domain component voltage in the set of frequency-domain component voltages based on the fitting results of frequency-varying dielectric parameters, thereby obtaining the corresponding frequency-domain electric field response results, and then superimposing the various frequency-domain electric field response results to finally obtain the temperature-varying electric field calculation results.

[0071] Step S30: Construct a model based on the temperature-varying electric field calculation results to obtain a temperature-varying electric field prediction model.

[0072] It is understood that in some embodiments, the process of building a model based on the temperature-varying electric field calculation results is based on the similarity between the temperature-varying electric field variation law at the three junction points in the multiphase heterogeneous interface and the conductivity ratio term variation law with temperature. This includes, but is not limited to, first performing a correlation analysis based on the change in DC conductivity ratio and the change in temperature-varying electric field intensity at each temperature, then distinguishing the differences in the conductivity mechanism between the ceramic substrate and the silicon elastomer in the multiphase heterogeneous interface based on the correlation analysis results, and finally obtaining the corresponding corrected equation, i.e., the temperature-varying electric field prediction model.

[0073] Furthermore, the multiphase heterogeneous interface has a three-binding point structure formed by a ceramic substrate, a silicon elastomer, and a metal electrode.

[0074] It is understood that, in some embodiments, a multiphase heterogeneous interface refers to a transition region with unique properties and finite thickness formed when two or more substances that are chemically and / or physically distinct come into contact with each other.

[0075] Please refer to this as well. Figure 4 , Figure 4 yes Figure 2A schematic diagram of the sub-process of step S30. In some embodiments, a temperature-varying electric field prediction model can be obtained based on steps S31 to S32.

[0076] Step S31: Perform correlation analysis based on the temperature-varying electric field calculation results and the temperature-varying conductivity data to obtain the correlation analysis results.

[0077] It is understood that in some embodiments, the process of performing correlation analysis based on the temperature-varying electric field calculation results and the temperature-varying conductivity data is to use statistical methods to verify whether there is a significant linear relationship between the change in the ratio of DC conductivity in different regions inside the multiphase heterogeneous interface material with temperature and the change in electric field intensity at key locations, thereby obtaining the corresponding correlation analysis results.

[0078] It should also be noted that, in some embodiments, the process of performing correlation analysis based on the temperature-varying electric field calculation results and the conductivity temperature-varying data includes, but is not limited to, first using the change in the DC conductivity ratio in the conductivity temperature-varying data as the horizontal axis, then using the change in the temperature-varying electric field intensity in the temperature-varying electric field calculation results as the vertical axis, and finally performing correlation analysis using statistical methods. When the two are linearly related, it indicates that there is a positive correlation between the temperature-varying electric field intensity and the conductivity ratio term, that is, the correlation analysis result is positively correlated.

[0079] Step S32: Based on the correlation analysis results, construct the model to obtain the temperature-varying electric field prediction model.

[0080] It is understood that, in some embodiments, the process of model construction based on correlation analysis results includes, but is not limited to, first distinguishing the differences in the conductivity mechanisms between ceramic substrates and silicon elastomers based on the correlation analysis results, and then, for the differences in the conductivity mechanisms of different materials at the interface, using corresponding differentiated conductivity models to modify the existing temperature-varying electric field equations to obtain a temperature-varying electric field prediction model. Specifically, the conductivity mechanism of silicon elastomers is chain segment motion conductivity, while the conductivity mechanism of ceramic substrates is ion thermal activation conductivity; their corresponding differentiated conductivity models can be the VTF model for silicon elastomers and the Arrhenius Rate Equation model for ceramics, respectively.

[0081] Please refer to this as well. Figure 5 , Figure 5 yes Figure 4 A schematic diagram of the sub-process of step S32. In some embodiments, a temperature-varying electric field prediction model can be obtained based on steps S321 to S322.

[0082] Step S321: Read the correlation analysis results. The correlation analysis results can be expressed by the formula:

[0083]

[0084] In the formula, E0 is the first fitting parameter, A is the second fitting parameter, and σ a σ is the DC conductivity of the ceramic substrate. b is the DC conductivity of the silicon elastomer.

[0085] It is understood that in some embodiments, the first fitting parameter E0 and the second fitting parameter A are obtained through experimental measurement and data fitting. The process includes, but is not limited to, first measuring the conductivity data of the material at different temperatures through experiments, and then performing linear regression fitting, such as performing least squares fitting on the logarithmic equation, to obtain the corresponding fitting parameters.

[0086] Step S322: Based on the correlation analysis results, construct the model to obtain the temperature-varying electric field prediction model. The temperature-varying electric field prediction model can be expressed by the formula:

[0087]

[0088] In the formula, E is the temperature-varying electric field strength, E0 is the first fitting parameter, A is the second fitting parameter, T is the thermodynamic temperature, and E a E represents the activation energy corresponding to the ceramic substrate. a ' is the activation energy corresponding to the silicon elastomer, k B Let T0 be the Boltzmann function, and T0 be a quantity related to the glass transition temperature in the fitting of the conductivity of silicon elastomers.

[0089] It is understood that, in some embodiments, after the temperature-varying electric field prediction model is successfully constructed, the specific values ​​of the electric field intensity at a certain temperature range, such as 100°C to 200°C, at the multiphase heterogeneous interface of different ceramic substrates and silicon elastomers can be combined to perform temperature-varying electric field fitting, obtain the corresponding temperature-varying electric field fitting results, and then verify whether the temperature-varying electric field prediction model meets the requirements of accuracy and universality.

[0090] It should be noted that in some embodiments, if the fitting result of the temperature-varying electric field is greater than a preset standard, such as 0.99, it indicates that the temperature-varying electric field prediction model meets the accuracy and universality requirements and can be used for subsequent temperature-varying electric field prediction. Conversely, it indicates that the temperature-varying electric field prediction model does not meet the accuracy and universality requirements and needs to be revised.

[0091] Please refer to this as well. Figure 6 , Figure 6 yes Figure 5 A schematic diagram of the sub-process of step S322. In some embodiments, a temperature-varying electric field prediction model can be obtained based on steps S3221 to S3222.

[0092] Step S3221: Construct a temperature change model for the heterogeneous interface based on the correlation analysis results, and obtain the temperature change model for the ceramic substrate and the temperature change model for the silicon elastomer.

[0093] It is understood that in some embodiments, due to the differences in the conductivity mechanisms of different materials, corresponding differentiated conductivity models need to be used. These include, but are not limited to, the temperature change model for ceramic substrates, the Arrhenius Rate Equation model, and the temperature change model for silicon elastomers, including but not limited to the VTF (Vogel-Tammann-Fulcher) model.

[0094] Step S3222: Based on the correlation analysis results, the temperature change model of the ceramic substrate and the temperature change model of the silicon elastomer, a model is constructed to obtain the temperature change electric field prediction model.

[0095] It is understood that in some embodiments, the temperature change model of the ceramic substrate and the temperature change model of the silicon elastomer are substituted into the correlation analysis results to construct the model, thereby obtaining the temperature change electric field prediction model. Subsequently, this temperature change electric field prediction model can be used to predict the magnitude of the electric field intensity at the interface, thus providing guidance for the selection of materials for multiphase heterogeneous interfaces.

[0096] Step S40: Input the multiphase heterogeneous interface material data into the temperature-varying electric field prediction model for fitting calculation to obtain the temperature-varying electric field prediction result.

[0097] It is understood that in some embodiments, the multiphase heterogeneous interface material data includes, but is not limited to, data on different material combinations at the multiphase heterogeneous interface at various temperatures and corresponding electrical parameters such as DC conductivity. Among these, the data on different material combinations includes, but is not limited to, data on different ratios of ceramic substrate and silicon elastomer.

[0098] Furthermore, based on the correlation analysis results, a temperature change model for the heterogeneous interface was constructed, resulting in temperature change models for the ceramic substrate and the silicon elastomer. This includes: describing the relationship between the conductivity of the ceramic substrate and temperature change based on the correlation analysis results, thus obtaining the temperature change model for the ceramic substrate. The temperature change model for the ceramic substrate can be expressed by the formula:

[0099]

[0100] In the formula, σ1 is the temperature change model of the ceramic substrate, σ is a constant, and E a It is the activation energy, k B is Boltzmann's constant, and T is the thermodynamic temperature.

[0101] Based on the correlation analysis results, the relationship between the conductivity of silicon elastomers and temperature changes is described, resulting in a temperature-dependent model for silicon elastomers. This model can be expressed by the formula:

[0102]

[0103] In the formula, σ2 is the temperature change model of silicon elastomer, σ is a constant, Ea' is the activation energy of the corresponding silicon elastomer, and k B is Boltzmann constant, T is thermodynamic temperature, and T0 is a quantity related to the glass transition temperature in the fitting of the conductivity of silicon elastomers.

[0104] It is understood that, in some embodiments, the temperature change model σ1 of the ceramic substrate can be used to represent the DC conductivity σ of the ceramic substrate. a The temperature variation model σ² of silicon elastomers can be used to represent the DC conductivity σ of silicon elastomers. b Then, the two were combined with the correlation analysis results to construct a model and obtain a temperature-varying electric field prediction model.

[0105] Reference Figure 7 , Figure 7 This is a structural block diagram of the temperature-varying electric field prediction device based on a multiphase heterogeneous interface provided in an embodiment of this application. Figure 7 As shown, the temperature-varying electric field prediction device based on a multiphase heterogeneous interface includes a data acquisition module 10, a data calculation module 20, a model building module 30, and a calculation and prediction module 40, wherein...

[0106] The data acquisition module 10 is used to acquire a set of dielectric performance characterization data.

[0107] The data calculation module 20 is used to calculate the electric field distribution based on the dielectric property characterization data set and obtain the temperature-varying electric field calculation results.

[0108] The model building module 30 is used to build a model based on the temperature-varying electric field calculation results to obtain a temperature-varying electric field prediction model.

[0109] The calculation and prediction module 40 is used to input multiphase heterogeneous interface material data into the temperature-varying electric field prediction model for fitting calculation and to obtain the temperature-varying electric field prediction result.

[0110] This embodiment provides a method, apparatus, device, and storage medium for predicting temperature-varying electric fields based on multiphase heterogeneous interfaces. It acquires a set of dielectric property characterization data; calculates the electric field distribution based on the dielectric property characterization data to obtain the temperature-varying electric field calculation results; constructs a model based on the temperature-varying electric field calculation results to obtain a temperature-varying electric field prediction model; and inputs the multiphase heterogeneous interface material data into the temperature-varying electric field prediction model for fitting calculation to obtain the temperature-varying electric field prediction results. This embodiment first constructs a temperature-varying electric field prediction model based on the dielectric property characterization data set, and then performs fitting calculations through the temperature-varying electric field prediction model to obtain the temperature-varying electric field prediction results, achieving high-precision electric field prediction and improving the insulation reliability of the equipment.

[0111] In addition, refer to Figure 8 , Figure 8 This is another structural block diagram of the temperature-varying electric field prediction device based on a multiphase heterogeneous interface provided in this application embodiment. The temperature-varying electric field prediction device 2000 based on a multiphase heterogeneous interface includes a processor 1001 and a memory 1005. The memory 1005 stores programs, instructions, or code for executing the aforementioned temperature-varying electric field prediction method based on a multiphase heterogeneous interface. The processor 1001 executes the programs, instructions, or code stored in the memory 1005. The programs, instructions, or code stored in the memory 1005 are executable. Figures 2 to 6 The embodiments shown include some or all of the steps of the temperature-varying electric field prediction method based on multiphase heterogeneous interfaces.

[0112] Since this storage medium adopts all the technical solutions of all the above embodiments, it has at least all the beneficial effects brought about by the technical solutions of the above embodiments, which will not be repeated here.

[0113] Furthermore, embodiments of this application also propose a computer-readable storage medium storing a computer program, wherein the storage medium stores a temperature-varying electric field prediction program based on a multiphase heterogeneous interface, and when the temperature-varying electric field prediction program based on a multiphase heterogeneous interface is executed by a processor, it implements the steps of the temperature-varying electric field prediction method based on a multiphase heterogeneous interface as described above.

[0114] It should be understood that the above are merely illustrative examples and do not constitute any limitation on the technical solution of this application. In specific applications, those skilled in the art can make settings as needed, and this application does not impose any restrictions on this.

[0115] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of this application. In practical applications, those skilled in the art can select some or all of it to achieve the purpose of this embodiment according to actual needs, and no restrictions are imposed here.

[0116] In addition, for technical details not described in detail in this embodiment, please refer to the temperature-varying electric field prediction method based on multiphase heterogeneous interface provided in any embodiment of this application, which will not be repeated here.

[0117] Furthermore, it should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0118] The sequence numbers of the embodiments in this application are for description only and do not represent the superiority or inferiority of the embodiments.

[0119] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory (ROM) / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0120] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for predicting temperature-dependent electric field based on a multiphase heterointerface, characterized in that, The method comprises: acquiring a dielectric performance characterization data set; decomposing a time-domain voltage excitation to obtain a frequency-domain component voltage set; performing model fitting based on the dielectric performance characterization data set to obtain a frequency-dependent dielectric parameter fitting result; and performing electric field distribution calculation based on the frequency-domain component voltage set and the frequency-dependent dielectric parameter fitting result to obtain a temperature-dependent electric field calculation result; performing model construction based on the temperature-dependent electric field calculation result to obtain a temperature-dependent electric field prediction model; inputting multi-phase heterogeneous interface material data into the temperature-dependent electric field prediction model for fitting calculation to obtain a temperature-dependent electric field prediction result. 2.The temperature change electric field prediction method based on a hetero-interface of multiple phases according to claim 1, wherein, The model construction based on the temperature-dependent electric field calculation result to obtain a temperature-dependent electric field prediction model comprises: performing correlation analysis based on the temperature-dependent electric field calculation result and temperature-dependent conductivity data to obtain a correlation analysis result; performing model construction based on the correlation analysis result to obtain the temperature-dependent electric field prediction model.

3. The method of claim 1 or 2, wherein the method is a temperature change electric field prediction method based on a hetero-interface of multiple phases. The multi-phase heterogeneous interface has a three-joint structure formed by a ceramic substrate, a silicon elastomer, and a metal electrode. 4.The temperature change electric field prediction method based on a hetero-interface of multiple phases according to claim 2, wherein, The model construction based on the correlation analysis result to obtain a temperature-dependent electric field prediction model comprises: reading the correlation analysis result, which can be expressed by a formula as follows: where E0is a first fitting parameter, A is a second fitting parameter, σ a is the DC conductivity of the ceramic substrate, σ b is the DC conductivity of the silicone elastomer; the temperature-dependent electric field prediction model can be expressed by a formula as follows: where E is the electric field strength, E0is the first fitting parameter, A is the second fitting parameter, T is the thermodynamic temperature, E a is the activation energy for the ceramic substrate, E a ’ is the activation energy for the silicon elastomer, k B is the Boltzmann function, and T0is a quantity related to the glass transition temperature in the fitting of the silicon elastomer conductivity. 5.The temperature-dependent electric field prediction method based on a hetero-interface of multiple phases according to claim 4, wherein, The model construction based on the correlation analysis result to obtain a temperature-dependent electric field prediction model comprises: constructing a heterogeneous interface temperature-dependent model based on the correlation analysis result to obtain a ceramic substrate temperature-dependent model and a silicon elastomer temperature-dependent model; performing model construction based on the correlation analysis result, the ceramic substrate temperature-dependent model, and the silicon elastomer temperature-dependent model to obtain the temperature-dependent electric field prediction model. 6.The temperature-dependent electric field prediction method based on a hetero-interface of multiple phases according to claim 5, wherein, The construction of a heterogeneous interface temperature-dependent model based on the correlation analysis result to obtain a ceramic substrate temperature-dependent model and a silicon elastomer temperature-dependent model comprises: describing the relationship between the conductivity of the ceramic substrate and temperature change based on the correlation analysis result to obtain the ceramic substrate temperature-dependent model, which can be expressed by a formula as follows: where σ1 is a ceramic substrate temperature change model, σ is a constant, E a is an activation energy, k B is a Boltzmann constant, and T is a thermodynamic temperature; describing the relationship between the conductivity of the silicon elastomer and temperature change based on the correlation analysis result to obtain the silicon elastomer temperature-dependent model, which can be expressed by a formula as follows: where σ2is the temperature-dependent model of the silicon elastomer, σ is a constant, Ea' is the corresponding activation energy of the silicon elastomer, k B is the Boltzmann constant, T is the thermodynamic temperature, and T0is a quantity related to the glass transition temperature in the silicon elastomer conductivity fit.

7. A temperature change electric field prediction device based on a multiphase heterogeneous interface, characterized by, The device comprises: a data acquisition module configured to acquire a dielectric performance characterization data set; a data calculation module configured to decompose a time-domain voltage excitation to obtain a frequency-domain component voltage set; perform model fitting based on the dielectric performance characterization data set to obtain a frequency-dependent dielectric parameter fitting result; and perform electric field distribution calculation based on the frequency-domain component voltage set and the frequency-dependent dielectric parameter fitting result to obtain a temperature-dependent electric field calculation result; a model construction module configured to perform model construction based on the temperature-dependent electric field calculation result to obtain a temperature-dependent electric field prediction model; a calculation and prediction module configured to input multi-phase heterogeneous interface material data into the temperature-dependent electric field prediction model for fitting calculation to obtain a temperature-dependent electric field prediction result.

8. An electronic device, comprising: The electronic device includes a memory, a processor, one or more computer programs stored in the memory, the one or more computer programs including instructions that, when executed by the processor, enable the multi-phase heterojunction-based temperature-varying electric field prediction method of any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes instructions that, when executed by a processor, enable the multi-phase heterojunction-based temperature-varying electric field prediction method of any one of claims 1 to 6.

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