A method, device, medium and product for predicting the service life of a hybrid power device based on thermo-acoustic combination

Abstract

The application discloses a kind of based on thermo-acoustic joint's hybrid power device life prediction method, equipment, medium and product, it is related to electronic device life prediction field, this method includes: based on electrical signal establishes power loss model, and combining thermal network model establishes transient junction temperature calculation model to determine junction temperature variation, extracts thermal characteristic parameter;Under preset time interval, frequency domain analysis is carried out to acoustic emission signal, and the amplitude of fundamental frequency in frequency spectrum is extracted to obtain mechanical aging characteristic quantity;Thermal characteristic parameter and mechanical aging characteristic quantity are normalized with reference respectively to obtain uniform characteristic parameter;Using thermo-acoustic joint life model, according to uniform characteristic parameter determines the life index of hybrid power device, and combining preset threshold obtains life index determination result, to determine life prediction result and health status information.The application can solve the problem that life is not balanced in the prior art, mechanical level aging lacks effective characterization means and is difficult to meet the actual engineering application demand.

Description

Technical Field

[0001] This application relates to the field of electronic device lifetime prediction, and in particular to a method, device, medium and product for predicting the lifetime of hybrid power devices based on thermo-acoustic combination. Background Technology

[0002] With the widespread application of power electronic systems in new energy, electric transportation, and high-end equipment, higher requirements are placed on the reliability and lifespan of power devices. To balance efficiency and cost, hybrid power devices, consisting of Si IGBTs and SiCMOSFETs connected in parallel, are widely used in engineering to optimize performance. However, due to significant differences in material properties, switching characteristics, and thermal characteristics between the two types of devices, electro-thermal stress mismatch is prone to occur during operation, leading to different aging rates and uneven lifespan.

[0003] Existing lifetime prediction methods mainly rely on temperature-sensitive electrical parameters or thermal characteristics for evaluation, which can only reflect the aging process at the electrical level of the device, but lack effective characterization methods for mechanical aging such as package structure degradation and bond wire fatigue. In addition, traditional methods usually require invasive measurements or complex modeling, which is difficult to meet the needs of practical engineering applications.

[0004] Studies have shown that power devices generate acoustic emission signals related to changes in internal stress during operation, which can reflect the microscopic damage evolution of the device's mechanical structure. However, current technologies have not effectively combined acoustic emission signals with thermal characteristics to construct a unified lifetime prediction model. Therefore, there is an urgent need for a multidimensional lifetime prediction method that can integrate electrical and mechanical information to improve the accuracy and reliability of lifetime assessment for hybrid power devices. Summary of the Invention

[0005] To address the aforementioned problems in the existing technology, this application provides a method, device, medium, and product for predicting the lifetime of hybrid power devices based on thermo-acoustic combination, so as to improve the accuracy and reliability of lifetime assessment of hybrid power devices.

[0006] To achieve the above objectives, this application provides the following solution: In a first aspect, this application provides a method for predicting the lifetime of hybrid power devices based on a combined thermo-acoustic approach, including: Acquire electrical and acoustic emission signals during the operation of hybrid power devices; A power loss model is established based on the electrical signal, and a transient junction temperature calculation model is established in conjunction with the thermal network model to determine the junction temperature change and extract thermal characteristic parameters. Frequency domain analysis is performed on the acoustic emission signal at a preset time interval to extract the fundamental frequency amplitude in the spectrum, and the fundamental frequency amplitude is used as a mechanical aging characteristic quantity. The thermal characteristic parameters and the mechanical aging characteristic quantities are normalized with the benchmarks established for each working condition to obtain unified characteristic parameters. A thermo-acoustic combined lifetime model is used to determine the lifetime index of the hybrid power device based on the unified characteristic parameters. The lifespan index determination result is obtained based on the lifespan index and the preset threshold. Based on the lifespan index determination results, lifespan prediction results and health status information are determined.

[0007] In a second aspect, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described thermo-acoustic combined hybrid power device lifetime prediction method.

[0008] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described thermo-acoustic hybrid power device lifetime prediction method.

[0009] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described thermo-acoustic combined hybrid power device lifetime prediction method.

[0010] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a method, device, medium, and product for predicting the lifetime of hybrid power devices based on thermo-acoustic co-processing. It establishes a power loss model based on electrical signals and combines it with a thermal network model to establish a transient junction temperature calculation model to determine junction temperature changes and extract thermal characteristic parameters. This allows for characterization of the thermal fatigue effect caused by power loss and junction temperature changes. Furthermore, by performing frequency domain analysis on acoustic emission signals at preset time intervals, the fundamental frequency amplitude in the spectrum is extracted and used as a mechanical aging characteristic quantity. This characterizes the mechanical damage process caused by material thermal expansion mismatch, bond wire fatigue, and packaging structure degradation. Both types of features are independently extracted during lifetime prediction and, after normalization, input into a unified thermo-acoustic co-processing lifetime model. This achieves a unified description of electrical and mechanical aging of the device, thereby solving the problem of a lack of effective characterization methods for mechanical aging. By adopting a thermo-acoustic combined lifetime model and determining the lifetime index of hybrid power devices based on unified characteristic parameters, the cumulative aging effect of devices during long-term operation and the impact of environmental temperature changes on lifetime can be reflected, thereby improving the accuracy and stability of lifetime prediction results. This makes the model applicable to the lifetime assessment needs of hybrid power devices under different operating conditions and at different operating stages, and can solve the problems of uneven lifetime and difficulty in meeting the needs of practical engineering applications in the existing technology. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A schematic flowchart illustrating a hybrid power device lifetime prediction method based on thermo-acoustic combination provided in an embodiment of this application; Figure 2 A schematic diagram of the implementation architecture of a hybrid power device lifetime prediction method based on thermo-acoustic combination provided in an embodiment of this application; Figure 3 A schematic diagram of the aging evolution process of a hybrid power device under the combined action of electrical and mechanical stress, provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0014] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0015] In one exemplary embodiment, this application provides a method for predicting the lifetime of hybrid power devices based on thermo-acoustic combined methods. This method is executed by a computer device, specifically a terminal or server, or both. In this embodiment, the method is described using a server as an example. Figure 1 As shown, the method includes: Step 100: Acquire electrical and acoustic emission signals during the operation of the hybrid power device. The electrical signals include voltage and current signals.

[0016] Step 101: Establish a power loss model based on the electrical signal, and combine it with the thermal network model to establish a transient junction temperature calculation model to determine the junction temperature change and extract thermal characteristic parameters. The power loss model is expressed as follows: .

[0017] In the formula, For switching losses, The switching frequency of the hybrid power device. To account for activation losses, To shut down losses.

[0018] The transient junction temperature calculation model is expressed as follows: .

[0019] In the formula, For transient junction temperature, (t) represents the hybrid power device in t Power loss generated at all times, For the thermal impedance of hybrid power devices, For the heat capacities of hybrid power devices at various orders, This refers to the case temperature of the hybrid power device. For intermediate parameters, .

[0020] Step 102: Perform frequency domain analysis on the acoustic emission signal at a preset time interval, extract the fundamental frequency amplitude in the spectrum, and use the fundamental frequency amplitude as a mechanical aging characteristic quantity.

[0021] Step 103: Normalize the thermal characteristic parameters and mechanical aging characteristic quantities with the benchmarks established for each working condition to obtain unified characteristic parameters.

[0022] Step 104: Using a thermo-acoustic combined lifetime model, determine the lifetime index of the hybrid power device based on unified characteristic parameters.

[0023] Step 105: Obtain the lifespan index determination result based on the lifespan index and the preset threshold. The lifespan index is represented as follows: (Conduction voltage drop, acoustic emission signal fundamental frequency amplitude, conduction current, leakage current, threshold voltage) and determine whether the preset threshold is reached. When the life index exceeds the preset threshold, an early warning signal is output.

[0024] Step 106: Determine life prediction results and health status information based on life index assessment results.

[0025] By implementing steps 100-106 above, this application can achieve high-precision prediction of the lifetime of hybrid power devices without adding additional sensors or damaging the device structure, and is applicable to different operating conditions, thus having good engineering application value.

[0026] In an exemplary embodiment of this application, the gate-source voltage, drain-source voltage, and drain-source current change throughout the entire process of the hybrid power device's turn-on and turn-off, manifesting as carrier movement within the semiconductor layer. Based on this, the location of the acoustic emission source is determined according to existing conditions. The location of the power chip is determined using acoustic emission propagation delay localization technology, which is the primary reason for the change in particle energy levels, leading to the generation of moving charged particles. This can be achieved through the formula... The behavior of the flow carriers during the switching process and the origin of the acoustic emission source were derived.

[0027] In the formula, The height of the Schottky barrier. The intrinsic Fermi level of a semiconductor in a hybrid power device. This represents the intrinsic carrier concentration of the semiconductor in a hybrid power device. This represents the equilibrium hole concentration in the semiconductor of a hybrid power device. The Fermi level in the semiconductor bulk region. For elementary charge, Absolute temperature is the Boltzmann constant.

[0028] In an exemplary embodiment of this application, in order to eliminate the influence of differences between installation conditions and initial amplitude, the implementation process of step 103 above includes: Using formula The thermal characteristic parameters are normalized to the thermal baseline established for each operating condition to obtain the normalized thermal characteristics. Where, For the first Normalized thermal characteristics under sub-power cycles For the first Thermal characteristic parameters under sub-power cycles A thermal baseline was established for each operating condition.

[0029] Using formula The mechanical aging characteristics are normalized to the acoustic emission references established for each operating condition to obtain normalized acoustic emission characteristics. Where, For the first Normalized acoustic emission characteristics under sub-power cycles. For the first Mechanical aging characteristics under the next power cycle, i.e., the first The fundamental frequency amplitude output by the AE sensor under one power cycle. Acoustic emission references are established for each operating condition, where the acoustic emission characteristics under the current operating condition are the acoustic emission characteristics under the reference operating condition.

[0030] Normalized thermal characteristics and normalized acoustic emission characteristics are used as unified characteristic parameters.

[0031] In one exemplary embodiment of this application, a thermo-acoustic joint lifetime model is established by fusing thermal characteristic parameters and acoustic emission characteristic parameters. Wherein: Lifetime representation based on thermal model: .

[0032] In the formula, For fusion thermal characteristic parameters, For harmonic complementary coupling correction coefficients, This represents the junction temperature fluctuation amplitude. Junction temperature index For switching frequency, To activate energy, is the Boltzmann constant, i.e., the average junction temperature.

[0033] Lifetime representation based on AE: .

[0034] In the formula, These are acoustic emission characteristic parameters, which are used to characterize the aging degree of power devices at the mechanical structure level and have a clear degradation mapping relationship with lifetime indicators. This is the scaling factor for the acoustic emission lifetime model. To normalize acoustic emission characteristics, For power cycle number, The acoustic emission degradation index, For harmonic complementary coupling coefficients, Let be the wave complementary coupling sensitivity coefficient, and exp() be the exponential function. and All of these are model parameters identified through data-driven fitting based on power cycling aging test data under different operating conditions, used to describe the quantitative relationship between acoustic emission characteristics and device lifetime.

[0035] Based on the above description, the final thermo-acoustic combined lifetime model is expressed as follows: .

[0036] In the formula, This represents the lifetime value predicted from acoustic emission characteristics.

[0037] Specifically, as the number of power cycles increases, mechanical damage such as bonding wire fatigue, solder layer peeling, and chip-substrate interface delamination gradually accumulates inside the power device. The fundamental frequency amplitude of the acoustic emission signal in the frequency domain shows a monotonically increasing trend and is highly sensitive to mechanical structure degradation, which can be used as the core feature for monitoring mechanical aging.

[0038] In an exemplary embodiment of this application, in order to improve prediction accuracy, the lifetime prediction results obtained in step 106 above can be used for parameter identification through experimental data and verified through multi-condition power cycle experiments.

[0039] In an exemplary embodiment of this application, the implementation process of the thermo-acoustic combined lifetime prediction method for hybrid power devices provided above is illustrated using a hybrid power device consisting of Si IGBTs and SiC MOSFETs connected in parallel as an example. In practical applications, in this embodiment, the method provided by this application may include three parts: a lifetime feature extraction method based on electro-thermal coupling, a mechanical aging characterization method based on acoustic emission signals, and a thermo-acoustic combined lifetime modeling method.

[0040] (1) Lifetime feature extraction method based on electro-thermal coupling.

[0041] This method is primarily used to obtain the junction temperature and thermal stress characteristics of devices. Specifically, by establishing a power loss model and a thermal network model, the junction temperature changes and thermal stress characteristics of the devices are obtained, enabling quantitative analysis of electrical aging. During the operation of hybrid power devices, real-time data such as voltage and current are collected to establish a device power loss model, including conduction and switching losses. Combined with the device's thermal impedance network model, the dynamic change process of the junction temperature is calculated. Key thermal characteristic parameters are extracted by analyzing the junction temperature time series.

[0042] (2) Mechanical aging characterization method based on acoustic emission signals.

[0043] This method is used to extract mechanical degradation characteristics of devices. Specifically, by performing time-frequency analysis on the acoustic emission signal, features such as the fundamental frequency amplitude in the frequency domain are extracted, enabling non-invasive monitoring of device mechanical degradation. During device operation, microscopic mechanical stress and structural vibrations are generated within the device due to carrier motion, changes in electromagnetic force, and the thermal expansion effect of materials, thereby exciting acoustic emission signals.

[0044] (3) Thermal-acoustic combined lifetime modeling method.

[0045] This method is used to construct a thermo-acoustic joint lifetime prediction model through feature fusion. Specifically, the thermal features (on-state voltage, junction temperature) extracted by method (1) are fused with the acoustic emission features (fundamental frequency amplitude) extracted by method (2) to construct a unified lifetime prediction model and achieve multi-dimensional lifetime assessment. Based on this, this application proposes a thermo-acoustic joint lifetime calculation formula that does not rely on empirical weights, realizing the symmetrical fusion of thermal stress and acoustic feature information and improving the robustness of lifetime prediction.

[0046] In one exemplary embodiment of this application, based on such Figure 2The implementation architecture shown, taking a hybrid power device consisting of Si IGBTs and SiCMOSFETs in parallel as an example, achieves multi-dimensional prediction of device lifetime by comprehensively analyzing the voltage, current, and acoustic emission signals during device operation. In this embodiment, the voltage and current signals at the inverter output are collected, and a power loss model is established by combining the switching frequency and device operating status to calculate conduction and switching losses, thereby obtaining the power loss variation of the device under different operating conditions. Further, combined with a preset thermal impedance network model, thermal mapping calculations are performed on the power loss to obtain the dynamic process of the device junction temperature changing over time, and thermal characteristic parameters such as junction temperature fluctuation amplitude and average junction temperature are extracted to characterize the aging degree of the device under electrical stress. Simultaneously, during device operation, an acoustic emission sensor located outside the device collects the internal micro-mechanical vibration signals, and the collected acoustic emission signals are filtered and analyzed in the frequency domain to extract the fundamental frequency amplitude at a specific frequency as a characteristic quantity reflecting the degradation of the device packaging structure and the fatigue degree of the bond wires. Based on this, the thermal and acoustic emission parameters obtained under different operating conditions are normalized to eliminate the influence of operating condition differences on the characteristic quantities, and a unified lifetime assessment index is constructed through feature fusion. A lifetime prediction model is established based on the fused characteristic parameters to calculate the equivalent lifetime or remaining lifetime of the hybrid power device, and the device health status is assessed in conjunction with preset state criteria, thereby achieving real-time monitoring and lifetime prediction of the hybrid power device's operating status.

[0047] In this embodiment, the application does not require modification of the internal structure of the device or the addition of additional invasive testing methods. It can achieve a comprehensive evaluation of the electrical and mechanical aging of the device simply by acquiring and analyzing external signals, which has good engineering applicability and promotion value.

[0048] In one exemplary embodiment of this application, such as Figure 3 As shown, this reflects the aging evolution process of hybrid power devices under the combined action of electrical and mechanical stress. Thermal features are used to characterize the thermal fatigue effect caused by power loss and junction temperature changes, while acoustic emission features are used to characterize the mechanical damage process caused by material thermal expansion mismatch, bond wire fatigue, and packaging structure degradation. The two types of features are extracted independently during the lifetime prediction process and then normalized before being input into a unified fusion model (i.e., the thermo-acoustic joint lifetime model). A comprehensive lifetime assessment index is constructed through weighted superposition or equivalent damage superposition, thereby achieving a unified description of the electrical and mechanical aging of the device.

[0049] In practical applications, the thermo-acoustic combined lifetime model provided in this application can introduce time accumulation factors and environmental correction parameters according to different operating conditions to reflect the aging accumulation effect of the device during long-term operation and the impact of environmental temperature changes on lifetime. Furthermore, the model parameters can be identified and optimized through experimental data, thereby improving the accuracy and stability of lifetime prediction results and making it applicable to the lifetime assessment needs of hybrid power devices under different operating conditions and at different operating stages.

[0050] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 4 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores lifetime prediction data for hybrid power devices based on thermo-acoustic co-processing. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a lifetime prediction method for hybrid power devices based on thermo-acoustic co-processing.

[0051] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer equipment to which the present application is applied. Specific computer equipment may include, for example, [the following is a list of possible additional structures]. Figure 4 The diagram shows more or fewer components, or combinations of certain components, or different component arrangements.

[0052] As an optional implementation, the processor may include a data acquisition module, a thermal feature calculation module, an acoustic emission analysis module, a feature fusion module, and a lifetime assessment module.

[0053] The system comprises several modules: a data acquisition module, a thermal characteristic calculation module, and a acoustic emission module. The former uses electrical signals to acquire electrical and acoustic emission signals during the operation of the hybrid power device. The latter uses a thermal network model to establish a transient junction temperature calculation model, determining junction temperature changes and extracting thermal characteristic parameters. The latter performs frequency domain analysis on the acoustic emission signals at preset time intervals, extracting the fundamental frequency amplitude and using it as a mechanical aging characteristic. The former normalizes the thermal and mechanical aging characteristic parameters with benchmarks established for each operating condition, obtaining unified characteristic parameters. The latter uses a combined thermo-acoustic lifetime model to determine the lifetime index of the hybrid power device based on the unified characteristic parameters. The lifetime index is then used to determine the lifetime prediction result and health status information based on the lifetime index determination result.

[0054] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0055] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0056] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0057] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0058] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (RRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0059] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0060] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0061] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for predicting the lifetime of hybrid power devices based on thermo-acoustic co-processing, characterized in that, include: Acquire electrical and acoustic emission signals during the operation of hybrid power devices; A power loss model is established based on the electrical signal, and a transient junction temperature calculation model is established in conjunction with the thermal network model to determine the junction temperature change and extract thermal characteristic parameters. Frequency domain analysis is performed on the acoustic emission signal at a preset time interval to extract the fundamental frequency amplitude in the spectrum, and the fundamental frequency amplitude is used as a mechanical aging characteristic quantity. The thermal characteristic parameters and the mechanical aging characteristic quantities are normalized with the benchmarks established for each working condition to obtain unified characteristic parameters. A thermo-acoustic combined lifetime model is used to determine the lifetime index of the hybrid power device based on the unified characteristic parameters. The lifespan index determination result is obtained based on the lifespan index and the preset threshold. Based on the lifespan index determination results, lifespan prediction results and health status information are determined.

2. The method for predicting the lifetime of hybrid power devices based on thermo-acoustic coupling according to claim 1, characterized in that, The power loss model is expressed as follows: ； In the formula, For switching losses, The switching frequency of the hybrid power device. To account for activation losses, To shut down losses.

3. The method for predicting the lifetime of hybrid power devices based on thermo-acoustic co-processing according to claim 1, characterized in that, The transient junction temperature calculation model is expressed as follows: ； In the formula, For transient junction temperature, (t) represents the hybrid power device in t Power loss generated at all times, For the thermal impedance of hybrid power devices, For the heat capacities of hybrid power devices at various orders, For the case temperature of hybrid power devices; For intermediate parameters, .

4. The method for predicting the lifetime of hybrid power devices based on thermo-acoustic co-processing according to claim 1, characterized in that, In the process of acquiring acoustic emission signals, the propagation delay localization technique of acoustic emission is used to determine the location of the power chip in the hybrid power device as the location of the acoustic emission source.

5. The method for predicting the lifetime of hybrid power devices based on thermo-acoustic co-processing according to claim 1, characterized in that, In the process of acquiring acoustic emission signals, the formula is used. Determine the carrier behavior and acoustic emission source origin during the switching process; In the formula, The height of the Schottky barrier. The intrinsic Fermi level of a semiconductor in a hybrid power device. This represents the intrinsic carrier concentration of the semiconductor in a hybrid power device. This represents the equilibrium hole concentration in the semiconductor of a hybrid power device. The Fermi level in the semiconductor bulk region. For elementary charge, Absolute temperature is the Boltzmann constant.

6. The method for predicting the lifetime of hybrid power devices based on thermo-acoustic co-processing according to claim 1, characterized in that, The thermal characteristic parameters and the mechanical aging characteristic quantities are normalized with the benchmarks established for each operating condition to obtain unified characteristic parameters, including: Using formula The thermal characteristic parameters are normalized against the thermal benchmarks established for each operating condition to obtain normalized thermal characteristics; where, For the first Normalized thermal characteristics under sub-power cycles For the first Thermal characteristic parameters under sub-power cycles Thermal references were established for each operating condition; Using formula The mechanical aging characteristics are normalized to the acoustic emission references established for each working condition to obtain normalized acoustic emission characteristics; where, For the first Normalized acoustic emission characteristics under sub-power cycles For the first Mechanical aging characteristics under power cycles; Acoustic emission references were established for various operating conditions; The normalized thermal characteristics and the normalized acoustic emission characteristics are used as the unified feature parameters.

7. The method for predicting the lifetime of hybrid power devices based on thermo-acoustic co-processing according to claim 1, characterized in that, The thermo-acoustic joint lifetime model is established by fusing thermal characteristic parameters and acoustic emission characteristic parameters; the thermo-acoustic joint lifetime model is expressed as: ； In the formula, The lifetime value is predicted by acoustic emission characteristics; acoustic emission characteristic parameters are used to characterize the aging degree of power devices at the mechanical structure level, and there is a clear degradation mapping relationship between them and lifetime indicators. For fusion thermal characteristic parameters, , For harmonic complementary coupling correction coefficients, This represents the junction temperature fluctuation amplitude. Junction temperature index For switching frequency, To activate energy, Boltzmann's constant; These are acoustic emission characteristic parameters. , This is the scaling factor for the acoustic emission lifetime model. To normalize acoustic emission characteristics, For power cycle number, The acoustic emission degradation index, For harmonic complementary coupling coefficients, is the harmonic complementary coupling sensitivity coefficient, and exp() is the exponential function.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that the processor executes the computer program to implement the thermo-acoustic combined hybrid power device lifetime prediction method according to any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the hybrid power device lifetime prediction method based on thermo-acoustic combination as described in any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the hybrid power device lifetime prediction method based on thermo-acoustic combination as described in any one of claims 1-7.

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