A performance simulation method for power modules

Abstract

The present invention relates to the technical field of performance simulation analysis, and in particular to a performance simulation method for a power module. The method comprises the following steps: obtaining initial physical characteristic data and initial electrical characteristic data of the power module; pre-processing and simulating the initial physical characteristic data and initial electrical characteristic data to obtain the physical field distribution characteristics inside the power module; using an adaptive grid generation algorithm and a sparse matrix solution to perform multi-physical field simulation analysis on the power module based on the physical field distribution characteristics inside the power module to obtain simulation result data; adjusting and optimizing the parameters of the simulation model of the power module according to the simulation result data to generate optimized simulation model parameter data; simplifying the design of the power module according to the optimized simulation model parameter data to establish a simplified model. The present invention can significantly improve the performance and control accuracy of the power module and improve the stability and efficiency of the system through multi-stage simulation and optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of performance simulation analysis, and in particular to a performance simulation method for a power module. Background Art

[0002] Power modules, as key components of power electronics systems, are widely used in a variety of fields, including electric vehicles, renewable energy systems, and smart grids. Their performance directly impacts the overall efficiency and stability of the system. However, with the advancement of power electronics technology and the increasing complexity of its application scenarios, accurately predicting power module performance during the design phase and effectively optimizing its performance have become significant challenges in this field.

[0003] Currently, the design and simulation analysis of power modules on the market mostly rely on traditional experimental testing and simulation tools. Traditional simulation methods are usually based on single physical field simulation, ignoring the coupling between multiple physical fields in the working environment of the power module. For example, during operation, the power module not only generates an electric field, but also complex multi-physical phenomena such as heat and mechanical stress. If you rely solely on electric field simulation, you cannot fully predict its actual performance, which can easily lead to design deviations. In addition, traditional simulation tools mostly perform calculations based on fixed grids and cannot dynamically adjust the simulation accuracy, resulting in a waste of computing resources and long calculation times. Especially for power modules with complex geometric structures, the accuracy of the simulation results is difficult to meet the design requirements, and the amount of calculation increases significantly, resulting in an extension of the design cycle.

[0004] Traditional simulation methods suffer from inefficient utilization of computing resources, making it difficult to fully leverage the advantages of modern computing technologies. This is especially true when faced with large-scale parallel computing requirements, where single-machine computing often fails to meet the demands of efficient simulation. While distributed computing platforms have been gradually adopted in the simulation field, efficient management of computing resources and dynamic adjustment of task scheduling remain pressing challenges. Existing distributed simulation platforms typically employ fixed resource allocation strategies that are unable to adjust resource allocation based on the demands of real-time simulation tasks, resulting in wasted computing resources or extended simulation times. Traditional simulation processes also lack effective feedback control mechanisms. Summary of the Invention

[0005] Based on this, it is necessary for the present invention to provide a performance simulation method for a power module to solve at least one of the above technical problems.

[0006] To achieve the above object, a performance simulation method for a power module includes the following steps:

[0007] Step S1: obtaining initial physical characteristic data and initial electrical characteristic data of the power module; preprocessing and simulating the initial physical characteristic data and initial electrical characteristic data to obtain physical field distribution characteristics within the power module; performing multi-physical field simulation analysis on the power module based on the physical field distribution characteristics within the power module using an adaptive grid generation algorithm and a sparse matrix solver to obtain simulation result data; adjusting and optimizing parameters of a simulation model of the power module based on the simulation result data to generate optimized simulation model parameter data;

[0008] Step S2: Simplify the design of the power module to establish a simplified model based on the optimized simulation model parameter data; perform performance evaluation and optimization based on the simplified model to obtain optimization solution data;

[0009] Step S3: Perform simulation demand analysis on the optimization scheme data to obtain simulation tasks for the power module; utilize a distributed computing platform to perform large-scale parallel computing on the simulation tasks and perform simulation analysis to obtain real-time simulation data; dynamically adjust computing resources based on the real-time simulation data to obtain real-time optimization data;

[0010] Step S4: Pre-calculating preset working parameters based on real-time optimization data and establishing a simulation result library; performing rapid simulation and optimization on the power module based on the simulation result library to obtain rapid simulation data;

[0011] Step S5: performing real-time monitoring of the power module according to the simulation result library and the fast simulation data to obtain simulation detection results; dynamically adjusting the simulation detection results through feedback control to obtain optimized simulation result data;

[0012] Step S6: Performing performance simulation on the power module based on the optimization simulation result data, and constructing a comprehensive performance control model of the power module; and implementing dynamic optimization control of the power module based on the comprehensive performance control model.

[0013] This paper proposes a comprehensive multi-step simulation and optimization process that utilizes multi-physics simulation, adaptive mesh generation algorithms, distributed computing, and feedback control technologies to improve the design efficiency and performance stability of power modules. Through an adaptive mesh generation algorithm and sparse matrix solver, this method performs multi-physics simulation analysis based on the initial physical and electrical properties of the power module. This enables the simulation to better simulate the interactions of different physical fields in a real-world operating environment, such as the coupling of electric fields, heat, and mechanical stress, thereby improving simulation accuracy. By simplifying model construction, computational complexity is reduced. This method utilizes simplified models to evaluate and optimize the performance of power module designs while ensuring simulation accuracy. This method reduces reliance on highly complex models, effectively reducing computing resource consumption and computation time, thereby improving simulation efficiency. By utilizing a distributed computing platform for large-scale parallel computation of simulation tasks, this method can process a large number of complex simulation tasks in a short period of time. This enables rapid iteration of power module design and simulation, effectively shortening the development cycle and ensuring an efficient design verification process. Through real-time simulation data feedback and dynamic adjustment of computing resources, this method can flexibly allocate computing resources based on actual simulation needs. This dynamic resource management mechanism not only improves resource utilization efficiency but also automatically adjusts based on task complexity, ensuring that computing tasks are executed under the optimal resource allocation. The establishment of a simulation result library further optimizes the simulation process. Leveraging existing simulation data, simulation results can be quickly generated, reducing recalculation. This simulation result library allows engineers to quickly access relevant data during the design process, shortening decision-making time. By implementing a real-time monitoring mechanism for power module performance and integrating it with a feedback control system for dynamic adjustment, real-time adjustment of simulation test results enables optimization based on the actual operating conditions of the power module, effectively reducing the risk of system failure or performance degradation and improving system reliability. By constructing a comprehensive performance control model for the power module, the optimization of individual modules is integrated with overall system performance, achieving system-level dynamic optimization control. This approach not only focuses on the performance of the power module itself but also considers its interaction with the entire system, ensuring efficient and stable operation of the power module in complex system environments. The present invention has significant benefits, particularly in terms of simulation accuracy, computational efficiency, resource optimization management, real-time monitoring, and feedback control, significantly improving the design and optimization capabilities of power modules. Through a multi-step systematic process, this method provides a complete and efficient solution for the performance evaluation and optimization of complex power modules, which can meet the efficient design and rapid iteration requirements of modern power electronic equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0015] Figure 1 Schematic diagram of the steps of the performance simulation method of the power module of the present invention;

[0016] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0017] Figure 3 for Figure 1 Detailed step flow diagram of step S2;

[0018] Figure 4 for Figure 1 Detailed step flow diagram of step S3;

[0019] Figure 5 for Figure 1 Detailed step flow diagram of step S4; DETAILED DESCRIPTION

[0020] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0021] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0022] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0023] To achieve this, please refer to Figures 1 to 5 The present invention provides a performance simulation method for a power module, the method comprising the following steps:

[0024] Step S1: obtaining initial physical characteristic data and initial electrical characteristic data of the power module; preprocessing and simulating the initial physical characteristic data and initial electrical characteristic data to obtain physical field distribution characteristics within the power module; performing multi-physical field simulation analysis on the power module based on the physical field distribution characteristics within the power module using an adaptive grid generation algorithm and a sparse matrix solver to obtain simulation result data; adjusting and optimizing parameters of a simulation model of the power module based on the simulation result data to generate optimized simulation model parameter data;

[0025] Step S2: Simplify the design of the power module to establish a simplified model based on the optimized simulation model parameter data; perform performance evaluation and optimization based on the simplified model to obtain optimization solution data;

[0026] Step S3: Perform simulation demand analysis on the optimization scheme data to obtain simulation tasks for the power module; utilize a distributed computing platform to perform large-scale parallel computing on the simulation tasks and perform simulation analysis to obtain real-time simulation data; dynamically adjust computing resources based on the real-time simulation data to obtain real-time optimization data;

[0027] Step S4: Pre-calculating preset working parameters based on real-time optimization data and establishing a simulation result library; performing rapid simulation and optimization on the power module based on the simulation result library to obtain rapid simulation data;

[0028] Step S5: performing real-time monitoring of the power module according to the simulation result library and the fast simulation data to obtain simulation detection results; dynamically adjusting the simulation detection results through feedback control to obtain optimized simulation result data;

[0029] Step S6: Performing performance simulation on the power module based on the optimization simulation result data, and constructing a comprehensive performance control model of the power module; and implementing dynamic optimization control of the power module based on the comprehensive performance control model.

[0030] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of a performance simulation method for a power module according to the present invention. In this example, the performance simulation method for a power module includes the following steps:

[0031] Step S1: obtaining initial physical characteristic data and initial electrical characteristic data of the power module; preprocessing and simulating the initial physical characteristic data and initial electrical characteristic data to obtain physical field distribution characteristics within the power module; performing multi-physical field simulation analysis on the power module based on the physical field distribution characteristics within the power module using an adaptive grid generation algorithm and a sparse matrix solver to obtain simulation result data; adjusting and optimizing parameters of a simulation model of the power module based on the simulation result data to generate optimized simulation model parameter data;

[0032] The embodiment of the present invention obtains initial physical and electrical characteristic data of the power module, such as material properties, electrical conductivity, thermal conductivity, etc., which are the basis for simulation. An adaptive grid generation algorithm is used to generate a suitable grid model based on this initial data, ensuring finer grid division in key areas to improve simulation accuracy. A sparse matrix solution method is used to perform coupled simulation of multiple physical fields (such as electric fields, thermal fields, etc.), calculate the behavior of the power module under different physical conditions, and generate simulation result data. Model parameters are adjusted based on the simulation results to optimize the simulation model.

[0033] Step S2: Simplify the design of the power module to establish a simplified model based on the optimized simulation model parameter data; perform performance evaluation and optimization based on the simplified model to obtain optimization solution data;

[0034] The embodiment of the present invention simplifies the design of the power module based on optimized simulation model parameter data. This process can optimize the model by reducing unnecessary complex features, thereby reducing computing resources and time consumption. The simplified model must still retain key characteristics to ensure that its performance is close to that of the original model. Based on the simplified model, the performance of the power module is evaluated, and its behavior under different operating conditions is analyzed to ensure its accuracy and reliability. Based on the evaluation results, the model is further optimized to generate optimized solution data for the simplified model, laying the foundation for subsequent simulation tasks.

[0035] Step S3: Perform simulation demand analysis on the optimization scheme data to obtain simulation tasks for the power module; utilize a distributed computing platform to perform large-scale parallel computing on the simulation tasks and perform simulation analysis to obtain real-time simulation data; dynamically adjust computing resources based on the real-time simulation data to obtain real-time optimization data;

[0036] The embodiment of the present invention conducts a simulation demand analysis based on the optimization solution data of the simplified model to determine the simulation tasks of the power module, including the simulation scope, accuracy requirements, and time span. The distributed computing platform is used to decompose the simulation tasks and assign them to different computing nodes, enabling large-scale parallel computing and improving simulation efficiency. Parallel computing is used to perform multi-physics field simulation analysis on the power module and obtain real-time simulation data. Computing resource allocation is dynamically adjusted based on real-time simulation data, ensuring efficient use of computing resources while maintaining the accuracy and timeliness of simulation results, thereby obtaining real-time optimized data.

[0037] Step S4: Pre-calculating preset working parameters based on real-time optimization data and establishing a simulation result library; performing rapid simulation and optimization on the power module based on the simulation result library to obtain rapid simulation data;

[0038] The embodiments of the present invention pre-calculate the preset operating parameters of the power module based on real-time optimization data. These pre-calculated results are used to construct a simulation result library, which stores simulation data and optimization solutions for different operating conditions. Leveraging the data in this library, rapid simulation and optimization are performed for different operating states of the power module. This simulation process reduces recalculation time by leveraging existing data, resulting in rapidly responsive simulation data. This process improves simulation efficiency and accuracy, providing a foundation for subsequent performance monitoring and feedback adjustments.

[0039] Step S5: performing real-time monitoring of the power module according to the simulation result library and the fast simulation data to obtain simulation detection results; dynamically adjusting the simulation detection results through feedback control to obtain optimized simulation result data;

[0040] This embodiment of the present invention monitors power modules in real time based on a simulation results library and rapid simulation data. This monitoring process continuously tracks the operating status of the power modules, ensuring timely access to performance data. A feedback control mechanism dynamically adjusts simulation results. This feedback control system adjusts power module performance based on real-time data to optimize system response. Through multiple iterations and adjustments, optimized simulation results are ultimately obtained. This process ensures the efficiency and reliability of the power modules in dynamic operating environments.

[0041] Step S6: Performing performance simulation on the power module based on the optimization simulation result data, and constructing a comprehensive performance control model of the power module; and implementing dynamic optimization control of the power module based on the comprehensive performance control model.

[0042] This embodiment of the present invention utilizes optimization simulation results to perform system-level performance simulation of power modules. This simulation integrates various performance data from the power modules to construct a comprehensive performance control model. This model covers the performance of the power modules under different operating conditions and has dynamic response capabilities. This comprehensive performance control model provides real-time dynamic optimization control of the power modules. The model automatically adjusts and optimizes based on the real-time system status, ensuring that the power modules maintain optimal performance and stability under various operating conditions.

[0043] The present invention acquires initial physical and electrical characteristic data and utilizes an adaptive grid generation algorithm and sparse matrix solvers to perform multi-physics field simulation, enabling high-precision analysis of power module performance. This approach ensures the accuracy of simulation results and provides a reliable foundation for subsequent model optimization. Simplifying the optimized simulation model significantly reduces model complexity while preserving key performance characteristics. Simplifying the model's performance evaluation and optimization improves computational efficiency, reduces resource consumption, and rapidly generates effective optimization solution data. Utilizing a distributed computing platform for large-scale parallel computing and real-time simulation significantly shortens computation time and improves simulation analysis efficiency. Dynamically adjusting computing resources further optimizes the computational process, ensuring rapid processing of real-time data. By establishing a simulation result library and performing rapid simulation, the simulation process is accelerated. This enables rapid data response in a shorter timeframe, facilitating timely decision-making and adjustments. Real-time monitoring and feedback control enable dynamic adjustment of simulation results, ensuring system stability and optimization during operation. This feedback mechanism facilitates rapid response to changes in system operation and improves overall performance. The integrated performance control model enables system-level dynamic optimization control of the power module, further enhancing overall system performance and reliability. This model enables comprehensive control and optimization of power modules, improving system efficiency and stability. The performance simulation method of the present invention significantly improves the efficiency and reliability of power module design and operation through high-precision simulation, simplified models, optimized computing resource management, fast-response simulation, dynamic monitoring, and comprehensive performance control.

[0044] Preferably, step S1 includes the following steps:

[0045] Step S11: Acquire the size, temperature, and weight data of the power module through experimental measurement; and acquire the material property data of the power module through the specification data provided by the manufacturer;

[0046] Step S12: Calculating and analyzing the size, temperature, weight data, and material property data using a numerical simulation method to obtain initial physical property data of the power module;

[0047] Step S13: Using a digital multimeter, a power analyzer, and a spectrum analyzer to measure the electrical characteristics of the power module to obtain electrical characteristic measurement data; verifying the electrical characteristic measurement data based on the electrical specification data provided by the manufacturer to obtain initial electrical characteristic data;

[0048] Step S14: pre-processing the initial physical characteristic data and the initial electrical characteristic data to remove data noise and outliers to obtain pre-processed cleaned data; establishing an initial simulation model of the power module based on the pre-processed cleaned data;

[0049] Step S15: Using finite element analysis and computational fluid dynamics based on the initial simulation model of the power module, simulate the stress, heat distribution, air flow, and heat dissipation effect of the power module to obtain the physical field distribution characteristics inside the power module;

[0050] Step S16: Meshing the power module using an adaptive mesh generation algorithm based on the physical field distribution characteristics within the power module to obtain a power module meshing strategy, wherein the specific meshing rule is to use a finer mesh in mechanical stress concentration areas, high temperature areas, and high current density areas, and a coarser mesh in other areas;

[0051] Step S17: constructing a sparse matrix based on the physical field distribution characteristics inside the power module using a multi-grid method to obtain a sparse matrix solution algorithm;

[0052] Step S18: performing multi-physics field simulation on the power module based on the pre-processed cleaned data according to the power module grid partitioning strategy and sparse matrix solving algorithm to obtain simulation result data;

[0053] Step S19: According to the simulation result data, the simulation model of the power module is adjusted and optimized to generate optimized simulation model parameter data.

[0054] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S1 in the embodiment of the present invention, step S1 includes the following steps:

[0055] Step S11: Acquire the size, temperature, and weight data of the power module through experimental measurement; and acquire the material property data of the power module through the specification data provided by the manufacturer;

[0056] This embodiment of the present invention uses experimental measurement methods to measure the size, temperature, and weight of the power module to obtain its physical dimensions, thermal performance, and mass data. Material property data for the power module, including thermal conductivity and electrical conductivity, is extracted from manufacturer-provided specifications. This data provides the foundation for subsequent numerical simulation analysis, enabling accurate assessment of the power module's physical properties.

[0057] Step S12: Calculating and analyzing the size, temperature, weight data, and material property data using a numerical simulation method to obtain initial physical property data of the power module;

[0058] This embodiment of the present invention utilizes numerical simulation methods to input acquired dimensions, temperature, weight, and material property data into simulation software. This data is then used for computational analysis to simulate the physical properties of the power module and obtain initial physical property data. These simulation results provide a foundation for further analysis and optimization of the power module, ensuring that the actual design meets the expected performance.

[0059] Step S13: Using a digital multimeter, a power analyzer, and a spectrum analyzer to measure the electrical characteristics of the power module to obtain electrical characteristic measurement data; verifying the electrical characteristic measurement data based on the electrical specification data provided by the manufacturer to obtain initial electrical characteristic data;

[0060] In this embodiment, a digital multimeter, power analyzer, and spectrum analyzer are used to perform detailed electrical characteristic measurements on the power module, recording data such as voltage, current, power, and frequency. This measurement data is then compared and verified with the manufacturer's electrical specifications to confirm the accuracy of the measurement results, ultimately obtaining the initial electrical characteristic data for the power module. This data provides a key basis for subsequent simulation model development and optimization.

[0061] Step S14: pre-processing the initial physical characteristic data and the initial electrical characteristic data to remove data noise and outliers to obtain pre-processed cleaned data; establishing an initial simulation model of the power module based on the pre-processed cleaned data;

[0062] This embodiment of the present invention performs data preprocessing on the initial physical and electrical characteristic data, primarily removing noise and outliers. The resulting preprocessed, cleaned data is used to build an initial simulation model of the power module. This model forms the basis for subsequent simulation analysis, ensuring the accuracy and reliability of the simulation results.

[0063] Step S15: Using finite element analysis and computational fluid dynamics based on the initial simulation model of the power module, simulate the stress, heat distribution, air flow, and heat dissipation effect of the power module to obtain the physical field distribution characteristics inside the power module;

[0064] This embodiment of the present invention utilizes finite element analysis and computational fluid dynamics methods to perform detailed simulations on the initial simulation model of the power module. The analysis includes stress distribution, thermal distribution, air flow, and heat dissipation within the power module. These simulations help determine the physical field distribution characteristics of the power module under actual operating conditions, providing key data for subsequent meshing and multi-physics simulations.

[0065] Step S16: Meshing the power module using an adaptive mesh generation algorithm based on the physical field distribution characteristics within the power module to obtain a power module meshing strategy, wherein the specific meshing rule is to use a finer mesh in mechanical stress concentration areas, high temperature areas, and high current density areas, and a coarser mesh in other areas;

[0066] This embodiment of the present invention utilizes an adaptive mesh generation algorithm to mesh the power module. This meshing strategy is tailored to the physical field distribution within the power module. Finer meshes are used in areas of concentrated mechanical stress, high temperature, and high current density to improve computational accuracy, while coarser meshes are used in other areas to optimize computing resources. This approach ensures simulation accuracy in critical areas while reducing computational costs.

[0067] Step S17: constructing a sparse matrix based on the physical field distribution characteristics inside the power module using a multi-grid method to obtain a sparse matrix solution algorithm;

[0068] This embodiment of the present invention uses a multigrid method to construct a sparse matrix of the physical field distribution within the power module. This method improves the accuracy of capturing complex physical field characteristics through hierarchical mesh refinement. The physical fields of the power module are hierarchically gridded, a sparse matrix is ​​constructed to optimize computational efficiency, and the solution is performed using a sparse matrix solver algorithm. This method can effectively handle large-scale problems while reducing computing resource requirements.

[0069] Step S18: performing multi-physics field simulation on the power module based on the pre-processed cleaned data according to the power module grid partitioning strategy and sparse matrix solving algorithm to obtain simulation result data;

[0070] This embodiment of the present invention utilizes a gridding strategy for power modules and a sparse matrix solver algorithm to perform multi-physics simulation based on pre-processed and cleaned data. The gridding strategy is applied to the simulation model to ensure appropriate mesh density in critical areas. A sparse matrix solver algorithm is used to process complex physical field data, improving computational efficiency. Multi-physics simulation of the power module generates detailed simulation results for subsequent analysis and optimization.

[0071] Step S19: According to the simulation result data, the simulation model of the power module is adjusted and optimized to generate optimized simulation model parameter data.

[0072] This embodiment of the present invention adjusts the parameters of the power module simulation model based on the simulation results data. The simulation results data is analyzed to identify performance bottlenecks and optimization points. The simulation model is optimized by adjusting model parameters, such as material properties, geometry, or boundary conditions. This generates optimized simulation model parameter data to improve the performance, efficiency, or reliability of the power module. These adjustments provide an improved model foundation for subsequent simulations and practical applications.

[0073] The present invention obtains the size, temperature, weight and material properties of the power module through experimental measurement and manufacturer specification data to ensure the accuracy of preliminary data; uses numerical simulation method to calculate and analyze the acquired data to obtain the initial physical property data of the power module, providing a basis for subsequent optimization; obtains electrical characteristic data through multiple measuring instruments and verifies according to manufacturer specifications to ensure the reliability of electrical characteristic data; pre-processes the physical and electrical characteristic data, removes noise and outliers, and establishes an initial simulation model to improve the accuracy of the model; uses finite element analysis and computational fluid dynamics to simulate stress, heat distribution and heat dissipation effect, obtains physical field distribution characteristics, and provides a basis for subsequent meshing; adopts adaptive mesh generation algorithm and multi-grid method to construct sparse matrix solution algorithm, optimize meshing, and improve calculation accuracy and efficiency; performs multi-physics field simulation based on optimized mesh and sparse matrix solution algorithm, obtains detailed simulation result data, and generates improved simulation model parameter data through parameter adjustment and optimization. The present invention can significantly improve the simulation accuracy and efficiency of the power module, reduce computing cost, speed up the design optimization process, and ultimately improve the overall performance and reliability of the power module.

[0074] Preferably, step S15 includes the following steps:

[0075] Step S151: setting boundary conditions for the initial simulation model based on the constraints and load conditions in the actual working condition to obtain initial simulation boundary condition data;

[0076] In this embodiment of the present invention, the initial simulation model is configured based on the actual operating conditions of the power module, combined with the system's constraints and load conditions. The model's boundary conditions are determined based on the external environment, operating temperature, stress load, and other factors encountered during actual operation of the power module. This operating condition information is converted into input for the numerical model, forming initial simulation boundary condition data that serves as the basis for subsequent simulation calculations and analysis, ensuring that the simulation process reflects the actual operation of the power module under actual operating conditions.

[0077] Step S152: Calculating heat convection data based on the initial physical property data using finite element analysis and computational fluid dynamics;

[0078] This embodiment of the present invention uses finite element analysis to mesh the power module structure and computational fluid dynamics to analyze the fluid flow within and on the module surface. Based on these analysis results, the heat transfer path and distribution characteristics during module operation are determined, generating thermal convection data that provides a foundation for subsequent coupled analysis.

[0079] Step S153: performing stress state analysis on the stressed area using the dynamic finite element method based on the load conditions to obtain transient analysis data;

[0080] The present invention uses different time steps to calculate the transient stress distribution in the stressed area under varying loads, generating data including stress values, displacements, and deformations. This transient analysis data reflects the dynamic changes in stress over time during module operation, providing a basis for subsequent dynamic stress distribution diagrams and the identification of stress concentration points.

[0081] Step S154: analyzing the transient analysis data based on the time variation trend to obtain a dynamic stress distribution diagram, wherein the time variation trend range is the duration of three cycles of vibration;

[0082] This embodiment of the present invention tracks stress fluctuations over time and calculates how the stress distribution changes over the duration of three vibration cycles to generate a dynamic stress distribution map. This map can show the stress variation trends of the power module over multiple operating cycles, helping to identify areas of stress concentration and possible fatigue points within the module, providing a basis for subsequent analysis.

[0083] Step S155: Analyze the dynamic stress distribution diagram to identify stress concentration point data;

[0084] Embodiments of the present invention perform a detailed analysis of the dynamic stress distribution map to identify stress concentration points within the map. This is achieved by examining the peak stress values ​​within the dynamic stress distribution map. Particular attention is paid to areas where stress values ​​are significantly higher than those in the surrounding areas; these areas typically represent stress concentration points. By identifying these stress concentration points, potential weaknesses in the power module under actual operating conditions can be assessed, guiding further design improvements or material selection.

[0085] Step S156: performing coupling analysis on the stress concentration point data and the fluid flow data to obtain thermo-mechanical coupling data;

[0086] This embodiment of the present invention combines stress concentration point data with fluid flow data to perform thermo-mechanical coupling analysis. Fluid dynamics data is used to determine the heat flow around the stress concentration point. This heat convection data is then combined with stress data and analyzed using a thermo-mechanical coupling model. This process involves calculating the heat conduction effect of the stress concentration point and its feedback on the overall stress state, generating thermo-mechanical coupling data to assess the impact of temperature changes on stress distribution.

[0087] Step S157: using the initial simulation model to perform simulation based on the initial simulation boundary conditions and the thermo-mechanical coupling data to obtain the physical field distribution characteristics inside the power module.

[0088] This embodiment of the present invention combines thermo-mechanical coupling data with initial simulation boundary conditions and utilizes the initial simulation model to conduct a comprehensive simulation. This process involves applying the thermo-mechanical coupling data to the model and updating the model's thermal and mechanical field distributions. By comprehensively analyzing the temperature and stress fields within the power module, the physical field distribution characteristics of the power module under actual operating conditions are determined, helping to optimize design and improve performance.

[0089] The present invention sets boundary conditions for the initial simulation model and performs thermal convection and stress analysis, which can accurately capture the thermal and mechanical behavior of the power module in actual working conditions. These steps ensure the accuracy and reliability of the model in an actual working environment. Dynamic stress is analyzed over time to generate a dynamic stress distribution diagram, which can clearly identify the stress changes of the module under vibration conditions. This helps to understand the response of the structure under dynamic loads and discover potential problems in advance. By analyzing the dynamic stress distribution diagram and identifying stress concentration points, possible structural weaknesses can be effectively located, guiding design optimization and reducing the risk of failure. The stress concentration point data is coupled with the fluid flow data for analysis to obtain thermo-mechanical coupling data, which ensures the comprehensive consideration of thermal and mechanical effects, helps to optimize the heat dissipation design and improve the overall performance of the module. By combining these data for simulation, detailed physical field distribution characteristics inside the power module are obtained. This data supports further design optimization and performance improvement, ensuring efficient and stable operation of the module under actual working conditions.

[0090] Preferably, step S16 includes the following steps:

[0091] Step S161: discretizing the initial simulation model according to the physical field distribution characteristics and the power module grid division strategy to obtain a discretized model;

[0092] The present invention determines the area and scale that require discretization based on the physical field distribution characteristics of the power module. Based on the meshing strategy, meshing is performed within the physical field distribution map, discretizing the model into multiple mesh cells. Based on the meshing results, the original simulation model is converted into a discretized model composed of discrete mesh cells, preparing for subsequent finite element analysis. This ensures that the simulation model is discretized to an appropriate level of detail, providing an accurate foundation for subsequent finite element method matrix construction.

[0093] Step S162: using the finite element method to transform each discrete point in the discretized model to obtain matrix elements to construct a sparse matrix;

[0094] The present invention processes the discretized model, converting the physical properties and boundary conditions of each grid cell into the data format required by the finite element method. Based on the physical properties of each discrete point in the discretized model, the corresponding matrix elements are calculated using the finite element method. All matrix elements are assembled into a sparse matrix, reflecting the physical and mathematical properties of the discretized model and providing a foundation for subsequent solution algorithms. Converting the physical model into a mathematical model facilitates efficient numerical calculations.

[0095] Step S163: using a multigrid method to solve the sparse matrix and obtain a preliminary solution algorithm;

[0096] The embodiment of the present invention selects an appropriate multi-grid strategy to process sparse matrices and construct a multi-level grid structure to improve solution efficiency. A preliminary solution is performed on a coarse grid, and then refined calculations are gradually performed on fine grids to accelerate convergence and reduce the amount of calculation. The solution of the sparse matrix is ​​optimized through the iterative mechanism of the multi-grid method to obtain a preliminary solution algorithm that operates on different levels of grids. This improves the solution efficiency and accuracy of large-scale sparse matrices.

[0097] Step S164: Utilize parallel computing to optimize the efficiency of the solution algorithm to obtain a sparse matrix solution algorithm.

[0098] This embodiment of the present invention decomposes the sparse matrix solution algorithm into multiple parallel tasks, allowing them to run simultaneously on multiple computing cores or processors, thereby improving computational speed. It effectively schedules parallel tasks to ensure balanced load on each computing core, avoiding computational bottlenecks and idle resources. It adjusts the parallel computing strategy based on the hardware architecture and the characteristics of the solution task to maximize the efficiency and responsiveness of the solution algorithm. This optimizes the efficiency of sparse matrix solutions and improves overall simulation performance.

[0099] The present invention discretizes the initial simulation model of the power module, which can refine the continuous physical field distribution into discrete points, thereby improving the simulation accuracy. The discretized model is transformed using the finite element method to obtain matrix elements and construct a sparse matrix, laying the foundation for subsequent efficient solution. The multi-grid method is used to solve the sparse matrix, which can effectively reduce the computational complexity and improve the solution efficiency. Through parallel computing optimization, the processing speed and efficiency of the solution algorithm are significantly improved, and the simulation time is shortened. The implementation of these steps improves the accuracy and efficiency of the simulation model, providing strong support for the performance optimization and design of the power module.

[0100] Preferably, step S2 includes the following steps:

[0101] Step S21: simplifying the optimized simulation model parameter data using the characteristic orthogonal decomposition method to obtain simplified model parameter data;

[0102] Step S22: Using the simplified model parameter data, a simplified model is established based on the preset main physical behaviors and performance characteristics of the power module.

[0103] Step S23: Evaluate the performance of the power module using the simplified model to obtain performance evaluation data;

[0104] Step S24: adjusting and optimizing the parameters and design of the simplified model according to the performance evaluation data to obtain performance optimization data;

[0105] Step S25: Generate optimization solution data based on the performance evaluation data and the performance optimization data, wherein the optimization solution data includes a design optimization solution, optimization parameters, and adjustment suggestions.

[0106] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 1 Detailed step flow diagram of step S2 in the embodiment of the present invention, step S2 includes the following steps:

[0107] Step S21: simplifying the optimized simulation model parameter data using the characteristic orthogonal decomposition method to obtain simplified model parameter data;

[0108] The present invention utilizes a feature orthogonal decomposition method to simplify the parameter data of an optimized simulation model. By analyzing the key features of the simulation model, high-dimensional data is converted into a low-dimensional space, preserving key feature parameters and eliminating redundant information. This method extracts the most important features of the model through orthogonal decomposition, reducing computational complexity while maintaining model accuracy. The resulting simplified model parameter data provides a foundation for subsequent simplified model construction and performance evaluation.

[0109] Step S22: Using the simplified model parameter data, a simplified model is established based on the preset main physical behaviors and performance characteristics of the power module.

[0110] This embodiment of the present invention utilizes simplified model parameter data to establish a simplified model based on the pre-set primary physical behaviors and performance characteristics of the power module. By identifying the key physical behaviors of the power module, its primary performance characteristics are determined. The simplified model parameter data is applied to these characteristics to establish a simplified model that effectively simulates the primary functions of the power module. This simplified model is used for subsequent performance evaluation and optimization, reducing computational complexity and improving efficiency.

[0111] Step S23: Evaluate the performance of the power module using the simplified model to obtain performance evaluation data;

[0112] This embodiment of the present invention utilizes a simplified model to evaluate the performance of a power module. Based on the simplified model's key physical behaviors and performance characteristics, the actual operating state of the power module is simulated. By analyzing its performance under different loads and operating conditions, key parameters such as the model's thermal performance, mechanical stress distribution, and electrical characteristics are evaluated. These evaluation results are compared with pre-set performance standards to generate performance evaluation data, which is used to determine whether the power module design meets actual application requirements.

[0113] Step S24: adjusting and optimizing the parameters and design of the simplified model according to the performance evaluation data to obtain performance optimization data;

[0114] This embodiment of the present invention adjusts and optimizes the parameters and design of the simplified model based on performance evaluation data. It analyzes the performance evaluation data for areas that do not meet design requirements or have room for improvement, identifying key parameters that affect power module performance. By adjusting these parameters, the simplified model's structural design or material selection is improved. Simulation evaluation is then performed again using the optimized parameters to ensure improved performance and efficiency of the optimized model. This results in performance optimization data, providing a more optimal solution for power module design.

[0115] Step S25: Generate optimization solution data based on the performance evaluation data and the performance optimization data, wherein the optimization solution data includes a design optimization solution, optimization parameters, and adjustment suggestions.

[0116] This embodiment of the present invention generates optimization solution data based on performance evaluation data and performance optimization data. Combining the evaluation results with optimized parameters, a design optimization solution for the power module is developed, identifying any design details or parameters that require adjustment. Validation is performed based on the optimized parameters to ensure the solution's feasibility and performance improvement. Complete optimization solution data, including the design optimization solution, optimization parameters, and adjustment recommendations, is generated to guide further design improvements and production optimization of the power module.

[0117] The present invention simplifies the simulation model parameter data through the characteristic orthogonal decomposition method, greatly reducing the amount of calculation and resource consumption; 2. A simplified model is established based on the main physical behavior and performance characteristics of the power module to ensure that the simplified model can still effectively reflect the core characteristics of the system, thereby improving the modeling accuracy; through performance evaluation and parameter optimization, the performance bottleneck of the power module can be accurately identified, and the design of the model can be precisely adjusted to further improve its performance; the optimization scheme data not only provides a reference for design optimization, but also includes optimization parameters and adjustment suggestions, making the subsequent design and manufacturing process more instructive, and ultimately improving the overall performance and market competitiveness of the product.

[0118] Preferably, step S3 includes the following steps:

[0119] Step S31: performing simulation requirement analysis on the optimization scheme data to obtain a simulation task, wherein the simulation task includes a simulation target, a calculation scope, boundary conditions, and input data;

[0120] Step S32: splitting the simulation task based on task independence to obtain multiple simulation subtasks;

[0121] Step S33: performing large-scale parallel simulation analysis on multiple simulation subtasks through a distributed computing platform to obtain real-time simulation data;

[0122] Step S34: performing consistency and result trend analysis on the real-time simulation data to obtain real-time simulation data analysis results;

[0123] Step S35: dynamically adjusting computing resources according to the real-time simulation data analysis results to obtain dynamically adjusted data;

[0124] Step S36: Perform simulation analysis on the dynamic adjustment data to generate real-time optimization data, wherein the real-time optimization data includes optimized computing configuration, improved simulation strategy and optimization suggestions.

[0125] As an embodiment of the present invention, refer to Figure 4 As shown, Figure 1 Detailed step flow diagram of step S3 in the embodiment of the present invention, step S3 includes the following steps:

[0126] Step S31: performing simulation requirement analysis on the optimization scheme data to obtain a simulation task, wherein the simulation task includes a simulation target, a calculation scope, boundary conditions, and input data;

[0127] This embodiment of the present invention conducts a detailed simulation requirements analysis on the optimization solution data to clarify the various elements of the simulation task. This includes defining the specific simulation objectives, such as performance evaluation or optimization parameter verification; determining the spatial or temporal scope of the simulation, specifically the region and time period to be simulated; setting the necessary physical or mathematical boundary conditions for the simulation task; and preparing relevant input data, such as model parameters and environmental variables, based on the simulation requirements. This analysis ensures a comprehensive and clear definition of the simulation task.

[0128] Step S32: splitting the simulation task based on task independence to obtain multiple simulation subtasks;

[0129] The present invention splits the entire simulation task into multiple independent subtasks based on the independence of the simulation tasks. This splitting is based on the dependencies between the subtasks, the independence of the computational scope, the relative stability of the boundary conditions, and the locality of the input data. By splitting the tasks based on their independence, each subtask can run independently and in parallel on a distributed computing platform, thereby improving overall simulation efficiency, reducing redundant use of computing resources, and ensuring efficient execution of simulation analysis tasks.

[0130] Step S33: performing large-scale parallel simulation analysis on multiple simulation subtasks through a distributed computing platform to obtain real-time simulation data;

[0131] This embodiment of the present invention distributes multiple simulation subtasks onto a distributed computing platform, using parallel computing technology to perform simulation analysis on these subtasks simultaneously. Each subtask runs independently on a different computing node, reducing computation time and improving efficiency. The distributed computing platform dynamically allocates computing resources based on the computational requirements of the simulation task and monitors data changes during the simulation process in real time to ensure efficient execution of the simulation task and generate real-time simulation data. This data is used for subsequent analysis and dynamic adjustment of optimization strategies.

[0132] Step S34: performing consistency and result trend analysis on the real-time simulation data to obtain real-time simulation data analysis results;

[0133] The embodiment of the present invention performs consistency checks on the real-time simulation data generated by multiple simulation subtasks to ensure that there are no conflicts or data deviations between the subtask results. Trend analysis of the simulation results assesses changes in key parameters during the simulation process and identifies abnormal or unexpected results. Trend analysis can utilize statistical methods, time series analysis, or machine learning algorithms to assess the overall trend and direction of change in the data, ensuring the accuracy and reliability of the simulation data and providing a basis for subsequent resource adjustment and optimization strategies.

[0134] Step S35: dynamically adjusting computing resources according to the real-time simulation data analysis results to obtain dynamically adjusted data;

[0135] The present invention uses real-time simulation data analysis results to evaluate the current computing resource utilization efficiency and resource allocation. Data analysis identifies resource bottlenecks or redundancies and determines which computing resources require adjustment. Dynamic adjustments include optimizing computing node allocation, adjusting computing load balancing, and increasing or decreasing computing resource allocation. Using automated tools or algorithms for dynamic resource allocation ensures higher computing efficiency and more accurate results in the subsequent analysis phase of the simulation task.

[0136] Step S36: Perform simulation analysis on the dynamic adjustment data to generate real-time optimization data, wherein the real-time optimization data includes optimized computing configuration, improved simulation strategy and optimization suggestions.

[0137] This embodiment of the present invention conducts further simulation analysis on the dynamically adjusted data to evaluate the effects of resource adjustments. New simulation tasks are used to verify the effectiveness of the adjusted computing configuration and strategy. Real-time optimization data is generated, including the optimized computing configuration, improved simulation strategy, and specific optimization recommendations. This data is used to optimize subsequent simulation processes, improving efficiency and accuracy.

[0138] The present invention can efficiently generate multiple simulation subtasks and realize large-scale parallel simulation analysis by performing a comprehensive simulation demand analysis and task splitting on the optimization scheme data. This not only shortens the simulation time, but also improves the data processing capability. The consistency of real-time simulation data and the result trend analysis can quickly identify potential problems and guide the dynamic adjustment of computing resources, making resource allocation more reasonable and improving computing efficiency. Through further simulation analysis of the dynamically adjusted data, the generated real-time optimization data can provide accurate optimization configuration and improvement strategy to ensure the continuous optimization of the simulation process. This series of steps effectively improves the efficiency and accuracy of simulation analysis, optimizes the utilization of computing resources, and enhances the overall performance and adaptability of the system.

[0139] Preferably, step S4 includes the following steps:

[0140] Step S41: pre-processing the real-time optimization data to remove noise and outliers to obtain a clean real-time optimization data set;

[0141] Step S42: using a distributed computing platform, based on a clean real-time optimization data set, performing large-scale parallel pre-calculation of preset working parameters to generate pre-calculation result data;

[0142] Step S43: classify and organize the pre-calculated result data to establish a simulation result library;

[0143] Step S44: using the simulation result library to quickly simulate the power module and obtain a quick simulation result;

[0144] Step S45: Optimize and adjust the performance of the power module according to the rapid simulation results to generate rapid simulation data.

[0145] As an embodiment of the present invention, refer to Figure 5 As shown, Figure 1 Detailed step flow diagram of step S4 in the embodiment of the present invention, step S4 includes the following steps:

[0146] Step S41: pre-processing the real-time optimization data to remove noise and outliers to obtain a clean real-time optimization data set;

[0147] This embodiment of the present invention preprocesses real-time optimization data, removing noise and outliers through data cleaning techniques. Data denoising is performed, using filters or smoothing algorithms to reduce interference signals. Statistical analysis or anomaly detection methods are then applied to identify and remove anomalous data points that do not meet expectations. The clean, processed data set is saved for subsequent analysis and calculation. This step ensures data accuracy and reliability, laying the foundation for subsequent large-scale parallel precomputation.

[0148] Step S42: using a distributed computing platform, based on a clean real-time optimization data set, performing large-scale parallel pre-calculation of preset working parameters to generate pre-calculation result data;

[0149] The embodiments of the present invention utilize a clean, real-time optimized dataset for large-scale parallel pre-computation on a distributed computing platform. The dataset is divided into multiple subsets and assigned to different computing nodes. Each node performs calculations based on preset operating parameters, allowing independent processing tasks to be executed in parallel. After the calculations are complete, the results from each node are aggregated to generate pre-computation result data. This approach accelerates data processing and improves computational efficiency and accuracy.

[0150] Step S43: classify and organize the pre-calculated result data to establish a simulation result library;

[0151] The present invention classifies and organizes pre-calculation result data, categorizing it according to different features and parameters. Using data tags or features, pre-calculation results are grouped and stored in a simulation result library. Data organization includes removing duplicate data, merging related data sets, and establishing an efficient index structure for fast retrieval. Once the simulation result library is established, the data can be used for subsequent rapid simulation analysis, ensuring data integrity and accuracy.

[0152] Step S44: using the simulation result library to quickly simulate the power module and obtain a quick simulation result;

[0153] This embodiment of the present invention leverages an established simulation results library and extracts relevant pre-calculated results based on the design requirements and parameter configuration of the power module. These results are then applied to the power module's rapid simulation model for real-time simulation analysis. Through rapid simulation, the power module's performance under different operating conditions is evaluated, generating rapid simulation results. This process aims to rapidly evaluate model performance using existing data without requiring a full re-simulation calculation.

[0154] Step S45: Optimize and adjust the performance of the power module according to the rapid simulation results to generate rapid simulation data.

[0155] This embodiment of the present invention evaluates the performance of power modules based on rapid simulation results. It analyzes the gaps between the performance indicators in the simulation results and the design requirements, identifying potential areas for improvement. Based on these analysis results, the design parameters or operating conditions of the power modules are adjusted to optimize their performance. New rapid simulation data is generated, including optimized design recommendations, parameter adjustment plans, and further improvement strategies. This data will be used to guide subsequent design optimization and performance improvement.

[0156] The present invention ensures the accuracy and reliability of the data by pre-processing the real-time optimization data to remove noise and outliers, thereby providing a high-quality data foundation for subsequent calculations. The use of a distributed computing platform for large-scale parallel pre-computation significantly improves the calculation speed and efficiency, and greatly shortens the time for processing complex problems. The pre-calculation result data is classified and organized, and a systematic simulation result library is established, which not only improves the efficiency of data organization and retrieval, but also provides convenience for future analysis and reference. Rapid simulation is performed through the simulation result library, and the performance of the power module can be quickly evaluated, providing immediate performance feedback, and accelerating the design and optimization process. The performance of the power module is optimized and adjusted according to the rapid simulation results, and optimization data is generated, which can quickly respond to performance problems and formulate effective improvement strategies to further improve the overall performance of the power module. The present invention effectively combines data processing, calculation, result management and optimization adjustment to form a set of efficient and systematic simulation analysis and optimization processes, which greatly improves the design and performance optimization capabilities of the power module.

[0157] Preferably, step S43 includes the following steps:

[0158] Step S431: classifying the pre-calculation result data based on physical characteristics, operating conditions and time series to obtain a pre-calculation result classification data set;

[0159] Embodiments of the present invention classify pre-calculation result data into groups based on physical properties, operating conditions, and time series. These classification criteria can reflect the multidimensional nature of the data. This classified data is organized into a pre-calculation result classification dataset to facilitate subsequent data management and analysis. The classification process may involve data labeling, application of a grouping algorithm, and determination of a data storage format.

[0160] Step S432: using the distributed computing platform to classify the data set according to the pre-calculated results and establish a simulation result database;

[0161] This embodiment of the present invention utilizes a distributed computing platform to upload and store pre-computed classification datasets in a distributed system. The system distributes data across multiple computing nodes for efficient data processing and storage. This process involves selecting an appropriate distributed database system, configuring data storage strategies, and ensuring data consistency and integrity across nodes. Data may be compressed and encrypted during storage to improve storage efficiency and security.

[0162] Step S433: Performing structural design on the simulation result database to obtain a simulation result library, wherein the structural design includes index design and query optimization.

[0163] This embodiment of the present invention structures the simulation results database, creates indexes to improve data retrieval efficiency, and designs indexing strategies based on data access patterns and query requirements. Query optimization is performed by analyzing the execution plans of common queries, adjusting database configuration, and optimizing query statements to reduce response time. The design process also includes data partitioning, compression strategies, and backup mechanisms to improve database performance and reliability.

[0164] The present invention forms a structured pre-calculation result classification data set by classifying the pre-calculation result data based on physical properties, operating conditions, and time series. These data sets help establish an efficient simulation result database using a distributed computing platform. The database can support large-scale data storage and rapid access, improving the processing power of simulation analysis. By structurally designing the database, including index design and query optimization, the efficiency of data retrieval and query can be significantly improved, thereby accelerating the acquisition of simulation results. These measures combined can significantly improve the speed and accuracy of power module simulation analysis, making the optimization process more efficient and accurate.

[0165] Preferably, step S5 includes the following steps:

[0166] Step S51: Using sensors and a high-speed data acquisition system, the power module is monitored in real time to obtain operating data, and the operating data is pre-processed and standardized to obtain real-time monitoring data;

[0167] This embodiment of the present invention uses sensors and a high-speed data acquisition system to monitor power modules in real time, acquiring operational data. This operational data is preprocessed, including noise and outlier removal, and normalization to ensure consistency and accuracy. This processed data forms real-time monitoring data for further analysis and comparison. This approach ensures high data quality and provides a reliable foundation for subsequent simulation, testing, and optimization.

[0168] Step S52: performing comparative analysis using the real-time monitoring data, the simulation result library, and the rapid simulation data to obtain simulation detection results;

[0169] This embodiment of the present invention compares real-time monitoring data with historical simulation results and rapid simulation data stored in a simulation result library. This comparative analysis assesses the consistency between the power module's performance under actual operating conditions and the simulation results. Specific implementations include extracting relevant data, performing data matching and error analysis to determine discrepancies between simulation results and actual operating conditions, and generating simulation test results. This helps identify potential performance issues and provides a basis for subsequent adjustments.

[0170] Step S53: performing abnormal performance analysis on the simulation detection results to obtain adjustment operating parameters of the power module;

[0171] This embodiment of the present invention compares simulation test results to identify anomalies in expected performance. By analyzing this anomaly data, it identifies potential problems with the power module during actual operation. Using data mining and statistical analysis techniques, it extracts key operating parameters associated with the anomaly. Based on these analysis results, it develops a strategy for adjusting these operating parameters to optimize the power module's performance. These adjusted operating parameters are then used in the subsequent feedback control algorithm.

[0172] Step S54: constructing a feedback control algorithm based on real-time monitoring data and adjusting operating parameters;

[0173] Embodiments of the present invention develop and implement a feedback control algorithm based on real-time monitoring data and adjusted operating parameters. This algorithm dynamically adjusts the operating parameters of the power module based on changes in real-time monitoring data to maintain or improve its performance. Feedback control algorithms typically include data acquisition, real-time processing, and adjustment mechanisms to respond to detected performance deviations and ensure that the power module maintains optimal operation during actual operation. In this way, dynamic optimization and stability control of power module performance can be achieved.

[0174] Step S55: dynamically adjusting the parameters of the simulation test results through a feedback control algorithm to obtain optimized performance data of the power module;

[0175] This embodiment of the present invention utilizes a feedback control algorithm to dynamically adjust the operating parameters of the power module based on real-time monitoring data. The algorithm continuously acquires and analyzes real-time data, identifies performance deviations, and generates adjustment instructions to optimize the performance of the power module. These adjustment instructions are applied to the power module's control system, enabling it to respond to performance changes in real time, ensuring optimal system operation and improving overall efficiency and stability.

[0176] Step S56: Analyze the optimization performance data to obtain an adjustment optimization plan;

[0177] This embodiment of the present invention conducts in-depth analysis of optimized performance data to assess the performance of the power module after adjustment. By comparing the data before and after optimization, performance improvements and potential issues are identified, and an optimization plan is formulated. The analysis may include the magnitude of performance improvement, operational stability, and other key indicators. The analysis results are converted into a specific optimization plan, including further parameter adjustments and operational recommendations, to improve the overall efficiency of the power module.

[0178] Step S57: Dynamically adjust the preset operating parameters by adjusting the optimization scheme to obtain real-time control parameters of the power module;

[0179] This embodiment of the present invention dynamically adjusts the preset operating parameters of power modules based on an optimization scheme. This process involves applying the optimization scheme's recommendations to actual operations, adjusting the module's operating parameters to achieve optimal performance. This adjustment process requires real-time monitoring and feedback to ensure the effectiveness of parameter adjustments. Real-time control parameters are generated and applied to ensure optimal performance of the power modules during actual operation.

[0180] Step S58: updating the simulation result database according to the real-time control parameters and the real-time monitoring data to obtain optimized simulation result data.

[0181] This embodiment of the present invention uses real-time control parameters and real-time monitoring data to update the simulation results database. Real-time control parameters and monitoring data are collected, compared, and integrated with existing data in the simulation results database. Based on the new real-time data, the records in the simulation results database are updated to ensure that the data in the database reflects the actual performance of the current power module. This update optimizes the simulation results data and enhances the accuracy and reliability of future simulation analysis.

[0182] The present invention ensures the accuracy and consistency of monitoring data, reduces data noise, and improves data reliability through efficient data acquisition and preprocessing. By comparing real-time monitoring data with the simulation result library, it is possible to promptly detect performance anomalies of the module, quickly identify and adjust operating parameters, and thus reduce potential performance problems. By constructing a feedback control algorithm and dynamically adjusting the parameters, the operating performance and stability of the power module are effectively improved. The optimized performance data is analyzed, and the generated adjustment optimization scheme can dynamically adjust the operating parameters, thereby further improving the efficiency and reliability of the module. The simulation result library is updated according to the latest real-time control parameters and monitoring data, making the simulation data more timely and accurate, and supporting future performance analysis and optimization. The present invention improves the operating efficiency and system performance of the power module, ensuring that the module performance can be quickly responded to and optimized in actual applications.

[0183] Preferably, step S6 includes the following steps:

[0184] Step S61: Using dynamic system modeling technology, based on preset performance indicators and optimization simulation result data, construct a comprehensive performance control model of the power module, wherein the comprehensive performance control model includes input-output relationships and system constraints;

[0185] This embodiment of the present invention collects preset performance indicators and optimization simulation results to ensure the accuracy and completeness of the input data. Using dynamic system modeling techniques, a comprehensive performance control model for the power module is constructed based on the collected data. This model includes input-output relationships and system constraints. This ensures that the model reflects the actual system behavior and constraints, including the system's dynamic response and stability requirements. This comprehensive control model, which truly reflects the power module's performance, provides a foundation for the subsequent design of dynamic optimization control solutions.

[0186] Step S62: Designing a dynamic optimization control scheme for the power module based on the comprehensive performance control model;

[0187] Based on a comprehensive performance control model, the embodiments of the present invention define the optimization objectives of the power module, such as improving efficiency, reducing energy consumption, or meeting specific performance indicators. A dynamic optimization control scheme, including control algorithms and adjustment strategies, is developed to achieve the optimization objectives. During the design process, the control scheme is ensured to operate effectively within the system constraints. Preliminary verification is performed to verify that the designed control scheme meets the performance indicators in the model, and necessary adjustments and optimizations are performed. This ensures that the control scheme can effectively improve the performance of the power module in actual operation.

[0188] Step S63: using simulation software to evaluate and analyze the dynamic optimization control scheme to obtain dynamic optimization control scheme evaluation data;

[0189] In this embodiment of the present invention, the parameters and control scheme of the comprehensive performance control model are set in the simulation software to ensure that the simulation conditions are consistent with the actual system. Simulation analysis is performed, and the designed dynamic optimization control scheme is applied to observe the model's response under different operating conditions. Simulation results are recorded, including system response, performance indicator changes, and any deviations. The effectiveness of the control scheme is evaluated, its impact on system performance is analyzed, and whether the preset performance indicators are achieved is confirmed. The dynamic optimization control scheme design is ensured to be validated through simulation testing.

[0190] Step S64: performing a comprehensive evaluation of the comprehensive performance control model based on the dynamic optimization control scheme evaluation data to obtain a comprehensive performance control model evaluation result;

[0191] This embodiment of the present invention collects dynamic optimization control scheme evaluation data, including system response, performance indicators, and control effectiveness. Statistical and analytical methods are used to compare the evaluation data with pre-set performance indicators to assess the accuracy and effectiveness of the comprehensive performance control model. Deviations between the model and actual performance are determined, and the model's strengths and weaknesses, along with their impact on the control scheme, are analyzed. An evaluation report is compiled summarizing the model's performance, improvement suggestions, and optimization directions, providing a basis for subsequent optimization.

[0192] Step S65: Optimizing the comprehensive performance control model based on the control scheme and model parameters according to the comprehensive performance control model evaluation result to achieve dynamic optimization control.

[0193] Based on the evaluation results, the present invention identifies model deficiencies and areas for optimization, including performance deviations and areas of poor control effectiveness. Based on the evaluation results, the parameters and system constraints in the comprehensive performance control model are adjusted to improve the model's accuracy and responsiveness. The control scheme is adjusted to better align with the optimization objectives and enhance the model's adaptability to dynamic changes. The optimized model is re-simulated and tested to verify that the improvements meet expectations, ensuring the implementation of dynamic optimal control.

[0194] The present invention can accurately describe the behavior of the power module through dynamic system modeling and the construction of a comprehensive performance control model, ensuring that the control scheme can effectively respond to the dynamic changes of the system and improve control accuracy. A dynamic optimization control scheme based on the comprehensive performance control model is designed to enable the power module to perform optimal performance under various working conditions, reduce energy waste and improve efficiency. By using simulation software to evaluate the control scheme, potential problems can be identified before implementation, and continuous improvement and optimization of the system can be achieved by adjusting model parameters and optimizing the control scheme. By comprehensively evaluating the model and optimizing the control scheme, the system's adaptability and reliability to different working conditions are improved, and the failure rate and maintenance costs are reduced. These effects combined can significantly improve the overall performance and economic benefits of the power module and achieve more stable and efficient operation.

[0195] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0196] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A performance simulation method for a power module, characterized in that: The following steps are involved: Step S1: Acquire initial physical characteristic data and initial electrical characteristic data of the power module; Preprocessing and simulating the initial physical characteristic data and initial electrical characteristic data to obtain the physical field distribution characteristics within the power module; using an adaptive grid generation algorithm and a sparse matrix solution algorithm, multi-physics field simulation analysis is performed on the power module based on the physical field distribution characteristics within the power module to obtain simulation result data; adjusting and optimizing the parameters of the simulation model of the power module based on the simulation result data to generate optimized simulation model parameter data; Step S2: Simplify the design of the power module to establish a simplified model based on the optimized simulation model parameter data; perform performance evaluation and optimization based on the simplified model to obtain optimization solution data; Step S3: Perform simulation demand analysis on the optimization scheme data to obtain simulation tasks for the power module; utilize a distributed computing platform to perform large-scale parallel computing on the simulation tasks, and perform simulation analysis to obtain real-time simulation data; Dynamically adjust computing resources based on real-time simulation data to obtain real-time optimization data; Step S4: Pre-calculate the preset working parameters according to the real-time optimization data and establish a simulation result library; quickly simulate and optimize the power module according to the simulation result library to obtain fast simulation data. Step S4 includes the following steps: Step S41: pre-processing the real-time optimization data to remove noise and outliers to obtain a clean real-time optimization data set; Step S42: using a distributed computing platform, based on a clean real-time optimization data set, performing large-scale parallel pre-calculation of preset working parameters to generate pre-calculation result data; Step S43: Classify and organize the pre-calculated result data to establish a simulation result library. Step S43 includes the following steps: Step S431: classifying the pre-calculation result data based on physical characteristics, operating conditions and time series to obtain a pre-calculation result classification data set; Step S432: using the distributed computing platform to classify the data set according to the pre-calculated results and establish a simulation result database; Step S433: performing structural design on the simulation result database to obtain a simulation result library, wherein the structural design includes index design and query optimization; Step S44: using the simulation result library to quickly simulate the power module and obtain a quick simulation result; Step S45: Optimizing and adjusting the performance of the power module according to the rapid simulation results to generate rapid simulation data; Step S5: performing real-time monitoring of the power module according to the simulation result library and the fast simulation data to obtain simulation detection results; dynamically adjusting the simulation detection results through feedback control to obtain optimized simulation result data; Step S6: Performing performance simulation on the power module based on the optimization simulation result data, and constructing a comprehensive performance control model of the power module; and implementing dynamic optimization control of the power module based on the comprehensive performance control model.

2. The performance simulation method of the power module according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire the size, temperature, and weight data of the power module through experimental measurement; and acquire the material property data of the power module through the specification data provided by the manufacturer; Step S12: Calculating and analyzing the size, temperature, weight data, and material property data using a numerical simulation method to obtain initial physical property data of the power module; Step S13: Using a digital multimeter, a power analyzer, and a spectrum analyzer to measure the electrical characteristics of the power module to obtain electrical characteristic measurement data; verifying the electrical characteristic measurement data based on the electrical specification data provided by the manufacturer to obtain initial electrical characteristic data; Step S14: pre-processing the initial physical characteristic data and the initial electrical characteristic data to remove data noise and outliers to obtain pre-processed cleaned data; establishing an initial simulation model of the power module based on the pre-processed cleaned data; Step S15: Using finite element analysis and computational fluid dynamics based on the initial simulation model of the power module, simulate the stress, heat distribution, air flow, and heat dissipation effect of the power module to obtain the physical field distribution characteristics inside the power module; Step S16: Meshing the power module using an adaptive mesh generation algorithm according to the physical field distribution characteristics inside the power module to obtain a power module meshing strategy; Step S17: constructing a sparse matrix based on the physical field distribution characteristics inside the power module using a multi-grid method to obtain a sparse matrix solution algorithm; Step S18: performing multi-physics field simulation on the power module based on the pre-processed cleaned data according to the power module grid partitioning strategy and sparse matrix solving algorithm to obtain simulation result data; Step S19: According to the simulation result data, the simulation model of the power module is adjusted and optimized to generate optimized simulation model parameter data.

3. The performance simulation method of the power module according to claim 2, characterized in that: Step S15 includes the following steps: Step S151: setting boundary conditions for the initial simulation model based on the constraints in the actual working conditions to obtain initial simulation boundary condition data; Step S152: Calculating heat convection data based on the initial physical property data using finite element analysis and computational fluid dynamics; Step S153: performing stress state analysis on the stressed area using the dynamic finite element method based on the load conditions to obtain transient analysis data; Step S154: analyzing the transient analysis data based on the time variation trend to obtain a dynamic stress distribution diagram, wherein the time variation trend range is the duration of three cycles of vibration; Step S155: Analyze the dynamic stress distribution diagram to identify stress concentration point data; Step S156: performing coupling analysis on the stress concentration point data and the fluid flow data to obtain thermo-mechanical coupling data; Step S157: using the initial simulation model to perform simulation based on the initial simulation boundary conditions and the thermo-mechanical coupling data to obtain the physical field distribution characteristics inside the power module.

4. The performance simulation method of the power module according to claim 2, characterized in that: Step S17 includes the following steps: Step S171: discretizing the initial simulation model according to the physical field distribution characteristics and the power module grid division strategy to obtain a discretized model; Step S172: using the finite element method to transform each discrete point in the discretized model to obtain matrix elements to construct a sparse matrix; Step S173: Solve the sparse matrix using a multigrid method to obtain a preliminary solution algorithm; Step S174: Utilize parallel computing to optimize the efficiency of the solution algorithm to obtain a sparse matrix solution algorithm.

5. The performance simulation method of the power module according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: simplifying the optimized simulation model parameter data using the characteristic orthogonal decomposition method to obtain simplified model parameter data; Step S22: using the simplified model parameter data to establish a simplified model based on the preset main physical behaviors and performance characteristics of the power module; Step S23: Evaluate the performance of the power module using the simplified model to obtain performance evaluation data; Step S24: adjusting and optimizing the parameters and design of the simplified model according to the performance evaluation data to obtain performance optimization data; Step S25: Generate optimization solution data based on the performance evaluation data and the performance optimization data, wherein the optimization solution data includes a design optimization solution, optimization parameters, and adjustment suggestions.

6. The performance simulation method of the power module according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing simulation requirement analysis on the optimization scheme data to obtain a simulation task, wherein the simulation task includes a simulation target, a calculation scope, boundary conditions, and input data; Step S32: splitting the simulation task based on task independence to obtain multiple simulation subtasks; Step S33: performing large-scale parallel simulation analysis on multiple simulation subtasks through a distributed computing platform to obtain real-time simulation data; Step S34: performing consistency and result trend analysis on the real-time simulation data to obtain real-time simulation data analysis results; Step S35: dynamically adjusting computing resources according to the real-time simulation data analysis results to obtain dynamically adjusted data; Step S36: Perform simulation analysis on the dynamic adjustment data to generate real-time optimization data, wherein the real-time optimization data includes optimized computing configuration, improved simulation strategy and optimization suggestions.

7. The performance simulation method of the power module according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: Using sensors and a high-speed data acquisition system, the power module is monitored in real time to obtain operating data, and the operating data is pre-processed and standardized to obtain real-time monitoring data; Step S52: performing comparative analysis using the real-time monitoring data, the simulation result library, and the rapid simulation data to obtain simulation detection results; Step S53: performing abnormal performance analysis on the simulation detection results to obtain adjustment operating parameters of the power module; Step S54: constructing a feedback control algorithm based on real-time monitoring data and adjusting operating parameters; Step S55: dynamically adjusting the parameters of the simulation test results through a feedback control algorithm to obtain optimized performance data of the power module; Step S56: Analyze the optimization performance data to obtain an adjustment optimization plan; Step S57: Dynamically adjust the preset operating parameters by adjusting the optimization scheme to obtain real-time control parameters of the power module; Step S58: updating the simulation result database according to the real-time control parameters and the real-time monitoring data to obtain optimized simulation result data.

8. The performance simulation method of the power module according to claim 1, characterized in that: Step S6 includes the following steps: Step S61: Using dynamic system modeling technology, based on preset performance indicators and optimization simulation result data, construct a comprehensive performance control model of the power module, wherein the comprehensive performance control model includes input-output relationships and system constraints; Step S62: Designing a dynamic optimization control scheme for the power module based on the comprehensive performance control model; Step S63: using simulation software to evaluate and analyze the dynamic optimization control scheme to obtain dynamic optimization control scheme evaluation data; Step S64: performing a comprehensive evaluation of the comprehensive performance control model based on the dynamic optimization control scheme evaluation data to obtain a comprehensive performance control model evaluation result; Step S65: Optimizing the comprehensive performance control model based on the control scheme and model parameters according to the comprehensive performance control model evaluation result to achieve dynamic optimization control.

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