Performance simulation method of power module

Abstract

The invention relates to the technical field of performance simulation analysis, in particular to a performance simulation method of a power module. The method comprises the following steps: acquiring initial physical characteristic data and initial electrical characteristic data of a power module; the initial physical characteristic data and the initial electrical characteristic data are preprocessed and simulated, and physical field distribution characteristics in the power module are obtained; performing multi-physical field simulation analysis on the power module based on physical field distribution characteristics in the power module by using an adaptive grid generation algorithm and sparse matrix solution to obtain simulation result data; performing parameter adjustment and optimization on a simulation model of the power module according to the simulation result data to generate optimized simulation model parameter data; and according to the optimization simulation model parameter data, performing simplification processing on the design of the power module to establish a simplified model. Through multi-stage simulation and optimization, the performance and the control precision of the power module can be remarkably improved, and the stability and the efficiency of the system are improved.

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] As a key component of power electronics systems, power modules are widely used in electric vehicles, renewable energy systems, smart grids and other fields. Their performance directly affects the overall efficiency and stability of the system. However, with the development of power electronics technology and the increasing complexity of application scenarios, how to accurately predict the working performance of power modules during the design stage and effectively optimize their performance has become an important challenge in the current technology 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, the power module not only generates an electric field during operation, but also complex multi-physical phenomena such as heat and mechanical stress. If you only rely on electric field simulation, you cannot fully predict its actual performance, which can easily lead to design deviations. In addition, traditional simulation tools are mostly based on fixed grids for calculations, and cannot dynamically adjust the simulation accuracy, resulting in a waste of computing resources and long calculation time. 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] In traditional simulation methods, the utilization efficiency of computing resources is low, and it is difficult to fully utilize the advantages of modern computing technology. Especially when faced with large-scale parallel computing needs, single-machine computing often cannot meet the needs of efficient simulation. Although distributed computing platforms have been gradually applied to the simulation field, how to efficiently manage computing resources and dynamically adjust task scheduling is still an urgent problem to be solved. Existing distributed simulation platforms usually adopt fixed resource allocation strategies and cannot adjust resource allocation according to the needs of real-time simulation tasks, resulting in waste of computing resources or extended simulation time. 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 of 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 the 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;

[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; use the 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 according to the real-time simulation data to obtain real-time optimization data;

[0010] Step S4: pre-calculate the preset working parameters according to the real-time optimization data and establish a simulation result library; perform rapid simulation and optimization on the power module according to the simulation result library to obtain rapid simulation data;

[0011] Step S5: 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 according to 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 according to the comprehensive performance control model.

[0013] The present invention proposes a complete multi-step simulation and optimization process, which uses multi-physics field simulation, adaptive grid generation algorithm, distributed computing and feedback control to improve the design efficiency and performance stability of the power module. Through the adaptive grid generation algorithm and sparse matrix solution, the method can perform multi-physics field simulation analysis according to the initial physical characteristics and electrical characteristics of the power module. This enables the simulation to better simulate the interaction effects of different physical fields in the real working environment, such as the coupling of electric field, heat and mechanical stress, thereby improving the simulation accuracy. By simplifying the establishment of the model, the computational complexity is reduced. The method can use the simplified model to evaluate and optimize the design of the power module under the premise of ensuring the simulation accuracy. This method reduces the dependence on high-complexity models, effectively reduces the consumption of computing resources and computing time, thereby improving the efficiency of the simulation. By using a distributed computing platform to perform large-scale parallel computing on simulation tasks, the method can process a large number of complex simulation tasks in a short time. This enables the design and simulation of the power module to be quickly iterated, effectively reduces the development cycle, and ensures an efficient design verification process. Through the feedback of real-time simulation data and the dynamic adjustment of computing resources, the method can flexibly dispatch computing resources according to actual simulation needs. This dynamic resource management mechanism not only improves the utilization efficiency of resources, but also can automatically adjust according to the complexity of the task to ensure that the computing task is executed under the optimal resource configuration. By establishing a simulation result library, the simulation process is further optimized. Using the existing simulation data, simulation results can be quickly generated to reduce repeated calculations. The establishment of this simulation result library allows engineers to quickly obtain corresponding data during the design process and shorten the decision-making time. Through a real-time monitoring mechanism for the performance of the power module and dynamic adjustment in combination with a feedback control system. Through real-time adjustment of the simulation detection results, it can be optimized according to the actual operating conditions of the power module, effectively reducing the risk of system failure or performance degradation and improving the reliability of the system. By constructing a comprehensive performance control model of the power module, the optimization of a single module is combined with the overall system performance, and system-level dynamic optimization control is achieved. This method not only focuses on the performance of the power module itself, but also takes into account the interaction between it and the entire system, ensuring that the power module can operate efficiently and stably in a complex system environment. The present invention has significant beneficial effects, especially in terms of simulation accuracy, calculation efficiency, resource optimization management, real-time monitoring and feedback control, and significantly improves the design and optimization capabilities of the power module. 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 requirements of efficient design and rapid iteration of modern power electronic equipment. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0015] Figure 1 A 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 technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0021] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

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

[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 the 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;

[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; use the 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 according to the real-time simulation data to obtain real-time optimization data;

[0027] Step S4: pre-calculate the preset working parameters according to the real-time optimization data and establish a simulation result library; perform rapid simulation and optimization on the power module according to the simulation result library to obtain rapid simulation data;

[0028] Step S5: 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 according to 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 according to 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 of a power module of the present invention. In this example, the performance simulation method of the 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 the 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;

[0032] The embodiment of the present invention obtains the initial physical characteristic data and electrical characteristic data of the power module, such as material properties, electrical conductivity, thermal conductivity, etc., which are the basis of simulation. An adaptive grid generation algorithm is used to generate a suitable grid model based on these initial data to ensure 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. Adjust the model parameters according to 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 the optimized simulation model parameter data. This process can optimize the model by reducing unnecessary complex features to reduce computing resources and time consumption. The simplified model still needs to retain key features to ensure that its performance is close to the original model. The performance of the power module is evaluated based on the simplified model, and its behavior under different working conditions is analyzed to ensure its accuracy and reliability. The model is further optimized in combination with the evaluation results, and the optimization solution data of the simplified model is generated to lay 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; use the 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 according to the real-time simulation data to obtain real-time optimization data;

[0036] The embodiment of the present invention performs simulation demand analysis based on the optimization scheme data of the simplified model to determine the simulation tasks of the power module, including the simulation scope, accuracy requirements and time span. The simulation tasks are decomposed by using a distributed computing platform, and the tasks are assigned to different computing nodes to achieve large-scale parallel computing and improve simulation efficiency. Multi-physics field simulation analysis is performed on the power module through parallel computing to obtain real-time simulation data. The computing resource allocation is dynamically adjusted according to the real-time simulation data to ensure the accuracy and timeliness of the simulation results while ensuring the efficient use of computing resources, and to obtain real-time optimization data.

[0037] Step S4: pre-calculate the preset working parameters according to the real-time optimization data and establish a simulation result library; perform rapid simulation and optimization on the power module according to the simulation result library to obtain rapid simulation data;

[0038] The embodiment of the present invention pre-calculates the preset working parameters of the power module based on real-time optimization data. These pre-calculated results are used to build a simulation result library, which stores simulation data and optimization schemes under different working conditions. Using the data in the simulation result library, fast simulation and optimization are performed for different working states of the power module. This simulation process reduces the time of repeated calculations by calling existing data, thereby obtaining fast-response simulation data. This process improves the efficiency and accuracy of the simulation and provides a basis for subsequent performance monitoring and feedback adjustment.

[0039] Step S5: 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] The embodiment of the present invention monitors the power module in real time based on the simulation result library and fast simulation data. The monitoring process includes continuous tracking of the operating status of the power module to ensure timely acquisition of performance data. The simulation detection results are dynamically adjusted using a feedback control mechanism. The feedback control system can adjust the performance of the power module according to real-time data to optimize the system response. Through multiple iterations and adjustments, the optimized simulation result data is finally obtained. This process ensures the efficiency and reliability of the power module in a dynamic working environment.

[0041] Step S6: Performing performance simulation on the power module according to 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 according to the comprehensive performance control model.

[0042] The embodiment of the present invention uses the optimization simulation result data to perform system-level performance simulation on the power module. The simulation constructs a comprehensive performance control model by integrating various performance data of the power module. The model can cover the performance of the power module under different working conditions and has dynamic response capabilities. The power module is dynamically optimized and controlled in real time through the comprehensive performance control model. The model automatically adjusts and optimizes according to the real-time status of the system, thereby ensuring that the power module can maintain optimal performance and stability under various operating conditions.

[0043] The present invention can analyze the performance of the power module with high precision by acquiring initial physical characteristics and electrical characteristics data, and using an adaptive grid generation algorithm and sparse matrix solution to perform multi-physics field simulation. This method ensures the accuracy of the simulation results and provides a reliable basis for subsequent model optimization. Simplifying the optimized simulation model can significantly reduce the complexity of the model while retaining key performance characteristics. By simplifying the performance evaluation and optimization of the model, the computing efficiency can be improved, resource consumption can be reduced, and effective optimization scheme data can be quickly obtained. Using a distributed computing platform for large-scale parallel computing and real-time simulation can significantly shorten the computing time and improve the efficiency of simulation analysis. Dynamically adjusting computing resources further optimizes the computing process and ensures the rapid processing of real-time data. By establishing a simulation result library and performing rapid simulation, the simulation process is accelerated. This enables fast-response data to be obtained in a short time, which is convenient for timely decision-making and adjustment. Through real-time monitoring and feedback control, the simulation results can be dynamically adjusted to ensure the stability and optimization of the system during operation. This feedback mechanism helps to quickly respond to changes in system operation and improve overall performance. The comprehensive performance control model can realize system-level dynamic optimization control of the power module and further improve the overall performance and reliability of the system. Through this model, the comprehensive control and optimization of the power module can be achieved, and the working efficiency and stability of the system can be improved. The performance simulation method of the present invention significantly improves the efficiency and reliability of the design and operation of the power module through high-precision simulation, simplified model, optimized computing resource management, fast-response simulation, dynamic monitoring and comprehensive performance control.

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

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

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

[0047] Step S13: Use 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; perform verification based on the electrical specification data provided by the manufacturer according to the electrical characteristic measurement data to obtain initial electrical characteristic data;

[0048] Step S14: preprocessing the initial physical characteristic data and the initial electrical characteristic data, removing data noise and abnormal values, and obtaining preprocessing cleaning data; establishing an initial simulation model of the power module according to the preprocessing cleaning data;

[0049] Step S15: using finite element analysis and computational fluid dynamics according to the initial simulation model of the power module, simulating the stress, heat distribution, air flow and heat dissipation effect of the power module, and obtaining the physical field distribution characteristics inside the power module;

[0050] 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 meshing strategy for the power module, wherein a specific rule for meshing is to use finer meshes in mechanical stress concentration areas, high temperature areas, and high current density areas, and to use coarser meshes in other areas;

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

[0052] Step S18: According to the grid division strategy of the power module and the sparse matrix solving algorithm, based on the pre-processed cleaning data, multi-physical field simulation is performed on the power module 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: obtaining the size, temperature and weight data of the power module through experimental measurement; obtaining the material property data of the power module through the specification data provided by the manufacturer;

[0056] The embodiment of the present invention actually measures the size, temperature and weight of the power module through experimental measurement methods to obtain its physical size, thermal performance and quality data. The material property data of the power module, including the thermal conductivity and electrical conductivity of the material, are extracted from the specification data provided by the manufacturer. These data provide a basis for subsequent numerical simulation analysis, so that the physical properties of the power module can be accurately evaluated.

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

[0058] The embodiment of the present invention uses a numerical simulation method to input the acquired size, temperature, weight data and material property data into the simulation software. The data are used for calculation and analysis to simulate the physical properties of the power module and obtain its initial physical property data. These simulation results provide a basis for further analysis and optimization of the power module, ensuring that the actual design meets the expected performance.

[0059] Step S13: Use 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; perform verification based on the electrical specification data provided by the manufacturer according to the electrical characteristic measurement data to obtain initial electrical characteristic data;

[0060] The embodiment of the present invention uses a digital multimeter, a power analyzer and a spectrum analyzer to perform detailed electrical characteristic measurements on the power module, and records data such as voltage, current, power and frequency. These measurement data are compared and verified with the electrical specification data provided by the manufacturer to confirm the accuracy of the measurement results, and finally the initial electrical characteristic data of the power module are obtained. These data provide a key basis for the subsequent establishment and optimization of the simulation model.

[0061] Step S14: preprocessing the initial physical characteristic data and the initial electrical characteristic data, removing data noise and abnormal values, and obtaining preprocessing cleaning data; establishing an initial simulation model of the power module according to the preprocessing cleaning data;

[0062] The embodiment of the present invention performs data preprocessing on the initial physical property data and electrical property data, mainly including removing noise and outliers in the data. After processing, the generated preprocessed cleaned data is used to establish an initial simulation model of the power module. This model is 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 according to the initial simulation model of the power module, simulating the stress, heat distribution, air flow and heat dissipation effect of the power module, and obtaining the physical field distribution characteristics inside the power module;

[0064] The embodiment of the present invention uses finite element analysis and computational fluid dynamics methods to simulate the initial simulation model of the power module in detail. The analysis content includes the stress distribution, heat distribution, air flow and heat dissipation effect of the power module. These simulations help determine the physical field distribution characteristics of the power module under actual working conditions, and provide key data for subsequent meshing and multi-physics field simulation.

[0065] 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 meshing strategy for the power module, wherein a specific rule for meshing is to use finer meshes in mechanical stress concentration areas, high temperature areas, and high current density areas, and to use coarser meshes in other areas;

[0066] The embodiment of the present invention uses an adaptive grid generation algorithm to grid the power module. The grid division strategy is based on the physical field distribution characteristics inside the power module, where finer grids are used in mechanical stress concentration areas, high temperature areas, and high current density areas to improve calculation accuracy; coarser grids are used in other areas to optimize the use of computing resources. This method ensures simulation accuracy in key areas while reducing computing costs.

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

[0068] The embodiment of the present invention uses a multi-grid method to construct a sparse matrix of the physical field distribution inside the power module. This method improves the accuracy of capturing complex physical field characteristics through hierarchical grid refinement. The physical field of the power module is hierarchically gridded, a sparse matrix is ​​constructed to optimize computing efficiency, and a sparse matrix solving algorithm is used to solve it. This method can effectively handle large-scale problems while reducing computing resource requirements.

[0069] Step S18: According to the grid division strategy of the power module and the sparse matrix solving algorithm, based on the pre-processed cleaning data, multi-physical field simulation is performed on the power module to obtain simulation result data;

[0070] The embodiment of the present invention performs multi-physics simulation based on pre-processed and cleaned data according to the grid partitioning strategy and sparse matrix solution algorithm of the power module. The grid partitioning strategy is applied to the simulation model to ensure that the appropriate grid density is used in key areas. The sparse matrix solution algorithm is used to process complex physical field data to improve computing efficiency. By performing multi-physics simulation on the power module, detailed simulation result data is generated 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] The embodiment of the present invention adjusts the parameters of the simulation model of the power module according to the simulation result data. The simulation result data is analyzed to identify performance bottlenecks and optimization points. The simulation model is optimized by adjusting the parameters in the model, such as material properties, geometric structures or boundary conditions. The optimized simulation model parameter data is generated to improve the performance, efficiency or reliability of the power module. These adjustments will provide an improved model basis for subsequent further simulation and practical application.

[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 property data through a variety of measuring instruments, and verifies according to manufacturer specifications to ensure the reliability of electrical property data; pre-processes the physical and electrical property 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 effects, obtains physical field distribution characteristics, and provides a basis for subsequent meshing; uses adaptive mesh generation algorithm and multi-grid method to construct sparse matrix solution algorithm, optimizes meshing, and improves calculation accuracy and efficiency; performs multi-physical 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 calculation costs, speed up the design optimization process, and ultimately improve the overall performance and reliability of the power module.

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

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

[0076] The embodiment of the present invention sets the initial simulation model based on the operating conditions of the power module under actual working conditions, combined with the constraints and load conditions of the system. The boundary conditions of the model are determined according to the external environment, operating temperature, stress load, etc. of the power module in actual operation. These working condition information are converted into the input of the numerical model to form the initial simulation boundary condition data, which serves as the basis for subsequent simulation calculations and analysis, ensuring that the simulation process can reflect the operation of the power module under actual working conditions.

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

[0078] The embodiment of the present invention meshes the structure of the power module through finite element analysis, and analyzes the fluid flow inside and on the surface of the module through computational fluid dynamics. Based on these analysis results, the heat transfer path and distribution characteristics of the module during operation are determined, thereby generating thermal convection data, which provides a basis for subsequent coupling analysis.

[0079] Step S153: Based on the load condition, a dynamic finite element method is used to perform stress state analysis on the stress-bearing area to obtain transient analysis data;

[0080] The embodiment of the present invention calculates the transient stress distribution of the stress area under load changes by applying different time steps, and obtains data including stress value, displacement and deformation. These transient analysis data reflect the dynamic change of stress over time during the operation of the module, and provide a basis for the subsequent dynamic stress distribution diagram 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] The embodiment of the present invention tracks the fluctuation of stress over time, calculates the change of stress distribution within the duration of three cycles of vibration, and generates a dynamic stress distribution diagram. The distribution diagram can show the stress change trend of the power module in multiple working cycles, help identify the stress concentration areas and possible fatigue points in the module, and provide a basis for subsequent analysis.

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

[0084] The embodiment of the present invention performs a detailed analysis of the dynamic stress distribution diagram to identify stress concentration points in the diagram. This is achieved by examining the peak area of ​​the stress value in the dynamic stress distribution diagram. Focus is placed on the parts where the stress value is significantly higher than the surrounding area, which usually represent stress concentration points. By identifying these stress concentration points, the potential weaknesses of the power module under actual working conditions can be evaluated to guide further design improvements or material selection.

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

[0086] The embodiment of the present invention combines stress concentration point data with fluid flow data to perform thermo-mechanical coupling analysis. The heat flow around the stress concentration point is determined using fluid dynamics data, and these heat convection data are combined with stress data to apply a thermo-mechanical coupling model for analysis. 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 evaluate 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] The embodiment of the present invention combines the thermo-mechanical coupling data with the initial simulation boundary conditions and uses the initial simulation model to perform a comprehensive simulation. This process involves applying the thermo-mechanical coupling data to the model and updating the thermal and mechanical field distribution of the model. By comprehensively analyzing the temperature and stress fields inside the power module, the physical field distribution characteristics of the power module under actual working conditions are obtained to help optimize the design and improve performance.

[0089] The present invention can accurately capture the thermal and mechanical behavior of the power module in actual working conditions by setting boundary conditions for the initial simulation model and performing heat convection and stress analysis. These steps ensure the accuracy and reliability of the model in the actual working environment. The dynamic stress is analyzed for time change trend, and a dynamic stress distribution diagram is generated, which can clearly identify the stress change 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, design optimization can be guided, and the risk of failure can be reduced. The stress concentration point data is coupled and analyzed with the fluid flow data to obtain thermal-mechanical coupling data, which ensures the comprehensive consideration of thermal and mechanical effects, and 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. The data supports further design optimization and performance improvement, ensuring efficient and stable operation of the module in actual working conditions.

[0090] Preferably, step S16 comprises 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 embodiment of the present invention determines the area and scale that need to be discretized according to the physical field distribution characteristics of the power module. According to the meshing strategy, meshing is performed in the physical field distribution map to discretize the model into multiple mesh units. According to the meshing results, the original simulation model is converted into a discretized model composed of discrete mesh units to prepare for subsequent finite element analysis. Ensure that the simulation model is discretized with an appropriate degree of refinement, thereby providing an accurate basis for the matrix construction of the subsequent finite element method.

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

[0094] The embodiment of the present invention processes the discretized model and converts the physical characteristics and boundary conditions of each grid unit into the data format required by the finite element method. According to the physical characteristics of each discrete point in the discretized model, the corresponding matrix elements are calculated by the finite element method. All matrix elements are assembled into a sparse matrix to reflect the physical and mathematical characteristics of the discretized model and provide a basis for subsequent solution algorithms. The physical model is converted into a mathematical model to facilitate efficient numerical calculations.

[0095] Step S163: using a multi-grid 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, processes the sparse matrix, and constructs a multi-level grid structure to improve the solution efficiency. A preliminary solution is performed on a coarse grid, and then fine calculations are gradually performed on fine grids to accelerate the convergence speed 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, which operates on grids of different levels. Improve 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] The embodiment of the present invention decomposes the sparse matrix solution algorithm into multiple parallel tasks so that they can be run simultaneously on multiple computing cores or processors to improve the calculation speed. Effectively schedule parallel tasks to ensure that each computing core is load balanced, avoiding computing bottlenecks and idle resources. According to the characteristics of the hardware architecture and the solution task, adjust the parallel computing strategy to maximize the efficiency and response speed of the solution algorithm. The efficiency of sparse matrix solution is optimized and the overall simulation performance is improved.

[0099] The present invention can refine the continuous physical field distribution into discrete points by discretizing the initial simulation model of the power module, 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, and provides strong support for the performance optimization and design of the power module.

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

[0101] Step S21: simplifying the optimization 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: using the simplified model to evaluate the performance of the power module 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 optimization simulation model parameter data using the characteristic orthogonal decomposition method to obtain simplified model parameter data;

[0108] The embodiment of the present invention uses the characteristic orthogonal decomposition method to simplify the parameter data of the optimization simulation model. By analyzing the main features in the simulation model, high-dimensional data is converted into a low-dimensional space, thereby retaining key characteristic parameters and eliminating redundant information. The method extracts the most important features in the model through orthogonal decomposition, reduces the computational complexity, and maintains the accuracy of the model. The simplified model parameter data is obtained, which provides a basis for the subsequent establishment of a simplified model 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] The embodiment of the present invention uses simplified model parameter data to establish a simplified model based on the preset main physical behaviors and performance characteristics of the power module. By identifying the key physical behaviors of the power module, its main performance characteristics are determined. The simplified model parameter data is applied to these characteristics to establish a simplified model that can effectively simulate the main functions of the power module. The simplified model is used for subsequent performance evaluation and optimization to reduce calculation complexity and improve efficiency.

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

[0112] The embodiment of the present invention uses a simplified model to evaluate the performance of the power module. The actual working state of the power module is simulated based on the main physical behaviors and performance characteristics of the simplified model. By analyzing its performance under different loads and working conditions, the main parameters of the model such as thermal performance, mechanical stress distribution, and electrical characteristics are evaluated. These evaluation results are compared with the preset performance standards to generate performance evaluation data to determine whether the design of the power module meets the 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] The embodiment of the present invention adjusts and optimizes the parameters and design of the simplified model based on the performance evaluation data. Analyze the parts of the performance evaluation data that do not meet the design requirements or have room for improvement, and identify the key parameters that affect the performance of the power module. By adjusting these parameters, the structural design or material selection of the simplified model is improved. The simulation evaluation is performed again using the optimized parameters to ensure that the optimized model has improved performance and efficiency, obtain performance optimization data, and provide a better solution for the 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] The embodiment of the present invention generates optimization scheme data based on performance evaluation data and performance optimization data. In combination with the evaluation results and optimized parameters, a design optimization scheme for the power module is formulated to clarify the design details or parameters that need to be adjusted. Verification is performed based on the optimized parameters to ensure the feasibility and performance improvement of the scheme. Complete optimization scheme data including design optimization schemes, optimization parameters and adjustment suggestions 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 and improve 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 comprises 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 range, 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 the 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 range, boundary conditions and input data;

[0127] The embodiment of the present invention performs a detailed simulation requirement analysis on the optimization scheme data to clarify the various elements of the simulation task. The simulation task includes defining the specific goals of the simulation, such as performance evaluation or optimization parameter verification; determining the spatial or temporal scope of the simulation, and clarifying the area and time period that need to be simulated; setting the necessary physical or mathematical boundary conditions for the simulation task; and preparing relevant input data according to the simulation requirements, such as model parameters, environmental variables, etc. Through these analyses, the definition of the simulation task is ensured to be comprehensive and clear.

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

[0129] The embodiment of the present invention splits the entire simulation task into multiple independent simulation subtasks according to the independence of the simulation task. The basis for the splitting includes the dependency between each subtask, the independence of the calculation range, the relative fixity of the boundary conditions and the locality of the input data. By splitting the task independence, it is ensured that each subtask can be independently and parallelly run on the distributed computing platform, thereby improving the overall simulation efficiency, reducing the redundant use of computing resources, and ensuring that the simulation analysis task can be carried out efficiently.

[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] The embodiment of the present invention allocates multiple simulation subtasks to a distributed computing platform, and performs simulation analysis on these subtasks simultaneously through parallel computing technology. Each subtask runs independently on different computing nodes to reduce computing time and improve efficiency. The distributed computing platform dynamically allocates computing resources according to the computing requirements of the simulation task, monitors data changes in the simulation process in real time, ensures efficient execution of the simulation task, and generates real-time simulation data. These data are 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 check on the real-time simulation data generated by multiple simulation subtasks to ensure that there is no conflict or data deviation between the results of each subtask. By analyzing the trend of the simulation results, the changes in key parameters in the simulation process are evaluated, and abnormal or expected results are identified. Trend analysis can use statistical methods, time series analysis or machine learning algorithms to evaluate the overall trend and change direction of the data, ensure the accuracy and reliability of the simulation data, and provide a basis for subsequent resource adjustment and optimization strategies.

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

[0135] The embodiment of the present invention evaluates the current computing resource utilization efficiency and resource allocation based on the real-time simulation data analysis results. Resource bottlenecks or redundancies are identified through data analysis, and computing resources that need to be adjusted are determined. Dynamic adjustment includes 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 that simulation tasks can obtain higher computing efficiency and more accurate results in the next analysis phase.

[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] The embodiment of the present invention further simulates and analyzes the dynamically adjusted data to evaluate the effect of resource adjustment. The new simulation task verifies 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 suggestions. These data are used to optimize the subsequent simulation process and improve efficiency and accuracy.

[0138] The present invention can efficiently generate multiple simulation subtasks by performing comprehensive simulation demand analysis and task splitting on optimization scheme data, and realize large-scale parallel simulation analysis by using a distributed computing platform. This not only shortens the simulation time, but also improves the data processing capability. The consistency of real-time simulation data and 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 dynamically adjusted data, the generated real-time optimization data can provide accurate optimization configuration and improvement strategy to ensure 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 comprises the following steps:

[0140] Step S41: preprocessing 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 sort the pre-calculation result data to establish a simulation result library;

[0143] Step S44: using the simulation result library to quickly simulate the power module to 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: preprocessing the real-time optimization data to remove noise and outliers to obtain a clean real-time optimization data set;

[0147] The embodiment of the present invention preprocesses the real-time optimization data and removes noise and outliers through data cleaning technology. The data is denoised and interference signals are reduced using filters or smoothing algorithms. Statistical analysis or anomaly detection methods are applied to identify and remove abnormal data points that do not meet expectations. The processed clean data set is saved for subsequent analysis and calculation. This step ensures the accuracy and reliability of the data and lays the foundation for subsequent large-scale parallel pre-calculation.

[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 embodiment of the present invention uses a clean real-time optimized data set to perform large-scale parallel pre-computation on a distributed computing platform. The data set is divided into multiple subsets and assigned to different computing nodes. Each node performs calculations based on preset working parameters, and the processing tasks are independent and can be executed in parallel. After the calculation is completed, the calculation results of each node are summarized to generate pre-computation result data. This method accelerates the data processing process and improves computing efficiency and accuracy.

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

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

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

[0153] The embodiment of the present invention utilizes the established simulation result library and extracts relevant pre-calculated results from the library according to the design requirements and parameter configuration of the power module. These results are applied to the fast simulation model of the power module for real-time simulation analysis. Through fast simulation, the performance of the power module under different operating conditions is evaluated and fast simulation results are generated. This process is intended to use existing data to quickly evaluate model performance without having to re-perform comprehensive simulation calculations.

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

[0155] The embodiment of the present invention performs performance evaluation on the power module based on the rapid simulation results. The gap between the performance indicators in the simulation results and the design requirements is analyzed to identify potential areas for improvement. Based on these analysis results, the design parameters or operating conditions of the power module are adjusted to optimize its performance. New rapid simulation data is generated, including optimized design suggestions, parameter adjustment schemes, and further improvement strategies. These 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 preprocessing the real-time optimization data, removing noise and outliers, and thus providing a high-quality data foundation for subsequent calculations. The distributed computing platform is used for large-scale parallel pre-computation, which significantly improves the calculation speed and efficiency, and greatly shortens the time for processing complex problems. The pre-computation result data is classified and sorted, 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 instant performance feedback, and accelerating the design and optimization process. According to the rapid simulation results, the performance of the power module is optimized and adjusted, 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] The embodiment of the present invention classifies the pre-calculation result data and groups them according to physical characteristics, operating conditions, and time series. These classification bases can reflect the multi-dimensional characteristics of the data. These classified data are organized into pre-calculation result classification data sets to facilitate subsequent data management and analysis. The classification process may involve data labeling, application of grouping algorithms, and determination of data storage formats.

[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] The embodiment of the present invention utilizes a distributed computing platform to upload and store the pre-calculated classification data set in a distributed system. The system distributes the data to multiple computing nodes to achieve efficient data processing and storage. This process involves selecting a suitable distributed database system, configuring a data storage strategy, and ensuring the consistency and integrity of the data between nodes. The data may be compressed and encrypted during storage to improve storage efficiency and security.

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

[0163] The embodiment of the present invention performs a structural design on the simulation result database, creates an index to improve data retrieval efficiency, and designs an index strategy based on the data access mode and query requirements. Query optimization is performed by analyzing the execution plan of common queries, adjusting the database configuration and optimizing the query statement to reduce the response time. The design process also includes data partitioning, compression strategy and backup mechanism to improve database performance and reliability.

[0164] The present invention can form a structured pre-calculation result classification data set by classifying the pre-calculation result data based on physical characteristics, operating conditions and time series. These data sets help to establish an efficient simulation result database using a distributed computing platform. The database can support large-scale data storage and fast access, and improve 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 speed 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 comprises the following steps:

[0166] Step S51: using sensors and a high-speed data acquisition system to build and monitor the power module in real time to obtain operation data, and pre-processing and standardizing the operation data to obtain real-time monitoring data;

[0167] The embodiment of the present invention uses sensors and a high-speed data acquisition system to monitor the power module in real time to obtain operating data. These operating data are preprocessed, including removing noise and outliers, and standardizing to ensure the consistency and accuracy of the data. The processed data forms real-time monitoring data for further analysis and comparison. This method ensures high data quality and provides a reliable basis for subsequent simulation detection and optimization.

[0168] Step S52: using the real-time monitoring data to compare and analyze with the simulation result library and the fast simulation data to obtain the simulation detection results;

[0169] The embodiment of the present invention compares the real-time monitoring data with the historical simulation results and fast simulation data stored in the simulation result library. Through comparative analysis, the consistency between the performance of the power module under actual operating conditions and the simulation results is evaluated. The specific implementation method includes extracting relevant data, performing data matching and error analysis to determine the difference between the simulation results and the actual operating conditions, and generating simulation detection results. This helps to identify potential performance issues and provide a basis for subsequent adjustments.

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

[0171] The embodiment of the present invention compares the simulation test results to identify anomalies with expected performance. By analyzing these abnormal data, the problems that may occur in the actual operation of the power module are determined. Using data mining and statistical analysis techniques, key operating parameters related to abnormal performance are extracted. Based on these analysis results, a strategy for adjusting the operating parameters is formulated to optimize the performance of the power module. These adjusted operating parameters will be 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] The embodiment of the present invention develops and implements a feedback control algorithm based on real-time monitoring data and adjustment of operating parameters. The algorithm dynamically adjusts the operating parameters of the power module according to changes in real-time monitoring data to maintain or improve its performance. The feedback control algorithm generally includes data acquisition, real-time processing and adjustment mechanisms to respond to detected performance deviations to ensure that the power module maintains the best state in actual operation. In this way, dynamic optimization and stability control of the power module performance can be achieved.

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

[0175] The embodiment of the present invention utilizes a feedback control algorithm, and according to the feedback control algorithm, dynamically adjusts the operating parameters of the power module through 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 control system of the power module to enable it to respond to performance changes in real time, ensuring that the system operates in the best state, thereby improving overall efficiency and stability.

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

[0177] The embodiment of the present invention conducts an in-depth analysis of the optimized performance data to evaluate the performance of the power module after adjustment. By comparing the data before and after optimization, performance improvement points and potential problems are identified, and an optimization plan is formulated. The analysis content may include the performance improvement range, operation stability and other key indicators. The analysis results are converted into a specific adjustment optimization plan, including further parameter adjustments and operation suggestions, to improve the overall performance of the power module.

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

[0179] The embodiment of the present invention dynamically adjusts the preset operating parameters of the power module based on the adjustment optimization scheme. This process involves applying the suggestions in the optimization scheme to actual operation and adjusting the operating parameters of the module to achieve optimal performance. The adjustment process requires real-time monitoring and feedback to ensure the effectiveness of the parameter adjustment. Generate and apply real-time control parameters to ensure the performance optimization of the power module in 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] The embodiment of the present invention uses real-time control parameters and real-time monitoring data to update the simulation result library. Real-time control parameters and monitoring data are collected, and these data are compared and integrated with existing data in the simulation result library. Based on the new real-time data, the records in the simulation result library are updated to ensure that the data in the database reflects the actual performance of the current power module. This update optimizes the simulation result 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 collection and preprocessing. By comparing real-time monitoring data with the simulation result library, it is possible to promptly discover 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, so that the simulation data is more timely and accurate, and supports future performance analysis and optimization. The present invention improves the operating efficiency and system performance of the power module, and ensures that the module performance can be quickly responded to and optimized in practical applications.

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

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

[0185] The embodiment of the present invention collects preset performance indicators and optimization simulation result data to ensure the accuracy and completeness of the input data. Using dynamic system modeling technology, a comprehensive performance control model of the power module is constructed based on the collected data. This model includes input-output relationships and system constraints. Ensure that the model can reflect the actual behavior and constraints of the system, including the dynamic response and stability requirements of the system. Construct a comprehensive control model that can truly reflect the performance of the power module, providing a basis for the subsequent dynamic optimization control solution design.

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

[0187] The embodiment of the present invention clarifies the optimization goals of the power module based on the comprehensive performance control model, such as improving efficiency, reducing energy consumption or meeting specific performance indicators. A dynamic optimization control scheme is formulated, including a control algorithm and an adjustment strategy, to achieve the optimization goal. During the design process, it is ensured that the control scheme can operate effectively within the system constraints. Preliminary verification is performed to determine whether the designed control scheme can meet the performance indicators in the model, and necessary adjustments and optimizations are made. It is ensured 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] The embodiment of the present invention sets the parameters and control scheme of the comprehensive performance control model in the simulation software to ensure that the simulation conditions are consistent with the actual system. Perform simulation analysis, apply the designed dynamic optimization control scheme, and observe the response of the model under different working conditions. Record the simulation results, including system response, performance indicator changes, and any deviations. Evaluate the effectiveness of the control scheme, analyze its impact on system performance, and confirm whether the preset performance indicators are achieved. Ensure that the design of the dynamic optimization control scheme can verify its actual effect through simulation testing.

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

[0191] The embodiment of the present invention collects dynamic optimization control scheme evaluation data, including system response, performance indicators and control effects. : Apply statistical and analytical methods to compare the evaluation data with the preset performance indicators to evaluate the accuracy and effectiveness of the comprehensive performance control model. Determine the deviation between the model and the actual performance, analyze the advantages and disadvantages of the model and its impact on the control scheme. Write an evaluation result report to summarize the model performance, improvement suggestions and optimization direction, and provide 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 evaluation result of the comprehensive performance control model to achieve dynamic optimization control.

[0193] According to the evaluation results, the embodiment of the present invention identifies the deficiencies and optimization space in the model, including performance deviations and areas with poor control effects. According to the evaluation results, the parameters and system constraints in the comprehensive performance control model are adjusted to improve the accuracy and response performance of the model. The control scheme is adjusted to better meet the optimization objectives and enhance the model's adaptability to dynamic changes. By resimulating and testing the optimized model, it is verified whether the improvement effect meets expectations to ensure the realization of dynamic optimization 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 the control accuracy. A dynamic optimization control scheme based on a comprehensive performance control model is designed to enable the power module to perform optimal performance under various operating conditions, reduce energy waste and improve efficiency. By evaluating the control scheme using simulation software, 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 operating conditions can be improved, and the failure rate and maintenance costs can be 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] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0196] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A 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; Preprocess and simulate the initial physical characteristic data and the initial electrical characteristic data to obtain the physical field distribution characteristics inside the power module; use the adaptive grid generation algorithm and 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; adjust and optimize the parameters of the simulation model of the power module according to 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; use 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 according to 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; perform rapid simulation and optimization on the power module according to the simulation result library to obtain rapid simulation data; Step S5: 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: simulating the performance of the power module according to the optimization simulation result data, and constructing a comprehensive performance control model of the power module; Dynamic optimization control of the power module is achieved 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: obtaining the size, temperature and weight data of the power module through experimental measurement; obtaining the material property data of the power module through the specification data provided by the manufacturer; Step S12: using a numerical simulation method to calculate and analyze the size, temperature, weight data and material property data to obtain initial physical property data of the power module; Step S13: Use 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; perform verification based on the electrical specification data provided by the manufacturer according to the electrical characteristic measurement data to obtain initial electrical characteristic data; Step S14: preprocessing the initial physical characteristic data and the initial electrical characteristic data, removing data noise and abnormal values, and obtaining preprocessing cleaning data; establishing an initial simulation model of the power module according to the preprocessing cleaning data; Step S15: using finite element analysis and computational fluid dynamics according to the initial simulation model of the power module, simulating the stress, heat distribution, air flow and heat dissipation effect of the power module, and obtaining the physical field distribution characteristics inside the power module; Step S16: gridding the power module using an adaptive grid generation algorithm according to the physical field distribution characteristics inside the power module to obtain a grid division strategy for the power module; Step S17: constructing a sparse matrix based on the physical field distribution characteristics inside the power module through a multi-grid method to obtain a sparse matrix solution algorithm; Step S18: According to the grid division strategy of the power module and the sparse matrix solving algorithm, based on the pre-processed cleaning data, multi-physical field simulation is performed on the power module 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 constraint conditions in the actual working condition to obtain initial simulation boundary condition data; Step S152: Calculating thermal convection data based on initial physical property data using finite element analysis and computational fluid dynamics; Step S153: Based on the load condition, a dynamic finite element method is used to perform stress state analysis on the stress-bearing area 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: analyzing the dynamic stress distribution diagram to identify stress concentration point data; Step S156: performing coupling analysis on stress concentration point data and 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 3, characterized in that: Step S16 includes the following steps: 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; Step S162: using the finite element method to transform each discrete point in the discretization model to obtain matrix elements to construct a sparse matrix; Step S163: using a multi-grid method to solve the sparse matrix and obtain a preliminary solution algorithm; Step S164: 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 4, characterized in that: Step S2 includes the following steps: Step S21: simplifying the optimization 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: using the simplified model to evaluate the performance of the power module 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 5, 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 range, 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 the 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 6, characterized in that: Step S4 includes the following steps: Step S41: preprocessing 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 sort the pre-calculation result data to establish a simulation result library; Step S44: using the simulation result library to quickly simulate the power module to obtain a quick simulation result; Step S45: Optimize and adjust the performance of the power module according to the rapid simulation results to generate rapid simulation data.

8. The performance simulation method of the power module according to claim 7, characterized in that: 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 structured design on the simulation result database to obtain a simulation result library, wherein the structured design includes index design and query optimization.

9. The performance simulation method of the power module according to claim 8, characterized in that: Step S5 includes the following steps: Step S51: using sensors and a high-speed data acquisition system to build and monitor the power module in real time to obtain operation data, and pre-processing and standardizing the operation data to obtain real-time monitoring data; Step S52: using the real-time monitoring data to compare and analyze with the simulation result library and the fast simulation data to obtain the simulation detection results; Step S53: performing abnormal performance analysis on the simulation detection results to obtain adjustment operation 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 detection 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 adjusting the preset working 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.

10. The performance simulation method of the power module according to claim 9, 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, constructing a comprehensive performance control model of the power module, wherein the comprehensive performance control model includes input-output relationship 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 on 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 evaluation result of the comprehensive performance control model to achieve dynamic optimization control.

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