A structural parameter optimization design method based on hardware

Abstract

The present invention relates to the technical field of design parameter processing, and in particular to a structural parameter optimization design method based on hardware. The method comprises the following steps: obtaining hardware material structure information data and performing structural design parameter conversion processing and three-dimensional simulation modeling processing to generate a three-dimensional simulation space model of the hardware structure; performing structural performance simulation testing and performance optimization demand analysis on the three-dimensional simulation space model of the hardware structure to obtain performance optimization demand data of the hardware structure simulation model; determining the structural optimization target and constraint conditions of the three-dimensional simulation space model of the hardware structure to obtain the optimization target of the hardware structure model and the constraint conditions of the hardware structure model; performing structural parameter optimization design on the hardware material structure design parameter set to obtain an optimized combination of hardware material structure design parameters. The present invention can realize an efficient optimization design process for hardware structure parameters.

Description

Technical Field

[0001] The invention relates to the technical field of design parameter processing, and in particular to a structural parameter optimization design method based on hardware. Background Art

[0002] In mechanical design, hardware is a common component. The hardware structure design process includes the processing and production of precision hardware and plastic parts in industries such as photography and video equipment, automobile modification accessories, audio equipment, and robots. The reasonable design of the hardware structure parameters has an important influence on the performance and stability of the overall structure. By optimizing the structural parameters of the hardware, the performance of the hardware products can be improved, the cost can be reduced, and the reliability and maintainability can be enhanced. The design process involves computer-aided design, finite element analysis, optimization algorithms and other technologies. Computer-aided design is one of the key technologies for realizing the digitalization of mechanical design. It provides a wealth of design tools and functions, including three-dimensional modeling, assembly design, motion simulation, etc., and provides a design platform for structural parameter optimization. Finite element analysis technology is a numerical calculation method used to analyze the stress, deformation, fatigue and other aspects of the structure. This can be used to evaluate the structural performance under different parameter combinations and guide the direction of optimization design. The optimization algorithm is a mathematical method for finding the optimal solution, including genetic algorithm, particle swarm algorithm, simulated annealing algorithm, etc. In structural parameter optimization, these algorithms can be used to search for the optimal parameter combination to meet the design requirements and constraints. However, traditional hardware design methods often rely on experience and simple optimization models, which leads to inefficient parameter design and difficulty in ensuring the optimality of design results. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a structural parameter optimization design method based on hardware to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a structural parameter optimization design method based on hardware includes the following steps:

[0005] Step S1: acquiring hardware material structure information data, and performing structural design parameter conversion processing on the hardware material structure information data to obtain a hardware material structure design parameter set;

[0006] Step S2: performing three-dimensional simulation modeling processing on the hardware material structure information data based on the hardware material structure design parameter set to generate a three-dimensional simulation space model of the hardware structure; performing structural performance simulation test on the three-dimensional simulation space model of the hardware structure to obtain performance test result data of the hardware structure simulation model;

[0007] Step S3: Performing performance optimization demand analysis on the performance test result data of the hardware structure simulation model to obtain performance optimization demand data of the hardware structure simulation model; determining the structural optimization target and constraint conditions of the three-dimensional simulation space model of the hardware structure based on the performance optimization demand data of the hardware structure simulation model to obtain the optimization target of the hardware structure model and the constraint conditions of the hardware structure model;

[0008] Step S4: Based on the optimization target of the hardware structural model and the constraint conditions of the hardware structural model, the structural parameter optimization design of the hardware material structure parameter set is performed to obtain an optimized combination of the hardware material structure design parameters.

[0009] The present invention first collects and processes the material structure information of the hardware. The key to this step is to collect and organize the material structure information of the hardware, including data on material type, structure size, structure shape, material properties, etc. Through these data, the structure and characteristics of the hardware can be deeply understood, thereby providing basic data guarantee for subsequent analysis and design. At the same time, by performing characteristic parameter analysis on the hardware material structure information data, the key to this step is to analyze and extract the key characteristic parameters of the hardware structure, including characteristic parameters of geometric aspects such as type and shape, size, and material geometric connection characteristics. Through these characteristic parameters, the structural characteristics of the hardware can be more accurately described, thereby providing more specific guidance for subsequent design and manufacturing. In addition, by performing structural design parameter conversion processing on the characteristic parameters obtained by the analysis, the purpose of this step is to convert the characteristic parameters into specific parameters that can be used for design and manufacturing, so as to support the optimization design of the hardware structural parameters. Through these design parameter sets, the design and manufacture of hardware can be carried out more effectively, ensuring that the converted structural design parameters meet the requirements and have good performance. Secondly, by combining the hardware material structure design parameter set with the hardware material structure information data for three-dimensional simulation modeling, a three-dimensional space model of the main structure of the hardware material can be constructed. This model will accurately reflect the morphology and structural characteristics of the hardware, thereby providing a reliable data basis for understanding and analyzing the performance of the hardware. In addition, by conducting structural performance simulation tests on the three-dimensional simulation space model of the hardware structure, the mechanical, thermodynamic and other properties of the model can be simulated and analyzed, so that the performance of the hardware structure under different conditions can be evaluated, and the corresponding test result data can be obtained. These data will provide important references for evaluating and optimizing the design of hardware, thereby helping to ensure the reliability and effectiveness of the design parameter optimization. Then, by analyzing the performance optimization needs of the hardware structure simulation model performance test result data, such an analysis and processing process can help determine the specific needs of performance optimization, including which aspects need to be improved and the focus of improvement. By analyzing the cause-effect relationship and the degree of compliance with the performance goals, specific requirements for performance optimization can be put forward in a targeted manner to achieve continuous improvement and optimization of the hardware structure performance. The structural optimization objectives and constraints of the three-dimensional simulation space model of the hardware structure are determined based on the performance optimization requirement data of the hardware structure simulation model. The purpose of this step is to establish specific goals and constraints for structural optimization to ensure that the implementation of the optimization plan can effectively meet performance requirements and follow relevant constraints. By determining the optimization objectives and constraints, the optimization direction can be clarified in the design stage and ensure that the final hardware structure model can meet the expected performance requirements.Finally, by combining the optimization objectives of the hardware structural model and the constraints of the hardware structural model obtained through analysis, the structural parameter optimization design of the hardware material structure design parameter set is carried out to comprehensively consider the design objectives and constraints, and optimize the parameter selection from the aspects of the hardware material, shape, size, etc. The key to this step is to ensure that the optimized design parameter set can meet the structural performance requirements while maximizing the material utilization and structural stability, thereby providing a good basic data guarantee for subsequent simulation analysis. The purpose of this step is to determine the optimal design parameter combination. By comparing the verification score values ​​of different design parameter combinations, the design scheme that best meets the optimization objectives and constraints can be found. The key to this step is to provide a scientific method to select the optimal design parameter combination to maximize the structural performance and achieve the design objectives, thereby improving the efficiency of structural parameter design and ensuring the optimality of the hardware structure design results. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] 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:

[0011] Figure 1 It is a schematic diagram of the steps of the structural parameter optimization design method based on hardware of the present invention;

[0012] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0013] Figure 3 for Figure 2 Detailed step flow chart of step S13 in FIG. DETAILED DESCRIPTION

[0014] The following is a clear and complete description of the technical method of the present invention 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.

[0015] 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.

[0016] 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.

[0017] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for optimizing the structural parameters of hardware, the method comprising the following steps:

[0018] Step S1: acquiring hardware material structure information data, and performing structural design parameter conversion processing on the hardware material structure information data to obtain a hardware material structure design parameter set;

[0019] Step S2: performing three-dimensional simulation modeling processing on the hardware material structure information data based on the hardware material structure design parameter set to generate a three-dimensional simulation space model of the hardware structure; performing structural performance simulation test on the three-dimensional simulation space model of the hardware structure to obtain performance test result data of the hardware structure simulation model;

[0020] Step S3: Performing performance optimization demand analysis on the performance test result data of the hardware structure simulation model to obtain performance optimization demand data of the hardware structure simulation model; determining the structural optimization target and constraint conditions of the three-dimensional simulation space model of the hardware structure based on the performance optimization demand data of the hardware structure simulation model to obtain the optimization target of the hardware structure model and the constraint conditions of the hardware structure model;

[0021] Step S4: Based on the optimization target of the hardware structural model and the constraint conditions of the hardware structural model, the structural parameter optimization design of the hardware material structure parameter set is performed to obtain an optimized combination of the hardware material structure design parameters.

[0022] In the embodiment of the present invention, please refer to Figure 1 As shown, it is a schematic diagram of the steps of the structural parameter optimization design method based on hardware of the present invention. In this example, the structural parameter optimization design method based on hardware includes the following steps:

[0023] Step S1: acquiring hardware material structure information data, and performing structural design parameter conversion processing on the hardware material structure information data to obtain a hardware material structure design parameter set;

[0024] The embodiment of the present invention collects the material structure information of the hardware by using sensors or measuring instruments to collect and organize the material structure information of the hardware, including data on material type, structure size, structure shape, material properties, etc., so as to obtain the material structure information data of the hardware. At the same time, the structural type and geometric shape of the hardware material obtained by analysis are analyzed using a characteristic statistical analysis method to explore the structural characteristics and geometric shape characteristics of different types of hardware from a statistical perspective, and at the same time, the structural characteristics and geometric shape characteristics obtained by analysis are parameterized to convert the corresponding feature data into parameterized feature parameters, and the converted parameterized feature parameters are mapped and converted using computer-aided design technology to map the original feature parameters to the computer-aided design software and convert them into a parameter set that can be used for design, and finally obtain the hardware material structure design parameter set.

[0025] Step S2: performing three-dimensional simulation modeling processing on the hardware material structure information data based on the hardware material structure design parameter set to generate a three-dimensional simulation space model of the hardware structure; performing structural performance simulation test on the three-dimensional simulation space model of the hardware structure to obtain performance test result data of the hardware structure simulation model;

[0026] The embodiment of the present invention converts the parameter format of the hardware material structure design parameter set to convert the corresponding structural design parameters from the original data format to a data format suitable for modeling and simulation, and applies the converted hardware material structure design parameter set to CAD (i.e., computer-aided design software) software or other three-dimensional modeling tools to model the main structure of the corresponding hardware material to create a three-dimensional space model of the main structure of the corresponding hardware material. Secondly, the hardware material structure design parameter set is analyzed by using a data statistical analysis method to analyze the requirements and restrictions in the design parameter set, such as material strength, size requirements, assembly requirements, etc., and understand the basic situation of the design requirements, including the range, restrictions and design goals of each parameter. The corresponding three-dimensional space model of the main structure of the hardware material is re-adjusted and assembled by combining the design requirement data obtained by the analysis, including adjusting the size, shape or position of the structural model to ensure that the generated simulation space model can meet the design requirements and goals while maintaining consistency with the actual hardware structure, thereby generating a three-dimensional simulation space model of the hardware structure. Then, the generated three-dimensional simulation space model of the hardware structure is tested by using the corresponding simulation testing software, so that the three-dimensional simulation space model of the hardware structure is applied to the simulation testing software for testing and analysis of mechanical, thermodynamic and other properties, and the performance of the hardware structure model under different conditions is evaluated, and finally the performance test result data of the hardware structure simulation model is obtained.

[0027] Step S3: Performing performance optimization demand analysis on the performance test result data of the hardware structure simulation model to obtain performance optimization demand data of the hardware structure simulation model; determining the structural optimization target and constraint conditions of the three-dimensional simulation space model of the hardware structure based on the performance optimization demand data of the hardware structure simulation model to obtain the optimization target of the hardware structure model and the constraint conditions of the hardware structure model;

[0028] The embodiment of the present invention performs statistical analysis of performance indicators on the performance test result data of the hardware structure simulation model by using performance indicator statistical analysis, such as performance indicators such as strength, stiffness, and weight, to obtain relevant performance indicators of the hardware structure simulation model, and analyzes the causal relationship between the obtained performance indicators by using a causal relationship mining analysis method, that is, which performance indicators have a direct or indirect impact on other performance indicators, and evaluates the degree of conformity between the performance indicators and the design goals, so as to better understand the inherent correlation between the hardware structure performance, and at the same time, performs demand analysis on the corresponding hardware structure simulation model performance test result data to determine which aspects of the corresponding hardware structure simulation model need to be improved to improve the performance of the hardware structure, and helps determine the specific needs of performance optimization, including which aspects need to be improved and the focus of improvement, thereby obtaining the performance optimization demand data of the hardware structure simulation model. Secondly, the corresponding hardware structure three-dimensional simulation space model is analyzed in combination with the performance optimization demand data of the hardware structure simulation model obtained by analysis, so as to identify the parts with poor performance or obvious defects in the current hardware structure three-dimensional simulation space model, and help to deeply understand the problems and shortcomings of the model, and identify and explore the areas or parts where the hardware structure performance can be improved as the structural optimization target of the subsequent processing process, so as to obtain the optimization target of the hardware structure model. Then, the corresponding hardware structure three-dimensional simulation space model is simulated and analyzed by combining the performance optimization demand data of the hardware structure simulation model obtained by analysis, so as to simulate the load conditions of the hardware structure three-dimensional simulation space model under actual working conditions, such as static load, dynamic load, etc., and the corresponding hardware structure three-dimensional simulation space model is determined by combining the load conditions obtained by analysis, so as to convert the load limit range of the hardware structure simulation model into the constraint conditions of the model, including the maximum stress, displacement, frequency and other constraint conditions, and ensure that it is used to limit or guide the design of the structure in the subsequent temptation process, and finally obtain the constraint conditions of the hardware structure model.

[0029] Step S4: Based on the optimization target of the hardware structural model and the constraint conditions of the hardware structural model, the structural parameter optimization design of the hardware material structure parameter set is performed to obtain an optimized combination of the hardware material structure design parameters.

[0030] The embodiment of the present invention selects and optimizes the hardware material structure design parameter set by combining the hardware structure model optimization target obtained by analysis and the hardware structure model constraint conditions, so as to determine the structural design parameter combination that needs to be optimized from the hardware material structure design parameter set, and optimizes the determined structural design parameter combination by using an appropriate optimization algorithm (such as a genetic algorithm, a particle swarm algorithm or a simulated annealing algorithm, etc.). At the same time, by using finite element analysis software, a topological simulation analysis of the structure is performed on each optimized hardware material structure design parameter combination, and a hardware structure model corresponding to each optimized parameter combination is generated by topological simulation. Secondly, the accuracy and credibility of the constructed hardware structure model are verified, and the verification score value of the simulation result and the actual situation is compared by calculation. Then, the parameter combination of the corresponding hardware material structure selection optimization design parameter set is sorted by combining the verification score value of the hardware material structure model obtained by verification calculation, and the optimal design parameter combination is selected to finally obtain the hardware material structure design parameter optimization combination.

[0031] The present invention first collects and processes the material structure information of the hardware. The key to this step is to collect and organize the material structure information of the hardware, including data on material type, structure size, structure shape, material properties, etc. Through these data, the structure and characteristics of the hardware can be deeply understood, thereby providing basic data guarantee for subsequent analysis and design. At the same time, by performing characteristic parameter analysis on the hardware material structure information data, the key to this step is to analyze and extract the key characteristic parameters of the hardware structure, including characteristic parameters of geometric aspects such as type and shape, size, and material geometric connection characteristics. Through these characteristic parameters, the structural characteristics of the hardware can be more accurately described, thereby providing more specific guidance for subsequent design and manufacturing. In addition, by performing structural design parameter conversion processing on the characteristic parameters obtained by the analysis, the purpose of this step is to convert the characteristic parameters into specific parameters that can be used for design and manufacturing, so as to support the optimization design of the hardware structural parameters. Through these design parameter sets, the design and manufacture of hardware can be carried out more effectively, ensuring that the converted structural design parameters meet the requirements and have good performance. Secondly, by combining the hardware material structure design parameter set with the hardware material structure information data for three-dimensional simulation modeling, a three-dimensional space model of the main structure of the hardware material can be constructed. This model will accurately reflect the morphology and structural characteristics of the hardware, thereby providing a reliable data basis for understanding and analyzing the performance of the hardware. In addition, by conducting structural performance simulation tests on the three-dimensional simulation space model of the hardware structure, the mechanical, thermodynamic and other properties of the model can be simulated and analyzed, so that the performance of the hardware structure under different conditions can be evaluated, and the corresponding test result data can be obtained. These data will provide important references for evaluating and optimizing the design of hardware, thereby helping to ensure the reliability and effectiveness of the design parameter optimization. Then, by analyzing the performance optimization needs of the hardware structure simulation model performance test result data, such an analysis and processing process can help determine the specific needs of performance optimization, including which aspects need to be improved and the focus of improvement. By analyzing the cause-effect relationship and the degree of compliance with the performance goals, specific requirements for performance optimization can be put forward in a targeted manner to achieve continuous improvement and optimization of the hardware structure performance. The structural optimization objectives and constraints of the three-dimensional simulation space model of the hardware structure are determined based on the performance optimization requirement data of the hardware structure simulation model. The purpose of this step is to establish specific goals and constraints for structural optimization to ensure that the implementation of the optimization plan can effectively meet performance requirements and follow relevant constraints. By determining the optimization objectives and constraints, the optimization direction can be clarified in the design stage and ensure that the final hardware structure model can meet the expected performance requirements.Finally, by combining the optimization objectives of the hardware structural model and the constraints of the hardware structural model obtained through analysis, the structural parameter optimization design of the hardware material structure design parameter set is carried out to comprehensively consider the design objectives and constraints, and optimize the parameter selection from the aspects of the hardware material, shape, size, etc. The key to this step is to ensure that the optimized design parameter set can meet the structural performance requirements while maximizing the material utilization and structural stability, thereby providing a good basic data guarantee for subsequent simulation analysis. The purpose of this step is to determine the optimal design parameter combination. By comparing the verification score values ​​of different design parameter combinations, the design scheme that best meets the optimization objectives and constraints can be found. The key to this step is to provide a scientific method to select the optimal design parameter combination to maximize the structural performance and achieve the design objectives, thereby improving the efficiency of structural parameter design and ensuring the optimality of the hardware structure design results.

[0032] Preferably, step S1 comprises the following steps:

[0033] Step S11: collecting and processing the material structure information of the hardware to obtain the material structure information data of the hardware;

[0034] Step S12: performing material structure attribute recognition analysis on the hardware material structure information data to obtain the hardware material structure attribute information data;

[0035] Step S13: performing material structure classification processing on the hardware material structure information data based on the hardware material structure attribute information data to obtain the structure information data of various hardware materials;

[0036] Step S14: performing characteristic parameter analysis according to the structural information data of various hardware materials to obtain the structural type characteristic parameters of the hardware materials and the structural geometric characteristic parameters of the hardware materials;

[0037] Step S15: performing structural design parameter conversion processing on the hardware material structure type characteristic parameters and the hardware material structure geometric characteristic parameters to obtain a hardware material structure design parameter set.

[0038] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S1 in FIG. 1 , in this embodiment, step S1 includes the following steps:

[0039] Step S11: collecting and processing the material structure information of the hardware to obtain the material structure information data of the hardware;

[0040] The embodiment of the present invention collects material structure information of hardware by using sensors or measuring instruments to collect and organize the material structure information of the hardware, including data on material type, structure size, structure shape, material properties, etc., and finally obtains the material structure information data of the hardware.

[0041] Step S12: performing material structure attribute recognition analysis on the hardware material structure information data to obtain the hardware material structure attribute information data;

[0042] The embodiment of the present invention analyzes the hardware material structure information data by using the attribute recognition and analysis method to identify and extract various attribute information of the hardware material structure, such as material type, shape characteristics, geometric characteristics, hardness, strength, density, etc., and finally obtains the hardware material structure attribute information data.

[0043] Step S13: performing material structure classification processing on the hardware material structure information data based on the hardware material structure attribute information data to obtain the structure information data of various hardware materials;

[0044] The embodiment of the present invention analyzes the hardware material structure attribute information data by using a feature analysis method to extract attribute features that are of great significance to the hardware material structure from the original data, and identifies and extracts attribute information related to the hardware structure, such as the physical properties and geometric shape of the material, and analyzes the extracted hardware material structure attribute feature data by using a discrimination statistical analysis method to evaluate the ability of each attribute feature in distinguishing different types of hardware structures, and understand the importance of each attribute feature in distinguishing the hardware structure. At the same time, by combining the importance of distinguishing each hardware material structure attribute feature obtained by analysis, the corresponding hardware material structure attribute feature data is subjected to feature screening processing to screen out the most representative and distinguishing features for distinguishing different types of hardware structures. Then, by combining the screened hardware material structure distinguishing features, a machine learning classification algorithm is used to construct a material structure classification model, and the constructed material structure classification model is used to process the hardware material structure information data to divide the hardware material structure information data into different categories, so as to more accurately analyze and manage different types of hardware, and finally obtain the structural information data of various types of hardware materials.

[0045] Step S14: performing characteristic parameter analysis according to the structural information data of various hardware materials to obtain the structural type characteristic parameters of the hardware materials and the structural geometric characteristic parameters of the hardware materials;

[0046] The embodiment of the present invention analyzes the structural information data of various types of hardware materials by using a structural type recognition method to identify and classify the structural types of different types of hardware, and determine the structural type to which they belong, such as plate type, profile type, pipe type, etc., and uses a characteristic statistical analysis method to analyze the structural information data of various types of hardware materials by combining the structural types of various types of hardware materials obtained by analysis, so as to explore the structural characteristics of different types of hardware from a statistical perspective, and at the same time, the structural type characteristics of the hardware materials obtained by analysis are parameterized to convert the corresponding characteristic data into parameterized characteristic parameters, such as mean value, variance, etc., so as to obtain the structural type characteristic parameters of the hardware materials. Secondly, the structural information data of various hardware materials are analyzed by using a geometric shape recognition algorithm to identify and describe the geometric shapes of different types of hardware, and understand the shapes of different types of hardware, such as plane shapes, curved shapes, concave and convex shapes, etc., and the structural geometric shape status data of various hardware materials are obtained through combined analysis. The structural information data of various hardware materials are analyzed using a characteristic statistical analysis method, and the structural characteristics of different types of hardware are also explored from a geometric perspective. Then, the structural geometric characteristics of the hardware materials obtained by the analysis are parameterized to convert the corresponding feature data into parameterized feature parameters, and finally the structural geometric feature parameters of the hardware materials are obtained.

[0047] Step S15: performing structural design parameter conversion processing on the hardware material structure type characteristic parameters and the hardware material structure geometric characteristic parameters to obtain a hardware material structure design parameter set.

[0048] In the embodiment of the present invention, the hardware material structure type characteristic parameters and the hardware material structure geometric characteristic parameters are mapped and converted by using computer-aided design technology, so as to map the original characteristic parameters into computer-aided design software and convert them into a parameter set that can be used for design, and the hardware material structure type characteristic parameters and the hardware material structure geometric characteristic parameters obtained by analysis are evaluated and analyzed by using a correlation evaluation algorithm and an influence evaluation algorithm, so as to determine the degree of correlation and influence between the hardware material structure type characteristic parameters and the hardware material structure geometric characteristic parameters. At the same time, by combining the degree of correlation and influence between the type characteristic parameters and the geometric characteristic parameters obtained by analysis, the corresponding hardware material structure type characteristic parameters and the hardware material structure geometric characteristic parameters are analyzed by using data mining technology (such as association law mining or potential association mining analysis method) to find the potential correlation relationship between different characteristic parameters, and then, according to the potential correlation relationship obtained by analysis, the corresponding hardware material structure design parameters are subjected to parameter expansion processing to add new design parameters or adjust the value range of existing parameters to better reflect the correlation relationship between the characteristic parameters, and finally obtain the hardware material structure design parameter set.

[0049] The present invention first collects and processes the material structure information of the hardware. The key to this step is to collect and organize the material structure information of the hardware, including data on material type, structure size, structure shape, material properties, etc. Through these data, the structure and characteristics of the hardware can be deeply understood, thereby providing basic data guarantee for subsequent analysis and design. Secondly, by performing material structure attribute identification analysis on the hardware material structure information data, this step aims to identify various properties of the hardware material structure, such as hardness, strength, density, etc., so as to more comprehensively describe the structural characteristics of the hardware. Through these attribute information, the quality and performance of the hardware can be evaluated, thereby providing a reference basis for subsequent design and application. Then, by performing material structure classification processing on the hardware material structure information data based on the hardware material structure attribute information data, the purpose of this step is to classify the hardware according to the characteristics of the material structure, so as to more accurately analyze and manage different types of hardware. Through classification processing, the characteristics of hardware of different material structure types can be better understood, and targeted design and application can be carried out, thereby providing basic data guarantee for subsequent processing. Next, by analyzing the characteristic parameters based on the structural information data of various hardware materials, the key to this step is to be able to analyze and extract the key characteristic parameters of the hardware structure, including type and geometric characteristic parameters such as shape, size, and material geometric connection characteristics. Through these characteristic parameters, the structural characteristics of the hardware can be more accurately described, thereby providing more specific guidance for subsequent design and manufacturing. Finally, by converting the structural design parameters of the hardware material structure type characteristic parameters and the hardware material structure geometric characteristic parameters, the purpose of this step is to convert the characteristic parameters into specific parameters that can be used for design and manufacturing, so as to support the optimization design of the hardware structural parameters. Through these design parameter sets, the design and manufacture of hardware can be carried out more effectively, ensuring that the converted structural design parameters meet the requirements and have good performance.

[0050] Preferably, step S13 comprises the following steps:

[0051] Step S131: extracting the attribute characteristics of the hardware material structure attribute information data to obtain the hardware material structure attribute characteristic data;

[0052] Step S132: performing standardization processing on the structural attribute characteristic data of the hardware material to obtain standardized characteristic data of the structural attribute of the hardware material; performing feature dimension reduction processing on the standardized characteristic data of the structural attribute of the hardware material to obtain reduced dimension characteristic data of the structural attribute of the hardware material;

[0053] Step S133: performing discrimination evaluation and analysis on the dimension reduction feature data of the hardware material structure attribute to obtain discrimination degree data of the structural attribute features of each hardware material;

[0054] Step S134: performing discrimination feature screening processing on the hardware material structure attribute dimensionality reduction feature data based on the discrimination degree data of each hardware material structure attribute feature to obtain hardware material structure attribute discrimination feature data;

[0055] Step S135: performing material structure classification processing on the hardware material structure information data based on the hardware material structure attribute discrimination characteristic data to obtain the structure information data of various hardware materials.

[0056] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 2 FIG. 1 is a schematic diagram of a detailed step flow chart of step S13 in FIG. 1 . In this embodiment, step S13 includes the following steps:

[0057] Step S131: extracting the attribute characteristics of the hardware material structure attribute information data to obtain the hardware material structure attribute characteristic data;

[0058] The embodiment of the present invention analyzes the hardware material structure attribute information data by using a feature analysis method to extract attribute features that are important to the hardware material structure from the original data, and identifies and extracts attribute information related to the hardware structure, such as the physical properties and geometric shape of the material, and finally obtains the hardware material structure attribute feature data.

[0059] Step S132: performing standardization processing on the structural attribute characteristic data of the hardware material to obtain standardized characteristic data of the structural attribute of the hardware material; performing feature dimension reduction processing on the standardized characteristic data of the structural attribute of the hardware material to obtain reduced dimension characteristic data of the structural attribute of the hardware material;

[0060] The embodiment of the present invention standardizes the characteristic data of the structural attributes of hardware materials by using the z-score standardization method to eliminate the dimensional differences between different attributes and ensure that they have the same scale and range, thereby obtaining the standardized characteristic data of the structural attributes of hardware materials. Then, the standardized characteristic data of the structural attributes of hardware materials are subjected to dimensionality reduction processing by using the principal component analysis method to reduce the dimension and complexity of the data while retaining the main characteristic information, and finally obtaining the dimensionality reduction characteristic data of the structural attributes of hardware materials.

[0061] Step S133: performing discrimination evaluation and analysis on the dimension reduction feature data of the hardware material structure attribute to obtain discrimination degree data of the structural attribute features of each hardware material;

[0062] The embodiment of the present invention analyzes the dimensionality reduction feature data of the hardware material structure attributes by using a discrimination statistical analysis method to evaluate the ability of each attribute feature in distinguishing different types of hardware structures, and understand the importance of each attribute feature in distinguishing hardware structures, and finally obtain the discrimination degree data of each hardware material structure attribute feature.

[0063] Step S134: performing discrimination feature screening processing on the hardware material structure attribute dimensionality reduction feature data based on the discrimination degree data of each hardware material structure attribute feature to obtain hardware material structure attribute discrimination feature data;

[0064] The embodiment of the present invention performs feature screening processing on the corresponding hardware material structure attribute dimensionality reduction feature data by combining the differentiation degree data of each hardware material structure attribute feature obtained by analysis, so as to screen out the most representative and distinguishing features for distinguishing different types of hardware structures, and finally obtain the hardware material structure attribute differentiation feature data.

[0065] Step S135: performing material structure classification processing on the hardware material structure information data based on the hardware material structure attribute discrimination characteristic data to obtain the structure information data of various hardware materials.

[0066] The embodiment of the present invention constructs a material structure classification model by combining the hardware material structure attribute discrimination feature data obtained by screening with a machine learning classification algorithm, and processes the hardware material structure information data by using the constructed material structure classification model to divide the hardware material structure information data into different categories, so as to more accurately analyze and manage different types of hardware, and finally obtain the structural information data of various types of hardware materials.

[0067] The present invention firstly extracts the attribute characteristics of the hardware material structure from the original data by performing attribute feature extraction processing on the attribute information data of the hardware material structure. Through this step, the attribute information related to the hardware structure, such as the physical properties and geometric shape of the material, can be identified and extracted. These attribute feature data provide a basis for subsequent analysis and processing, so that the structural characteristics and differences of the hardware can be understood more deeply. Secondly, by standardizing the attribute feature data of the hardware material structure, the purpose of this step is to eliminate the dimensional differences between different attributes so that they have the same scale and range, and the standardized feature data helps to improve the convergence speed and performance of the subsequent processing process, so that the subsequent analysis and processing can be carried out more effectively. In addition, through feature dimensionality reduction processing, the dimension of the data can be reduced, the calculation efficiency can be improved, and overfitting can be prevented, so as to better mine the information of the data. Then, by performing discrimination evaluation analysis on the attribute reduction feature data of the hardware material structure, it is intended to evaluate the ability of each attribute feature in distinguishing different types of hardware structures. Through the discrimination evaluation analysis, we can understand the importance of each attribute feature in distinguishing the hardware structure, so as to provide guidance for the subsequent feature selection and classification processing. The key to this step is to help more targeted selection of the most discriminative features, thereby improving the accuracy and stability of the subsequent classification process. Next, by performing discrimination feature screening on the dimensionality reduction feature data of the hardware material structure attribute based on the discrimination degree data of the attribute features of each hardware material structure, we can screen out the most representative and discriminative features for distinguishing different types of hardware structures, thereby improving the effect and accuracy of the subsequent classification processing. The key to this step is to be able to select appropriate features so that the subsequent classification processing process can better understand and distinguish different types of hardware structures. Finally, by performing material structure classification processing on the hardware material structure information data based on the discrimination feature data of the hardware material structure attribute, the hardware material structure information data can be divided into different categories to more accurately analyze and manage different types of hardware. Through this step, hardware can be classified according to the distinctiveness of attribute characteristics, so that each type of hardware has similar structural characteristics and performance, which helps to optimize the design and production process, improve product quality and performance, and meet the needs of different application scenarios.

[0068] Preferably, step S14 comprises the following steps:

[0069] Step S141: performing structural type identification analysis on the structural information data of various hardware materials to obtain structural type status data of various hardware materials;

[0070] The embodiment of the present invention analyzes the structural information data of various types of hardware materials by using a structural type recognition method to identify and classify the structural types of different types of hardware, and determine the structural type to which they belong, such as plate type, profile type, pipe type, etc., and finally obtains the structural type status data of various types of hardware materials.

[0071] Step S142: performing type characteristic statistical analysis on the structural information data of various hardware materials based on the structural type status data of various hardware materials to obtain structural type characteristic data of the hardware materials; performing type characteristic parameterization processing on the structural type characteristic data of the hardware materials to obtain structural type characteristic parameters of the hardware materials;

[0072] The embodiment of the present invention analyzes the structural information data of various types of hardware materials by combining the structural type status data of various types of hardware materials obtained by analysis with a characteristic statistical analysis method, so as to explore the structural characteristics of different types of hardware from a statistical perspective, thereby obtaining the structural type characteristic data of the hardware materials. Then, the structural type characteristic data of the hardware materials obtained by analysis is parameterized to convert the corresponding characteristic data into parameterized characteristic parameters, such as mean value, variance, etc., so that the structural characteristics of different types of hardware can be described and compared more accurately, and finally the structural type characteristic parameters of the hardware materials are obtained.

[0073] Step S143: performing geometric shape recognition analysis on the structural information data of various hardware materials to obtain structural geometric shape status data of various hardware materials;

[0074] The embodiment of the present invention analyzes the structural information data of various types of hardware materials by using a geometric shape recognition algorithm to identify and describe the geometric shapes of different types of hardware, and understand the shape conditions of different types of hardware, such as plane shapes, curved shapes, concave and convex shapes, etc., and finally obtains the structural geometric shape status data of various types of hardware materials.

[0075] Step S144: performing geometric feature statistical analysis on the structural information data of various hardware materials based on the structural geometric shape status data of various hardware materials to obtain the structural geometric feature data of the hardware materials; performing geometric feature parameterization processing on the structural geometric feature data of the hardware materials to obtain the structural geometric feature parameters of the hardware materials.

[0076] The embodiment of the present invention obtains the structural geometric shape status data of various types of hardware materials by combining analysis and uses the characteristic statistical analysis method to analyze the structural information data of various types of hardware materials, and also explores the structural characteristics of different types of hardware from a geometric perspective, thereby obtaining the structural geometric characteristic data of the hardware materials. Then, the structural geometric characteristic data of the hardware materials obtained by the analysis is parameterized to convert the corresponding characteristic data into parameterized characteristic parameters, and the distribution of geometric characteristics of different types of hardware, such as length, width, thickness, curvature, etc., is understood, and finally the structural geometric characteristic parameters of the hardware materials are obtained.

[0077] The present invention firstly performs structural type identification and analysis on the structural information data of various types of hardware materials. The purpose of this step is to identify and classify the structural types of different types of hardware. By analyzing the structural information data of each type of hardware, the structural type to which it belongs can be determined, such as plate type, profile type, pipe type, etc., so as to establish the structural type status data of various types of hardware, which helps to systematically understand the structural characteristics of different types of hardware, thereby providing a basis for subsequent analysis and processing. Secondly, by performing type characteristic statistical analysis on the structural information data of various types of hardware materials based on the structural type status data of various types of hardware materials, it is intended to explore the structural characteristics of different types of hardware from a statistical perspective. By performing statistical analysis on the characteristics of the structural type, the distribution of the structural characteristics of different types of hardware can be understood, such as size, shape, proportion, etc. Further, by performing type characteristic parameterization processing on the structural type characteristic data of the hardware materials, the corresponding characteristic data can be converted into parameterized characteristic parameters, such as mean value, variance, etc. These parameterized characteristics can more accurately describe and compare the structural characteristics of different types of hardware. Then, the geometric shape recognition and analysis of the structural information data of various hardware materials is performed. The purpose of this step is to identify and describe the geometric shapes of different types of hardware. Through the recognition and analysis of geometric shapes, the shape characteristics of different types of hardware can be understood, such as plane shape, curved surface shape, concave and convex features, etc., which helps to understand the structural characteristics of hardware more comprehensively, thus providing a basis for subsequent feature analysis and classification processing. Finally, the geometric feature statistical analysis of the structural information data of various hardware materials based on the structural geometric shape status data of various hardware materials can explore the structural characteristics of different types of hardware from a geometric perspective. This step can understand the distribution of geometric features of different types of hardware through the statistical analysis of geometric features, such as length, width, thickness, curvature, etc. Similarly, by performing geometric feature parameterization processing on the structural geometric feature data of hardware materials, the geometric features of different types of hardware can be more accurately described and compared, thereby providing more specific guidance for subsequent design and manufacturing.

[0078] Preferably, step S15 comprises the following steps:

[0079] Step S151: using computer-aided design technology to perform feature parameter mapping conversion on the hardware material structure type feature parameters and the hardware material structure geometric feature parameters to obtain an initial design parameter set of the hardware material structure;

[0080] The embodiment of the present invention uses computer-aided design technology to map and convert the characteristic parameters of the hardware material structure type and the geometric characteristic parameters of the hardware material structure, so as to map the original characteristic parameters into computer-aided design software and convert them into a parameter set that can be used for design, and finally obtain the initial design parameter set of the hardware material structure.

[0081] Step S152: performing correlation evaluation and analysis on the hardware material structure type characteristic parameters and the hardware material structure geometric characteristic parameters to obtain the correlation degree coefficient between the type characteristic parameters and the geometric characteristic parameters;

[0082] The embodiment of the present invention uses a correlation evaluation algorithm to evaluate and analyze the analyzed hardware material structure type characteristic parameters and the hardware material structure geometric characteristic parameters to determine the degree of correlation between the hardware material structure type characteristic parameters and the hardware material structure geometric characteristic parameters, that is, the strength of the linear or nonlinear relationship between them, and finally obtains the correlation degree coefficient between the type characteristic parameters and the geometric characteristic parameters.

[0083] Step S153: performing an impact assessment analysis on the hardware material structure type characteristic parameters and the hardware material structure geometric characteristic parameters to obtain an impact degree coefficient between the type characteristic parameters and the geometric characteristic parameters;

[0084] The embodiment of the present invention evaluates and analyzes the type characteristic parameters of the hardware material structure and the geometric characteristic parameters of the hardware material structure by using an impact assessment algorithm to evaluate the degree of influence between different characteristic parameters, that is, the degree of influence of the change of one characteristic parameter on the change of another characteristic parameter, and finally obtains the influence degree coefficient between the type characteristic parameters and the geometric characteristic parameters.

[0085] Step S154: performing potential association mining analysis on the hardware material structure type characteristic parameters and the hardware material structure geometric characteristic parameters based on the correlation degree coefficient and the influence degree coefficient between the type characteristic parameters and the geometric characteristic parameters, so as to obtain potential association relationship data between the hardware material structure characteristic parameters;

[0086] The embodiment of the present invention uses data mining technology (such as association rule mining or potential association mining analysis method) to analyze the corresponding hardware material structure type characteristic parameters and hardware material structure geometric characteristic parameters by combining the correlation degree coefficient and influence degree coefficient between the type characteristic parameters and the geometric characteristic parameters obtained by analysis, so as to discover the potential correlation relationship between different characteristic parameters, so as to further understand the interaction between different characteristic parameters, and finally obtain the potential correlation relationship data between the hardware material structure characteristic parameters.

[0087] Step S155: performing design parameter expansion processing on the initial design parameter set of the hardware material structure according to the potential correlation relationship data between the characteristic parameters of the hardware material structure, so as to obtain the hardware material structure design parameter set.

[0088] The embodiment of the present invention performs parameter expansion processing on the corresponding initial design parameter set of the hardware material structure according to the potential correlation data between the characteristic parameters of the hardware material structure obtained by analysis, so as to add new design parameters or adjust the value range of existing parameters to better reflect the correlation between the characteristic parameters, and finally obtain the hardware material structure design parameter set.

[0089] The present invention firstly uses computer-aided design technology to map and convert the characteristic parameters of the hardware material structure type and the geometric characteristic parameters of the hardware material structure. This process can combine the structural type characteristics and geometric characteristics of the hardware, and use the corresponding computer-aided design technology to convert them into a parameter set that can be used for design, thereby laying the foundation for subsequent design work. The key to this step is to be able to integrate and convert the various characteristic parameters of the hardware material structure for subsequent analysis and processing. Through this step, an initial set containing various design parameters can be obtained, thereby laying the foundation for subsequent analysis and design work. Secondly, by performing correlation evaluation and analysis on the characteristic parameters of the hardware material structure type and the geometric characteristic parameters of the hardware material structure, the degree of correlation between different types of characteristic parameters and geometric characteristic parameters can be understood, which helps to further understand the characteristics and interrelationships of the hardware material structure. This analysis result is very important for subsequent steps and can guide decision-making in the subsequent design and optimization process. Then, by conducting an impact assessment analysis on the hardware material structure type characteristic parameters and the hardware material structure geometric characteristic parameters, we can understand the degree of influence of different characteristic parameters on the hardware material structure, which helps to identify and understand the importance of each parameter in the design process. This evaluation result can provide a basis and guidance for the subsequent optimization of design parameters. Next, by conducting a potential association mining analysis on the hardware material structure type characteristic parameters and the hardware material structure geometric characteristic parameters based on the correlation degree coefficient and the influence degree coefficient between the type characteristic parameters and the geometric characteristic parameters, this analysis and processing process aims to discover the potential correlation between different characteristic parameters. Even if there may be no direct correlation between them on the surface, by analyzing their correlation and influence degree, the hidden relationship between them can be discovered. The results of this step can provide new perspectives and ideas for the subsequent design parameter selection and optimization. Finally, by expanding the design parameter set of the initial design parameter set of the hardware material structure according to the potential correlation data between the characteristic parameters of the hardware material structure, this step can increase or adjust the design parameters to better meet the design requirements and optimization goals, and obtain a more complete and comprehensive hardware material structure design parameter set, thereby providing more choices and possibilities for subsequent design and optimization work.

[0090] Preferably, step S2 comprises the following steps:

[0091] Step S21: converting the hardware material structure design parameter set into a model parameter format to obtain a hardware material structure space model format parameter set;

[0092] The embodiment of the present invention converts the parameter format of the hardware material structure design parameter set to convert the corresponding structural design parameters from the original data format into a data format suitable for modeling and simulation, and ensures that the design parameters can be directly applied to subsequent three-dimensional modeling and simulation analysis, and finally obtains the hardware material structure space model format parameter set.

[0093] Step S22: performing structural three-dimensional simulation modeling on the hardware material structure information data based on the hardware material structure space model format parameter set to obtain a three-dimensional space model of the main structure of the hardware material;

[0094] The embodiment of the present invention combines the converted hardware material structure space model format parameter set with CAD (computer-aided design software) software or other three-dimensional modeling tools to model the main structure of the corresponding hardware material, so as to create a three-dimensional space model of the main structure of the corresponding hardware material, accurately reflect the morphology and structural characteristics of the hardware, and finally obtain a three-dimensional space model of the main structure of the hardware material.

[0095] Step S23: Performing design requirement statistical analysis on the hardware material structure design parameter set to obtain hardware material structure design requirement data;

[0096] The embodiment of the present invention analyzes the hardware material structure design parameter set by using a data statistical analysis method to analyze the requirements and restrictions in the design parameter set, such as material strength, size requirements, assembly requirements, etc., and understands the basic situation of the design requirements, including the range of each parameter, restrictions and design goals, and finally obtains the hardware material structure design requirement data.

[0097] Step S24: adjusting and assembling the three-dimensional spatial model of the main structure of the hardware material according to the hardware material structure design requirement data to generate a three-dimensional simulation spatial model of the hardware structure;

[0098] The embodiment of the present invention readjusts and assembles the corresponding three-dimensional spatial model of the main structure of the hardware material by combining the hardware material structure design requirement data obtained through analysis, including adjusting the size, shape or position of the structural model, to ensure that the generated simulation space model can meet the design requirements and objectives, while maintaining consistency with the actual hardware structure, and finally generating a three-dimensional simulation space model of the hardware structure.

[0099] Step S25: Perform structural performance simulation test on the three-dimensional simulation space model of the hardware structure to obtain performance test result data of the hardware structure simulation model.

[0100] The embodiment of the present invention tests the generated three-dimensional simulation space model of the hardware structure by using corresponding simulation testing software, so as to apply the three-dimensional simulation space model of the hardware structure to the simulation testing software for testing and analysis of mechanical, thermodynamic and other properties, and evaluate the performance of the hardware structure model under different conditions, and finally obtain the performance test result data of the hardware structure simulation model.

[0101] The present invention first converts the parameter format into a format parameter set suitable for the spatial model by converting the hardware material structure design parameter set. This conversion process is to ensure that the design parameters can be directly applied to subsequent three-dimensional modeling and simulation analysis, and provide the necessary data basis for the entire design process. Through this step, a parameter set suitable for the construction format of the hardware material structure spatial model can be obtained, thereby preparing for the next step of three-dimensional simulation modeling. Secondly, by performing structural three-dimensional simulation modeling on the hardware material structure information data based on the hardware material structure spatial model format parameter set, a three-dimensional spatial model of the main structure of the hardware material can be constructed. The model will accurately reflect the morphology and structural characteristics of the hardware, thereby providing a reliable data basis for understanding and analyzing the performance of the hardware. Then, by performing design requirement statistical analysis on the hardware material structure design parameter set, the basic situation of the design requirements can be understood, including the range of various parameters, restrictions and design goals. This analysis result is crucial for subsequent adjustment and optimization work, and can guide decision-making and strategies in the design process. Next, the three-dimensional spatial model of the main structure of the hardware material is adjusted and assembled according to the hardware material structure design requirements. This step is to ensure that the generated simulation space model can meet the design requirements and goals while maintaining consistency with the actual hardware structure. Through adjustment and assembly, a complete three-dimensional simulation space model of the hardware structure can be generated, thus preparing for the subsequent performance simulation test. Finally, by performing structural performance simulation tests on the three-dimensional simulation space model of the hardware structure, the mechanical, thermodynamic and other properties of the model can be simulated and analyzed, so that the performance of the hardware structure under different conditions can be evaluated, and the corresponding test result data can be obtained. These data will provide important references for evaluating and optimizing the design of hardware, thereby helping to ensure the reliability and effectiveness of the design parameter optimization.

[0102] Preferably, step S25 comprises the following steps:

[0103] Step S251: performing a structural mechanics simulation test on the three-dimensional simulation space model of the hardware structure to obtain a simulation test result of the mechanical properties of the hardware structure;

[0104] The embodiment of the present invention imports the three-dimensional simulation space model of the hardware structure into the structural mechanics simulation software, and defines the boundary conditions and loading conditions of the simulation test, including the force, pressure, constraints, etc. applied to the model. Then, the simulation test is performed by running the structural mechanics simulation software, so that the structural mechanics simulation software can calculate and evaluate the performance test performance of the hardware structure model under stress conditions, including the stress distribution, deformation and bearing capacity of the stress-bearing parts, and finally obtain the simulation test results of the mechanical properties of the hardware structure.

[0105] Step S252: performing a structural thermal simulation test on the three-dimensional simulation space model of the hardware structure to obtain a simulation test result of the thermal performance of the hardware structure;

[0106] The embodiment of the present invention imports a three-dimensional simulation space model of a hardware structure into structural thermal simulation software, and defines boundary conditions and loading conditions of the simulation test, including heat sources, temperatures, constraints, etc. applied to the model. Then, the structural thermal simulation software is run to perform the test, so that the structural thermal simulation software can calculate and evaluate the thermal properties of the hardware structure model under different temperature conditions, including test results of heat conduction, thermal expansion, etc., and finally obtain the simulation test results of the thermal properties of the hardware structure.

[0107] Step S253: merging the hardware structure mechanical performance simulation test results and the hardware structure thermal performance simulation test results to obtain hardware structure simulation model performance test result data.

[0108] The embodiment of the present invention integrates the hardware structure mechanical performance simulation test results and the hardware structure thermal performance simulation test results into the same data set and ensures the consistency of the two test result data sets, and finally obtains the hardware structure simulation model performance test result data.

[0109] The present invention first performs a structural mechanics simulation test on a three-dimensional simulation space model of the hardware structure. This test process is intended to evaluate the performance of the hardware under stress conditions, including stress distribution, deformation, and load-bearing capacity of the stress-bearing parts. Through the simulation test of structural mechanics, the stress conditions under different working conditions can be simulated, providing an important data basis for evaluating the strength and stability of the hardware. These test results have a guiding role in the optimization and improvement of the design. Then, a structural thermal simulation test is performed on the three-dimensional simulation space model of the hardware structure. This test is intended to evaluate the thermal properties of the hardware under different temperature conditions, including characteristics such as heat conduction and thermal expansion. Through the simulation test, the thermal performance of the hardware in the actual working environment can be simulated, providing a basis for optimizing its working temperature range, material selection, etc. These thermal performance simulation test results are crucial to ensuring the stability and reliability of the hardware in various working environments. Finally, by merging the test results of the mechanical properties simulation of the hardware structure and the thermal properties simulation of the hardware structure, the test results of the two aspects can be combined to obtain a more comprehensive and integrated hardware structure simulation model performance test result data. This data will provide designers and engineers with deeper insights and help make more accurate and reliable design decisions, thereby ensuring that the performance of the hardware under various working conditions meets the expected goals.

[0110] Preferably, step S3 comprises the following steps:

[0111] Step S31: extracting performance indicators from the hardware structure simulation model performance test result data to obtain a key performance indicator parameter set of the hardware structure simulation model;

[0112] The embodiment of the present invention performs statistical analysis of performance indicators on the performance test result data of the hardware structure simulation model by using performance indicator statistical analysis, such as performance indicators such as strength, stiffness, and weight, to obtain relevant performance indicators of the hardware structure simulation model, and analyzes the degree of influence between the performance indicators obtained by using an impact assessment algorithm. At the same time, the parameter set with the greatest influence on the hardware structure performance is screened out from the performance indicator data obtained by the analysis according to the degree of influence between the performance indicators, and finally a key performance indicator parameter set of the hardware structure simulation model is obtained.

[0113] Step S32: performing causal relationship mining analysis on the key performance indicator parameter set of the hardware structure simulation model to obtain causal relationship data between the key performance indicators of the hardware structure;

[0114] The embodiment of the present invention mines and analyzes the causal relationship between the key performance indicators of each hardware structure simulation model in the key performance indicator parameter set of the hardware structure simulation model by using a causal relationship mining and analysis method to find out the causal relationship between the performance indicators, that is, which performance indicators have a direct or indirect impact on other performance indicators, so as to better understand the intrinsic correlation of the hardware structure performance and finally obtain the causal relationship data between the key performance indicators of the hardware structure.

[0115] Step S33: performing a quantitative analysis of the degree of compliance with the design target on the performance test result data of the hardware structure simulation model based on the key performance indicator parameter set of the hardware structure simulation model, and obtaining the degree of compliance with the performance target of the hardware structure simulation model;

[0116] The embodiment of the present invention analyzes the corresponding performance design targets in the performance test result data of the hardware structure simulation model by combining the key performance indicator parameter set of the hardware structure simulation model obtained by analysis, so as to compare the key performance indicators of the hardware structure simulation model with the corresponding performance design targets, and evaluate the degree of conformity between the performance of the simulation model and the design targets, which helps to determine whether the existing design has achieved the expected goals and which aspects need further improvement, and finally obtains the performance target conformity data of the hardware structure simulation model.

[0117] Step S34: performing performance optimization demand analysis on the performance test result data of the hardware structure simulation model according to the causal relationship data between the key performance indicators of the hardware structure and the performance target compliance data of the hardware structure simulation model to obtain the performance optimization demand data of the hardware structure simulation model;

[0118] The embodiment of the present invention performs demand analysis on the corresponding hardware structure simulation model performance test result data based on the causal relationship data between the key performance indicators of the hardware structure and the performance target compliance data of the hardware structure simulation model, so as to determine which aspects of the corresponding hardware structure simulation model need to be improved to improve the performance of the hardware structure, and help determine the specific needs of performance optimization, including which aspects need to be improved and the focus of improvement, and finally obtain the performance optimization demand data of the hardware structure simulation model.

[0119] Step S35: Determine the structural optimization target and constraint conditions of the three-dimensional simulation space model of the hardware structure based on the performance optimization requirement data of the hardware structure simulation model to obtain the optimization target of the hardware structure model and the constraint conditions of the hardware structure model.

[0120] The embodiment of the present invention analyzes the corresponding hardware structure three-dimensional simulation space model in combination with the performance optimization demand data of the hardware structure simulation model obtained by analysis, so as to identify the parts with poor performance or obvious defects in the current hardware structure three-dimensional simulation space model, and help to gain an in-depth understanding of the problems and shortcomings of the model. At the same time, by using a data mining analysis algorithm, the improvement space of the problems and shortcomings obtained by analysis is analyzed to identify and explore the areas or parts where the hardware structure performance can be improved as the structural optimization target of the subsequent processing process, thereby obtaining the hardware structure model optimization target. Secondly, by combining the performance optimization demand data of the hardware structure simulation model obtained by analysis, the model load simulation analysis is carried out on the corresponding hardware structure three-dimensional simulation space model to simulate the load conditions of the hardware structure three-dimensional simulation space model under actual working conditions, such as static load, dynamic load, etc., and by combining the load conditions obtained by analysis, the constraint conditions of the corresponding hardware structure three-dimensional simulation space model are determined to convert the load limit range of the hardware structure simulation model into the constraint conditions of the model, including maximum stress, displacement, frequency and other constraints, and ensure that it is used to limit or guide the design of the structure in the subsequent temptation process, and finally obtain the constraint conditions of the hardware structure model.

[0121] The present invention firstly extracts key performance indicator parameter sets by performing performance indicator extraction processing on the performance test result data of the hardware structure simulation model, which means that the parameters that have the greatest impact on the performance of the hardware structure can be screened out from a large amount of data, providing a basis for subsequent analysis and optimization. The extraction of these key performance indicator parameters can help to more deeply understand the performance characteristics of the hardware structure and lay the foundation for subsequent work. Secondly, by performing causal relationship mining and analysis on the key performance indicator parameter set of the hardware structure simulation model, the causal relationship data between different performance indicators can be revealed. Such an analysis and processing process helps to determine which performance indicators have the most significant impact on the overall performance of the hardware structure, thereby providing a direction for performance optimization. By mining the causal relationship between key performance indicators, the working mechanism and performance of the hardware structure can be better understood. Then, by conducting a quantitative analysis of the degree of compliance of the hardware structure simulation model performance test result data with the design goal based on the key performance indicator parameter set of the hardware structure simulation model, the degree of compliance with the performance goal of the hardware structure simulation model can be quantitatively evaluated. Such an analysis helps to determine whether the existing design has achieved the expected goal and which aspects need further improvement. By quantitatively analyzing the degree of compliance with the performance goal, the advantages and disadvantages of the design can be more clearly understood, and corresponding improvement strategies can be formulated. Next, by conducting a performance optimization demand analysis on the hardware structure simulation model performance test result data based on the causal relationship data between the key performance indicators of the hardware structure and the degree of compliance with the performance goal of the hardware structure simulation model, such an analysis and processing process can help determine the specific needs of performance optimization, including which aspects need to be improved and the focus of improvement. By analyzing the causal relationship and the degree of compliance with the performance goal, specific requirements for performance optimization can be put forward in a targeted manner to achieve continuous improvement and optimization of the hardware structure performance. Finally, the structural optimization objectives and constraints of the three-dimensional simulation space model of the hardware structure are determined based on the performance optimization requirement data of the hardware structure simulation model. The purpose of this step is to establish specific goals and constraints for structural optimization to ensure that the implementation of the optimization plan can effectively meet performance requirements and follow relevant constraints. By determining the optimization objectives and constraints, the optimization direction can be clarified in the design stage and ensure that the final hardware structure model can meet the expected performance requirements.

[0122] Preferably, step S35 includes the following steps:

[0123] Step S351: Based on the performance optimization requirement data of the hardware structure simulation model, the three-dimensional simulation space model of the hardware structure is subjected to structural performance shortcoming identification and analysis to obtain performance shortcoming data of the hardware structure simulation model;

[0124] The embodiment of the present invention analyzes the corresponding hardware structure three-dimensional simulation space model in combination with the performance optimization demand data of the hardware structure simulation model obtained by analysis, so as to identify the parts with poor performance or obvious defects in the current hardware structure three-dimensional simulation space model, and help to gain an in-depth understanding of the problems and shortcomings of the model, and finally obtain the performance short board data of the hardware structure simulation model.

[0125] Step S352: performing improvement space mining analysis on the performance short board data of the hardware structure simulation model to obtain the performance improvement space of the hardware structure simulation model;

[0126] The embodiment of the present invention uses a data mining analysis algorithm to analyze the improvement space of corresponding problems in the performance shortcomings data of the hardware structure simulation model, so as to identify and explore the areas or parts where the hardware structure performance can be improved, and determine targeted structural improvement measures to solve the structural performance shortcomings of the model, and finally obtain the performance improvement space of the hardware structure simulation model.

[0127] Step S353: taking the performance improvement space of the hardware structure simulation model as the structure optimization target to obtain the hardware structure model optimization target;

[0128] The embodiment of the present invention uses the performance improvement space of the hardware structure simulation model obtained by analysis as the structural optimization target of the subsequent processing process to convert the corresponding performance improvement space of the hardware structure simulation model into a mathematical expression or an optimization objective function, and finally obtains the optimization target of the hardware structure model.

[0129] Step S354: performing model load simulation analysis on the three-dimensional simulation space model of the hardware structure based on the performance optimization requirement data of the hardware structure simulation model to obtain the load limit range of the hardware structure simulation model;

[0130] The embodiment of the present invention performs a simulation analysis of the model load on the corresponding three-dimensional simulation space model of the hardware structure by combining the performance optimization requirement data of the hardware structure simulation model obtained by analysis, so as to simulate the load conditions of the three-dimensional simulation space model of the hardware structure under actual working conditions, such as static load, dynamic load, etc., and understand the pressure and load range that the model is subjected to in actual work, and finally obtain the load limit range of the hardware structure simulation model.

[0131] Step S355: determining the constraint conditions of the three-dimensional simulation space model of the hardware structure based on the load limit range of the hardware structure simulation model to obtain the constraint conditions of the hardware structure model.

[0132] The embodiment of the present invention determines the constraint conditions of the corresponding three-dimensional simulation space model of the hardware structure by combining the load limit range of the hardware structure simulation model obtained by analysis, so as to convert the load limit range of the hardware structure simulation model into the constraint conditions of the model, including the maximum stress, displacement, frequency and other constraint conditions, and ensure that it is used to limit or guide the design of the structure in the subsequent temptation process, and finally obtain the constraint conditions of the hardware structure model.

[0133] The present invention firstly analyzes the structural performance shortcomings of the three-dimensional simulation space model of the hardware structure by combining the performance optimization demand data of the hardware structure simulation model obtained by analysis, and can identify the parts with poor performance or obvious defects in the current three-dimensional simulation space model of the hardware structure. These parts are called "short boards". This analysis can help to deeply understand the problems and shortcomings of the model and provide guidance for subsequent performance improvements. The obtained hardware structure simulation model performance short board data will guide the subsequent process to carry out targeted optimization and improvement. Secondly, by performing improvement space mining analysis on the hardware structure simulation model performance short board data, potential improvement directions and spaces can be determined, which means that targeted improvement measures can be determined to solve the short board problems of the model. By analyzing the improvement space, it can be clear which aspects can be optimized to improve the overall performance level. Then, by taking the performance improvement space of the hardware structure simulation model as the structural optimization target, the optimization direction and target of the subsequent hardware model can be established. The key to this step is that the improvement space can be converted into a specific optimization target, so that the subsequent optimization work is more specific and targeted. By taking the improvement space as the optimization goal, the desired performance level and improvement direction can be clearly defined, thus providing guidance for subsequent optimization work. Next, the load simulation analysis of the three-dimensional simulation space model of the hardware structure is carried out based on the performance optimization demand data of the hardware structure simulation model to simulate the load range of the model under actual working conditions. This analysis can help understand the pressure and load conditions that the model is subjected to in actual work, provide a basis for determining the constraints, and guide the determination of the constraints in the subsequent process. Finally, the constraints of the three-dimensional simulation space model of the hardware structure are determined based on the load limit range of the hardware structure simulation model to ensure that the model will not exceed the bearing range in actual work. The key to this step is to determine the constraints of the model design and optimization to ensure that the model can meet the requirements of actual working conditions during the design stage. By determining the constraints, it can be ensured that the optimized model has good stability and reliability, thereby improving the overall performance level.

[0134] Preferably, step S4 comprises the following steps:

[0135] Step S41: optimizing the structural parameter selection of the hardware material structure design parameter set based on the hardware structure model optimization target and the hardware structure model constraint conditions to generate a hardware material structure selection optimization design parameter set;

[0136] The embodiment of the present invention selects and optimizes the hardware material structure design parameter set by combining the hardware structure model optimization target and the hardware structure model constraint conditions obtained by analysis, so as to determine the combination of structural design parameters that need to be optimized from the hardware material structure design parameter set, and optimizes the determined combination of structural design parameters by using an appropriate optimization algorithm (such as a genetic algorithm, a particle swarm algorithm or a simulated annealing algorithm, etc.), and finally generates a hardware material structure selection optimization design parameter set.

[0137] Step S42: using finite element analysis software to perform structural topology simulation analysis on the hardware material structure selection optimization design parameter set to obtain a hardware material structure selection optimization simulation model;

[0138] The embodiment of the present invention uses finite element analysis software to perform structural topological simulation analysis on each optimized hardware material structure selection optimization design parameter set, and generates a hardware structure model corresponding to each optimization parameter set through topological simulation construction, and finally obtains a hardware material structure selection optimization simulation model.

[0139] Step S43: performing model verification calculation on the hardware material structure selection optimization simulation model to obtain a hardware material structure selection optimization model verification score;

[0140] The embodiment of the present invention uses a model verification calculation method to perform verification calculation on the hardware material structure selection optimization simulation model obtained after topological simulation analysis, so as to verify the accuracy and credibility of the hardware material structure selection optimization simulation model, and finally obtains the hardware material structure selection optimization model verification score value by calculating and comparing the simulation results with the verification score value of the actual situation.

[0141] Step S44: sorting and screening the hardware material structure selection optimization design parameter set based on the hardware material structure selection optimization model verification score to obtain an optimized combination of hardware material structure design parameters.

[0142] The embodiment of the present invention sorts the parameter combinations of the corresponding hardware material structure selection optimization design parameter set by combining the hardware material structure selection optimization model verification score value obtained by verification calculation, so as to sort the corresponding design parameter combinations according to their verification score values, and select and determine the optimal design parameter combination, and finally obtain the optimized combination of hardware material structure design parameters.

[0143] The present invention first optimizes the hardware material structure design parameter set by combining the optimization target of the hardware structure model and the constraint condition of the hardware structure model obtained by analysis, so as to comprehensively consider the design target and the constraint condition, and optimize the parameter selection from the aspects of the material, shape, size, etc. of the hardware. The key of this step is to ensure that the optimized design parameter set can improve the utilization rate of the material and the stability of the structure as much as possible while meeting the structural performance requirements, so as to provide a good basic data guarantee for the subsequent simulation analysis. Secondly, by using finite element analysis software to perform structural topology simulation analysis on the hardware material structure selection optimization design parameter set, it is possible to perform in-depth simulation analysis on the hardware material structure on the basis of considering the optimized design parameters. This analysis can help understand the stress condition, stress distribution and deformation condition of the structure, so as to evaluate the effectiveness and stability of the design. Through the obtained simulation model, the influence of the optimized design parameters on the structural performance can be better understood, so as to provide a basis for the subsequent model verification. Then, the model verification calculation is performed on the hardware material structure selection optimization simulation model to evaluate the accuracy and reliability of the optimization model. In this way, the degree of consistency between the simulation results and the actual situation can be compared to verify the accuracy and credibility of the hardware material structure selection optimization simulation model. The key to this step is to ensure that the optimization model can accurately reflect the actual structural performance, thereby providing a reliable basis for subsequent parameter optimization and design. Finally, the hardware material structure selection optimization design parameter set is sorted and screened based on the hardware material structure selection optimization model verification score. The purpose of this step is to determine the optimal design parameter combination. By comparing the verification score values ​​of different design parameter combinations, the design scheme that best meets the optimization objectives and constraints can be found. The key to this step is that it can provide a scientific method to select the optimal design parameter combination to maximize structural performance and achieve design goals.

[0144] 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.

[0145] 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 structural parameter optimization design method based on hardware, characterized in that: The following steps are involved: Step S1: Acquire the hardware material structure information data, and perform structural design parameter conversion processing on the hardware material structure information data to obtain a hardware material structure design parameter set; wherein step S1 includes the following steps: Step S11: collecting and processing the material structure information of the hardware to obtain the material structure information data of the hardware; Step S12: performing material structure attribute recognition analysis on the hardware material structure information data to obtain the hardware material structure attribute information data; Step S13: Based on the hardware material structure attribute information data, the hardware material structure information data is classified into material structure categories to obtain the structure information data of various hardware materials; wherein step S13 includes the following steps: Step S131: extracting the attribute characteristics of the hardware material structure attribute information data to obtain the hardware material structure attribute characteristic data; Step S132: performing standardization processing on the structural attribute characteristic data of the hardware material to obtain standardized characteristic data of the structural attribute of the hardware material; performing feature dimension reduction processing on the standardized characteristic data of the structural attribute of the hardware material to obtain reduced dimension characteristic data of the structural attribute of the hardware material; Step S133: performing discrimination evaluation and analysis on the dimension reduction feature data of the hardware material structure attribute to obtain discrimination degree data of the structural attribute features of each hardware material; Step S134: performing discrimination feature screening processing on the hardware material structure attribute dimensionality reduction feature data based on the discrimination degree data of each hardware material structure attribute feature to obtain hardware material structure attribute discrimination feature data; Step S135: performing material structure classification processing on the hardware material structure information data based on the hardware material structure attribute discrimination characteristic data to obtain the structure information data of various hardware materials; Step S14: Perform characteristic parameter analysis based on the structural information data of various hardware materials to obtain the structural type characteristic parameters of the hardware materials and the structural geometric characteristic parameters of the hardware materials; wherein step S14 includes the following steps: Step S141: performing structural type identification analysis on the structural information data of various hardware materials to obtain structural type status data of various hardware materials; Step S142: performing type characteristic statistical analysis on the structural information data of various hardware materials based on the structural type status data of various hardware materials to obtain structural type characteristic data of the hardware materials; performing type characteristic parameterization processing on the structural type characteristic data of the hardware materials to obtain structural type characteristic parameters of the hardware materials; Step S143: performing geometric shape recognition analysis on the structural information data of various hardware materials to obtain structural geometric shape status data of various hardware materials; Step S144: performing geometric feature statistical analysis on the structural information data of various hardware materials based on the structural geometric shape status data of various hardware materials to obtain structural geometric feature data of the hardware materials; performing geometric feature parameterization processing on the structural geometric feature data of the hardware materials to obtain structural geometric feature parameters of the hardware materials; Step S15: performing structural design parameter conversion processing on the hardware material structure type characteristic parameters and the hardware material structure geometric characteristic parameters to obtain a hardware material structure design parameter set; wherein step S15 includes the following steps: Step S151: using computer-aided design technology to perform feature parameter mapping conversion on the hardware material structure type feature parameters and the hardware material structure geometric feature parameters to obtain an initial design parameter set of the hardware material structure; Step S152: performing correlation evaluation and analysis on the hardware material structure type characteristic parameters and the hardware material structure geometric characteristic parameters to obtain the correlation degree coefficient between the type characteristic parameters and the geometric characteristic parameters; Step S153: performing an impact assessment analysis on the hardware material structure type characteristic parameters and the hardware material structure geometric characteristic parameters to obtain an impact degree coefficient between the type characteristic parameters and the geometric characteristic parameters; Step S154: performing potential association mining analysis on the hardware material structure type characteristic parameters and the hardware material structure geometric characteristic parameters based on the correlation degree coefficient and the influence degree coefficient between the type characteristic parameters and the geometric characteristic parameters, so as to obtain potential association relationship data between the hardware material structure characteristic parameters; Step S155: performing design parameter expansion processing on the initial design parameter set of the hardware material structure according to the potential correlation relationship data between the characteristic parameters of the hardware material structure, so as to obtain the hardware material structure design parameter set; Step S2: performing three-dimensional simulation modeling processing on the hardware material structure information data based on the hardware material structure design parameter set to generate a three-dimensional simulation space model of the hardware structure; performing structural performance simulation test on the three-dimensional simulation space model of the hardware structure to obtain performance test result data of the hardware structure simulation model; Step S3: Performing performance optimization demand analysis on the performance test result data of the hardware structure simulation model to obtain the performance optimization demand data of the hardware structure simulation model; determining the structural optimization target and constraint conditions of the three-dimensional simulation space model of the hardware structure based on the performance optimization demand data of the hardware structure simulation model to obtain the optimization target of the hardware structure model and the constraint conditions of the hardware structure model; wherein step S3 includes the following steps: Step S31: extracting performance indicators from the hardware structure simulation model performance test result data to obtain a key performance indicator parameter set of the hardware structure simulation model; Step S32: performing causal relationship mining analysis on the key performance indicator parameter set of the hardware structure simulation model to obtain causal relationship data between the key performance indicators of the hardware structure; Step S33: performing a quantitative analysis of the degree of compliance with the design target on the performance test result data of the hardware structure simulation model based on the key performance indicator parameter set of the hardware structure simulation model, and obtaining the degree of compliance with the performance target of the hardware structure simulation model; Step S34: performing performance optimization demand analysis on the performance test result data of the hardware structure simulation model according to the causal relationship data between the key performance indicators of the hardware structure and the performance target compliance data of the hardware structure simulation model to obtain the performance optimization demand data of the hardware structure simulation model; Step S35: determining the structural optimization target and constraint conditions of the three-dimensional simulation space model of the hardware structure based on the performance optimization requirement data of the hardware structure simulation model, so as to obtain the optimization target of the hardware structure model and the constraint conditions of the hardware structure model; wherein step S35 includes the following steps: Step S351: Based on the performance optimization requirement data of the hardware structure simulation model, the three-dimensional simulation space model of the hardware structure is subjected to structural performance shortcoming identification and analysis to obtain performance shortcoming data of the hardware structure simulation model; Step S352: performing improvement space mining analysis on the performance short board data of the hardware structure simulation model to obtain the performance improvement space of the hardware structure simulation model; Step S353: taking the performance improvement space of the hardware structure simulation model as the structure optimization target to obtain the hardware structure model optimization target; Step S354: performing model load simulation analysis on the three-dimensional simulation space model of the hardware structure based on the performance optimization requirement data of the hardware structure simulation model to obtain the load limit range of the hardware structure simulation model; Step S355: determining the constraint conditions of the three-dimensional simulation space model of the hardware structure based on the load limit range of the hardware structure simulation model to obtain the constraint conditions of the hardware structure model; Step S4: Based on the optimization target of the hardware structural model and the constraint conditions of the hardware structural model, the structural parameter optimization design of the hardware material structure parameter set is performed to obtain an optimized combination of the hardware material structure design parameters.

2. The structural parameter optimization design method based on hardware according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: converting the hardware material structure design parameter set into a model parameter format to obtain a hardware material structure space model format parameter set; Step S22: performing structural three-dimensional simulation modeling on the hardware material structure information data based on the hardware material structure space model format parameter set to obtain a three-dimensional space model of the main structure of the hardware material; Step S23: Performing design requirement statistical analysis on the hardware material structure design parameter set to obtain hardware material structure design requirement data; Step S24: adjusting and assembling the three-dimensional spatial model of the main structure of the hardware material according to the hardware material structure design requirement data to generate a three-dimensional simulation spatial model of the hardware structure; Step S25: Perform structural performance simulation test on the three-dimensional simulation space model of the hardware structure to obtain performance test result data of the hardware structure simulation model.

3. The structural parameter optimization design method based on hardware according to claim 2 is characterized in that: Step S25 includes the following steps: Step S251: performing a structural mechanics simulation test on the three-dimensional simulation space model of the hardware structure to obtain a simulation test result of the mechanical properties of the hardware structure; Step S252: performing a structural thermal simulation test on the three-dimensional simulation space model of the hardware structure to obtain a simulation test result of the thermal performance of the hardware structure; Step S253: merging the hardware structure mechanical performance simulation test results and the hardware structure thermal performance simulation test results to obtain hardware structure simulation model performance test result data.

4. The structural parameter optimization design method based on hardware according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: optimizing the structural parameter selection of the hardware material structure design parameter set based on the hardware structure model optimization target and the hardware structure model constraint conditions to generate a hardware material structure selection optimization design parameter set; Step S42: using finite element analysis software to perform structural topology simulation analysis on the hardware material structure selection optimization design parameter set to obtain a hardware material structure selection optimization simulation model; Step S43: performing model verification calculation on the hardware material structure selection optimization simulation model to obtain a hardware material structure selection optimization model verification score; Step S44: sorting and screening the hardware material structure selection optimization design parameter set based on the hardware material structure selection optimization model verification score to obtain an optimized combination of hardware material structure design parameters.

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