Optimal design method of electronic equipment chassis structure based on electromechanical, thermal and three-field coupling

Abstract

The present invention relates to the field of physical simulation technology, and in particular to an optimization design method for electronic equipment chassis structure based on electromechanical thermal three-field coupling. The method comprises the following steps: extracting structural features from electronic equipment chassis design data to form chassis structure data; performing component topology structure analysis to establish chassis component topology structure model; extracting design operating condition features to obtain operating condition data; integrating physical field simulation boundary conditions to generate boundary condition sets; constructing a multi-physical field coupled thermal flow model based on topology structure model and boundary conditions; performing operating condition simulation on the model to obtain simulation response data; using data to predict chassis working state to generate prediction data; evaluating chassis performance to obtain performance data; performing performance level classification on component topology model to obtain performance level data; optimizing chassis structure according to performance level data to generate optimized structure data. The present invention can improve the accuracy and reliability of equipment design.

Description

Technical Field

[0001] The invention relates to the technical field of physical simulation, and in particular to an electronic equipment chassis structure optimization design method based on electromechanical thermal three-field coupling. Background Art

[0002] With the rapid development of electronic equipment technology, the integration and power density of electronic equipment have increased significantly, which has put forward higher requirements for the design and optimization of chassis. Traditional chassis design methods usually focus on structural strength and appearance design, and fail to fully consider the comprehensive impact of the coupling of electromechanical, thermal and three-fields on equipment performance. This limitation has led to problems such as overheating, overload and insufficient thermal management in the actual use of the equipment, thus affecting the reliability and service life of the equipment. In the chassis design process of electronic equipment, the coupling problem of electromechanical, thermal and three-fields mainly involves electromagnetic field, mechanical field and thermal field. Electromagnetic field affects the electrical performance of electronic equipment, mechanical field involves the structural strength and vibration performance of the chassis, and thermal field determines the heat dissipation effect of the equipment. Traditional design methods usually deal with these field effects separately, and fail to consider the interaction between them, resulting in deformation caused by thermal expansion, increased electromagnetic interference and uneven heat dissipation in the chassis in actual applications. These problems not only affect the performance of the equipment, but may also lead to premature failure or safety hazards of the equipment. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide an electronic equipment chassis structure optimization design method based on electromechanical, thermal and three-field coupling to solve at least one of the above technical problems.

[0004] To achieve the above purpose, an electronic equipment chassis structure optimization design method based on electromechanical thermal three-field coupling includes the following steps:

[0005] Step S1: acquiring electronic equipment chassis design data, and extracting electronic equipment chassis structure features from the electronic equipment chassis design data, thereby obtaining electronic equipment chassis structure data; performing chassis component topology structure analysis based on the electronic equipment chassis structure data, thereby obtaining a chassis component topology structure model;

[0006] Step S2: extracting design condition features from the electronic equipment chassis design data to obtain design condition data, and integrating physical field simulation boundary conditions according to the design condition data to obtain a physical field simulation boundary condition set; constructing a multi-physical field coupling model according to the physical field simulation boundary condition set and the chassis component topology structure model to obtain a chassis multi-physical field coupling heat flow model;

[0007] Step S3: performing multi-physics field working condition simulation on the chassis multi-physics field coupled thermal flow model according to the physical field simulation boundary condition set, thereby obtaining simulation response data, and predicting the working state of the electronic equipment chassis according to the simulation response data, thereby obtaining chassis working state prediction data;

[0008] Step S4: performing chassis working performance evaluation on the chassis working state prediction data, thereby obtaining working performance evaluation data, and performing component working performance level classification on the chassis component topology structure model according to the working performance evaluation data, thereby obtaining chassis component working performance level data;

[0009] Step S5: Optimizing the structural performance of the electronic equipment chassis structure data based on the chassis component working performance level data, thereby obtaining optimized electronic equipment chassis structure data; and structurally optimizing and coupling the electronic equipment chassis design data based on the optimized electronic equipment chassis structure data, thereby obtaining optimized electronic equipment chassis design data.

[0010] The present invention can accurately establish a structural feature model of the chassis by acquiring and extracting design data. This lays a solid foundation for subsequent multi-physics field simulation. Topological structure analysis can reveal the relationship between the components of the chassis, which helps to understand how the components jointly affect the overall performance of the chassis. The obtained structural data and topological model provide a basis for further structural optimization, so that the design can better meet actual needs. Working condition feature extraction can help accurately understand the working environment of the chassis in actual use, including factors such as temperature and load, so as to more accurately set the simulation boundary conditions. By integrating the physical field simulation boundary conditions, a multi-physics field coupling model that comprehensively considers electromagnetic fields, mechanical fields and thermal fields can be constructed. This coupling model can more realistically simulate multiple effects in the actual use environment. Through multi-physics field simulation, the response of the chassis under different working conditions can be simulated in a virtual environment to predict its working state. This can discover potential problems in the design in advance, such as overheating or structural stress concentration. The simulation response data helps to evaluate the performance of the chassis under actual working conditions and provide a scientific basis for subsequent improvements. By evaluating the working state prediction data, the working performance of the chassis in actual use can be understood and components with insufficient performance can be identified. Performance grading helps identify which components perform well or poorly under specific conditions, so that targeted design improvements or component replacements can be made. By optimizing the chassis structural data, the overall performance of the chassis in actual applications can be improved. For example, optimizing the heat dissipation path to solve thermal management problems, or adjusting the structure to improve seismic performance. The optimized structural design can effectively solve previously discovered problems, improve the reliability and life of the overall design, and reduce maintenance costs. In summary, these steps solve the three-field coupling problem that is not fully considered in traditional design methods by systematically integrating and optimizing various aspects of chassis design data. They not only improve the scientificity and accuracy of the design, but also improve the reliability and service life of the equipment. Through this comprehensive design approach, the chassis of electronic equipment can better cope with various challenges in actual work, thereby ensuring stable operation and efficient heat dissipation of the equipment.

[0011] Optionally, step S1 specifically includes:

[0012] Step S11: acquiring electronic equipment chassis design data, and extracting electronic equipment chassis structural features from the electronic equipment chassis design data, thereby obtaining electronic equipment chassis structural data;

[0013] Step S12: classifying the electronic equipment chassis structure data by structure type, thereby obtaining chassis circuit structure data and chassis component structure data;

[0014] Step S13: performing chassis circuit topology analysis according to the chassis circuit structure data, thereby obtaining a chassis circuit topology model;

[0015] Step S14: performing chassis component topology analysis on the chassis component structure data, thereby obtaining a chassis component topology model;

[0016] Step S15: merging the chassis circuit topology structure model and the chassis component topology structure model to obtain a chassis component topology structure model.

[0017] The present invention can ensure that the data used is accurate and representative by obtaining the design data of the electronic device chassis. This step is the basis for subsequent analysis. By extracting the structural features of the chassis, key features such as the basic geometric shape, opening position, and partition position of the chassis can be identified. This provides necessary information for further structural analysis. The process of converting design data into structural data helps to standardize the data format, laying a good foundation for subsequent processing and analysis. Classifying the chassis structural data helps to systematically organize and manage different types of structural information. For example, the internal circuit layout of the chassis and the physical component layout of the chassis can be distinguished. Through classification, the circuit structure and component structure of the chassis can be targeted for analysis, thereby improving the accuracy and efficiency of the analysis. The classified structural data can be used to optimize the chassis design, such as improving circuit wiring or adjusting component layout to improve the performance and heat dissipation effect of the equipment. Through circuit topology analysis, potential problems in circuit design, such as unreasonable current path or signal interference, can be identified, which helps to optimize circuit design. Establishing a circuit topology model can help locate problems faster during fault diagnosis and reduce maintenance time and cost. It can verify whether the circuit design meets the design specifications and requirements, and improve the reliability and stability of the design. By analyzing the component topology, the arrangement of components can be improved, space utilization can be optimized, and the overall design quality of the chassis can be improved. The coordination and cooperation between different components can be ensured to avoid interference or conflict, and the overall performance of the chassis can be improved. The analysis results help to discover and correct deficiencies in the design, thereby improving the design of the chassis to make it more in line with actual needs. By merging the circuit topology model and the component topology model, a comprehensive topological view of the chassis can be obtained, thereby fully understanding the overall design of the chassis. The merged model can reveal the relationship between the internal structure and circuit of the chassis, and help optimize the overall system design, including space utilization, electrical performance, and thermal management. Comprehensive design verification and simulation can be performed to ensure the performance and stability of the chassis in actual use, thereby improving the reliability and market competitiveness of the product.

[0018] Optionally, step S13 is specifically:

[0019] Step S131: extracting electrical path features from chassis circuit structure data, thereby obtaining chassis electrical path data;

[0020] Step S132: performing electrical node distribution statistics on the chassis electrical path data, thereby obtaining electrical node distribution data;

[0021] Step S133: integrating the node path connection mode of the chassis electrical path data according to the electrical node distribution data, thereby obtaining node series connection path data and node parallel connection path data;

[0022] Step S134: performing a series connection path topology structure analysis according to the node series connection path data, thereby obtaining a series path topology structure model; performing a parallel connection path topology structure analysis according to the node parallel connection path data, thereby obtaining a parallel path topology structure model;

[0023] Step S135: merging the series path topology structure model and the parallel path topology structure model into circuit connection topology structures, thereby obtaining a chassis circuit topology structure model.

[0024] The electrical path feature extraction in the present invention helps to accurately capture the basic structure of the electrical path inside the chassis, lay a data foundation for subsequent analysis, and ensure the accuracy of the model. Electrical node distribution statistics provide distribution information of nodes in the chassis, which helps to identify important electrical connection points and support more effective circuit design and troubleshooting. The node path connection mode of the electrical path data is integrated so that series and parallel connections can be clearly distinguished, which helps to better understand the working mechanism of the circuit. Through the analysis of the series and parallel connection path topology structure, detailed circuit topology models can be generated, which help to optimize circuit design and improve performance and reliability. The series and parallel path topologies are merged to form an overall topology model of the chassis circuit, which is convenient for a comprehensive understanding of the circuit system, improves the accuracy and efficiency of the design, and helps to optimize and troubleshoot the overall circuit.

[0025] Optionally, step S14 is specifically:

[0026] Step S141: extracting connection structure features and component material features from chassis component structure data, thereby obtaining chassis component connection structure data and chassis component material data;

[0027] Step S142: performing connection structure similarity calculation on the chassis component connection structure data, thereby obtaining connection structure similarity data;

[0028] Step S143: integrating the chassis component structure data based on the connection structure similarity data to obtain chassis component connection relationship data;

[0029] Step S144: performing chassis component topology analysis according to chassis component connection relationship data, thereby obtaining a component topology model;

[0030] Step S145: performing material property analysis on chassis component material data to obtain component material property data;

[0031] Step S146: filling the component topology structure model with material characteristic parameters according to the component material characteristic data, thereby obtaining the chassis component topology structure model.

[0032] By extracting the connection structure characteristics of the chassis components, the present invention can understand the connection mode and position between the components in detail, which provides basic data for subsequent structural analysis and optimization. Obtaining the material properties of the components helps to evaluate the mechanical properties, durability and cost of different materials. This step provides the necessary basis for material selection and structural design optimization. By calculating the similarity of the connection structure, components with similar connection characteristics can be identified. This helps to achieve the standardization of components in chassis design, reduce design complexity and production costs. Similarity calculation can also be used to compare different design schemes, so as to select the optimal design to improve the overall performance and reliability of the chassis. Integrating connection relationship data can help better understand how chassis components work together and identify potential design defects or improvement opportunities. By integrating the connection relationship, the arrangement and connection mode of the components can be optimized, the strength and stability of the chassis can be improved, and the difficulty of manufacturing and assembly can be reduced. Analyzing the topological structure can generate a detailed model of the chassis components, which helps to visualize and understand the spatial relationship and connection mode between components. Through topological structure analysis, the rationality of the design can be verified, potential interference problems or structural weaknesses can be identified, and necessary adjustments and improvements can be made. Through material property analysis, we can gain in-depth understanding of the performance of different materials in actual applications, including strength, heat resistance, corrosion resistance, etc. It provides data support for selecting the most suitable materials, thereby optimizing the overall performance, durability and cost-effectiveness of chassis components. Filling material property parameters into the topology model enables the model to more realistically reflect the performance of actual materials. This is crucial for evaluating and predicting the performance of components in actual use. By combining material properties and topology, the design of chassis components can be optimized to achieve higher performance and better material utilization efficiency.

[0033] Optionally, step S2 specifically includes:

[0034] Step S21: extracting design operating condition features from the electronic equipment chassis design data, thereby obtaining design operating condition data;

[0035] Step S22: integrating the working condition simulation boundary conditions according to the design working condition data, thereby obtaining the working condition simulation boundary conditions; integrating the component material simulation boundary conditions according to the chassis component topology structure model, thereby obtaining the material simulation conditions;

[0036] Step S23: performing multi-physics field simulation boundary condition coupling on the working condition simulation boundary conditions and the material simulation conditions, thereby obtaining a set of physical field simulation boundary conditions;

[0037] Step S24: meshing the chassis geometry model of the chassis component topology structure model, thereby obtaining a physical field simulation mesh model;

[0038] Step S25: construct a multi-physics field coupling model according to the physical field simulation boundary condition set and the physical field simulation grid model, so as to obtain a multi-physics field coupling heat flow model of the chassis.

[0039] The present invention extracts the design operating condition characteristics to ensure that the simulation model accurately reflects the actual usage, thereby obtaining real operating condition data and providing basic data support for subsequent simulations. Integrating the operating condition simulation boundary conditions and material simulation conditions helps to ensure that the boundary conditions and material properties in the simulation process are complete and consistent, and improves the accuracy and reliability of the simulation. The coupling of multi-physics field simulation boundary conditions can comprehensively consider the interaction of multiple physical fields, provide more comprehensive simulation results, and help evaluate the performance of the chassis in complex environments. Meshing the chassis geometry model can convert complex geometric shapes into computable grid structures, improving the accuracy and efficiency of simulation calculations. Constructing a multi-physics field coupled heat flow model can comprehensively simulate the heat flow of the chassis and the coupling effects of other physical fields, which helps to evaluate the thermal management performance of the chassis under actual working conditions and optimize the design to meet usage requirements.

[0040] Optionally, step S25 is specifically:

[0041] Selecting simulation boundary conditions for the physical field simulation boundary condition set, thereby obtaining mechanical simulation boundary conditions, thermal flow simulation boundary conditions, and electrical simulation boundary conditions;

[0042] Performing model mesh division on the physical field simulation mesh model to obtain an electrical mesh model and a mechanical mesh model;

[0043] Performing mechanical physical field finite element simulation on the mechanical mesh model according to mechanical simulation boundary conditions, thereby obtaining mechanical field simulation data;

[0044] Performing electric physical field finite element simulation on the electric grid model according to the electric simulation boundary conditions, thereby obtaining electric field simulation data;

[0045] Performing thermal flow physics field finite element simulation on the physical field simulation grid model according to thermal flow simulation boundary conditions, thereby obtaining thermal flow simulation data;

[0046] Perform multi-physics field simulation coupling on mechanical field simulation data, electric field simulation data and thermal flow simulation data, so as to obtain multi-physics field coupling simulation data;

[0047] Based on the multi-physics field coupling simulation data, a multi-physics field coupling model is constructed for the physical field simulation grid model, so as to obtain a multi-physics field coupling thermal flow model of the chassis.

[0048] The present invention can ensure that the simulation model is consistent with the actual application conditions by selecting appropriate mechanical, thermal flow and electrical simulation boundary conditions from the physical field simulation boundary condition set. This helps to accurately predict the performance of the chassis under the actual working environment. Determining the correct boundary conditions helps to discover potential problems in the early stage and optimize the design, thereby improving the reliability and performance of the final product. Dividing the physical field simulation grid model into an electrical grid model and a mechanical grid model helps to refine the grid structure of the model, thereby improving the calculation accuracy and details of the simulation. Through appropriate grid division, the calculation efficiency can be improved while ensuring accuracy, reducing the consumption of computing resources and time overhead. Finite element simulation of the mechanical grid model according to the mechanical simulation boundary conditions can provide data such as stress and deformation of the chassis structure under various loads, helping designers to evaluate its mechanical properties. Analysis of mechanical field simulation data can find structural weaknesses and optimize them to improve the strength and durability of the chassis. Finite element simulation of the electrical grid model according to the electrical simulation boundary conditions can evaluate the electric field distribution, current density, etc. inside the chassis to ensure that the electrical components are within the normal working range. Electrical simulation data helps analyze the thermal effects caused by current, provides data support for thermal management design, and avoids overheating problems. By performing thermal flow simulation on the physical field simulation grid model, the temperature distribution data inside the chassis can be obtained to help design a reasonable heat dissipation solution and avoid high temperature damage to electronic equipment. Thermal flow simulation data can predict the thermal behavior of the chassis under high load, thereby optimizing the heat dissipation design and improving the reliability and stability of the equipment. By coupling the data of mechanical fields, electric fields, and thermal flow fields, the interaction of multiple physical effects can be comprehensively considered to provide a more comprehensive performance prediction. Coupling simulation can identify interaction effects that may be ignored in a single physical field simulation, thereby improving the accuracy and reliability of the overall simulation. The coupling model built based on multi-physics field coupling simulation data can comprehensively consider the structural, electrical, and thermal management performance of the chassis and provide a comprehensive performance prediction tool. By analyzing the multi-physics field coupling model, the chassis design can be optimized, its comprehensive performance in the actual working environment can be improved, and product quality and reliability can be improved.

[0049] Optionally, step S3 specifically includes:

[0050] Step S31: extracting the working condition simulation boundary conditions according to the physical field simulation boundary condition set, thereby obtaining the working condition simulation boundary conditions;

[0051] Step S32: performing multi-physics field working condition simulation on the chassis multi-physics field coupled thermal flow model according to the working condition simulation boundary conditions, thereby obtaining working condition simulation data, and performing physical field coupling effect calculation based on the working condition simulation data, thereby obtaining simulation response data;

[0052] Step S33: extracting chassis working mode features from the electronic equipment chassis design data to obtain chassis working mode data, and integrating chassis working status data from the chassis working mode data to obtain chassis working status data;

[0053] Step S34: performing working state association on the chassis working state data and the simulation response data, thereby obtaining working state association response data;

[0054] Step S35: constructing a chassis working state prediction model based on the working state associated response data, and predicting the working state of the electronic device chassis on the simulation response data through the chassis working state prediction model, thereby obtaining chassis working state prediction data.

[0055] Accurately extracting boundary conditions in the present invention helps to improve the accuracy of the simulation model and make it closer to actual use. The impact of different working conditions on the chassis can be better understood, and the design can be optimized to adapt to various working environments. Virtual simulation reduces the need for physical experiments and saves experimental and material costs. Multi-physics field simulation takes into account the coupling effects of multiple factors such as heat, fluid, and structure, and can comprehensively evaluate the performance of the chassis under different working conditions. By calculating the coupling effect, the performance of the chassis in actual applications can be more accurately predicted to avoid design defects. Effectively evaluate the heat flow and heat dissipation effects, help design more efficient heat dissipation systems, and improve the overall performance of the equipment. Extracting and integrating working mode features helps to systematically understand the behavior of the chassis under different working conditions. By integrating feature data, the state of the chassis under various working modes can be more comprehensively analyzed to improve the adaptability and flexibility of the design. Provide basic data for further decision-making, helping engineers to formulate more reasonable design and maintenance strategies. Through correlation analysis, the parameters that have the greatest impact on the working state of the chassis can be identified and targeted optimization can be performed. Increasing the depth of understanding of the operating state of the chassis helps to accurately predict the performance and reliability of the chassis. A better understanding of the relationship between the working state and the simulation response will help with early diagnosis and prediction of faults. The established prediction model can more accurately predict the working state of the chassis, supporting early warning and maintenance. Through the application of the prediction model, the working state of the chassis can be monitored in real time to improve the reliability and stability of the system. It helps to formulate more effective maintenance and management strategies, reduce the occurrence of unexpected failures, and reduce maintenance costs.

[0056] Optionally, step S4 is specifically:

[0057] Step S41: dividing the chassis working state prediction data into chassis working stage states, thereby obtaining chassis working stage state prediction data;

[0058] Step S42: Calculate the component temperature state stage proportion for the chassis working stage state prediction data, thereby obtaining component high temperature state stage proportion data, and cluster high-proportion high temperature state components according to the component high temperature state stage proportion data, thereby obtaining high-proportion high temperature state component data;

[0059] Step S43: extracting component material characteristics from the chassis component topology structure model to obtain component material characteristic data, and classifying the component material characteristic data into heat-resistant components to obtain high heat-resistant component data and low heat-resistant component data;

[0060] Step S44: performing component intersection calculation on high-temperature state high-ratio component data and high-heat-resistant component data, thereby obtaining high-performance component data; performing component intersection calculation on high-temperature state high-ratio component data and low-heat-resistant component data, thereby obtaining low-performance component data;

[0061] Step S45: merging the high performance component data and the low performance component data to obtain performance evaluation data;

[0062] Step S46: classifying the chassis component topology model into component working performance levels according to the working performance evaluation data, thereby obtaining chassis component working performance level data.

[0063] The present invention can understand the performance of the chassis in different working stages in detail by dividing the working state of the chassis into stages. This helps to accurately identify potential problems in each stage of the chassis. Dividing different working stages enables the prediction model to analyze the working state of the chassis more accurately, reduce prediction errors, and improve the reliability of prediction. It is possible to identify possible failure modes in advance before the working state of the chassis deteriorates, and take measures in advance for maintenance or adjustment. By calculating the stage proportion of component temperature, components with a high proportion in high temperature state can be identified, and these components are usually the focus of possible failure. Identifying components with a high proportion in high temperature state helps to optimize the heat dissipation design of the chassis, improve the heat dissipation performance in a targeted manner, and reduce the risk of overheating. Clustering of components with a high proportion of high temperature state can help engineers focus on the maintenance or replacement of these components and improve maintenance efficiency. Extracting the material properties of the components can evaluate their heat resistance and provide basic data for further analysis. Classifying components according to heat resistance (high heat resistance and low heat resistance) can help formulate corresponding use and maintenance strategies and optimize resource allocation. Understanding the heat resistance of components can guide the selection of appropriate materials in the design stage and improve the overall reliability and durability of the chassis. Through intersection calculation, components can be divided into high performance and low performance categories, which helps to clarify which components perform well under high temperature conditions and which perform poorly. The data of high performance components can be used to optimize design and production to ensure the stability and reliability of the chassis in high temperature environments. After identifying low performance components, measures can be taken to replace or improve them to reduce the failure rate in actual operation. By merging the data of high performance and low performance components, the overall performance of the chassis can be comprehensively evaluated to form a complete performance evaluation report. By comprehensively evaluating the data, the overall advantages and disadvantages of the system can be discovered, providing a basis for subsequent optimization and improvement. The merged data can provide decision makers with detailed performance evaluations to support more scientific decision-making and planning. By dividing components by performance level, the performance status of each component can be clearly understood, providing guidance for system maintenance and upgrades. Managing components according to performance levels helps to adopt different management and maintenance strategies for components of different levels during operation. By dividing components into performance levels, the reliability and stability of the overall system can be improved to ensure the good performance of the chassis under various working conditions.

[0064] Optionally, step S46 is specifically:

[0065] Step S461: performing performance component distribution statistics according to the work performance evaluation data, thereby obtaining high work performance component distribution data and low work performance component distribution data;

[0066] Step S462: performing component area division on the chassis component topology structure model, thereby obtaining a chassis component area division model;

[0067] Step S463: Associating the high-performance component distribution data and the chassis component area division model with the high-performance component area, thereby obtaining high-performance component area data; and associating the low-performance component distribution data and the chassis component area division model with the low-performance component area, thereby obtaining low-performance component area data;

[0068] Step S464: Perform component area integration on the high-performance level component area data and the low-performance level component area data, thereby obtaining chassis component working performance level data.

[0069] The present invention can accurately identify which components have excellent performance and which components have poor performance by statistically analyzing the performance data of each component. This provides basic data support for subsequent optimization. With the distribution data of high-performance and low-performance components, a data-driven decision-making basis can be provided for optimizing the chassis design, helping engineers to make targeted improvements. The component area division makes the internal structure of the chassis more systematic, which is convenient for detailed analysis and optimization of each area. By dividing the area, components can be arranged in each area in a targeted manner, heat dissipation and power distribution can be optimized, and the performance and reliability of the overall chassis can be improved. Associating high-performance components with the area where they are located can optimize the layout of these components and ensure that they are in the best position in the chassis to maximize their performance. By associating low-performance components with areas, areas that need to be improved can be identified, and targeted design adjustments can be made, thereby improving the performance of the overall system. By integrating the regional data of high-performance and low-performance components, comprehensive performance level data can be obtained. This helps to perform overall optimization and improve the working efficiency and stability of the chassis. The integrated data can be used to formulate a more optimized chassis design plan, balance the layout of high-performance components and low-performance components, and ensure that the system can perform well under various load conditions.

[0070] Optionally, step S5 specifically includes:

[0071] Step S51: extracting low-performance-level component regions from chassis component working performance level data, thereby obtaining low-performance-level component region data;

[0072] Step S52: extracting regional associated component material features and regional component distribution features from the low performance level component regional data, thereby obtaining regional associated component material data and regional component distribution data;

[0073] Step S53: optimizing the component structure material of the electronic device chassis structure data according to the regional component material data, thereby obtaining component material optimization data;

[0074] Step S54: optimizing the component distribution of the electronic equipment chassis structure data according to the regional component distribution data, thereby obtaining component distribution optimization data;

[0075] Step S55: performing component optimization integration on the component material optimization data and the component distribution optimization data, thereby obtaining optimized electronic equipment chassis structure data;

[0076] Step S56: structurally optimizing and coupling the electronic equipment chassis design data according to the optimized electronic equipment chassis structure data, thereby obtaining optimized electronic equipment chassis design data.

[0077] The present invention extracts regional data of low-performance grade components, which helps to identify and focus on improving areas with poor performance, and provides basic data for further analysis. Extracting material characteristics and distribution characteristic data of regional associated components enables a more accurate understanding of the actual use of materials and components, providing a detailed basis for optimization. Through component structural material optimization, the overall durability and performance of the chassis can be improved, material waste can be reduced, and cost-effectiveness can be improved. Optimizing component distribution can improve the utilization efficiency of the internal space of the chassis, enhance heat dissipation performance, reduce interference, and improve the rationality of the overall design. Integrating component material optimization and distribution optimization data can comprehensively improve the overall performance of the chassis structure, so that it can achieve the best balance in function and design. The final structural optimization coupling can ensure that the optimized design data is effectively applied, further improving the comprehensive performance and reliability of the chassis design. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] 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:

[0079] Figure 1 It is a schematic diagram of the steps of the electronic equipment chassis structure optimization design method based on the electromechanical thermal three-field coupling of the present invention;

[0080] Figure 2 Detailed step flow diagram of step S1 in the present invention;

[0081] Figure 3 Detailed step flow diagram of step S2 in the present invention;

[0082] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0083] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0084] 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.

[0085] 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.

[0086] To achieve this, please refer to Figures 1 to 3 The present invention provides an electronic equipment chassis structure optimization design method based on electromechanical thermal three-field coupling, the method comprising the following steps:

[0087] Step S1: acquiring electronic equipment chassis design data, and extracting electronic equipment chassis structure features from the electronic equipment chassis design data, thereby obtaining electronic equipment chassis structure data; performing chassis component topology structure analysis based on the electronic equipment chassis structure data, thereby obtaining a chassis component topology structure model;

[0088] In this embodiment, the 3D design data of the electronic device chassis is imported from the CAD software. The data format can be STEP or IGES, which can fully describe the geometry and structure of the chassis. The key structural features of the chassis are extracted using computer vision algorithms, such as the boundaries, hole positions, distribution of ventilation holes, internal support structures, etc. of the chassis. This can be achieved through algorithms such as edge detection and feature point matching. The extracted structural features are input into the topology optimization tool to analyze the component layout and connection mode of the chassis. For example, the arrangement of the chassis components is analyzed using a topology optimization algorithm (such as the topology optimization tool TOSCA), thereby generating a chassis component topology structure model. When using CAD data, SolidWorks can be used to obtain the design data, and then the image processing toolbox in MATLAB is used to extract the structural features. For topological structure analysis, the topology optimization module of ANSYS Workbench can be used for analysis to obtain the final chassis component topology structure model.

[0089] Step S2: extracting design condition features from the electronic equipment chassis design data to obtain design condition data, and integrating physical field simulation boundary conditions according to the design condition data to obtain a physical field simulation boundary condition set; constructing a multi-physical field coupling model according to the physical field simulation boundary condition set and the chassis component topology structure model to obtain a chassis multi-physical field coupling heat flow model;

[0090] In this embodiment, feature extraction is performed based on the use environment and working conditions (such as operating temperature range, air flow rate, equipment heat generation, etc.) of the electronic equipment chassis. These data can be obtained through the technical specifications of the equipment or on-site tests. Physical field boundary conditions, such as heat conduction boundary conditions, fluid flow boundary conditions, etc., are set in the simulation software. For example, in the thermal simulation model, the chassis surface is set as a convection boundary, and the heat source of the internal component is a quantitative heat source. Using multi-physics simulation software such as COMSOL Multiphysics, the integrated boundary conditions are combined with the chassis component topology model to construct a thermal-fluid coupling model. This includes coupled analysis of heat conduction, convection and radiation. Working condition feature extraction can use a data acquisition system (such as LabVIEW) to record the operating data of the equipment under actual working conditions. When establishing a coupling model in COMSOL, its built-in modules (such as heat conduction modules and fluid flow modules) are used to configure and calculate the model.

[0091] Step S3: performing multi-physics field working condition simulation on the chassis multi-physics field coupled thermal flow model according to the physical field simulation boundary condition set, thereby obtaining simulation response data, and predicting the working state of the electronic equipment chassis according to the simulation response data, thereby obtaining chassis working state prediction data;

[0092] In this embodiment, the operating condition characteristics are input into the multi-physics field coupling model, and multi-physics field simulation is performed to obtain response data such as the temperature distribution and heat flux density inside the chassis. By analyzing the simulation response data, the performance of the chassis under different working conditions is predicted. For example, the heat distribution data is used to predict whether the chassis will have an overheating problem or whether the temperature of certain components exceeds the safe range. ANSYS Fluent and ANSYS Mechanical can be used to perform heat flow and structural response simulations to obtain simulation response data. The simulation results can be input into a prediction model, such as using regression analysis or a machine learning model (such as a support vector machine) to predict the working state.

[0093] Step S4: performing chassis working performance evaluation on the chassis working state prediction data, thereby obtaining working performance evaluation data, and performing component working performance level classification on the chassis component topology structure model according to the working performance evaluation data, thereby obtaining chassis component working performance level data;

[0094] In this embodiment, the thermal performance of the chassis is evaluated based on the chassis working state prediction data. The evaluation indicators may include temperature uniformity, heat conduction efficiency, etc. The chassis components are divided into performance levels according to the evaluation results. For example, the components are divided into three categories: A, B, and C, representing high, medium, and low performance levels, respectively. The evaluation can use performance evaluation software (such as Matlab) to analyze the thermal response data. Standardized test methods, such as IPC standards, can be used to divide the components into performance levels.

[0095] Step S5: Optimizing the structural performance of the electronic equipment chassis structure data based on the chassis component working performance level data, thereby obtaining optimized electronic equipment chassis structure data; and structurally optimizing and coupling the electronic equipment chassis design data based on the optimized electronic equipment chassis structure data, thereby obtaining optimized electronic equipment chassis design data.

[0096] In this embodiment, based on the performance evaluation data, the chassis structure design is adjusted to optimize its performance. For example, the heat dissipation channel layout in the chassis is modified or a heat sink is added. The optimized structural data is fed back to the original design and coupled with the structural optimization to ensure that the optimized design can be implemented in actual production. The structure can be optimized using a topology optimization algorithm (such as HyperWorks). After optimization, the design data is updated using CAD software, and verification tests are performed to ensure performance improvements.

[0097] Optionally, step S1 specifically includes:

[0098] Step S11: acquiring electronic equipment chassis design data, and extracting electronic equipment chassis structural features from the electronic equipment chassis design data, thereby obtaining electronic equipment chassis structural data;

[0099] In this embodiment, a 3D scanner is used to scan the electronic device chassis to obtain the three-dimensional model data of the chassis. The scanned data is analyzed by a computer vision algorithm to extract the geometric features of the chassis, including the shape of the shell, the internal support structure, the hole position, and the precise position of the mounting hole. For example, the 3D point cloud data is processed using tools such as MeshLab or CloudCompare to obtain the shell and internal structure model of the chassis. These extracted data include the size of the chassis, the position of the support frame and their relative relationship, forming the chassis structure data.

[0100] Step S12: classifying the electronic equipment chassis structure data by structure type, thereby obtaining chassis circuit structure data and chassis component structure data;

[0101] In this embodiment, the extracted chassis structure data is divided into chassis circuit structure data and chassis component structure data using a data classification algorithm. First, the circuit board area (such as the mainboard, slot location, etc.) is identified through image recognition technology and the circuit structure data is generated. Next, the component area of ​​the chassis (such as the radiator, bracket, hard disk bracket, etc.) is identified and the component structure data is generated. Machine learning models such as support vector machines (SVM) or deep learning networks can be used to classify the data to ensure accurate separation of circuit and component data.

[0102] Step S13: performing chassis circuit topology analysis according to the chassis circuit structure data, thereby obtaining a chassis circuit topology model;

[0103] In this embodiment, a topological structure analysis is performed on the chassis circuit structure data to construct a chassis circuit topological structure model. Circuit design software (such as Altium Designer or KiCad) is used to import circuit layout data to analyze the connection relationship and topological structure of the circuit. The topological structure model of the circuit is obtained by mapping each node (such as chip, connector) and edge (such as wire, path) in the circuit to a graph model. The model shows the overall layout of the circuit, including power distribution and signal flow path.

[0104] Step S14: performing chassis component topology analysis on the chassis component structure data, thereby obtaining a chassis component topology model;

[0105] In this embodiment, the chassis component structure data is subjected to topological structure analysis and modeling is performed using CAD software (such as SolidWorks or AutoCAD). The chassis component topological structure model is generated by analyzing the installation position, connection relationship and spatial layout of the components. For example, the installation point and fixing method of each component are identified, and this information is converted into a geometric model or topological diagram to show the relative position and connection method between the components. This step includes modeling the spatial relationship of the components, such as how the bracket fixes the circuit board.

[0106] Step S15: merging the chassis circuit topology structure model and the chassis component topology structure model to obtain a chassis component topology structure model.

[0107] In this embodiment, the chassis circuit topology model and the chassis component topology model are merged, and the data of the two are integrated using a model merging tool (such as Fusion 360 or ParaView). First, the circuit topology model and the component topology model are aligned to ensure that they are in the same coordinate system. Then, the circuit model and the component model are combined through data fusion technology to form a comprehensive chassis topology model. This comprehensive model shows the comprehensive layout of the circuit and components, which is convenient for further optimization and verification.

[0108] Optionally, step S13 is specifically:

[0109] Step S131: extracting electrical path features from chassis circuit structure data, thereby obtaining chassis electrical path data;

[0110] In this embodiment, in the chassis circuit structure data, the circuit diagram is parsed by using an electronic design automation tool to extract the characteristic information of all electrical paths, such as resistance values, wire connection points, and layout of electrical components. Then, these characteristic data are converted into structured electrical path data, including the starting point and end point of each path, and the electrical connection relationship between the paths.

[0111] Step S132: performing electrical node distribution statistics on the chassis electrical path data, thereby obtaining electrical node distribution data;

[0112] In this embodiment, the electrical node distribution statistics are performed on the chassis electrical path data. The distribution of electrical nodes needs to be counted, including the number of connections, location and connected paths of each node. By analyzing the electrical path data, a distribution model of electrical nodes is established, and the node distribution is presented in a data table or graphical form. For example, a heat map of the node can be generated to show the density of the node and the current load distribution.

[0113] Step S133: integrating the node path connection mode of the chassis electrical path data according to the electrical node distribution data, thereby obtaining node series connection path data and node parallel connection path data;

[0114] In this embodiment, the node path connection mode of the chassis electrical path data is integrated according to the electrical node distribution data. The node distribution data is applied to the electrical path data to identify the connection mode between the nodes. Specifically, the connection relationship between the nodes is analyzed to distinguish which paths are connected in series and which are connected in parallel. According to these analysis results, the node series connection path data and the node parallel connection path data are classified and sorted. For example, for the series connection path, the order of all nodes of each series path is recorded, and for the parallel connection path, the parallel connection relationship of all nodes of the parallel path is recorded.

[0115] Step S134: performing a series connection path topology structure analysis according to the node series connection path data, thereby obtaining a series path topology structure model; performing a parallel connection path topology structure analysis according to the node parallel connection path data, thereby obtaining a parallel path topology structure model;

[0116] In this embodiment, the topological structure analysis of the serial connection path is performed based on the node serial connection path data. In specific implementation, a topological structure model of the serial path is constructed using a topological analysis tool, and each serially connected path and node is marked. A similar analysis is performed on the node parallel connection path data to generate a topological structure model of the parallel path. These models can be displayed using graphical tools, such as network diagrams, to clearly represent the topological structure of the path.

[0117] Step S135: merging the series path topology structure model and the parallel path topology structure model into circuit connection topology structures, thereby obtaining a chassis circuit topology structure model.

[0118] In this embodiment, the circuit connection topology structure of the series path topology structure model and the parallel path topology structure model is merged. The two topology models are integrated to generate a comprehensive chassis circuit topology structure model. This model should comprehensively consider the series and parallel connection modes to generate a complete circuit connection diagram. The circuit design software can be used for merging, automatically processing the overlap of connection points and path integration, and finally obtaining a comprehensive topology structure model of the chassis circuit. This process helps to understand the layout of the entire circuit, optimize the design and troubleshoot.

[0119] Optionally, step S14 is specifically:

[0120] Step S141: extracting connection structure features and component material features from chassis component structure data, thereby obtaining chassis component connection structure data and chassis component material data;

[0121] In this embodiment, connection structure data and material characteristics are extracted from the CAD file of the chassis component. Connection structure characteristics include screw hole positions, welding points, etc.; material characteristics include material type (such as aluminum alloy, steel) and thickness. In specific implementations, software tools (such as SolidWorks) can be used to automatically identify and extract these data and store them as structure feature tables and material attribute tables. Assume that the chassis component data includes connection holes, screw holes and other interface information. First, use CAD tools to extract the connection structure characteristics of each component of the chassis, such as the diameter, position and interconnection method of the bolt holes. Next, use material science analysis software to extract the material properties of each component, including material type (such as aluminum alloy, plastic) and its mechanical properties (such as tensile strength, hardness). Finally, these features are integrated to obtain detailed chassis component connection structure data and material data, such as the precise size of the connection hole and the physical property data of the material.

[0122] Step S142: performing connection structure similarity calculation on the chassis component connection structure data, thereby obtaining connection structure similarity data;

[0123] In this embodiment, the extracted chassis component connection structure data is input into a similarity calculation algorithm. A similarity evaluation method based on geometric shapes, such as Hausdorff distance or Euclidean distance, is used to calculate the similarity of the connection structures between different components. Assuming that the chassis includes multiple modules, the similarity of the shape and position of the connection holes between each pair of modules is calculated to obtain similarity data. For example, the spatial matching degree of the connection holes of two components is calculated, and the similarity is expressed as a percentage.

[0124] Step S143: integrating the chassis component structure data based on the connection structure similarity data to obtain chassis component connection relationship data;

[0125] In this embodiment, the calculated connection structure similarity data is used to group components with high similarity and integrate the connection relationships. Assuming that there are two components A and B, whose connection structure similarity is 95%, A and B are classified as the same type of connection relationship. In this way, the connection information of all components is integrated to generate a comprehensive chassis component connection relationship data graph, which describes the connection mode between components and their mutual relationship.

[0126] Step S144: performing chassis component topology analysis according to chassis component connection relationship data, thereby obtaining a component topology model;

[0127] In this embodiment, a topology analysis tool is used to analyze the integrated component connection relationship data. A topology structure model of the chassis components is constructed through a graph theory algorithm, where nodes represent components and edges represent connection relationships. For example, a graph model is established in which each node is a chassis component and edges represent physical connections between components. The analysis results show the connection sequence and hierarchical structure between components, forming an overall topology structure model of the chassis.

[0128] Step S145: performing material property analysis on chassis component material data to obtain component material property data;

[0129] In this embodiment, the material data of each chassis component is analyzed in detail, including the mechanical properties and thermal properties of the material. The material samples are subjected to tensile tests, hardness tests, etc. using material testing instruments, and the results are recorded. Assuming that the material data indicates that the aluminum alloy of a component has a tensile strength of 300 MPa and a hardness of 50 HRC, these data are integrated into the material property data of the component, showing its performance indicators and applicable range.

[0130] Step S146: filling the component topology structure model with material characteristic parameters according to the component material characteristic data, thereby obtaining the chassis component topology structure model.

[0131] In this embodiment, the material property data is applied to the previously constructed component topology model. Corresponding material property parameters, such as tensile strength, hardness, etc., are added to each component node. For example, the material property data of the aluminum alloy component is filled into the topology model to form a model in which each node has specific material properties. The resulting topology model not only shows the connection relationship of the components, but also contains the detailed material properties of each component.

[0132] Optionally, step S2 specifically includes:

[0133] Step S21: extracting design operating condition features from the electronic equipment chassis design data, thereby obtaining design operating condition data;

[0134] In this embodiment, the design operating condition feature extraction involves identifying and extracting key operating condition features from the design data of the electronic equipment chassis. This can be done by analyzing factors such as the equipment's usage environment, workload, temperature changes, vibration conditions, etc. For example, when designing a computer server chassis, the extracted data includes the internal temperature distribution during normal operation, the direction of the external cooling airflow, and possible vibration sources. These feature data help to accurately simulate the performance of the chassis under actual working conditions.

[0135] Step S22: integrating the working condition simulation boundary conditions according to the design working condition data, thereby obtaining the working condition simulation boundary conditions; integrating the component material simulation boundary conditions according to the chassis component topology structure model, thereby obtaining the material simulation conditions;

[0136] In this embodiment, the operating condition simulation boundary conditions are determined based on the extracted design operating condition data, such as the maximum power of the electronic device is 150W and the external ambient temperature of the chassis is 35°C. When setting the boundary conditions, the maximum power is used as the heat source input, and the heat source distribution and heat flux density are set. The integration of component material simulation boundary conditions is to integrate the material properties of each component of the chassis (such as thermal conductivity, elastic modulus) into the simulation model. For example, if the chassis material is aluminum alloy, its thermal conductivity needs to be set to 200 W / m·K. At the same time, these conditions need to be integrated to form a complete simulation boundary condition.

[0137] Step S23: performing multi-physics field simulation boundary condition coupling on the working condition simulation boundary conditions and the material simulation conditions, thereby obtaining a set of physical field simulation boundary conditions;

[0138] In this embodiment, the coupling of multi-physics simulation boundary conditions involves combining the boundary conditions of multiple physical fields such as thermal field, force field and fluid field. Taking electronic equipment as an example, it is necessary to couple the temperature field with the stress field and the air flow field to ensure that the simulation model accurately reflects the interaction of different physical effects. For example, the heat generation of electronic equipment will affect the airflow distribution in the chassis, and the stress distribution will also change with temperature changes.

[0139] Step S24: meshing the chassis geometry model of the chassis component topology structure model, thereby obtaining a physical field simulation mesh model;

[0140] In this embodiment, the meshing of the chassis geometry model involves dividing the chassis geometry into small meshes for accurate physical field simulation. Taking a standard computer chassis as an example, the meshing includes finely dividing the chassis shell, internal brackets, and fan area to ensure that the simulation can capture subtle geometric features. For example, a coarser mesh may be used for the shell, while a finer mesh may be used in the fan area to capture the details of air flow.

[0141] Step S25: construct a multi-physics field coupling model according to the physical field simulation boundary condition set and the physical field simulation grid model, so as to obtain a multi-physics field coupling heat flow model of the chassis.

[0142] In this embodiment, the construction of the multi-physics coupling model involves combining the obtained set of physical field simulation boundary conditions with the obtained mesh model to form a comprehensive thermal flow simulation model. For example, by applying the boundary conditions of heat conduction, electrical flow, and mechanical stress to the mesh model, a coupling model that includes these multiple physical effects can be constructed. Ultimately, this model can be used to analyze the thermal management and structural strength of the chassis under actual working conditions, thereby optimizing the design.

[0143] Optionally, step S25 is specifically:

[0144] Selecting simulation boundary conditions for the physical field simulation boundary condition set, thereby obtaining mechanical simulation boundary conditions, thermal flow simulation boundary conditions, and electrical simulation boundary conditions;

[0145] In this embodiment, the obtained physical field simulation boundary condition set includes mechanical simulation boundary conditions, thermal flow simulation boundary conditions and electrical simulation boundary conditions. First, select appropriate boundary conditions based on the design working condition data and the material properties of the chassis components. For example, for mechanical simulation boundary conditions, set the support points of the chassis as fixed boundaries, and apply external pressure or vibration loads; for thermal flow simulation boundary conditions, set the heat source distribution on the surface and inside of the chassis, such as thermal power density and ambient temperature; for electrical simulation boundary conditions, set the voltage and current conditions of the electrical connection points, as well as the boundaries of electrical insulation and conductors. Through these selections, the boundary conditions of mechanical, thermal flow and electrical simulations are determined to provide a basis for subsequent simulations.

[0146] Performing model mesh division on the physical field simulation mesh model to obtain an electrical mesh model and a mechanical mesh model;

[0147] In this embodiment, the meshing of the physical field simulation mesh model needs to obtain an electrical mesh model and a mechanical mesh model separately. For example, when meshing a chassis, select an appropriate mesh size to ensure the accuracy and computational efficiency of the simulation. For mechanical simulation, a coarser mesh is used to simplify the calculation, and a finer mesh is used in key areas (such as support points); for electrical simulation, ensure that the mesh at electrical components (such as wires and contact points) is fine enough to accurately capture the electric field distribution. The choice of mesh model should balance computing resources and simulation accuracy.

[0148] Performing mechanical physical field finite element simulation on the mechanical mesh model according to mechanical simulation boundary conditions, thereby obtaining mechanical field simulation data;

[0149] In this embodiment, mechanical simulation boundary conditions are used to perform finite element simulation on the mechanical grid model. For example, fixed boundary conditions are applied to the support points of the chassis, and loads are applied to possible external vibrations or pressures. Through finite element analysis, mechanical field simulation data are obtained, including stress, strain distribution, and deformation. These data are used to evaluate the structural strength and stability of the chassis under different loads.

[0150] Performing electric physical field finite element simulation on the electric grid model according to the electric simulation boundary conditions, thereby obtaining electric field simulation data;

[0151] In this embodiment, when performing electric physical field finite element simulation on the electrical grid model, the electrical parameters are set according to the electrical simulation boundary conditions. For example, the voltage and current boundary conditions of the electrical components inside the chassis are set, and the electrical insulation area is considered. Through simulation, data such as electric field distribution, current density, and electric potential are obtained. These data help analyze the performance of electrical components and possible electrical interference.

[0152] Performing thermal flow physics field finite element simulation on the physical field simulation grid model according to thermal flow simulation boundary conditions, thereby obtaining thermal flow simulation data;

[0153] In this embodiment, the heat flow simulation boundary conditions are used to perform heat flow finite element simulation on the physical field simulation grid model. For example, the position and power of the heat source inside the chassis are set, and the external environment temperature and heat conduction boundary are set. Through simulation, the temperature field distribution, heat flux density and heat conduction path are obtained. These data are used to evaluate the heat dissipation performance and thermal management effect of the chassis.

[0154] Perform multi-physics field simulation coupling on mechanical field simulation data, electric field simulation data and thermal flow simulation data, so as to obtain multi-physics field coupling simulation data;

[0155] In this embodiment, mechanical field simulation data, electric field simulation data, and heat flow simulation data are coupled by multi-physics field simulation. For example, the multi-physics field coupling algorithm is used to process these data by combining the influence of heat flow on material strength, the influence of electric field on heat distribution, and the influence of mechanical stress on electrical performance. This process integrates the interaction of various physical fields and provides a comprehensive simulation view.

[0156] Based on the multi-physics field coupling simulation data, a multi-physics field coupling model is constructed for the physical field simulation grid model, so as to obtain a multi-physics field coupling thermal flow model of the chassis.

[0157] In this embodiment, a multi-physics coupling model is constructed for the physical field simulation grid model based on the multi-physics coupling simulation data. For example, the data of heat flow, mechanical stress and electric field distribution are combined to update the various physical field parameters in the grid model. In this way, a multi-physics coupling heat flow model that comprehensively considers thermal, mechanical and electrical factors is obtained. This model is used to analyze the overall performance of the chassis under actual working conditions and to perform further optimization design.

[0158] Optionally, step S3 specifically includes:

[0159] Step S31: extracting the working condition simulation boundary conditions according to the physical field simulation boundary condition set, thereby obtaining the working condition simulation boundary conditions;

[0160] In this embodiment, a set of physical field simulation boundary conditions of the chassis is collected, including boundary conditions such as heat conduction, convection and radiation. These conditions can be measured experimentally or estimated based on previous simulation data. For example, if the chassis is designed with multiple radiators and fans, the heat flux density on the surface of each radiator and the air flow boundary conditions of the fan need to be defined. Then, these boundary conditions are input into the simulation software to set the working condition simulation boundary conditions.

[0161] Step S32: performing multi-physics field working condition simulation on the chassis multi-physics field coupled thermal flow model according to the working condition simulation boundary conditions, thereby obtaining working condition simulation data, and performing physical field coupling effect calculation based on the working condition simulation data, thereby obtaining simulation response data;

[0162] In this embodiment, the multi-physics field coupled heat flow model of the chassis is simulated using the obtained working condition simulation boundary conditions. The specific operation is to input the boundary conditions into the multi-physics field simulation software (such as COMSOL or ANSYS) and set the working conditions such as heat source and cooling conditions. After the simulation, the software will generate data such as temperature distribution and heat flow path. Based on these data, the physical field coupling effects inside the chassis, such as thermal expansion and stress distribution, are further calculated to obtain simulation response data.

[0163] Step S33: extracting chassis working mode features from the electronic equipment chassis design data to obtain chassis working mode data, and integrating chassis working status data from the chassis working mode data to obtain chassis working status data;

[0164] In this embodiment, the design data of the electronic equipment chassis is analyzed to extract the working mode characteristics of the chassis, such as heat output and heat dissipation efficiency under high load. By analyzing the chassis design drawings and operation manuals, the data of different working modes (such as normal operation and full load operation) are determined. For example, parameters such as temperature, wind speed and current of components in the chassis under different working modes can be extracted. These data are integrated into chassis working status data.

[0165] Step S34: performing working state association on the chassis working state data and the simulation response data, thereby obtaining working state association response data;

[0166] In this embodiment, the chassis working state data and the simulation response data are correlated and analyzed. The specific operation includes using statistical analysis or machine learning algorithms (such as correlation analysis or regression analysis) to find the relationship between the working state data (such as temperature change, load condition) and the simulation response data (such as heat flow distribution). Through these associations, the working state correlation response data is obtained, such as the predicted heat distribution under a certain working state.

[0167] Step S35: constructing a chassis working state prediction model based on the working state associated response data, and predicting the working state of the electronic device chassis on the simulation response data through the chassis working state prediction model, thereby obtaining chassis working state prediction data.

[0168] In this embodiment, a chassis working state prediction model is constructed based on the obtained working state associated response data. A regression model, a neural network or other prediction algorithms can be used. The constructed model is applied to the simulation response data to predict the working state of the chassis under different working conditions. For example, simulation data (such as heat flux density) is input into the prediction model to obtain the chassis working state prediction data (such as the expected maximum temperature, potential overheating problems).

[0169] Optionally, step S4 is specifically:

[0170] Step S41: dividing the chassis working state prediction data into chassis working stage states, thereby obtaining chassis working stage state prediction data;

[0171] In this embodiment, the chassis working state prediction data is divided into chassis working stage states, and a clustering algorithm can be used to group the data to divide the working state data into different stages, such as standby, light load, medium load, and heavy load. The working state prediction data of each stage includes temperature, humidity, and current, etc. Using these data, each stage can be marked and the chassis working stage state prediction data can be generated. This process needs to ensure that the data of each stage is accurately divided and recorded to ensure the accuracy of subsequent analysis.

[0172] Step S42: Calculate the component temperature state stage proportion for the chassis working stage state prediction data, thereby obtaining component high temperature state stage proportion data, and cluster high-proportion high temperature state components according to the component high temperature state stage proportion data, thereby obtaining high-proportion high temperature state component data;

[0173] In this embodiment, the temperature state stage proportion of the components is calculated for the chassis working stage state prediction data, and the temperature state of each component in different working stages can be determined by statistical methods. For example, the time proportion of each component in the high temperature state is calculated, and it is classified as a high-proportion component. Then, a clustering algorithm (such as K-means or DBSCAN) is applied to perform cluster analysis on these high-proportion high-temperature state components, and the data of high-proportion components that show high temperature is extracted, so as to further analyze the performance and requirements of these components in actual work.

[0174] Step S43: extracting component material characteristics from the chassis component topology structure model to obtain component material characteristic data, and classifying the component material characteristic data into heat-resistant components to obtain high heat-resistant component data and low heat-resistant component data;

[0175] In this embodiment, the component material characteristics of the chassis component topology structure model are extracted. First, the material data of each component, including its thermal conductivity, melting point, etc., is obtained. These characteristics are measured using material testing equipment (such as a thermal analyzer) and recorded as component material characteristic data. Then, these data are classified into high heat-resistant components and low heat-resistant components. For example, components with a heat-resistant temperature higher than 100°C are classified as high heat-resistant components, and components with a heat-resistant temperature lower than 100°C are classified as low heat-resistant components for subsequent analysis.

[0176] Step S44: performing component intersection calculation on high-temperature state high-ratio component data and high-heat-resistant component data, thereby obtaining high-performance component data; performing component intersection calculation on high-temperature state high-ratio component data and low-heat-resistant component data, thereby obtaining low-performance component data;

[0177] In this embodiment, the component intersection calculation is performed on the high-temperature state high-proportion component data and the high-heat-resistant component data, and the common part of the two sets of data can be determined by the data matching algorithm. For example, set operations are used to find components that belong to both high-temperature state and high-heat-resistant classifications to generate high-performance component data. Similarly, the intersection calculation is performed on the high-temperature state high-proportion component data and the low-heat-resistant component data to find the low-performance component data. This process can reveal the actual working performance of components under extreme conditions.

[0178] Step S45: merging the high performance component data and the low performance component data to obtain performance evaluation data;

[0179] In this embodiment, data merging is performed on high performance component data and low performance component data, and data fusion technology can be used to integrate these two types of data. The data needs to be normalized so that different types of data can be effectively combined. The merged data will form performance evaluation data, which is used to describe the performance of the component under various working conditions and provide a basis for the optimization of the chassis system.

[0180] Step S46: classifying the chassis component topology model into component working performance levels according to the working performance evaluation data, thereby obtaining chassis component working performance level data.

[0181] In this embodiment, the chassis component topology model is divided into component working performance levels according to the working performance evaluation data, and a classification algorithm (such as hierarchical clustering or a scoring system) can be used to classify the components. For example, the components are divided into three categories of excellent, medium and poor according to their working performance, and the chassis component working performance level data is generated. This process involves the formulation of clear scoring standards and evaluation indicators to ensure the accuracy and practicality of the classification results.

[0182] Optionally, step S46 is specifically:

[0183] Step S461: performing performance component distribution statistics according to the work performance evaluation data, thereby obtaining high work performance component distribution data and low work performance component distribution data;

[0184] In this embodiment, the performance evaluation data from different chassis components are collected, and the data are processed using statistical analysis tools such as the pandas library in Python. The distribution data of high-performance components and low-performance components are generated by calculating the performance index of each component. For example, the components are divided into three levels of high, medium and low according to performance using the K-means clustering algorithm, so as to obtain component data of high and low performance, and then the distribution of high performance components and the distribution of low performance components are visualized through spatial distribution according to the component spatial position information contained in the component data, so as to obtain high performance component distribution data and low performance component distribution data. Performance component division can also extract the component performance that has been defined in the performance evaluation data.

[0185] Step S462: performing component area division on the chassis component topology structure model, thereby obtaining a chassis component area division model;

[0186] In this embodiment, based on the chassis component topology model, computer-aided design (CAD) software is used to divide the chassis components into regions. The region division can be performed according to the functional requirements and spatial layout of the components. For example, the chassis is divided into a CPU region, a memory region, a graphics card region, etc., and each region corresponds to a specific component layout model.

[0187] Step S463: Associating the high-performance component distribution data and the chassis component area division model with the high-performance component area, thereby obtaining high-performance component area data; and associating the low-performance component distribution data and the chassis component area division model with the low-performance component area, thereby obtaining low-performance component area data;

[0188] In this embodiment, the high-performance component distribution data is matched with the chassis component area division model. Through an algorithm, such as the nearest neighbor algorithm (KNN), the high-performance component is assigned to its nearest chassis area to form high-performance level component area data. Similarly, the low-performance component is regionally associated to generate low-performance level component area data. This can be achieved by marking high and low performance areas in the CAD model.

[0189] Step S464: Perform component area integration on the high-performance level component area data and the low-performance level component area data, thereby obtaining chassis component working performance level data.

[0190] In this embodiment, finally, the high-performance component area data and the low-performance component area data are integrated. A merge operation, such as the merge function in the data frame, is used to merge the two data sets according to the component area to obtain the chassis component working performance level data. For example, the pivot table function in Excel can be used to merge to obtain the comprehensive performance level data of each area and perform visual analysis.

[0191] Optionally, step S5 specifically includes:

[0192] Step S51: extracting low-performance-level component regions from chassis component working performance level data, thereby obtaining low-performance-level component region data;

[0193] In this embodiment, in the working performance level data of the electronic equipment chassis, the temperature, vibration and power consumption data of each component are first collected and analyzed. Using machine learning algorithms, such as K-means clustering or hierarchical clustering, low-performance level components with poor performance are identified. Specifically, all components in the chassis are divided into several areas, and by setting performance thresholds, those areas with performance below the threshold are extracted. In this way, low-performance level component area data can be obtained, for example, through the measurement results of the temperature sensor and the power consumption detection module, the area where the temperature exceeds 70°C and the power consumption exceeds the standard is marked. It is also possible to extract the component area that has been defined as a low performance level contained in the chassis component working performance level data.

[0194] Step S52: extracting regional associated component material features and regional component distribution features from the low performance level component regional data, thereby obtaining regional associated component material data and regional component distribution data;

[0195] In this embodiment, for the extracted low-performance component area data, image processing technology and feature extraction algorithms, such as edge detection and texture analysis, are used to extract material features in the area. For example, a scanning electron microscope (SEM) is used to analyze the regional material to obtain the microstructure data of the material. The regional component distribution feature extraction uses spatial analysis algorithms, such as spatial clustering or Voronoi diagrams, to determine the distribution pattern of components in the chassis. This can help identify the relative positional relationship and layout characteristics between components, such as discovering that components in certain low-performance areas are concentrated in specific areas or arranged in specific shapes.

[0196] Step S53: optimizing the component structure material of the electronic device chassis structure data according to the regional component material data, thereby obtaining component material optimization data;

[0197] In this embodiment, based on the material characteristics extracted from the material data of the region-associated components, for example, through analysis, it is found that the material hardness of some areas is insufficient or the thermal conductivity is poor. You can choose to use an alloy material with high thermal conductivity and low thermal expansion for optimization. Finite element analysis (FEA) can be used to simulate the impact of different materials on the chassis structure, and then the best material can be selected for practical application. For example, the aluminum alloy material in the chassis is replaced with an improved magnesium alloy to improve heat dissipation performance and strength.

[0198] Step S54: optimizing the component distribution of the electronic equipment chassis structure data according to the regional component distribution data, thereby obtaining component distribution optimization data;

[0199] In this embodiment, based on the regional component distribution data, an optimization algorithm (such as a genetic algorithm or simulated annealing) is applied to re-layout the components inside the chassis. Three-dimensional modeling software can be used to simulate different component layout schemes, and the optimal layout can be determined by calculating the thermal management efficiency and space utilization. For example, high-power consumption components are moved from heat-sensitive areas to the vicinity of the heat sink of the chassis to reduce heat accumulation and improve overall heat dissipation efficiency.

[0200] Step S55: performing component optimization integration on the component material optimization data and the component distribution optimization data, thereby obtaining optimized electronic equipment chassis structure data;

[0201] In this embodiment, the component material optimization data and the component distribution optimization data are integrated, and the multi-objective optimization technology is used to comprehensively consider the effects of material improvement and layout adjustment. For example, by comprehensively considering the thermal conductivity of the material and the heat dissipation efficiency of the layout, the chassis structure is finally optimized. An optimization model can be created by a genetic algorithm or a multi-objective optimization algorithm, and the optimized material properties and layout scheme are input into the model for overall evaluation and adjustment to obtain the optimal chassis structure data.

[0202] Step S56: structurally optimizing and coupling the electronic equipment chassis design data according to the optimized electronic equipment chassis structure data, thereby obtaining optimized electronic equipment chassis design data.

[0203] In this embodiment, the optimized electronic device chassis structure data is applied to the design of the electronic device chassis. The specific method includes using computer-aided design (CAD) software to integrate the optimized component materials and layout data into the chassis design. When performing structural optimization coupling, ensure that the design meets the requirements of thermal management, mechanical strength and space utilization. For example, apply the properties of new materials in the CAD model, adjust the component positions to adapt to the new layout scheme, and perform actual structural analysis and verification to ensure that the final design meets the performance and reliability requirements.

[0204] 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.

[0205] 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. An electronic equipment chassis structure optimization design method based on electromechanical thermal three-field coupling, characterized in that: The following steps are involved: Step S1: obtaining electronic equipment chassis design data, and extracting electronic equipment chassis structure features from the electronic equipment chassis design data, thereby obtaining electronic equipment chassis structure data; performing chassis component topology structure analysis based on the electronic equipment chassis structure data, thereby obtaining a chassis component topology structure model, step S1 specifically includes: Step S11: acquiring electronic equipment chassis design data, and extracting electronic equipment chassis structural features from the electronic equipment chassis design data, thereby obtaining electronic equipment chassis structural data; Step S12: classifying the electronic equipment chassis structure data by structure type, thereby obtaining chassis circuit structure data and chassis component structure data; Step S13: performing chassis circuit topology analysis according to the chassis circuit structure data, thereby obtaining a chassis circuit topology model; Step S14: performing chassis component topology analysis on the chassis component structure data, thereby obtaining a chassis component topology model; Step S15: merging the chassis circuit topology structure model and the chassis component topology structure model to obtain a chassis component topology structure model; Step S2: extracting the design condition features of the electronic equipment chassis design data to obtain the design condition data, and integrating the physical field simulation boundary conditions according to the design condition data to obtain the physical field simulation boundary condition set; constructing a multi-physical field coupling model according to the physical field simulation boundary condition set and the chassis component topology structure model to obtain the chassis multi-physical field coupling heat flow model. Step S2 is specifically as follows: Step S21: extracting design operating condition features from the electronic equipment chassis design data, thereby obtaining design operating condition data; Step S22: integrating the working condition simulation boundary conditions according to the design working condition data, thereby obtaining the working condition simulation boundary conditions; integrating the component material simulation boundary conditions according to the chassis component topology structure model, thereby obtaining the material simulation conditions; Step S23: performing multi-physics field simulation boundary condition coupling on the working condition simulation boundary conditions and the material simulation conditions, thereby obtaining a set of physical field simulation boundary conditions; Step S24: meshing the chassis geometry model of the chassis component topology structure model, thereby obtaining a physical field simulation mesh model; Step S25: constructing a multi-physics field coupling model according to the physical field simulation boundary condition set and the physical field simulation grid model, thereby obtaining a chassis multi-physics field coupling heat flow model; Step S3: Perform multi-physics field working condition simulation on the chassis multi-physics field coupled thermal flow model according to the physical field simulation boundary condition set, so as to obtain simulation response data, and perform working state prediction of the electronic equipment chassis according to the simulation response data, so as to obtain chassis working state prediction data. Step S3 is specifically as follows: Step S31: extracting the working condition simulation boundary conditions according to the physical field simulation boundary condition set, thereby obtaining the working condition simulation boundary conditions; Step S32: performing multi-physics field working condition simulation on the chassis multi-physics field coupled thermal flow model according to the working condition simulation boundary conditions, thereby obtaining working condition simulation data, and performing physical field coupling effect calculation based on the working condition simulation data, thereby obtaining simulation response data; Step S33: extracting chassis working mode features from the electronic equipment chassis design data to obtain chassis working mode data, and integrating chassis working status data from the chassis working mode data to obtain chassis working status data; Step S34: performing working state association on the chassis working state data and the simulation response data, thereby obtaining working state association response data; Step S35: constructing a chassis working state prediction model based on the working state associated response data, and predicting the working state of the electronic device chassis on the simulation response data through the chassis working state prediction model, thereby obtaining chassis working state prediction data; Step S4: performing chassis working performance evaluation on the chassis working state prediction data, thereby obtaining working performance evaluation data, and performing component working performance level classification on the chassis component topology structure model according to the working performance evaluation data, thereby obtaining chassis component working performance level data; Step S5: Optimizing the structural performance of the electronic equipment chassis structure data based on the chassis component working performance level data, thereby obtaining optimized electronic equipment chassis structure data; and structurally optimizing and coupling the electronic equipment chassis design data based on the optimized electronic equipment chassis structure data, thereby obtaining optimized electronic equipment chassis design data.

2. The electronic equipment chassis structure optimization design method based on electromechanical thermal three-field coupling according to claim 1 is characterized in that: Step S13 is specifically as follows: Step S131: extracting electrical path features from chassis circuit structure data, thereby obtaining chassis electrical path data; Step S132: performing electrical node distribution statistics on the chassis electrical path data, thereby obtaining electrical node distribution data; Step S133: integrating the node path connection mode of the chassis electrical path data according to the electrical node distribution data, thereby obtaining node series connection path data and node parallel connection path data; Step S134: performing a series connection path topology structure analysis according to the node series connection path data, thereby obtaining a series path topology structure model; performing a parallel connection path topology structure analysis according to the node parallel connection path data, thereby obtaining a parallel path topology structure model; Step S135: merging the series path topology structure model and the parallel path topology structure model into circuit connection topology structures, thereby obtaining a chassis circuit topology structure model.

3. The electronic equipment chassis structure optimization design method based on electromechanical thermal three-field coupling according to claim 1 is characterized in that: Step S14 is specifically as follows: Step S141: extracting connection structure features and component material features from chassis component structure data, thereby obtaining chassis component connection structure data and chassis component material data; Step S142: performing connection structure similarity calculation on the chassis component connection structure data, thereby obtaining connection structure similarity data; Step S143: integrating the chassis component structure data based on the connection structure similarity data to obtain chassis component connection relationship data; Step S144: performing chassis component topology analysis according to chassis component connection relationship data, thereby obtaining a component topology model; Step S145: performing material property analysis on chassis component material data to obtain component material property data; Step S146: filling the component topology structure model with material characteristic parameters according to the component material characteristic data, thereby obtaining the chassis component topology structure model.

4. The electronic equipment chassis structure optimization design method based on electromechanical thermal three-field coupling according to claim 1 is characterized in that: Step S25 is specifically as follows: Selecting simulation boundary conditions for the physical field simulation boundary condition set, thereby obtaining mechanical simulation boundary conditions, thermal flow simulation boundary conditions, and electrical simulation boundary conditions; Performing model mesh division on the physical field simulation mesh model to obtain an electrical mesh model and a mechanical mesh model; Performing mechanical physical field finite element simulation on the mechanical mesh model according to mechanical simulation boundary conditions, thereby obtaining mechanical field simulation data; Performing electric physical field finite element simulation on the electric grid model according to the electric simulation boundary conditions, thereby obtaining electric field simulation data; Performing thermal flow physics field finite element simulation on the physical field simulation grid model according to thermal flow simulation boundary conditions, thereby obtaining thermal flow simulation data; Perform multi-physics field simulation coupling on mechanical field simulation data, electric field simulation data and thermal flow simulation data, so as to obtain multi-physics field coupling simulation data; Based on the multi-physics field coupling simulation data, a multi-physics field coupling model is constructed for the physical field simulation grid model, so as to obtain a multi-physics field coupling thermal flow model of the chassis.

5. The electronic equipment chassis structure optimization design method based on electromechanical thermal three-field coupling according to claim 1 is characterized in that: Step S4 is specifically as follows: Step S41: dividing the chassis working state prediction data into chassis working stage states, thereby obtaining chassis working stage state prediction data; Step S42: Calculate the component temperature state stage proportion for the chassis working stage state prediction data, thereby obtaining component high temperature state stage proportion data, and cluster high-proportion high temperature state components according to the component high temperature state stage proportion data, thereby obtaining high-proportion high temperature state component data; Step S43: extracting component material characteristics from the chassis component topology structure model to obtain component material characteristic data, and classifying the component material characteristic data into heat-resistant components to obtain high heat-resistant component data and low heat-resistant component data; Step S44: performing component intersection calculation on high-temperature state high-proportion component data and high-heat-resistant component data, thereby obtaining high-performance component data; The component intersection calculation is performed on the component data with a high proportion of high temperature state and the component data with low heat resistance, so as to obtain the component data with low working performance; Step S45: merging the high performance component data and the low performance component data to obtain performance evaluation data; Step S46: classifying the chassis component topology model into component working performance levels according to the working performance evaluation data, thereby obtaining chassis component working performance level data.

6. The electronic equipment chassis structure optimization design method based on electromechanical thermal three-field coupling according to claim 5 is characterized in that: Step S46 is specifically as follows: Step S461: performing performance component distribution statistics according to the work performance evaluation data, thereby obtaining high work performance component distribution data and low work performance component distribution data; Step S462: performing component area division on the chassis component topology structure model, thereby obtaining a chassis component area division model; Step S463: performing high-performance component region association on the high-performance component distribution data and the chassis component region division model, thereby obtaining high-performance grade component region data; Associating the low-performance component distribution data and the chassis component area division model with the low-performance component area, thereby obtaining low-performance level component area data; Step S464: Perform component area integration on the high-performance level component area data and the low-performance level component area data, thereby obtaining chassis component working performance level data.

7. The electronic equipment chassis structure optimization design method based on electromechanical thermal three-field coupling according to claim 1 is characterized in that: Step S5 is specifically as follows: Step S51: extracting low-performance-level component regions from chassis component working performance level data, thereby obtaining low-performance-level component region data; Step S52: extracting regional associated component material features and regional component distribution features from the low performance level component regional data, thereby obtaining regional associated component material data and regional component distribution data; Step S53: optimizing the component structure material of the electronic device chassis structure data according to the regional component material data, thereby obtaining component material optimization data; Step S54: optimizing the component distribution of the electronic equipment chassis structure data according to the regional component distribution data, thereby obtaining component distribution optimization data; Step S55: performing component optimization integration on the component material optimization data and the component distribution optimization data, thereby obtaining optimized electronic equipment chassis structure data; Step S56: structurally optimizing and coupling the electronic equipment chassis design data according to the optimized electronic equipment chassis structure data, thereby obtaining optimized electronic equipment chassis design data.

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