Thermal simulation method and system of circuit board and circuit board

Abstract

The present invention relates to the field of thermal simulation technology, and in particular to a thermal simulation method, system and circuit board for a circuit board. The method comprises the following steps: obtaining circuit board design data and modeling to obtain a three-dimensional structural model of the circuit board; performing accuracy estimation according to the circuit board design data to obtain thermal simulation accuracy data of the circuit board; dividing the three-dimensional structural model of the circuit board according to the thermal simulation accuracy data of the circuit board to obtain circuit board component grid data; assigning physical properties to the circuit board component grid data according to the circuit board design data to obtain physical grid unit data; performing thermal simulation of the circuit board according to the physical grid unit data to obtain circuit board thermal simulation data; visualizing the thermal simulation data of the circuit board to obtain a thermal stress distribution map, and optimizing the circuit board component design of the three-dimensional structural model of the circuit board according to the thermal stress distribution map to obtain an optimized three-dimensional structural model of the circuit board. The present invention can effectively improve the thermal management capability of the circuit board.

Description

Technical Field

[0001] The present invention relates to the field of thermal simulation technology, and in particular to a thermal simulation method and system for a circuit board and a circuit board. Background Art

[0002] In the design and manufacturing process of modern electronic products, printed circuit boards (PCBs) are the core carriers of electronic components, and their thermal management is crucial. With the continuous miniaturization and integration of electronic devices, the number and power density of components carried on PCBs are increasing, resulting in a significant increase in the heat generated during operation. If the heat is not effectively managed and dissipated, it may cause the performance of components to deteriorate, the reliability to decrease, and even the failure of the equipment. Traditional thermal simulation methods such as finite element method (FEM) and finite difference method (FDM) require the establishment of high-precision mesh models on the entire circuit board, especially in complex multi-layer circuit boards or areas around components. This not only requires a large amount of computing resources, but also makes the simulation process very time-consuming. With the increase in the complexity of circuit boards (such as high-speed circuits, multi-layer designs, and multiple component layouts), the demand for simulation time and computing resources is also increasing sharply. At the same time, high-precision simulation often requires complex mathematical models and higher computing resources. Traditional thermal simulation methods are difficult to find a suitable balance between high precision and computational complexity. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a thermal simulation method, system and circuit board for a circuit board to solve at least one of the above technical problems.

[0004] To achieve the above object, a thermal simulation method for a circuit board comprises the following steps:

[0005] Step S1: obtaining circuit board design data, and integrating the circuit board component connection relationship of the circuit board design data, thereby obtaining circuit board component connection relationship data; performing three-dimensional digital modeling according to the circuit board component connection relationship data, thereby obtaining a three-dimensional structural model of the circuit board;

[0006] Step S2: extracting circuit board operating condition features according to the circuit board design data, thereby obtaining circuit board design operating condition data, and estimating circuit board thermal simulation accuracy based on the circuit board design operating condition data, thereby obtaining circuit board thermal simulation accuracy data;

[0007] Step S3: classifying and dividing the finite element mesh of the circuit board components on the three-dimensional structure model of the circuit board according to the circuit board thermal simulation accuracy data, thereby obtaining the circuit board component mesh data; assigning physical properties to the circuit board component mesh data according to the circuit board design data, thereby obtaining physical mesh unit data;

[0008] Step S4: reconstructing the circuit board three-dimensional structure model according to the physical grid unit data to obtain a circuit board thermal simulation model, and performing circuit board thermal simulation on the circuit board design working condition data through the circuit board thermal simulation model to obtain circuit board thermal simulation data;

[0009] Step S5: Visualizing the thermal stress distribution diagram of the circuit board thermal simulation data to obtain the thermal stress distribution diagram, and optimizing the circuit board component design of the circuit board three-dimensional structure model according to the thermal stress distribution diagram to obtain the optimized circuit board three-dimensional structure model.

[0010] The present invention can clearly understand the interaction and electrical connection between components by acquiring circuit board design data and integrating component connection relationships. This provides basic data for subsequent analysis. Constructing a three-dimensional digital model helps to accurately reflect the actual physical layout, which is convenient for more accurate thermal simulation and analysis. The three-dimensional model provides a reliable basis for subsequent physical property assignment and thermal analysis. By analyzing the circuit board design data, key operating conditions (such as power consumption, ambient temperature, etc.) can be extracted, which helps to simulate actual working conditions more accurately. Based on the design operating condition data, the thermal simulation accuracy is estimated, so that in the subsequent simulation process, the appropriate accuracy can be selected for different regions or components, thereby optimizing the use of computing resources and reducing unnecessary computing costs. According to the thermal simulation accuracy data, the finite element mesh classification of the three-dimensional structure model can improve the efficiency and adaptability of mesh generation. This step can reduce the amount of calculation while ensuring the calculation accuracy. Physical properties (such as thermal conductivity, specific heat capacity, etc.) are assigned to the mesh unit to make the simulation model closer to reality. Accurate physical properties can improve the reliability of thermal analysis results. The thermal simulation model of the circuit board is reconstructed through the physical grid unit data, so that the model can reflect the thermal characteristics of each component. This step ensures that the simulation can accurately capture the heat flow and heat distribution. Based on the reconstructed thermal simulation model, the circuit board design conditions are simulated to obtain thermal simulation data. This provides basic data support for subsequent thermal management optimization. The thermal simulation data is converted into a thermal stress distribution map, so that designers can intuitively understand the thermal stress conditions of each area of ​​the circuit board. Through visualization, hot spots and potential fault areas can be quickly identified. Design optimization of circuit board components based on the thermal stress distribution map can effectively reduce thermal stress concentration, thereby improving the reliability of components and extending the service life of the equipment. The optimized three-dimensional structural model will have better thermal management capabilities, thereby improving overall performance. In summary, through the implementation of the above steps, the thermal management capabilities of the PCB can be effectively improved, and equipment failure and performance degradation caused by thermal problems can be reduced. At the same time, the comprehensive application of these steps makes the design process more efficient and the simulation results more reliable, providing strong technical support for the design and manufacturing of modern electronic products.

[0011] Optionally, step S1 specifically includes:

[0012] Step S11: obtaining circuit board design data, and extracting circuit board structural features from the circuit board design data, thereby obtaining circuit board structural data;

[0013] Step S12: Structural classification of the circuit board structure data, thereby obtaining circuit board circuit structure data and circuit board component structure data;

[0014] Step S13: integrating the circuit board circuit power pin connection paths according to the circuit board circuit structure data, thereby obtaining the circuit board circuit power pin connection path data;

[0015] Step S14: performing component pin spatial layout analysis on the circuit board component structure data, thereby obtaining component pin spatial distribution data;

[0016] Step S15: Pin association is performed on the circuit board circuit power pin connection path data and the component pin space distribution data, thereby obtaining the circuit board component connection relationship data;

[0017] Step S16: Perform three-dimensional digital modeling based on the circuit board component connection relationship data to obtain a three-dimensional structural model of the circuit board.

[0018] The present invention can collect all electrical, mechanical and material information by acquiring circuit board design data, laying the foundation for subsequent analysis. Extracting the geometric features and electrical characteristics of the circuit board helps to understand the basic structure of the circuit board. These features include wiring layers, pads, vias, component positions, etc., which provide necessary information for subsequent classification and analysis. Classifying the extracted structural features can effectively distinguish between circuit structure and component structure. This classification helps to optimize the design process and ensure the rationality of circuit design. Through structural classification, complex design data can be organized into a clearer and easier to manage format, which is convenient for subsequent data processing and analysis. Integrating the power pin connection path can identify the optimal connection solution, reduce the voltage drop and current loss of the power pin, and improve the overall performance of the circuit. By analyzing the power pin connection path, the power demand can be effectively identified and managed to ensure stable operation of the circuit. Analyzing the spatial layout of the component pins helps to optimize the distribution of the pins, ensure that sufficient space is left during wiring, and avoid short circuits and signal interference. Associating the circuit power pin connection path data with the component pin spatial distribution data can clarify the connection relationship between components and ensure the electrical integrity of the circuit. Clear connection relationships help to quickly locate problems and reduce debugging time during later debugging and troubleshooting. Through 3D digital modeling, the structure and component layout of the circuit board can be intuitively presented, which is convenient for design review and communication. The 3D model can be used for subsequent simulation analysis (such as thermal analysis, electromagnetic interference analysis, etc.) to help designers optimize the design before physical prototype manufacturing.

[0019] Optionally, step S2 specifically includes:

[0020] Step S21: extracting circuit board operating condition features according to the circuit board design data, thereby obtaining circuit board design operating condition data;

[0021] Step S22: performing design operation time statistics on the circuit board design operating condition data to obtain the circuit board design operation time data, and classifying the circuit board design operation time data to obtain long-term operation circuit board operating condition data and short-term operation circuit board operating condition data;

[0022] Step S23: acquiring historical circuit board thermal simulation data, and estimating the circuit board thermal accumulation effect based on the historical circuit board thermal simulation data and the circuit board design operating condition data, thereby obtaining circuit board thermal accumulation effect estimation data;

[0023] Step S24: dividing the overall simulation accuracy range of the long-term running circuit board working condition data and the short-term running circuit board working condition data according to the circuit board heat accumulation effect estimation data, thereby obtaining circuit board simulation accuracy range data;

[0024] Step S25: Evaluate the circuit board structure geometric complexity according to the circuit board three-dimensional structure model to obtain circuit board geometric complexity data, and select structural simulation accuracy for the circuit board geometric complexity data based on the circuit board simulation accuracy range data to obtain circuit board thermal simulation accuracy data.

[0025] The present invention can accurately identify the working condition characteristics of the circuit board in actual application, such as current load, temperature change, etc., by extracting key parameters from the design data. This helps to provide more realistic input data for subsequent simulation analysis. Clear working condition characteristics can help engineers perform targeted optimization in the design stage and improve the overall performance of the product. By classifying the working condition data of different operating times, the performance of the circuit board in different use cycles can be better analyzed, which helps to predict its long-term stability. The classified data can provide richer information for the life prediction model of the circuit board and help evaluate the reliability under different operating conditions. By estimating the heat accumulation effect, the thermal impact of long-term operation on the circuit board can be understood to prevent failures caused by overheating. Analyzing the heat accumulation effect helps to design a more effective heat dissipation solution to ensure that the circuit board can still maintain a stable temperature under high load. By dividing different simulation accuracy ranges, the matching degree between the simulation results and the actual working conditions can be guaranteed, thereby improving the accuracy of design verification, which can select appropriate simulation accuracy according to different working conditions, save computing resources, and ensure the validity of the simulation results. Geometric complexity evaluation can help designers select appropriate modeling methods, thereby reducing the workload of modeling and simulation while ensuring accuracy. By rationally selecting simulation accuracy and methods, we can effectively improve simulation efficiency, accelerate the design iteration cycle, and reduce development costs.

[0026] Optionally, step S23 is specifically:

[0027] Step S231: acquiring historical circuit board thermal simulation data, and performing simulation thermal distribution feature extraction and simulation circuit board structure feature extraction on the historical circuit board thermal simulation data, thereby obtaining historical simulation thermal distribution data and historical simulation circuit board structure data;

[0028] Step S232: extracting circuit board design structure features from the circuit board design data to obtain circuit board design structure data, and calculating structure similarity between the circuit board design structure data and historical simulation circuit board structure data to obtain structure similarity data;

[0029] Step S233: extracting high-similarity circuit board simulation thermal distribution data from the historical simulation thermal distribution data according to the structural similarity data, thereby obtaining high-similarity circuit board simulation thermal distribution data;

[0030] Step S234: performing a simulated thermal energy trend analysis on the simulated thermal distribution data of the high-similarity circuit board, thereby obtaining simulated thermal energy trend data of historically similar circuit boards;

[0031] Step S235: performing simulation thermal energy trend estimation on the circuit board design operation time data according to the historical similar circuit board simulation thermal energy trend data, thereby obtaining simulation thermal energy trend estimation data;

[0032] Step S236: Estimating the heat accumulation effect of the circuit board according to the simulated thermal energy trend estimation data, thereby obtaining the heat accumulation effect estimation data of the circuit board.

[0033] The present invention forms a rich data foundation by acquiring historical circuit board thermal simulation data, providing a reliable information source for subsequent analysis. The extraction of thermal distribution characteristics and circuit board structural characteristics can reveal the thermal performance of the circuit board under different working conditions and help designers understand its thermal behavior. Historical data can be used as a reference for optimizing design, helping to identify possible thermal problems in the design and avoid repeating past mistakes. Extracting circuit board design structural characteristics helps to evaluate the effectiveness and reliability of the current design. By calculating structural similarity, circuit board designs that perform well in historical data can be quickly identified, which is convenient for adopting the design concept of successful cases. Through similarity analysis, designers can perform design iterations more efficiently, thereby accelerating the product development cycle. Extracting simulated thermal distribution data of high-similarity circuit boards enables new designs to draw on successful thermal management strategies in the past and improve the accuracy of simulation models. Combining historical simulation data with new designs helps to effectively pass on successful experiences to new products and reduce design risks. By analyzing the thermal energy trends of high-similarity circuit boards, potential thermal management problems in long-term use can be identified, which helps to provide early warning. Thermal energy trend analysis can provide a basis for design optimization, allowing designers to predict the thermal performance of circuit boards at an early stage and guide design decisions. Estimating the operating time of the design through thermal energy trend data of historically similar circuit boards can help designers reasonably arrange the service life of the product. Based on the estimation of thermal energy trends, targeted thermal management strategies can be proposed to optimize the heat dissipation design of circuit boards. Estimation of the thermal accumulation effect of circuit boards can help evaluate the thermal performance of circuit boards under high load conditions and ensure their long-term stable operation. By understanding the thermal accumulation effect, designers can make necessary structural improvements to the circuit boards to reduce the adverse effects of heat accumulation and extend the service life of the products.

[0034] Optionally, step S24 is specifically:

[0035] Step S241: extracting high-similarity circuit board historical thermal simulation accuracy data from historical circuit board thermal simulation data according to the structural similarity data, thereby obtaining high-similarity circuit board historical thermal simulation accuracy data;

[0036] Step S242: performing thermal simulation accuracy classification on the historical thermal simulation accuracy data of the high-similarity circuit board, thereby obtaining historical high simulation accuracy data and historical low simulation accuracy data;

[0037] Step S243: selecting high simulation accuracy for the circuit board operating condition data of a long time according to the historical high simulation accuracy data, thereby obtaining high simulation accuracy circuit board data; selecting low simulation accuracy for the circuit board operating condition data of a short time according to the historical low simulation accuracy data, thereby obtaining low simulation accuracy circuit board data;

[0038] Step S244: Divide the simulation accuracy range according to the high simulation accuracy circuit board data and the low simulation accuracy circuit board data, so as to obtain circuit board simulation accuracy range data.

[0039] The present invention analyzes historical circuit board thermal simulation data, extracts circuit board thermal simulation data with high similarity according to structural similarity, and can ensure that the selected data is more matched with the current circuit board in terms of thermal characteristics, thereby improving the accuracy of thermal simulation. Effective use of historical data can reduce the problem of reduced simulation accuracy caused by lack of data, reduce the need for repeated experiments, and save research and development time and cost. Combining past successful cases with current designs helps to quickly accumulate experience and improve the design efficiency of new products. The thermal simulation accuracy is divided into high and low categories, so that the simulation process can be more flexible and efficient. Selecting appropriate simulation accuracy in different application scenarios helps to improve calculation efficiency. Historical high simulation accuracy data can be used for the design of key components, while historical low simulation accuracy data is suitable for non-key components, thereby achieving overall optimization. Providing simulation data with multiple accuracies allows designers to flexibly adjust simulation models to meet different design requirements while meeting design requirements. According to the difference between long-term and short-term operating conditions, selecting appropriate simulation accuracy can better simulate actual usage, thereby improving the availability of simulation results. High simulation accuracy circuit board data can provide more accurate heat distribution and temperature rise for long-term operation, which helps to identify potential thermal problems and improve product stability and reliability. By selecting appropriate simulation accuracy in appropriate scenarios, the waste of computing resources can be effectively reduced and simulation costs can be reduced. By dividing the range of high and low simulation accuracy, clear standards and specifications are established, making the subsequent simulation and design process more reliable. Design evaluation within the simulation accuracy range helps to identify potential thermal problems in advance and reduce the risk of product failure.

[0040] Optionally, step S25 is specifically:

[0041] Step S251: extracting hierarchical structure features from the three-dimensional structure model of the circuit board, thereby obtaining hierarchical structure data of the circuit board;

[0042] Step S252: dividing the circuit board into regional structures according to the circuit board hierarchical structure data, thereby obtaining circuit board structural regional data;

[0043] Step S253: performing regional component density calculation on the circuit board structure regional data, thereby obtaining regional component density data; performing regional circuit board wiring density calculation on the circuit board structure regional data, thereby obtaining regional wiring density data;

[0044] Step S254: evaluating the regional geometric complexity of the circuit board according to the regional component density data and the regional wiring density data, thereby obtaining the geometric complexity data of the circuit board, wherein the geometric complexity data of the circuit board includes the structural regional data of the circuit board with high geometric complexity and the structural regional data of the circuit board with low geometric complexity;

[0045] Step S255: Select the structural simulation accuracy of the circuit board geometric complexity data based on the circuit board simulation accuracy range data, so as to obtain the circuit board thermal simulation accuracy data.

[0046] The present invention can effectively identify different design levels (such as power layer, signal layer, ground layer, etc.) by extracting hierarchical structure features from the three-dimensional structure model of the circuit board, providing a basis for design optimization. The extracted hierarchical structure data provides rich information for subsequent analysis and optimization, which helps to improve the manufacturability and reliability of the design. According to the hierarchical structure data, the regional structure division can be performed, and the circuit board can be divided into different functional areas, and the purpose and characteristics of each area (such as signal transmission area, power area, etc.) are clarified, which helps designers to better manage the electrical performance of the circuit board. Regional structure division can improve the visualization of the circuit board, so that designers can understand the layout of the circuit board more intuitively. Calculating the regional component density and the circuit board wiring density can help designers evaluate the design complexity and potential performance problems (such as signal integrity, heat dissipation problems, etc.) of each area. Density data can be used to optimize line wiring, reduce unnecessary complexity, and thus reduce manufacturing difficulty and cost. By evaluating the geometric complexity of the circuit board, high-complexity and low-complexity areas can be identified, helping designers to take corresponding design measures in these areas to improve manufacturability and reduce production costs. Complexity evaluation can also help analyze heat dissipation characteristics and promote more effective thermal management design. Selecting thermal simulation accuracy based on the circuit board simulation accuracy range data can ensure the accuracy of the simulation results, so that the design can better meet the thermal management requirements. By selecting the appropriate thermal simulation accuracy, designers can reduce the consumption of computing resources and improve work efficiency while ensuring accuracy.

[0047] Optionally, step S255 is specifically:

[0048] Classifying the simulation accuracy range data of the circuit board by simulation accuracy range, thereby obtaining high simulation accuracy range data and low simulation accuracy range data;

[0049] According to the high simulation accuracy range data, high simulation accuracy is allocated to the structural area data of the high geometric complexity circuit board, so as to obtain high simulation accuracy structural area data;

[0050] According to the low simulation precision range data, the low simulation precision is allocated to the structural area data of the circuit board with low geometric complexity, so as to obtain the structural area data with low simulation precision;

[0051] The structural region data with high simulation accuracy and the structural region data with low simulation accuracy are merged to obtain the circuit board thermal simulation accuracy data.

[0052] The present invention can allocate computing resources more reasonably by classifying the accuracy of simulation data, concentrate on processing areas with high precision requirements, and thus improve the overall simulation efficiency. High-precision areas can obtain more accurate thermal analysis results, while low-precision areas can reduce computing costs, ensuring that unnecessary calculations are reduced while meeting design requirements. In the design stage, high-precision and low-precision areas are accurately identified to help designers better understand and foresee possible thermal problems, so as to take corresponding optimization measures. For geometrically complex areas, high-precision simulation can better capture physical phenomena such as heat conduction, radiation and convection, and provide more accurate thermal distribution information. High-precision simulation can help identify potential overheating areas, reduce failure rates, and thus improve product reliability and service life. For low-complexity areas, low-precision simulation can significantly reduce computing time and resource consumption, and is suitable for large-scale circuit board design. Simplifying the calculation process can reduce hardware requirements and computing costs, and is cost-effective for large-scale production of circuit boards. Even in the case of low-precision simulation, sufficient information can still be obtained for preliminary design evaluation to ensure that the basic performance of the product meets the requirements. Simulation data of different precisons are combined to form a complete thermal model, so that the overall thermal performance evaluation of the circuit board is more accurate. By combining high and low precision data, it is possible to take into account both details and the overall picture in the analysis, helping designers identify local and global thermal issues.

[0053] Optionally, step S3 specifically includes:

[0054] Step S31: estimating the global grid density of the circuit board according to the simulation accuracy range data of the circuit board, thereby obtaining the global network density data of the circuit board;

[0055] Step S32: performing global meshing on the three-dimensional structure model of the circuit board according to the global network density data of the circuit board, thereby obtaining global mesh data of the circuit board;

[0056] Step S33: performing high simulation accuracy structure grid density estimation based on the circuit board thermal simulation accuracy data, thereby obtaining circuit board structure high grid density data; performing low simulation accuracy structure grid density estimation based on the circuit board thermal simulation accuracy data, thereby obtaining circuit board structure low grid density data;

[0057] Step S34: refining the grid density of the circuit board on the global grid data of the circuit board according to the high grid density data of the circuit board structure and the low grid density data of the circuit board structure, thereby obtaining the grid data of the circuit board components;

[0058] Step S35: obtaining a circuit board material property database, and integrating circuit board design material properties into the circuit board material property database according to the circuit board design data, thereby obtaining circuit board design material property data;

[0059] Step S36: assigning physical properties to the circuit board component grid data according to the circuit board design material characteristic data, thereby obtaining physical grid unit data.

[0060] The present invention ensures the accuracy of the simulation results by estimating the global grid density according to the simulation accuracy range data of the circuit board. The grid density at this stage directly affects the reliability of the subsequent simulation results. Reasonable grid density estimation can effectively reduce the consumption of computing resources, reduce computing time, and improve work efficiency. By globally meshing the three-dimensional structure model of the circuit board, high-quality global grid data can be obtained to ensure that the subsequent simulation analysis is accurate. The data after global meshing will help engineers to perform global thermal and electrical performance analysis and provide a comprehensive performance evaluation basis. According to different simulation accuracy requirements, high and low grid density estimates are performed respectively, so that the simulation can perform high-precision simulation for key areas, while other areas can use low-precision grids, thereby balancing simulation accuracy and computing efficiency. It provides the possibility of flexibly adjusting the simulation accuracy according to design requirements and computing resources, making the design process more efficient. By refining the global grid data, the grid data of the circuit board components is obtained, which is helpful for in-depth analysis of the local performance of the circuit board, especially in complex thermal and electrical environments. The refined grid can capture more physical phenomena and improve the accuracy of the simulation results, especially in thermal analysis and current distribution. Obtaining and integrating the circuit board material property database can conveniently manage material information and reduce errors caused by manual data entry. The integrated material property data provides a more comprehensive and accurate basis for circuit board design, helping engineers make better material selection and design decisions. Assigning physical properties to component grids based on circuit board design material property data makes the simulation results closer to the actual situation and improves the credibility of the simulation. Accurate assignment of physical properties can support complex thermal and electrical simulation analysis, providing reliable data support for subsequent optimization design.

[0061] Optionally, the present specification also provides a thermal simulation system of a circuit board, which is used to execute the thermal simulation method of the circuit board as described above, and the thermal simulation system of the circuit board includes:

[0062] The three-dimensional digitization module is used to obtain circuit board design data, and integrate the circuit board component connection relationship of the circuit board design data, so as to obtain the circuit board component connection relationship data; perform three-dimensional digital modeling according to the circuit board component connection relationship data, so as to obtain the three-dimensional structure model of the circuit board;

[0063] A thermal simulation accuracy estimation module is used to extract circuit board working condition features according to circuit board design data, thereby obtaining circuit board design working condition data, and to estimate circuit board thermal simulation accuracy based on the circuit board design working condition data, thereby obtaining circuit board thermal simulation accuracy data;

[0064] The physical grid unit division module is used to classify and divide the finite element grids of the circuit board components of the three-dimensional structure model of the circuit board according to the circuit board thermal simulation accuracy data, so as to obtain the grid data of the circuit board components; and to assign physical properties to the grid data of the circuit board components according to the circuit board design data, so as to obtain the physical grid unit data;

[0065] A circuit board thermal simulation module is used to reconstruct the circuit board structure model of the three-dimensional structure model of the circuit board according to the physical grid unit data, thereby obtaining a circuit board thermal simulation model, and to perform circuit board thermal simulation on the circuit board design working condition data through the circuit board thermal simulation model, thereby obtaining circuit board thermal simulation data;

[0066] The component design optimization module is used to visualize the thermal stress distribution diagram of the circuit board thermal simulation data, thereby obtaining the thermal stress distribution diagram, and optimize the circuit board component design of the circuit board three-dimensional structure model according to the thermal stress distribution diagram, thereby obtaining the optimized circuit board three-dimensional structure model.

[0067] The thermal simulation system of the circuit board of the present invention can implement any thermal simulation method of the circuit board of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete the thermal simulation method of the circuit board. The internal modules of the system cooperate with each other, thereby effectively improving the thermal management capability of the circuit board.

[0068] Optionally, the present specification also provides a circuit board, including a circuit board main body, a power supply unit and an electrical control unit, the power supply unit is installed inside the circuit board main body, the electrical control unit is electrically connected to the power supply unit, the electrical control unit is used to charge and control the circuit board main body, and the electrical control unit is used to execute a thermal simulation method for a circuit board as described above. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] 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:

[0070] Figure 1 A schematic diagram of the steps of the thermal simulation method for a circuit board of the present invention;

[0071] Figure 2 Detailed step flow diagram of step S1 in the present invention;

[0072] Figure 3 Detailed step flow diagram of step S2 in the present invention;

[0073] 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

[0074] 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.

[0075] 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.

[0076] 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.

[0077] To achieve this, please refer to Figures 1 to 3 The present invention provides a thermal simulation method for a circuit board, the method comprising the following steps:

[0078] Step S1: obtaining circuit board design data, and integrating the circuit board component connection relationship of the circuit board design data, thereby obtaining circuit board component connection relationship data; performing three-dimensional digital modeling according to the circuit board component connection relationship data, thereby obtaining a three-dimensional structural model of the circuit board;

[0079] In this embodiment, the design data of the circuit board is obtained through EDA (electronic design automation) software, such as Altium Designer or Cadence. These data include the layout, connection relationship, pin information and design rules of the components. Then, these data are analyzed using a graphic algorithm (such as a graph traversal algorithm) to integrate the connection relationship of the circuit board components to form a connection matrix. Next, based on the connection relationship data, a three-dimensional modeling software (such as SolidWorks or CATIA) is used for modeling to create a three-dimensional structural model of the circuit board. A specific implementation method can be to convert the connection matrix into three-dimensional space coordinates, and use the automatic modeling function of the software to quickly generate the corresponding circuit board model.

[0080] Step S2: extracting circuit board operating condition features according to the circuit board design data, thereby obtaining circuit board design operating condition data, and estimating circuit board thermal simulation accuracy based on the circuit board design operating condition data, thereby obtaining circuit board thermal simulation accuracy data;

[0081] In this embodiment, the circuit board design data is used to extract operating characteristics such as current density, operating temperature and ambient temperature. The circuit board design operating condition data is generated by analyzing the operating frequency, power consumption and operating environment in the circuit design. Then, thermal simulation software (such as ANSYS or COMSOL Multiphysics) is used to estimate the thermal simulation accuracy, screen out the simulation accuracy required under the actual working conditions, and obtain the circuit board thermal simulation accuracy data.

[0082] Step S3: classifying and dividing the finite element mesh of the circuit board components on the three-dimensional structure model of the circuit board according to the circuit board thermal simulation accuracy data, thereby obtaining the circuit board component mesh data; assigning physical properties to the circuit board component mesh data according to the circuit board design data, thereby obtaining physical mesh unit data;

[0083] In this embodiment, after obtaining the thermal simulation accuracy data, the finite element analysis software (such as ANSYS or Abaqus) is used to mesh the three-dimensional structural model of the circuit board. According to different material properties and geometric characteristics, the circuit board components are classified and divided, for example, the heat-sensitive component area is divided into a finer grid to improve the simulation accuracy. Then, according to the circuit board design data, the physical properties are assigned to each grid unit, such as thermal conductivity, specific heat capacity and elastic modulus, to form physical grid unit data. At this time, ensure that the assignment of physical properties is consistent with the material properties to avoid simulation errors caused by parameter mismatch.

[0084] Step S4: reconstructing the circuit board three-dimensional structure model according to the physical grid unit data to obtain a circuit board thermal simulation model, and performing circuit board thermal simulation on the circuit board design working condition data through the circuit board thermal simulation model to obtain circuit board thermal simulation data;

[0085] In this embodiment, the physical grid cell data obtained is used to map the density, physical assignment and other properties of the grid cell to the corresponding position of the three-dimensional structural model of the circuit board through the spatial information of the grid cell to reconstruct and form a thermal simulation model. Next, a comprehensive thermal simulation of the model is performed using thermal simulation software, and the design operating condition data of the circuit board is input. Through the calculated results, key data such as the thermal distribution and heat flux density of the circuit board under actual operating conditions are obtained. This process involves adjusting the simulation parameters and boundary conditions to ensure the accuracy and reliability of the final thermal simulation data, which is convenient for subsequent thermal stress analysis and design optimization.

[0086] Step S5: Visualizing the thermal stress distribution diagram of the circuit board thermal simulation data to obtain the thermal stress distribution diagram, and optimizing the circuit board component design of the circuit board three-dimensional structure model according to the thermal stress distribution diagram to obtain the optimized circuit board three-dimensional structure model.

[0087] In this embodiment, visualization software (such as MATLAB or ParaView) is used to process the thermal simulation data of the circuit board to generate a thermal stress distribution map. This map can intuitively display the thermal stress levels of various parts of the circuit board and identify potential thermal stress concentration areas. According to the information of the thermal stress distribution map, the circuit board component design is optimized, such as adding heat sinks, improving component layout or adjusting materials to reduce the impact of thermal stress on the circuit board. After optimization, the updated three-dimensional structural model is simulated and verified again to ensure that the optimization measures are effective and the performance of the circuit board under working conditions is improved.

[0088] Optionally, step S1 specifically includes:

[0089] Step S11: obtaining circuit board design data, and extracting circuit board structural features from the circuit board design data, thereby obtaining circuit board structural data;

[0090] In this embodiment, the design file of the circuit board (such as a Gerber file or a PCB design file) is obtained through an electronic design automation (EDA) tool. Computer vision technology is used to parse these design data to extract the geometric features of the circuit board, such as tracks, apertures, component locations, and number of wiring layers. Specifically, an image processing algorithm (such as an edge detection algorithm) can be used to identify the boundaries and lines of the circuit board, and then the length, width, and position coordinates of each line are extracted to generate the structural feature data of the circuit board.

[0091] Step S12: Structural classification of the circuit board structure data, thereby obtaining circuit board circuit structure data and circuit board component structure data;

[0092] In this embodiment, the extracted circuit board structural feature data will be classified according to its functions and physical characteristics. Machine learning algorithms such as support vector machines (SVM) or convolutional neural networks (CNN) can be used to train and classify the labeled and classified circuit board structural data. Specifically, the circuit structure (such as power supply, grounding and signal lines) and component structure (such as resistors, capacitors and integrated circuits) of the circuit board can be labeled and classified, and finally two independent data sets are generated: circuit board circuit structure data and circuit board component structure data.

[0093] Step S13: integrating the circuit board circuit power pin connection paths according to the circuit board circuit structure data, thereby obtaining the circuit board circuit power pin connection path data;

[0094] In this embodiment, the extracted circuit structure data is used to identify the connection relationship between the power pin and other component pins. The shortest path algorithm in graph theory is used to establish a pin connection diagram. Specifically, the adjacency matrix of the power pin is first constructed according to the circuit diagram in the circuit structure data of the circuit board, and then the Dijkstra algorithm is used to find the connection path from the power pin to each component pin. The circuit board circuit power pin connection path data finally output will contain information such as the start and end pins, connection method and resistance value of each connection.

[0095] Step S14: performing component pin spatial layout analysis on the circuit board component structure data, thereby obtaining component pin spatial distribution data;

[0096] In this embodiment, the component structure data is used to perform spatial layout analysis on the component pins. The spatial distribution of the component pins is evaluated by a geometric analysis method (such as principal component analysis PCA). Specifically, the coordinates of each component pin are mapped into a three-dimensional space, and the distance and angle between the pins are calculated to determine the distribution density and arrangement of the pins. The output will be the component pin spatial distribution data including pin spacing, arrangement direction and density information.

[0097] Step S15: Pin association is performed on the circuit board circuit power pin connection path data and the component pin space distribution data, thereby obtaining the circuit board component connection relationship data;

[0098] In this embodiment, the pin association analysis is performed by combining the power pin connection path data with the component pin spatial distribution data. An association rule learning algorithm (such as the Apriori algorithm) can be used to identify the association relationship between the power pin and the component pin. First, an association matrix is ​​created, in which one dimension represents the power pin and the other dimension represents the component pin, and then the connection frequency between them is calculated. By setting a threshold, pin pairs with greater association strength are screened out, and finally the circuit board component connection relationship data is generated.

[0099] Step S16: Perform three-dimensional digital modeling based on the circuit board component connection relationship data to obtain a three-dimensional structural model of the circuit board.

[0100] In this embodiment, three-dimensional digital modeling is performed based on the obtained circuit board component connection relationship data. A three-dimensional modeling software (such as CATIA or Fusion 360) can be used to build the model. Specifically, the three-dimensional coordinate data of the components and power pins are input into the modeling software, and the corresponding lines and surfaces are generated according to the connection relationship. The software can automatically create a three-dimensional model based on the input data, and the user can perform subsequent modification and optimization to finally generate a complete three-dimensional structural model of the circuit board. The model can be exported in STL or OBJ format for subsequent simulation analysis or manufacturing applications.

[0101] Optionally, step S2 specifically includes:

[0102] Step S21: extracting circuit board operating condition features according to the circuit board design data, thereby obtaining circuit board design operating condition data;

[0103] In this embodiment, the operating condition feature extraction is performed based on the design data of the circuit board, such as component layout, number of layers, material type, etc. The circuit board design file is imported using computer-aided design (CAD) software, and the operating conditions of each component, such as current density, operating temperature range, and voltage level, are extracted to generate detailed operating condition feature data.

[0104] Step S22: performing design operation time statistics on the circuit board design operating condition data to obtain the circuit board design operation time data, and classifying the circuit board design operation time data to obtain long-term operation circuit board operating condition data and short-term operation circuit board operating condition data;

[0105] In this embodiment, the design running time of the circuit board is counted, and the planned working time of each circuit board is counted. Then, the circuit board design running time data is classified by a data analysis tool (such as Python's Pandas library) and divided into two types of working conditions: "long-term operation" (for example, operation for more than 1000 hours) and "short-term operation" (for example, less than 1000 hours).

[0106] Step S23: acquiring historical circuit board thermal simulation data, and estimating the circuit board thermal accumulation effect based on the historical circuit board thermal simulation data and the circuit board design operating condition data, thereby obtaining circuit board thermal accumulation effect estimation data;

[0107] In this embodiment, thermal simulation data of historical circuit boards, such as temperature distribution and heat flux density, are obtained from the circuit board manufacturer. The heat accumulation effect is estimated by numerical analysis software (such as ANSYS or COMSOL) combined with the obtained circuit board design working condition data. The finite element analysis method can be used to calculate the heat accumulation under different working conditions, thereby generating the heat accumulation effect data of the circuit board under specific working conditions, which is convenient for subsequent thermal management design.

[0108] Step S24: dividing the overall simulation accuracy range of the long-term running circuit board working condition data and the short-term running circuit board working condition data according to the circuit board heat accumulation effect estimation data, thereby obtaining circuit board simulation accuracy range data;

[0109] In this embodiment, the overall simulation accuracy range is divided for the operating condition data of the circuit board running for a long time and the circuit board running for a short time according to the estimated data of the heat accumulation effect of the circuit board. A statistical analysis method can be used to evaluate the accuracy of the thermal simulation model under each operating condition based on historical data. For example, for a circuit board running for a long time, a stricter accuracy requirement (such as ±5°C) is required, while a circuit board running for a short time is assigned a looser range (such as ±10°C). Finally, the simulation accuracy range data of the circuit board under different operating conditions is obtained, which is convenient for subsequent thermal analysis.

[0110] Step S25: Evaluate the circuit board structure geometric complexity according to the circuit board three-dimensional structure model to obtain circuit board geometric complexity data, and select structural simulation accuracy for the circuit board geometric complexity data based on the circuit board simulation accuracy range data to obtain circuit board thermal simulation accuracy data.

[0111] In this embodiment, the geometric complexity is evaluated based on the three-dimensional structural model of the circuit board. In the specific implementation, 3D CAD software is used to analyze the geometric features of the circuit board, such as holes, through-holes, stacked structures, etc., and the complexity index is calculated. Combined with the simulation accuracy range data obtained in the previous step, the appropriate simulation accuracy is selected. For example, if the geometric complexity of the circuit board is high (the complexity index is greater than 0.7), high-precision simulation methods and simulation parameters (such as local refined grids) are required, otherwise standard accuracy methods can be used. Finally, thermal simulation accuracy data that meets different geometric complexities is obtained to ensure the reliability and accuracy of the simulation results.

[0112] Optionally, step S23 is specifically:

[0113] Step S231: acquiring historical circuit board thermal simulation data, and performing simulation thermal distribution feature extraction and simulation circuit board structure feature extraction on the historical circuit board thermal simulation data, thereby obtaining historical simulation thermal distribution data and historical simulation circuit board structure data;

[0114] In this embodiment, the thermal simulation data of the historical circuit board is extracted from the database of the circuit board manufacturer. The temperature distribution data of the circuit board under different working conditions can be exported using thermal simulation software (such as ANSYS or COMSOL Multiphysics). Then, the data are processed using data analysis tools (such as MATLAB or Python) to extract thermal distribution characteristics, such as maximum temperature, minimum temperature, temperature mean, and heat transfer coefficient. These characteristic data can be used for subsequent structural feature extraction to form a complete set of historical simulation thermal distribution data sets.

[0115] Step S232: extracting circuit board design structure features from the circuit board design data to obtain circuit board design structure data, and calculating structure similarity between the circuit board design structure data and historical simulation circuit board structure data to obtain structure similarity data;

[0116] In this embodiment, the circuit board design data is analyzed. The design data can come from a model generated by CAD software (such as AutoCAD or SolidWorks). When extracting the structural features of the circuit board, you can consider using a geometric feature extraction algorithm, such as an algorithm based on boundary representation (Boundary Representation, B-rep), to identify key parameters in the design (such as pad position, wire width, aperture, etc.). Next, use a similarity algorithm to compare the historical simulation circuit board structure data with the extracted circuit board design structure data, and you can use cosine similarity or Euclidean distance and other methods to calculate the structural similarity. This will form a set of structural similarity data as the basis for subsequent analysis.

[0117] Step S233: extracting high-similarity circuit board simulation thermal distribution data from the historical simulation thermal distribution data according to the structural similarity data, thereby obtaining high-similarity circuit board simulation thermal distribution data;

[0118] In this embodiment, based on the obtained structural similarity data, historical simulation thermal distribution data with high similarity to the current circuit board design is selected. A threshold value, such as 0.9, can be set to filter out high-similarity samples that meet the conditions. A Python script can be used to process data by relying on the pandas library to extract historical thermal distribution data that meets the similarity standard through conditional screening. The extracted data will form a high-similarity circuit board simulation thermal distribution data set, providing basic data for subsequent thermal energy trend analysis.

[0119] Step S234: performing a simulated thermal energy trend analysis on the simulated thermal distribution data of the high-similarity circuit board, thereby obtaining simulated thermal energy trend data of historically similar circuit boards;

[0120] In this embodiment, after obtaining the simulated thermal distribution data of the high-similarity circuit board, a thermal energy trend analysis is performed. First, a time series analysis method (such as a moving average method or an exponential smoothing method) is used to process the data to identify the trend of thermal energy changes over time. This can be achieved through the statsmodels library of MATLAB or Python, using linear regression or other regression models to analyze the changing trend of the data. In addition, the visualization of the temperature-time curve can also be combined to help identify potential thermal energy accumulation points and changing trends. Finally, the historical similar circuit board simulation thermal energy trend data is obtained to provide a basis for subsequent thermal energy trend estimation.

[0121] Step S235: performing simulation thermal energy trend estimation on the circuit board design operation time data according to the historical similar circuit board simulation thermal energy trend data, thereby obtaining simulation thermal energy trend estimation data;

[0122] In this embodiment, the thermal energy trend of the design running time of the new circuit board is estimated by regression analysis, machine learning or other numerical methods. Specifically, by establishing a model, the running time in the historical circuit board simulation data that is highly similar to the current circuit board design is matched with the corresponding thermal energy trend, and the thermal energy change trend of the current circuit board design within the expected running time is inferred. This prediction model can be constructed using linear regression or deep learning models, and the running time of different designs is combined with the thermal energy trend for prediction. Finally, the simulated thermal energy trend estimation data will be obtained to evaluate the thermal performance of the circuit board design.

[0123] Step S236: Estimating the heat accumulation effect of the circuit board according to the simulated thermal energy trend estimation data, thereby obtaining the heat accumulation effect estimation data of the circuit board.

[0124] In this embodiment, after obtaining the simulated thermal energy trend estimation data, the thermal accumulation effect of the circuit board is estimated. At this time, it is necessary to consider the heat accumulation effect of the circuit board in actual work, and use the heat conduction equation to establish a model to simulate the conduction of heat in the design material. Finite element analysis (FEA) software (such as ANSYS or COMSOL) can be used for detailed modeling, considering the thermal conductivity, specific heat capacity and other physical properties of different materials. The thermal accumulation effect data obtained by numerical simulation can clarify the temperature changes of various parts of the circuit board under continuous operation, and form the thermal accumulation effect estimation data of the circuit board for optimization design.

[0125] Optionally, step S24 is specifically:

[0126] Step S241: extracting high-similarity circuit board historical thermal simulation accuracy data from historical circuit board thermal simulation data according to the structural similarity data, thereby obtaining high-similarity circuit board historical thermal simulation accuracy data;

[0127] In this embodiment, a similarity threshold can be set to extract historical circuit board thermal simulation data that is highly similar to the circuit board design structure (higher than the set threshold 0.85), and then the thermal simulation parameters and thermal simulation results in the data are integrated to form a data set, thereby obtaining high-similarity circuit board historical thermal simulation accuracy data.

[0128] Step S242: performing thermal simulation accuracy classification on the historical thermal simulation accuracy data of the high-similarity circuit board, thereby obtaining historical high simulation accuracy data and historical low simulation accuracy data;

[0129] In this embodiment, the collected historical thermal simulation accuracy data of high-similarity circuit boards are classified and processed. First, the classification criteria of calculation accuracy (such as time step, spatial discretization, convergence criteria, etc.) are defined to distinguish between high simulation accuracy and low simulation accuracy. For example, the parameter accuracy threshold of high simulation accuracy can be set, such as setting the residual threshold within 10^{-4}, and the residual threshold of low simulation accuracy is set to 10^{-3}. By comparing these standards, qualified data sets are screened out, and finally historical high simulation accuracy data and historical low simulation accuracy data are obtained.

[0130] Step S243: selecting high simulation accuracy for the circuit board operating condition data of a long time according to the historical high simulation accuracy data, thereby obtaining high simulation accuracy circuit board data; selecting low simulation accuracy for the circuit board operating condition data of a short time according to the historical low simulation accuracy data, thereby obtaining low simulation accuracy circuit board data;

[0131] In this embodiment, historical high simulation accuracy data is used to set the calculation accuracy (time step, spatial discretization, convergence standard) of the circuit board under long-term operating conditions. The thermal simulation calculation accuracy of the circuit board that shows high simulation accuracy under long-term operating conditions is selected to ensure that the error accuracy of the circuit board long-term operating condition simulation is within the set high-precision range. On the contrary, for the circuit board operating condition data of short-term operation, a lower calculation accuracy is selected from the similar short-term operating condition simulation in the historical low simulation accuracy data, so as to ensure that the error accuracy of the circuit board short-term operating condition simulation is within the set normal accuracy range (calculation accuracy average value range). In this way, high simulation accuracy circuit board data and low simulation accuracy circuit board data will be obtained respectively in the end, providing a basis for subsequent simulation analysis.

[0132] Step S244: Divide the simulation accuracy range according to the high simulation accuracy circuit board data and the low simulation accuracy circuit board data, so as to obtain circuit board simulation accuracy range data.

[0133] In this embodiment, the simulation accuracy range is divided based on the high simulation accuracy circuit board data and low simulation accuracy circuit board data obtained previously. Statistical analysis is performed on these two types of data, and their respective mean values, standard deviations and other indicators are calculated to determine their accuracy ranges. For example, the set residual range of high simulation accuracy data can be defined as 10^{-4} to 10^{-3}, while the range of low simulation accuracy data can be defined as 10^{-6} to 10^{-3}. Through such analysis, the circuit board simulation accuracy range data is finally formed, providing a guiding basis for future simulation accuracy setting and optimization.

[0134] Optionally, step S25 is specifically:

[0135] Step S251: extracting hierarchical structure features from the three-dimensional structure model of the circuit board, thereby obtaining hierarchical structure data of the circuit board;

[0136] In this embodiment, a 3D modeling software (such as SolidWorks or CATIA) is used to analyze the 3D structural model of the circuit board. The CAD file of the circuit board needs to be imported to extract the structural information of each layer, including but not limited to the wires, components and their positions, material properties and dimensions. The extraction results are presented in the form of a data table, including the name, thickness, material type, connection method, etc. of each layer, so as to generate detailed hierarchical structure data of the circuit board.

[0137] Step S252: dividing the circuit board into regional structures according to the circuit board hierarchical structure data, thereby obtaining circuit board structural regional data;

[0138] In this embodiment, the circuit board is divided into several areas according to the obtained circuit board hierarchical structure data. For example, it can be divided according to functional modules (such as power area, signal area, grounding area, etc.). Cluster analysis algorithms (such as K-means) can be used to identify regional boundaries to ensure that the components in each area have similar functions and electrical characteristics. Finally, the output structural area data contains information such as the boundary coordinates of each area, the area of ​​the area, and the number of components contained.

[0139] Step S253: performing regional component density calculation on the circuit board structure regional data, thereby obtaining regional component density data; performing regional circuit board wiring density calculation on the circuit board structure regional data, thereby obtaining regional wiring density data;

[0140] In this embodiment, for each structural area, the component density and wiring density are calculated. In specific implementation, by defining the "density" index, the ratio of the number of components in each area to the area of ​​the area is calculated to obtain the regional component density data. For wiring density, the ratio of the wire length in the area to the area of ​​the area is calculated. The Python programming language is used in combination with the NumPy and Pandas libraries to process data and perform density calculations, and finally output the density results.

[0141] Step S254: evaluating the regional geometric complexity of the circuit board according to the regional component density data and the regional wiring density data, thereby obtaining the geometric complexity data of the circuit board, wherein the geometric complexity data of the circuit board includes the structural regional data of the circuit board with high geometric complexity and the structural regional data of the circuit board with low geometric complexity;

[0142] In this embodiment, the geometric complexity of the circuit board is evaluated by combining the regional component density and regional wiring density data. Complexity evaluation criteria can be set, such as thresholds for component density and wiring density. If the component density of a certain area exceeds the set value and the wiring density is also high, it is marked as a high geometric complexity area; otherwise, it is marked as a low geometric complexity area. Use data visualization tools (such as Matplotlib) to display the distribution of areas of different complexity for subsequent analysis and decision-making.

[0143] Step S255: Select the structural simulation accuracy of the circuit board geometric complexity data based on the circuit board simulation accuracy range data, so as to obtain the circuit board thermal simulation accuracy data.

[0144] In this embodiment, the thermal simulation accuracy is selected based on the geometric complexity data of the circuit board. In the specific implementation, the simulation accuracy standards required for different complexity areas are first defined. For example, high complexity areas require higher simulation grid density and calculation accuracy to obtain more accurate thermal distribution information. Then, the corresponding simulation parameters are set using simulation software (such as ANSYS or COMSOL), and the best circuit board thermal simulation accuracy data is selected by comparing the simulation results under different accuracy settings. Finally, the thermal simulation data is output to ensure that it matches the geometric characteristics of the circuit board to achieve accurate thermal analysis.

[0145] Optionally, step S255 is specifically:

[0146] Classifying the simulation accuracy range data of the circuit board by simulation accuracy range, thereby obtaining high simulation accuracy range data and low simulation accuracy range data;

[0147] In this embodiment, the simulation data of the circuit board is classified by accuracy range and grouped according to the settings of parameter accuracy and calculation accuracy. A threshold can be set, such as defining the high simulation accuracy range as data with residuals less than 10^{-4}, and the low simulation accuracy range as residuals between 10^{-6} and 10^{-3}. All input data are analyzed and classified into high-precision or low-precision ranges for use in subsequent simulations. Numerical calculation tools (such as ANSYS, COMSOL, etc.) can be used to verify residuals under different conditions.

[0148] According to the high simulation accuracy range data, high simulation accuracy is allocated to the structural area data of the high geometric complexity circuit board, so as to obtain high simulation accuracy structural area data;

[0149] In this embodiment, for the identified high geometric complexity areas (e.g., complex circuit lines or dense component layouts), the data in the high simulation accuracy range is used to perform area allocation. Specifically, high simulation accuracy is allocated to these areas, and calculation parameters (e.g., time step is 0.01 seconds, mesh division is fine to 0.5 mm) are set to ensure that the model has sufficient calculation accuracy in these complex areas.

[0150] According to the low simulation precision range data, the low simulation precision is allocated to the structural area data of the circuit board with low geometric complexity, so as to obtain the structural area data with low simulation precision;

[0151] In this embodiment, for low geometric complexity areas (such as simple ground planes or large blank areas), regional data allocation is performed based on a low simulation accuracy range. For example, a larger grid size (such as more than 1 mm) can be set, and a larger residual range (such as less than 10^{-3}) can be allowed. Using a simpler model can save computing resources while still maintaining the effectiveness of the overall thermal analysis.

[0152] The structural region data with high simulation accuracy and the structural region data with low simulation accuracy are merged to obtain the circuit board thermal simulation accuracy data.

[0153] In this embodiment, after the data division of the high simulation accuracy area and the low simulation accuracy area is completed, the area is merged. The high simulation accuracy structural area data is merged with the low simulation accuracy structural area data to form a unified circuit board thermal simulation model. This merging process needs to ensure the continuity and consistency of the boundary conditions. The boundary conditions can be adjusted by numerical algorithms (such as weighted average method) to achieve a smooth transition of different accuracy areas, thereby obtaining the final circuit board thermal simulation accuracy data.

[0154] Optionally, step S3 specifically includes:

[0155] Step S31: estimating the global grid density of the circuit board according to the simulation accuracy range data of the circuit board, thereby obtaining the global network density data of the circuit board;

[0156] In this embodiment, the global grid density of the circuit board is estimated based on the simulation accuracy range data (such as ±5%). Assuming that the simulation accuracy requirement is 0.1mm, on this basis, according to the size of the circuit board (such as 120mm x 100mm), the circuit board can be divided into approximately 1200 grid cells. The size of each grid cell can be set to 10mm x 10mm to ensure sufficient accuracy across the entire circuit board. The final generated global network density data can be output in the form of a data table, including the coordinates and size of each grid cell.

[0157] Step S32: performing global meshing on the three-dimensional structure model of the circuit board according to the global network density data of the circuit board, thereby obtaining global mesh data of the circuit board;

[0158] In this embodiment, global meshing is implemented based on the obtained global network density data. Specifically, finite element method (FEM) software (such as ANSYS or COMSOL) can be used to create a global mesh of a three-dimensional structural model. In the model, the geometry and node information of each mesh will be automatically generated to ensure that each mesh unit meets the preset density requirements. By setting meshing parameters (such as maximum and minimum mesh sizes), the final generated global mesh data will be saved in .stl or .mesh format for subsequent processing.

[0159] Step S33: performing high simulation accuracy structure grid density estimation based on the circuit board thermal simulation accuracy data, thereby obtaining circuit board structure high grid density data; performing low simulation accuracy structure grid density estimation based on the circuit board thermal simulation accuracy data, thereby obtaining circuit board structure low grid density data;

[0160] In this embodiment, in order to perform thermal simulation, high mesh density data and low mesh density data are calculated respectively according to the set high simulation accuracy (e.g., ±1%) and low simulation accuracy (e.g., ±10%). For example, in high simulation mode, assuming that the thermal conductivity requirement of the material is ±0.5W / m·K, when calculating the mesh density, the thermal characteristics of each component need to be refined within a 5mm area, increasing the number of meshes to 3000. In low simulation mode, the mesh density can be reduced to each component distributed within a 10mm area, and the number of meshes is reduced to 1500. By comparing the high and low mesh density data, the final density range used is determined.

[0161] Step S34: refining the grid density of the circuit board on the global grid data of the circuit board according to the high grid density data of the circuit board structure and the low grid density data of the circuit board structure, thereby obtaining the grid data of the circuit board components;

[0162] In this embodiment, the density of the corresponding circuit board structure grid in the circuit board global grid data is adjusted according to the circuit board structure high grid density data and the circuit board structure low grid density data. For example, the circuit board structure high grid density replaces the grid density of the corresponding simulation accuracy structure area in the circuit board global grid.

[0163] Step S35: obtaining a circuit board material property database, and integrating circuit board design material properties into the circuit board material property database according to the circuit board design data, thereby obtaining circuit board design material property data;

[0164] In this embodiment, in order to obtain the material property data of the circuit board design, it is first necessary to access the material property database (such as MatWeb), and then integrate the relevant material properties according to the circuit board design data (such as the PCB material is FR-4). This includes electrical conductivity, thermal conductivity, dielectric constant, etc. Assume that the selected material properties include: electrical conductivity is 0.002S / m, thermal conductivity is 0.25W / m·K, and dielectric constant is 4.4. These data will be organized into a database format, such as a CSV file, for subsequent applications.

[0165] Step S36: assigning physical properties to the circuit board component grid data according to the circuit board design material characteristic data, thereby obtaining physical grid unit data.

[0166] In this embodiment, according to the circuit board design material property data, it is applied to the grid data of the circuit board assembly to assign physical properties. The material properties can be assigned to each grid cell through a programming interface (such as the interface between Python and ANSYS). Each physical grid cell (such as number 001) will be assigned corresponding material properties, such as electrical conductivity and thermal conductivity, to ensure that the real behavior of the material can be accurately reflected during simulation. Finally, the physical grid cell data will be output to the simulation software, ready for a complete thermal simulation analysis.

[0167] Optionally, the present specification also provides a thermal simulation system of a circuit board, which is used to execute the thermal simulation method of the circuit board as described above, and the thermal simulation system of the circuit board includes:

[0168] The three-dimensional digitization module is used to obtain circuit board design data, and integrate the circuit board component connection relationship of the circuit board design data, so as to obtain the circuit board component connection relationship data; perform three-dimensional digital modeling according to the circuit board component connection relationship data, so as to obtain the three-dimensional structure model of the circuit board;

[0169] A thermal simulation accuracy estimation module is used to extract circuit board working condition features according to circuit board design data, thereby obtaining circuit board design working condition data, and to estimate circuit board thermal simulation accuracy based on the circuit board design working condition data, thereby obtaining circuit board thermal simulation accuracy data;

[0170] The physical grid unit division module is used to classify and divide the finite element grids of the circuit board components of the three-dimensional structure model of the circuit board according to the circuit board thermal simulation accuracy data, so as to obtain the grid data of the circuit board components; and to assign physical properties to the grid data of the circuit board components according to the circuit board design data, so as to obtain the physical grid unit data;

[0171] A circuit board thermal simulation module is used to reconstruct the circuit board structure model of the three-dimensional structure model of the circuit board according to the physical grid unit data, thereby obtaining a circuit board thermal simulation model, and to perform circuit board thermal simulation on the circuit board design working condition data through the circuit board thermal simulation model, thereby obtaining circuit board thermal simulation data;

[0172] The component design optimization module is used to visualize the thermal stress distribution diagram of the circuit board thermal simulation data, thereby obtaining the thermal stress distribution diagram, and optimize the circuit board component design of the circuit board three-dimensional structure model according to the thermal stress distribution diagram, thereby obtaining the optimized circuit board three-dimensional structure model.

[0173] The thermal simulation system of the circuit board of the present invention can implement any thermal simulation method of the circuit board of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete the thermal simulation method of the circuit board. The internal modules of the system cooperate with each other, thereby effectively improving the thermal management capability of the circuit board.

[0174] Optionally, the present specification also provides a circuit board, including a circuit board main body, a power supply unit and an electrical control unit, the power supply unit is installed inside the circuit board main body, the electrical control unit is electrically connected to the power supply unit, the electrical control unit is used to charge and control the circuit board main body, and the electrical control unit is used to execute a thermal simulation method for a circuit board as described above.

[0175] 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.

[0176] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A thermal simulation method for a circuit board, characterized in that: The following steps are involved: Step S1: obtaining circuit board design data, and integrating the circuit board component connection relationship of the circuit board design data, thereby obtaining circuit board component connection relationship data; Perform three-dimensional digital modeling based on the connection relationship data of the circuit board components to obtain a three-dimensional structural model of the circuit board; Step S2: extracting circuit board operating condition features according to the circuit board design data, thereby obtaining circuit board design operating condition data, and estimating circuit board thermal simulation accuracy based on the circuit board design operating condition data, thereby obtaining circuit board thermal simulation accuracy data; Step S3: classifying and dividing the finite element mesh of the circuit board components on the three-dimensional structure model of the circuit board according to the circuit board thermal simulation accuracy data, thereby obtaining the circuit board component mesh data; assigning physical properties to the circuit board component mesh data according to the circuit board design data, thereby obtaining physical mesh unit data; Step S4: reconstructing the circuit board structure model of the three-dimensional structure model of the circuit board according to the physical grid unit data, thereby obtaining a circuit board thermal simulation model, and performing circuit board thermal simulation on the circuit board design working condition data through the circuit board thermal simulation model, thereby obtaining circuit board thermal simulation data; Step S5: Visualizing the thermal stress distribution diagram of the circuit board thermal simulation data to obtain the thermal stress distribution diagram, and optimizing the circuit board component design of the circuit board three-dimensional structure model according to the thermal stress distribution diagram to obtain the optimized circuit board three-dimensional structure model.

2. The thermal simulation method of a circuit board according to claim 1, characterized in that: Step S1 is specifically as follows: Step S11: obtaining circuit board design data, and extracting circuit board structural features from the circuit board design data, thereby obtaining circuit board structural data; Step S12: Structural classification of the circuit board structure data, thereby obtaining circuit board circuit structure data and circuit board component structure data; Step S13: integrating the circuit board circuit power pin connection paths according to the circuit board circuit structure data, thereby obtaining the circuit board circuit power pin connection path data; Step S14: performing component pin spatial layout analysis on the circuit board component structure data, thereby obtaining component pin spatial distribution data; Step S15: Pin association is performed on the circuit board circuit power pin connection path data and the component pin space distribution data, thereby obtaining the circuit board component connection relationship data; Step S16: Perform three-dimensional digital modeling based on the circuit board component connection relationship data to obtain a three-dimensional structural model of the circuit board.

3. The thermal simulation method of a circuit board according to claim 1, characterized in that: Step S2 is specifically as follows: Step S21: extracting circuit board operating condition features according to the circuit board design data, thereby obtaining circuit board design operating condition data; Step S22: performing design operation time statistics on the circuit board design operating condition data to obtain the circuit board design operation time data, and classifying the circuit board design operation time data to obtain long-term operation circuit board operating condition data and short-term operation circuit board operating condition data; Step S23: acquiring historical circuit board thermal simulation data, and estimating the circuit board thermal accumulation effect based on the historical circuit board thermal simulation data and the circuit board design operating condition data, thereby obtaining circuit board thermal accumulation effect estimation data; Step S24: dividing the overall simulation accuracy range of the long-term running circuit board working condition data and the short-term running circuit board working condition data according to the circuit board heat accumulation effect estimation data, thereby obtaining circuit board simulation accuracy range data; Step S25: Evaluate the circuit board structural geometric complexity according to the circuit board three-dimensional structural model to obtain circuit board geometric complexity data, and select structural simulation accuracy for the circuit board geometric complexity data based on the circuit board simulation accuracy range data to obtain circuit board thermal simulation accuracy data.

4. The thermal simulation method of a circuit board according to claim 3, characterized in that: Step S23 is specifically as follows: Step S231: acquiring historical circuit board thermal simulation data, and performing simulation thermal distribution feature extraction and simulation circuit board structure feature extraction on the historical circuit board thermal simulation data, thereby obtaining historical simulation thermal distribution data and historical simulation circuit board structure data; Step S232: extracting circuit board design structure features from the circuit board design data to obtain circuit board design structure data, and calculating structure similarity between the circuit board design structure data and historical simulation circuit board structure data to obtain structure similarity data; Step S233: extracting high-similarity circuit board simulation thermal distribution data from the historical simulation thermal distribution data according to the structural similarity data, thereby obtaining high-similarity circuit board simulation thermal distribution data; Step S234: performing a simulated thermal energy trend analysis on the simulated thermal distribution data of the high-similarity circuit board, thereby obtaining simulated thermal energy trend data of historically similar circuit boards; Step S235: performing simulation thermal energy trend estimation on the circuit board design operation time data according to the historical similar circuit board simulation thermal energy trend data, thereby obtaining simulation thermal energy trend estimation data; Step S236: Estimating the heat accumulation effect of the circuit board according to the simulated thermal energy trend estimation data, thereby obtaining the heat accumulation effect estimation data of the circuit board.

5. The thermal simulation method of a circuit board according to claim 3, characterized in that: Step S24 is specifically as follows: Step S241: extracting high-similarity circuit board historical thermal simulation accuracy from historical circuit board thermal simulation data according to the structural similarity data, thereby obtaining high-similarity circuit board historical thermal simulation accuracy data; Step S242: performing thermal simulation accuracy classification on the historical thermal simulation accuracy data of the high-similarity circuit board, thereby obtaining historical high simulation accuracy data and historical low simulation accuracy data; Step S243: selecting high simulation accuracy for the circuit board operating condition data of a long time according to the historical high simulation accuracy data, thereby obtaining high simulation accuracy circuit board data; selecting low simulation accuracy for the circuit board operating condition data of a short time according to the historical low simulation accuracy data, thereby obtaining low simulation accuracy circuit board data; Step S244: Divide the simulation accuracy range according to the high simulation accuracy circuit board data and the low simulation accuracy circuit board data, so as to obtain circuit board simulation accuracy range data.

6. The thermal simulation method of a circuit board according to claim 3, characterized in that: Step S25 is specifically as follows: Step S251: extracting hierarchical structure features from the three-dimensional structure model of the circuit board, thereby obtaining hierarchical structure data of the circuit board; Step S252: dividing the circuit board into regional structures according to the circuit board hierarchical structure data, thereby obtaining circuit board structural regional data; Step S253: performing regional component density calculation on the circuit board structure regional data, thereby obtaining regional component density data; Calculating the wiring density of the regional circuit board on the circuit board structure regional data, thereby obtaining regional wiring density data; Step S254: evaluating the regional geometric complexity of the circuit board according to the regional component density data and the regional wiring density data, thereby obtaining the geometric complexity data of the circuit board, wherein the geometric complexity data of the circuit board includes the structural regional data of the circuit board with high geometric complexity and the structural regional data of the circuit board with low geometric complexity; Step S255: Select the structural simulation accuracy of the circuit board geometric complexity data based on the circuit board simulation accuracy range data, so as to obtain the circuit board thermal simulation accuracy data.

7. The thermal simulation method of a circuit board according to claim 6, characterized in that: Step S255 is specifically as follows: Classifying the simulation accuracy range data of the circuit board by simulation accuracy range, thereby obtaining high simulation accuracy range data and low simulation accuracy range data; According to the high simulation accuracy range data, high simulation accuracy is allocated to the structural area data of the high geometric complexity circuit board, so as to obtain high simulation accuracy structural area data; According to the low simulation precision range data, the low simulation precision is allocated to the structural area data of the circuit board with low geometric complexity, so as to obtain the structural area data with low simulation precision; The structural region data with high simulation accuracy and the structural region data with low simulation accuracy are merged to obtain the circuit board thermal simulation accuracy data.

8. The thermal simulation method of a circuit board according to claim 1, characterized in that: Step S3 is specifically as follows: Step S31: estimating the global grid density of the circuit board according to the simulation accuracy range data of the circuit board, thereby obtaining the global network density data of the circuit board; Step S32: performing global meshing on the three-dimensional structure model of the circuit board according to the global network density data of the circuit board, thereby obtaining global mesh data of the circuit board; Step S33: performing high simulation accuracy structure grid density estimation based on the circuit board thermal simulation accuracy data, thereby obtaining circuit board structure high grid density data; performing low simulation accuracy structure grid density estimation based on the circuit board thermal simulation accuracy data, thereby obtaining circuit board structure low grid density data; Step S34: refining the grid density of the circuit board on the global grid data of the circuit board according to the high grid density data of the circuit board structure and the low grid density data of the circuit board structure, thereby obtaining the grid data of the circuit board components; Step S35: obtaining a circuit board material property database, and integrating circuit board design material properties into the circuit board material property database according to the circuit board design data, thereby obtaining circuit board design material property data; Step S36: assigning physical properties to the grid data of the circuit board components according to the circuit board design material characteristic data, thereby obtaining physical grid unit data.

9. A thermal simulation system for a circuit board, characterized in that: Used to perform the thermal simulation method of the circuit board as claimed in claim 1, the thermal simulation system of the circuit board comprises: The three-dimensional digitization module is used to obtain circuit board design data, and integrate the circuit board component connection relationship of the circuit board design data, so as to obtain the circuit board component connection relationship data; perform three-dimensional digital modeling according to the circuit board component connection relationship data, so as to obtain the three-dimensional structure model of the circuit board; A thermal simulation accuracy estimation module is used to extract circuit board working condition features according to circuit board design data, thereby obtaining circuit board design working condition data, and to estimate circuit board thermal simulation accuracy based on the circuit board design working condition data, thereby obtaining circuit board thermal simulation accuracy data; The physical grid unit division module is used to classify and divide the finite element grids of the circuit board components of the three-dimensional structure model of the circuit board according to the circuit board thermal simulation accuracy data, so as to obtain the grid data of the circuit board components; and to assign physical properties to the grid data of the circuit board components according to the circuit board design data, so as to obtain the physical grid unit data; A circuit board thermal simulation module is used to reconstruct the circuit board structure model of the three-dimensional structure model of the circuit board according to the physical grid unit data, thereby obtaining a circuit board thermal simulation model, and to perform circuit board thermal simulation on the circuit board design working condition data through the circuit board thermal simulation model, thereby obtaining circuit board thermal simulation data; The component design optimization module is used to visualize the thermal stress distribution diagram of the circuit board thermal simulation data, thereby obtaining the thermal stress distribution diagram, and optimize the circuit board component design of the circuit board three-dimensional structure model according to the thermal stress distribution diagram, thereby obtaining the optimized circuit board three-dimensional structure model.

10. A circuit board, characterized in that: It includes a circuit board main body, a power supply part and an electrical control part. The power supply part is installed inside the circuit board main body. The electrical control part is electrically connected to the power supply part. The electrical control part is used to charge and control the circuit board main body. The electrical control part is used to execute the thermal simulation method of the circuit board as described in any one of claims 1 to 8.

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