A lightweight design method based on lamps

Abstract

The present invention relates to the technical field of structural optimization, and in particular to a lightweight design method based on lamps. The method comprises the following steps: obtaining lamp design data, and performing circuit board design feature extraction according to the lamp design data, thereby obtaining circuit board design data; performing current path analysis according to the circuit board design data, thereby obtaining current path data; performing component design layout feature extraction and component design material feature extraction according to the circuit board design data, thereby obtaining component design layout data and component design material data; performing thermal performance analysis according to the component design layout data and the component design material data, thereby obtaining circuit board thermal performance data; performing heat generation analysis on the circuit board thermal performance data according to the current path data, thereby obtaining current heat generation data. The present invention improves the heat dissipation performance of the lamp, reduces weight and optimizes its overall design based on structural optimization technology.

Description

Technical Field

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

[0002] Traditional lightweight design methods usually rely on conventional lightweight materials, such as aluminum alloys and plastics. Although these materials are lightweight to a certain extent, they are insufficient in strength, durability and heat dissipation performance, which makes the lamps prone to damage or performance degradation during long-term use. In traditional design methods, thermal management often relies on simple heat sink design, and fails to conduct detailed analysis and optimization of the thermal load of the lamp. Insufficient consideration of heat flow distribution and temperature gradient leads to local overheating, affecting lamp performance and even shortening service life. Traditional design methods are mostly manual design and experience accumulation, lacking automation and intelligent support for the entire lamp design process. There are not enough simulation tools and optimization algorithms, resulting in long design iterations and verification times and low efficiency. Summary of the invention

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

[0004] To achieve the above purpose, a lightweight design method based on lamps includes the following steps:

[0005] Step S1: Acquire lamp design data, and perform circuit board design feature extraction based on the lamp design data to obtain circuit board design data; perform current path analysis based on the circuit board design data to obtain current path data;

[0006] Step S2: extracting component design layout features and component design material features according to the circuit board design data, thereby obtaining component design layout data and component design material data; performing thermal performance analysis according to the component design layout data and the component design material data, thereby obtaining circuit board thermal performance data;

[0007] Step S3: performing heat generation analysis on the thermal performance data of the circuit board according to the current path data, thereby obtaining current heat generation data; and drawing a thermodynamic map according to the current heat generation data, thereby obtaining a current heat generation thermodynamic map;

[0008] Step S4: extracting design features of heat dissipation components according to the lamp design data, thereby obtaining heat dissipation component design data; performing heat dissipation optimization analysis according to the heat dissipation component design data and the current heat generation thermodynamic diagram, thereby obtaining circuit board heat dissipation optimization data;

[0009] Step S5: Perform circuit material weight reduction analysis based on the circuit board heat dissipation optimization data to obtain circuit material weight reduction data, and upload it to the lamp management platform to perform the lamp lightweight design task.

[0010] The present invention can provide basic data for subsequent current path analysis by extracting lamp design data and circuit board design features, so that the current path of the circuit board can be accurately described, thereby ensuring the efficiency and safety of current flow. Further component layout and material property extraction can provide accurate component positioning and material data for subsequent thermal performance analysis, which is helpful for comprehensive evaluation of the thermal load of the circuit board. These steps ensure that the thermal performance analysis can accurately reflect the thermal behavior of different components on the circuit board, provide effective data information for subsequent heat generation and thermal map drawing, and ensure the rationality of the thermal management strategy. By combining and analyzing the current path data and thermal performance data, the key areas where heat is generated on the circuit board can be identified, and the heat generation situation can be intuitively displayed in the form of a thermal map, helping designers to quickly identify potential overheating areas. Subsequently, combined with the heat dissipation component design data and the thermal map, the heat dissipation performance of the circuit board can be effectively improved through heat dissipation optimization analysis to avoid heat accumulation causing component failure. This heat dissipation optimization not only improves the performance of the lamp by improving the heat dissipation design, but also provides a guarantee for the long-term stable operation of the lamp. Through the optimization analysis of circuit board materials and the weight reduction of materials, lightweight design can be achieved while ensuring the overall performance of the lamp. This step not only reduces the weight of the lamp, but also reduces production costs, and helps to improve the convenience and installation flexibility of the lamp. By uploading to the lamp management platform, real-time design feedback and data updates can be further carried out to ensure the consistency between design optimization and actual production. This series of steps effectively integrates multiple aspects such as circuit design, thermal management, and material optimization, which can comprehensively improve the performance, reliability, and service life of the lamp, and has high technical value and application prospects.

[0011] Optionally, step S1 specifically includes:

[0012] Step S11: Acquire lamp design data, and extract circuit board design features based on the lamp design data, thereby obtaining circuit board design data;

[0013] Step S12: extracting circuit diagram features according to the circuit board design data, thereby obtaining a circuit diagram;

[0014] Step S13: identifying the power interface of the circuit diagram, thereby obtaining power interface data;

[0015] Step S14: performing ground port identification on the circuit diagram to obtain ground port data;

[0016] Step S15: constructing a current path model according to the power interface data and the ground port data, thereby obtaining a current path model;

[0017] Step S16: Perform current path simulation according to the current path model to obtain current path data.

[0018] The present invention can accurately understand and analyze the design requirements of the lamp circuit by acquiring the lamp design data and extracting the circuit board design features, laying the foundation for the subsequent circuit diagram feature extraction. After the circuit diagram feature extraction, the overall layout of the circuit board can be clearly displayed, providing key data for the subsequent identification of the power interface and the ground port. The implementation of these steps ensures the rationality of the circuit design in terms of function and safety, and provides data support for further optimizing the current path. Through the effective combination of the power interface data and the ground port data, an accurate current path model can be constructed, and the current path simulation can be performed to help designers accurately grasp the current flow. Through the simulation analysis of these data, potential current overload or unevenly distributed areas can be identified, and the current path and circuit board layout can be further optimized to improve the stability of the electrical performance of the lamp. Through intelligent current path modeling and simulation, a lot of design and verification time is saved. In addition, the construction and simulation of the current path model not only helps to ensure the reliability of the circuit design, but also provides an important basis for the subsequent thermal performance analysis and heat dissipation optimization, ultimately achieving the purpose of improving lamp performance, extending service life and enhancing user experience. This full-process data support from circuit diagram to current path optimization ensures the efficiency, intelligence and high reliability of lamp design.

[0019] Optionally, step S2 specifically includes:

[0020] Step S21: extracting component design layout features and component design material features according to the circuit board design data, thereby obtaining component design layout data and component design material data;

[0021] Step S22: performing thermal convection analysis on the component design layout data, thereby obtaining the component design layout thermal convection data;

[0022] Step S23: performing thermal expansion analysis on the component design material data, thereby obtaining the component design material thermal expansion data;

[0023] Step S24: Perform thermal performance integration based on the component design layout thermal convection data and the component design material thermal expansion data to obtain the circuit board thermal performance data.

[0024] The present invention ensures that the various components and material data of the lamp can accurately provide a basis for subsequent thermal analysis through the extraction of component design layout features and component design material features. These steps provide key input data for thermal convection analysis and thermal expansion analysis, ensuring that under different heat load conditions, the design of the circuit board can not only meet the heat dissipation requirements, but also withstand thermal deformation and expansion. By performing thermal convection analysis on the component design layout data, the heat flow distribution of the circuit board can be effectively evaluated, and the areas where heat is concentrated can be identified, so as to optimize these areas and avoid component damage or performance degradation caused by local overheating. In addition, thermal expansion analysis has conducted in-depth research on the thermal expansion characteristics of component design materials, which can help identify material deformation and stress problems caused by temperature changes, which is crucial to improving the durability and service life of the components. After obtaining the thermal convection data of the component design layout and the thermal expansion data of the component design materials, thermal performance integration is performed to combine the results of thermal convection and thermal expansion analysis to comprehensively consider the overall impact of heat load on the lamp. By integrating and analyzing these data, we can obtain comprehensive PCB thermal performance data, which can guide designers to make more scientific and optimized decisions in material selection, heat dissipation design, component layout, etc., to ensure that the lamps can still work stably under high load conditions and reduce the probability of failure.

[0025] Optionally, step S22 is specifically:

[0026] Step S221: extracting component coordinate features from component design layout data to obtain component coordinate data;

[0027] Step S222: Obtaining component heat consumption power data;

[0028] Step S223: constructing a three-dimensional thermal convection model according to the component heat consumption power data and the component coordinate data, thereby obtaining a three-dimensional thermal convection model;

[0029] Step S224: performing air flow field analysis according to the three-dimensional thermal convection model to obtain air flow field data;

[0030] Step S225: Performing thermal convection visualization analysis based on the air flow field data to obtain component design layout thermal convection data.

[0031] The present invention can accurately obtain the position data of each component on the circuit board by extracting the component coordinate features from the component design layout data, which provides accurate spatial coordinate input for the subsequent construction of the thermal convection model. At the same time, the heat consumption power data of the component is obtained, so that the heat generated by each component during the working process can be clearly identified, ensuring that the heat load of each component can be fully evaluated during thermal analysis. A three-dimensional thermal convection model is constructed based on the component heat consumption power data and the component coordinate data, which can accurately simulate the heat transfer of each component in space. This step provides detailed model support for the subsequent air flow field analysis, which can simulate the distribution and flow of heat inside the lamp, thereby identifying potential high-temperature areas, avoiding local overheating caused by heat concentration, and thus affecting the performance and service life of the lamp. Further air flow field analysis can accurately obtain detailed data of air flow in the lamp, help identify airflow dead corners and turbulent areas, and provide accurate air flow paths, which is crucial for optimizing heat dissipation solutions. Through the visualization analysis of thermal convection of air flow field data, the heat distribution and flow can be clearly presented, providing intuitive data support for designers and helping to carry out more effective heat dissipation design. The thermal management effect of lamp components can be fully evaluated, and the heat dissipation structure, materials and design layout of lamps can be optimized to ensure that lamps can maintain a stable temperature during long-term operation and avoid performance degradation or damage due to overheating. This refined thermal management analysis not only improves the accuracy and efficiency of the design, but also significantly extends the service life of lamps and avoids the uneven heat dissipation and overheating problems that exist in traditional design methods.

[0032] Optionally, step S224 is specifically:

[0033] The air density is set according to the three-dimensional thermal convection model to obtain air density data;

[0034] The inlet and outlet boundaries are set according to the three-dimensional thermal convection model, thereby obtaining the inlet and outlet boundary data;

[0035] Perform turbulence simulation according to air density data and inlet and outlet boundary data to obtain turbulence simulation data;

[0036] The air flow field is solved for the turbulence simulation data to obtain the air flow field data.

[0037] The present invention sets the air density based on a three-dimensional thermal convection model to ensure that the thermal physical properties of air are accurately reflected in the thermal simulation, thereby providing the necessary basic data for subsequent thermal flow analysis. The correct setting of air density is crucial for fluid dynamics analysis, which can affect the heat transfer efficiency and the flow pattern of airflow. Next, the setting of the inlet and outlet boundaries ensures that the boundary conditions of the air flow are correctly described, further improving the accuracy of the model, so that the thermal simulation can more realistically reflect the airflow conditions in the actual environment. Through these two settings, more accurate initial conditions can be provided for turbulence simulation, thereby obtaining higher quality simulation data. After obtaining air density data and inlet and outlet boundary data, turbulence simulation can further capture the details of air flow, especially high temperature areas and places where airflow is not smooth. Turbulence simulation helps reveal the distribution of heat flow in complex spaces, identify potential heat dissipation bottlenecks, and provide a basis for optimizing heat dissipation solutions. These simulation results are key data for optimizing the design, ensuring that the lamp can achieve effective thermal management under high heat load conditions. Solving the air flow field of the turbulence simulation data enables a comprehensive understanding of the dynamic process of air flow in the lamp, revealing the heat transfer path and efficiency between the various components. The air flow field data provides a clear view for the final thermal optimization design, which can help designers locate areas with low heat dissipation efficiency and make targeted improvements. Through this refined thermal management and flow field analysis, problems such as lamp performance degradation and shortened service life caused by overheating can be avoided, ensuring that the lamp can work stably under different usage conditions. In addition, the entire process relies on efficient simulation, which not only improves design efficiency, but also greatly reduces the number of iterations and verification costs that rely on manual adjustments in traditional methods.

[0038] Optionally, step S23 is specifically:

[0039] Step S231: extracting the thermal expansion coefficient characteristics of the component design material data, thereby obtaining the thermal expansion coefficient of the material;

[0040] Step S232: extracting material geometric dimension features according to the lamp design data to obtain material geometric dimension data, and constructing a three-dimensional material geometric model according to the material geometric dimension data to obtain a three-dimensional material geometric model;

[0041] Step S233: performing thermal deformation simulation according to the material thermal expansion coefficient and the three-dimensional material geometric model to obtain thermal deformation data;

[0042] Step S234: calculating the material deformation amount of the thermal deformation data, thereby obtaining the material deformation amount data;

[0043] Step S235: performing thermal expansion calculation according to the material deformation data and the material thermal expansion coefficient, thereby obtaining the thermal expansion data of the component design material.

[0044] The present invention provides important physical property parameters for simulating the thermal response of materials by extracting the thermal expansion coefficient. The acquisition of this data enables the expansion behavior of materials caused by temperature changes to be accurately described in subsequent thermal analysis. Then, by extracting the material geometric size data and constructing a three-dimensional material geometric model, a practical physical model can be provided for thermal simulation, ensuring that the simulation results are more representative and reliable. Through this geometric model, thermal deformation simulation can be accurately performed to obtain the deformation of the material under different temperature environments, providing a basis for subsequent material selection and structural design. Further, the calculation of the material deformation helps to determine the deformation of the material under the action of thermal load, and then evaluate the structural failure or stress concentration area, ensuring the reliability of the lamp in actual use. By comprehensively considering the thermal expansion characteristics of the material and the geometric model, and performing thermal deformation simulation, the performance of the lamp can be more comprehensively evaluated, avoiding local deformation or uneven stress distribution caused by thermal expansion, and then optimizing the structural design. In addition, thermal expansion calculation provides a clear standard for the long-term stability of the material, and can accurately predict the thermal stability changes of the material during long-term use, avoiding material aging, cracking or deformation caused by excessive thermal stress. Overall, through these steps, all-round thermal management optimization from material selection to design optimization can be achieved, avoiding the shortcomings of traditional design methods such as insufficient precision in thermal management analysis, neglect of details and lack of intelligent optimization, thereby significantly improving design efficiency and the quality and performance of the final product.

[0045] Optionally, step S4 is specifically:

[0046] Step S41: extracting heat dissipation component design features according to the lamp design data, thereby obtaining heat dissipation component design data;

[0047] Step S42: Optimizing the heat dissipation geometry structure according to the heat dissipation element design data, thereby obtaining heat dissipation geometry structure data;

[0048] Step S43: performing fluid dynamics analysis based on the current heat generated thermodynamic diagram, thereby obtaining fluid dynamics data;

[0049] Step S44: integrating the heat dissipation optimization characteristics of the circuit board according to the heat dissipation geometry data and the fluid dynamics data, thereby obtaining the heat dissipation optimization data of the circuit board.

[0050] By extracting the design features of the heat dissipation element, the present invention can accurately obtain the geometric structure, size and material properties of the heat dissipation element, and provide basic data for subsequent heat dissipation optimization. Next, the implementation of the heat dissipation geometry optimization can be carried out by finely analyzing the structure and adjusting the shape and layout of the heat dissipation element to maximize the heat dissipation effect. Unlike traditional methods, this step relies on precise calculation and optimization, avoiding the defects of relying solely on experience and intuition in the design. Furthermore, by performing fluid dynamics analysis based on the current heat generation thermogram, the air flow, turbulence effect and heat exchange characteristics in the heat dissipation process can be simulated and analyzed to ensure that heat can be effectively transferred from the high temperature area to the low temperature area. This analysis provides a scientific basis for optimizing the heat dissipation path and avoiding local overheating. Finally, the integration of the heat dissipation geometry data and the fluid dynamics data can be comprehensively optimized within the overall design framework to maximize the heat dissipation efficiency and avoid equipment failure or performance degradation caused by heat accumulation. This intelligent and precise design method can not only effectively predict and solve heat dissipation problems in the early stages of design, but also achieve design iteration and rapid verification through simulation and optimization tools, significantly reducing the manual design and trial-and-error process in traditional design, thereby improving design efficiency and extending the service life of lamps.

[0051] Optionally, step S42 is specifically:

[0052] Step S421: extracting heat sink fin features according to heat sink component design data, thereby obtaining heat sink fin data;

[0053] Step S422: performing fin shape scanning on the heat dissipation fin data to obtain heat dissipation fin shape data;

[0054] Step S423: performing corrugation optimization according to the heat dissipation fin shape data, thereby obtaining the heat dissipation fin corrugation shape data;

[0055] Step S424: performing airflow distribution simulation on the heat dissipation fin data to obtain airflow distribution simulation data;

[0056] Step S425: drawing an airflow velocity distribution diagram according to the airflow distribution simulation data, thereby obtaining an airflow velocity distribution diagram;

[0057] Step S426: Calculating the airflow efficiency according to the airflow velocity distribution diagram, thereby obtaining airflow efficiency data;

[0058] Step S427: extracting low-efficiency features according to the airflow efficiency data, thereby obtaining low-efficiency airflow data, and identifying low-efficiency areas of the airflow velocity distribution map according to the low-efficiency airflow data, thereby obtaining low-efficiency airflow area data;

[0059] Step S428: adjusting the heat dissipation fin spacing based on the low-efficiency airflow passing area data, thereby obtaining heat dissipation fin spacing adjustment data;

[0060] Step S429: performing heat dissipation geometry integration according to the heat dissipation fin spacing adjustment data and the heat dissipation fin corrugation shape data, so as to obtain heat dissipation geometry data.

[0061] The present invention can obtain detailed heat dissipation fin data through heat dissipation fin feature extraction, ensuring that the geometric characteristics of the heat dissipation element are accurately collected, which lays the foundation for subsequent optimization and adjustment. Next, the shape scanning of the heat dissipation fin can help understand the shape characteristics of the fin in the current design and provide a specific direction for optimization. Further, through corrugation optimization, the shape of the fin can be adjusted to adapt to different airflow environments and improve the heat dissipation effect. This optimization process enables the fin to contact the air more effectively, thereby accelerating the release of heat. Airflow distribution simulation and airflow velocity distribution map drawing can scientifically display the heat distribution of the heat dissipation area. In this way, areas with poor airflow or poor heat dissipation can be accurately identified. The airflow further optimizes the heat dissipation path through efficiency calculation and low-efficiency feature extraction, helps identify specific areas with poor airflow, and solves the problem of uneven airflow distribution by adjusting the spacing of the heat dissipation fins. Finally, through the adjustment of the heat dissipation fin spacing and the integration of heat dissipation geometry, the entire heat dissipation system can be refined and optimized to ensure that the heat can be evenly distributed and reduce the phenomenon of local overheating. Compared with traditional methods, this design method based on intelligent simulation and optimization analysis not only improves the heat dissipation effect of lamps, but also reduces the time waste and inaccuracy caused by lack of experience or over-reliance on manual adjustments in traditional designs. The effectiveness of this method lies in its data-driven, automated, and precise characteristics, which can achieve comprehensive optimization of heat dissipation design, extend the service life of lamps, and improve their overall performance.

[0062] Optionally, step S43 is specifically:

[0063] Step S431: Calculate the temperature gradient according to the current heat generated thermodynamic diagram, so as to obtain temperature gradient data;

[0064] Step S432: identifying airflow dead corners according to the temperature gradient data, thereby obtaining airflow dead corner data;

[0065] Step S433: optimizing the fin spacing of the airflow dead angle data, thereby obtaining fin spacing optimization data;

[0066] Step S434: Evaluate the turbulence intensity based on the fin spacing optimization data to obtain fluid dynamics data.

[0067] The present invention can accurately obtain the temperature changes inside and outside the lamp by calculating the temperature gradient based on the current heat generation thermogram, thereby identifying the heat concentration area and the place where the heat load is too high. This provides reliable basic data for subsequent airflow optimization and heat dissipation improvement. Then, the airflow dead corner identification is performed through the temperature gradient data, and the area where the airflow is not smooth, that is, the "dead corner" area, can be effectively identified. These areas often cause local overheating, affecting the heat dissipation efficiency and service life of the lamp. After identifying these dead corners, they can be optimized by adjusting the heat dissipation fin spacing, thereby optimizing the flow path and distribution of the airflow, so that the heat can be more evenly dispersed and avoid overheating problems. Further, the fin spacing optimization can effectively adjust the design of the heat dissipation element according to the airflow dead corner data to increase the airflow throughput and reduce heat accumulation. On this basis, the turbulence intensity evaluation of the fin spacing optimization data can provide a detailed analysis for fluid dynamics, evaluate the intensity and distribution of the airflow, and ensure that the heat dissipation system can work under optimal conditions. Through these optimization steps, not only the thermal management performance of the lamp is improved, but also the energy consumption and overheating risk are reduced, and the service life of the lamp is extended. Compared with traditional design methods, this design approach based on thermal analysis, airflow optimization and turbulence intensity assessment can more accurately improve the heat dissipation performance of lamps, reduce unnecessary energy waste, and avoid the waste of time and resources caused by reliance on manual experience and trial and error in traditional designs, thereby improving design efficiency and optimization accuracy.

[0068] Optionally, step S432 is specifically:

[0069] Extract high temperature gradient features according to the temperature gradient data, so as to obtain high temperature gradient data;

[0070] Acquire fluid flow data;

[0071] Perform flow velocity statistics based on fluid flow data to obtain low flow velocity data;

[0072] The air flow velocity distribution diagram is divided into regions according to the low flow velocity data, so as to obtain low flow velocity region data;

[0073] The current heat generation thermodynamic map is divided into regions according to the high temperature gradient data, thereby obtaining high temperature gradient region data;

[0074] The area intersection operation is performed based on the low flow velocity area data and the high temperature gradient area data to obtain the airflow dead corner data.

[0075] The present invention can obtain temperature difference data inside and outside the lamp by extracting high temperature gradient features from temperature gradient data, and these data can help accurately identify areas with higher temperatures. By understanding these high temperature areas, the location where the heat load is concentrated can be discovered in advance, thereby providing guidance for subsequent thermal management optimization. Then, by acquiring fluid flow data and performing flow velocity statistics, the flow of the airflow can be analyzed, especially the low flow velocity area can be identified. Low flow velocity areas usually cause poor air flow, resulting in heat accumulation, which leads to overheating problems. For these low flow velocity areas, the airflow velocity distribution map can be divided into regions to help determine the airflow path that needs to be optimized. Further, by dividing the high temperature gradient data and the current heat generation thermodynamic map into regions, the high temperature gradient area data can be accurately obtained, and these data are subjected to regional intersection operations with the low flow velocity area data to obtain the airflow dead angle area. These dead angle areas are often the main source of heat accumulation, so by identifying and optimizing these areas, the heat dissipation performance of the lamp can be significantly improved. Compared with traditional design methods, this refined thermal analysis and fluid optimization method can solve the heat dissipation problem of lamps more efficiently and accurately, thereby extending the service life of lamps, reducing performance degradation caused by overheating, and effectively reducing maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] 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:

[0077] Figure 1 It is a schematic diagram of the steps of the lightweight design method of the lamp based on the present invention;

[0078] Figure 2 Detailed step flow diagram of step S1 in the present invention;

[0079] Figure 3 Detailed step flow diagram of step S2 in the present invention;

[0080] 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

[0081] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0082] 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.

[0083] 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.

[0084] To achieve this, please refer to Figures 1 to 3 The present invention provides a lightweight design method based on a lamp, the method comprising the following steps:

[0085] Step S1: Acquire lamp design data, and perform circuit board design feature extraction based on the lamp design data to obtain circuit board design data; perform current path analysis based on the circuit board design data to obtain current path data;

[0086] In this embodiment, the overall design data of the lamp is obtained from the lamp design platform, including structural design, component layout, interface information, etc. The circuit board design feature extraction is performed by importing the circuit diagram, including information such as component distribution, line connection method, and hierarchy. The circuit board design data after feature extraction includes the calibration value, size data, power requirements, etc. of each component. For current path analysis, the power and ground ports defined in the circuit design data are used to generate a current path diagram in combination with the current flow direction, contact points, and resistance parameters. The path diagram identifies each part of the circuit board through which the current passes, and can accurately calculate the current intensity of each path, thereby providing basic data for subsequent thermal performance analysis.

[0087] Step S2: extracting component design layout features and component design material features according to the circuit board design data, thereby obtaining component design layout data and component design material data; performing thermal performance analysis according to the component design layout data and the component design material data, thereby obtaining circuit board thermal performance data;

[0088] In this embodiment, the component design layout features are extracted from the circuit board design data to obtain the precise coordinates, arrangement and relative position of each component with other components. The component design material feature extraction obtains the material information of the component from the circuit board design file, including its physical properties such as electrical conductivity, thermal conductivity, thermal expansion coefficient, etc. By inputting these layout and material data into the thermal performance analysis model, a thermal analysis method based on the finite element method is used to simulate the thermal distribution of the circuit board under working conditions. Taking into account the power dissipation of different components and combining the thermal characteristics of the material, a thermal performance analysis is performed to finally obtain the thermal performance data of the circuit board, such as temperature distribution, hot spot area and heat conduction path.

[0089] Step S3: performing heat generation analysis on the thermal performance data of the circuit board according to the current path data, thereby obtaining current heat generation data; and drawing a thermodynamic map according to the current heat generation data, thereby obtaining a current heat generation thermodynamic map;

[0090] In this embodiment, the heat generation formula (for example, P = I 2 R) for heat generation analysis. This analysis calculates the heat generated when current passes through resistive materials and combines it with the current path data to calculate the heat generation of each component and circuit connection. The heat generation value of each component and current path is solved based on its power dissipation, line resistance, and current intensity. After obtaining the current heat generation data, thermodynamic analysis software (such as ANSYS or COMSOL) is further used to draw a thermal map. The thermal map shows the temperature distribution in different areas of the circuit board, identifies the hot spots, and can predict the areas that cause material aging or failure based on temperature changes.

[0091] Step S4: extracting design features of heat dissipation components according to the lamp design data, thereby obtaining heat dissipation component design data; performing heat dissipation optimization analysis according to the heat dissipation component design data and the current heat generation thermodynamic diagram, thereby obtaining circuit board heat dissipation optimization data;

[0092] In this embodiment, the design features of the heat dissipation components are extracted from the lamp design data, including the type, size, shape and position of the radiator. Combined with the current heat generation thermogram, the heat dissipation optimization analysis will evaluate the impact of different heat dissipation component configurations on the heat dissipation performance of the circuit board. By optimizing the radiator size, material (such as aluminum alloy, copper) and heat dissipation fin design, its thermal conductivity and heat dissipation efficiency are calculated. During the analysis, the heat transfer effect under different configurations is simulated, and the heat dissipation effect is calculated. The airflow and temperature changes of the heat dissipation system are simulated through simulation tools (such as CFD software), and finally the design of the heat dissipation components is optimized to ensure that the temperature of the circuit board during operation remains within a safe range and reduce local overheating.

[0093] Step S5: Perform circuit material weight reduction analysis based on the circuit board heat dissipation optimization data to obtain circuit material weight reduction data, and upload it to the lamp management platform to perform the lamp lightweight design task.

[0094] In this embodiment, the circuit board heat dissipation optimization data is obtained, which includes the heat dissipation performance indicators of the circuit board under different heat load conditions, such as heat dissipation efficiency, thermal conductivity path, hot spot temperature distribution, etc. These data are used as the input basis for weight reduction analysis. Then, according to the design requirements of the circuit board, combined with the existing characteristic data of the circuit board material, including the density of the material (g / cm 3 ), thermal conductivity (W / m·K), mechanical strength (MPa) and heat resistance (℃), and conduct a comprehensive analysis of the weight reduction potential of circuit materials. By establishing a material substitution analysis model, the substitutability of existing materials is simulated and evaluated. The main evaluation contents include whether the thermal conductivity meets the requirements of heat dissipation optimization, whether the mechanical strength is sufficient to support the physical stability of the circuit board in the working environment, and whether the thermal expansion coefficient matches the materials of other components to avoid thermal stress imbalance. After the material substitution evaluation is completed, the weight reduction target is set according to the analysis results. The target value is usually determined according to the weight reduction ratio of the current circuit board weight (such as 10%-20%), and a specific weight reduction strategy is selected based on the set target value, including material replacement or material thickness optimization. In the weight reduction simulation stage, CAE (computer-aided engineering) software is used to simulate the performance of alternative materials under actual working conditions. The simulation content covers heat dissipation performance verification, mechanical stress distribution analysis and thermal expansion behavior simulation. Through multiple simulation iterations, different material substitution schemes are compared and optimized, and finally a material substitution scheme that meets both heat dissipation performance and minimizes weight is selected. After completing the optimization plan, the weight reduction analysis data is output based on the simulation results. The data includes the total weight of the circuit board after weight reduction, the thermal conductivity of the new material, mechanical strength, density change value, and specific weight reduction ratio. In addition, the optimized heat dissipation performance indicators are recorded to verify the reliability of the design. Subsequently, the generated circuit material weight reduction data is uploaded to the lighting management platform in the form of a standardized data file. The data content includes a weight reduction optimization report, a new material selection plan, simulation verification results, and design specifications for subsequent manufacturing and testing. After receiving the data, the lighting management platform automatically triggers the lightweight design task, promotes further material processing, lighting assembly and performance testing, and ensures that the lightweight goal is achieved while meeting the heat dissipation performance and mechanical reliability requirements of the lighting.

[0095] Optionally, step S1 specifically includes:

[0096] Step S11: Acquire lamp design data, and extract circuit board design features based on the lamp design data, thereby obtaining circuit board design data;

[0097] In this embodiment, the lamp design data is directly exported by reading the CAD file or through the lamp design software, including but not limited to the characteristic data such as the circuit board size, layout distribution, and interface position. By using a parsing tool (such as a DXF or Gerber file parser), the lamp design data is feature extracted, and the extracted content should include the number of layers of the circuit board, the wire width, the circuit board thickness, the pad diameter and its spatial layout in the design. When processing data, a structured database needs to be established to record the precise parameters of each feature. For example, the circuit board thickness (unit: mm) is set within the range of 1.6±0.05mm, and the pad diameter is read according to the design input value, and the precise value is taken. After the feature extraction is completed, the structured data is organized into a CSV file or a JSON file for subsequent processing steps.

[0098] Step S12: extracting circuit diagram features according to the circuit board design data, thereby obtaining a circuit diagram;

[0099] In the present embodiment, circuit design software (such as Altium Designer or KiCAD) is used to import circuit board design data, parse out the connection, node and component symbols therein, and generate a standard circuit diagram file (such as SPICE netlist or PDF format circuit diagram). The connection relationship and position marking of key components (such as capacitors, inductors, diodes) need to be paid attention to during feature extraction, and node and path forms are generated. The parameters (such as impedance, capacitance, inductance) of each component are matched by consulting the component database and indicated in the circuit diagram. Ensure that the extracted circuit diagram file is completely corresponding to the circuit board design data, and the circuit diagram output should include node numbering and component pin numbering.

[0100] Step S13: identifying the power interface of the circuit diagram, thereby obtaining power interface data;

[0101] In this embodiment, the identification of the power interface is completed by analyzing the interface symbols (such as +Vcc, GND logo) in the circuit diagram. After the parsing tool identifies the interface symbol through pattern matching, it is further confirmed in combination with the corresponding connection relationship in the node table. When identifying the power interface, it is necessary to verify whether the input voltage range meets the design standards. For example, for low-voltage lamp design, the input voltage range is generally between 5V and 24V. All power interface positions, types and connection relationships need to be stored in the database, and the record format includes interface type (input / output), position coordinates (unit: mm) and rated voltage value (unit: V).

[0102] Step S14: performing ground port identification on the circuit diagram to obtain ground port data;

[0103] In this embodiment, the circuit analysis algorithm is used to extract the ground port in the circuit diagram, and the automatic marking is completed by identifying the "GND" or ground symbol. The actual connection relationship of the ground port is verified in combination with the circuit board design data to ensure that all ground ports are connected to the common ground point through the ground grid. The ground port data needs to be stored in a list form, including the port number, location coordinates (unit: mm) and connection network number, which is convenient for subsequent simulation and current path modeling.

[0104] Step S15: constructing a current path model according to the power interface data and the ground port data, thereby obtaining a current path model;

[0105] In this embodiment, based on the extracted power interface and ground port data, a current path modeling tool (such as MATLAB or COMSOL Multiphysics) is used to generate a current path model. During the model construction process, the complete current path from the power interface to the ground port, including all branch paths, is identified by combining the circuit topology information with the path tracing algorithm. The path model needs to consider the impedance characteristics of all components in the circuit and set the equivalent resistance value (unit: Ω) of the current path. The data of all paths are stored in a hierarchical topology for simulation analysis.

[0106] Step S16: Perform current path simulation according to the current path model to obtain current path data.

[0107] In this embodiment, based on the current path model, simulation software is imported to perform current path simulation analysis. The applied current value is calculated according to the design rated power of the lamp. For example, when the power is 10W and the input voltage is 5V, the current value is set to 2A. The simulation software calculates the current distribution, voltage drop and power dissipation of each path through finite element analysis (FEM). The output simulation data includes the current intensity (unit: A), voltage drop (unit: V) and total path impedance (unit: Ω) of each path. The simulation results need to be verified with the design standards to ensure that the path current does not exceed the design rated value, and record abnormal paths for subsequent optimization analysis.

[0108] Optionally, step S2 specifically includes:

[0109] Step S21: extracting component design layout features and component design material features according to the circuit board design data, thereby obtaining component design layout data and component design material data;

[0110] In this embodiment, the circuit board design data is imported into the feature extraction module through a parsing tool (such as a Gerber file analyzer). Component design layout feature extraction includes analysis of component coordinates, connection relationships, spacing, and layout density. The parsing tool locates the coordinate position of each component (unit: mm) and generates a coordinate matrix and records it in the database. For example, the spacing between components must be kept within the range of 0.2±0.05mm. For component design material features, it is necessary to combine the material library data and obtain the thermal conductivity (unit: W / m·K), thermal expansion coefficient (unit: 1 / ℃) and density (unit: g / cm 3 ). The material data needs to be compared and verified with the heat resistance requirements in the design specifications to ensure that the extracted material data meets the product performance requirements. Finally, the component design layout data and component design material data are organized into structured files for subsequent use.

[0111] Step S22: performing thermal convection analysis on the component design layout data, thereby obtaining the component design layout thermal convection data;

[0112] In this embodiment, a three-dimensional thermal convection model is constructed based on the component design layout data using a fluid dynamics analysis tool (such as ANSYS Fluent or a CFD tool). The component coordinate data in the layout is meshed, and the grid unit size is controlled at 0.5±0.1mm to ensure the calculation accuracy. According to the input component heat consumption power (unit: W) and layout gap, the initial air velocity is set to 2m / s. By applying boundary conditions, such as setting the inlet temperature to 25°C and the outlet pressure to 0Pa, the simulation calculation of the airflow distribution and thermal convection process is started. The calculation process uses a turbulence model (such as a k-ε model) for turbulence simulation, and the output data includes a fluid velocity field (unit: m / s) and a temperature field (unit: °C). Finally, the thermal convection data generated by the simulation is integrated with the component layout features, and the component design layout thermal convection data file is output for use in the thermal performance integration step.

[0113] Step S23: performing thermal expansion analysis on the component design material data, thereby obtaining the component design material thermal expansion data;

[0114] In this embodiment, based on the component design material data, a finite element analysis tool (such as ABAQUS or ANSYS) is used to simulate thermal expansion. The thermal expansion coefficient of each material (unit: 1 / °C) is obtained from the material database, and combined with the geometric size data of the material to generate a three-dimensional geometric model. The model mesh is divided into tetrahedral units, and the side length of the mesh unit is controlled at 0.1±0.05mm to improve the accuracy of thermal stress calculation. According to the initial temperature field input of the thermal load, the temperature variation range is set to 25°C to 85°C. During the simulation process, fixed boundary conditions and symmetric boundary conditions are applied, and static thermal expansion simulation is run to calculate thermal stress (unit: MPa) and thermal deformation (unit: mm). The simulation results derive key data, including total deformation, stress distribution and stress concentration area, and record the high temperature deformation position of the material. Finally, the calculation results are organized into a thermal expansion data file of the component design material.

[0115] Step S24: Perform thermal performance integration based on the component design layout thermal convection data and the component design material thermal expansion data to obtain the circuit board thermal performance data.

[0116] In this embodiment, thermal performance integration is completed in a comprehensive analysis tool (such as COMSOL Multiphysics) based on thermal convection data and thermal expansion data. Import thermal convection data and thermal expansion data into the thermal performance integration module. By matching the coordinate data and material properties of the components, the comprehensive thermal performance index of each component is calculated. A thermal coupling algorithm is used in the calculation to superimpose the temperature gradient caused by thermal convection and the stress distribution caused by thermal expansion. For each component, its maximum temperature (unit: ° C), local stress value (unit: MPa) and critical deformation (unit: mm) are recorded. The integrated analysis results need to be compared with the design requirements to ensure that all indicators are within an acceptable range, such as the maximum temperature of the component does not exceed 85 ° C, and the thermal stress is less than 80% of the yield strength of the material. The generated thermal performance data is organized into reports and visualization files (such as 3D temperature field and stress distribution diagrams), and output as circuit board thermal performance data for further analysis and optimization.

[0117] Optionally, step S22 is specifically:

[0118] Step S221: extracting component coordinate features from component design layout data to obtain component coordinate data;

[0119] In this embodiment, the component design layout data is imported into the feature extraction module, and the specific coordinates of each component on the circuit board are identified by analyzing the relative position relationship and wiring information of the components. The layout file is scanned using the CAD parsing algorithm to read the center point coordinates, boundary dimensions, and spacing relationship of each component, and record them as two-dimensional plane coordinate data (unit: mm). The extraction process requires strict verification of the accuracy of the layout, for example, the minimum spacing between each component must be greater than 0.2 mm. The extraction results are stored in a matrix format, with each row corresponding to one component. The data includes the coordinate position (x, y) and the boundary range for subsequent thermal convection analysis.

[0120] Step S222: Obtaining component heat consumption power data;

[0121] In this embodiment, the thermal power data is simulated and calculated by a circuit simulation tool (such as LTspice or PSIM), or obtained from the specification sheet provided by the component supplier. For each component, the unit of thermal power is watt (W). For example, the thermal power calculation formula of a power transistor is: P = V × IP = V\timesIP = V × I, where V is voltage (unit: V) and I is current (unit: A). In the simulation, DC and AC loads are applied to the circuit, and a thermal power distribution table is generated by calculating the average power consumption of each component under normal working conditions. The extracted data needs to be matched with the component coordinates to form a complete thermal power input matrix for subsequent model construction.

[0122] Step S223: constructing a three-dimensional thermal convection model according to the component heat consumption power data and the component coordinate data, thereby obtaining a three-dimensional thermal convection model;

[0123] In this embodiment, computational fluid dynamics software (such as ANSYS Fluent or COMSOL) is used to load component heat consumption power data and component coordinate data to generate a three-dimensional thermal convection model. The heat source position is defined according to the coordinate data of the component, and the heat consumption power is mapped to the corresponding component area. The circuit board is geometrically modeled to build a complete three-dimensional geometric model containing components, air flow channels and boundary conditions to ensure that the spatial accuracy of the model is within 0.1mm. The boundary conditions of the model include an inlet air velocity of 2m / s, an ambient temperature set to 25°C, and an outlet pressure of 0Pa. A turbulence model (such as a k-ε model) is used to model the influence of air flow, and the grid is set to tetrahedral units, and the grid size is controlled within 1±0.2mm to improve the calculation accuracy. Run the simulation to obtain the intermediate results of the thermal convection simulation, including the fluid temperature field and velocity field distribution.

[0124] Step S224: performing air flow field analysis according to the three-dimensional thermal convection model to obtain air flow field data;

[0125] In this embodiment, based on the three-dimensional thermal convection model, the air flow field distribution is calculated using CFD tools. By setting the air physical parameters (such as density of 1.2 kg / m 3 , the viscosity is 1.8×10 -5 Pa·s), and the velocity distribution, pressure distribution and turbulence intensity of the flow field are solved. The steady-state calculation method is used to analyze the flow characteristics of air on the surface of the circuit board. The areas with lower flow velocity in the flow field are marked as potential airflow dead spots, and the turbulence intensity distribution is statistically analyzed to identify areas with uneven air flow. The analysis results are output in the form of a three-dimensional vector diagram, and the generated data include the velocity (unit: m / s), pressure (unit: Pa) and turbulent kinetic energy (unit: m 2 / s 2 ).

[0126] Step S225: Performing thermal convection visualization analysis based on the air flow field data to obtain component design layout thermal convection data.

[0127] In this embodiment, the air flow field data is input into a visualization module (such as ParaView or Tecplot), and the air flow and temperature distribution characteristics are intuitively displayed through streamlines and isotherms. The main parameters of visualization include the magnitude and direction of air velocity, fluid temperature gradient, and turbulent kinetic energy distribution. Streamline diagrams are used to display the air flow path, and isotherms are used to mark the temperature distribution of each area. The local flow field around specific components on the circuit board is enlarged and analyzed to identify areas with uneven heat dissipation. The generated visualization results are saved as 3D image files and data reports, including flow field vector field data and temperature distribution data, and recorded as component design layout thermal convection data for subsequent optimization steps.

[0128] Optionally, step S224 is specifically:

[0129] The air density is set according to the three-dimensional thermal convection model to obtain air density data;

[0130] In this embodiment, the three-dimensional thermal convection model is imported into computational fluid dynamics software (such as ANSYS Fluent or COMSOL), and the air density is set in the physical property setting module. The air density value is set to 1.225 kg / m under standard atmospheric pressure according to the experimental conditions. 3 , or select from the fluid property library based on actual working conditions. For high temperature conditions or significant density changes, use the ideal gas formula:

[0131]

[0132] Where P is pressure (unit: Pa), R is the air gas constant (287 J / kg·K), and T is the absolute temperature (unit: K). Through calculation, the air density value at each location in the model is dynamically adjusted and stored as a density distribution table as a key input parameter for subsequent simulations.

[0133] The inlet and outlet boundaries are set according to the three-dimensional thermal convection model, thereby obtaining the inlet and outlet boundary data;

[0134] In this embodiment, the boundary conditions of the air inlet and outlet are set in the three-dimensional thermal convection model. The air inlet is usually set as the velocity inlet, and the inlet air velocity value is set to 2m / s according to the design requirements, and the direction enters the model vertically. The air outlet boundary is set as the pressure outlet, and the outlet pressure is 0Pa (relative to atmospheric pressure) to ensure the stability of the internal flow field. In the specific implementation process, the model inlet and outlet are divided by geometric modeling tools, and the boundary setting function of the CFD software is used to apply corresponding conditions to each boundary. All boundary parameters are saved in the boundary condition configuration file so that they can be called during the simulation run, and the inlet and outlet boundary data table is generated, including position coordinates, flow velocity direction and related physical property parameters.

[0135] Perform turbulence simulation according to air density data and inlet and outlet boundary data to obtain turbulence simulation data;

[0136] In this embodiment, the air density data and the inlet and outlet boundary data are input into the turbulence model module, and the k-turbulence model is used to simulate the air flow. The main parameters of the turbulence model include turbulent kinetic energy k and turbulent dissipation rate, and the initial values ​​are set by experimental experience or default values. The calculation area is gridded, and an unstructured grid is used. The side length of each unit is controlled between 0.5mm-1mm to ensure the calculation accuracy. During the simulation process, the fluid control equations (including the continuity equation, momentum equation and turbulent energy equation) are iteratively solved to record the velocity field, turbulence intensity and vortex distribution of each unit. The results are saved as a turbulence simulation data file in the form of a three-dimensional vector.

[0137] The air flow field is solved for the turbulence simulation data to obtain the air flow field data.

[0138] In this embodiment, based on the turbulence simulation results, a complete air flow field distribution is generated by the flow field solution module. The flow field solution is mainly based on the Navier-Stokes equations, and the velocity field, pressure field and temperature field of the air are calculated in combination with the boundary conditions and turbulence characteristics. The discretized equations are iteratively solved using numerical methods (such as the finite volume method), and the convergence standard for each iteration is set to a residual error less than 10 -4 After the calculation is completed, three-dimensional flow field data containing information such as air velocity, direction, pressure and temperature are generated and output as a visualization file for subsequent thermal convection optimization and design improvement.

[0139] Optionally, step S23 is specifically:

[0140] Step S231: extracting the thermal expansion coefficient characteristics of the component design material data, thereby obtaining the thermal expansion coefficient of the material;

[0141] In this embodiment, the thermal expansion coefficient of the component material is queried in the material database. The coefficient is usually measured by experimental data and is expressed in μm / m·K (linear expansion rate per degree Celsius). For example, for aluminum alloy materials, the thermal expansion coefficient is 22-24 μm / m·K, while for copper it is 16.5-17.5 μm / m·K. The thermal expansion coefficient data of the selected material is imported into the calculation tool and recorded as a material characteristic parameter file for subsequent thermal simulation. If custom materials are used, the thermal expansion coefficient curve of the material within a specified temperature range must be measured with the help of experimental equipment such as a thermomechanical analyzer (TMA) to ensure the accuracy of the calculation parameters.

[0142] Step S232: extracting material geometric dimension features according to the lamp design data to obtain material geometric dimension data, and constructing a three-dimensional material geometric model according to the material geometric dimension data to obtain a three-dimensional material geometric model;

[0143] In this embodiment, according to the lamp design data, the geometric size parameters of the components, such as length, width and thickness, are extracted in millimeters and recorded as a structural size table. A three-dimensional material geometric model is constructed based on the extracted size information using three-dimensional modeling software (such as SolidWorks or CATIA), and the model type is selected as a solid geometry based on the actual component shape. For complex structural components, scanning or reverse engineering equipment (such as a 3D scanner) is used to obtain surface point cloud data, and an accurate geometric model is generated through a reconstruction algorithm. All geometric models need to be meshed, and the mesh unit size is controlled between 0.1-0.5mm to meet the calculation accuracy requirements of subsequent thermal analysis.

[0144] Step S233: performing thermal deformation simulation according to the material thermal expansion coefficient and the three-dimensional material geometric model to obtain thermal deformation data;

[0145] In this embodiment, the three-dimensional geometric model is imported into finite element analysis software (such as ANSYS or ABAQUS), and the thermal expansion coefficient, elastic modulus and Poisson's ratio of the material are set. A thermal load is applied to the model, and the temperature gradient range is set from room temperature to the maximum operating temperature, for example, 25°C to 150°C, with a step size of 5°C. During the simulation process, a nonlinear thermal stress analysis method is used to solve the internal stress distribution and geometric deformation caused by temperature changes. The boundary conditions of the model are set, such as fixed boundaries to limit the overall translation to ensure the stability of the calculation. The thermal deformation data finally generated are output in the form of numerical tables and three-dimensional deformation diagrams, and the data format includes information such as stress (unit: MPa) and displacement (unit: mm).

[0146] Step S234: calculating the material deformation amount of the thermal deformation data, thereby obtaining the material deformation amount data;

[0147] In this embodiment, the thermal deformation simulation results are imported into the data processing tool to extract the displacement data of the key nodes using the formula:

[0148] ΔL=α·L·ΔT;

[0149] Calculate the material deformation, where ΔL is the deformation (unit: mm), α is the thermal expansion coefficient, L is the initial length (unit: mm), and ΔT is the temperature difference (unit: K). Take the temperature of each node output by the simulation as input, calculate the material deformation point by point, and count the maximum and minimum deformation values ​​of the entire model to form the deformation distribution data. All calculation results are stored in the form of two-dimensional tables and curve graphs for subsequent analysis.

[0150] Step S235: performing thermal expansion calculation according to the material deformation data and the material thermal expansion coefficient, thereby obtaining the thermal expansion data of the component design material.

[0151] In this embodiment, the volume expansion of each part of the material is calculated using a formula in combination with the deformation data and the thermal expansion coefficient of the material. The volume expansion calculation formula is:

[0152] ΔV=β·V·ΔT;

[0153] Where β=3α is the volume expansion coefficient, V is the initial volume of the material (unit: mm 3 ). Compare the calculation results with the original geometric parameters of the material to generate a thermal expansion distribution diagram, and record the shape data and volume change data of the material after expansion. The final thermal expansion data of the component design material includes local expansion value, overall expansion rate and deformation area annotation, which is used to optimize the design and evaluate the reliability of the material.

[0154] Optionally, step S4 is specifically:

[0155] Step S41: extracting heat dissipation component design features according to the lamp design data, thereby obtaining heat dissipation component design data;

[0156] In this embodiment, the design features of components related to heat dissipation are extracted by parsing the lamp design data, including parameters such as the number, size, material, installation position and surface treatment process of the heat sink. For example, the thickness of the heat sink is 1-3mm, the length is 50-100mm, the material is aluminum alloy or copper with excellent thermal conductivity, and the surface treatment is anodizing or nickel plating to enhance the heat dissipation capacity. The heat dissipation design data is feature classified using the database matching method, and each parameter is recorded in the design file to ensure that the design features of all heat dissipation components can meet the overall structure and thermal management requirements of the lamp. After the heat dissipation component feature extraction is completed, the generated heat dissipation component design data is stored in the form of tables and three-dimensional geometric diagrams for subsequent optimization analysis.

[0157] Step S42: Optimizing the heat dissipation geometry structure according to the heat dissipation element design data, thereby obtaining heat dissipation geometry structure data;

[0158] In this embodiment, according to the extracted design data of the heat dissipation element, a computing tool (such as SolidWorks or AutoCAD) is used to construct an initial geometric structure model, and the geometric parameter range and optimization target are set. For example, the adjustment range of the heat sink spacing is 2-5mm, and the thickness optimization range is 1.5-2mm. The parametric design method is used to optimize the geometric structure, and the arrangement form, fin angle and spacing of the heat dissipation element are adjusted to improve the heat dissipation area and airflow efficiency. The optimized geometric model is input into a finite element analysis tool (such as ANSYS Fluent) for thermal fluid simulation verification to ensure that the heat dissipation performance of the optimized design meets the actual needs of the lamp. After the optimization is completed, the generated heat dissipation geometric structure data includes parameters such as the new shape, number, spacing, material thickness and arrangement of the heat sink.

[0159] Step S43: performing fluid dynamics analysis based on the current heat generated thermodynamic diagram, thereby obtaining fluid dynamics data;

[0160] In this embodiment, the current heat generation thermogram is imported into the fluid dynamics simulation software (such as ANSYS Fluent or COMSOL Multiphysics), and the initial boundary conditions are set according to the temperature gradient and heat load distribution in the thermogram, including the ambient temperature of 25°C and the air flow velocity of 2-5m / s. When performing airflow simulation, a turbulence model (such as the standard k-model) is set to describe the turbulent behavior of the fluid, and the simulation area is gridded, and the unit size is set to 0.1-0.5mm to improve the calculation accuracy. During the simulation process, the airflow distribution is observed, the distribution characteristics of the airflow dead corners, low-speed areas and eddy areas are recorded, and the fluid dynamics data are generated by analyzing the parameters such as turbulence intensity, airflow velocity distribution and air convection path. The output fluid dynamics data includes a turbulence intensity distribution diagram, an air velocity distribution curve and an airflow efficiency report.

[0161] Step S44: integrating the heat dissipation optimization characteristics of the circuit board according to the heat dissipation geometry data and the fluid dynamics data, thereby obtaining the heat dissipation optimization data of the circuit board.

[0162] In this embodiment, the heat dissipation geometry data and fluid dynamics data are input into the heat dissipation optimization integration algorithm tool, and the optimal heat dissipation geometry and fluid dynamics characteristics are matched through interactive calculation. During the optimization and integration process, the focus is on identifying airflow dead corners and high-temperature areas, and adjusting the heat dissipation geometry parameters to eliminate airflow obstacles. For example, the heat sink spacing in the high-temperature area is further reduced, with a specific adjustment amount of 0.5-1mm, and the improvement in its heat dissipation performance is re-simulated to verify the improvement. The optimized parameters are used to generate circuit board heat dissipation optimization data through the output module, including the final heat sink layout plan, airflow path adjustment suggestions and overall heat dissipation performance indicators (such as thermal resistance reduction value, temperature distribution diagram). All optimization data are saved in a standardized format for subsequent design updates and production and manufacturing of lamps.

[0163] Optionally, step S42 is specifically:

[0164] Step S421: extracting heat sink fin features according to heat sink component design data, thereby obtaining heat sink fin data;

[0165] In this embodiment, by analyzing the design data of the heat dissipation element, the design features related to the heat dissipation fins are extracted, including the number, length, thickness, surface roughness, material properties and other key parameters of the fins. For example, the number of fins is set to 10-30 pieces, the length of a single piece is in the range of 40-80mm, the thickness is 1.0-2.5mm, the surface roughness is controlled below Ra1.6μm, and the material is selected from aluminum alloy (thermal conductivity is 205W / m·K) or copper (thermal conductivity is 385W / m·K) with good thermal conductivity. Use a database tool to record the extracted parameters and generate a heat dissipation fin data file, which is stored in a table form for subsequent analysis and optimization steps.

[0166] Step S422: performing fin shape scanning on the heat dissipation fin data to obtain heat dissipation fin shape data;

[0167] In this embodiment, the extracted heat sink fin data is input into a three-dimensional scanner, and the three-dimensional shape data of the fin is generated by laser or optical scanning technology. The scanning accuracy is set to ±0.01mm to ensure the integrity and accuracy of the shape data. After data cleaning and fitting, the scanning results are converted into a standardized CAD format file (such as .STEP or .IGES format) to record the curvature change, edge shape and overall geometric features of the fin to form the heat sink fin shape data. The scanned data is compared with the original design parameters through the inspection tool to ensure the consistency of the data with the actual fin shape.

[0168] Step S423: performing corrugation optimization according to the heat dissipation fin shape data, thereby obtaining the heat dissipation fin corrugation shape data;

[0169] In this embodiment, the fin corrugated structure is optimized and analyzed using computational fluid dynamics software (such as ANSYS Fluent) based on the shape data of the heat dissipating fins, with the goal of improving the heat transfer efficiency of the fin surface. The corrugation amplitude is set to 1-3mm, the corrugation spacing is set to 5-10mm, the corrugation design scheme within the parameter range is adjusted, and the heat transfer coefficient and pressure loss of different schemes are analyzed through simulation. Finally, a set of corrugation parameters with the best heat transfer coefficient and a pressure loss of less than 10% is selected. The optimized corrugated shape data is recorded with a three-dimensional modeling software and output as a standardized data file for use in the production and manufacturing process.

[0170] Step S424: performing airflow distribution simulation on the heat dissipation fin data to obtain airflow distribution simulation data;

[0171] In this embodiment, based on the fin shape data and the corrugation optimization results, a three-dimensional airflow simulation model of the heat sink fin is constructed, and the initial boundary conditions are set, including an inlet wind speed of 3m / s, an ambient temperature of 25°C, and an air density of 1.225kg / m 3. Computational fluid dynamics tools are used to simulate the airflow distribution and analyze the flow trajectory, velocity change and vortex distribution of the airflow between the fins. The simulation results record the airflow distribution characteristics and generate airflow distribution simulation data files including velocity field, pressure field and flow distribution for subsequent visual analysis.

[0172] Step S425: drawing an airflow velocity distribution diagram according to the airflow distribution simulation data, thereby obtaining an airflow velocity distribution diagram;

[0173] In this embodiment, the air flow distribution simulation data is imported into data visualization software (such as Tecplot or ParaView), and the air flow velocity distribution map is drawn by means of vector diagrams and contour maps. The air flow velocity distribution map uses different color areas to represent the velocity size. For example, the low-speed area is represented by blue (the speed is less than 1m / s), and the high-speed area is represented by red (the speed is greater than 5m / s). The image resolution is set to 300dpi to ensure that the image is clear and easy to identify the air flow characteristics and the position of the low-speed area.

[0174] Step S426: Calculating the airflow efficiency according to the airflow velocity distribution diagram, thereby obtaining airflow efficiency data;

[0175] In this embodiment, according to the air flow velocity distribution diagram, the air flow efficiency calculation formula is defined as: the ratio of the effective flow velocity of the passing area to the inlet flow velocity (in %). The area ratio of the low-speed area to the high-speed area in the air flow velocity distribution diagram is quantified, and the air flow efficiency is calculated by combining the inlet flow velocity and the outlet flow velocity data. For example, if the inlet flow velocity is 3m / s and the average outlet flow velocity is 2.5m / s, the air flow efficiency is 83.3%. The calculation results are recorded and the air flow efficiency data file is output.

[0176] Step S427: extracting low-efficiency features according to the airflow efficiency data, thereby obtaining low-efficiency airflow data, and identifying low-efficiency areas of the airflow velocity distribution map according to the low-efficiency airflow data, thereby obtaining low-efficiency airflow area data;

[0177] In this embodiment, the efficiency threshold is set to 80% through the airflow efficiency data, the airflow passing area below this threshold is identified, and the corresponding area position and area are marked in the airflow velocity distribution map. The flow velocity, pressure, and temperature characteristics of the low-efficiency area are exported using a data analysis tool to generate low-efficiency airflow passing data. A detailed analysis of the low-efficiency area is performed based on the data file to identify the key factors affecting the airflow efficiency, and the low-efficiency airflow passing area data is output to guide the fin optimization design.

[0178] Step S428: adjusting the heat dissipation fin spacing based on the low-efficiency airflow passing area data, thereby obtaining heat dissipation fin spacing adjustment data;

[0179] In this embodiment, the spacing of the heat sink fins is adjusted according to the data of the area where the inefficient airflow passes, and the optimization range is 2-6mm, with the goal of reducing the airflow dead angle and eddy effect. The model is remodeled through the parametric design tool, and the effect of the adjusted spacing on the airflow velocity and temperature distribution is simulated and evaluated. After the spacing optimization is completed, an adjustment data file containing the fin spacing and airflow efficiency improvement index is generated for use in the subsequent structural integration step.

[0180] Step S429: performing heat dissipation geometry integration according to the heat dissipation fin spacing adjustment data and the heat dissipation fin corrugation shape data, so as to obtain heat dissipation geometry data.

[0181] In this embodiment, the heat dissipation fin spacing adjustment data and the corrugation shape data are input into a 3D design software (such as SolidWorks) to complete the final geometric structure integration. During the integration process, the overall rigidity of the fins is ensured to be consistent with the heat dissipation performance optimization goals, and the improvement of the airflow efficiency by the fin structure is verified. The completed heat dissipation geometric structure data includes parameters such as fin shape, spacing, corrugation design, material thickness, etc., and the standardized file is output for production and subsequent verification.

[0182] Optionally, step S43 is specifically:

[0183] Step S431: Calculate the temperature gradient according to the current heat generated thermodynamic diagram, so as to obtain temperature gradient data;

[0184] In this embodiment, the thermogram data is generated according to the heat of the current, and the temperature gradient of each area is determined by calculating the temperature changes in different areas of the thermogram. Use a special thermodynamic calculation tool, such as ANSYS or Fluent, to analyze the thermogram. Import the thermogram into the tool, set the analysis area, select an appropriate calculation grid size (for example, 0.5mm), and solve according to the preset boundary conditions to obtain temperature gradient data. The data includes the temperature change value per unit area, usually expressed in ° C / m, reflecting the rate of temperature change at different locations. In specific implementation, high temperature areas and low temperature areas are identified through temperature distribution analysis to help subsequent airflow optimization analysis.

[0185] Step S432: identifying airflow dead corners according to the temperature gradient data, thereby obtaining airflow dead corner data;

[0186] In this embodiment, a fluid dynamics analysis tool (such as Fluent) is used to identify airflow blind spots based on temperature gradient data. The temperature gradient data is combined with the airflow model, and different airflow conditions are set in the simulation environment, such as a flow rate of 3m / s and an air density of 1.225kg / m 3Etc. The airflow simulation tool is used to analyze the flow path of airflow under different temperature gradients, and identify the airflow stagnation area caused by temperature difference, that is, the airflow dead corner. The identification process requires setting a threshold, and usually the area where the temperature gradient changes by more than 5℃ / m is selected as the potential airflow dead corner area. Through this step, the airflow dead corner data is obtained, including the dead corner location, area and impact on airflow efficiency.

[0187] Step S433: optimizing the fin spacing of the airflow dead angle data, thereby obtaining fin spacing optimization data;

[0188] In this embodiment, the spacing of the heat sink fins is further optimized based on the airflow dead corner data. Computational fluid dynamics (CFD) tools (such as Fluent or CFX) are used to analyze the dead corner area, set the fin spacing optimization range to 2mm to 6mm, and perform multiple simulations within this range. After each optimization, monitor the changes in airflow efficiency, and select the spacing with a flow rate greater than 1m / s in the airflow dead corner area and a heat transfer efficiency of more than 20% as the final solution. During the optimization process, the fin spacing is adjusted, and the optimization results are recorded in the form of a data table, including the impact of each spacing on airflow velocity, temperature, and thermal conductivity, to generate the final fin spacing optimization data.

[0189] Step S434: Evaluate the turbulence intensity based on the fin spacing optimization data to obtain fluid dynamics data.

[0190] In this embodiment, the turbulence intensity is evaluated based on the fin spacing optimization data. A turbulence model (such as the k-ε model) is used to perform fluid dynamics simulation on the optimized fin spacing scheme to analyze the turbulence intensity of the fluid at different spacings. The boundary conditions are set to an ambient temperature of 25°C and a flow velocity of 3m / s, and the entire flow field is simulated. By calculating the turbulence intensity (usually expressed by turbulent kinetic energy and turbulent dissipation rate), fluid dynamics data is obtained, including turbulence intensity distribution, velocity distribution, and pressure changes. This data helps to further optimize the distribution of the airflow, making the flow of the fluid more uniform, reducing the airflow retention area, and ultimately improving the heat dissipation performance.

[0191] Optionally, step S432 is specifically:

[0192] Extract high temperature gradient features according to the temperature gradient data, so as to obtain high temperature gradient data;

[0193] In this embodiment, based on the temperature gradient data obtained from the current heat generation thermogram, a special thermal analysis software (such as ANSYS Fluent) is used to extract high temperature gradient features. The thermogram data is imported into the software and the corresponding area is set. By calculating the temperature change rate in the area, the area with large temperature change is identified. The threshold of the high temperature gradient is set to the area where the temperature gradient exceeds 15°C / m, and these areas are selected as the high temperature gradient feature extraction area. The thermal gradient of each area is calculated by combining the imported temperature gradient with the heat flux formula, and the high temperature gradient data of these areas are extracted. The data includes the thermal gradient intensity, temperature change range and affected area of ​​each high temperature area.

[0194] Acquire fluid flow data;

[0195] In this embodiment, fluid flow data is obtained by CFD software (such as ANSYS Fluent). First, a fluid flow model in the lamp is established, and input parameters such as air velocity of 2m / s, ambient temperature of 25°C, and pressure set to normal atmospheric pressure are set. According to the designed shape and internal structure of the lamp, the fluid flow is obtained by numerical simulation. During the simulation process, by setting an appropriate turbulence model (such as the k-ε model), the air flow velocity, pressure, temperature, and turbulent kinetic energy are solved. The simulation results include the velocity distribution of the entire flow field, the turbulence intensity, and its effect on the heat dissipation inside and outside the lamp.

[0196] Perform flow velocity statistics based on fluid flow data to obtain low flow velocity data;

[0197] In this embodiment, flow velocity statistics are performed in the obtained fluid flow data. The flow velocity distribution data of each area inside the lamp is extracted by CFD software. The area with a flow velocity lower than 1.5m / s is marked as a low flow velocity area. The flow velocity threshold is set to 1.5m / s, and the low flow velocity areas inside the entire lamp are counted. These low flow velocity areas usually affect the flow efficiency of the air, so they need to be optimized. In the data processing process, the distribution of the low flow velocity area, the influencing factors and their impact on the heat dissipation efficiency are analyzed, and the specific coordinates and area of ​​the low flow velocity area are output.

[0198] The air flow velocity distribution diagram is divided into regions according to the low flow velocity data, so as to obtain low flow velocity region data;

[0199] In this embodiment, the air flow velocity distribution diagram is divided into regions according to the low flow velocity data. Using numerical simulation tools, based on the existing flow velocity distribution diagram, different areas inside the lamp are delineated according to the low flow velocity area data. These areas are divided into low flow velocity, normal flow velocity and high speed areas according to the different flow velocities. The flow velocity of each area is marked by CFD software, and the coordinates, area and influencing factors of the low flow velocity area are output according to the set flow velocity standard (such as 1.5m / s or less is a low flow velocity area). Finally, the low flow velocity area data is formed to help the subsequent optimization design.

[0200] The current heat generation thermodynamic map is divided into regions according to the high temperature gradient data, thereby obtaining high temperature gradient region data;

[0201] In this embodiment, the current heat generation thermogram is divided into regions according to the high temperature gradient data. Use thermal analysis tools, such as the thermal simulation module of ANSYS, to input the high temperature gradient data into the system. Set the temperature gradient threshold, and select the area where the thermal gradient exceeds 15°C / m as the high temperature area. Subdivide the entire thermogram, mark the areas with different temperature gradient intensities, and determine the specific location, area and thermal intensity of the high temperature area. Based on this data, generate high temperature gradient area data to help optimize the airflow path and thermal management solution later.

[0202] The area intersection operation is performed based on the low flow velocity area data and the high temperature gradient area data to obtain the airflow dead corner data.

[0203] In this embodiment, the data of low velocity area and high temperature gradient area are input into CFD analysis software for regional overlap analysis. Through geometric overlap algorithm, the intersection of low velocity area and high temperature gradient area is found and marked as airflow dead angle area. The position, size and factors affecting airflow of these dead angle areas are recorded to generate airflow dead angle data. These areas usually have poor flow and heat accumulation, which affects the heat dissipation effect of the lamp and must be optimized first.

[0204] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0205] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A lightweight design method based on lamps, characterized in that: The following steps are involved: Step S1: Acquire lamp design data, and extract circuit board design features based on the lamp design data, thereby obtaining circuit board design data; Performing current path analysis according to circuit board design data to obtain current path data; Step S2: extracting component design layout features and component design material features according to the circuit board design data, thereby obtaining component design layout data and component design material data; performing thermal performance analysis according to the component design layout data and the component design material data, thereby obtaining circuit board thermal performance data; Step S3: performing heat generation analysis on the circuit board thermal performance data according to the current path data, thereby obtaining current heat generation data; A thermodynamic map is drawn based on the current heat generation data, thereby obtaining a current heat generation thermodynamic map; Step S4: extracting the design features of the heat dissipation component according to the lamp design data, thereby obtaining the heat dissipation component design data; Perform heat dissipation optimization analysis based on heat dissipation component design data and current heat generation thermodynamic diagram to obtain circuit board heat dissipation optimization data; Step S5: Perform circuit material weight reduction analysis based on the circuit board heat dissipation optimization data to obtain circuit material weight reduction data, and upload it to the lamp management platform to execute the lamp lightweight design task.

2. The light-weight design method based on lamps according to claim 1, characterized in that: Step S1 is specifically as follows: Step S11: Acquire lamp design data, and extract circuit board design features based on the lamp design data, thereby obtaining circuit board design data; Step S12: extracting circuit diagram features according to the circuit board design data, thereby obtaining a circuit diagram; Step S13: identifying the power interface of the circuit diagram, thereby obtaining power interface data; Step S14: performing ground port identification on the circuit diagram to obtain ground port data; Step S15: constructing a current path model according to the power interface data and the ground port data, thereby obtaining a current path model; Step S16: Perform current path simulation according to the current path model to obtain current path data.

3. The light-weight design method based on lamps according to claim 1, characterized in that: Step S2 is specifically as follows: Step S21: extracting component design layout features and component design material features according to the circuit board design data, thereby obtaining component design layout data and component design material data; Step S22: performing thermal convection analysis on the component design layout data, thereby obtaining the component design layout thermal convection data; Step S23: performing thermal expansion analysis on the component design material data, thereby obtaining the component design material thermal expansion data; Step S24: Perform thermal performance integration based on the component design layout thermal convection data and the component design material thermal expansion data to obtain the circuit board thermal performance data.

4. The light-weight design method based on lamps according to claim 3, characterized in that: Step S22 is specifically as follows: Step S221: extracting component coordinate features from component design layout data to obtain component coordinate data; Step S222: Obtaining component heat consumption power data; Step S223: constructing a three-dimensional thermal convection model according to the component heat consumption power data and the component coordinate data, thereby obtaining a three-dimensional thermal convection model; Step S224: performing air flow field analysis according to the three-dimensional thermal convection model to obtain air flow field data; Step S225: Performing thermal convection visualization analysis based on the air flow field data to obtain component design layout thermal convection data.

5. The light-weight design method based on lamps according to claim 4, characterized in that: Step S224 is specifically as follows: The air density is set according to the three-dimensional thermal convection model to obtain air density data; The inlet and outlet boundaries are set according to the three-dimensional thermal convection model, thereby obtaining the inlet and outlet boundary data; Perform turbulence simulation according to air density data and inlet and outlet boundary data to obtain turbulence simulation data; The air flow field is solved for the turbulence simulation data to obtain the air flow field data.

6. The light-weight design method based on lamps according to claim 3, characterized in that: Step S23 is specifically as follows: Step S231: extracting the thermal expansion coefficient characteristics of the component design material data, thereby obtaining the thermal expansion coefficient of the material; Step S232: extracting material geometric dimension features according to the lamp design data to obtain material geometric dimension data, and constructing a three-dimensional material geometric model according to the material geometric dimension data to obtain a three-dimensional material geometric model; Step S233: performing thermal deformation simulation according to the material thermal expansion coefficient and the three-dimensional material geometric model to obtain thermal deformation data; Step S234: calculating the material deformation amount of the thermal deformation data, thereby obtaining the material deformation amount data; Step S235: performing thermal expansion calculation according to the material deformation data and the material thermal expansion coefficient, thereby obtaining the thermal expansion data of the component design material.

7. The light-weight design method based on lamps according to claim 1, characterized in that: Step S4 is specifically as follows: Step S41: extracting heat dissipation component design features according to the lamp design data, thereby obtaining heat dissipation component design data; Step S42: Optimizing the heat dissipation geometry structure according to the heat dissipation element design data, thereby obtaining heat dissipation geometry structure data; Step S43: performing fluid dynamics analysis based on the current heat generated thermodynamic diagram, thereby obtaining fluid dynamics data; Step S44: integrating the heat dissipation optimization characteristics of the circuit board according to the heat dissipation geometry data and the fluid dynamics data, thereby obtaining the heat dissipation optimization data of the circuit board.

8. The light-weight design method based on lamps according to claim 7, characterized in that: Step S42 is specifically as follows: Step S421: extracting heat sink fin features according to heat sink component design data, thereby obtaining heat sink fin data; Step S422: performing fin shape scanning on the heat dissipation fin data to obtain heat dissipation fin shape data; Step S423: performing corrugation optimization according to the heat dissipation fin shape data, thereby obtaining the heat dissipation fin corrugation shape data; Step S424: performing airflow distribution simulation on the heat dissipation fin data to obtain airflow distribution simulation data; Step S425: drawing an airflow velocity distribution diagram according to the airflow distribution simulation data, thereby obtaining an airflow velocity distribution diagram; Step S426: Calculating the airflow efficiency according to the airflow velocity distribution diagram, thereby obtaining airflow efficiency data; Step S427: extracting low-efficiency features according to the airflow efficiency data, thereby obtaining low-efficiency airflow data, and identifying low-efficiency areas of the airflow velocity distribution map according to the low-efficiency airflow data, thereby obtaining low-efficiency airflow area data; Step S428: adjusting the heat dissipation fin spacing based on the low-efficiency airflow passing area data, thereby obtaining heat dissipation fin spacing adjustment data; Step S429: performing heat dissipation geometry integration according to the heat dissipation fin spacing adjustment data and the heat dissipation fin corrugation shape data, so as to obtain heat dissipation geometry data.

9. The light-weight design method based on lamps according to claim 7, characterized in that: Step S43 is specifically as follows: Step S431: Calculate the temperature gradient according to the current heat generated thermodynamic diagram, so as to obtain temperature gradient data; Step S432: identifying airflow dead corners according to the temperature gradient data, thereby obtaining airflow dead corner data; Step S433: optimizing the fin spacing of the airflow dead angle data, thereby obtaining fin spacing optimization data; Step S434: Evaluate the turbulence intensity based on the fin spacing optimization data to obtain fluid dynamics data.

10. The light-weight design method based on lamps according to claim 9, characterized in that: Step S432 is specifically as follows: Extract high temperature gradient features according to the temperature gradient data, so as to obtain high temperature gradient data; Acquire fluid flow data; Perform flow velocity statistics based on fluid flow data to obtain low flow velocity data; The air flow velocity distribution diagram is divided into regions according to the low flow velocity data, so as to obtain low flow velocity region data; The current heat generation thermodynamic map is divided into regions according to the high temperature gradient data, thereby obtaining high temperature gradient region data; The area intersection operation is performed based on the low flow velocity area data and the high temperature gradient area data to obtain the airflow dead corner data.

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