Automatic design generation method based on tool table lamp structure model

Abstract

The invention relates to the technical field of table lamp production, and discloses an automatic design generation method based on a tool table lamp structure model, which allows a user to select design parameters of a table lamp through a user interface, including shape, size, color, material type and the like, and then automatically generates a three-dimensional structure model of the table lamp by using a parametric modeling method. Constructing a performance analysis model through a machine learning algorithm, and generating performance expression data of the table lamp, including environmental protection, durability and manufacturing cost analysis; optical simulation software is used for simulating the illumination effect of the table lamp, illumination index data are calculated, and meanwhile circuit simulation software is used for conducting function verification and testing on a circuit system of the table lamp; and finally, generating a detailed output file of the desk lamp design for production and manufacturing. Clear and accurate guidance is provided for production and manufacturing, and the production difficulty and cost are reduced.

Description

Technical Field

[0001] The invention relates to the technical field of table lamp production, and in particular to an automatic design generation method based on a tool table lamp structure model. Background Art

[0002] As an indispensable lighting tool in daily life, the design and manufacturing process of desk lamps are gradually developing towards intelligence and automation.

[0003] In the traditional design process, designers need to manually draw a large number of drawings and adjust design parameters through repeated trials, which leads to long design cycles, low efficiency, and difficulty in meeting the rapidly changing market needs. In addition, the performance of desk lamps, such as environmental protection, durability, and manufacturing cost, traditionally relies on designers' empirical estimates or simple tests, lacking systematic and accurate analysis methods, which limits the performance optimization of design solutions. To this end, we propose an automatic design generation method based on the tool desk lamp structure model. Summary of the invention

[0004] The purpose of the present invention is to provide an automatic design generation method based on a tool desk lamp structure model to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: an automatic design generation method based on a tool desk lamp structure model, the method comprising:

[0006] S1: The user selects design parameters of the desk lamp through a user interface, including shape, size, color, and material type; the material type includes the type of lamp beads, shell material, and battery model;

[0007] S2: According to the design parameters selected by the user, a three-dimensional structural model of the desk lamp is automatically generated using a parametric modeling method;

[0008] S3: constructing a performance analysis model using a machine learning algorithm, wherein the performance analysis model is used to generate performance data of the desk lamp according to the design parameters selected by the user, wherein the performance data includes an analysis of the environmental performance, durability and manufacturing cost of the desk lamp;

[0009] S4: simulating the lighting effect of the desk lamp by using optical simulation software, and calculating lighting index data according to the design parameters of the desk lamp and the characteristics of the selected light source, wherein the lighting index data includes light intensity distribution, color temperature and illumination;

[0010] S5: Use circuit simulation software to perform functional verification and testing on the circuit system of the desk lamp;

[0011] S6: After the user confirms the final design plan, a detailed output file of the desk lamp design is generated, including 2D drawings, 3D model files and BOM tables for production and manufacturing, as well as performance data output in step S3, lighting index data output in step S4 and circuit system test data output in step S5.

[0012] Preferably, in step S2, the specific steps of the parametric modeling method include:

[0013] Determine the basic shape and size of the table lamp, including overall height, base diameter and lampshade shape, based on the design parameters selected by the user;

[0014] According to the material type selected by the user, the corresponding material properties are assigned to different parts of the desk lamp, including the lamp bead type, shell material and battery model;

[0015] Using a predefined parametric modeling rule library, a three-dimensional structural model of the desk lamp is automatically generated according to the basic shape, size and material properties of the desk lamp, wherein the parametric modeling rule library defines the corresponding relationship between different design parameters and the geometric shape of the model;

[0016] The design of the three-dimensional structural model is further optimized using optimization algorithms.

[0017] Preferably, in step S2, a finite element analysis method is used to evaluate the stress distribution and deformation of the three-dimensional structural model in different usage scenarios, the structural strength and stability of the three-dimensional structural model are analyzed, and the basic structural design of the desk lamp is adjusted accordingly according to the analysis results. The steps of the finite element analysis method include:

[0018] Step 1: Divide the 3D structural model of the desk lamp into multiple finite element meshes;

[0019] Step 2: Apply material properties to each finite element mesh, including elastic modulus, Poisson’s ratio, and density;

[0020] Step 3: Apply boundary conditions and loads to simulate stress distribution and deformation under different usage scenarios;

[0021] Step 4: Calculate the displacement and stress of each finite element mesh using the following formula:

[0022] [K]{U}={F}

[0023] Where [K] is the stiffness matrix, {U} is the displacement vector, and {F} is the load vector;

[0024] Step 5: Evaluate the structural strength and stability of the desk lamp based on the calculation results, and adjust the basic structural design of the desk lamp as needed.

[0025] Preferably, in step S2, when constructing the three-dimensional structural model of the desk lamp, an optimization algorithm is used to automatically adjust the size, shape and material distribution of the desk lamp structure to optimize weight, cost and performance. The specific method is:

[0026] Step 1: Randomly generate a set of initial design solutions of the size, shape and material distribution of the table lamp structure as a population;

[0027] Step 2: Based on the environmental protection, material durability and cost-effectiveness constraints set by the user, as well as the performance requirements of the desk lamp, the fitness function value of each design scheme is calculated. The parameters of the fitness function include the weight, cost and performance of the desk lamp. The calculation formula of the fitness function value is:

[0028] F=α*(W_max-W)+β*(C_max-C)+γ*P

[0029] Where W, C, and P represent the weight, cost, and performance of the desk lamp, respectively. W_max and C_max are the maximum allowable weight and maximum allowable cost set by the user, respectively. α, β, and γ are weight coefficients used to adjust the relative importance of weight, cost, and performance in the fitness function.

[0030] Step 3: According to the fitness function value, select the design with higher fitness as the parent generation to generate the next generation of design solutions;

[0031] Step 4: Perform crossover and mutation operations on the selected parent design to generate a new design; the crossover operation generates a new solution by combining the features of the parent design, while the mutation operation randomly changes some features of the design;

[0032] Step 5: Add the newly generated design to the population and replace the design with lower fitness to keep the population size unchanged;

[0033] Step 6: Repeat steps 2 to 5 until the preset number of iterations is met or the fitness function value reaches the preset optimization target;

[0034] Step 7: Select the design with the highest fitness from the final population as the optimal structural size, shape, and material distribution of the desk lamp.

[0035] Preferably, the performance analysis model is obtained in the following manner:

[0036] Collect a large amount of table lamp design sample data, each sample contains the design parameters of the table lamp and its corresponding performance data;

[0037] Preprocess the collected sample data, including data cleaning, feature extraction and normalization;

[0038] A performance analysis model is constructed using a neural network algorithm, and the model is trained using preprocessed sample data, with design parameters as input features and performance data as output targets;

[0039] The cross-validation method is used to verify and tune the trained machine learning model; when the model performance meets the specified requirements, the model is saved and deployed.

[0040] Preferably, the method for obtaining lighting index data is: in the optical simulation software, according to the light source characteristics selected by the user, the light source parameters in the optical simulation software are set, including light intensity, color temperature and light efficiency; using the optical simulation software, the lighting effect of the desk lamp in actual use is simulated, and according to the simulation results, the light intensity distribution diagram, color temperature distribution diagram and illuminance distribution diagram of the desk lamp are calculated; finally, the lighting effect simulation results of the desk lamp and the corresponding lighting index data are obtained.

[0041] Preferably, according to the table lamp circuit system solution, a corresponding circuit model is built in the circuit simulation software; and circuit simulation parameters are set, including power supply voltage, operating frequency and simulation time; the built circuit model is functionally verified using the circuit simulation software to check the circuit's working state, voltage and current waveforms and power consumption;

[0042] Analyze the simulation results to determine whether the circuit system meets the design requirements, including circuit stability, safety, and energy efficiency; if the circuit system does not meet the design requirements, modify and optimize the circuit solution, and re-simulate and verify; ultimately obtain a functionally verified desk lamp circuit system solution.

[0043] Preferably, the three-dimensional structural model is exported to a common 3D model file format, and computer-aided design software CAD is used to generate 2D drawings for production and manufacturing based on the three-dimensional structural model, including detailed dimensions and assembly relationships of various components.

[0044] Preferably, a BOM table of the desk lamp is generated according to the material selection and BOM table template in the design plan, including the name, quantity, material specification and supplier information of each component.

[0045] Preferably, all generated output files, including 2D drawings, 3D model files, BOM tables, and performance and lighting index reports, are packaged and archived; a detailed output file package containing all necessary information for desk lamp design is obtained for subsequent production and quality control.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] The present invention allows users to quickly select design parameters through a user interface, and automatically generates a three-dimensional structural model of a desk lamp using a parametric modeling method, greatly shortening the time required for traditional manual drawing and improving design efficiency. By using a machine learning algorithm to build a performance analysis model, the present invention can generate performance data of the desk lamp, including environmental protection, durability, and manufacturing cost analysis, providing designers with a systematic and accurate design evaluation, which helps to optimize the design solution.

[0048] After the user confirms the final design plan, the present invention can automatically generate detailed design files, including 2D drawings, 3D model files and BOM tables for production and manufacturing. These files provide clear and accurate guidance for production and manufacturing, reducing production difficulty and cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is an overall step diagram of the method of the present invention;

[0050] Figure 2 A diagram showing the steps for analyzing a three-dimensional structural model using the finite element analysis method;

[0051] Figure 3 Diagram of the steps for automatically adjusting the size, shape, and material distribution of a desk lamp structure using an optimization algorithm. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] See also Figure 1-3 The present invention provides a technical solution: an automatic design generation method based on a tool desk lamp structure model, the specific steps are as follows:

[0054] S1. User selects design parameters: The user selects the design parameters of the desk lamp through a graphical user interface. The design reference parameters include but are not limited to the shape (such as round, square, etc.), size (height, diameter, etc.), color (shell color, light source color, etc.) and material type (such as lamp bead type, shell material, battery model, etc.) of the desk lamp. The user interface provides intuitive options and preview functions to help users easily select and adjust design parameters.

[0055] S2. Automatically generate a three-dimensional structural model: Based on the design parameters selected by the user, a three-dimensional structural model of the desk lamp is automatically generated using a parametric modeling method. Parametric modeling allows the system to dynamically adjust the model based on the specific parameter values ​​entered by the user, ensuring that the generated model fully matches the user's selection.

[0056] S3. Constructing a performance analysis model and generating performance data: A performance analysis model is constructed using a machine learning algorithm. The model can predict and generate performance data of the desk lamp based on the design parameters selected by the user. The performance data includes the environmental performance (such as energy consumption, material recyclability, etc.), durability (such as expected service life, damage resistance, etc.) and manufacturing cost analysis (including material cost, production cost, etc.) of the desk lamp.

[0057] S4. Simulate lighting effects and calculate lighting index data: Use optical simulation software to simulate the lighting effect of the desk lamp. According to the design parameters of the desk lamp and the characteristics of the selected light source, the software calculates and outputs lighting index data, which includes light intensity distribution (i.e., the distribution of light in space), color temperature (the color temperature of the light source) and illumination (the illumination intensity of the light source on a specific surface).

[0058] S5. Functional verification and testing of the circuit system: Use circuit simulation software to perform functional verification and testing on the circuit system of the desk lamp, including checking the connectivity of the circuit, verifying the functionality of the circuit (such as switch control, brightness adjustment, etc.), and ensuring that the circuit system will not malfunction or have safety hazards in actual use.

[0059] S6. Generate detailed output files: After the user confirms the final design plan, generate detailed output files of the desk lamp design. The output files include 2D drawings (such as plan views, cross-section views, etc.) for production and manufacturing, 3D model files (such as files in STL, IGES and other formats) and BOM tables (i.e., bill of materials, listing all materials and components required to manufacture the desk lamp). They also include the performance data in step S3, the lighting index data in step S4 and the circuit system test data in step S5 for the user's reference and use.

[0060] The present invention will be further described below in conjunction with Examples 1 to 4:

[0061] Embodiment 1:

[0062] In step S2, the specific implementation of the parameterized modeling method is as follows:

[0063] Based on the design parameters selected by the user through the user interface, the basic shape and size of the table lamp are first determined, including the overall height of the table lamp, the base diameter, and the shape of the lampshade. For example, if the user selects "round" as the shape of the table lamp and specifies a specific height and base diameter, the basic geometry of the table lamp is set based on this information.

[0064] According to the material type selected by the user, corresponding material properties are assigned to different parts of the desk lamp, including the type of lamp beads (such as LED, halogen, etc.), the material of the shell (such as metal, plastic, etc.), and the battery model (such as lithium-ion battery, alkaline battery, etc.). These material properties will directly affect the performance and appearance of the desk lamp.

[0065] Using the predefined parametric modeling rule library, the 3D structural model of the desk lamp is automatically generated based on the basic shape, size and material properties of the desk lamp. The parametric modeling rule library is a database containing the correspondence between different design parameters and model geometry. By retrieving and applying the defined rules, a 3D model that matches the user's selection can be quickly constructed.

[0066] Finally, the design of the generated 3D structural model is further optimized using an optimization algorithm. The optimization algorithm may consider a variety of factors, such as material utilization, structural stability, and manufacturing feasibility. By adjusting the optimization algorithm, it can be ensured that the generated 3D model not only meets the user's design requirements, but also has good manufacturability and practicality.

[0067] See attached Figure 2 , the finite element analysis method is used to analyze the structural strength and stability of the three-dimensional structural model:

[0068] The three-dimensional structural model of the desk lamp is divided into multiple finite element meshes. The finite element mesh of pollen is the basic analysis unit of the model, which is used to simulate the deformation and stress distribution of the material during the calculation process. Assume that the three-dimensional structural model of the desk lamp has been divided into 1000 finite element meshes, each mesh represents a small part of the model.

[0069] Material properties, including elastic modulus, Poisson’s ratio, and density, are applied to each finite element mesh. These properties are the basic characteristics of materials in mechanical behavior and are used to accurately simulate the stress distribution and deformation of the desk lamp. For example, assume that the outer shell of the desk lamp is made of aluminum alloy with an elastic modulus of 70 GPa, a Poisson’s ratio of 0.33, and a density of 2700 kg / m 3 , these property values ​​are assigned to all finite element meshes representing the shell.

[0070] Apply boundary conditions and loads to simulate stress distribution and deformation in different usage scenarios. Boundary conditions define the constraints of the model, such as fixed points or support points; loads represent the external forces that the model may be subjected to during use, such as gravity or human-applied pressure.

[0071] The displacement and stress of each finite element mesh are calculated using the formula: [K]{U}={F}, where [K] is the stiffness matrix, representing the stiffness characteristics of the model; {U} is the displacement vector, representing the deformation of the model under load; and {F} is the load vector, representing the external force acting on the model. By solving this formula, the displacement and stress values ​​of each finite element mesh can be obtained.

[0072] Evaluate the structural strength and stability of the lamp based on the calculation results. Check the stress distribution and deformation in the model to determine if there are structural weaknesses or unstable areas. If necessary, adjust the basic structural design of the lamp based on the analysis results to enhance its structural strength and stability. If high stress values ​​are found in certain areas, these areas may be structural weaknesses. To enhance the structural strength, thicken these areas or change the material, and then re-perform finite element analysis to verify the improvement.

[0073] See attached Figure 3 In step S2, when constructing the three-dimensional structure model of the desk lamp, an optimization algorithm is used to automatically adjust the size, shape and material distribution of the desk lamp structure to optimize weight, cost and performance. The specific method is as follows:

[0074] Step 1: Randomly generate a set of initial design solutions for the size, shape, and material distribution of the table lamp structure as a population. These design solutions are the starting point of the optimization algorithm. The diversity of the population is ensured by random generation. Each design solution contains the specific size, shape, and material distribution information of the table lamp. For example, randomly generate a set of initial design solutions containing 100 table lamp structure sizes, shapes, and material distributions as a population. Each design solution contains specific table lamp size parameters (such as height, base diameter, etc.), shape descriptions (such as round, square, etc.), and material distribution information (such as shell material, lamp bead type, etc.).

[0075] Step 2: Based on the environmental protection, material durability and cost-effectiveness constraints set by the user, as well as the performance requirements of the desk lamp, the fitness function value of each design scheme is calculated. The parameters of the fitness function include the weight, cost and performance of the desk lamp. The calculation formula of the fitness function value is:

[0076] F=α*(W_max-W)+β*(C_max-C)+γ*P

[0077] Among them, W, C, and P represent the weight, cost, and performance of the desk lamp, respectively. W_max and C_max are the maximum allowable weight and maximum allowable cost set by the user, respectively. α, β, and γ are weight coefficients used to adjust the relative importance of weight, cost, and performance in the fitness function.

[0078] Assume that the maximum allowable weight W_max set by the user is 2kg, the maximum allowable cost C_max is 100 yuan, and the weight coefficients α, β and γ are 0.5, 0.3 and 0.2 respectively. For a certain design scheme, its weight W is 1.5kg, the cost C is 80 yuan, and the performance P score is 90 (assuming that the higher the performance score, the better the performance). Then the fitness function value of the design scheme is:

[0079] Fitness=α*(1-W / W_max)+β*(1-C / C_max)+γ*P

[0080] =0.5*(1-1.5 / 2)+0.3*(1-80 / 100)+0.2*90

[0081] =0.5*0.25+0.3*0.2+0.2*90

[0082] =0.125+0.06+18

[0083] =18.185

[0084] The fitness function value of each design scheme in the population is calculated using this method.

[0085] Step 3: According to the fitness function value, select the design with higher fitness as the parent generation to generate the next generation of design. The selection process is based on the ranking of fitness values ​​or roulette wheel, etc., to ensure that the design with higher fitness has more chances to be selected and used to generate new design, gradually optimizing the structural size, shape and material distribution of the desk lamp to meet the needs and constraints of users. Assume that the top 20 design solutions in fitness are selected as the parent generation.

[0086] Step 4: Perform crossover and mutation operations on the selected parent design to generate new designs. The crossover operation creates new designs by combining the features of different parent designs, which helps introduce diversity into the population. The mutation operation randomly changes some features of the design to increase the exploration ability of the population and may discover better designs.

[0087] Step 5: Add the newly generated design to the current population and replace the design with lower fitness. Assuming that 20 new designs are generated, add these new designs to the population and replace the 20 designs with the lowest fitness in the original population. The purpose of this step is to keep the size of the population unchanged while ensuring that the designs in the population gradually evolve in a better direction. By continuously replacing designs with lower fitness, the overall fitness level of the population will gradually increase.

[0088] Step 6: Repeat steps 2 to 5 until the preset number of iterations is met or the fitness function value reaches the preset optimization goal. In each iteration, the design scheme is evaluated and selected according to the fitness function value, and then a new design scheme is generated through crossover and mutation operations. This process will be repeated until the optimal design scheme that meets the user's requirements is found.

[0089] Step 7: After the iteration, the design with the highest fitness is selected from the final population as the optimal structural size, shape, and material distribution of the desk lamp.

[0090] This design is the best solution found through optimization algorithm under the environmental protection, material durability and cost-effectiveness constraints set by the user, as well as the performance requirements of the desk lamp.

[0091] Embodiment 2:

[0092] A neural network algorithm is used to construct a performance analysis model, and the preprocessed table lamp design sample data is used for training. The performance analysis model is obtained in the following way:

[0093] Collect a large amount of table lamp design sample data to ensure that each sample contains the design parameters of the table lamp and its corresponding performance data. Design parameters include the size of the table lamp (such as height, base diameter, etc.), shape description (such as round, square, etc.), material distribution information (such as shell material, lamp bead type, etc.), while performance data includes performance indicators in actual use such as light intensity, color temperature, and energy consumption.

[0094] The collected sample data is preprocessed to ensure the quality and consistency of the data. The preprocessing steps include data cleaning, that is, removing duplicate, missing or abnormal data points; feature extraction, that is, extracting features meaningful to model training from the original data, such as key dimensions and material properties in design parameters; and normalization, converting feature data of different dimensions to the same dimension so that the model can better learn and understand the data. The Z-score algorithm is used to normalize the data, subtracting its mean from each data value and dividing it by its standard deviation, so that the processed data conforms to the standard normal distribution. The Z-score algorithm formula is: Z = (X-μ) / σ, where X is the original data, μ is the mean of the original data, σ is the standard deviation of the original data, and Z is the normalized data value.

[0095] A performance analysis model is constructed using a neural network algorithm, and the model is trained using preprocessed sample data. Design parameters are used as input features, and performance data are used as output targets. Through the learning mechanism of the neural network, the model can gradually learn the complex mapping relationship between design parameters and performance. In order to ensure the generalization ability and stability of the model, the cross-validation method is used to verify and tune the trained machine learning model. Cross-validation divides the data set into a training set and a validation set, trains and validates the model multiple times to evaluate the average performance of the model, and tunes the model parameters based on the validation results to improve the accuracy of the model.

[0096] When the model performance meets the specified requirements, the model is saved and deployed. Deploying a model is to integrate the trained performance analysis model into the relevant application or system of desk lamp design so that it can be used in the actual design process. The deployed model can quickly predict the performance of the desk lamp based on the input design parameters, providing designers with strong decision support, helping them optimize the design of desk lamps, and improving design efficiency and quality. The following is a specific training example:

[0097] Assume that 1,000 table lamp design sample data are collected and preprocessed. Each sample contains the design parameters of the table lamp (such as height, base diameter, material type, etc.) and the corresponding performance data (such as light intensity, color temperature, energy consumption, etc.).

[0098] A neural network architecture suitable for regression tasks was selected, such as a fully connected neural network with an input layer, two hidden layers (100 neurons each), and an output layer. ReLU was used as the activation function, and the Adam optimizer and mean squared error (MSE) were selected as the loss function.

[0099] During the training process, the preprocessed sample data is divided into a training set (80%) and a validation set (20%). The training set is used to train the neural network, while the validation set is used to evaluate the performance of the model and perform tuning.

[0100] The weights and biases of the neural network are optimized through multiple iterations of training to minimize the loss function on the training set. At the same time, the performance of the model on the validation set is evaluated after each iteration to monitor the generalization ability of the model.

[0101] Assume that after 50 iterations of training, the performance of the model on the validation set meets the specified requirements (for example, MSE is lower than a certain threshold). At this point, it is considered that the model has fully learned the mapping relationship between design parameters and performance, and has good generalization ability.

[0102] Finally, save the trained neural network model as a file for subsequent deployment and use.

[0103] Embodiment 3:

[0104] The method for obtaining lighting index data is:

[0105] In the optical simulation software, according to the characteristics of the light source selected by the user, the light source parameters in the optical simulation software are set in detail, including the precise input of the light intensity value to ensure that the simulated light source brightness is consistent with the actual; select the appropriate color temperature value to simulate the true color performance of the light source; and set the light effect parameters to reflect the energy conversion efficiency of the light source. Use the functions of the optical simulation software to simulate the lighting effect of the desk lamp in actual use. The optical simulation software calculates optical phenomena such as the propagation, reflection and refraction of light in the structure of the desk lamp to generate realistic lighting simulation results. For example, in the optical simulation software, according to the characteristics of an LED light source selected by the user, the light source parameters are set in detail. Specifically, the input light intensity is 800 lux (Lx), the color temperature value of 4000K is selected to create a warm and comfortable lighting environment, and the light effect is set to 90 lumens / watt (Lm / W) to reflect the high efficiency and energy saving characteristics of the light source.

[0106] According to the simulation results, the light intensity distribution diagram, color temperature distribution diagram and illuminance distribution diagram of the desk lamp are further calculated. The light intensity distribution diagram shows the light intensity of the desk lamp in different directions, which helps to understand the uniformity and intensity of the lighting; the color temperature distribution diagram reflects the color of the light source of the desk lamp in different areas, which helps to evaluate the consistency and comfort of the color temperature; and the illuminance distribution diagram shows the illuminance distribution of the desk lamp on the work surface, which is an important indicator for evaluating the lighting quality. For example, the light intensity distribution diagram shows that the light intensity of the desk lamp is the highest in the front direction, reaching 800Lx, and gradually decreases to the surroundings; the color temperature distribution diagram shows that the light source color of the entire desk lamp remains consistent, and the color temperature fluctuation range is within ±200K; and the illuminance distribution diagram shows that on the work surface, the illuminance is evenly distributed between 300Lx and 500Lx, meeting the lighting quality requirements.

[0107] Through the above method, the lighting effect simulation results of the desk lamp and the corresponding lighting index data are obtained to help designers evaluate the lighting performance of different design schemes.

[0108] According to the circuit system plan of the desk lamp, the corresponding circuit model is built in the circuit simulation software, including the layout and connection of various components in the circuit plan, such as power supply, switch, resistor, capacitor, inductor and LED lamp beads, in the simulation software according to the actual connection method.

[0109] Set the circuit simulation parameters to ensure the authenticity and effectiveness of the simulation environment, including setting the power supply voltage to simulate the actual power supply in use; setting the operating frequency to reflect the signal change speed of the circuit during normal operation; and determining the simulation time to be sufficient to observe the stable working state and potential problems of the circuit. Use the functions of the circuit simulation software to perform functional verification on the constructed circuit model. The simulation software simulates the actual working state of the circuit, generates waveform diagrams of voltage and current, and calculates the power consumption of the circuit. By observing and analyzing these simulation results, you can check whether the working state of the circuit is normal, whether the waveforms of voltage and current meet expectations, and whether the power consumption is within a reasonable range.

[0110] After obtaining the simulation results, an in-depth analysis is conducted to determine whether the circuit system meets the design requirements, including evaluating the stability of the circuit, that is, whether the circuit can maintain stable performance under long-term operation; checking the safety of the circuit to ensure that the circuit can operate safely under various abnormal conditions and will not cause dangers such as fire or electric shock; and evaluating the energy efficiency of the circuit, that is, whether the circuit can effectively use electrical energy and reduce energy waste while providing the required lighting.

[0111] If the circuit system does not meet the design requirements, the circuit solution needs to be modified and optimized, including adjusting the parameters of the components, changing the circuit layout, or using more advanced circuit technology. After the modification is completed, re-simulation verification is performed to ensure that the new circuit solution can meet all design requirements.

[0112] Embodiment 4:

[0113] Export the constructed 3D structural model to a common 3D model file format, such as STL, OBJ or IGES. Utilize the powerful functions of computer-aided design software CAD to generate 2D drawings for manufacturing based on the exported 3D structural model. In this process, the size, shape, position of each component and the assembly relationship between them need to be marked in detail to ensure accuracy and efficiency in the manufacturing process. The generation of 2D drawings follows industry standards and manufacturing specifications, providing clear and easy-to-understand guidance for production personnel.

[0114] According to the material selection and BOM template in the design plan, the BOM of the desk lamp was generated. The BOM detailed the name, quantity, material specifications and supplier information of each component, providing accurate data support for the procurement and production departments.

[0115] Finally, all generated output files are packaged and archived, including 2D drawings, 3D model files, BOM tables, performance and lighting index reports, etc. These files are organized into a detailed output file package, which contains all the necessary information required for desk lamp design, providing great convenience for subsequent production and quality control, ensuring the smooth implementation of the design plan and the final quality of the product.

[0116] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0117] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An automatic design generation method based on a tool lamp structure model, characterized in that: The method comprises: S1: The user selects design parameters of the desk lamp through a user interface, including shape, size, color, and material type; the material type includes the type of lamp beads, shell material, and battery model; S2: According to the design parameters selected by the user, a three-dimensional structural model of the desk lamp is automatically generated using a parametric modeling method; S3: constructing a performance analysis model using a machine learning algorithm, wherein the performance analysis model is used to generate performance data of the desk lamp according to the design parameters selected by the user, wherein the performance data includes an analysis of the environmental performance, durability and manufacturing cost of the desk lamp; S4: simulating the lighting effect of the desk lamp by using optical simulation software, and calculating lighting index data according to the design parameters of the desk lamp and the characteristics of the selected light source, wherein the lighting index data includes light intensity distribution, color temperature and illumination; S5: Use circuit simulation software to perform functional verification and testing on the circuit system of the desk lamp; S6: After the user confirms the final design plan, a detailed output file of the desk lamp design is generated, including 2D drawings, 3D model files and BOM tables for production and manufacturing, as well as performance data output in step S3, lighting index data output in step S4 and circuit system test data output in step S5.

2. The automatic design generation method based on the tool lamp structure model according to claim 1 is characterized in that: In step S2, the specific steps of the parametric modeling method include: Determine the basic shape and size of the table lamp, including overall height, base diameter and lampshade shape, based on the design parameters selected by the user; According to the material type selected by the user, the corresponding material properties are assigned to different parts of the desk lamp, including the lamp bead type, shell material and battery model; Using a predefined parametric modeling rule library, a three-dimensional structural model of the desk lamp is automatically generated according to the basic shape, size and material properties of the desk lamp, wherein the parametric modeling rule library defines the corresponding relationship between different design parameters and the geometric shape of the model; The design of the three-dimensional structural model is further optimized using optimization algorithms.

3. The automatic design generation method based on the tool lamp structure model according to claim 2 is characterized in that: In step S2, a finite element analysis method is used to evaluate the stress distribution and deformation of the three-dimensional structure model in different usage scenarios, and the structural strength and stability of the three-dimensional structure model are analyzed, and the basic structure design of the desk lamp is adjusted accordingly according to the analysis results. The steps of the finite element analysis method include: Step 1: Divide the 3D structural model of the desk lamp into multiple finite element meshes; Step 2: Apply material properties to each finite element mesh, including elastic modulus, Poisson’s ratio, and density; Step 3: Apply boundary conditions and loads to simulate stress distribution and deformation under different usage scenarios; Step 4: Calculate the displacement and stress of each finite element mesh using the following formula: [K]{U}={F} Where [K] is the stiffness matrix, {U} is the displacement vector, and {F} is the load vector; Step 5: Evaluate the structural strength and stability of the desk lamp based on the calculation results, and adjust the basic structural design of the desk lamp as needed.

4. The automatic design generation method based on the tool table lamp structure model according to claim 3 is characterized in that: In step S2, when constructing the three-dimensional structural model of the desk lamp, an optimization algorithm is used to automatically adjust the size, shape and material distribution of the desk lamp structure to optimize weight, cost and performance. The specific method is as follows: Step 1: Randomly generate a set of initial design solutions of the size, shape and material distribution of the table lamp structure as a population; Step 2: Based on the environmental protection, material durability and cost-effectiveness constraints set by the user, as well as the performance requirements of the desk lamp, the fitness function value of each design scheme is calculated. The parameters of the fitness function include the weight, cost and performance of the desk lamp. The calculation formula of the fitness function value is: F=α*(W_max-W)+β*(C_max-C)+γ*P Where W, C, and P represent the weight, cost, and performance of the desk lamp, respectively. W_max and C_max are the maximum allowable weight and maximum allowable cost set by the user, respectively. α, β, and γ are weight coefficients used to adjust the relative importance of weight, cost, and performance in the fitness function. Step 3: According to the fitness function value, select the design with higher fitness as the parent generation to generate the next generation of design solutions; Step 4: Perform crossover and mutation operations on the selected parent design to generate a new design; the crossover operation generates a new solution by combining the features of the parent design, while the mutation operation randomly changes some features of the design; Step 5: Add the newly generated design to the population and replace the design with lower fitness to keep the population size unchanged; Step 6: Repeat steps 2 to 5 until the preset number of iterations is met or the fitness function value reaches the preset optimization target; Step 7: Select the design with the highest fitness from the final population as the optimal structural size, shape, and material distribution of the desk lamp.

5. The automatic design generation method based on the tool lamp structure model according to claim 4 is characterized in that: The way to obtain the performance analysis model is: Collect a large amount of table lamp design sample data, each sample contains the design parameters of the table lamp and its corresponding performance data; Preprocess the collected sample data, including data cleaning, feature extraction and normalization; A performance analysis model is constructed using a neural network algorithm, and the model is trained using preprocessed sample data, with design parameters as input features and performance data as output targets; The cross-validation method is used to verify and tune the trained machine learning model; when the model performance meets the specified requirements, the model is saved and deployed.

6. The automatic design generation method based on the tool lamp structure model according to claim 5 is characterized in that: The method for obtaining the illumination index data is as follows: in the optical simulation software, according to the characteristics of the light source selected by the user, the light source parameters in the optical simulation software are set, including light intensity, color temperature and light effect; Optical simulation software is used to simulate the lighting effect of the desk lamp in actual use. According to the simulation results, the light intensity distribution diagram, color temperature distribution diagram and illuminance distribution diagram of the desk lamp are calculated; finally, the lighting effect simulation results of the desk lamp and the corresponding lighting index data are obtained.

7. The automatic design generation method based on the tool lamp structure model according to claim 6 is characterized by: According to the circuit system scheme of the desk lamp, build the corresponding circuit model in the circuit simulation software; and set the circuit simulation parameters, including power supply voltage, operating frequency and simulation time; use the circuit simulation software to verify the function of the built circuit model, and check the working status, voltage and current waveforms and power consumption of the circuit; Analyze the simulation results to determine whether the circuit system meets the design requirements, including circuit stability, safety, and energy efficiency; if the circuit system does not meet the design requirements, modify and optimize the circuit solution, and re-simulate and verify; ultimately obtain a functionally verified desk lamp circuit system solution.

8. The automatic design generation method based on the tool lamp structure model according to claim 1 is characterized by: The 3D structural model is exported to a common 3D model file format, and computer-aided design software CAD is used to generate 2D drawings for production and manufacturing based on the 3D structural model, including the detailed dimensions and assembly relationships of each component.

9. The automatic design generation method based on the tool table lamp structure model according to claim 1 is characterized by: Based on the material selection and BOM template in the design plan, generate the BOM of the desk lamp, including the name, quantity, material specification and supplier information of each component.

10. The automatic design generation method based on the tool table lamp structure model according to claim 9 is characterized in that: Package and archive all generated output files, including 2D drawings, 3D model files, BOM tables, and performance and lighting index reports; Get a detailed output package containing all necessary information about your lamp design for subsequent manufacturing and quality control.

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