A personalized frame lightweight design method and system

Abstract

The application provides a personalized frame lightweight design method and system, and belongs to the technical field of frame optimization and personalized design. The method comprises the following steps: receiving user input personalized frame design requirements, determining a target design frame template, analyzing the personalized frame design requirements to obtain frame personalized design parameters, fusing the frame personalized design parameters and a frame data set to obtain a lightweight design data set, and constructing a frame finite element model based on the lightweight design data set; and solving the frame finite element model. The system is used to realize the method. The technical scheme of the application can fully consider the personalized requirements of users when performing frame lightweight design, can maximize the fusion of user personalized requirement parameters on the premise of meeting design specifications, so that the frame optimization design process realizes interaction with the user, and the product experience can be improved.

Description

Technical Field

[0001] This invention belongs to the field of chassis optimization and personalized design technology, and particularly relates to a personalized lightweight chassis design method and system, a computer-readable storage medium for implementing the method, a computer program product, and an electronic device. Background Technology

[0002] Automotive lightweighting technology reduces vehicle weight by employing lightweight materials such as aluminum alloys, magnesium alloys, and high-strength steel, along with optimized design and manufacturing processes. Automotive lightweighting improves material utilization efficiency, reduces waste and resource waste, helps reduce carbon emissions, and maintains vehicle strength and safety. The lightweight chassis is a key element in overall vehicle lightweighting design, playing a crucial role in load-bearing and connectivity. The static and dynamic performance of the chassis is closely related to overall vehicle stiffness, NVH (noise, vibration, and shock), and durability. Therefore, rapid lightweight chassis design has garnered widespread attention in the industry.

[0003] In lightweight chassis design, commonly used methods include finite element model analysis, response surface methodology, decision tree method, and deep neural network model method. Among these, the finite element model analysis method is the most mature. For example, the literature (Optimization of Torsional Stiffness for Heavy Commercial Vehicle Chassis Frame[J]. Gawande SH et al.; Automotive Innovation, 2018, 1(4)) established a stiffness characteristic model of the heavy commercial vehicle chassis beam structure through finite element analysis, and achieved stiffness characteristic optimization under weight constraints through cross-section optimization design; Chinese invention patent publication CN118656912A proposed a lightweight chassis design method for unmanned sightseeing vehicles. By establishing a finite element model of the chassis and considering multiple working conditions, multi-objective optimization of the unmanned sightseeing vehicle chassis was carried out, achieving lightweight design.

[0004] With the increasing prevalence of intelligent technology in the automotive industry, car design will place greater emphasis on user interaction and experience. Ordinary users will be able to participate in the entire car design process, even submitting personalized customizations. Personalized design has become a crucial direction for future automotive design. However, how to maximize the integration of user-specific requirements while meeting design specifications to achieve lightweight chassis design remains a challenge, and no relevant technical reports have been found. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention proposes a personalized lightweight frame design method, a computer-readable storage medium for implementing the method, a computer program product, and an electronic device.

[0006] In a first aspect of the invention, a personalized lightweight frame design method is proposed, the method being implemented using electronic devices;

[0007] The method includes the following steps:

[0008] S110: Receives personalized frame design requirements input by the user;

[0009] S120: Based on the personalized frame design requirements, determine the target frame design template from multiple candidate frame design templates;

[0010] S130: Analyze the personalized frame design requirements to obtain at least one personalized frame design parameter;

[0011] S140: Analyze the target design frame template to obtain a frame dataset, and fuse the at least one personalized frame design parameter with the frame dataset to obtain a lightweight design dataset;

[0012] S150: Construct a finite element model of the chassis based on the aforementioned lightweight design dataset;

[0013] S160: Solve the finite element model of the vehicle frame and determine whether the solution result meets the preset requirements;

[0014] If so, output the finite element model of the vehicle frame;

[0015] If not, then optimize the finite element model of the chassis.

[0016] In step S160, if the model solution result does not meet the preset requirements, the finite element model of the chassis is optimized, specifically including:

[0017] After optimizing at least part of the network architecture in the finite element model of the chassis, the optimized finite element model of the chassis is re-solved, and it is determined whether the solution result of the model meets the preset requirements.

[0018] If the model solution still does not meet the preset requirements, the user is prompted to adjust the personalized chassis design requirements and the process returns to step S110.

[0019] Furthermore, in a second aspect of the invention, another personalized lightweight frame design method is proposed, the method comprising the following steps:

[0020] S210: Receives personalized frame design requirements input by the user;

[0021] S220: Based on the personalized frame design requirements, determine the target frame design template from multiple candidate frame design templates, and analyze the target frame design template to obtain a basic frame dataset;

[0022] S230: Analyze the personalized frame design requirements to obtain N personalized frame design parameters.

[0023] S240: Let i=1;

[0024] S250: Incorporating personalized design parameters Integrate into the basic frame dataset to obtain a lightweight design dataset;

[0025] S260: Construct a finite element model of the chassis based on the aforementioned lightweight design dataset;

[0026] S270: Solve the finite element model of the vehicle frame and determine whether the solution result meets the preset requirements;

[0027] If the model solution meets the preset requirements, then let i = i + 1; determine whether i ≤ N is true.

[0028] If i≤N is true, then the lightweight design dataset is used as the basic frame dataset, and the process returns to step S250.

[0029] If i≤N is not true, then output the finite element model of the chassis.

[0030] Step S230 specifically includes:

[0031] Analyze the personalized frame design requirements to obtain N personalized frame design parameters;

[0032] The N frame customization parameters are arranged in descending order of priority to obtain the N frame customization parameters. .

[0033] In step S270, if the model solution result does not meet the preset requirements, then after optimizing at least part of the network architecture in the frame finite element model, the optimized frame finite element model is solved again to determine whether the model solution result meets the preset requirements.

[0034] If the model solution still does not meet the preset requirements, the user is prompted to adjust the personalized chassis design requirements and the process returns to step S210.

[0035] If the model's solution does not meet the preset requirements, the user will be prompted to abandon the personalized design parameters. ;

[0036] If the user agrees to waive personalized design parameters , then let i=i+1;

[0037] Determine whether i ≤ N is true;

[0038] If i≤N is true, then the lightweight design dataset is used as the basic frame dataset, and the process returns to step S250.

[0039] If i≤N is not true, then output the finite element model of the chassis.

[0040] The aforementioned two methods for personalized lightweight chassis design can be implemented automatically through various forms of electronic devices and computer program instructions; the computer program instructions can be stored in different forms of storage media and loaded into computer electronic devices for execution.

[0041] Therefore, in a third aspect of the invention, a computer-readable storage medium is also provided for storing computer instructions that, when executed on an electronic device, cause the electronic device to perform the personalized lightweight chassis design method of the foregoing two aspects.

[0042] In a fourth aspect of the invention, a computer device is also provided, the computer device including a processor and a memory, the memory for storing instructions, and the processor for calling the instructions in the memory, causing the computer device to execute the personalized lightweight chassis design method of the foregoing two aspects.

[0043] In a fifth aspect of the invention, a computer program product is also provided, the product comprising a computer program that, when executed, implements the personalized lightweight frame design methods of the two aforementioned aspects.

[0044] To achieve the personalized lightweight frame design method of the first aspect mentioned above, in the sixth aspect of the present invention, a personalized lightweight frame design system is also proposed, the system comprising:

[0045] Receiving unit: Receives personalized chassis design requirements input by the user;

[0046] Target design frame template matching unit: Based on the personalized frame design requirements, determine the target design frame template from multiple candidate frame design templates;

[0047] Analysis Unit: Analyzes the personalized frame design requirements to obtain at least one personalized frame design parameter;

[0048] Fusion Unit: Analyzes the target design frame template to obtain a frame dataset, and fuses the at least one personalized frame design parameter with the frame dataset to obtain a lightweight design dataset;

[0049] Finite element model building unit: Construct a finite element model of the chassis based on the aforementioned lightweight design dataset;

[0050] Finite element model solving unit: solves the finite element model of the vehicle frame and determines whether the model solution results meet the preset requirements;

[0051] If so, output the finite element model of the vehicle frame;

[0052] If not, then optimize the finite element model of the chassis.

[0053] If the model solution does not meet the preset requirements, the finite element model of the chassis will be optimized, specifically including:

[0054] After optimizing at least part of the network architecture in the finite element model of the chassis, the optimized finite element model of the chassis is re-solved, and it is determined whether the solution result of the model meets the preset requirements.

[0055] If the model solution still does not meet the preset requirements, the user will be prompted to adjust the personalized chassis design requirements.

[0056] To achieve the personalized lightweight frame design method of the second aspect mentioned above, in the seventh aspect of the present invention, a personalized lightweight frame design system is also proposed, the system comprising:

[0057] Receiving unit: Receives personalized chassis design requirements input by the user;

[0058] Matching and Analysis Unit: Based on the personalized frame design requirements, the target frame design template is determined from multiple candidate frame design templates, and the target frame design template is analyzed to obtain a basic frame dataset;

[0059] Analysis Unit: Analyzes the personalized frame design requirements to obtain N personalized frame design parameters.

[0060] A lightweight design unit, wherein the lightweight design unit performs the following steps:

[0061] Step 1: Let i = 1;

[0062] Step 2: Personalize the design parameters Integrate into the basic frame dataset to obtain a lightweight design dataset;

[0063] Step 3: Construct a finite element model of the chassis based on the aforementioned lightweight design dataset;

[0064] Step 4: Solve the finite element model of the vehicle frame and determine whether the solution results meet the preset requirements;

[0065] If the model solution meets the preset requirements, then let i = i + 1; determine whether i ≤ N is true.

[0066] If i≤N is true, then the lightweight design dataset is used as the basic frame dataset, and the process returns to Step 2.

[0067] If i≤N is not true, then output the finite element model of the chassis.

[0068] The technical solution of this invention can fully consider the personalized needs of users when designing lightweight frames. It can integrate personalized user needs parameters to the maximum extent while meeting design specifications, thereby enabling the frame optimization design process to interact with users and improve the product experience.

[0069] Further advantages of the present invention will be further detailed in the Specific Embodiments section in conjunction with the accompanying drawings. Attached Figure Description

[0070] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0071] Figure 1 This is a schematic diagram of the main process of a personalized lightweight frame design method according to an embodiment of the present invention;

[0072] Figure 2 This is a schematic diagram of the main flow of a personalized lightweight frame design method according to another embodiment of the present invention;

[0073] Figure 3 These are schematic diagrams of the original target design chassis template and the finite element model of the chassis after personalized design;

[0074] Figure 4 This is a schematic diagram of the finite element model of the chassis output after the user adjusts their personalized chassis design requirements;

[0075] Figure 5 This is a schematic diagram of the functional units of a personalized lightweight frame design system according to an embodiment of the present invention;

[0076] Figure 6 This is a schematic diagram of the functional units of a personalized lightweight frame design system according to another embodiment of the present invention. Detailed Implementation

[0077] First, it should be noted that the embodiments of the personalized lightweight frame design method mentioned in this section can be implemented by computer programs on electronic devices or systems equipped with memory and processors. The electronic devices or systems can be physical machines, virtual machines, servers, clusters, or any combination thereof.

[0078] Preferably, the electronic device can also be a human-computer interaction terminal, which can be a desktop terminal, a smart handheld terminal, a mobile terminal, etc., with a human-computer interaction interface.

[0079] First see Figure 1 , Figure 1 This is a flowchart of a personalized lightweight frame design method according to an embodiment of the present invention. Figure 1 The method includes steps S110-S160, and the specific implementation of each step is as follows:

[0080] S110: Receives personalized frame design requirements input by the user;

[0081] S120: Based on the personalized frame design requirements, determine the target frame design template from multiple candidate frame design templates;

[0082] S130: Analyze the personalized frame design requirements to obtain at least one personalized frame design parameter;

[0083] S140: Analyze the target design frame template to obtain a frame dataset, and fuse the at least one personalized frame design parameter with the frame dataset to obtain a lightweight design dataset;

[0084] S150: Construct a finite element model of the chassis based on the aforementioned lightweight design dataset;

[0085] S160: Solve the finite element model of the vehicle frame and determine whether the solution result meets the preset requirements;

[0086] If so, output the finite element model of the vehicle frame;

[0087] If not, then optimize the finite element model of the chassis.

[0088] If the model solution does not meet the preset requirements, the finite element model of the chassis will be optimized, specifically including:

[0089] After optimizing at least part of the network architecture in the finite element model of the chassis, the optimized finite element model of the chassis is re-solved, and it is determined whether the solution result of the model meets the preset requirements.

[0090] If the model solution still does not meet the preset requirements, the user is prompted to adjust the personalized chassis design requirements and the process returns to step S110.

[0091] Next, through illustrative examples, we will... Figure 1 The steps of the method described will be explained in detail.

[0092] Step S110: Receive the user's personalized frame design requirements.

[0093] It's understandable that the users here are ordinary users. Unlike professional frame designers, ordinary users generally use natural language to describe their personalized frame design needs.

[0094] As an example, for ordinary users, different parts of the entire vehicle frame can be described as the front, body, rear, chassis, driver's seat, passenger seat, and rear space, while professional designers may use professional terms such as "A-pillar," "hood," and "luggage compartment."

[0095] As an example, a typical user's personalized frame design request could be:

[0096] "I want a square-shaped front design with pointed edges; a chassis clearance of at least XX1cm; and to eliminate the passenger seat and replace it with a platform mat-less rear seat."

[0097] Next, proceed to step S120: Based on the personalized frame design requirements, determine the target frame design template from multiple candidate frame design templates.

[0098] A frame design template database can be pre-built to store design templates for different types of frames.

[0099] As an example, a chassis design template database is shown in Table 1 below:

[0100] Serial Number Car front shape Rear shape chassis height Passenger seat Template A Adjustable Not adjustable Adjustable within (XX1, XX2) Not customizable Template B Not adjustable Not adjustable Adjustable within (XX0, XX1) Customizable Template C Adjustable Adjustable Adjustable within (XX1, XX3) Customizable Template D Not adjustable Adjustable Not adjustable Not customizable

[0101] Table 1

[0102] It is understood that each frame design template corresponds to an original basic finite element model in the frame design template database, for example... Figure 3 and Figure 4 The left image.

[0103] The basic finite element model shows the frame as being formed by connecting hollow tubular components of varying thicknesses and lengths. The main structure of the frame is a spatial triangular structure, including a small number of rectangular and polygonal structures (circular shapes are fitted using an N-sided polygon).

[0104] Continuing with the previous example, based on the personalized frame design requirements ("I want a square front design with a pointed end; the chassis should be no less than XX1cm; the passenger seat should be removed and replaced with a platform without a backrest"), the target frame design template is determined as template C from multiple candidate frame design templates;

[0105] Next, proceed to step S130: analyze the personalized frame design requirements and obtain at least one personalized frame design parameter;

[0106] Continuing with the previous example, the set of personalized frame design parameters P could be:

[0107] Car front: Square (rectangle with right angle of 90°) + pointed corner in the middle (triangle with vertex angle of 150°);

[0108] Chassis height: > XX1cm;

[0109] Passenger seat: Circular (diameter YYCM) (in actual design, the circle is fitted with an N-sided polygon).

[0110] Preferably, the process of analyzing the personalized frame design requirements and obtaining at least one personalized frame design parameter can be completed by professional designers, or the personalized frame design requirements can be input into the pre-trained parameter recognition model after the parameter recognition model is pre-trained, and the pre-trained parameter recognition model can output at least one personalized frame design parameter.

[0111] Preferably, the training samples of the pre-trained parameter recognition model are pairs of "personalized frame design requirement text + standard design parameters".

[0112] At this point, the target design frame template C and the set of personalized frame design parameters P have been obtained, and the personalized fusion and design stage is entered, namely step S140: analyze the target design frame template to obtain the frame dataset, and fuse the at least one personalized frame design parameter with the frame dataset to obtain the lightweight design dataset.

[0113] Preferably, the step of analyzing the target design frame template to obtain the frame dataset specifically includes:

[0114] Based on the target design chassis template C, obtain the corresponding basic finite element model Lc;

[0115] The basic finite element model Lc was subjected to DOE (Design of Experiments) analysis to obtain the chassis performance table data as the original chassis dataset DLc;

[0116] Then, the set of personalized frame design parameters P is fused with the original frame dataset DLc to obtain the lightweight design dataset DPc.

[0117] Preferably, when performing DOE (Design of Experiments) analysis on the basic finite element model Lc to obtain chassis performance table data, multiple feature points and feature variables are selected from the basic finite element model Lc based on engineering design experience, and then the chassis performance table data is derived.

[0118] The selected feature points and feature variables include at least the feature points corresponding to the set of personalized chassis design parameters. Continuing with the previous example, the feature points corresponding to the set of personalized chassis design parameters include the front of the vehicle, the chassis, and the passenger seat.

[0119] At this point, the personalized design parameters of the frame corresponding to the front, chassis and passenger seat can be updated to the "standard design parameters of the frame corresponding to the front, chassis and passenger seat" in the frame performance table data, thus obtaining the lightweight design dataset DPc.

[0120] Then, step S150 yields the updated finite element model Lc' of the chassis;

[0121] That is, in step S150, a finite element model Lc' of the chassis is constructed based on the lightweight design dataset.

[0122] Next, we proceed to the solution and optimization stage of the updated finite element model, namely step S160.

[0123] Preferably, the finite element model solving and optimization stage of step S160 specifically includes:

[0124] (1) Determine the material properties of each element in the model, such as elastic modulus, density, Poisson's ratio, etc. These properties can truly reflect the performance of the material and are crucial to the behavior and response of the model.

[0125] (2) Mesh generation: Discretizing the structure into a finite number of small elements is called finite element method. This process is called mesh generation. Mesh generation needs to take into account the complexity of the geometry and the accuracy requirements of the analysis. The quality and accuracy of the mesh generation result have a significant impact on the calculation result.

[0126] (3) Establish loading and boundary conditions: Define loading conditions and boundary conditions in the model. Loading conditions may include force, pressure, temperature, etc., and need to be added according to the actual situation. Boundary conditions specify the constraints and conditions at the boundaries of the model.

[0127] (4) Solving: Solve the finite element model using numerical methods. This involves solving linear or nonlinear equations, which is usually done using computer software.

[0128] (5) Results Analysis: After obtaining the solution scheme for the model, the results are analyzed and interpreted. This may involve stress analysis, deformation analysis, displacement analysis, etc., to evaluate whether the performance and behavior of the structure meet the requirements.

[0129] (6) Validation and optimization: Validate the simulation results by comparing them with experimental data or theoretical predictions. If necessary, the model can be optimized to improve its performance or meet design requirements.

[0130] In one embodiment, if the frame finite element model is solved through the above steps (1)-(6), and the solution result of the model meets the preset requirements (e.g., the performance and behavior of the structure meet the requirements, the simulation results match the experimental data or theoretical prediction results, etc.), the frame finite element model can be output, that is, all the user's personalized requirements can be met, and the personalized design frame finite element model is output.

[0131] Figure 3 These are schematic diagrams of the original target design chassis template and the finite element model of the chassis after personalized design. Figure 3 The left figure is a schematic diagram of the original basic finite element model Lc. Figure 3 The right figure is a schematic diagram of the finite element model of the vehicle frame output after incorporating user-personalized requirement parameters. Figure 3 The right figure uses arrows to indicate updated features.

[0132] In another example, if the finite element model of the frame is solved after the above steps (1)-(6), and it is determined that the solution result does not meet the preset requirements (e.g., the performance and behavior of the structure do not meet the requirements, the simulation results do not match the experimental data or theoretical predictions, etc.), then it is necessary to optimize at least part of the network architecture in the finite element model of the frame and then re-solve the optimized finite element model of the frame to determine whether the solution result meets the preset requirements.

[0133] If the model solution still does not meet the preset requirements, the user is prompted to adjust the personalized chassis design requirements and the process returns to step S110.

[0134] In this scenario, it means that although the user submits a personalized chassis design requirement, and the chassis design template database can match the original basic finite element model Lc that meets the user's personalized chassis design requirement, the updated chassis finite element model Lc' obtained after data fusion cannot pass the design specifications. These design specifications include, but are not limited to, crash tests, stress tests, stiffness tests, etc. Therefore, the updated chassis finite element model Lc' cannot be used as a lightweight chassis design solution.

[0135] At this point, at least some of the network architecture in the frame finite element model can be adjusted, such as by adding connecting rods or adjusting material properties, and the optimized frame finite element model can be solved again to determine whether the model solution results meet the preset requirements; if so, the frame finite element model is output.

[0136] If the model solution still does not meet the preset requirements, it means that under the current conditions, it is objectively impossible to meet the user's personalized needs. In this case, the user is prompted to adjust the personalized frame design requirements and the process returns to step S110.

[0137] In one scenario, the user adjusts the personalized chassis design requirements and re-executes the process. Figure 1 Following the method described above, a new personalized finite element model of the vehicle frame can be output.

[0138] Figure 4 This is a schematic diagram of the finite element model of the chassis output after the user adjusts their personalized chassis design requirements. In this diagram, the user no longer requires a "square front end with a pointed tip," thus enabling the creation of the finite element model of the chassis under the adjusted personalized design. Figure 4 (Right image).

[0139] As you can see, Figure 1 The method described above takes into account multiple personalized design parameters of the user's frame, aiming to achieve personalized design while satisfying all personalized design parameters, thereby maximizing the satisfaction of the user's personalized needs.

[0140] In extreme cases, Figure 1 In the method, step S160 first solves the finite element model of the frame. If the solution result does not meet the preset requirements, at least part of the network architecture in the finite element model of the frame is optimized, and the optimized finite element model of the frame is solved again to determine whether the solution result meets the preset requirements. If the solution result still does not meet the preset requirements, the user is prompted to adjust the personalized frame design requirements. The method is then returned to step S110 and executed again. At this time, when the method is executed to step S160 again, the solution result may still not meet the preset requirements, which means that the personalized lightweight frame design scheme still fails after repeated attempts, which will also bring a bad experience to the user.

[0141] Therefore, the inventors further noted that although the limitations of design specifications (including but not limited to crash tests, stress tests, stiffness tests, etc.) often cannot fully (simultaneously) meet multiple personalized needs of users, that is, the tolerance for some of these personalized requirements is relatively high.

[0142] Therefore, in order to improve the success rate of personalized lightweight frame design solutions and further enhance the user experience, Figure 2 The embodiments provide a more preferred method for personalized lightweight frame design.

[0143] Figure 2 The flowchart includes steps S210-S270, and the specific implementation of each step is as follows:

[0144] S210: Receives personalized frame design requirements input by the user;

[0145] S220: Based on the personalized frame design requirements, determine the target frame design template from multiple candidate frame design templates, and analyze the target frame design template to obtain a basic frame dataset;

[0146] S230: Analyze the personalized frame design requirements to obtain N personalized frame design parameters.

[0147] S240: Let i=1;

[0148] S250: Incorporating personalized design parameters Integrate into the basic frame dataset to obtain a lightweight design dataset;

[0149] S260: Construct a finite element model of the chassis based on the aforementioned lightweight design dataset;

[0150] S270: Solve the finite element model of the vehicle frame and determine whether the solution result meets the preset requirements;

[0151] If the model solution meets the preset requirements, then let i = i + 1; determine whether i ≤ N is true.

[0152] If i≤N is true, then the lightweight design dataset is used as the basic frame dataset, and the process returns to step S250.

[0153] If i≤N is not true, then output the finite element model of the chassis.

[0154] As can be seen, with Figure 1 Compared to the previous embodiment, Figure 2 The main improvements begin with step S240, which is after obtaining N personalized frame design parameters. Figure 2 The implementation method attempts to gradually integrate the individual frame design parameters, while Figure 1 An example of this is to package and integrate N personalized frame design parameters.

[0155] Obviously, Figure 2 The improvement method, which integrates individual components, can greatly improve the success rate of outputting personalized lightweight frame design solutions, because it takes into account that the model and design specifications have a high tolerance for some personalized requirements.

[0156] Furthermore, when a user has multiple personalized frame design parameters, the preference for each personalized frame design parameter may differ. Therefore, in a more preferred embodiment, step S230 specifically includes:

[0157] Analyze the personalized frame design requirements to obtain N personalized frame design parameters;

[0158] The N frame customization parameters are arranged in descending order of priority to obtain the N frame customization parameters. .

[0159] when When sorted in descending order of priority, it can be seen that the personalized design parameter T1, which has the highest user preference, will be considered first, further improving the user experience.

[0160] On the other hand, if the current personalized parameter Ta cannot be incorporated (and an improved personalized lightweight frame design cannot be output), further user guidance can be provided to continue considering other personalized parameters Tb (Ta≠Tb), thereby further improving the success rate of outputting personalized lightweight frame design solutions.

[0161] Specifically, if the model solution does not meet the preset requirements, the user will be prompted to abandon the personalized design parameters;

[0162] If the user agrees to forgo personalized design parameters, then let i = i + 1;

[0163] Determine whether i ≤ N is true;

[0164] If i≤N is true, then the lightweight design dataset is used as the basic frame dataset, and the process returns to step S250.

[0165] If i≤N is not true, then output the finite element model of the chassis.

[0166] As can be seen, compared to Figure 1 , Figure 2 The implementation is further improved by trying to integrate personalized design parameters one by one, and continuing to integrate the next personalized design parameter if the current personalized design parameter cannot be met. This can maximize the integration of user personalized demand parameters while meeting design specifications, and meet more personalized frame design needs of users as much as possible, further enhancing the personalized interactive experience.

[0167] The aforementioned personalized lightweight frame design method can be automatically implemented through various forms of electronic devices and computer-readable program instructions; the computer-readable program instructions can be stored in different forms of storage media and loaded into computer electronic devices for execution.

[0168] According to an exemplary implementation of this disclosure, a computer-readable storage medium is provided that stores computer-executable instructions thereon, wherein the computer-executable instructions are executed by a processor to implement the methods described above. According to an exemplary implementation of this disclosure, a computer program product is also provided, which is tangibly stored on a non-transitory computer-readable medium and includes computer-executable instructions, which are executed by a processor to implement the methods described above.

[0169] and Figure 1 , Figure 2 Corresponding to the method implementation examples, Figures 5-6 The embodiments respectively provide schematic diagrams of the functional unit composition of a corresponding personalized lightweight frame design system.

[0170] exist Figure 5 The image shows a personalized lightweight chassis design system using an integrated bus architecture, which includes a receiving unit, a parsing unit, a fusion unit, a target design chassis template matching unit, a finite element model building unit, and a finite element model solving unit; the receiving unit, parsing unit, fusion unit, target design chassis template matching unit, finite element model building unit, and finite element model solving unit are all connected to the integrated bus;

[0171] Figure 5 The functions of each component are as follows:

[0172] Receiving unit: Receives personalized chassis design requirements input by the user;

[0173] Target design frame template matching unit: Based on the personalized frame design requirements, determine the target design frame template from multiple candidate frame design templates;

[0174] Analysis Unit: Analyzes the personalized frame design requirements to obtain at least one personalized frame design parameter;

[0175] Fusion Unit: Analyzes the target design frame template to obtain a frame dataset, and fuses the at least one personalized frame design parameter with the frame dataset to obtain a lightweight design dataset;

[0176] Finite element model building unit: Construct a finite element model of the chassis based on the aforementioned lightweight design dataset;

[0177] Finite element model solving unit: solves the finite element model of the vehicle frame and determines whether the model solution results meet the preset requirements;

[0178] If so, output the finite element model of the vehicle frame;

[0179] If not, then optimize the finite element model of the chassis.

[0180] If the model solution does not meet the preset requirements, the finite element model of the chassis will be optimized, specifically including:

[0181] After optimizing at least part of the network architecture in the finite element model of the chassis, the optimized finite element model of the chassis is re-solved, and it is determined whether the solution result of the model meets the preset requirements.

[0182] If the model solution still does not meet the preset requirements, the user will be prompted to adjust the personalized chassis design requirements.

[0183] Figure 6 Further examples of personalized lightweight frame design systems are provided. Figure 2 Corresponding to the method embodiment, the system includes:

[0184] Receiving unit: Receives personalized chassis design requirements input by the user;

[0185] Matching and Analysis Unit: Based on the personalized frame design requirements, the target frame design template is determined from multiple candidate frame design templates, and the target frame design template is analyzed to obtain a basic frame dataset;

[0186] Analysis Unit: Analyzes the personalized frame design requirements to obtain N personalized frame design parameters.

[0187] A lightweight design unit, wherein the lightweight design unit performs the following steps:

[0188] Step 1: Let i = 1;

[0189] Step 2: Personalize the design parameters Integrate into the basic frame dataset to obtain a lightweight design dataset;

[0190] Step 3: Construct a finite element model of the chassis based on the aforementioned lightweight design dataset;

[0191] Step 4: Solve the finite element model of the vehicle frame and determine whether the solution results meet the preset requirements;

[0192] If the model solution meets the preset requirements, then let i = i + 1; determine whether i ≤ N is true.

[0193] If i≤N is true, then the lightweight design dataset is used as the basic frame dataset, and the process returns to Step 2.

[0194] If i≤N is not true, then output the finite element model of the chassis.

[0195] The technical solution of this invention can fully consider the personalized needs of users when designing lightweight frames. It can integrate personalized user needs parameters to the maximum extent while meeting design specifications, thereby enabling the frame optimization design process to interact with users and improve the product experience.

[0196] Other technologies, principles, algorithms, or models not elaborated in detail in this application can be found in the prior art.

[0197] In the foregoing embodiments section, the present invention provides multiple embodiments, each of which can constitute an independent technical solution and may contribute to the prior art, and solve corresponding technical problems. However, it should be noted that different embodiments can be combined with each other without violating logic; at the same time, each embodiment can solve at least one technical problem, but it is not required that each individual embodiment solve multiple or all technical problems.

[0198] Furthermore, in specific embodiments of this application, if user-related data is involved, user permission or consent must be obtained when the embodiments of this application are applied to specific products or technologies, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0199] Various implementations of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed implementations. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described implementations. The terminology used herein is chosen to best explain the principles, practical applications, or improvements to technology in the market, or to enable others skilled in the art to understand the various implementations disclosed herein.

Claims

1. A method for individualized frame lightweight design, characterized in that, The method includes the following steps: S110: Receives personalized frame design requirements input by the user; S120: Based on the personalized frame design requirements, determine the target frame design template from multiple candidate frame design templates; S130: Analyze the personalized frame design requirements to obtain at least one personalized frame design parameter; S140: Analyze the target design frame template to obtain a frame dataset, and fuse the at least one personalized frame design parameter with the frame dataset to obtain a lightweight design dataset; S150: Construct a finite element model of the chassis based on the aforementioned lightweight design dataset; S160: Solve the finite element model of the vehicle frame and determine whether the solution result meets the preset requirements; If so, output the finite element model of the vehicle frame; If not, then optimize the finite element model of the chassis, specifically including: After optimizing at least part of the network architecture in the finite element model of the chassis, the optimized finite element model of the chassis is re-solved, and it is determined whether the solution result of the model meets the preset requirements. If the model solution still does not meet the preset requirements, the user is prompted to adjust the personalized chassis design requirements and the process returns to step S110.

2. A method for individualized frame lightweight design, characterized in that, The method includes the following steps: S210: Receives personalized frame design requirements input by the user; S220: Based on the personalized frame design requirements, determine the target frame design template from multiple candidate frame design templates, and analyze the target frame design template to obtain a basic frame dataset; S230: Analyze the personalized frame design requirements to obtain N personalized frame design parameters; arranging the N frame individualized design parameters in descending order of priority to obtain the N frame individualized design parameters ; S240: Let i=1; S250: personalize design parameters fused into the basic frame dataset, obtaining a lightweight design dataset; S260: Construct a finite element model of the chassis based on the aforementioned lightweight design dataset; S270: Solve the finite element model of the vehicle frame and determine whether the solution result meets the preset requirements; If the model solution meets the preset requirements, then let i = i + 1; determine whether i ≤ N is true. If i≤N holds true, then the lightweight design dataset is used as the basic frame dataset, and the process returns to step S250. If i≤N is not true, then output the finite element model of the chassis; If the model solution result does not meet the preset requirement, the user is prompted to abandon the personalized design parameter ; If the user agrees to give up the personalized design parameters then let i = i + 1; Determine whether i ≤ N is true; If i≤N is true, then the lightweight design dataset is used as the basic frame dataset, and the process returns to step S250. If i≤N is not true, then output the finite element model of the chassis.

3. A system for individualized frame lightweight design, characterized by, The system includes: Receiving unit: Receives personalized chassis design requirements input by the user; Target design frame template matching unit: Based on the personalized frame design requirements, determine the target design frame template from multiple candidate frame design templates; Analysis Unit: Analyzes the personalized frame design requirements to obtain at least one personalized frame design parameter; Fusion Unit: Analyzes the target design frame template to obtain a frame dataset, and fuses the at least one personalized frame design parameter with the frame dataset to obtain a lightweight design dataset; Finite element model building unit: Construct a finite element model of the chassis based on the aforementioned lightweight design dataset; Finite element model solving unit: solves the finite element model of the vehicle frame and determines whether the model solution results meet the preset requirements; If so, output the finite element model of the vehicle frame; If not, then optimize the finite element model of the chassis, specifically including: After optimizing at least part of the network architecture in the finite element model of the chassis, the optimized finite element model of the chassis is re-solved, and it is determined whether the solution result of the model meets the preset requirements. If the model solution still does not meet the preset requirements, the user will be prompted to adjust the personalized chassis design requirements.

4. A system for individualized frame lightweight design, characterized by, The system includes: Receiving unit: Receives personalized chassis design requirements input by the user; Matching and Analysis Unit: Based on the personalized frame design requirements, the target frame design template is determined from multiple candidate frame design templates, and the target frame design template is analyzed to obtain a basic frame dataset; Analyzing unit: analyzing the individualized frame design requirements to obtain N frame individualized design parameters A lightweight design unit, wherein the lightweight design unit performs the following steps: Step 1: Let i = 1; Step 2: fuse the personalized design parameters into the basic frame dataset to obtain a lightweight design dataset ​ Step 3: Construct a finite element model of the chassis based on the aforementioned lightweight design dataset; Step 4: Solve the finite element model of the vehicle frame and determine whether the solution results meet the preset requirements; If the model solution meets the preset requirements, then let i = i + 1; determine whether i ≤ N is true. If i≤N is true, then the lightweight design dataset is used as the basic frame dataset, and the process returns to Step 2. If i≤N is not true, then output the finite element model of the chassis.

5. A computer program product comprising a computer program or computer-executable instructions, wherein when the computer program or computer-executable instructions are executed by a processor, they implement the personalized lightweight frame design method according to any one of claims 1 to 2.

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