Method, device and equipment for predicting vehicle roof modality through CAS, and medium

Abstract

The invention relates to the technical field of vehicle design, and discloses a method, device, equipment and medium for predicting a vehicle ceiling modal through a CAS, and the method comprises the steps: obtaining ceiling CAS data of a target vehicle; intercepting the first CAS data, and determining a ceiling cross beam theoretical height between the ceiling cross beam upper plate and the ceiling cross beam lower plate according to the intercepted first CAS data and the intercepted second CAS data; determining the actual height range and the actual width range of the ceiling cross beam matched with the theoretical height of the ceiling cross beam, and creating a lower plate grid model of the ceiling cross beam; and performing modal analysis on the ceiling cross beam lower plate grid model to obtain modal frequencies, and comparing the modal frequencies to optimize the ceiling of the target vehicle according to a comparison result. According to the technical scheme provided by the invention, the ceiling modal can be predicted through the CAS data in the early stage of vehicle design, and the problem of low-frequency ear pressing is avoided.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle design, and particularly relates to a CAS-predicted vehicle roof modal method, device, equipment and medium. BACKGROUND

[0002] In vehicle design, the modal characteristics of the roof cross beam have an important influence on the NVH performance. In particular, when driving at low or high speed, the vibration excited by the interaction between the tire and the road surface may be coupled with the roof modal frequency, resulting in low-frequency booming sound and discomfort to passengers.

[0003] Traditional modal analysis is usually performed in the product engineering stage, and the optimization space is limited, and the adjustment cycle is long and the cost is high, so it is difficult to effectively predict and adjust the roof modal in the early design stage, resulting in a long development cycle and high cost.

[0004] Therefore, how to predict the roof modal through CAS data in the early stage of vehicle design to avoid low-frequency ear pressure problems, thereby realizing low-cost and short-cycle roof development and optimization is a technical problem that needs to be solved at present. SUMMARY

[0005] The present application provides a CAS-predicted vehicle roof modal method, device, equipment and medium, which achieves the technical effect of predicting the roof modal through CAS data in the early stage of vehicle design to avoid low-frequency ear pressure problems, thereby realizing low-cost and short-cycle roof development and optimization.

[0006] In order to achieve the above purpose, the main technical scheme adopted by the present application includes: In a first aspect, the present application provides a CAS-predicted vehicle roof modal method, which comprises: obtaining roof CAS data of a target vehicle; wherein the roof CAS data comprises first CAS data for representing the shape of the roof and second CAS data for representing the structure of the roof; performing interception processing on the first CAS data, and determining the theoretical height of the roof cross beam between the upper plate of the roof cross beam and the lower plate of the roof cross beam according to the intercepted first CAS data and the intercepted second CAS data; determining a roof cross beam actual height range and a roof cross beam actual width range matched with the roof cross beam theoretical height, and creating a roof cross beam lower plate grid model according to the roof cross beam actual height range and the roof cross beam actual width range; performing modal analysis on the roof cross beam lower plate grid model to obtain modal frequencies, and comparing the modal frequencies to optimize the roof of the target vehicle according to the comparison result.

[0007] The method for predicting the modal of the vehicle roof provided by the embodiment can calculate the theoretical height of the roof cross beam by intercepting the first CAS data and combining the second CAS data. Then, the actual height range and the actual width range of the roof cross beam that match the theoretical height of the roof cross beam are determined, and a grid model of the lower plate of the roof cross beam is created based on the data. Subsequently, modal analysis is performed to obtain the modal frequency of the roof, and the design of the roof is optimized by comparing the results. The method can predict the modal characteristics of the roof at an early stage of vehicle design, avoid the problem of low-frequency ear pressure, and thus realize low-cost and short-cycle development and optimization of the roof.

[0008] In one embodiment, the manner of intercepting the first CAS data includes: determining a vehicle coordinate system; wherein the X-axis positive direction of the vehicle coordinate system is the front-to-rear direction of the vehicle, the Y-axis positive direction is the left-to-right direction of the vehicle, and the Z-axis positive direction is the from-bottom-to-top direction of the vehicle; performing first intercepting processing on the first CAS data, taking the most front end in the Y=0 plane as the starting point, and intercepting the first CAS data of a specified length along the X-axis positive direction to obtain a preliminary intercepting surface; performing second intercepting processing on the preliminary intercepting surface, taking the most front end in the Y=0 plane as the starting point, and intercepting along the Y-axis positive direction to obtain a target intercepting surface; wherein the target intercepting surface contains the first CAS data after intercepting and the second CAS data after intercepting.

[0009] The embodiment determines the vehicle coordinate system, thereby providing a clear reference framework for subsequent data processing and ensuring accurate spatial positioning. Then, first intercepting is performed to simplify the CAS data along the X-axis positive direction (from the front to the rear of the vehicle), concentrate on analyzing the key area of the roof cross beam, and reduce the interference of irrelevant areas. Subsequently, second intercepting is performed to further finely intercept along the Y-axis positive direction, thereby ensuring that all important parts of the roof cross beam are analyzed in detail. This method enables early prediction and optimization of the roof modal, avoids the problem of low-frequency ear pressure, reduces feedback and modification work in the test phase, reduces development costs, shortens the cycle, and ultimately improves the NVH performance of the roof.

[0010] In one embodiment, determining the theoretical height of the roof cross beam between the upper plate of the roof cross beam and the lower plate of the roof cross beam based on the first CAS data after intercepting and the second CAS data after intercepting includes: determining the distance between the highest point of the upper plate of the roof cross beam in the first CAS data after intercepting and the lowest point of the lower plate of the roof cross beam in the second CAS data after intercepting; determining the distance as the theoretical height of the roof cross beam between the upper plate of the roof cross beam and the lower plate of the roof cross beam.

[0011] The embodiment obtains the maximum available height of the ceiling space by accurately measuring the maximum vertical distance between the highest point of the upper plate of the ceiling cross beam and the lowest point of the lower plate of the ceiling cross beam, which is crucial for space adjustment and noise control in subsequent design. The distance is taken as the theoretical height of the ceiling cross beam, serving as a reference for ceiling design. Based on the reference, the structure, material selection and space layout can be optimized to improve the NVH performance of the vehicle and ensure passenger comfort. Overall, accurate measurement and modal analysis using CAS data can improve design efficiency, reduce modification costs, and improve vehicle comfort and functionality.

[0012] In one embodiment, the ceiling cross beam actual height range is determined as follows: The ceiling cross beam actual height range is determined according to the product of the ceiling cross beam theoretical height and the preset proportion threshold range.

[0013] In one embodiment, creating a ceiling cross beam lower plate grid model according to the ceiling cross beam actual height range and the ceiling cross beam actual width range comprises: determining a change interval for changes in the ceiling cross beam actual height range and the ceiling cross beam actual width range; generating a plurality of size combinations including ceiling cross beam lower plates according to the ceiling cross beam actual height range, the ceiling cross beam actual width range and the change interval; creating a ceiling cross beam lower plate grid model matching each geometric combination.

[0014] The embodiment generates a plurality of size combinations by determining a change interval between the ceiling cross beam actual height range and the ceiling cross beam actual width range, and creates a ceiling cross beam lower plate grid model for each size combination. This method can perform modal analysis at an early stage, optimize the design scheme, avoid adverse resonance phenomena, and improve the comfort and safety of the vehicle ceiling. Through reasonable size adjustment and modal optimization, the ceiling development can be completed in a short time, reducing the demand for physical tests, thereby realizing low-cost and short-cycle ceiling development. In addition, by optimizing the calculation efficiency, reducing redundant calculations and saving resources, the ceiling design can be completed more efficiently and accurately, ultimately providing high-quality NVH performance optimization support.

[0015] In one embodiment, comparing the modal frequency to optimize the ceiling of the target vehicle according to the comparison result comprises: Comparing the modal frequency, if the modal frequency is within the low-frequency ear pressure sensitive frequency band, optimizing the ceiling of the target vehicle; wherein the optimization method includes adjusting the ceiling surface curvature, the front windshield and ceiling joint position or the ceiling cross beam height.

[0016] In one embodiment, the method further comprises: After optimizing the roof of the target vehicle, updating the roof cross beam lower panel grid model and re-performing modal analysis to obtain updated modal frequencies; Comparing the updated modal frequencies until the updated modal frequencies are not in the low-frequency ear-pressing sensitive frequency band to obtain a target roof cross beam lower panel grid model.

[0017] The embodiment optimizes the roof of the target vehicle by first updating the roof cross beam lower panel grid model according to the CAS data to reflect the design changes. Then, modal analysis is re-performed to obtain updated modal frequencies. Subsequently, these modal frequencies are compared with the low-frequency ear-pressing sensitive frequency band. If the updated modal frequencies are no longer in the sensitive frequency band, the optimization is successfully completed; otherwise, the design needs to be further adjusted and optimized until the modal frequencies are no longer in the sensitive range. This process ensures that the roof design achieves ideal vibration characteristics by continuously updating the grid model and repeatedly optimizing, avoids low-frequency ear-pressing problems, and ultimately realizes efficient and precise roof design optimization.

[0018] In a second aspect, the embodiments of the present application provide a device for predicting modal of vehicle roof based on CAS, the device comprising: a data acquisition unit configured to acquire roof CAS data of a target vehicle; wherein the roof CAS data comprises first CAS data for representing shape of the roof and second CAS data for representing structure of the roof; a theoretical height determination unit configured to perform intercepting processing on the first CAS data, and determine a roof cross beam theoretical height between a roof cross beam upper panel and a roof cross beam lower panel according to the intercepted first CAS data and the intercepted second CAS data; a grid model creation unit configured to determine a roof cross beam actual height range and a roof cross beam actual width range matching the roof cross beam theoretical height, and create a roof cross beam lower panel grid model according to the roof cross beam actual height range and the roof cross beam actual width range; a modal analysis unit configured to perform modal analysis on the roof cross beam lower panel grid model to obtain modal frequencies, and compare the modal frequencies to optimize the roof of the target vehicle according to a comparison result.

[0019] In a third aspect, the embodiments of the present application provide a computer device, comprising: a memory and a processor, which are communicatively connected with each other, and the memory stores computer instructions, and the processor executes the computer instructions to perform the method for predicting modal of vehicle roof based on CAS.

[0020] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, and the computer readable storage medium stores computer instructions. The computer instructions are used to make a computer execute the method for predicting a modal of a vehicle roof by CAS. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the specific embodiments or prior art of the present application, the drawings needed in the specific embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0022] Figure 1 A flow chart of a method for predicting a modal of a vehicle roof by CAS provided by an embodiment of the present application; Figure 2 A schematic diagram of an internal structure of a vehicle roof provided by an embodiment of the present application; Figure 3 A schematic diagram of a constraint area provided by an embodiment of the present application; Figure 4 A modal analysis comparison diagram provided by an embodiment of the present application; Figure 5 A comparison diagram before and after optimization provided by an embodiment of the present application; Figure 6 A flow chart of a method for intercepting first CAS data provided by an embodiment of the present application; Figure 7 A first intercepting processing diagram provided by an embodiment of the present application; Figure 8 A second intercepting processing diagram provided by an embodiment of the present application; Figure 9 A flow chart of a method for determining a theoretical height of a roof beam provided by an embodiment of the present application; Figure 10 A flow chart of a method for creating a roof beam lower panel grid model provided by an embodiment of the present application; Figure 11 A roof beam lower panel grid model schematic diagram provided by an embodiment of the present application; Figure 12 A flow chart after optimization provided by an embodiment of the present application; Figure 13 A block diagram of a device for predicting a modal of a vehicle roof by CAS provided by an embodiment of the present application; Figure 14 A structural schematic diagram of a computer device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0023] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] In vehicle design, optimizing noise, vibration, and harshness (NVH) performance is a crucial and indispensable aspect, especially in roof design, where the modal characteristics of the roof crossbeams have a significant impact on the overall NVH performance of the vehicle. Particularly during low-speed driving on rough roads or high-speed driving on smooth roads, the interaction between the tire and the road surface excites the tire to generate X-direction torsional modes. These vibrations are transmitted through the chassis to various body panels, particularly the roof area. If the modal frequencies of the roof crossbeams couple with the tire excitation frequencies, a low-frequency booming sound may be generated inside the vehicle. This noise often occurs in the low-frequency pressure-sensitive frequency range, causing noticeable pressure and discomfort to passengers, manifesting as symptoms such as irritability and dizziness.

[0025] Therefore, NVH performance development in vehicle design requires meticulous attention to the modal characteristics of the roof crossbeams, and advance planning of their modal distribution range to avoid indentation problems during actual driving. Traditional modal analysis methods are generally conducted during the product engineering development phase, which usually requires waiting for the release of detailed vehicle body structural data before finite element modeling can be used to analyze the roof modes. If the analysis reveals that the roof modal frequencies overlap with the low-frequency indentation sensitive frequency band, optimization and rectification are necessary. However, the optimization space at this stage is limited because the styling, overall layout, and human-machine interface are mostly determined. Optimization of the roof structure usually only involves increasing the crossbeam thickness or other methods to reduce vibration response. While this approach can reduce noise, it cannot fundamentally change the modal characteristics of the roof.

[0026] If the modal characteristics of the roof beam are found to be coupled with low-frequency pressure lugs during the prototype testing phase, it is often necessary to install mass blocks or vibration absorbers at the beam. This is not only time-consuming and costly, but also seriously affects the project schedule and makes it difficult to achieve effective feedback from the design phase to the testing phase. Therefore, traditional methods have significant limitations in modal analysis and optimization, especially in the difficulty of effectively predicting and adjusting the roof modes from the early design stage, resulting in high development costs and long cycles.

[0027] Therefore, how to predict roof modes through CAS data in the early stages of vehicle design to avoid low-frequency piezoresistive problems, thereby achieving low-cost and short-cycle roof development and optimization, is a technical problem that urgently needs to be solved.

[0028] To address the aforementioned technical problems, according to an embodiment of this application, a method embodiment for predicting vehicle roof modes using CAS is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0029] This embodiment provides a method for predicting vehicle roof modes using CAS (Computer-Assisted Design). Figure 1 A flowchart of a method for predicting vehicle roof modes using CAS provided in this application embodiment is shown below. Figure 1 As shown, the process includes the following steps: Step S1: Obtain the roof CAS data of the target vehicle; wherein, the roof CAS data includes first CAS data for characterizing the roof shape and second CAS data for characterizing the roof structure.

[0030] Specifically, the first set of CAS data includes the external shape data of the windshield, roof, and A-pillars. This data is crucial for analyzing the external shape and aerodynamic characteristics of the roof. The second set of CAS data includes the internal structural data of the roof and A-pillars. This data is essential for understanding the internal support structure and reinforcement layout of the roof, as they directly affect the modal characteristics and structural strength of the roof. By obtaining detailed roof CAS data, the accuracy of modal analysis can be improved, thereby more effectively predicting and avoiding low-frequency noise problems.

[0031] Step S3: The first CAS data is truncated, and the theoretical height of the ceiling beam between the upper plate and the lower plate of the ceiling beam is determined based on the truncated first CAS data and the truncated second CAS data.

[0032] Specifically, truncating the first CAS data is a crucial step in vehicle roof modal analysis. Its purpose is to simplify the model and focus on the areas most influential on modal characteristics. This typically involves truncating a specified length of data, such as 400mm, along the positive X-axis of the roof, based on the vehicle's overall coordinate system. After truncating, by comparing the truncated first CAS data (representing the roof's external shape) with the second CAS data (representing the roof's internal structure), the theoretical height H between the upper and lower roof beams can be determined. maxThis theoretical height is obtained by measuring the vertical distance between the highest point of the upper plate of the ceiling beam in the first CAS data and the lowest point of the lower plate of the ceiling beam in the second CAS data. This distance reflects the maximum height that the ceiling beam can reach without internal structural interference. This theoretical height is used to assess whether the height of the ceiling beam in the actual design is sufficient and whether adjustments are needed to avoid resonance in the sensitive low-frequency noise range.

[0033] Step S5: Determine the actual height range and actual width range of the ceiling beam that match the theoretical height of the ceiling beam, and create a grid model of the lower plate of the ceiling beam based on the actual height range and actual width range of the ceiling beam.

[0034] Specifically, the actual height range of the ceiling beams is usually based on the theoretical height H. max The actual height is determined by design requirements. For example, a certain percentage (e.g., 30%, 40%) of the theoretical height might be chosen as the starting point for the actual height, and then this range is adjusted based on structural strength, stiffness requirements, and the arrangement of internal components (e.g., wiring harnesses, reading lights). This is done to ensure that the beams meet strength and stiffness requirements while effectively avoiding low-frequency noise-sensitive bands. Similarly, the actual width range of the ceiling beams also needs to be determined based on design requirements and the internal space layout. The choice of width affects the beam's load-bearing capacity and modal frequencies, therefore, structural performance and space utilization efficiency need to be considered comprehensively. Once the actual height and width ranges of the ceiling beams are determined, a mesh model of the lower plate of the ceiling beams can be created based on these parameters for subsequent modal analysis to predict and evaluate the beam's behavior under different vibration modes, thus providing a scientific basis for design optimization.

[0035] In a preferred embodiment, the actual height range of the ceiling beam is determined by multiplying the theoretical height of the ceiling beam with a preset percentage threshold range.

[0036] Specifically, theoretically, the theoretical height H of the ceiling beam between the upper and lower plates of the roof beam is... max It can reach the height between the first and second CAS data points. This means that, without interference from internal structures (such as wiring harnesses, reading lights, interior rearview mirrors, etc.), the height of the ceiling beam can fully utilize the space between the first and second CAS data points. However, in practice, please refer to... Figure 2 This is a schematic diagram of the internal structure of the vehicle roof provided in the embodiments of this application. These internal structures occupy a certain amount of space, resulting in the actual usable height H usually being less than H0. maxTo quantify this difference, a percentage threshold range is typically set. Assuming a preset percentage threshold range of 30% to 40%, this means the actual usable height is usually the theoretical height H. max The actual width W, excluding height, is usually set based on experience, generally ranging from 90mm to 120mm. This range is also based on design considerations for interior space, comfort, and the rational configuration of other components. For example, designers choose the most suitable width based on factors such as the layout of the interior space, the dimensions of various components, and ergonomics.

[0037] Step S7: Perform modal analysis on the mesh model of the lower plate of the roof beam to obtain the modal frequencies, and compare the modal frequencies to optimize the roof of the target vehicle based on the comparison results.

[0038] Specifically, modal analysis can identify modal frequencies that may lead to resonance, which can cause uncomfortable noise and vibration during vehicle operation. Before performing modal analysis, appropriate constraints need to be imposed on the model; please refer to [link to relevant documentation]. Figure 3 This diagram illustrates the constraint region provided in this embodiment, constraining the translational degrees of freedom of the lower and rear sections of the A-pillar. These constraints simulate the fixing and support conditions of a vehicle in actual use. The mesh model of the lower plate of the roof beam is modally solved using finite element analysis software to obtain the mode shapes and frequencies. The obtained modal frequencies are compared with the frequency range of low-frequency road noise (e.g., 30Hz-50Hz). If the modal frequencies fall within this sensitive frequency band, the roof design needs to be optimized to avoid resonance. Optimization measures include adjusting structural parameters such as the curvature of the outer CAS surface, the joint position between the windshield and the roof, and the height of the beam. These parameters are the main factors affecting the modal frequencies of the roof; changing them can alter the modal frequencies, thereby avoiding the sensitive frequency band.

[0039] It should be noted that by comparing the mode shapes obtained using different methods, the accuracy and reliability of different design or analysis methods can be evaluated. Figure 4 For example, Figure 4 This is a comparison diagram of modal analysis provided in the embodiments of this application. Figure 4 (a) in the figure is an embodiment of this application. Figure 4 (b) in the figure represents the current traditional modal analysis method. Red indicates large deformation, and blue indicates small deformation. The mode shapes obtained by these two methods are basically consistent in the area of ​​the ceiling beam, indicating that these methods are similar in predicting the modal characteristics of this area.

[0040] In a preferred embodiment, the modal frequencies are compared, and if the modal frequencies are within the low-frequency piezoresistive frequency band, the roof of the target vehicle is optimized; wherein, the optimization method includes adjusting the curvature of the roof surface, the position of the joint between the windshield and the roof, or the height of the roof beam.

[0041] Specifically, the obtained modal frequencies are compared with the frequency range of low-frequency road noise (e.g., 30Hz-50Hz). If the modal frequencies fall within this sensitive frequency band, the ceiling design needs to be optimized to avoid resonance. For example, see... Figure 5 This is a comparison diagram of the before and after optimization provided in the embodiments of this application. By adjusting the design parameters, the two influencing factors in the first CAS data—the curvature of the CAS surface and the position of the joint between the windshield and the roof—can be optimized. Figure 5 In (a) Base state, the modal frequency of the top cover beam increases from the sensitive frequency band (e.g., 46.4Hz) to... Figure 5 (b) Higher frequencies (e.g., 58.3Hz) in the OPT state (optimized state) thus avoid the sensitive frequency band of low-frequency pressure.

[0042] This embodiment provides a method for predicting vehicle roof modal characteristics using CAS (Computational Sensing and Analysis). By processing first CAS data and combining it with second CAS data, the theoretical height of the roof crossbeam is calculated. Next, based on this theoretical height, the actual height and width ranges of the roof crossbeam are determined, and a mesh model of the lower plate of the roof crossbeam is created based on this data. Subsequently, modal analysis is performed to obtain the modal frequencies of the roof, and the roof design is optimized by comparing the results. This method can predict the modal characteristics of the roof in the early stages of vehicle design, avoiding low-frequency pressure ear problems, thereby achieving low-cost, short-cycle roof development and optimization.

[0043] Figure 6 A flowchart illustrating a method for extracting and processing first CAS data according to an embodiment of this application is provided. This process may include the following steps: Step S311: Determine the vehicle coordinate system; wherein, in the vehicle coordinate system, the positive X-axis is from the front to the rear of the vehicle, the positive Y-axis is from the left to the right of the vehicle, and the positive Z-axis is from the bottom to the top of the vehicle.

[0044] Specifically, before processing the model, a vehicle coordinate system is first defined as the reference coordinate system. The positive X-axis runs from the front to the rear of the vehicle and is used to define the vehicle's longitudinal position. The positive Y-axis runs from the left to the right of the vehicle and is used to define the vehicle's lateral position. The positive Z-axis runs from bottom to top of the vehicle and is used to define the vehicle's vertical position.

[0045] Step S313: Perform the first truncation process on the first CAS data. Starting from the foremost point in the Y=0 plane, truncate the first CAS data of a specified length along the positive X-axis to obtain the preliminary truncation surface.

[0046] Specifically, to facilitate the analysis of the modal characteristics of the ceiling beam, the first CAS data needs to be truncated. This data truncation process is crucial, helping to simplify the complex model, reduce computational load, and ensure focus is placed on the target component (i.e., the ceiling beam). The first truncation is performed along the positive X-axis, starting from the very front of the Y=0 plane, and extracting a specified length of data. This truncation reduces model complexity, improves computational efficiency, and ensures that the analysis focuses on the ceiling beam region. For an illustrative example, please refer to [link to illustrative example]. Figure 7 The first cut-off image provided in this application embodiment is based on the vehicle coordinate system. The direction from the front to the rear of the vehicle is the positive X-axis, the direction from the left to the right side of the vehicle is the positive Y-axis, the left and right symmetrical center planes of the roof are the Y=0 plane, and the direction from bottom to top is the positive Z-axis. The cut-off surface is perpendicular to the X-axis and located on the YOZ plane. On the "outer CAS surface of the roof (green part)" in the left image, the foremost point in the Y=0 plane (the foremost point of the X-axis) is selected as the starting point. Data is cut from this point along the positive X-axis to a distance of 400mm to obtain the preliminary cut-off surface (from the perspective of the vehicle coordinate system, the direction is from back to front). The right image shows the model after the cut-off.

[0047] Step S315: Perform a second cutting process on the initial cutting surface. Starting from the foremost point in the Y=0 plane, cut along the positive Y-axis to obtain the target cutting surface. The target cutting surface includes the first CAS data and the second CAS data after cutting.

[0048] Specifically, based on the first cut, a second cut is performed, this time along the positive Y-axis. This further refines the model, ensuring that the target cut surface accurately includes the key parts of the ceiling beam. This precise cut allows for more accurate analysis of the ceiling beam's modal characteristics, reduces the model size, and thus lowers the computational complexity and time required for modal analysis. For an illustrative example, please refer to [link to illustrative example]. Figure 8 The second cut-out processing diagram provided in this application embodiment shows the target cut-out surface located in the Y=0 plane (XOZ plane), perpendicular to the Y-axis. On this section, the target cut-out surface at the front crossbeam of the roof can be obtained (viewed from the vehicle coordinate system, the direction is from left to right). The upper and lower plates of the roof crossbeam are located between the inner and outer CAS sections.

[0049] This embodiment provides a clear reference framework for subsequent data processing by establishing the vehicle coordinate system, ensuring accurate spatial positioning. Next, a first segmentation is performed, refining the CAS data along the positive X-axis (from front to rear) to focus on analyzing key areas of the roof crossbeams and reduce interference from irrelevant areas. Subsequently, a second segmentation is performed, further refining the segmentation along the positive Y-axis to ensure detailed analysis of all important parts of the roof crossbeams. This method enables early prediction and optimization of roof modes, avoids low-frequency pressure ear problems, reduces feedback and modification work during the testing phase, lowers development costs, shortens the cycle, and ultimately improves the NVH performance of the roof.

[0050] Figure 9 The flowchart for determining the theoretical height of the ceiling beam provided in this application embodiment may include the following steps: Step S331: Determine the distance between the highest point of the upper plate of the ceiling beam in the first CAS data and the lowest point of the lower plate of the ceiling beam in the second CAS data.

[0051] Specifically, measuring tools are used to determine the vertical distance between the highest point of the upper plate of the ceiling beam in the first CAS data segment and the lowest point of the lower plate of the ceiling beam in the second CAS data segment. This distance represents the maximum usable height, which is the maximum space between the plates when designing the ceiling beam, reflecting the maximum space capacity that the design can provide without internal interference. For example, when the design involves airflow or sound control, knowing this maximum height can help adjust the structural or material selection to ensure that the ceiling does not experience low-frequency noise or ear pressure issues. The maximum achievable height H between the upper and lower plates of the ceiling beam is... max .

[0052] Step S333: Determine the distance as the theoretical height of the ceiling beam between the upper and lower plates of the ceiling beam.

[0053] Specifically, the measured distance is determined as the theoretical height H of the ceiling beam. max This is an important reference value when designing ceiling beams. It provides basic data for subsequent design optimization and modal analysis.

[0054] This embodiment obtains the maximum usable height of the roof space by accurately measuring the maximum vertical distance between the highest point of the upper plate and the lowest point of the lower plate of the roof crossbeam. This data is crucial for subsequent space adjustments and noise control in the design. This distance is used as the theoretical height of the roof crossbeam, becoming the benchmark for roof design. Using this benchmark, the structure can be optimized, material selection and spatial layout adjusted, vehicle NVH performance improved, and passenger comfort ensured. Overall, using CAS data for precise measurement and modal analysis can improve design efficiency, reduce modification costs, and simultaneously enhance vehicle comfort and functionality.

[0055] Figure 10 The flowchart for creating a mesh model of the lower plate of the ceiling beam provided in this application embodiment may include the following steps: Step S51: Determine the variation interval of the actual height range and the actual width range of the ceiling beam.

[0056] Specifically, when designing ceiling beams, in order to deeply analyze the influence of height and width on the modal characteristics of the ceiling beams, it is first necessary to determine an appropriate variation interval. The variation interval determines how the dimensions gradually change within a given height and width range, thus enabling simulations and analyses of different combinations. For example, if the variation interval is set to 5mm, then the beam dimensions will increase by 5mm in each calculation until the entire actual height and width range of the ceiling beam are covered.

[0057] Step S53: Based on the actual height range, actual width range and variation interval of the ceiling beam, generate multiple size combinations including the lower plate of the ceiling beam.

[0058] Specifically, after determining the actual height range, actual width range, and variation intervals of the ceiling beams, the next step is to generate all possible dimensional combinations based on these parameters. These combinations will help analyze how the modal characteristics of the ceiling beams change under different design dimensions. Starting from the minimum height value, the dimensions are gradually increased according to the set variation intervals until the maximum height value is reached. Similarly, for the width, the dimensions are gradually increased from the minimum width value until the maximum width value is reached. This means that for each set of height values, there will be multiple combinations of width values, forming a multi-dimensional combination of height and width. All possible H and W combinations are recorded. For example, if the actual height of the ceiling beam ranges from 20mm to 40mm, and the actual width of the ceiling beam ranges from 90mm to 120mm, with a variation interval of 5mm, then possible size combinations include [20,120], [25,115], [30,110], [35,105], [40,100], [20,115], [25,110], [30,105], [35,100], [40,95], etc. This method of generating size combinations not only covers all possible heights and widths but also ensures that the impact of each possible size combination on modal characteristics can be analyzed step-by-step during the calculation process.

[0059] Step S55: Create a mesh model of the lower plate of the ceiling beam that matches each set of geometric combinations.

[0060] Specifically, a mesh model of the lower slab of the ceiling beam is created based on the values ​​of H and W for each set of size combinations. Please refer to [link to relevant documentation]. Figure 11 This is a schematic diagram of the mesh model of the lower plate of the roof beam provided in an embodiment of this application. By creating multiple mesh models of the lower plate of the roof beam, the influence of different heights and widths on the modal characteristics of the roof beam can be analyzed. This method helps to find the optimal roof beam dimensions to avoid resonance in the sensitive frequency range, thereby improving the NVH performance of the vehicle. In addition, by defining the variation interval, computational resources can be effectively managed, avoiding unnecessary analysis.

[0061] This embodiment generates multiple size combinations by determining the variation interval between the actual height range and the actual width range of the roof beam, and creates a mesh model of the lower plate of the roof beam for each size combination. This method enables modal analysis at an early stage, optimizes the design scheme, avoids adverse resonance phenomena, and improves the comfort and safety of the vehicle roof. Through reasonable size adjustment and modal optimization, roof development can be completed in a short time, reducing the need for physical testing, thereby achieving low-cost and short-cycle roof development. In addition, by optimizing computational efficiency, reducing redundant calculations, and saving resources, roof design can be completed more efficiently and accurately, ultimately providing high-quality NVH performance optimization support.

[0062] Figure 12 The optimized flowchart provided for the embodiments of this application may include the following steps: Step S91: After optimizing the roof of the target vehicle, update the mesh model of the lower plate of the roof beam and re-perform modal analysis to obtain the updated modal frequencies. Step S93: Compare the updated modal frequencies until the updated modal frequencies are no longer within the low-frequency piezoresistive frequency band, and obtain the mesh model of the lower plate of the target ceiling beam.

[0063] Specifically, after optimizing the roof, the mesh model of the lower plate of the roof beams is updated to reflect the design changes. Modal analysis is then performed again to evaluate the optimization effect. Based on the optimized roof design parameters (such as the curvature of the outer CAS surface, the location of the joint between the windshield and the roof, and the beam height), the mesh model of the lower plate of the roof beams is updated. Modal analysis is performed using the updated mesh model to obtain new modal frequencies and mode shapes. The updated modal frequencies are compared with the low-frequency pressure-sensitive frequency band (such as 30Hz-50Hz). If the updated modal frequencies are not within the sensitive frequency band, the optimization is successful; if they are still within the sensitive frequency band, further design optimization is required.

[0064] This embodiment optimizes the roof design of the target vehicle. First, the mesh model of the lower plate of the roof crossbeam is updated based on CAS data to reflect design changes. Next, modal analysis is performed again to obtain the updated modal frequencies. Subsequently, these modal frequencies are compared with the low-frequency pressure plate sensitive frequency band. If the updated modal frequencies are no longer within the sensitive frequency band, the optimization is successful; otherwise, the design needs further adjustment and optimization until the modal frequencies are no longer within the sensitive range. This process, through continuous mesh model updates and iterative optimization, ensures that the roof design achieves ideal vibration characteristics, avoids low-frequency pressure plate problems, and ultimately achieves efficient and accurate roof design optimization.

[0065] Accordingly, please refer to Figure 13 A block diagram of a CAS (Computer-Assisted Design) device for predicting vehicle roof modes provided in this application embodiment, the device comprising: The data acquisition unit 101 is used to acquire the roof CAS data of the target vehicle; wherein, the roof CAS data includes first CAS data for characterizing the roof shape and second CAS data for characterizing the roof structure; The theoretical height determination unit 103 is used to extract and process the first CAS data, and determine the theoretical height of the ceiling beam between the upper plate and the lower plate of the ceiling beam based on the extracted first CAS data and the extracted second CAS data; the mesh model creation unit 105 is used to determine the actual height range and actual width range of the ceiling beam that match the theoretical height of the ceiling beam, and create a mesh model of the lower plate of the ceiling beam based on the actual height range and actual width range of the ceiling beam. Modal analysis unit 107 is used to perform modal analysis on the mesh model of the lower plate of the roof beam, obtain modal frequencies, and compare modal frequencies to optimize the roof of the target vehicle based on the comparison results.

[0066] In some alternative implementations, the theoretical height determination unit 103 includes: Determine the vehicle coordinate system; where the positive X-axis of the vehicle coordinate system is from the front to the rear of the vehicle, the positive Y-axis is from the left to the right of the vehicle, and the positive Z-axis is from the bottom to the top of the vehicle. The first CAS data is processed by first-time interception. Starting from the foremost point in the Y=0 plane, the first CAS data of a specified length is intercepted along the positive X-axis to obtain the preliminary interception surface. The initial cut surface is subjected to a second cut process. Starting from the foremost point in the Y=0 plane, the cut is made along the positive Y-axis to obtain the target cut surface. The target cut surface contains the first CAS data and the second CAS data after the cut.

[0067] In some alternative implementations, the theoretical height determination unit 103 includes: Determine the distance between the highest point of the upper plate of the ceiling beam in the first CAS data after extraction and the lowest point of the lower plate of the ceiling beam in the second CAS data after extraction; The distance is defined as the theoretical height of the ceiling beam between the upper and lower sections of the ceiling beam.

[0068] In some alternative implementations, the mesh model creation unit 105 includes: The actual height range of the ceiling beam is determined by multiplying the theoretical height of the ceiling beam by the preset percentage threshold range.

[0069] In some alternative implementations, the mesh model creation unit 105 includes: Determine the variation intervals of the actual height range and the actual width range of the ceiling beams; Based on the actual height range, actual width range and variation interval of the ceiling beam, generate multiple size combinations including the lower plate of the ceiling beam; Create a mesh model of the lower plate of the ceiling beam that matches each set of geometric combinations.

[0070] In some alternative implementations, the modal analysis unit 107 includes: If the modal frequencies are compared and found to be within the low-frequency piezoresistive band, the roof of the target vehicle will be optimized. Optimization methods include adjusting the curvature of the roof surface, the position of the gap between the windshield and the roof, or the height of the roof beams.

[0071] In some alternative embodiments, the apparatus further includes: After optimizing the roof of the target vehicle, the mesh model of the lower plate of the roof beam is updated, and modal analysis is performed again to obtain the updated modal frequencies. The updated modal frequencies are compared until they are no longer within the low-frequency piezoresistive frequency band, thus obtaining the mesh model of the lower plate of the target ceiling beam.

[0072] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0073] In this embodiment, a device for predicting vehicle roof modes using CAS is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above-mentioned functions.

[0074] Please see Figure 14 , Figure 14 This application provides a schematic diagram of the structure of a computer device, as shown in the embodiment of the present application. Figure 14 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 14 Take a processor 10 as an example.

[0075] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0076] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0077] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0078] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0079] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0080] This application also provides a computer-readable storage medium. The methods described in this application can be implemented in hardware or firmware, or implemented as recordable on a storage medium, or implemented as computer code downloaded over a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and subsequently stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the methods shown in the above embodiments are implemented.

[0081] The apparatus or unit described in the above embodiments can be implemented by a computer chip or entity, or by a product having a certain function. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0082] For ease of description, the above devices are described separately by function as various units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware.

[0083] Those skilled in the art will understand that the embodiments of this application can be provided as methods or apparatus. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0084] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatuses, and devices according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0085] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0086] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0087] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0088] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the apparatus embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0089] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

[0090] Although embodiments of this application have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of this application, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for predicting vehicle roof modes using CAS (Computer-Assisted Design), characterized in that, The method includes: Acquire the roof CAS data of the target vehicle; wherein the roof CAS data includes first CAS data for characterizing the roof shape and second CAS data for characterizing the roof structure; The first CAS data is truncated, and the theoretical height of the ceiling beam between the upper and lower plates of the ceiling beam is determined based on the truncated first CAS data and the truncated second CAS data. Determine the actual height range and actual width range of the roof beam that match the theoretical height of the roof beam, and create a mesh model of the lower plate of the roof beam based on the actual height range and actual width range of the roof beam; perform modal analysis on the mesh model of the lower plate of the roof beam to obtain the modal frequencies, and compare the modal frequencies to optimize the roof of the target vehicle based on the comparison results.

2. The method according to claim 1, characterized in that, The method for truncating the first CAS data includes: Determine the vehicle coordinate system; wherein, in the vehicle coordinate system, the positive X-axis is from the front to the rear of the vehicle, the positive Y-axis is from the left to the right of the vehicle, and the positive Z-axis is from the bottom to the top of the vehicle. The first CAS data is subjected to a first truncation process. Starting from the foremost point in the Y=0 plane, a specified length of the first CAS data is truncated along the positive X-axis to obtain a preliminary truncation surface. The initial cut surface is subjected to a second cut process. Starting from the foremost point in the Y=0 plane, the cut is made along the positive Y-axis to obtain the target cut surface. The target cut surface includes the first CAS data and the second CAS data after the cut.

3. The method according to claim 1, characterized in that, The determination of the theoretical height of the ceiling beam between the upper and lower slabs of the ceiling beam based on the extracted first CAS data and second CAS data includes: Determine the distance between the highest point of the upper plate of the ceiling beam in the first CAS data and the lowest point of the lower plate of the ceiling beam in the second CAS data; The distance is defined as the theoretical height of the ceiling beam between the upper and lower plates of the ceiling beam.

4. The method according to claim 1, characterized in that, The actual height range of the ceiling beam is determined as follows: the actual height range of the ceiling beam is determined by multiplying the theoretical height of the ceiling beam by a preset percentage threshold range.

5. The method according to claim 1, characterized in that, The process of creating a mesh model for the lower plate of the ceiling beam based on the actual height range and the actual width range of the ceiling beam includes: Determine the variation interval of the actual height range and the actual width range of the ceiling beam; based on the actual height range, the actual width range, and the variation interval, generate multiple size combinations including the lower plate of the ceiling beam; Create a mesh model of the lower plate of the ceiling beam that matches each set of geometric combinations.

6. The method according to claim 1, characterized in that, The comparison of the modal frequencies to optimize the roof of the target vehicle based on the comparison results includes: If the modal frequencies are compared and found to be within the low-frequency piezoresistive frequency band, the roof of the target vehicle is optimized. The optimization methods include adjusting the curvature of the roof surface, the position of the joint between the windshield and the roof, or the height of the roof beam.

7. The method according to claim 6, characterized in that, The method further includes: After optimizing the roof of the target vehicle, the mesh model of the lower plate of the roof beam is updated, and modal analysis is performed again to obtain the updated modal frequencies. The updated modal frequencies are compared until they are no longer within the low-frequency piezoresistive frequency band, thus obtaining the mesh model of the lower plate of the target ceiling beam.

8. A device for predicting vehicle roof modes using CAS (Computer-Assisted Design), characterized in that, The device includes: A data acquisition unit is used to acquire CAS data of the roof of a target vehicle; wherein the roof CAS data includes first CAS data for characterizing the shape of the roof and second CAS data for characterizing the roof structure; The theoretical height determination unit is used to extract and process the first CAS data, and determine the theoretical height of the ceiling beam between the upper plate and the lower plate of the ceiling beam based on the extracted first CAS data and the extracted second CAS data; the mesh model creation unit is used to determine the actual height range and actual width range of the ceiling beam that match the theoretical height of the ceiling beam, and create a mesh model of the lower plate of the ceiling beam based on the actual height range and the actual width range of the ceiling beam. The modal analysis unit is used to perform modal analysis on the mesh model of the lower plate of the roof beam, obtain the modal frequencies, and compare the modal frequencies to optimize the roof of the target vehicle based on the comparison results.

9. A computer device, characterized in that, include: A memory and a processor are communicatively connected, the memory storing computer instructions, and the processor executing the computer instructions to perform the CAS method for predicting vehicle roof modes according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a computer to perform the CAS method for predicting vehicle roof modes according to any one of claims 1 to 7.

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