A method and related equipment for automatic vehicle modal extraction

Abstract

This invention discloses an automatic vehicle modal extraction method and related equipment. The method includes: obtaining basic order information of the first-order torsional mode and the first-order bending mode corresponding to the basic model, and basic model displacement information of the target point, respectively, through modal simulation analysis; obtaining modal calculation information corresponding to multiple parametric models through simulation analysis; calculating the similarity information between the displacement information of the parametric models corresponding to different parametric models and the displacement information of the basic model; and obtaining the torsional frequency information and torsional order information of the parametric models corresponding to the first-order torsional mode, and the bending frequency information and bending order information of the parametric models corresponding to the first-order bending mode, based on the similarity information. The method provided in this application can significantly reduce the post-processing time of simulation results, achieve automatic identification of modal information of parametric models, and improve simulation efficiency in the design phase.

Description

Technical Field

[0001] This specification relates to the automotive field, and more specifically, to a method and related equipment for automatic extraction of vehicle modes. Background Technology

[0002] Currently, most vehicle parameter optimization processes involve stiffness, modal dynamics, and corresponding crash performance. For stiffness and crash performance, the displacement or acceleration of a specific node can be directly obtained. Modal dynamics can also be directly read from the modal order, facilitating subsequent parameter optimization. However, when the structural parameters change drastically, the structural performance will change significantly, and the modal order will change. Determining which order corresponds to the original mode requires modal tracking and identification.

[0003] Currently, commonly used methods for modal identification of different models include the VTF method and the four-point method. VTF (Vibration Transfer Function) refers to the ratio of the Laplace transform of the system output to the Laplace transform of the input that caused the output under zero initial conditions. The transfer function is determined by the inherent characteristics of the system and is independent of the input. The four-point method is a method that can reduce the influence of other local modal combinations, thereby accurately extracting the low-order modes of the vehicle body, especially the first-order bending and torsional modes. However, currently, modal identification using these two methods usually relies on engineers manually identifying the modes based on the simulation results of the parametric model and the deformation characteristics of different modes, which cannot achieve automatic identification and tracking of a large number of model modes. Summary of the Invention

[0004] The summary section introduces a series of simplified concepts, which will be further explained in detail in the detailed description section. The summary section of this invention is not intended to limit the key features and essential technical features of the claimed technical solution, nor is it intended to determine the scope of protection of the claimed technical solution.

[0005] In a first aspect, the present invention proposes an automatic vehicle mode extraction method, the method comprising:

[0006] Modal simulation analysis is used to obtain the basic order information of the first-order torsional mode and the first-order bending mode corresponding to the basic model, as well as the basic model displacement information of the target point. The basic order information includes torsional order information and bending order information, and the basic model displacement information includes torsional displacement information and bending displacement information of the basic model.

[0007] Modal calculation information corresponding to multiple parametric models is obtained through simulation analysis. The modal calculation information includes the displacement and frequency information of the target point under different order modes. At least one of the beam position, cross-sectional shape and material type is different for different parametric models.

[0008] Calculate the similarity information between the displacement information of the above-mentioned parameterized model and the displacement information of the above-mentioned basic model for different parameterized models;

[0009] Based on the above similarity information, we obtain the torsional frequency information and torsional order information of the parameterized model corresponding to the first-order torsional mode, and the bending frequency information and bending order information of the parameterized model corresponding to the first-order bending mode.

[0010] Optionally, the similarity information between the displacement information of the parameterized model and the displacement information of the basic model corresponding to different parameterized models is calculated, including:

[0011] The similarity information between the displacement information of the above-mentioned parametric models and the displacement information of the above-mentioned basic models corresponding to different parametric models was calculated using the meta software.

[0012] Optionally, the similarity information between the displacement information of the parametric model and the displacement information of the basic model corresponding to different parametric models calculated using meta software includes:

[0013] Write the preset subroutines into folders corresponding to the displacement information of different parameterized models. The preset subroutines include parameterized subroutines and approximation template files.

[0014] The meta software was used to read and import the displacement information of each parametric model and the displacement information of the basic model mentioned above.

[0015] The above parameterization subroutine is imported into the above meta software, and similarity calculation is performed on the translational degrees of freedom of the above parameterization model information and the above basic model displacement information to obtain similarity information. The parameterization subroutine is used to extract the displacement information of the target degree of freedom of the target point in the parameterization model information and the basic model information.

[0016] Store the above similarity information in the above similarity template file.

[0017] Optionally, the above-mentioned parametric model displacement information and the basic model displacement information of each parametric model are read and imported separately using the meta software, including:

[0018] Each of the above-mentioned parametric models and the above-mentioned basic models are read separately using the meta software;

[0019] Import the parametric model displacement information and the basic model displacement information mentioned above into the Modal / FRF Correlation module of the Meta software.

[0020] Optionally, the above-mentioned acquisition of the torsional frequency information and torsional order information of the parameterized model corresponding to the first-order torsional mode and the bending frequency information and bending order information of the parameterized model corresponding to the first-order bending mode based on the above similarity information includes:

[0021] The frequency information and order information that have the highest similarity to the torsional displacement information of the basic model in the same parametric model are selected as the torsional frequency information and torsional order information of the above parametric model.

[0022] The frequency information and order information that have the highest similarity to the bending displacement information of the basic model in the same parametric model are selected as the bending frequency information and bending order information of the parametric model.

[0023] Optionally, the above-mentioned preset subroutines also include a maximum value selection program and a result output template file;

[0024] The above-mentioned acquisition of torsional frequency information and torsional order information of the parameterized model corresponding to the first-order torsional mode and bending frequency information and bending order information of the parameterized model corresponding to the first-order bending mode based on the above similarity information includes:

[0025] The maximum value selection procedure described above selects the maximum torsional similarity information and the maximum bending similarity information corresponding to each parameterized model in the approximation template file. The maximum torsional similarity information is the maximum similarity information between the displacement information of the parameterized model and the torsional displacement information of the basic model, and the maximum bending similarity information is the maximum similarity information between the displacement information of the parameterized model and the bending displacement information of the basic model.

[0026] The frequency and order information corresponding to the maximum torsional similarity information are written into the above result output template file as the torsional frequency information and torsional order information of the parameterized model;

[0027] The frequency and order information corresponding to the maximum bending similarity information are written into the above result output template file as the bending frequency information and bending order information of the parameterized model.

[0028] Optionally, the aforementioned target points are symmetrically arranged about the center of the vehicle, and are respectively located at the front bumper, rear bumper, lower A-pillar connector, lower B-pillar connector, lower C-pillar connector, upper A-pillar connector, upper B-pillar connector, and upper D-pillar connector.

[0029] Secondly, the present invention also proposes an automatic vehicle mode extraction device, comprising:

[0030] The first acquisition unit is used to acquire the basic order information of the first-order torsional mode and the first-order bending mode corresponding to the basic model and the basic model displacement information of the target point through modal simulation analysis, wherein the basic order information includes torsional order information and bending order information, and the basic model displacement information includes basic model torsional displacement information and basic model bending displacement information.

[0031] The second acquisition unit is used to acquire modal calculation information corresponding to multiple parametric models through simulation analysis. The modal calculation information includes the displacement and frequency information of the target point under different order modes. At least one of the beam position, cross-sectional shape and material type is different for different parametric models.

[0032] The calculation unit is used to calculate the similarity information between the displacement information of the above-mentioned parameterized model and the displacement information of the above-mentioned basic model corresponding to different parameterized models.

[0033] The third acquisition unit is used to acquire, based on the above similarity information, the torsional frequency information and torsional order information of the parameterized model corresponding to the first-order torsional mode of different parameterized models, and the bending frequency information and bending order information of the parameterized model corresponding to the first-order bending mode.

[0034] Thirdly, an electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program stored in the memory to implement the steps of the vehicle mode automatic extraction method as described in any of the first aspects above.

[0035] Fourthly, the present invention also proposes a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the vehicle mode automatic extraction method of any of the above claims in the first aspect.

[0036] In summary, the automatic vehicle modal extraction method of this application includes: obtaining basic order information of the first-order torsional mode and the first-order bending mode corresponding to the basic model and basic model displacement information of the target point through modal simulation analysis, wherein the basic order information includes torsional order information and bending order information, and the basic model displacement information includes basic model torsional displacement information and basic model bending displacement information; obtaining modal calculation information corresponding to multiple parametric models through simulation analysis, wherein the modal calculation information includes parametric model displacement information and frequency information of the target point under different order modes, and at least one of the beam position, cross-sectional shape and material type corresponding to different parametric models is different; calculating the similarity information between the parametric model displacement information corresponding to different parametric models and the basic model displacement information; and obtaining parametric model torsional frequency information and parametric model torsional order information corresponding to the first-order torsional mode and parametric model bending frequency information and parametric model bending order information corresponding to the first-order bending mode based on the similarity information. The automatic vehicle modal extraction method proposed in this application obtains the basic model displacement information of the basic model and the parametric model displacement information of the parametric model through simulation analysis. It calculates the similarity between the parametric model displacement information and the basic model displacement information corresponding to the first-order torsional mode and the first-order bending mode, and uses the frequency and order information with the highest similarity as the torsional frequency information, bending frequency information, torsional order information, and bending order information of the parametric model. The method provided in this application can significantly reduce the post-processing time of simulation results, automatically identify the modal information of the parametric model, improve simulation efficiency in the design stage, track whether the mode skips an order, and achieve structural optimization of the vehicle model based on the automatically identified order and frequency information.

[0037] The automatic vehicle modality extraction method proposed in this application, other advantages, objectives and features of the present invention will be apparent in part from the following description, and in part will be understood by those skilled in the art through study and practice of the invention. Attached Figure Description

[0038] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit this specification. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0039] Figure 1 This is a schematic flowchart of an automatic vehicle modality extraction method provided in an embodiment of this application;

[0040] Figure 2This application provides a schematic diagram of the folder relationship for modal calculation results.

[0041] Figure 3 This is a schematic diagram of similarity information results provided in an embodiment of this application;

[0042] Figure 4 This is a schematic diagram illustrating the principle of a four-point method for vehicle modal recognition, as provided in an embodiment of this application.

[0043] Figure 5 This application provides a schematic diagram illustrating the principle of automatic vehicle modality recognition.

[0044] Figure 6 This is a schematic diagram of the structure of an automatic vehicle modality extraction device provided in an embodiment of this application;

[0045] Figure 7 This is a schematic diagram of an electronic device for automatic vehicle modality extraction provided in an embodiment of this application. Detailed Implementation

[0046] The automatic vehicle modal extraction method proposed in this application obtains the basic model displacement information of the basic model and the parametric model displacement information of the parametric model through simulation analysis. It calculates the similarity between the parametric model displacement information and the basic model displacement information corresponding to the first-order torsional mode and the first-order bending mode, and uses the frequency and order information with the highest similarity as the torsional frequency information, bending frequency information, torsional order information, and bending order information of the parametric model. The method provided in this application can significantly reduce the post-processing time of simulation results, automatically identify the modal information of the parametric model, improve simulation efficiency in the design stage, track whether the mode skips an order, and achieve structural optimization of the vehicle model based on the automatically identified order and frequency information.

[0047] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus. The technical solutions of the embodiments of this application will now be clearly and completely described in conjunction with the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them.

[0048] Please see Figure 1 This is a schematic diagram of a vehicle modal automatic extraction method provided in an embodiment of this application, which may specifically include:

[0049] S110. Through modal simulation analysis, obtain the basic order information of the first-order torsional mode and the first-order bending mode corresponding to the basic model and the basic model displacement information of the target point, wherein the basic order information includes torsional order information and bending order information, and the basic model displacement information includes basic model torsional displacement information and basic model bending displacement information.

[0050] For example, a basic vehicle body model is constructed using simulation software, and modal simulation analysis is performed. The torsional order information and torsional displacement information of the first-order torsional mode corresponding to the basic model are extracted, as are the bending order information and bending displacement information of the first-order bending mode corresponding to the basic model. Simulation software such as ANSYS and ABQUS can be used.

[0051] S120. Modal calculation information corresponding to multiple parametric models is obtained through simulation analysis. The modal calculation information includes the displacement and frequency information of the target point under different order modes. At least one of the beam position, cross-sectional shape and material type is different for different parametric models.

[0052] For example, multiple parametric models are obtained by changing at least one of the following: the position of the crossbeam, the cross-sectional shape of the vehicle body structure, or the type of material, based on the basic model. Simulation software is then used to perform simulation analysis and obtain modal calculation information corresponding to each parametric model. The calculation information for each parametric model includes displacement and frequency information of the target point under different order modes. It should be noted that the target points in the parametric models are set at the same locations as the target points in the basic model, such as on the front and rear bumpers, or at important connection nodes.

[0053] S130. Calculate the similarity information between the displacement information of the above-mentioned parameterized model and the displacement information of the above-mentioned basic model corresponding to different parameterized models.

[0054] For example, the similarity information of the displacement information of different parameterized models obtained through simulation analysis is calculated with the position information of the basic model. This yields the similarity between the displacement information of different orders of modes in different parameterized models and the displacement information of the first torsional mode and the first bending mode in the basic model. It should be noted that the similarity information is obtained by comparing and calculating the translational degrees of freedom of the target point; only translational degrees of freedom need to be selected.

[0055] S140. Based on the above similarity information, obtain the torsional frequency information and torsional order information of the parameterized model corresponding to the first-order torsional mode of different parameterized models, and the bending frequency information and bending order information of the parameterized model corresponding to the first-order bending mode.

[0056] For example, based on similarity information, the displacement information with the highest similarity to the first-order torsional mode and the first-order bending mode of the basic model is selected as the torsional frequency information and torsional order information of the parameterized model corresponding to the first-order torsional mode, and the bending frequency information and bending order information of the parameterized model corresponding to the first-order bending mode.

[0057] In summary, the automatic vehicle modal extraction method proposed in this application obtains the basic model displacement information of the basic model and the parametric model displacement information of the parametric model through simulation analysis. It calculates the similarity between the parametric model displacement information and the basic model displacement information corresponding to the first-order torsional mode and the first-order bending mode, and uses the frequency and order information with the highest similarity as the torsional frequency information, bending frequency information, torsional order information, and bending order information of the parametric model. The method provided in this application can significantly reduce the post-processing time of simulation results, automatically identify the modal information of the parametric model, improve simulation efficiency in the design stage, track whether the mode skips an order, and achieve structural optimization of the vehicle model based on the automatically identified order and frequency information.

[0058] In some examples, the similarity information between the displacement information of the parameterized model corresponding to different parameterized models and the displacement information of the basic model is calculated, including:

[0059] The similarity information between the displacement information of the above-mentioned parametric models and the displacement information of the above-mentioned basic models corresponding to different parametric models was calculated using the meta software.

[0060] For example, meta can process data from mainstream solvers such as ABAQUS and ANSYS, and has comprehensive visualization capabilities. By using meta software to calculate the displacement information of parametric models of different parametric models and the displacement information of the basic model corresponding to the first-order torsional mode and the first-order bending mode of the basic model, similarity information can be quickly obtained.

[0061] In some examples, the similarity information between the displacement information of the aforementioned parametric models and the displacement information of the aforementioned basic models, calculated using meta software for different parametric models, includes:

[0062] Write the preset subroutines into folders corresponding to the displacement information of different parameterized models. The preset subroutines include parameterized subroutines and approximation template files.

[0063] The meta software was used to read and import the displacement information of each parametric model and the displacement information of the basic model mentioned above.

[0064] The above parameterization subroutine is imported into the above meta software, and similarity calculation is performed on the translational degrees of freedom of the above parameterization model information and the above basic model displacement information to obtain similarity information. The parameterization subroutine is used to extract the displacement information of the target degree of freedom of the target point in the parameterization model information and the basic model information.

[0065] Store the above similarity information in the above similarity template file.

[0066] For example, by designing corresponding subroutines, it is possible to automatically calculate similarity information using simulation results from a parametric model combined with Meta software, and write the approximation information into an approximation template file. Specifically, as follows... Figure 2 The image shows a folder containing simulation results for different parametric models. Preset subroutines, including parametric subroutines (node.npt) and approximation template files (MAC.csv), are written into folders corresponding to the displacement information of different parametric models. The Meta post-processing software is run to read and import the parametric model displacement information and the aforementioned basic model displacement information for each parametric model. The Meta software is then used to calculate the similarity between the translational degrees of freedom of the parametric model information and the aforementioned basic model displacement information, and this similarity information is written into the approximation template file (MAC.csv). Figure 3 This is an approximation template file for a certain parametric model. The first column, 2 and 4, represent the orders of the first torsional and first bending modes of the basic model, respectively. The first row corresponds to the sequence numbers of the first 17 modes of one of the parametric models (only 17 modes are shown due to calculations only up to 70Hz). The second row contains the corresponding values; the third row shows the overall similarity between the mode shape at 16 nodes and the first torsional mode of the basic model; the fourth row shows the overall similarity between the mode shape at 16 nodes and the first bending mode of the basic model.

[0067] It should be noted that the parameterization subroutine is used to extract the displacement information of the target degrees of freedom of the target points from the parameterized model information and the basic model information. Specifically, after processing by the parameterization program, the target nodes can be stored in the following format:

[0068] 300001,300001,,,123

[0069] In this context, the first 300001 is the first node number for the basic model comparison, the second 30001 is the first node number for the Nth parametric model comparison, and 123 represents the translational degrees of freedom of that node.

[0070] In summary, the automatic vehicle modal extraction method proposed in this application can automatically calculate and obtain the similarity between the first-order torsional mode and the first-order bending mode of different parametric models and the basic model by writing parametric subroutines and approximation template files into the simulation calculation result folder of the parametric model. This enables automatic calculation of similarity information and batch processing.

[0071] In some examples, the above-mentioned parametric model displacement information and the basic model displacement information of each parametric model are read and imported separately using the meta software, including:

[0072] Each of the above-mentioned parametric models and the above-mentioned basic models are read separately using the meta software;

[0073] Import the parametric model displacement information and the basic model displacement information mentioned above into the Modal / FRF Correlation module of the Meta software.

[0074] For example, a parametric model and a basic model are read through the meta software, and the two models coexist. The displacement information of the parametric model and the displacement information of the basic model are imported under the Modal / FRF Correlation module of the meta software. The displacement information of the basic model for the first bending and first torsional modes of the basic model is selected, and the translational degrees of freedom of the target node are compared and similarity is calculated with the parametric model information.

[0075] In some examples, the above-mentioned similarity information is used to obtain the torsional frequency information and torsional order information of the parameterized model corresponding to the first-order torsional mode and the bending frequency information and bending order information of the parameterized model corresponding to the first-order bending mode, including:

[0076] The frequency information and order information that have the highest similarity to the torsional displacement information of the basic model in the same parametric model are selected as the torsional frequency information and torsional order information of the above parametric model.

[0077] The frequency information and order information that have the highest similarity to the bending displacement information of the basic model in the same parametric model are selected as the bending frequency information and bending order information of the parametric model.

[0078] For example, when calculating similarity information, the displacement information of each parameterized model needs to be compared with the torsional displacement information and the bending displacement information to obtain similarity information. The frequency information and order information with the largest value in the two sets of similarity information are selected as the torsional frequency information and torsional order information of the parameterized model corresponding to the first torsional mode, and the bending frequency information and bending order information of the parameterized model corresponding to the first bending mode, respectively.

[0079] In some examples, the above-mentioned preset subroutines also include a maximum value selection procedure and a result output template file;

[0080] The above-mentioned acquisition of torsional frequency information and torsional order information of the parameterized model corresponding to the first-order torsional mode and bending frequency information and bending order information of the parameterized model corresponding to the first-order bending mode based on the above similarity information includes:

[0081] The maximum value selection procedure described above selects the maximum torsional similarity information and the maximum bending similarity information corresponding to each parameterized model in the approximation template file. The maximum torsional similarity information is the maximum similarity information between the displacement information of the parameterized model and the torsional displacement information of the basic model, and the maximum bending similarity information is the maximum similarity information between the displacement information of the parameterized model and the bending displacement information of the basic model.

[0082] The frequency and order information corresponding to the maximum torsional similarity information are written into the above result output template file as the torsional frequency information and torsional order information of the parameterized model;

[0083] The frequency and order information corresponding to the maximum bending similarity information are written into the above result output template file as the bending frequency information and bending order information of the parameterized model.

[0084] For example, the pre-defined subroutines written to the modal calculation results folders of different parameterized models also include a maximum value selection program (readmacvalue.py) and an output template file (result.txt). After obtaining the approximation template file (MAC.csv) containing approximation information, the maximum value selection program (readmacvalue.py) is run to read the results. Figure 3 The maximum value from the 3rd row and 3rd column of the MAC file shown (where the first two columns are the header and the first two rows are the modal termination and modal frequency) is used to assign the first and second rows of the corresponding columns to the parametric model torsional order information (i.e., torsional modal order a) and parametric model torsional frequency information (i.e., torsional modal frequency b), respectively. The `readmacvalue.py` file is then run to read the maximum value from the 4th row and 3rd column of the MAC file. The first and second rows of the corresponding columns are then assigned to the parametric model bending order information (i.e., bending modal order c) and parametric model bending frequency information (i.e., bending modal frequency d), respectively. Finally, the parametric model torsional frequency information, parametric model torsional order information, parametric model bending frequency information, and parametric model bending order information are written to the output template file (result.txt). Figure 3 The output template file corresponding to the approximation template file shown is:

[0085] TorsionFreqq=42.4535 Torsionmode=2 BendFreq=47.5053 Bendmode=4

[0086] Each time a folder is run, a new line is added to this file, eventually forming a file with N lines. After processing, useful information is extracted to obtain an N*4 matrix.

[0087] In summary, the automatic vehicle modal extraction method proposed in this application can automatically obtain the maximum value of the approximation information by writing the maximum value selection program and the result output template file into the modal calculation folder of different parameterized models. The order information and frequency information corresponding to the maximum approximation information are written into the output template file. The approximation model can be built and optimized by using the order information and frequency information recorded in the output template file.

[0088] In some examples, the aforementioned target points are symmetrically arranged about the center of the vehicle, and are respectively located at the front bumper, rear bumper, lower A-pillar connector, lower B-pillar connector, lower C-pillar connector, upper A-pillar connector, upper B-pillar connector, and upper D-pillar connector.

[0089] For example, in existing methods, such as Figure 4 As shown, the four-point method is often used to identify vehicle modes. The four-point method identifies bending modes when the two front points 1 and 2 and the two rear points 3 and 4 are simultaneously moving upwards or downwards. However, when local modes (such as floor modes) are coupled with the overall vehicle bending modes, their similarity can be very similar, leading to misclassification. This application can set 16 target points on the vehicle structure, symmetrically arranged about the left and right sides of the vehicle, such as... Figure 5 As shown, the target points are located on the front and rear bumpers (300001 and 300005), respectively. The remaining target points are located at the key node joints of the vehicle body force transmission structure, namely: the lower joint of the A-pillar (300002), the lower joint of the B-pillar (300003), the lower joint of the C-pillar (300003), and the upper joint of the A-pillar (300006), the upper joint of the B-pillar (300007), and the upper joint of the D-pillar (300008).

[0090] In summary, the automatic vehicle modal extraction method proposed in this application uses 16 points to identify the vehicle's modalities, which can reflect the mechanical state at key stress points and improve the success rate of modal identification.

[0091] Please see Figure 6 One embodiment of the vehicle modal automatic extraction device in this application may include:

[0092] The first acquisition unit 21 is used to acquire the basic order information of the first torsional mode and the first bending mode corresponding to the basic model and the basic model displacement information of the target point through modal simulation analysis, wherein the basic order information includes torsional order information and bending order information, and the basic model displacement information includes basic model torsional displacement information and basic model bending displacement information.

[0093] The second acquisition unit 22 is used to acquire modal calculation information corresponding to multiple parameterized models through simulation analysis. The modal calculation information includes the displacement information and frequency information of the target point under different order modes. At least one of the beam position, cross-sectional shape and material type corresponding to different parameterized models is different.

[0094] The calculation unit 23 is used to calculate the similarity information between the displacement information of the above-mentioned parameterized model and the displacement information of the above-mentioned basic model corresponding to different parameterized models;

[0095] The third acquisition unit 24 is used to acquire, based on the above similarity information, the torsional frequency information and torsional order information of the parameterized model corresponding to the first-order torsional mode of different parameterized models, and the bending frequency information and bending order information of the parameterized model corresponding to the first-order bending mode.

[0096] like Figure 7 As shown, this application embodiment also provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor. When the processor 320 executes the computer program 311, it implements the steps of any of the above-described methods for automatic vehicle modality extraction.

[0097] Since the electronic device described in this embodiment is the device used to implement the vehicle modality automatic extraction device in the embodiments of this application, those skilled in the art can understand the specific implementation method and various variations of the electronic device in this embodiment based on the method described in the embodiments of this application. Therefore, how the electronic device implements the method in the embodiments of this application will not be described in detail here. Any device used by those skilled in the art to implement the method in the embodiments of this application falls within the scope of protection of this application.

[0098] In practical implementation, when the computer program 311 is executed by the processor, it can achieve the following: Figure 1 Any of the corresponding implementation methods in the embodiments.

[0099] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0100] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. 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.

[0101] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products 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 computer, 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, create a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0102] 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.

[0103] 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.

[0104] This application also provides a computer program product, which includes computer software instructions that, when executed on a processing device, cause the processing device to perform actions such as... Figure 1 The process for automatic vehicle mode extraction in the corresponding embodiment.

[0105] A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0106] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0107] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0108] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0109] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0110] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0111] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for automatic extraction of vehicle modes, characterized in that, include: Modal simulation analysis is used to obtain the basic order information of the first-order torsional mode and the first-order bending mode of the basic model and the basic model displacement information of the target point. The basic order information includes torsional order information and bending order information, and the basic model displacement information includes torsional displacement information and bending displacement information of the basic model. Modal calculation information corresponding to multiple parametric models is obtained through simulation analysis. The modal calculation information includes the displacement and frequency information of the target point under different order modes. At least one of the following is different: the position, cross-sectional shape, and material type of the vehicle body beam corresponding to different parametric models. Calculate the similarity information between the displacement information of the parameterized model and the displacement information of the basic model corresponding to different parameterized models; Based on the similarity information, obtain the torsional frequency information and torsional order information of the parameterized model corresponding to the first-order torsional mode and the bending frequency information and bending order information of the parameterized model corresponding to the first-order bending mode, including: The frequency information and order information that have the highest similarity to the torsional displacement information of the basic model in the same parameterized model are selected as the torsional frequency information and torsional order information of the parameterized model. The frequency information and order information that have the highest similarity to the bending displacement information of the basic model in the same parametric model are selected as the bending frequency information and bending order information of the parametric model.

2. The method as described in claim 1, characterized in that, The step of calculating the similarity information between the displacement information of the parameterized model and the displacement information of the base model corresponding to different parameterized models includes: The similarity information between the displacement information of the parameterized model and the displacement information of the basic model corresponding to different parameterized models is calculated using the meta software.

3. The method as described in claim 2, characterized in that, The step of using meta-software to calculate the similarity information between the displacement information of the parameterized model corresponding to different parameterized models and the displacement information of the base model includes: Write the preset subroutines into folders corresponding to the displacement information of different parameterized models, wherein the preset subroutines include parameterized subroutines and approximation template files; The meta software is used to read and import the displacement information of the parametric model and the displacement information of the basic model for each parametric model. The parameterization subroutine is imported into the meta software, and similarity calculation is performed on the translational degrees of freedom of the parameterized model displacement information and the basic model displacement information to obtain similarity information. The parameterization subroutine is used to extract the displacement information of the target degree of freedom of the target point in the parameterized model displacement information and the basic model displacement information. The similarity information is stored in the similarity template file.

4. The method as described in claim 3, characterized in that, The step of reading and importing the parametric model displacement information of each parametric model and the basic model displacement information using the meta software includes: Each parameterized model and the base model are read separately using the meta software; Import the parametric model displacement information and the basic model displacement information into the Modal / FRF Correlation module of the Meta software.

5. The method as described in claim 3, characterized in that, The preset subroutine also includes a maximum value selection procedure and a result output template file; The process of obtaining the torsional frequency information and torsional order information of the parameterized model corresponding to the first-order torsional mode and the bending frequency information and bending order information of the parameterized model corresponding to the first-order bending mode based on the similarity information includes: The maximum value selection procedure selects the maximum torsional similarity information and the maximum bending similarity information corresponding to each parameterized model in the approximation template file. The maximum torsional similarity information is the maximum similarity information between the displacement information of the parameterized model and the torsional displacement information of the basic model, and the maximum bending similarity information is the maximum similarity information between the displacement information of the parameterized model and the bending displacement information of the basic model. The frequency and order information corresponding to the maximum torsional similarity information are written into the result output template file as the torsional frequency information and torsional order information of the parameterized model. The frequency and order information corresponding to the maximum bending similarity information are written into the result output template file as the bending frequency information and bending order information of the parameterized model.

6. The method as described in claim 1, characterized in that, The target points are symmetrically arranged about the center of the vehicle, and are respectively located at the front bumper, rear bumper, lower A-pillar connector, lower B-pillar connector, lower C-pillar connector, upper A-pillar connector, upper B-pillar connector, and upper D-pillar connector.

7. An automatic vehicle modal extraction device, characterized in that, include: The first acquisition unit is used to acquire, through modal simulation analysis, the basic order information of the first-order torsional mode and the first-order bending mode corresponding to the basic model and the basic model displacement information of the target point, wherein the basic order information includes torsional order information and bending order information, and the basic model displacement information includes basic model torsional displacement information and basic model bending displacement information. The second acquisition unit is used to acquire modal calculation information corresponding to multiple parameterized models through simulation analysis. The modal calculation information includes the displacement information and frequency information of the target point under different order modes. At least one of the beam position, cross-sectional shape and material type is different for different parameterized models. The calculation unit is used to calculate the similarity information between the displacement information of the parameterized model and the displacement information of the basic model corresponding to different parameterized models; The third acquisition unit is used to acquire, based on the similarity information, the torsional frequency information and torsional order information of the parameterized model corresponding to the first-order torsional mode of different parameterized models, and the bending frequency information and bending order information of the parameterized model corresponding to the first-order bending mode, including: The frequency information and order information that have the highest similarity to the torsional displacement information of the basic model in the same parameterized model are selected as the torsional frequency information and torsional order information of the parameterized model. The frequency information and order information that have the highest similarity to the bending displacement information of the basic model in the same parametric model are selected as the bending frequency information and bending order information of the parametric model.

8. An electronic device, comprising: The memory and processor are characterized in that the processor is used to execute a computer program stored in the memory to implement the steps of the vehicle mode automatic extraction method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the vehicle modality automatic extraction method as described in any one of claims 1-6.

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