Model parameter determination method and device, electronic equipment and storage medium

Abstract

The invention provides a model parameter determination method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a computer-aided engineering (CAE) model corresponding to a simulation target, the CAE model comprising at least one piece of parameter information of the simulation target; judging whether the at least one piece of parameter information meets a preset performance requirement or not; if the preset performance requirement is not met, generating target parameter information of at least two different parameter values; wherein the target parameter information is attribute information which does not meet the preset performance requirement; generating a plurality of corresponding simulation analysis models according to the target parameter information of the at least two different parameter values; and analyzing the plurality of simulation analysis models to determine optimal model parameters corresponding to the CAE model. Compared with the prior art, according to the embodiment of the invention, a plurality of simulation analysis models are generated according to the target parameter information to determine the optimal model parameters; the optimization of the CAE model can be realized based on the solved optimal model parameters, and the efficiency of optimization design can be improved.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to a method and device for determining model parameters, an electronic device, and a storage medium. Background Art

[0002] In the related art, when optimizing the parameters of the vehicle body model, the structural parameters are first optimized using CAD software, and then the CAE simulation model is re-simulated and analyzed based on the optimized structural parameters. After the structural parameters are changed, the mesh division in the CAE simulation model needs to be re-performed and the connection relationship with the surrounding parts needs to be adjusted; since the simulation analysis cannot determine whether the optimization scheme is effective after one optimization, multiple repeated optimization adjustments are required, resulting in a large workload and low analysis efficiency. Summary of the invention

[0003] The present disclosure provides a method and device for determining model parameters, an electronic device and a storage medium, the main purpose of which is to solve the problem of low optimization efficiency of current simulation models.

[0004] According to a first aspect of the present disclosure, a method for determining a model parameter is provided, comprising:

[0005] Acquire a computer-aided engineering (CAE) model corresponding to a simulation target, wherein the CAE model includes at least one parameter information of the simulation target;

[0006] Determining whether the at least one parameter information meets a preset performance requirement;

[0007] If the preset performance requirement is not met, generating at least two target parameter information with different parameter values; wherein the target parameter information is the attribute information that does not meet the preset performance requirement;

[0008] Generate a plurality of corresponding simulation analysis models according to the target parameter information of the at least two different parameter values;

[0009] The multiple simulation analysis models are analyzed to determine optimal model parameters corresponding to the CAE model.

[0010] Optionally, the target parameter information for generating at least two different parameter values ​​includes:

[0011] Calling a preset parameter generation algorithm to generate at least two different parameter values ​​for the target parameter information, wherein the preset parameter generation algorithm includes a MeshWorks software algorithm;

[0012] The target parameter information of the at least two different parameter values ​​are recorded in a first model file and a second model file respectively, wherein the first model file and the second model file are in different storage formats.

[0013] Optionally, generating corresponding multiple simulation analysis models according to the target parameter information of the at least two different parameter values ​​includes:

[0014] Calling a preset optimization algorithm to redefine the target parameter information based on the second model file, wherein the preset optimization algorithm includes a HyperStudy software algorithm;

[0015] The preset parameter generation algorithm is called and target parameter information based on at least two different parameter values ​​in the first model file is used to generate corresponding multiple simulation analysis models.

[0016] Optionally, the determining whether the at least one parameter information meets a preset performance requirement includes:

[0017] Parsing the CAE model to obtain components constituting the simulation target, each component including at least one parameter information;

[0018] Based on the preset performance requirement, it is determined whether at least one parameter information corresponding to each component meets the preset performance requirement.

[0019] Optionally, analyzing the multiple simulation analysis models to determine the optimal model parameters corresponding to the CAE model includes:

[0020] The preset solver is called to calculate the optimal solution for the multiple simulation analysis models according to the preset performance requirements, so as to obtain the optimal model parameters corresponding to the CAE model.

[0021] Optionally, before obtaining a computer-aided engineering (CAE) model corresponding to the simulation target, the method further includes:

[0022] Create the corresponding CAE model based on computer-aided design CAD.

[0023] Optionally, after analyzing the multiple simulation analysis models to determine the optimal model parameters corresponding to the CAE model, the method further includes:

[0024] According to the optimal model parameters corresponding to the CAE model, the corresponding parameters in the CAD digital model structure are adjusted.

[0025] According to a second aspect of the present disclosure, there is provided a device for determining a model parameter, comprising:

[0026] An acquisition unit, used to acquire a computer-aided engineering (CAE) model corresponding to a simulation target, wherein the CAE model includes at least one parameter information of the simulation target;

[0027] A judging unit, configured to judge whether the at least one parameter information meets a preset performance requirement;

[0028] A first generating unit, configured to generate target parameter information of at least two different parameter values ​​when the at least one parameter information does not meet the preset performance requirement; wherein the target parameter information is attribute information that does not meet the preset performance requirement;

[0029] A second generating unit, configured to generate a plurality of corresponding simulation analysis models according to the target parameter information of the at least two different parameter values;

[0030] The analysis unit is used to analyze the multiple simulation analysis models to determine the optimal model parameters corresponding to the CAE model.

[0031] Optionally, the first generating unit includes:

[0032] A first generating module, used for calling a preset parameter generating algorithm to generate at least two different parameter values ​​for the target parameter information, wherein the preset parameter generating algorithm includes a MeshWorks software algorithm;

[0033] The storage module is used to record the target parameter information of the at least two different parameter values ​​in the first model file and the second model file respectively, wherein the first model file and the second model file are in different storage formats.

[0034] Optionally, the second generating unit includes:

[0035] A definition module, used to call a preset optimization algorithm and redefine the target parameter information based on the second model file, wherein the preset optimization algorithm includes a HyperStudy software algorithm;

[0036] The second generation module is used to call the preset parameter generation algorithm and generate corresponding multiple simulation analysis models based on the target parameter information of at least two different parameter values ​​in the first model file.

[0037] Optionally, the judging unit includes:

[0038] A parsing module, used for parsing the CAE model to obtain components constituting the simulation target, each component including at least one parameter information;

[0039] The judgment module is used to judge whether at least one parameter information corresponding to each component meets the preset performance requirement based on the preset performance requirement.

[0040] Optionally, the analysis unit is further used for:

[0041] The preset solver is called to calculate the optimal solution for the multiple simulation analysis models according to the preset performance requirements, so as to obtain the optimal model parameters corresponding to the CAE model.

[0042] Optionally, the device further comprises:

[0043] The creation unit is used to create a corresponding CAE model according to the computer-aided design (CAD) before obtaining a computer-aided engineering (CAE) model corresponding to the simulation target.

[0044] Optionally, the device further comprises:

[0045] The adjustment unit is used to adjust the corresponding parameters in the CAD digital model structure according to the optimal model parameters corresponding to the CAE model after analyzing the multiple simulation analysis models to determine the optimal model parameters corresponding to the CAE model.

[0046] According to a third aspect of the present disclosure, there is provided an electronic device, including:

[0047] at least one processor; and

[0048] a memory communicatively connected to the at least one processor; wherein,

[0049] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect.

[0050] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute the method described in the first aspect.

[0051] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the method as described in the first aspect above.

[0052] The present disclosure provides a method and device for determining model parameters, an electronic device and a storage medium, which obtain a computer-aided engineering CAE model corresponding to a simulation target, wherein the CAE model includes at least one parameter information of the simulation target; determines whether the at least one parameter information meets a preset performance requirement; if the preset performance requirement is not met, generates target parameter information of at least two different parameter values; wherein the target parameter information is attribute information that does not meet the preset performance requirement; generates corresponding multiple simulation analysis models based on the target parameter information of at least two different parameter values; and analyzes the multiple simulation analysis models to determine the optimal model parameters corresponding to the CAE model. Compared with the related art, the embodiment of the present disclosure generates multiple simulation analysis models based on the target parameter information to determine the optimal model parameters; based on the solved optimal model parameters, the optimization of the CAE model can be achieved, and the efficiency of the optimization design can be improved.

[0053] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0055] Figure 1 A flow chart of a method for determining model parameters provided in an embodiment of the present disclosure;

[0056] Figure 2 A flowchart of another method for determining model parameters provided by an embodiment of the present disclosure;

[0057] Figure 3 A flowchart of another method for determining model parameters provided by an embodiment of the present disclosure;

[0058] Figure 4 A schematic diagram of the structure of a device for determining model parameters provided by an embodiment of the present disclosure;

[0059] Figure 5 A schematic diagram of the structure of another device for determining model parameters provided by an embodiment of the present disclosure;

[0060] Figure 6 A schematic block diagram of an exemplary electronic device 400 provided for an embodiment of the present disclosure. DETAILED DESCRIPTION

[0061] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0062] The following describes a method and apparatus for determining model parameters, an electronic device, and a storage medium according to an embodiment of the present disclosure with reference to the accompanying drawings.

[0063] Figure 1 A flowchart of a method for determining model parameters provided in an embodiment of the present disclosure.

[0064] like Figure 1 As shown, the method comprises the following steps:

[0065] Step 101: Obtain a computer-aided engineering (CAE) model corresponding to a simulation target, wherein the CAE model includes at least one parameter information of the simulation target.

[0066] In the existing technology, the traditional body structure optimization process is to analyze the structural weaknesses based on the simulation results, and engineers use CAD software to optimize the structure and modify it, and then re-simulate the analysis. However, after the structure is changed, it is necessary to re-grid and adjust the connection relationship with the surrounding parts. Moreover, since it is uncertain whether the optimization plan is effective, it is necessary to analyze each alternative plan separately to find the optimal plan, resulting in a large workload and low analysis efficiency.

[0067] A CAE model is established based on the CAD data of the simulation target, which may be a vehicle body structure or other target model that needs to be simulated and analyzed. The embodiments of the present disclosure do not constitute a limitation on the simulation target. Parameter information includes information such as mesh, connection, material properties, working conditions, etc. The embodiments of the present disclosure do not constitute a limitation on the parameter information.

[0068] Step 102: determine whether the at least one parameter information meets a preset performance requirement.

[0069] The CAE model of the simulation target is analyzed, and whether the parameters in at least one parameter information of the simulation target meet the requirements is determined according to the preset performance requirements. For example, when simulating the vehicle body structure, if you want to analyze and determine whether the door thickness meets the requirements, you need to set the preset performance requirement threshold of the door thickness in advance. Whether the door thickness meets the requirements is determined according to the simulation analysis results and the preset performance requirements.

[0070] Step 103: If the preset performance requirement is not met, generate target parameter information of at least two different parameter values; wherein the target parameter information is attribute information that does not meet the preset performance requirement.

[0071] If it is determined that a certain parameter information of the simulation target does not meet the preset performance requirements, it is necessary to optimize the parameter information of a certain component that does not meet the preset performance requirements according to the results of the simulation analysis. In the embodiment of the present disclosure, the CAE model of the simulation target to be optimized is imported into the MeshWorks software, parametric design is performed, and a file with parametric settings is output; the parametric settings are completed by setting the target parameter information with different ladder values.

[0072] Step 104: Generate a plurality of corresponding simulation analysis models according to the target parameter information of the at least two different parameter values.

[0073] In the disclosed embodiment, according to the software with parameterized settings, MeshWorks software is called by HyperStudy software to generate multiple simulation analysis models. According to the CAE model of the simulation target, multiple simulation analysis models are generated by utilizing the mesh deformation function and parameterization function of MeshWorks software. Compared with the existing technology, the disclosed embodiment uses MeshWorks software to directly modify the structure on the mesh model, which can quickly achieve the same function as the CAD software to achieve the purpose of changing the structure shape.

[0074] Step 105: Analyze the multiple simulation analysis models to determine the optimal model parameters corresponding to the CAE model.

[0075] The optimization function of HyperStudy software is used to optimize the CAE model of the simulation target, and finite element analysis is used to analyze and calculate multiple simulation analysis models to obtain the analysis results of each simulation analysis model. When performing simulation analysis, the CAE model of the simulation target may have parameter information of multiple components that need to be optimized. After obtaining the analysis and calculation results of multiple simulation analysis models, HyperStudy software is also used to obtain the parameters of the optimal model of the CAE model of the simulation target.

[0076] The present disclosure provides a method for determining model parameters, obtaining a computer-aided engineering (CAE) model corresponding to a simulation target, wherein the CAE model includes at least one parameter information of the simulation target; determining whether the at least one parameter information meets a preset performance requirement; if the preset performance requirement is not met, generating target parameter information of at least two different parameter values; wherein the target parameter information is attribute information that does not meet the preset performance requirement; generating corresponding multiple simulation analysis models based on the target parameter information of at least two different parameter values; analyzing the multiple simulation analysis models to determine the optimal model parameters corresponding to the CAE model. Compared with the related art, the embodiment of the present disclosure generates multiple simulation analysis models based on the target parameter information to determine the optimal model parameters; based on the solved optimal model parameters, the CAE model can be optimized, and the efficiency of the optimization design can be improved.

[0077] In order to clearly illustrate the embodiments of the present disclosure, the embodiments of the present disclosure provide Figure 2 A flow chart of another method for determining model parameters is shown in FIG. Figure 2 As shown,

[0078] Step 201, creating a corresponding CAE model according to computer-aided design (CAD).

[0079] When designing a mechanical structure, it is usually necessary to create a corresponding CAE model based on the CAD digital model structure for simulation analysis and calculation. Generally, the CAE model is analyzed and calculated using the finite element analysis method.

[0080] Step 202: parse the CAE model to obtain components constituting the simulation target, each component including at least one parameter information.

[0081] When performing simulation analysis, the CAE model is meshed, and at least one component of the simulation target in the CAE model is obtained after simulation analysis calculation. When performing simulation analysis, the simulation target may also be a single component. Each component contains at least one parameter information, such as: mesh, connection, material properties, working conditions and other information.

[0082] Step 203: Based on the preset performance requirement, determine whether at least one parameter information corresponding to each component meets the preset performance requirement.

[0083] The components in the simulation target that do not meet the preset performance requirements are judged according to the preset performance requirements; for example, assuming that the simulation target includes a bolt component, the length and diameter of the bolt may not meet the preset performance requirements. It can be understood that multiple parameter information of a component may not meet the preset performance requirements, or the same parameter information of multiple components may not meet the preset performance requirements.

[0084] Step 204, calling a preset parameter generation algorithm to generate at least two different parameter values ​​for the target parameter information, wherein the preset parameter generation algorithm includes a MeshWorks software algorithm.

[0085] In the disclosed embodiment, the CAE model of the simulation target to be optimized is used with MeshWorks software to generate a series of parameter values ​​of target parameter information of gradients from the parameter information of the component to be optimized.

[0086] Step 205 : Record the target parameter information of the at least two different parameter values ​​in the first model file and the second model file respectively, wherein the first model file and the second model file are in different storage formats.

[0087] According to the model of the simulation target with parameterized settings, a model file (.smw format) with parameterized settings, a parameterized information file (.txt format), and an interface file (.bat format) for calling the parameterized model are output, wherein the interface file for calling the parameterized model is used to realize the control of the model simulation analysis.

[0088] Step 206, calling a preset optimization algorithm to redefine the target parameter information based on the second model file, wherein the preset optimization algorithm includes a HyperStudy software algorithm.

[0089] In the HyperStudy software, the target parameter information is redefined according to the parameterized information file so as to optimize the simulation target model using the HyperStudy software.

[0090] Step 207 , calling the preset parameter generation algorithm and the target parameter information based on at least two different parameter values ​​in the first model file to generate corresponding multiple simulation analysis models.

[0091] When calling MeshWorks software, each component has target parameter information with at least two different parameter values. Different simulation analysis models are generated according to these parameter values ​​and different component arrangements and combinations.

[0092] Step 208 , calling a preset solver, performing optimal solution calculations on the multiple simulation analysis models according to the preset performance requirements, and obtaining optimal model parameters corresponding to the CAE model.

[0093] By using the solver software, MatLab can be used for solving but is not limited to it; all simulation analysis models are solved and calculated to obtain the optimal model parameters corresponding to the CAE model.

[0094] Step 209: adjusting corresponding parameters in the CAD digital model structure according to the optimal model parameters corresponding to the CAE model.

[0095] According to the calculated optimal model parameters, the corresponding parameters in the CAD digital model structure are adjusted, which can simplify the design process and shorten the optimization cycle.

[0096] In order to clearly illustrate the embodiments of the present disclosure, the embodiments of the present disclosure provide Figure 3 A flow chart of another method for determining model parameters is shown in FIG. Figure 3 As shown,

[0097] S1. Create a CAE simulation model and submit the calculation.

[0098] S2. Import the basic model to be optimized into MeshWorks software for parametric design. Output the model file with parametric settings (.smw format), parametric information file (.txt format), and interface file (.bat format) for calling the parametric model.

[0099] S3. Open the HyperStudy software, import the parameterized information file to define the design variables, and save it as a solution file in .tpl format.

[0100] S4. Import the solution file into the HyperStudy software to create an optimization model, call the MeshWorks software to generate an analysis model of the optimization solution, and then call the solver software to calculate the generated analysis model.

[0101] S5. Find the optimal solution according to the set optimization algorithm.

[0102] S6. Adjust the CAD digital model structure according to the final optimization result.

[0103] For the description of S1-S6, please refer to the above embodiment, and the embodiments of the present disclosure will not be described one by one.

[0104] Corresponding to the above-mentioned method for determining model parameters, the present invention also proposes a device for determining model parameters. Since the device embodiment of the present invention corresponds to the above-mentioned method embodiment, details not disclosed in the device embodiment can be referred to the above-mentioned method embodiment, and will not be repeated in the present invention.

[0105] Figure 3 A schematic diagram of a structure of a device for determining model parameters provided by an embodiment of the present disclosure, such as Figure 3 As shown, including:

[0106] An acquisition unit 31 is used to acquire a computer-aided engineering (CAE) model corresponding to a simulation target, wherein the CAE model includes at least one parameter information of the simulation target;

[0107] A judging unit 32, configured to judge whether the at least one parameter information meets a preset performance requirement;

[0108] A first generating unit 33 is configured to generate target parameter information of at least two different parameter values ​​when the at least one parameter information does not meet the preset performance requirement; wherein the target parameter information is attribute information that does not meet the preset performance requirement;

[0109] A second generating unit 34 is used to generate a corresponding plurality of simulation analysis models according to the target parameter information of the at least two different parameter values;

[0110] The analysis unit 35 is used to analyze the multiple simulation analysis models to determine the optimal model parameters corresponding to the CAE model.

[0111] Furthermore, in a possible implementation of this embodiment, as Figure 4 As shown, the first generating unit 33 includes:

[0112] A first generating module 331 is used to call a preset parameter generating algorithm to generate at least two different parameter values ​​for the target parameter information, wherein the preset parameter generating algorithm includes a MeshWorks software algorithm;

[0113] The storage module 332 is used to record the target parameter information of the at least two different parameter values ​​in the first model file and the second model file respectively, wherein the first model file and the second model file are in different storage formats.

[0114] Furthermore, in a possible implementation of this embodiment, as Figure 4 As shown, the second generating unit 34 includes:

[0115] A definition module 341, used to call a preset optimization algorithm and redefine the target parameter information based on the second model file, wherein the preset optimization algorithm includes a HyperStudy software algorithm;

[0116] The second generating module 342 is used to call the preset parameter generating algorithm and generate corresponding multiple simulation analysis models based on the target parameter information of at least two different parameter values ​​in the first model file.

[0117] Furthermore, in a possible implementation of this embodiment, as Figure 4 As shown, the judging unit 32 includes:

[0118] The parsing module 321 is used to parse the CAE model to obtain components constituting the simulation target, each component including at least one parameter information;

[0119] The judgment module 322 is used to judge whether at least one parameter information corresponding to each component meets the preset performance requirement based on the preset performance requirement.

[0120] Furthermore, in a possible implementation of this embodiment, the analysis unit 35 is further configured to:

[0121] The preset solver is called to calculate the optimal solution for the multiple simulation analysis models according to the preset performance requirements, so as to obtain the optimal model parameters corresponding to the CAE model.

[0122] Furthermore, in a possible implementation of this embodiment, as Figure 4 As shown, the device also includes:

[0123] The creation unit 36 ​​is used to create a corresponding CAE model according to the computer-aided design (CAD) model before obtaining the computer-aided engineering (CAE) model corresponding to the simulation target.

[0124] Furthermore, in a possible implementation of this embodiment, as Figure 4 As shown, the device also includes:

[0125] The adjustment unit 37 is used to adjust the corresponding parameters in the CAD digital model structure according to the optimal model parameters corresponding to the CAE model after analyzing the multiple simulation analysis models to determine the optimal model parameters corresponding to the CAE model.

[0126] It should be noted that the above explanation of the method embodiment is also applicable to the device of this embodiment, and the principle is the same, which is not limited in this embodiment.

[0127] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0128] Figure 5A schematic block diagram of an example electronic device 400 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0129] like Figure 5 As shown, the device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a ROM (Read-Only Memory) 402 or a computer program loaded from a storage unit 408 to a RAM (Random Access Memory) 403. In the RAM 403, various programs and data required for the operation of the device 400 can also be stored. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An I / O (Input / Output) interface 405 is also connected to the bus 404.

[0130] A number of components in the device 400 are connected to the I / O interface 405, including: an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a disk, an optical disk, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows the device 400 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0131] The computing unit 401 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a CPU (Central Processing Unit), a GPU (Graphic Processing Units), various dedicated AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, a DSP (Digital Signal Processor), and any appropriate processor, controller, microcontroller, etc. The computing unit 401 performs the various methods and processes described above, such as a method for determining model parameters. For example, in some embodiments, the method for determining model parameters may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 400 via the ROM 402 and / or the communication unit 409. When the computer program is loaded into the RAM 403 and executed by the computing unit 401, one or more steps of the method described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to execute the aforementioned method for determining the model parameters in any other appropriate manner (for example, by means of firmware).

[0132] Various embodiments of the systems and techniques described above herein may be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application Specific Standard Products), SOCs (System On Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs that may be executed and / or interpreted on a programmable system including at least one programmable processor that may be a dedicated or general-purpose programmable processor that may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0133] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0134] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include electrical connections based on one or more lines, portable computer disks, hard disks, RAM, ROM, EPROM (Electrically Programmable Read-Only-Memory) or flash memory, optical fiber, CD-ROM (Compact Disc Read-Only Memory), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0135] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball), through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0136] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: LAN (Local Area Network), WAN (Wide Area Network), the Internet, and blockchain networks.

[0137] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server may also be a server of a distributed system, or a server combined with a blockchain.

[0138] It should be noted that artificial intelligence is a discipline that studies how computers can simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.), and includes both hardware-level and software-level technologies. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, and big data processing; artificial intelligence software technologies mainly include computer vision technology, speech recognition technology, natural language processing technology, as well as machine learning / deep learning, big data processing technology, knowledge graph technology, and other major directions.

[0139] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0140] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for determining model parameters, characterized in that: include: Acquire a computer-aided engineering (CAE) model corresponding to a simulation target, wherein the CAE model includes at least one parameter information of the simulation target; Determining whether the at least one parameter information meets a preset performance requirement; If the preset performance requirement is not met, generating at least two target parameter information with different parameter values; wherein the target parameter information is the attribute information that does not meet the preset performance requirement; Generate a plurality of corresponding simulation analysis models according to the target parameter information of the at least two different parameter values; The multiple simulation analysis models are analyzed to determine optimal model parameters corresponding to the CAE model.

2. The method according to claim 1, characterized in that The target parameter information for generating at least two different parameter values ​​includes: Calling a preset parameter generation algorithm to generate at least two different parameter values ​​for the target parameter information, wherein the preset parameter generation algorithm includes a MeshWorks software algorithm; The target parameter information of the at least two different parameter values ​​are recorded in a first model file and a second model file respectively, wherein the first model file and the second model file are in different storage formats.

3. The method according to claim 2, further characterized in that: The generating corresponding multiple simulation analysis models according to the target parameter information of the at least two different parameter values ​​comprises: Calling a preset optimization algorithm to redefine the target parameter information based on the second model file, wherein the preset optimization algorithm includes a HyperStudy software algorithm; The preset parameter generation algorithm is called and target parameter information based on at least two different parameter values ​​in the first model file is used to generate corresponding multiple simulation analysis models.

4. The method according to claim 3, characterized in that The determining whether the at least one parameter information meets the preset performance requirement includes: Parsing the CAE model to obtain components constituting the simulation target, each component including at least one parameter information; Based on the preset performance requirement, it is determined whether at least one parameter information corresponding to each component meets the preset performance requirement.

5. The method according to claim 4, characterized in that The analyzing the multiple simulation analysis models to determine the optimal model parameters corresponding to the CAE model comprises: The preset solver is called to calculate the optimal solution for the multiple simulation analysis models according to the preset performance requirements, so as to obtain the optimal model parameters corresponding to the CAE model.

6. The method according to any one of claims 1 to 5, characterized in that Before obtaining the computer-aided engineering (CAE) model corresponding to the simulation target, the method further includes: Create the corresponding CAE model based on computer-aided design CAD.

7. The method according to claim 6, characterized in that After analyzing the multiple simulation analysis models to determine the optimal model parameters corresponding to the CAE model, the method further includes: According to the optimal model parameters corresponding to the CAE model, the corresponding parameters in the CAD digital model structure are adjusted.

8. A device for determining model parameters, characterized in that: include: An acquisition unit, configured to acquire a computer-aided engineering (CAE) model corresponding to a simulation target, wherein the CAE model includes at least one parameter information of the simulation target; A judging unit, configured to judge whether the at least one parameter information meets a preset performance requirement; A first generating unit, configured to generate target parameter information of at least two different parameter values ​​when the at least one parameter information does not meet the preset performance requirement; wherein the target parameter information is attribute information that does not meet the preset performance requirement; A second generating unit, configured to generate a plurality of corresponding simulation analysis models according to the target parameter information of the at least two different parameter values; The analysis unit is used to analyze the multiple simulation analysis models to determine the optimal model parameters corresponding to the CAE model.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-7.

11. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 7.

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