Digital twin model parameter optimization method, device, equipment and storage medium

Abstract

The present invention discloses a method, device, equipment and storage medium for optimizing parameters of a digital twin model, and relates to the field of digital twin models. The method comprises: constructing a structural model; obtaining an initial structural parameter model according to the structural model; extracting and operating the initial structural parameter model to obtain operating parameters; performing maturity evaluation on the operating parameters to obtain evaluation results; and obtaining a final structural parameter model according to the evaluation results. By performing maturity evaluation on the generated model to obtain the final structural parameter model, a model that better meets user needs can be established, making parameter optimization more accurate, applicable to various occasions, and saving costs.

Description

Technical Field

[0001] The present invention relates to the field of digital twin models, and in particular to a method, device, equipment and storage medium for optimizing digital twin model parameters. Background Art

[0002] The existing parameter optimization methods for the conceptual models of electromechanical products such as screw conveyors have initially developed a relatively complete parameter design process. However, in the existing technology, once a problem occurs in the established model, the parameters need to be redesigned and simulated, which greatly increases the cost of digital twin production. Summary of the invention

[0003] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention proposes a digital twin model parameter optimization method, device, equipment and storage medium, which can evaluate the maturity of the parameter model, make the parameter optimization more accurate, suitable for various occasions, and save costs.

[0004] A method for optimizing parameters of a digital twin model according to an embodiment of the first aspect of the present invention. The method comprises:

[0005] Constructing structural models;

[0006] Obtaining an initial structural parameter model according to the structural model;

[0007] Extracting and running the initial structural parameter model to obtain operating parameters;

[0008] Performing maturity evaluation on the operating parameters to obtain evaluation results;

[0009] A final structural parameter model is obtained according to the evaluation results.

[0010] The digital twin model parameter optimization method according to the embodiment of the present invention has at least the following technical effects: the method performs the following steps: constructing a structural model; obtaining an initial structural parameter model according to the structural model; extracting and running the initial structural parameter model to obtain operating parameters; performing maturity evaluation on the operating parameters to obtain evaluation results; and obtaining a final structural parameter model according to the evaluation results. By performing maturity evaluation on the generated model to obtain the final structural parameter model, a model that better meets user needs can be established, making parameter optimization more accurate, applicable to various occasions, and saving costs.

[0011] According to the digital twin model parameter optimization method of an embodiment of the present invention, constructing the structural model includes: constructing the structural model using three-dimensional modeling software.

[0012] According to the digital twin model parameter optimization method of the embodiment of the present invention, before performing maturity evaluation on the operating parameters to obtain the evaluation results, the method further includes:

[0013] Obtain demand analysis parameters;

[0014] A control evaluation parameter is obtained according to the demand analysis parameter.

[0015] According to the digital twin model parameter optimization method of an embodiment of the present invention, the demand analysis parameters include dynamic control parameters and dynamic analysis parameters, the control evaluation parameters include system control parameters and dynamic evaluation index parameters, and the control evaluation parameters obtained according to the demand analysis parameters include:

[0016] The system control parameters are obtained according to the kinetic control parameters, and the kinetic evaluation index parameters are obtained according to the kinetic analysis parameters.

[0017] According to the digital twin model parameter optimization method of an embodiment of the present invention, the step of performing maturity evaluation on the operating parameters to obtain an evaluation result includes:

[0018] According to the system control parameters and the dynamics evaluation index parameters, the maturity evaluation is performed on the operating parameters to obtain the evaluation results.

[0019] According to the digital twin model parameter optimization method of an embodiment of the present invention, obtaining the final structural parameter model according to the evaluation result includes:

[0020] comparing the evaluation result with a preset maturity evaluation threshold;

[0021] If the evaluation result is less than the preset maturity evaluation threshold, the evaluation result is fed back to the initial structural parameter model to re-evaluate the maturity;

[0022] If the evaluation result is not less than the preset maturity evaluation threshold, the initial structural parameter model corresponding to the evaluation result is determined as the final structural parameter model.

[0023] According to the digital twin model parameter optimization method of an embodiment of the present invention, extracting and running the initial structural parameter model to obtain operating parameters includes: extracting and running the initial structural parameter model based on Labview software to obtain operating parameters.

[0024] The digital twin model parameter optimization device of the second embodiment of the present invention includes:

[0025] Unit 1 is used to build a structural model;

[0026] The second unit is used to obtain an initial structural parameter model according to the structural model

[0027] The third unit is used for extracting and running the initial structural parameter model to obtain operating parameters;

[0028] The fourth unit is used to perform maturity evaluation on the operating parameters to obtain evaluation results.

[0029] According to the digital twin model parameter optimization device of the embodiment of the present invention, there are at least the following technical effects: the device executes the method of the first aspect embodiment, including: constructing a structural model; obtaining an initial structural parameter model according to the structural model; extracting and running the initial structural parameter model to obtain operating parameters; performing maturity evaluation on the operating parameters to obtain evaluation results; and obtaining a final structural parameter model according to the evaluation results. By performing maturity evaluation on the generated model to obtain the final structural parameter model, a model that better meets user needs can be established, making parameter optimization more accurate, applicable to various occasions, and saving costs.

[0030] The device of the embodiment of the third aspect of the present invention also includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that when the processor executes the computer program, the digital twin model parameter optimization method described in any one of the first aspects is implemented.

[0031] The computer-readable storage medium of the fourth aspect of the embodiment of the present invention stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute any one of the digital twin model parameter optimization methods described in the first aspect.

[0032] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0034] Figure 1 Schematic diagram of the process of the digital twin model parameter optimization method according to an embodiment of the present invention;

[0035] Figure 2 A schematic flow chart of a method for optimizing parameters of a digital twin model according to another embodiment of the present invention;

[0036] Figure 3 yes Figure 2 Schematic diagram of the sub-step flow of step S220;

[0037] Figure 4 yes Figure 1 Schematic diagram of the sub-step flow chart of step S140;

[0038] Figure 5 yes Figure 1 Schematic diagram of the sub-step flow chart of step S150;

[0039] Figure 6 is a structural schematic diagram of a digital twin model parameter optimization device according to an embodiment of the present invention;

[0040] Figure 7 It is a schematic diagram of the structure of a device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0042] In the embodiments of the present invention, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B may be singular or plural. The text " / " generally indicates that the previous and subsequent associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, c can be single or multiple.

[0043] At present, the production and simulation of digital twin models of electromechanical products has become one of the key technologies driving the development of intelligent manufacturing. Compared with traditional electromechanical product manufacturing, the design method of digital twin structural parameter models is more efficient and has better product quality, but there is still a problem of lack of intelligence, and the model parameter design process still needs to be optimized. In the related art, the design of digital twin parameter models lacks a feedback process, and often only establishes a structural parameter model of electromechanical products according to market demand, and does not evaluate the constructed parameter model, resulting in the constructed model being unusable. Based on this, the present invention proposes a digital twin model parameter optimization method, device, equipment and storage medium, which can evaluate the maturity of the parameter model, so that the parameter optimization is more accurate, suitable for various occasions, and saves costs.

[0044] refer to Figure 1 , Figure 1A schematic flow chart of a digital twin model parameter optimization method provided in an embodiment of the present invention is shown, and the method includes but is not limited to step S110, step S120, step S130, step S140 and step S150.

[0045] Step S110, constructing a structural model.

[0046] In one embodiment, constructing a structural model includes: constructing a structural model using a three-dimensional modeling software, which may be SolidWorks software or Unigraphics NX software (UG software for short). It should be noted that SolidWorks software is powerful and has many components. It can provide different design solutions, reduce errors in the design process, and improve product quality. It is a mainstream three-dimensional modeling software. UG software provides a practically verified solution for the user's virtual product design and process design needs, as well as meeting various industrialization needs. It can realize the construction of various complex entities and shapes, and is also a mainstream three-dimensional modeling software. The above-mentioned mainstream three-dimensional modeling software is simple and easy to learn, and is convenient for quickly creating models.

[0047] Step S120, obtaining an initial structural parameter model according to the structural model.

[0048] It can be understood that, according to the above-constructed structural model, the corresponding initial parameters are set for each component in the structural model to obtain the initial structural parameter model. Exemplarily, SolidWorks is used to construct the structural model of the screw rod of the electromechanical product, and the parameters of each component in the screw rod structural model are set, which are rod length L = 8000mm (screw length 7200mm), screw conveyor spiral blade outer diameter D = 460mm, screw shaft diameter d = 0.3D = 140mm, screw conveyor pitch length P = 0.8D = 360mm, shaft screw conveyor spiral blade thickness δ = 0.125P = 45mm, spiral hollow shaft inner diameter d1 = 100mm, steel pipe thickness d1 = 40mm, and spiral barrel length 6800mm. After the above settings, the initial structural parameter model of the screw rod is established, which provides convenience for subsequent operations.

[0049] Step S130, extracting the initial operating structural parameter model to obtain operating parameters.

[0050] In one implementation, extracting the initial structural parameter model of operation to obtain the operation parameters includes extracting the initial structural parameter model of operation based on Labview software to obtain the operation parameters. It should be noted that Solidworks is provided with an interface, and the initial parameter model constructed by the Solidworks software is imported into the Labview environment through the interface, and the operation parameters are obtained by extracting the initial structural parameter model of operation based on the Labview software. Labview is a graphical programming language that uses icons instead of text lines to create applications, and has a huge function library for completing programming tasks, and the function library includes data acquisition, serial port control, data analysis, data display and data storage, etc. The operation parameters obtained by the above software can be adjusted to build a usable model.

[0051] Step S140, performing maturity evaluation on the operating parameters to obtain evaluation results.

[0052] In one embodiment, before performing maturity evaluation on the operating parameters to obtain the evaluation results, refer to Figure 2 The above method also includes but is not limited to the following steps:

[0053] Step S210, obtaining demand analysis parameters.

[0054] It can be understood that the demand analysis parameters are parameters obtained by analyzing user or market demand, and the demand parameters under different circumstances are obtained by analyzing according to customer or market demand. For example, in different circumstances, the requirements for operating speed, power, conveying volume, conveying soil type, etc. in the screw conveyor are different. In terms of speed and power, if the speed or power is too large, it will cause serious wear of the conveyor, greatly increasing the maintenance cost; in terms of conveying soil, there are many kinds of soil in reality, and different parameters need to be modified for different conveying requirements. When there are different circumstances such as large or small particles in the conveyed soil, it is necessary to change the pitch of the screw rod in the screw conveyor to facilitate the transportation of such objects; when the length of the channel to be transported is different, the length of the screw rod of the screw conveyor must also be changed. According to different customer or market demands, different demand analysis parameters are obtained. Therefore, the model constructed according to the demand analysis parameters can increase the availability of the model.

[0055] Step S220, obtaining control evaluation parameters according to the demand analysis parameters.

[0056] In one embodiment, the demand analysis parameters include dynamic control parameters and dynamic analysis parameters, and the control evaluation parameters include system control parameters and dynamic evaluation index parameters. Figure 3 , obtaining control evaluation parameters according to demand analysis parameters includes but is not limited to step S221.

[0057] Step S221, obtaining system control parameters according to the dynamics control parameters, and obtaining dynamics evaluation index parameters according to the dynamics analysis parameters.

[0058] It should be noted that ADAMS software (Automatic Dynamic Analysis of Mechanical Systems) is used to analyze user or market needs. ADAMS software has multiple interfaces. The constructed model is imported into ADAMS software for dynamic simulation. After analyzing the motion parameters, dynamic evaluation index parameters and system control parameters are generated.

[0059] It should also be noted that, on the one hand, ADAMS software is an application software for virtual prototype analysis, and users can use the software to perform statics, kinematics and dynamics analysis on virtual mechanical systems; on the other hand, it is also a virtual prototype analysis development tool, and its open program structure and multiple interfaces can become a secondary development tool platform for users in special industries to perform special types of virtual prototype analysis. Therefore, using ADAMS software can very conveniently perform dynamic analysis and dynamic simulation according to needs, obtain system control parameters and dynamic evaluation index parameters, and make the constructed model usable.

[0060] refer to Figure 4 , performing maturity evaluation on the operating parameters to obtain evaluation results includes but is not limited to step S141.

[0061] Step S141, performing maturity evaluation on the operating parameters according to the system control parameters and the dynamics evaluation index parameters to obtain evaluation results.

[0062] It can be understood that, on the one hand, the maturity of the model can be evaluated directly through the initial parameter model and the operating parameters obtained by extracting and running the initial structural parameter model based on Labview software to obtain evaluation results; on the other hand, the initial structural parameter model can be dynamically simulated and adjusted according to the system control parameters, and then the operating parameters obtained by running the initial structural parameter model are extracted, and then the maturity of the model is evaluated to obtain evaluation results; on the other hand, the initial structural parameter model is dynamically simulated and adjusted according to the system control parameters, and then the operating parameters obtained by running the initial structural parameter model are extracted, and the maturity of the model is evaluated in combination with the dynamic evaluation index parameters obtained according to the dynamic analysis parameters to obtain evaluation results, so as to prepare for the subsequent judgment of whether the constructed model meets the requirements.

[0063] Step S150, obtaining a final structural parameter model according to the evaluation results.

[0064] refer to Figure 5 , Figure 5The overall flow chart of the digital twin model parameter optimization method provided by the embodiment of the present invention is shown. The final structural parameter model is obtained according to the evaluation results, including but not limited to the following steps:

[0065] Step S151, comparing the evaluation result with a preset maturity evaluation threshold;

[0066] Step S152, if the evaluation result is less than the preset maturity evaluation threshold, the evaluation result is fed back to the initial structural parameter model to re-evaluate the maturity;

[0067] Step S153: if the evaluation result is not less than the preset maturity evaluation threshold, the initial structural parameter model corresponding to the evaluation result is determined as the final structural parameter model.

[0068] It can be understood that when the evaluation result of a run is less than the preset maturity evaluation threshold, the evaluation result is fed back to the initial structural parameter model for re-maturity evaluation until the evaluation result is not less than the preset maturity evaluation threshold, and the final structural parameter model is obtained; when the evaluation result of the initial structural parameter model constructed by a run is not less than the preset maturity evaluation threshold, the initial structural parameter model is the final structural parameter model. Through the above steps, a structural parameter model that meets the requirements can be obtained, making parameter optimization more accurate, applicable to various occasions, and saving costs.

[0069] It should be noted that the evaluation result is fed back to the initial structural parameter model for re-maturity evaluation until the evaluation result is not less than the preset maturity evaluation threshold. This process is not an infinite loop process. If the final structural parameter model is not obtained after a certain number of cycles, a prompt is issued for human intervention. The maximum number of cycles can be set to 50 times, or other values, which can be set according to needs. The preset maturity evaluation threshold is a value with reference data, which is set according to needs and can be changed for easy adjustment.

[0070] refer to Figure 6 , Figure 6 The digital twin model parameter optimization device 600 provided by the embodiment of the present invention is shown. The device executes the above-mentioned digital twin model parameter optimization method, and the first unit constructs a structural model; the second unit obtains an initial structural parameter model according to the structural model; the third unit extracts and runs the initial structural parameter model to obtain operating parameters; the fourth unit performs maturity evaluation on the operating parameters to obtain evaluation results, and obtains the final structural parameter model according to the evaluation results. By performing maturity evaluation on the generated model to obtain the final structural parameter model, a model that better meets user needs can be established, making parameter optimization more accurate, applicable to various occasions, and saving costs.

[0071] Figure 7The device 700 provided in the embodiment of the present invention is shown. The device 700 may be a server or a terminal, and the internal structure of the device 700 includes but is not limited to:

[0072] A memory 710, used for storing programs;

[0073] The processor 720 is used to execute the program stored in the memory 710. When the processor 720 executes the program stored in the memory 710, the processor 720 is used to execute the above-mentioned digital twin model parameter optimization method.

[0074] The processor 720 and the memory 710 may be connected via a bus or in other ways.

[0075] The memory 710 is a non-transient computer-readable storage medium that can be used to store non-transient software programs and non-transient computer executable programs, such as the digital twin model parameter optimization method described in any embodiment of the present invention. The processor 720 implements the above-mentioned digital twin model parameter optimization method by running the non-transient software program and instructions stored in the memory 710.

[0076] The memory 710 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function; the data storage area may store and execute the above-mentioned digital twin model parameter optimization method. In addition, the memory 710 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory 710 may optionally include a memory remotely disposed relative to the processor 720, and these remote memories may be connected to the processor 720 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0077] The non-transient software programs and instructions required to implement the above-mentioned digital twin model parameter optimization method are stored in the memory 710. When executed by one or more processors 720, the digital twin model parameter optimization method provided by any embodiment of the present invention is executed.

[0078] An embodiment of the present invention also provides a computer-readable storage medium storing computer-executable instructions, which are used to execute the above-mentioned digital twin model parameter optimization method.

[0079] In one embodiment, the storage medium stores computer executable instructions, which are executed by one or more control processors 720, for example, by a processor 720 in the above-mentioned device 700, so that the above-mentioned one or more processors 720 can execute the digital twin model parameter optimization method provided by any embodiment of the present invention.

[0080] The above described embodiments are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0081] It will be appreciated by those skilled in the art that all or some of the steps and systems in the disclosed method above may be implemented as software, firmware, hardware and appropriate combinations thereof. Some physical components or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor or a microprocessor, or may be implemented as hardware, or may be implemented as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or a non-transitory medium) and a communication medium (or a temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that may be used to store desired information and may be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically include computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0082] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the above implementation. Those skilled in the art can make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present invention.

Claims

1. A digital twin model parameter optimization method, characterized in that: include: Constructing structural models; Obtaining an initial structural parameter model according to the structural model; Extract and run the initial structural parameter model based on Labview software to obtain operating parameters; Performing maturity evaluation on the operating parameters to obtain evaluation results; Obtaining a final structural parameter model according to the evaluation results; Before performing maturity evaluation on the operating parameters to obtain evaluation results, the method further includes: Acquiring demand analysis parameters, wherein the demand analysis parameters include dynamic control parameters and dynamic analysis parameters; Obtaining system control parameters according to the dynamic control parameters; The kinetic evaluation index parameters are obtained according to the kinetic analysis parameters.

2. The digital twin model parameter optimization method according to claim 1, characterized in that: The constructing of the structural model comprises: constructing the structural model by using three-dimensional modeling software.

3. The digital twin model parameter optimization method according to claim 1, characterized in that: The step of performing maturity evaluation on the operating parameters to obtain an evaluation result includes: According to the system control parameters and the dynamics evaluation index parameters, the maturity evaluation is performed on the operating parameters to obtain the evaluation results.

4. The digital twin model parameter optimization method according to claim 1, characterized in that: The step of obtaining a final structural parameter model according to the evaluation results comprises: comparing the evaluation result with a preset maturity evaluation threshold; If the evaluation result is less than the preset maturity evaluation threshold, the evaluation result is fed back to the initial structural parameter model to re-evaluate the maturity; If the evaluation result is not less than the preset maturity evaluation threshold, the initial structural parameter model corresponding to the evaluation result is determined as the final structural parameter model.

5. A digital twin model parameter optimization device, characterized in that: Also includes: Unit 1 is used to build a structural model; The second unit is used to obtain an initial structural parameter model according to the structural model; The third unit is used for extracting and running the initial structural parameter model based on Labview software to obtain operating parameters; The fourth unit is configured to perform maturity evaluation on the operating parameters to obtain an evaluation result, wherein before performing maturity evaluation on the operating parameters to obtain the evaluation result, the method further includes: Acquiring demand analysis parameters, wherein the demand analysis parameters include dynamic control parameters and dynamic analysis parameters; Obtaining system control parameters according to the dynamic control parameters; The kinetic evaluation index parameters are obtained according to the kinetic analysis parameters.

6. A device, characterized in that: The device includes a memory and a processor, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by one or more of the processors, the one or more processors execute the digital twin model parameter optimization method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that: The storage medium can be read and written by a processor, and the storage medium stores computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the digital twin model parameter optimization method as described in any one of claims 1 to 4.

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