CN112668223B - Electromechanical equipment simulation method and system based on digital twin lightweight model - Google Patents
Electromechanical equipment simulation method and system based on digital twin lightweight model Download PDFInfo
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Abstract
The disclosure provides an electromechanical equipment simulation method and system based on a digital twin lightweight model, which are used for acquiring performance state data of parts of electromechanical equipment; when the performance state of the part is changed, recognizing the performance attenuation position and the attenuation amount by using the sensing data, and updating the performance attenuation of the electromechanical equipment part model through finite element analysis; replacing the original model with the updated part model to complete the preliminary update of the electromechanical equipment digital twin model and obtain a consistent electromechanical equipment digital twin model; aiming at different application services of the electromechanical equipment, obtaining the requirement guided by the application service through application service requirement analysis, and guiding the self-configuration and self-optimization of a digital twin model of the electromechanical equipment according to the requirement to obtain a lightweight model guided by the application service; the method and the device have the advantages that the lightweight model of the electromechanical equipment, which takes application as guidance, is obtained, and the solving efficiency is improved on the basis of ensuring the precision.
Description
Technical Field
The disclosure relates to the technical field of intellectualization and digitization of electromechanical equipment, in particular to an electromechanical equipment simulation method and system based on a digital twin lightweight model, and specifically relates to a model optimization method for reconfiguring and reducing orders of the digital twin model of the electromechanical equipment based on a self-configuration and self-optimization technology.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
With the development of CPS (Cyber-Physical Systems) technology, Digital Twin (Digital Twin) technology is becoming a hot spot of academic research. The digital twin technology fully utilizes data such as a physical model, sensor updating, operation history and the like, integrates a multidisciplinary, multi-physical quantity, multi-scale and multi-probability simulation process, and completes mapping in a virtual space, so that corresponding entity equipment is truly reflected. That is, the digital twin is a digital copy of various physical assets, processes, people, locations, systems, and devices. This fundamentally ensures the accuracy of the digital twin model.
With the continuous development of the technology, the design of electromechanical equipment becomes more and more complex, the digitization degree is higher and higher, and the requirement on the upgrading and upgrading period is shorter and shorter.
However, the inventor finds that to accurately model a complete engineering system, a very huge computing model is generally required, which requires a large amount of computing resources to evaluate, and in many electromechanical equipment application services, a digital twin is required to provide near real-time insight so as to effectively use the model for operation decision, which requires that a lightweight model guided by the application service can be rapidly acquired according to different application scenes of the electromechanical equipment, and model simulation analysis is rapidly performed to provide analysis and prediction services, whereas most of the existing lightweight model acquisition methods for electromechanical equipment have long solution time and insufficient real-time performance, which hinders the development of the digital twin.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides an electromechanical equipment simulation method and system based on a digital twin lightweight model, the lightweight model of the electromechanical equipment taking application as guidance is obtained, and the solving efficiency is improved on the basis of ensuring the precision.
In order to achieve the purpose, the following technical scheme is adopted in the disclosure:
the disclosure provides an electromechanical equipment simulation method based on a digital twin lightweight model.
A simulation method of electromechanical equipment based on a digital twin lightweight model comprises the following steps:
acquiring performance state data of parts of the electromechanical equipment;
when the performance state of the part changes, recognizing the performance attenuation position and the attenuation quantity by using the sensing data, and updating the performance attenuation of the electromechanical equipment part model through finite element analysis;
replacing the original model with the updated part model to complete the preliminary update of the electromechanical equipment digital twin model and obtain a consistent electromechanical equipment digital twin model;
aiming at different application services of the electromechanical equipment, the requirements guided by the application services are obtained through application service requirement analysis, the self-configuration and self-optimization of a digital twin model of the electromechanical equipment are guided according to the requirements, a lightweight model guided by the application services is obtained, and the working process simulation of the electromechanical equipment is carried out according to the obtained lightweight model.
A second aspect of the present disclosure provides a digital twinning-based electromechanical equipment lightweight model acquisition system.
A digital twinning-based electromechanical equipment lightweight model acquisition system, comprising:
a data acquisition module configured to: acquiring performance state data of parts of the electromechanical equipment;
a parts model update module configured to: when the performance state of the part is changed, recognizing the performance attenuation position and the attenuation amount by using the sensing data, and updating the performance attenuation of the electromechanical equipment part model through finite element analysis;
a digital twin module acquisition module configured to: replacing the original model with the updated part model to complete the preliminary update of the electromechanical equipment digital twin model and obtain a consistent electromechanical equipment digital twin model;
a lightweight model simulation module configured to: aiming at different application services of the electromechanical equipment, the requirements guided by the application services are obtained through application service requirement analysis, the self-configuration and self-optimization of a digital twin model of the electromechanical equipment are guided according to the requirements, a lightweight model guided by the application services is obtained, and the working process simulation of the electromechanical equipment is carried out according to the obtained lightweight model.
A third aspect of the present disclosure provides a computer-readable storage medium having stored thereon a program which, when executed by a processor, implements the steps in a method of simulating electromechanical equipment based on a digital twin weight model according to the first aspect of the present disclosure.
A fourth aspect of the present disclosure provides an electronic device comprising a memory, a processor and a program stored on the memory and executable on the processor, the processor implementing the steps in the method for simulating an electromechanical equipment based on a digital twinning lightweight model according to the first aspect of the present disclosure when executing the program.
Compared with the prior art, the beneficial effect of this disclosure is:
1. the method, the system, the medium or the electronic equipment provided by the disclosure have the advantages that the application-oriented lightweight model of the electromechanical equipment is obtained, the solving efficiency is improved on the basis of ensuring the precision, the computing resources (a computer, a server and the like) can be greatly saved, and the improvement of the efficiency of the application based on the digital twin model can be promoted.
2. According to the method, the system, the medium or the electronic equipment, the lightweight model taking application as guidance is obtained, and the obstacles that the real-time performance is insufficient and the development of the digital twin is hindered due to long solving time can be further weakened.
Advantages of additional aspects of the disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the disclosure.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
Fig. 1 is a block diagram of an electromechanical equipment simulation method based on a digital twin lightweight model and based on a digital twin according to embodiment 1 of the present disclosure.
Fig. 2 is a schematic diagram of an application-oriented electromechanical equipment model self-configuration strategy provided in embodiment 1 of the present disclosure.
Fig. 3 is a schematic diagram of model self-configuration guided by an electromechanical equipment life prediction application service provided in embodiment 1 of the present disclosure.
Fig. 4 is a self-optimization block diagram of an electromechanical equipment model based on application as a guide provided in embodiment 1 of the present disclosure.
Fig. 5 is a schematic diagram of an adaptive error limit obtaining method of an application-oriented electromechanical equipment model self-optimization strategy according to embodiment 1 of the present disclosure.
Fig. 6 is an implementation flow of self-optimization of an electromechanical equipment model based on application as a guide according to embodiment 1 of the present disclosure.
Detailed Description
The present disclosure is further described with reference to the following drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
Example 1:
as shown in fig. 1, an embodiment 1 of the present disclosure provides a method for simulating an electromechanical device based on a digital twinning lightweight model, which includes different application scenario analyses, a consistent electromechanical device digital twinning model, and application-oriented model self-optimization and self-configuration;
different application scene analysis provides application service guidance for self-optimization and self-configuration of the model; the consistency electromechanical equipment digital twin model provides a high-fidelity model foundation for obtaining the lightweight model.
As shown in fig. 1, there are three ways to obtain a lightweight model:
(1) obtaining a reduced-order model taking application as guidance, namely a reduced-order lightweight model, through a model self-optimization scheme;
(2) obtaining a configuration model taking application as guidance, namely a lightweight model with reduced model scale, through a model self-configuration strategy;
(3) and obtaining the lightweight model with reduced scale and reduced order of the model by taking application as a guide through a model self-configuration strategy and a model self-optimization scheme.
Specifically, the method for acquiring the digital twin model of the consistent electromechanical equipment comprises the following steps:
firstly, performing sensing monitoring on the performance state change of the parts of the numerical control machine tool by analyzing state data, if the state change is monitored, identifying performance attenuation positions, attenuation amounts and the like by using the sensing data, and realizing model performance attenuation updating of the parts of the numerical control machine tool by finite element analysis;
and then replacing the original model with the updated part model to finish the primary update of the numerical control machine tool digital twin model, then carrying out consistency verification on the model, and carrying out model consistency correction if necessary so as to improve the virtual and real consistency of the numerical control machine tool digital twin model.
And (3) application scene analysis, namely aiming at different application services of the electromechanical equipment, combing the requirements guided by the application services through application service requirement analysis to guide the self-configuration and self-optimization of the model, further obtaining a lightweight model guided by the application services, and guiding the production services according to the obtained lightweight model.
As shown in fig. 2, the application-oriented model self-configuration mainly includes three parts: model decomposition strategy, model self-configuration strategy and meta-model interface design.
The model decomposition strategy mainly comprises a model decomposition criterion taking application as guidance, a model decomposition method, model decomposition evaluation and the like.
(1) The model decomposition criterion is obtained by using application service as a guide for demand analysis guidance and is used for guiding the model decomposition;
(2) under the guidance of the decomposition criterion, performing model decomposition on the electromechanical equipment by using methods such as fuzzy clustering and the like to obtain an electromechanical equipment decomposition scheme which takes application service requirements as guidance;
(3) if multiple decomposition schemes exist, evaluating and quantifying each scheme through a TOPSIS algorithm, and selecting an optimal decomposition scheme to guide the design of a meta-model interface and the formulation of a model self-configuration scheme.
The model self-configuration strategy is mainly oriented to application requirement analysis, selects parts of the electromechanical equipment system with larger influence on application service by using an Analytic Hierarchy Process (AHP), and forms the electromechanical equipment system model aiming at the application requirement analysis by self-configuration by combining the coupling between subsystems and the contact matching relationship between the parts.
Taking the service of the numerically controlled machine tool life prediction application as an example, a technical route diagram is shown in fig. 3:
the method comprises the steps of selecting key parts of the numerical control machine tool which are most prone to damage to predict service life, wherein influence factors (namely a criterion layer) comprise secondary wear degree, crack expansion, fatigue damage and the like, quantizing the key parts of a scheme layer through the criterion layer factors, selecting the parts most prone to damage through an analytic hierarchy process, and then constructing a self-configuration model (namely a light configuration model for numerical control machine tool service life prediction) with the service life prediction application service as a guide by combining coupling characteristics and assembly relations of the selected parts.
As shown in FIG. 4, the model self-optimization using application as a guide mainly includes two parts of model coupling analysis and adaptive model reduction.
The self-adaptive model reduction mainly utilizes an influence evaluation method based on a time domain maximum error limit and a Krylov subspace projection method to research a self-adaptive reduction method of a digital twin model of the electromechanical equipment, packages the self-adaptive reduction method to obtain a dynamic link library, carries out autonomous optimization reduction processing on the digital twin model of the electromechanical equipment, generates a high-fidelity reduced-order model, and reserves necessary behavior characteristics and leading effects of a system.
The self-adaptive error limit is mainly determined by using application service as a guide for demand analysis and combining an analytic hierarchy process, the structural diagram of the analytic hierarchy process is shown in figure 5, the calculation mode is shown in formulas (1) to (7), and each element of the F vector is the self-optimization self-adaptive error limit of the electromechanical equipment digital twin model using the application service as a guide.
Obtaining a triangular fuzzy judgment matrix shown in table 1 through expert evaluation by utilizing triangular fuzzy numbers and based on a constructed optimal evaluation index structure model which is oriented by taking application as a guide, wherein C is (C) ij ) n×n ＝(l ij m ij u ij ) n×n 。
Table 1: triangular fuzzy judgment matrix
(1) Calculating a comprehensive fuzzy value of the ith index element of the m layers, wherein the comprehensive fuzzy value is shown as formula (1):
(2) calculating normalized weight values for evaluation criteria
The possibility that two triangular blur numbers M1 are more than or equal to M2 is shown in formula (2):
each criterion layer fuzzy weight vector can be derived from equation (3):
in the formula
As shown in formula (4):
the weight vector of the formula (3) is normalized to obtain the weight value of each element of a certain criterion layer, and the vector is represented as the formula (5):
in the formula
As shown in formula (6):
F＝(W s-c ) T *TW (7)
as shown in the figure, the specific implementation process of model self-optimization is as follows: firstly, obtaining model characteristic information files full and emat through finite element analysis, wherein the files contain information such as a model quality matrix and a rigidity matrix; model characteristic information is led into an application service analysis environment (such as TwinBuilder) through Socket communication, and model reduction is carried out through a Krylov subspace method (written based on C language and packaged into a reduced-order component for application environment identification).
Example 2:
the embodiment 2 of the present disclosure provides a electromechanical device lightweight model acquisition system based on digital twinning, including:
a data acquisition module configured to: acquiring performance state data of parts of the electromechanical equipment;
a parts model update module configured to: when the performance state of the part changes, recognizing the performance attenuation position and the attenuation quantity by using the sensing data, and updating the performance attenuation of the electromechanical equipment part model through finite element analysis;
a digital twin module acquisition module configured to: replacing the original model with the updated part model to complete the preliminary update of the electromechanical equipment digital twin model and obtain a consistent electromechanical equipment digital twin model;
a lightweight model acquisition module configured to: aiming at different application services of the electromechanical equipment, the application service requirement analysis is carried out to obtain the requirement guided by the application service, and the self-configuration and self-optimization of the electromechanical equipment digital twin model are guided according to the requirement to obtain the lightweight model guided by the application service.
The working method of the system is the same as the electromechanical equipment simulation method based on the digital twin lightweight model provided in embodiment 1, and details are not repeated here.
Example 3:
a third embodiment 3 of the present disclosure provides a computer-readable storage medium, on which a program is stored, which when executed by a processor, implements the steps in the method for simulating an electromechanical device based on a digital twin lightweight model according to embodiment 1 of the present disclosure, the steps being:
acquiring performance state data of parts of the electromechanical equipment;
when the performance state of the part is changed, recognizing the performance attenuation position and the attenuation amount by using the sensing data, and updating the performance attenuation of the electromechanical equipment part model through finite element analysis;
replacing the original model with the updated part model to complete the preliminary update of the electromechanical equipment digital twin model and obtain a consistent electromechanical equipment digital twin model;
aiming at different application services of the electromechanical equipment, the application service requirement analysis is carried out to obtain the requirement guided by the application service, and the self-configuration and self-optimization of the electromechanical equipment digital twin model are guided according to the requirement to obtain the lightweight model guided by the application service.
The detailed steps are the same as those of the electromechanical equipment simulation method based on the digital twin lightweight model provided in the embodiment 1, and are not repeated here.
Example 4:
the embodiment 4 of the present disclosure provides an electronic device, which includes a memory, a processor, and a program stored in the memory and capable of running on the processor, and when the processor executes the program, the processor implements the steps in the electromechanical equipment simulation method based on the digital twin lightweight model according to embodiment 1 of the present disclosure, where the steps are:
acquiring performance state data of parts of the electromechanical equipment;
when the performance state of the part changes, recognizing the performance attenuation position and the attenuation quantity by using the sensing data, and updating the performance attenuation of the electromechanical equipment part model through finite element analysis;
replacing the original model with the updated part model to complete the preliminary update of the electromechanical equipment digital twin model and obtain a consistent electromechanical equipment digital twin model;
aiming at different application services of the electromechanical equipment, the application service requirement analysis is carried out to obtain the requirement guided by the application service, and the self-configuration and self-optimization of the electromechanical equipment digital twin model are guided according to the requirement to obtain the lightweight model guided by the application service.
The detailed steps are the same as those of the electromechanical equipment simulation method based on the digital twin lightweight model provided in the embodiment 1, and are not described again here.
As will be appreciated by one of skill in the art, embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Claims (7)
1. A simulation method of electromechanical equipment based on a digital twin lightweight model is characterized by comprising the following steps:
acquiring performance state data of parts of the electromechanical equipment;
when the performance state of the part is changed, recognizing the performance attenuation position and the attenuation amount by using the sensing data, and updating the performance attenuation of the electromechanical equipment part model through finite element analysis;
replacing the original model with the updated part model to complete the preliminary update of the electromechanical equipment digital twin model and obtain a consistent electromechanical equipment digital twin model;
aiming at different application services of the electromechanical equipment, obtaining the requirements guided by the application services through application service requirement analysis, guiding self-configuration and self-optimization of a digital twin model of the electromechanical equipment according to the requirements to obtain a lightweight model guided by the application services, and simulating the working process of the electromechanical equipment according to the obtained lightweight model;
self-optimization of a digital twin model of electromechanical equipment, comprising: model coupling analysis and adaptive model reduction;
the self-adaptive model order reduction utilizes an influence evaluation method based on a time domain maximum error limit and a Krylov subspace projection method to obtain a dynamic link library, and the autonomous optimization order reduction processing is carried out on the electromechanical equipment digital twin model;
the self-adaptive error limit is obtained by combining an analytic hierarchy process and application service oriented demand analysis guidance;
self-configuration of a digital twinning model of electromechanical equipment, comprising: the method comprises the following steps of designing a model decomposition strategy, a model self-configuration strategy and a meta-model interface, wherein the model decomposition strategy comprises an application-oriented model decomposition criterion, a model decomposition method and model decomposition evaluation;
performing model decomposition on the electromechanical equipment by using a fuzzy clustering method to obtain an electromechanical equipment decomposition scheme which takes application service requirements as guidance;
when multiple decomposition schemes exist, each scheme is evaluated and quantized through a TOPSIS algorithm, an optimal decomposition scheme is selected, and meta-model interface design and model self-configuration scheme formulation are guided.
2. The method of simulating electromechanical equipment based on a digital twinning weight model of claim 1,
the model self-configuration strategy is guided by application requirement analysis, parts of the electromechanical equipment system with large influence on application service are selected by utilizing an analytic hierarchy process, and the electromechanical equipment system model aiming at the application requirement analysis is formed by self-configuration by combining coupling among subsystems and contact matching relation among the parts.
3. The method of simulating electromechanical equipment based on a digital twin lightweight model according to claim 1,
when the application service is life prediction, selecting the most easily damaged key parts of the electromechanical equipment to predict the life;
quantifying key parts of a scheme layer through a criterion layer factor, and selecting parts which are most easily damaged through an analytic hierarchy process;
and constructing a self-configuration model guided by the service life prediction application service of the numerical control machine tool by combining the coupling characteristics and the assembly relation of the selected parts.
4. The method for simulating electromechanical equipment based on a digital twin lightweight model according to claim 1, wherein the self-optimization of the electromechanical equipment digital twin model comprises:
obtaining a model characteristic information file by using finite element analysis;
importing the model characteristic information into an application service analysis environment by Socket communication;
and (5) performing model reduction by using a Krylov subspace method.
5. An electromechanical equipment simulation system based on a digital twin lightweight model, comprising:
a data acquisition module configured to: acquiring performance state data of parts of the electromechanical equipment;
a parts model update module configured to: when the performance state of the part is changed, recognizing the performance attenuation position and the attenuation amount by using the sensing data, and updating the performance attenuation of the electromechanical equipment part model through finite element analysis;
a digital twin module acquisition module configured to: replacing the original model with the updated part model to complete the preliminary update of the electromechanical equipment digital twin model and obtain a consistent electromechanical equipment digital twin model;
a lightweight model simulation module configured to: aiming at different application services of the electromechanical equipment, obtaining the requirements guided by the application services through application service requirement analysis, guiding self-configuration and self-optimization of a digital twin model of the electromechanical equipment according to the requirements to obtain a lightweight model guided by the application services, and simulating the working process of the electromechanical equipment according to the obtained lightweight model;
self-optimization of a digital twin model of electromechanical equipment, comprising: model coupling analysis and adaptive model reduction;
the self-adaptive model order reduction utilizes an influence evaluation method based on a time domain maximum error limit and a Krylov subspace projection method to obtain a dynamic link library, and the autonomous optimization order reduction processing is carried out on the electromechanical equipment digital twin model;
the self-adaptive error limit is obtained by combining an analytic hierarchy process and a demand analysis guide which takes application service as guidance;
self-configuration of a digital twinning model of electromechanical equipment, comprising: the method comprises the following steps of designing a model decomposition strategy, a model self-configuration strategy and a meta-model interface, wherein the model decomposition strategy comprises an application-oriented model decomposition criterion, a model decomposition method and model decomposition evaluation;
performing model decomposition on the electromechanical equipment by using a fuzzy clustering method to obtain an electromechanical equipment decomposition scheme which takes application service requirements as guidance;
when multiple decomposition schemes exist, each scheme is evaluated and quantified through a TOPSIS algorithm, an optimal decomposition scheme is selected, and the interface design of a meta-model and the formulation of a model self-configuration scheme are guided.
6. A computer-readable storage medium, on which a program is stored, which program, when being executed by a processor, is characterized by carrying out the steps in the method of simulating an electromechanical device based on a digital twinning lightweight model according to any one of claims 1-4.
7. An electronic device comprising a memory, a processor and a program stored on the memory and executable on the processor, wherein the processor implements the steps in the method of simulating electromechanical equipment based on a digital twinning lightweight model according to any of claims 1-4 when executing the program.
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