CN115356949B - Digital twin model consistency maintaining system and method - Google Patents
Digital twin model consistency maintaining system and method Download PDFInfo
- Publication number
- CN115356949B CN115356949B CN202211052645.0A CN202211052645A CN115356949B CN 115356949 B CN115356949 B CN 115356949B CN 202211052645 A CN202211052645 A CN 202211052645A CN 115356949 B CN115356949 B CN 115356949B
- Authority
- CN
- China
- Prior art keywords
- model
- digital twin
- twin model
- data
- consistency
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 84
- 238000004088 simulation Methods 0.000 claims abstract description 31
- 238000012795 verification Methods 0.000 claims abstract description 18
- 230000008859 change Effects 0.000 claims description 34
- 238000007792 addition Methods 0.000 claims description 18
- 238000012217 deletion Methods 0.000 claims description 10
- 230000037430 deletion Effects 0.000 claims description 10
- 238000007781 pre-processing Methods 0.000 claims description 10
- 230000001502 supplementing effect Effects 0.000 claims description 10
- 230000035945 sensitivity Effects 0.000 claims description 5
- 238000010276 construction Methods 0.000 claims description 2
- 238000012423 maintenance Methods 0.000 claims 1
- 230000008569 process Effects 0.000 description 9
- 238000005457 optimization Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 230000006978 adaptation Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B17/00—Systems involving the use of models or simulators of said systems
- G05B17/02—Systems involving the use of models or simulators of said systems electric
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a system and a method for keeping consistency of a digital twin model, wherein the system comprises the steps of (1) reading data of a physical entity and a simulation result of the digital twin model, taking a difference value of the data and the simulation result as a model deviation value, and comparing the model deviation value with a consistency threshold value of the digital twin model to realize consistency judgment of the digital twin model; step (2), analyzing the model deviation reason according to the model consistency judging result and the model structural characteristics, and dynamically evolving the structure and parameters of the digital twin model; and (3) reading the data of the physical entity and the digital twin model simulation result after model evolution, calculating a model deviation value, and comparing the model deviation value with a consistency threshold value to realize consistency verification of the digital twin model. The invention can provide a dynamic maintaining method for the consistency of the digital twin model and lays a foundation for providing effective application and service for the digital twin model to a certain extent.
Description
Technical Field
The invention belongs to the fields of electronic engineering and computer science, and particularly relates to a digital twin model consistency maintaining system and method.
Background
The digital twin model driven based on real-time data can describe the state and characteristics of the physical entity. The consistency of the characteristics described by the digital twin model and the characteristics of the physical entity in the dynamic running process of the model is the core key of the implementation of the digital twin model. The current discussion of the consistency problem of the digital twin model mainly surrounds a judging method (patent application number 201910920573.9) of the consistency of the digital twin model and a consistency maintaining method (patent application number 201910099067.8) of an electromechanical physical model, but the methods have obvious defects and cannot consider the two conditions of structural change and parameter change of a physical entity. Therefore, the invention discloses a system and a method for keeping consistency of a digital twin model, which can keep consistency of the digital twin model in a dynamic operation process to a certain extent through consistency verification, dynamic evolution and consistency verification of the digital twin model.
Disclosure of Invention
In order to solve the technical problems, the invention discloses a digital twin model consistency maintaining system and a digital twin model consistency maintaining method, which are suitable for a digital twin model with multidimensional characteristics, in particular to a digital twin model of complex equipment, so that when a physical entity actually works, the digital twin model consistency maintaining method can be used for realizing high-precision operation of the model for subsequent control, prediction and optimization of the physical entity.
The invention solves the technical problems by adopting the following technical scheme:
the invention provides a digital twin model consistency maintaining system, which comprises: the system comprises a consistency judging module, an evolution module and a consistency verifying module;
The consistency judging module is used for determining a consistency threshold value of a digital twin model of a physical entity aiming at the physical entity, wherein the consistency threshold value ensures that the simulation of the digital twin model meets basic requirements; taking the difference value of the physical entity data and the digital twin model simulation result as a digital twin model deviation value, comparing the digital twin model deviation value with a consistency threshold value of the digital twin model, and if the digital twin model deviation value is lower than the consistency threshold value of the digital twin model, conforming the digital twin model to the consistency requirement of the digital twin model to obtain a consistency judgment result of the digital twin model; if the consistency requirement of the digital twin model is not met, the digital twin model is required to be sent to an evolution module for evolution;
The evolution module divides the deviation of the digital twin model into two types of structural deviation and parameter deviation according to the structure of the digital twin model of the physical entity; determining the deviation type of the digital twin model according to the consistency judging result of the digital twin model in the consistency judging module and the digital twin model structure; if the deviation type of the digital twin model is structural deviation, adopting a structural evolution method to obtain the digital twin model after structural evolution; if the deviation type of the digital twin model is parameter deviation, adopting a parameter evolution method to obtain a digital twin model after parameter evolution;
The consistency verification module judges whether the parameters of the evolved digital twin model meet the structural requirements of the digital twin model, and if the parameters do not meet the requirements, the digital twin model is evolved again, namely the repeated evolution module is used; reading the evolved digital twin model simulation result and the physical entity data, and taking the difference value of the two as the accuracy of the digital twin model; comparing the accuracy of the digital twin model with the consistency threshold, if the accuracy of the digital twin model is lower than the consistency threshold, conforming the evolved digital twin model to the consistency requirement, if the accuracy of the digital twin model is higher than the consistency threshold, not conforming to the consistency requirement, and needing to be re-evolved, namely repeating the content of the evolution module, so as to finally obtain the consistency verification result of the digital twin model.
In the evolution module, the structure evolution method comprises the following steps:
① For a certain physical entity, the physical entity consists of different components, a digital twin model of the physical entity consists of a component model, and the component model describes the components of the physical entity; the components of the physical entity are composed of functional modules, the component model is composed of functional module models, and the functional module models describe functions of the components of the physical entity; the digital twin model, the component model and the functional module model are all stored in a digital twin model library;
② Determining the structural change condition of a physical entity, wherein the structural change is divided into an external structural change and an internal structural change; external structural changes include additions and deletions of components, internal structural changes include additions and deletions of functional modules;
③ Acquiring data of a physical entity, judging whether a component to which the data acquired from the physical entity belongs changes, if so, performing an evolution method of step ④, and if not, performing an evolution method of step ⑤, if so, performing an internal structure change;
④ Aiming at the component addition condition of the data acquired from the physical entity, a newly added component model is required to be matched in a digital twin model library, and an I/O interface of the component model is added to obtain a digital twin model after the structure evolution; aiming at the condition of deleting components to which the data acquired from the physical entity belong, deleting the corresponding digital twin model and the I/O interface to obtain a digital twin model after the structure evolution; the components of the physical entity are composed of functional modules, and the functional modules are changed due to the component change, so that after the external structure of the digital twin model is evolved, the internal structure is required to be evolved, namely, the step ⑤ is executed;
⑤ Aiming at the function module adding condition of the data acquired from the physical entity, adding the association relation between the component model and the function module model to obtain a digital twin model after parameter evolution; aiming at the condition of deleting the functional module to which the data acquired from the physical entity belongs, the association relation between the component model and the functional module model is required to be deleted, and a digital twin model after parameter evolution is obtained.
In the evolution module, the parameter evolution method comprises the following steps:
① Determining characteristic parameters of the digital twin model according to the parameter sensitivity of the digital twin model, storing data required for constructing the digital twin model into a digital twin model construction data set, and judging an evolution strategy by determining the similarity between the data characteristics of a physical entity and the data set characteristics, wherein the judging method comprises the following steps: if the similarity is high, the current physical entity does not generate a new state, and the evolution method of the step ② is performed; otherwise, if the similarity is low, the current physical entity is indicated to generate a new state, and the evolution method of step ③ is performed;
② Acquiring data of a physical entity, selecting sample data and performing data preprocessing to remove error/redundant data, then supplementing the processed data into a data set, and updating digital twin model parameters according to the updated data set to obtain a digital twin model after parameter evolution;
③ Acquiring data of a physical entity, selecting sample data, performing data preprocessing, performing similarity judgment on the sample data and data of a data set, selecting and removing data with lowest similarity in the data set, supplementing the sample data into the data set, and updating digital twin model parameters according to the updated data set to obtain a digital twin model after parameter evolution;
The invention discloses a method for keeping consistency of a digital twin model, which comprises the following steps:
step 1, consistency judgment is carried out, and the method is concretely realized as follows:
① Determining a consistency threshold of the digital twin model aiming at a certain physical entity, wherein the consistency threshold needs to ensure that the simulation of the digital twin model meets basic requirements, and the consistency threshold needs to be determined according to simulation requirements;
② Reading data of a physical entity and a simulation result of the digital twin model, and taking a difference value of the data and the simulation result as a deviation value of the digital twin model;
③ Comparing the digital twin model deviation value with a consistency threshold of the digital twin model, if the digital twin model deviation value is lower than the consistency threshold of the digital twin model, conforming the digital twin model to the consistency requirement of the digital twin model, otherwise, not conforming to the consistency requirement of the digital twin model, and needing further evolution so as to obtain a consistency judging result of the digital twin model;
And 2, evolution, wherein the method is specifically realized as follows:
① Dividing model deviation into two types of model structure deviation and model parameter deviation according to the structure of the digital twin model;
② Determining the deviation type of the digital twin model according to the consistency judging result of the digital twin model in the step 1 and the digital twin model structure;
③ If the deviation type is model structure deviation, adopting a model structure evolution method; if the deviation type is model parameter deviation, a model parameter evolution method is adopted, so that a digital twin model after model evolution is obtained;
And 3, performing consistency verification, wherein the method is specifically realized as follows:
① Judging whether the parameters of the digital twin model accord with the structural requirements of the digital twin model based on the digital twin model obtained in the step 2 after the model evolution, and if the parameters do not accord with the structural requirements, carrying out the digital twin model evolution again, namely repeating the step 2;
② Reading a digital twin model simulation result and physical entity data after model evolution, and taking the difference value of the digital twin model simulation result and the physical entity data as the accuracy of the digital twin model;
③ Comparing the accuracy of the digital twin model with the consistency threshold of the digital twin model in the step 1, if the accuracy of the digital twin model is lower than the consistency threshold of the digital twin model, the digital twin model after model evolution accords with the consistency requirement of the digital twin model, and if the accuracy of the digital twin model is higher than the consistency threshold of the digital twin model, the digital twin model does not accord with the consistency requirement of the digital twin model, and the step 2 needs to be evolved again, namely, the step 2 is repeated, so that the consistency verification result of the digital twin model is obtained.
In the step 2, the model structure deviation is realized by adopting a model structure evolution method, and the specific evolution method comprises the following steps:
① For a certain physical entity, the physical entity consists of different components, a digital twin model of the physical entity consists of a component model, and the component model needs to describe the components of the physical entity; the components of the physical entity consist of functional modules, the component model consists of functional module models, and the functional module models need to describe the functions of the components of the physical entity; the digital twin model, the component model and the functional module model are all required to be stored in a digital twin model library;
② Determining the structural change condition of a physical entity, wherein the structural change is divided into an external structural change and an internal structural change; external structural changes include additions and deletions of components, internal structural changes include additions and deletions of functional modules;
③ Acquiring data of a physical entity, judging whether a component to which the data belongs changes, if so, the external structure changes, wherein the evolution method is step ④, the component to which the data belongs does not change, and the functional module to which the data belongs changes, and the internal structure changes, and the evolution method is step ⑤;
④ Aiming at the component addition condition of the data, a newly added component model is required to be matched in a digital twin model library, and the I/O interface of the component model is added; aiming at the condition of deleting the components to which the data belong, deleting the corresponding digital twin model and the I/O interface; the components of the physical entity are composed of functional modules, and the functional modules are changed due to the component change, so that after the external structure of the digital twin model is evolved, the internal structure is required to be evolved, namely, the step ⑤ is executed;
⑤ Aiming at the situation of adding the functional modules to which the data belong, the association relationship between the component model and the functional module model is required to be added; aiming at the deleting condition of the functional module to which the data belongs, the association relation between the component model and the functional module model is required to be deleted;
in the step 2, the model parameter deviation adopts a model parameter evolution method, and the model parameter evolution method comprises the following steps:
determining model characteristic parameters according to the parameter sensitivity of the digital twin model, and judging a model evolution strategy by determining the similarity of the data characteristics of a physical entity and the data characteristics in a model data set, wherein the judging method comprises the following steps: if the similarity is high, the current physical entity is not in a new state, and a sample replacement strategy is adopted at the moment; otherwise, if the similarity is low, the current physical entity is indicated to generate a new state, and a sample adding strategy is adopted at the moment; updating a model dataset according to the acquired data of the physical entity, so as to update the digital twin model parameters and obtain an updated digital twin model;
The model data set is a set of data required for constructing a digital twin model;
the sample addition policy: acquiring data of a physical entity, selecting sample data, performing data preprocessing to remove error/redundant data, and then supplementing the processed data into a model data set;
The sample replacement strategy: and acquiring data of a physical entity, selecting sample data, preprocessing the data, judging the similarity between the sample data and the data of the model data set, selecting and removing the data with the lowest similarity in the model data set, and finally supplementing the sample data into the model data set.
The method is suitable for digital twin models with different dimensions, including a physical model, a behavior model and a rule model.
Compared with the prior art, the invention has the advantages that: the digital twin model is updated by dividing the digital twin model deviation reasons into model structures and model parameters and selecting a corresponding model evolution method. Through multiple iterations of the steps of consistency judgment, dynamic evolution, consistency verification and the like of the digital twin model, the consistency between the digital twin model and the physical entity characteristics in the operation process of the digital twin model is maintained, so that the model can be operated with high precision through a digital twin consistency maintaining method when the physical entity actually works, and the digital twin model is used for subsequent control, prediction and optimization of the physical entity.
The invention comprises the step of judging the consistency of the digital twin model driven by dynamic data, and realizes the evaluation of the consistency of the model in the dynamic operation process of the digital twin model; analyzing the model deviation reasons, and selecting an evolution method to dynamically evolve model parameters and model structures so as to realize dynamic update of the digital twin model; and comparing the digital twin model simulation result after model evolution with the data of the physical entity to realize model consistency verification. Based on iterative loops of consistency judgment, model evolution and consistency verification, consistency in the dynamic operation process of the digital twin model is finally realized, and technical support is provided for application of the digital twin model. The invention can solve the problem that the dynamic operation of the digital twin model is inaccurate due to the physical entity structure or parameter change to a certain extent, so that the digital twin model has the capability of keeping consistency when the physical entity actually works, and is used for subsequent control, prediction and optimization of the physical entity.
Drawings
FIG. 1 is a block diagram of a digital twin model consistency maintaining system of the present invention;
FIG. 2 is a flow chart of a method for evolving a digital twin model structure according to the present invention;
FIG. 3 is a flow chart of the digital twin model parameter evolution method of the present invention.
Detailed Description
The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments, and all other embodiments obtained by those skilled in the art without the inventive effort based on the embodiments of the present invention are within the scope of protection of the present invention.
The dynamic operation of the data-driven digital twin model is a key characteristic of digital twin, so that keeping the digital twin model consistent with the characteristics of a physical entity in the operation process is a key of digital twin floor application. In the embodiment of the invention, the digital twin model has the characteristics of physics, behavior and rules in multiple dimensions, and the consistency of the digital twin model needs to cover the characteristics of each dimension of the model. To this end, according to an embodiment of the present invention, a digital twin model consistency maintaining method is proposed, which is applicable to a digital twin model having multi-dimensional characteristics. The consistency judgment of the digital twin model driven by dynamic data is included, so that the evaluation of the model accuracy in the dynamic operation process of the digital twin model is realized; analyzing the model deviation reasons, and selecting an evolution method to dynamically evolve the model parameters and the model structure so as to realize the dynamic update of the model parameters; and comparing the digital twin model simulation result after model evolution with the data of the physical entity to realize model consistency verification. Based on iterative loop processes of consistency judgment, model evolution and consistency verification, consistency in a dynamic operation process of the digital twin model is finally realized, the problem that dynamic operation of the digital twin model is inaccurate due to physical entity structure or parameter change is solved, and the digital twin model has consistency maintaining capability when a physical entity actually works, so that the digital twin model is used for subsequent control, prediction and optimization of the physical entity.
The general block diagram of the invention is shown in fig. 1, and a digital twin model consistency maintaining system of the invention comprises: a consistency judging module 1, an evolution module 2 and a consistency verifying module 3;
The flow chart of the model structure evolution method is shown in fig. 2, the flow chart of the model parameter evolution method is shown in fig. 3, and the specific implementation manner is as follows:
(1) And aiming at a certain physical entity, reading the data of the physical entity and the simulation result of the digital twin model, and comparing the difference value of the data and the simulation result of the digital twin model with a consistency threshold value of the digital twin model to realize the judgment of the consistency of the digital twin model. The specific implementation flow is as follows:
① Determining a consistency threshold of the digital twin model aiming at a certain physical entity, wherein the consistency threshold needs to ensure that the simulation of the digital twin model meets basic requirements, and the consistency threshold needs to be determined according to simulation requirements;
② Reading data of a physical entity and a simulation result of the digital twin model, and taking a difference value of the data and the simulation result as a deviation value of the digital twin model;
③ Comparing the digital twin model deviation value with a consistency threshold of the digital twin model, if the digital twin model deviation value is lower than the consistency threshold of the digital twin model, conforming the digital twin model to the consistency requirement of the digital twin model, otherwise, not conforming to the consistency requirement of the digital twin model, and further evolution is required, wherein the evolution method is shown in the step (2), so that a consistency judgment result of the digital twin model is obtained;
(2) Dividing model deviation into two types, namely model structure deviation and model parameter deviation, according to the structure of the digital twin model, determining the deviation type of the digital twin model according to the consistency judging result of the digital twin model in the step (1) and the digital twin model structure, and if the deviation type is the model structure deviation, adopting a model structure evolution method; and if the deviation type is model parameter deviation, adopting a model parameter evolution method to obtain a digital twin model after model evolution.
As shown in fig. 2, the specific implementation flow of the model structure evolution method is as follows:
① For a certain physical entity, the physical entity is composed of different components, so that a digital twin model of the physical entity is composed of a component model, and the component model needs to describe the components of the physical entity; the components of the physical entity are composed of functional modules, so that the component model is composed of a functional module model, and the functional module model needs to describe the functions of the components of the physical entity; the digital twin model, the component model and the functional module model are all required to be stored in a digital twin model library;
② Determining the structural change condition of a physical entity, wherein the structural change is divided into an external structural change and an internal structural change; external structural changes include additions and deletions of components, internal structural changes include additions and deletions of functional modules;
③ Acquiring data of a physical entity, judging whether a component to which the data belongs changes, if so, the external structure changes, wherein the evolution method is step ④, the component to which the data belongs does not change, and the functional module to which the data belongs changes, and the internal structure changes, and the evolution method is step ⑤;
④ Aiming at the component addition condition of the data, a newly added component model is required to be matched in a digital twin model library, and the I/O interface of the component model is added; aiming at the condition of deleting the components to which the data belong, deleting the corresponding digital twin model and the I/O interface; the components of the physical entity are composed of functional modules, and the functional modules are changed due to the component change, so that after the external structure of the digital twin model is evolved, the internal structure is required to be evolved, namely, the step ⑤ is executed;
⑤ Aiming at the situation of adding the functional modules to which the data belong, the association relationship between the component model and the functional module model is required to be added; aiming at the deleting condition of the functional module to which the data belongs, the association relation between the component model and the functional module model needs to be deleted.
As shown in fig. 3, the specific implementation flow of the model parameter evolution method is as follows:
determining model characteristic parameters according to the parameter sensitivity of the digital twin model, and judging a model evolution strategy by determining the similarity of the data characteristics of a physical entity and the data characteristics in a model data set, wherein the judging method comprises the following steps: if the similarity is high, the current physical entity is not in a new state, and a sample replacement strategy is adopted at the moment; otherwise, if the similarity is low, the current physical entity is indicated to generate a new state, and a sample adding strategy is adopted at the moment; updating a model dataset according to the acquired data of the physical entity, so as to update the digital twin model parameters and obtain an updated digital twin model;
The model data set is a set of data required for constructing a digital twin model;
the sample addition policy: acquiring data of a physical entity, selecting sample data, performing data preprocessing to remove error/redundant data, and then supplementing the processed data into a model data set;
The sample replacement strategy: and acquiring data of a physical entity, selecting sample data, preprocessing the data, judging the similarity between the sample data and the data of the model data set, selecting and removing the data with the lowest similarity in the model data set, and finally supplementing the sample data into the model data set.
(3) And carrying out consistency verification on the digital twin model after model evolution. The specific implementation flow is as follows:
① Judging whether the parameters of the digital twin model accord with the structural requirements of the digital twin model based on the digital twin model obtained in the step (2) after the model evolution, and if the parameters do not accord with the structural requirements, carrying out the digital twin model evolution again, namely repeating the step (2);
② Reading a digital twin model simulation result and physical entity data after model evolution, and taking the difference value of the digital twin model simulation result and the physical entity data as the accuracy of the digital twin model;
③ Comparing the accuracy of the digital twin model with the consistency threshold of the digital twin model in the step (1), if the accuracy of the digital twin model is lower than the consistency threshold of the digital twin model, conforming the digital twin model after model evolution to the consistency requirement of the digital twin model, and if the accuracy of the digital twin model is higher than the consistency threshold of the digital twin model, not conforming to the consistency requirement of the digital twin model, and needing to be evolved again, namely repeating the step (2), so as to obtain the consistency verification result of the digital twin model.
What is not described in detail in the present specification belongs to the prior art known to those skilled in the art.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.
Claims (6)
1. A digital twin model consistency maintenance system, comprising: the system comprises a consistency judging module, an evolution module and a consistency verifying module;
The consistency judging module is used for determining a consistency threshold value of a digital twin model of a physical entity aiming at the physical entity, wherein the consistency threshold value ensures that the simulation of the digital twin model meets basic requirements; taking the difference value of the physical entity data and the digital twin model simulation result as a digital twin model deviation value, comparing the digital twin model deviation value with a consistency threshold value of the digital twin model, and if the digital twin model deviation value is lower than the consistency threshold value of the digital twin model, conforming the digital twin model to the consistency requirement of the digital twin model to obtain a consistency judgment result of the digital twin model; if the consistency requirement of the digital twin model is not met, the digital twin model is required to be sent to an evolution module for evolution;
The evolution module divides the deviation of the digital twin model into two types of structural deviation and parameter deviation according to the structure of the digital twin model of the physical entity; determining the deviation type of the digital twin model according to the consistency judging result of the digital twin model in the consistency judging module and the digital twin model structure; if the deviation type of the digital twin model is structural deviation, adopting a structural evolution method to obtain the digital twin model after structural evolution; if the deviation type of the digital twin model is parameter deviation, adopting a parameter evolution method to obtain a digital twin model after parameter evolution;
The consistency verification module judges whether the parameters of the evolved digital twin model meet the structural requirements of the digital twin model, and if the parameters do not meet the requirements, the digital twin model is evolved again, namely the repeated evolution module is used; reading the evolved digital twin model simulation result and the physical entity data, and taking the difference value of the two as the accuracy of the digital twin model; comparing the accuracy of the digital twin model with the consistency threshold, if the accuracy of the digital twin model is lower than the consistency threshold, conforming the evolved digital twin model to the consistency requirement, if the accuracy of the digital twin model is higher than the consistency threshold, not conforming to the consistency requirement, and needing to be re-evolved, namely repeating the content of the evolution module, so as to finally obtain the consistency verification result of the digital twin model.
2. The system for maintaining consistency of digital twin models according to claim 1, wherein in the evolution module, the structural evolution method comprises the following steps:
① For a certain physical entity, the physical entity consists of different components, a digital twin model of the physical entity consists of a component model, and the component model describes the components of the physical entity; the components of the physical entity are composed of functional modules, the component model is composed of functional module models, and the functional module models describe functions of the components of the physical entity; the digital twin model, the component model and the functional module model are all stored in a digital twin model library;
② Determining the structural change condition of a physical entity, wherein the structural change is divided into an external structural change and an internal structural change; external structural changes include additions and deletions of components, internal structural changes include additions and deletions of functional modules;
③ Acquiring data of a physical entity, judging whether a component to which the data acquired from the physical entity belongs changes, if so, performing an evolution method of step ④, and if not, performing an evolution method of step ⑤, if so, performing an internal structure change;
④ Aiming at the component addition condition of the data acquired from the physical entity, a newly added component model is required to be matched in a digital twin model library, and an I/O interface of the component model is added to obtain a digital twin model after the structure evolution; aiming at the condition of deleting components to which the data acquired from the physical entity belong, deleting the corresponding digital twin model and the I/O interface to obtain a digital twin model after the structure evolution; the components of the physical entity are composed of functional modules, and the functional modules are changed due to the component change, so that after the external structure of the digital twin model is evolved, the internal structure is required to be evolved, namely, the step ⑤ is executed;
⑤ Aiming at the function module adding condition of the data acquired from the physical entity, adding the association relation between the component model and the function module model to obtain a digital twin model after parameter evolution; aiming at the condition of deleting the functional module to which the data acquired from the physical entity belongs, the association relation between the component model and the functional module model is required to be deleted, and a digital twin model after parameter evolution is obtained.
3. The system for maintaining consistency of digital twin models as defined in claim 1, wherein the evolution module, the parameter evolution method comprises the steps of:
① Determining characteristic parameters of the digital twin model according to the parameter sensitivity of the digital twin model, storing data required for constructing the digital twin model into a digital twin model construction data set, and judging an evolution strategy by determining the similarity between the data characteristics of a physical entity and the data set characteristics, wherein the judging method comprises the following steps: if the similarity is high, the current physical entity does not generate a new state, and the evolution method of the step ② is performed; otherwise, if the similarity is low, the current physical entity is indicated to generate a new state, and the evolution method of step ③ is performed;
② Acquiring data of a physical entity, selecting sample data and performing data preprocessing to remove error/redundant data, then supplementing the processed data into a data set, and updating digital twin model parameters according to the updated data set to obtain a digital twin model after parameter evolution;
③ Obtaining data of a physical entity, selecting sample data, carrying out data preprocessing, carrying out similarity judgment on the sample data and data of a data set, selecting and removing data with lowest similarity in the data set, supplementing the sample data into the data set, and updating digital twin model parameters according to the updated data set to obtain a digital twin model after parameter evolution.
4. The digital twin model consistency maintaining method is characterized by comprising the following steps:
step 1, consistency judgment is carried out, and the method is concretely realized as follows:
① Determining a consistency threshold of the digital twin model aiming at a certain physical entity, wherein the consistency threshold needs to ensure that the simulation of the digital twin model meets basic requirements, and the consistency threshold needs to be determined according to simulation requirements;
② Reading data of a physical entity and a simulation result of the digital twin model, and taking a difference value of the data and the simulation result as a deviation value of the digital twin model;
③ Comparing the digital twin model deviation value with a consistency threshold of the digital twin model, if the digital twin model deviation value is lower than the consistency threshold of the digital twin model, conforming the digital twin model to the consistency requirement of the digital twin model, otherwise, not conforming to the consistency requirement of the digital twin model, and needing further evolution so as to obtain a consistency judging result of the digital twin model;
And 2, evolution, wherein the method is specifically realized as follows:
① Dividing model deviation into two types of model structure deviation and model parameter deviation according to the structure of the digital twin model;
② Determining the deviation type of the digital twin model according to the consistency judging result of the digital twin model in the step 1 and the digital twin model structure;
③ If the deviation type is model structure deviation, adopting a model structure evolution method; if the deviation type is model parameter deviation, a model parameter evolution method is adopted, so that a digital twin model after model evolution is obtained;
And 3, performing consistency verification, wherein the method is specifically realized as follows:
① Judging whether the parameters of the digital twin model accord with the structural requirements of the digital twin model based on the digital twin model obtained in the step 2 after the model evolution, and if the parameters do not accord with the structural requirements, carrying out the digital twin model evolution again, namely repeating the step 2;
② Reading a digital twin model simulation result and physical entity data after model evolution, and taking the difference value of the digital twin model simulation result and the physical entity data as the accuracy of the digital twin model;
③ Comparing the accuracy of the digital twin model with the consistency threshold of the digital twin model in the step 1, if the accuracy of the digital twin model is lower than the consistency threshold of the digital twin model, the digital twin model after model evolution accords with the consistency requirement of the digital twin model, and if the accuracy of the digital twin model is higher than the consistency threshold of the digital twin model, the digital twin model does not accord with the consistency requirement of the digital twin model, and the step 2 needs to be evolved again, namely, the step 2 is repeated, so that the consistency verification result of the digital twin model is obtained.
5. The method for maintaining consistency of digital twin models according to claim 4, wherein in the step 2, the model structure deviation is implemented by using a model structure evolution method, and the specific evolution method comprises the following steps:
① For a certain physical entity, the physical entity consists of different components, a digital twin model of the physical entity consists of a component model, and the component model needs to describe the components of the physical entity; the components of the physical entity consist of functional modules, the component model consists of functional module models, and the functional module models need to describe the functions of the components of the physical entity; the digital twin model, the component model and the functional module model are all required to be stored in a digital twin model library;
② Determining the structural change condition of a physical entity, wherein the structural change is divided into an external structural change and an internal structural change; external structural changes include additions and deletions of components, internal structural changes include additions and deletions of functional modules;
③ Acquiring data of a physical entity, judging whether a component to which the data belongs changes, if so, the external structure changes, wherein the evolution method is step ④, the component to which the data belongs does not change, and the functional module to which the data belongs changes, and the internal structure changes, and the evolution method is step ⑤;
④ Aiming at the component addition condition of the data, a newly added component model is required to be matched in a digital twin model library, and the I/O interface of the component model is added; aiming at the condition of deleting the components to which the data belong, deleting the corresponding digital twin model and the I/O interface; the components of the physical entity are composed of functional modules, and the functional modules are changed due to the component change, so that after the external structure of the digital twin model is evolved, the internal structure is required to be evolved, namely, the step ⑤ is executed;
⑤ Aiming at the situation of adding the functional modules to which the data belong, the association relationship between the component model and the functional module model is required to be added; aiming at the deleting condition of the functional module to which the data belongs, the association relation between the component model and the functional module model is required to be deleted;
6. The method for maintaining consistency of digital twin models according to claim 5, wherein in the step 2, model parameter deviation adopts a model parameter evolution method, and the model parameter evolution method comprises the following steps:
determining model characteristic parameters according to the parameter sensitivity of the digital twin model, and judging a model evolution strategy by determining the similarity of the data characteristics of a physical entity and the data characteristics in a model data set, wherein the judging method comprises the following steps: if the similarity is high, the current physical entity is not in a new state, and a sample replacement strategy is adopted at the moment; otherwise, if the similarity is low, the current physical entity is indicated to generate a new state, and a sample adding strategy is adopted at the moment; updating a model dataset according to the acquired data of the physical entity, so as to update the digital twin model parameters and obtain an updated digital twin model;
The model data set is a set of data required for constructing a digital twin model;
the sample addition policy: acquiring data of a physical entity, selecting sample data, performing data preprocessing to remove error/redundant data, and then supplementing the processed data into a model data set;
The sample replacement strategy: and acquiring data of a physical entity, selecting sample data, preprocessing the data, judging the similarity between the sample data and the data of the model data set, selecting and removing the data with the lowest similarity in the model data set, and finally supplementing the sample data into the model data set.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211052645.0A CN115356949B (en) | 2022-08-30 | 2022-08-30 | Digital twin model consistency maintaining system and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211052645.0A CN115356949B (en) | 2022-08-30 | 2022-08-30 | Digital twin model consistency maintaining system and method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115356949A CN115356949A (en) | 2022-11-18 |
| CN115356949B true CN115356949B (en) | 2024-05-07 |
Family
ID=84004107
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211052645.0A Active CN115356949B (en) | 2022-08-30 | 2022-08-30 | Digital twin model consistency maintaining system and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115356949B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109116751A (en) * | 2018-07-24 | 2019-01-01 | 西安西电电气研究院有限责任公司 | Digitization system and its construction method based on the twin technology of number |
| CN112905385A (en) * | 2021-01-27 | 2021-06-04 | 北京航空航天大学 | Digital twin model operation and iterative evolution method based on model backup |
| EP3944034A1 (en) * | 2020-07-21 | 2022-01-26 | Rockwell Automation Technologies, Inc. | Model-based design of linear synchronous motor transport systems |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11119882B2 (en) * | 2019-10-09 | 2021-09-14 | International Business Machines Corporation | Digital twin workflow simulation |
-
2022
- 2022-08-30 CN CN202211052645.0A patent/CN115356949B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109116751A (en) * | 2018-07-24 | 2019-01-01 | 西安西电电气研究院有限责任公司 | Digitization system and its construction method based on the twin technology of number |
| EP3944034A1 (en) * | 2020-07-21 | 2022-01-26 | Rockwell Automation Technologies, Inc. | Model-based design of linear synchronous motor transport systems |
| CN112905385A (en) * | 2021-01-27 | 2021-06-04 | 北京航空航天大学 | Digital twin model operation and iterative evolution method based on model backup |
Non-Patent Citations (1)
| Title |
|---|
| 数字孪生及其应用探索;陶飞;刘蔚然;刘检华;刘晓军;刘强;屈挺;胡天亮;张执南;向峰;徐文君;王军强;张映锋;刘振宇;李浩;程江峰;戚庆林;张萌;张贺;隋芳媛;何立荣;易旺民;程辉;;计算机集成制造系统;20180115(第01期);4-21 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115356949A (en) | 2022-11-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112597610B (en) | Optimization method, device and equipment for lightweight design of mechanical arm structure | |
| CN108509723B (en) | LRU Cache prefetching mechanism performance gain evaluation method based on artificial neural network | |
| WO2023284443A1 (en) | Method and apparatus for determining working condition of excavator | |
| CN102063449A (en) | Method and device for improving reliability of statistic information of data object in database | |
| CN114609994A (en) | Fault diagnosis method and device based on multi-granularity regularization rebalance incremental learning | |
| CN115687432A (en) | Method, apparatus, and medium for monitoring anomalous transaction data | |
| CN112000848A (en) | Graph data processing method and device, electronic equipment and storage medium | |
| CN116150190A (en) | Database query optimization processing method and system based on tree type QRNN | |
| CN118297271A (en) | Energy consumption enterprise time sequence data prediction method and system based on reversible calculation | |
| CN115356949B (en) | Digital twin model consistency maintaining system and method | |
| CN117318033B (en) | Power grid data management method and system combining data twinning | |
| CN113947237A (en) | AMS-TCN-based wind power error correction method | |
| CN111984662B (en) | Method and device for updating databases in batches | |
| CN117113682A (en) | Digital twin digital-analog linkage system and method | |
| CN115841343B (en) | Sales limit determining method and device | |
| CN116933657A (en) | Complex profile processing parameter feature extraction method, system, equipment and medium | |
| CN116448685A (en) | Urban carbon monitoring system and method based on artificial intelligence | |
| CN115017443A (en) | Natural circulation system reliability analysis method based on self-adaptive agent model | |
| CN118154139B (en) | Intelligent business process monitoring method and system based on AI | |
| CN117076141B (en) | High-applicability off-line data processing task issuing method and system | |
| CN118313141B (en) | Product reliability digital twin model construction method and computer equipment | |
| CN117521580B (en) | Circuit optimization method and device based on generalization model and storage medium | |
| CN111008238B (en) | Key mode automatic positioning and early warning method based on associated evolution big data | |
| CN117573199B (en) | Model difference comparison analysis method, device, equipment and medium | |
| CN115510170A (en) | Real-time index calculation method based on financial data incremental calculation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |