A digital twin numerical model linkage system and method

Abstract

The application discloses a kind of digital twin numerical model linkage system and method, comprising, for certain digital twin model, determine the model input parameter and available data source of the digital twin model, analyze and construct the digital twin model of number model mapping association, realize the construction of number model mapping association;According to the application scene requirement of digital twin model, analyze the simulation demand of the digital twin model, realize the number model linkage of digital twin model, obtain the dynamic operation of digital twin model;According to the structure of the digital twin model, analyze the number model mapping association deviation reason of digital twin model, evolution is carried out to the number model mapping association of deviation, the consistency of evolution after number model mapping association is judged, finally the number model mapping association satisfying consistency requirement is obtained.The application can provide effective method for the dynamic driving of digital twin, and to a certain extent, lay the foundation for the effective application and service of digital twin.

Description

Technical Field

[0001] This invention belongs to the fields of electronic engineering and computer science, and specifically relates to a digital twin analog-digital linkage system and method. Background Technology

[0002] A key characteristic of digital twins is simulation driven by real-time data. Dynamic driving of the digital twin model is achieved by inputting available real-time data into it in the correct data format. Therefore, constructing a mapping relationship between the available data source and the model input parameters, and combining this with the simulation requirements of the digital twin model to achieve dynamic driving, is the core key to digital twin implementation. Current research on digital twin model mapping and linkage mainly focuses on methods for verifying and maintaining model consistency considering model parameter updates and corrections, and on mapping and interaction between digital twins. The inventors have not yet found any relevant methods focusing on the mapping relationship between the data source and the input parameters of the digital twin model to drive its dynamic operation. Therefore, this invention discloses a digital twin-model linkage system and method, which, through the construction, linkage, and dynamic evolution of the digital twin-model mapping relationship, achieves real-time data-driven dynamic simulation of the digital twin to a certain extent. Summary of the Invention

[0003] To address the aforementioned technical issues, this invention discloses a digital twin-model linkage system and method, applicable to digital twin application scenarios requiring dynamic simulation. By establishing a mapping relationship between an acquireable data source and the input parameters of the digital twin model, and continuously evolving the mapping relationship during the dynamic operation of the digital twin, real-time data-driven dynamic simulation of the digital twin is achieved, which can then be used for subsequent control, prediction, and optimization of the physical entity.

[0004] The technical problem solved by this invention is achieved through the following technical solution:

[0005] This invention proposes a digital twin-model linkage system, comprising: a model-data mapping construction module, a model-data linkage module, and a model-data mapping association evolution module;

[0006] The digital twin mapping construction module, for a given digital twin model, determines the model input parameters and available data sources for that digital twin model. The model input parameters ensure that the simulation of the digital twin model meets basic requirements, and the available data sources are used to acquire data to drive model simulation. Based on the digital twin model input parameters and available data, the module analyzes and constructs the digital twin mapping association for that digital twin model. The digital twin mapping association ensures that each input parameter of the digital twin model has a corresponding available data source.

[0007] The digital twin linkage module analyzes the simulation requirements of the digital twin model based on the application scenario requirements; and realizes the digital twin linkage of the digital twin model based on the determined simulation requirements and the constructed digital twin mapping association to obtain a dynamically running digital twin model.

[0008] The digital twin mapping association evolution module analyzes the causes of deviations in the digital twin mapping association based on the structure of the digital twin model; evolves the deviated digital twin mapping association based on the determined causes of deviations; verifies the consistency of the evolved digital twin mapping association to obtain the consistency verification result; if the evolved digital twin mapping relationship does not meet the consistency requirements, it needs to be re-evolved, that is, the above steps are repeated, and finally a digital twin mapping association that meets the consistency requirements is obtained.

[0009] Furthermore, in the model mapping association construction module, the model mapping association analysis includes the following steps:

[0010] ① For a given digital twin model, which is composed of component models, each component model contains multiple model input parameters; these model input parameters are used to drive the dynamic operation of the component models.

[0011] ② Determine the input parameters of the component model based on its structural characteristics;

[0012] ③ Determine the available data sources based on the data acquisition device settings;

[0013] ④ Analyze the characteristics of the component model input parameters determined in step ②, and determine the data source corresponding to the component model input parameters. The corresponding data source should be included in the data source determined in step ③.

[0014] ⑤ Based on the data source determined in step ④, the correspondence between the input parameters of the digital twin model and the data source is obtained, that is, the digital twin mapping association.

[0015] Furthermore, in the digital-simulation linkage module, the simulation requirements analysis includes the following steps:

[0016] ① Based on the application requirements of the digital twin model, determine the model simulation frequency of each component model. The model simulation frequency can ensure that the dynamic operation of the digital twin model meets the basic application requirements.

[0017] ② To meet the simulation requirements of the digital twin model, a consistency threshold for the digital twin model is determined. The consistency threshold must ensure that the simulation of the digital twin model meets the basic requirements. This consistency threshold needs to be determined based on the simulation requirements.

[0018] Furthermore, in the analog-digital linkage module, the analog-digital linkage method includes the following steps:

[0019] ① Obtain data from available data sources, with a data acquisition frequency no less than the determined model simulation frequency, preprocess the acquired data, and store it in the database;

[0020] ② Determine whether the simulation of the digital twin model has ended. The frequency of this determination should not be less than the determined simulation frequency. If the determination result is that the simulation has ended, proceed to step ③. If the determination result is that the simulation has not ended, return to step ①.

[0021] ③ Read the data of the physical entity and the simulation results of the digital twin model, and use the difference between the two as the deviation value of the digital twin model;

[0022] ④ Compare the deviation value of the digital twin model with the consistency threshold of the digital twin model. If the deviation value of the digital twin model is lower than the consistency threshold of the digital twin model, then the digital twin model meets the consistency requirements of the digital twin model and proceed to step ⑤. Otherwise, it does not meet the consistency requirements of the digital twin model and needs to be further evolved.

[0023] ⑤ Select the data whose time attribute is closest to the current time from the database and use this data as the driving data for the digital twin model;

[0024] ⑥ Based on the structure of the model input parameters, the model-driven data obtained in step ⑤ is converted into a new format. The converted model-driven data must conform to the structural requirements of the model input parameters.

[0025] ⑦ Use the model-driven data obtained in ⑥ as the model input parameters to obtain a dynamically running digital twin model.

[0026] Furthermore, in the numerical model mapping correlation evolution module, the numerical model mapping correlation deviation analysis includes the following steps:

[0027] ① Read the simulation results of the digital twin model and determine whether the component model structure has been added or deleted. If such changes have occurred, the reason for the deviation in the digital twin mapping is the change in the component model structure.

[0028] ② Determine whether the input parameters of the component model have been added or deleted. If they have changed, the reason for the deviation in the numerical model mapping is the change in the model input parameters.

[0029] ③If the component model structure and model input parameters have not changed, the reason for the deviation in the numerical model mapping relationship is the change in the numerical model mapping relationship;

[0030] ④ Analyze the causes of the above-mentioned numerical model mapping correlation deviation and obtain the analysis results of the numerical model mapping correlation deviation.

[0031] Furthermore, in the numerical model mapping correlation evolution module, the numerical model mapping correlation evolution includes the following steps:

[0032] ① For changes in component model structure in the numerical model mapping correlation deviation results, identify the component models that have changed. If it is a case of adding components, analyze and construct the numerical model mapping correlation of the added component models. If it is a case of deleting components, delete the mapping correlation of the component models to obtain the updated numerical model mapping correlation.

[0033] ② For changes in component model input parameters in the numerical model mapping correlation deviation results, determine the changed model input parameters. If it is a case of increased model input parameters, it is necessary to analyze the available data sources corresponding to the component model input parameters to obtain the updated numerical model mapping correlation.

[0034] ③ To analyze the changes in the numerical model mapping correlation in the mapping correlation deviation results, analyze the characteristics of the model input parameters, determine the data source corresponding to the model input parameters, and obtain the updated numerical model mapping correlation.

[0035] Furthermore, in the numerical model mapping association evolution module, the numerical model mapping association consistency verification includes the following:

[0036] ① The input parameters of the digital twin model all have available data sources;

[0037] ② The digital twin model evolved from the digital model mapping association must meet the consistency requirements of the digital twin model.

[0038] Furthermore, the following steps are included:

[0039] Step 1: Construct the numerical model mapping association, specifically as follows:

[0040] ① For a given digital twin model, determine the model input parameters and available data for the digital twin model. The model input parameters ensure that the simulation of the digital twin model meets the basic requirements, and the available data source is used to acquire data to drive the model simulation.

[0041] ②Based on the input parameters of the digital twin model and the available data sources, analyze the digital twin model mapping association, which ensures that each input parameter of the digital twin model has a corresponding available data source;

[0042] ③ Construct a numerical model mapping association based on the results of the numerical model mapping association analysis;

[0043] Step 2: Perform digital-analog linkage, specifically as follows:

[0044] ①Based on the application scenario requirements of the digital twin model, analyze the simulation requirements of the digital twin model, determine the model simulation frequency of each component model, and ensure that the dynamic operation of the digital twin model meets the basic application requirements.

[0045] ② To meet the simulation requirements of the digital twin model, determine the consistency threshold of the digital twin model. The consistency threshold must ensure that the simulation of the digital twin model meets the basic requirements. The consistency threshold needs to be determined according to the simulation requirements.

[0046] ③ Obtain data from available data sources, with a data acquisition frequency no less than the determined model simulation frequency, preprocess the acquired data, and store it in the database;

[0047] ④ Determine whether the simulation of the digital twin model has ended. The frequency of this determination should not be less than the determined simulation frequency. If the determination result is that the simulation has ended, proceed to step ⑤. If the determination result is that the simulation has not ended, return to step ③.

[0048] ⑤ Read the data of the physical entity and the simulation results of the digital twin model, and use the difference between the two as the deviation value of the digital twin model;

[0049] ⑥ Compare the deviation value of the digital twin model with the consistency threshold of the digital twin model. If the deviation value of the digital twin model is lower than the consistency threshold of the digital twin model, then the digital twin model meets the consistency requirements of the digital twin model and proceed to step ⑦. Otherwise, it does not meet the consistency requirements of the digital twin model and needs to be further evolved.

[0050] ⑦ Select the data whose time attribute is closest to the current time from the database and use this data as the driving data for the digital twin model;

[0051] ⑧ Based on the structure of the model input parameters, the model-driven data obtained in step ⑦ is converted into a new format. The converted model-driven data must conform to the structural requirements of the model input parameters.

[0052] ⑨ Use the model-driven data obtained in step ⑧ as the model input parameters to obtain a dynamically running digital twin model.

[0053] Step 3: Perform the numerical model mapping correlation evolution, specifically implemented as follows:

[0054] ①Based on the structure of the digital twin model, analyze the reasons for the deviation in the digital twin model's mapping correlation;

[0055] ②Based on the determined causes of the digital-to-analog mapping deviation, the correlation between the deviation and the digital-to-analog mapping is evolved;

[0056] ③ Perform consistency verification on the evolved mathematical model mapping association to obtain the consistency verification result of the mathematical model mapping association; if the evolved mathematical model mapping relationship does not meet the consistency requirements, it needs to be re-evolved, that is, repeat the above steps, and finally obtain the mathematical model mapping association that meets the consistency requirements.

[0057] Furthermore, in step 1, the numerical model mapping correlation analysis includes the following steps:

[0058] ① For a given digital twin model, which is composed of component models, each component model contains multiple model input parameters; these model input parameters are used to drive the dynamic operation of the component models.

[0059] ② Determine the input parameters of the component model based on its structural characteristics;

[0060] ③ Determine the available data sources based on the data acquisition device settings;

[0061] ④ Analyze the characteristics of the model input parameters determined in step ②, and determine the data source corresponding to the component model input parameters. The corresponding data source should be included in the data source determined in step ③.

[0062] ⑤ Based on the data source determined in step ④, the correspondence between the input parameters of the digital twin model and the data source is obtained, that is, the digital twin mapping association.

[0063] Furthermore, in step 3, the numerical model mapping correlation deviation analysis includes the following steps:

[0064] ① Read the simulation results of the digital twin model and determine whether the component model structure has been added or deleted. If such changes have occurred, the reason for the deviation in the digital twin mapping is the change in the component model structure.

[0065] ② Determine whether the input parameters of the component model have been added or deleted. If they have changed, the reason for the deviation in the numerical model mapping is the change in the model input parameters.

[0066] ③If the component model structure and model input parameters have not changed, the reason for the deviation in the numerical model mapping relationship is the change in the numerical model mapping relationship;

[0067] ④ Analyze the causes of the above-mentioned numerical model mapping correlation deviation and obtain the analysis results of the numerical model mapping correlation deviation.

[0068] Furthermore, in step 3, the numerical model mapping correlation evolution includes the following steps:

[0069] ① For changes in component model structure in the numerical model mapping correlation deviation results, identify the component models that have changed. If it is a case of adding components, analyze and construct the numerical model mapping correlation of the added component models. If it is a case of deleting components, delete the numerical model mapping correlation of the component models to obtain the updated numerical model mapping correlation.

[0070] ② For changes in model input parameters in the numerical model mapping correlation deviation results, identify the changed model input parameters. If it is a case of increased model input parameters, it is necessary to analyze the available data sources corresponding to the model input parameters to obtain the updated numerical model mapping correlation.

[0071] ③ To analyze the changes in the numerical model mapping relationship in the mapping correlation deviation results, analyze the characteristics of the model input parameters, determine the data source corresponding to the model input parameters, and obtain the updated numerical model mapping relationship.

[0072] Furthermore, in step 3, the consistency determination includes the following:

[0073] ① The input parameters of the digital twin model all have available data sources;

[0074] ② The digital twin model evolved from the digital model mapping association must meet the consistency requirements of the digital twin model.

[0075] This invention is applicable to real-time data-driven digital twins.

[0076] The advantages of this invention compared to the prior art are:

[0077] By analyzing the mapping relationship between available data sources and digital twin model input parameters, a digital twin-model mapping association is established to guide real-time data-driven digital twin operation. Based on the application requirements and simulation needs of the digital twin model, real-time data-driven dynamic simulation of the digital twin is achieved through digital-model linkage. During the dynamic operation of the digital twin, the consistency of the digital twin-model mapping association is continuously assessed, and an appropriate evolution method is selected based on the mapping deviation to update the digital twin-model mapping association. Through multiple iterations of digital twin-model mapping association construction, digital-model linkage, and dynamic evolution, consistency between the digital twin and the physical entity characteristics is maintained during dynamic operation, achieving real-time data-driven dynamic simulation of the digital twin for subsequent control, prediction, and optimization of the physical entity.

[0078] This invention includes analyzing the model input parameters and available data sources of a digital twin model to construct a mapping relationship between the digital twin and the digital model; analyzing the simulation requirements of the digital twin model based on its application scenario to achieve digital-analog linkage; analyzing the causes of deviations in the digital twin model's mapping relationship based on its structure, evolving the deviated mapping relationships, and determining the consistency of the evolved mapping relationships to achieve the updating and evolution of the mapping relationships. This invention provides an effective method for the dynamic driving of digital twins and, to a certain extent, lays the foundation for the effective application and services of digital twins. Attached Figure Description

[0079] Figure 1 This is a structural block diagram of a digital twin analog-digital linkage system according to the present invention;

[0080] Figure 2 This is a flowchart of the digital twin analog-digital linkage method of the present invention. Detailed Implementation

[0081] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the protection scope of the present invention.

[0082] The dynamic operation of data-driven digital twin models is a key characteristic of digital twins. Therefore, constructing the digital twin-model mapping association and realizing digital twin-model linkage are crucial for the practical application of digital twins. To this end, according to an embodiment of the present invention, a digital twin-model linkage method is proposed, applicable to real-time data-driven digital twins. This method includes analyzing the model input parameters and available data sources of the digital twin model to construct the digital twin-model mapping association; analyzing the simulation requirements of the digital twin model according to the application scenario requirements to realize the digital twin-model linkage; analyzing the causes of deviations in the digital twin-model mapping association based on the structure of the digital twin model, evolving the deviated digital twin-model mapping association, and performing consistency judgment on the evolved digital twin-model mapping association to realize the update and evolution of the digital twin-model mapping association. This invention can provide an effective method for the dynamic driving of digital twins and, to a certain extent, lay the foundation for the effective application and service of digital twins.

[0083] The overall block diagram of the present invention is as follows: Figure 1 As shown, a digital twin analog-digital linkage system of the present invention includes: an analog-digital mapping construction module 1, an analog-digital linkage module 2, and an analog-digital mapping association evolution module 3;

[0084] The flowchart of the digital twin-model linkage method is as follows: Figure 2 As shown, the specific implementation method is as follows:

[0085] ①Based on the application scenario requirements of the digital twin model, analyze the simulation requirements of the digital twin model, determine the model simulation frequency of each component model, and ensure that the dynamic operation of the digital twin model meets the basic application requirements.

[0086] ② To meet the simulation requirements of the digital twin model, determine the consistency threshold of the digital twin model. The consistency threshold must ensure that the simulation of the digital twin model meets the basic requirements. The consistency threshold needs to be determined according to the simulation requirements.

[0087] ③ Obtain data from available data sources, with a data acquisition frequency no less than the determined model simulation frequency, preprocess the acquired data, and store it in the database;

[0088] ④ Determine whether the simulation of the digital twin model has ended. The frequency of this determination should not be less than the determined simulation frequency. If the determination result is that the simulation has ended, proceed to step ⑤. If the determination result is that the simulation has not ended, return to step ③.

[0089] ⑤ Read the data of the physical entity and the simulation results of the digital twin model, and use the difference between the two as the deviation value of the digital twin model;

[0090] ⑥ Compare the deviation value of the digital twin model with the consistency threshold of the digital twin model. If the deviation value of the digital twin model is lower than the consistency threshold of the digital twin model, then the digital twin model meets the consistency requirements of the digital twin model and proceed to step ⑦. Otherwise, it does not meet the consistency requirements of the digital twin model and needs to be further evolved.

[0091] ⑦ Select the data whose time attribute is closest to the current time from the database and use this data as the driving data for the digital twin model;

[0092] ⑧ Based on the structure of the model input parameters, the model-driven data obtained in step ⑦ is converted into a new format. The converted model-driven data must conform to the structural requirements of the model input parameters.

[0093] ⑨ Use the model-driven data obtained in step ⑧ as the model input parameters to obtain a dynamically running digital twin model.

[0094] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A digital twin analog-digital linkage system, characterized in that, include: The module includes a digital model mapping construction module, a digital model linkage module, and a digital model mapping association evolution module. The digital twin mapping construction module, for a given digital twin model, determines the model input parameters and available data sources for that digital twin model. The model input parameters ensure that the simulation of the digital twin model meets basic requirements, and the available data sources are used to acquire data to drive model simulation. Based on the digital twin model input parameters and available data sources, the module analyzes and constructs the digital twin mapping association for that digital twin model. The digital twin mapping association ensures that each input parameter of the digital twin model has a corresponding available data source. The digital twin linkage module analyzes the simulation requirements of the digital twin model based on the application scenario requirements; and realizes the digital twin linkage of the digital twin model based on the determined simulation requirements and the constructed digital twin mapping association to obtain a dynamically running digital twin model. The digital twin mapping association evolution module analyzes the causes of deviations in the digital twin mapping association based on the structure of the digital twin model; evolves the deviated digital twin mapping association based on the determined causes of deviations; verifies the consistency of the evolved digital twin mapping association to obtain the consistency verification result; if the evolved digital twin mapping relationship does not meet the consistency requirements, it needs to be re-evolved to finally obtain a digital twin mapping association that meets the consistency requirements.

2. The digital twin analog-digital linkage system according to claim 1, characterized in that, The model mapping construction module includes the following steps for implementing model mapping correlation analysis: ① For a given digital twin model, which is composed of component models, each component model contains multiple model input parameters; these model input parameters are used to drive the dynamic operation of the component models. ② Determine the input parameters of the component model based on its structural characteristics; ③ Determine the available data sources based on the data acquisition device settings; ④ Analyze the characteristics of the component model input parameters determined in step ②, and determine the data source corresponding to the component model input parameters. The corresponding data source should be included in the data source determined in step ③. ⑤ Based on the data source determined in step ④, the correspondence between the input parameters of the digital twin model and the data source is integrated to obtain the digital twin mapping association.

3. The digital twin analog-digital linkage system according to claim 2, characterized in that, The simulation requirements analysis in the digital-analog linkage module includes the following steps: ① Based on the application requirements of the digital twin model, determine the model simulation frequency of each component model. The model simulation frequency can ensure that the dynamic operation of the digital twin model meets the basic application requirements. ② To meet the simulation requirements of the digital twin model, determine the consistency threshold of the digital twin model. The consistency threshold must ensure that the simulation of the digital twin model meets the basic requirements. The consistency threshold needs to be determined according to the simulation requirements.

4. The digital twin analog-digital linkage system according to claim 1, characterized in that, The digital-analog linkage module includes the following steps in its digital-analog linkage method: ① Obtain data from available data sources, with a data acquisition frequency no less than the determined model simulation frequency, preprocess the acquired data, and store it in the database; ② Determine whether the simulation of the digital twin model has ended. The frequency of this determination should not be less than the determined simulation frequency. If the determination result is that the simulation has ended, proceed to step ③. If the determination result is that the simulation has not ended, return to step ①. ③ Read the data of the physical entity and the simulation results of the digital twin model, and use the difference between the two as the deviation value of the digital twin model; ④ Compare the deviation value of the digital twin model with the consistency threshold of the digital twin model. If the deviation value of the digital twin model is lower than the consistency threshold of the digital twin model, then the digital twin model meets the consistency requirements of the digital twin model and proceed to step ⑤. Otherwise, it does not meet the consistency requirements of the digital twin model and needs to be further evolved. ⑤ Select the data whose time attribute is closest to the current time from the database, and use this data as the driving data for the digital twin model; ⑥ Based on the structure of the model input parameters, the model-driven data obtained in step ⑤ is converted into a new format. The converted model-driven data must conform to the structural requirements of the model input parameters. ⑦ Use the model-driven data obtained in ⑥ as the model input parameters to obtain a dynamically running digital twin model.

5. A digital twin analog-digital linkage system according to claim 2, characterized in that, In the numerical model mapping correlation evolution module, the numerical model mapping correlation deviation analysis includes the following steps: ① Read the simulation results of the digital twin model and determine whether the component model structure has been added or deleted. If such changes have occurred, the reason for the deviation in the digital twin mapping is the change in the component model structure. ② Determine whether the input parameters of the component model have been added or deleted. If they have changed, the reason for the deviation in the numerical model mapping is the change in the model input parameters. ③ If the component model structure and model input parameters have not changed, the reason for the deviation in the numerical model mapping relationship is the change in the numerical model mapping relationship; ④ Analyze the causes of the above-mentioned numerical-to-analog mapping correlation deviation and obtain the analysis results of the numerical-to-analog mapping correlation deviation.

6. A digital twin analog-digital linkage system according to claim 5, characterized in that, The numerical model mapping correlation evolution module includes the following steps: ① For changes in component model structure in the numerical model mapping correlation deviation results, identify the component models that have changed. If it is a case of adding components, it is necessary to analyze and construct the numerical model mapping correlation of the added component models. If it is a case of deleting components, it is necessary to delete the mapping correlation of the component models and obtain the updated numerical model mapping correlation. ② For changes in component model input parameters in the numerical model mapping correlation deviation results, identify the changed model input parameters. If it is a case of increased model input parameters, it is necessary to analyze the available data sources corresponding to the component model input parameters to obtain the updated numerical model mapping correlation. ③ To analyze the changes in the numerical model mapping correlation in the mapping correlation deviation results, analyze the characteristics of the model input parameters, determine the data source corresponding to the model input parameters, and obtain the updated numerical model mapping correlation.

7. A digital twin analog-digital linkage system according to claim 1, characterized in that, In the numerical model mapping association evolution module, the numerical model mapping association consistency verification includes the following: ① The input parameters of the digital twin model all have available data sources; ② The digital twin model evolved from the digital model mapping association must meet the consistency requirements of the digital twin model.

8. A digital twin analog-digital linkage method, characterized in that, Includes the following steps: Step 1: Construct the numerical model mapping association, specifically as follows: 1.1 For a given digital twin model, determine the model input parameters and available data sources for the digital twin model. The model input parameters ensure that the simulation of the digital twin model meets the basic requirements, and the available data sources are used to acquire data to drive the model simulation. 1.2 Based on the input parameters of the digital twin model and the available data sources, analyze the digital twin model mapping association, which ensures that each input parameter of the digital twin model has a corresponding available data source; 1.3 Based on the results of the numerical model mapping correlation analysis, construct the numerical model mapping correlation; Step 2: Perform digital-analog linkage, specifically as follows: 2.1 Based on the application scenario requirements of the digital twin model, analyze the simulation requirements of the digital twin model, determine the model simulation frequency of each component model, and ensure that the dynamic operation of the digital twin model meets the basic application requirements. 2.2 To meet the simulation requirements of the digital twin model, a consistency threshold for the digital twin model is determined. The consistency threshold must ensure that the simulation of the digital twin model meets the basic requirements. This consistency threshold needs to be determined based on the simulation requirements. 2.3 Obtain data from available data sources, with a data acquisition frequency no less than the determined model simulation frequency. Perform data preprocessing on the acquired data and store it in the database. 2.4 Determine whether the simulation of the digital twin model has ended. The frequency of this determination should not be less than the determined simulation frequency. If the determination result is that the simulation has ended, proceed to step 2.

5. If the determination result is that the simulation has not ended, return to step ③. 2.5 Read the data of the physical entity and the simulation results of the digital twin model, and use the difference between the two as the deviation value of the digital twin model; 2.6 Compare the deviation value of the digital twin model with the consistency threshold of the digital twin model. If the deviation value of the digital twin model is lower than the consistency threshold of the digital twin model, the digital twin model meets the consistency requirements of the digital twin model and proceed to step 2.

7. Otherwise, it does not meet the consistency requirements of the digital twin model and needs to be further evolved. 2.7 Select the data whose time attribute is closest to the current time from the database and use this data as the driving data for the digital twin model; 2.8 Based on the structure of the model input parameters, the model-driven data obtained in step ⑦ is converted into a new format. The converted model-driven data must conform to the structural requirements of the model input parameters. 2.9 Use the model-driven data obtained in step 2.8 as the model input parameters to obtain a dynamically running digital twin model; Step 3: Perform the numerical model mapping correlation evolution, specifically implemented as follows: 3.1 Based on the structure of the digital twin model, analyze the reasons for the deviation in the digital twin model's mapping correlation; 3.2 Based on the determined causes of the digital-to-analog mapping deviation, the correlation between the deviation and the digital-to-analog mapping is evolved; 3.3 Perform consistency verification on the evolved mathematical model mapping association to obtain the consistency verification result of the mathematical model mapping association; if the evolved mathematical model mapping relationship does not meet the consistency requirements, it needs to be re-evolved to finally obtain a mathematical model mapping association that meets the consistency requirements.

9. A digital twin analog-digital linkage method according to claim 8, characterized in that, Step 1, the numerical model mapping correlation analysis includes the following steps: ① For a given digital twin model, which is composed of component models, each component model contains multiple model input parameters; these model input parameters are used to drive the dynamic operation of the component models. ② Determine the input parameters of the component model based on its structural characteristics; ③ Determine the available data sources based on the data acquisition device settings; ④ Analyze the characteristics of the model input parameters determined in step ②, and determine the data source corresponding to the component model input parameters. The corresponding data source should be included in the data source determined in step ③. ⑤ Based on the data source determined in step ④, the correspondence between the input parameters of the digital twin model and the data source is integrated to obtain the digital twin mapping association.

10. A digital twin analog-digital linkage method according to claim 8, characterized in that, Step 3, the numerical model mapping correlation deviation analysis, includes the following steps: ① Read the simulation results of the digital twin model and determine whether the component model structure has been added or deleted. If such changes have occurred, the reason for the deviation in the digital twin mapping is the change in the component model structure. ② Determine whether the input parameters of the component model have been added or deleted. If they have changed, the reason for the deviation in the numerical model mapping is the change in the model input parameters. ③ If the component model structure and model input parameters have not changed, the reason for the deviation in the numerical model mapping relationship is the change in the numerical model mapping relationship; ④ Analyze the causes of the above-mentioned numerical-to-analog mapping correlation deviation and obtain the analysis results of the numerical-to-analog mapping correlation deviation.

11. A digital twin analog-digital linkage method according to claim 10, characterized in that, Step 3, the numerical model mapping correlation evolution includes the following steps: ① For changes in component model structure in the deviation results of the numerical model mapping association, identify the component models that have changed. If it is a case of adding components, it is necessary to analyze and construct the numerical model mapping association of the added component models. If it is a case of deleting components, it is necessary to delete the numerical model mapping association of the component models and obtain the updated numerical model mapping association. ② For changes in model input parameters in the numerical model mapping correlation deviation results, identify the changed model input parameters. If it is a case of increased model input parameters, it is necessary to analyze the available data sources corresponding to the model input parameters to obtain the updated numerical model mapping correlation. ③ To address the changes in the numerical model mapping correlation in the mapping correlation deviation results, analyze the characteristics of the model input parameters, determine the data source corresponding to the model input parameters, and obtain the updated numerical model mapping correlation.

12. A digital twin analog-digital linkage method according to claim 8, characterized in that, In step 3, the consistency determination includes the following: ① The input parameters of the digital twin model all have available data sources; ② The digital twin model evolved from the digital model mapping association must meet the consistency requirements of the digital twin model.

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