A method for establishing a digital twin model

Abstract

The application relates to the field of digital twin modeling, in particular to a digital twin model establishment method, and the specific steps are as follows: S1, rule and constraint model; S2, attribute method modeling; S3, data model; S4, entity modeling; S5, fusion model; S6, data twin model; through the attribute method modeling of the application, the digital twin modeling steps are divided and disassembled to improve the universality of the modeling method, the disassembly of the digital twin model modeling steps is more suitable for the cooperation of a plurality of teams and the modular assembly model, the expansibility and utilization of the established model are improved; according to the evaluation standards of each modeling sub-link, the differences of similar links in different scene modeling processes are reduced, and the modular degree of the model is further improved.

Description

Technical Field

[0001] This invention relates to the field of digital twin modeling, specifically a method for establishing a digital twin model. Background Technology

[0002] Digital twin technology is developing rapidly, and its integration with various industries has become a trend. Existing digital twin applications exhibit considerable uniqueness in their modeling, meaning they address a specific problem by analyzing that problem and then building a model based on that analysis. Current modeling techniques are highly correlated with the usage scenarios, necessitating the establishment of an overall framework for the digital twin model after analyzing the usage scenarios. Summary of the Invention

[0003] To address the aforementioned problems, this invention proposes a method for establishing a digital twin model.

[0004] A method for establishing a digital twin model, the specific steps of which are as follows:

[0005] S1. Rule and Constraint Model: The rule and constraint information is stored in XML language. When it is necessary to assign these rules and constraints to the model, the corresponding XML language file is extracted and converted into a program file.

[0006] S2. Attribute-based modeling: Analyze the twin scene to determine the attributes and methods of each entity model component, conduct a comprehensive analysis of the required attributes and methods to extract common attributes and methods, and implement them in a modular and component-based programming manner;

[0007] S3. Data Model: Through the function and model analysis of the establishment process of different types of models, relevant data in the twin scenario are selected and discarded. After the relevant data is determined, the relationship between the data is compared and analyzed to determine one or more main data, namely the primary key, to standardize the storage and retrieval of data.

[0008] S4. Solid Modeling: Model the static solids in the twin scene. For the main components, try to restore the solids as much as possible, and simplify or ignore the secondary solid elements.

[0009] S5, Fusion Model:

[0010] a. By analyzing the interaction range between different components of the entity model and the attribute and method models, as well as the driving method and effect of the rule constraint model on the entity model and its attributes and methods;

[0011] b. By mapping action sequence relationships, process constraints, energy flow, information flow, and material flow into the model, the models generate logic and behavior that fit reality.

[0012] S6. Data Twin Model: Data interaction is performed in the order of entity model, attribute model, constraint / rule model, data model, and fusion model.

[0013] The method component in step S2 needs to provide a flexible external interface to enable method execution in different forms.

[0014] The restored entity in step S4 includes the robot model in the assembly workbench, the relative position of the robot model in the workbench, and the relative position of the assembly materials in the workbench.

[0015] The secondary entity elements in step S4 are specifically the wiring in the assembly workbench and the air pipes connecting the cylinders.

[0016] In step S5, the driving method and effect of the rule constraint model on the entity model and its attributes and methods are specifically the constraints on the physical properties of the assembly materials and the constraints on the robot's motion behavior in the assembly platform.

[0017] The entity model in step S6 needs to be compared and verified, and the design drawings need to be compared.

[0018] The attribute model in step S6 needs to be verified for consistency. Variable rules / constraints are manually set, and the behavior is compared to whether it is generated according to the predetermined rules / constraint logic.

[0019] The constraint / rule model in step S6 is specifically anchor point verification. It uses prefabricated or verified entity models and attribute models and sets anchor points at key positions or in logic on them to verify whether the running logic and behavior are consistent with the design.

[0020] The data model in step S6 needs to be verified by data comparison. That is, a copy of the data with clear expectations is distributed to the comparison stage before and after the model processing to verify the correctness of the data processing results. If the verification is passed in the research and development and debugging stage, it is considered qualified.

[0021] The fusion model in step S6 relies on the correctness and accuracy of the above model and is verified by running the complete design scenario or a specially designed scenario.

[0022] The beneficial effects of this invention are: by dividing and decomposing the digital twin modeling steps through the attribute method of this invention, the versatility of the modeling method is improved. The decomposition of the digital twin modeling steps is more suitable for multi-person team collaboration and modular assembly of models, thereby improving the scalability and utilization of the model. The evaluation criteria for each modeling sub-step reduce the differences in similar steps in different scenarios, further improving the modularity of the model. Attached Figure Description

[0023] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0024] Figure 1 This is a schematic diagram of the process structure of the present invention. Detailed Implementation

[0025] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below.

[0026] like Figure 1 As shown, a method for establishing a digital twin model includes the following specific steps:

[0027] S1. Rule and Constraint Model: The rule and constraint information is stored in XML language. When it is necessary to assign these rules and constraints to the model, the corresponding XML language file is extracted and converted into a program file.

[0028] S2. Attribute-based modeling: Analyze the twin scene to determine the attributes and methods of each entity model component, conduct a comprehensive analysis of the required attributes and methods to extract common attributes and methods, and implement them in a modular and component-based programming manner;

[0029] S3. Data Model: Through the function and model analysis of the establishment process of different types of models, relevant data in the twin scenario are selected and discarded. After the relevant data is determined, the relationship between the data is compared and analyzed to determine one or more main data, namely the primary key, to standardize the storage and retrieval of data.

[0030] S4. Solid Modeling: Model the static solids in the twin scene. For the main components, try to restore the solids as much as possible, and simplify or ignore the secondary solid elements.

[0031] S5, Fusion Model:

[0032] a. By analyzing the interaction range between different components of the entity model and the attribute and method models, as well as the driving method and effect of the rule constraint model on the entity model and its attributes and methods;

[0033] b. By mapping action sequence relationships, process constraints, energy flow, information flow, and material flow into the model, the models generate logic and behavior that fit reality.

[0034] S6. Data Twin Model: Data interaction is performed in the order of entity model, attribute model, constraint / rule model, data model, and fusion model.

[0035] Specifically, in step S1, after analyzing the twin scene, the extraction of rules and constraints related to or primarily related to the scene is analyzed to determine whether there are any formulas or data. This is because modeling real-world physical constraints in digital modeling is difficult and the results are inferior to other models. Therefore, only the main or relevant constraints and rules are extracted for modeling. The specific rules and constraints are as follows:

[0036]

[0037] Analysis results: Analysis of the table above shows that the constraints that have a significant impact on assembly accuracy during this assembly operation are gravity constraints, rigid body constraints, and linear motion constraints. Inertial constraints can be ignored under low speed and light load conditions.

[0038] The method component in step S2 needs to provide a flexible external interface to implement method execution in different forms, as detailed below:

[0039]

[0040] Analysis results: The analysis of the table above shows that the general properties in this example include mass, rigid body, and linear motion. For the mass property, the coordinates of the center of mass and the mass size are set as its main characteristics; for the rigid body property, a space set is set as its main characteristics; and for the linear motion property, its motion relative to the coordinate system is set as its main characteristics.

[0041] Specifically, step S3, the establishment of the data model, depends on the functions of the twin scene and the above three steps. The establishment of the data model should be interspersed among the various modeling steps.

[0042] Specifically, in step S3, taking the assembly workbench as an example, the data model selects the timestamp as the primary key, linking the motion data of each joint of the robot with the assembly status.

[0043] The restored entity in step S4 includes the robot model in the assembly workbench, the relative position of the robot model in the workbench, and the relative position of the assembly materials in the workbench.

[0044] The attribute-based modeling method of this invention breaks down the digital twin modeling steps to improve the versatility of the modeling method. The breakdown of the digital twin modeling steps is more suitable for multi-person team collaboration and modular assembly of models, improving the scalability and utilization of the model. The evaluation criteria for each modeling sub-step reduce the differences in similar steps in different scenarios, further improving the modularity of the model.

[0045] The secondary entity elements in step S4 are specifically the wiring in the assembly workbench and the air pipes connecting the cylinders.

[0046] Specifically, the specific rules for model fusion in step S5 for different scenarios and needs should be analyzed in detail.

[0047] In step S5, the driving method and effect of the rule constraint model on the entity model and its attributes and methods are specifically the constraints on the physical properties of the assembly materials and the constraints on the robot's motion behavior in the assembly platform.

[0048] The entity model in step S6 needs to be compared and verified, and the design drawings need to be compared.

[0049] The attribute model in step S6 needs to be verified for consistency. Variable rules / constraints are manually set, and the behavior is compared to whether it is generated according to the predetermined rules / constraint logic.

[0050] The constraint / rule model in step S6 is specifically anchor point verification. It uses prefabricated or verified entity models and attribute models and sets anchor points at key positions or in logic on them to verify whether the running logic and behavior are consistent with the design.

[0051] The data model in step S6 needs to be verified by data comparison. That is, a copy of the data with clear expectations is distributed to the comparison stage before and after the model processing to verify the correctness of the data processing results. If the verification is passed in the research and development and debugging stage, it is considered qualified.

[0052] The fusion model in step S6 relies on the correctness and accuracy of the above model and is verified by running the complete design scenario or a specially designed scenario.

[0053] Specifically, the foundation for data twins lies in data interaction. For a specific scenario, the first step in establishing its twin data model is to determine its data type and data volume. In general production practice, the amount of data generated and recorded is insufficient to reproduce the real world. Therefore, the general method for determining the data type and data volume is to summarize the data information of the hardware devices and sensors in the actual scenario, and use this as the basis for the subsequent establishment of the data model.

[0054] Specifically, after determining the overall data in step S6, a correlation analysis needs to be performed on all the data. For example, the industrial robot data that can be collected generally includes relative displacement and velocity. In data transmission within a local area network, displacement and velocity information can be considered identical, thus removing velocity data from the data model. After this step, the data model should contain all the obtainable, independent data. At this point, a portion of the data can be selected as the main data to identify the characteristics of the data, according to the needs of the twinning objective.

[0055] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely prisms of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for establishing a digital twin model, characterized in that: The specific steps are as follows: S1. Rule and Constraint Model: The rule and constraint information is stored in XML language. When it is necessary to assign these rules and constraints to the model, the corresponding XML language file is extracted and converted into a program file. S2. Attribute-based modeling: Analyze the twin scene to determine the attributes and methods of each entity model component, conduct a comprehensive analysis of the required attributes and methods to extract common attributes and methods, and implement them in a modular and component-based programming manner; S3. Data Model: Through the function and model analysis of the establishment process of different types of models, relevant data in the twin scenario are selected and discarded. After the relevant data is determined, the relationship between the data is compared and analyzed to determine one or more main data, namely the primary key, to standardize the storage and retrieval of data. S4. Entity Modeling: Model static entities in the twin scene, restore the entities for the main components, and simplify or ignore the secondary entity elements. S5, Fusion Model: a. By analyzing the interaction range between different components of the entity model and the attribute and method models, as well as the driving methods and effects of the rule and constraint models on the entity model and its attributes and methods; b. By mapping action sequence relationships, process constraints, energy flow, information flow, and material flow into the model, the models generate logic and behaviors that conform to reality. S6. Data Twin Model: Data interaction is performed in the order of entity model, attribute and method model, rule and constraint model, data model, and fusion model.

2. The method for establishing a digital twin model according to claim 1, characterized in that: The method in step S2 provides an external interface.

3. The method for establishing a digital twin model according to claim 1, characterized in that: The restored entity in step S4 includes the robot model in the assembly workbench, the relative position of the robot model in the workbench, and the relative position of the assembly materials in the workbench.

4. The method for establishing a digital twin model according to claim 1, characterized in that: The secondary entity elements in step S4 are specifically the wiring in the assembly workbench and the air pipes connecting the cylinders.

5. The method for establishing a digital twin model according to claim 1, characterized in that: In step S5, the driving method of the rule and constraint model on the entity model and its attributes and methods is specifically the constraint on the physical properties of the assembly materials and the constraint on the robot's motion behavior in the assembly platform.

6. The method for establishing a digital twin model according to claim 1, characterized in that: The entity model in step S6 needs to be compared and verified.

7. The method for establishing a digital twin model according to claim 1, characterized in that: The attribute model in step S6 needs to be verified for consistency. Variable rules / constraints are manually set, and the behavior is compared to whether it is generated according to the predetermined rules / constraint logic.

8. The method for establishing a digital twin model according to claim 1, characterized in that: The rules and constraints model in step S6 is verified using the anchor point verification method. Pre-made or verified entity models and attribute and method models are used, and anchor points are set at key locations or in the logic to verify whether the running logic and behavior are consistent with the design.

9. The method for establishing a digital twin model according to claim 1, characterized in that: The data model in step S6 needs to be verified by data comparison, that is, the data with clear expectations is compared and verified with the data processed by the model. If the verification is passed during the research and development and debugging stage, it is considered qualified.

10. The method for establishing a digital twin model according to claim 1, characterized in that: The fusion model in step S6 relies on the accuracy and precision of the entity model, attribute and method model, rule and constraint model, and data model, and is verified by running the complete design scenario or a specially designed scenario.

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