MBSE-based production line digital twin system design method
By employing the MBSE method and a SysML model-driven process flow, the problems of requirement acquisition and physical-digital traceability for production line digital twin systems were solved, enabling the design of high-fidelity, functionally consistent digital twin systems and improving design quality and maintenance efficiency.
Patent Information
- Application Number
- CN202511643793.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-11
- Publication Date
- 2026-01-30
AI Technical Summary
Existing digital twin development methods for production lines cannot effectively address the design of high-fidelity, traceable digital twin systems for existing physical production lines. This results in gaps in requirements acquisition and physical-digital traceability, leading to resource waste and functional mismatch issues.
Using an MBSE-based approach, a SysML model-driven process is employed to extract scenarios, requirements, activities, performance indicators, modules, functions, and parameters from the physical production line, constructing a digital twin system and establishing a cross-domain traceability link to ensure consistency between the digital model and the physical entity.
It achieves high-fidelity mapping and functional consistency of digital twins for production lines, improves design quality and lifecycle maintenance efficiency, and ensures the accuracy and efficiency of change management.
Smart Images
Figure CN121435535A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of digital twinning, in particular to a production line digital twinning system design method based on MBSE. BACKGROUND
[0002] Digital twinning provides strong technical support for the monitoring, optimization and prediction of production systems by constructing a high-fidelity virtual model of a physical entity and establishing real-time data interaction between the physical and virtual spaces. Among them, the production line digital twinning has great potential to improve production efficiency and optimize process flow. Existing production line digital twinning development mostly adopts a function-oriented approach. This approach starts from ideal digital function requirements and then reversely constructs a model. This approach is suitable for simple and independent unit-level devices. However, the production line is a complex system composed of many heterogeneous devices and complex process flows. Function-oriented development can easily lead to misjudgment of the actual capabilities of the physical production line. For example, developing a complex predictive maintenance software module for a conveyor belt that only needs to monitor speed monitoring eventually finds that its maintenance cost is much higher than the cost of regular replacement, resulting in a huge waste of resources, and the final digital twinning system cannot truly reflect the core constraints and production goals of the production line. This development method leads to a mismatch between the digital twinning and the production line and a demand traceability gap.
[0003] To solve the above problems, some research and applications introduce the MBSE method. MBSE has significant advantages in the forward design of a new system through the model-driven paradigm and can systematically capture the needs of stakeholders. However, the development of a production line digital twinning is not the creation of a new system from scratch, but a high-fidelity digital mapping of an existing and complex physical display. When applying traditional MBSE methods directly to the development of a production line digital twinning system, there are two new problems: demand acquisition gap and physical-digital traceability gap.
[0004] The demand activity gap refers to the fact that the core requirements of the production line digital twinning come from the objective facts of the physical production line and are embedded in its historical constraints and operation processes. Traditional MBSE methods are good at handling forward-looking subjective requirements, but lack a systematic framework to reverse engineer an existing physical entity, making it difficult to accurately and completely acquire the real requirements needed to build a digital twinning. The physical-digital traceability gap refers to the fact that even if some physical requirements are identified through certain means, existing MBSE methods have difficulty in effectively converting these physical constraints into traceable digital design specifications. Its traceability is limited to the internal digital model, and there is a lack of a bridge to establish the association between the physical and digital domains. When the physical production line changes or the digital twinning deviates, developers cannot quickly locate the root cause, which seriously hinders the verification, evolution and life cycle maintenance of the digital twinning.
[0005] Existing technologies, whether employing a function-oriented approach or the traditional MBSE method, cannot effectively solve the core challenge of developing high-fidelity, traceable digital twins for existing physical production lines. Therefore, there is an urgent need for a new method that can systematically derive and map requirements and designs from physical reality, and establish a complete traceability chain. Summary of the Invention
[0006] The purpose of this invention is to provide a design method for a production line digital twin system based on MBSE to solve the above-mentioned technical problems.
[0007] The objective of this invention can be achieved through the following technical solutions: A design method for a production line digital twin system based on MBSE includes the following steps: S1, Run Scenario Construction: Extract the physical operation scenarios of the production line to construct a physical operation scenario model of the production line, and construct a digital operation scenario model of the production line digital twin based on the physical operation scenario model.
[0008] S2, System Requirements Identification: Extract the system requirements that the physical production line meets to construct a physical system requirement model for the production line, and construct a digital twin digital operation scenario model for the production line based on the physical system requirement model, the digital operation scenario model, and stakeholder requirements.
[0009] S3, Activity Analysis: Extract the operational activities that the physical production line needs to perform to construct a physical operational activity model of the production line, and construct a digital operational activity model of the production line digital twin based on the physical operational activity model and the digital system requirement model.
[0010] S4, Validity Measurement Design: Extract the performance indicators that the physical production line meets to construct a physical effectiveness measurement model for the production line, and construct a digital effectiveness measurement model for the digital twin of the production line based on the physical effectiveness measurement model and the digital system requirement model.
[0011] S5, System Module Design: The physical production line's modular components are extracted to construct a physical system module model of the production line, and a digital system module model of the production line's digital twin is constructed based on the physical system module model and the digital operation activity model.
[0012] S6, System Function Planning: The system functions of the physical production line are extracted to construct a physical system function model of the production line. Based on the physical system function model, the digital operation activity model, the digital system module model, and the digital module component model, a digital system function model of the production line digital twin is constructed. S7, Modular Component Design: Extract the components of each module of the physical production line to construct a physical module component model of the production line, and construct a digital module component model of the production line digital twin based on the physical module component model, the digital system functional model, and the digital system module model.
[0013] S8, System Parameter Design: System parameters of each component in the physical production line are extracted to construct a physical system parameter model of the production line, and a digital system parameter model of the production line digital twin is constructed based on the physical system parameter model and the digital module component model.
[0014] Step S1 above includes the following sub-steps: S11, Production Line Operation Scenario Analysis: Based on the production line in the physical world, we analyze the stakeholders, operating environment, and target tasks of the production line, construct a physical operation scenario model, and represent it using SysML use case diagrams.
[0015] S12, Production Line Digital Twin Scenario Construction: Based on the physical operation scenario model, the stakeholders, operating environment, and target tasks of the production line digital twin are identified, and a digital operation scenario model is constructed and represented using SysML use case diagrams.
[0016] Step S2 includes the following steps: S21, Production Line Demand Analysis: Based on the production line in the physical world, we analyze the capability and quality of the production line system, distinguish between the functional and non-functional requirements of the production line, construct a physical system requirement model, and represent it using SysML requirement diagrams.
[0017] S22, Digital Twin Requirements Identification for Production Lines: Based on the digital operation scenario model in S12 and the physical system requirement model in S21, the top-level requirements of stakeholders are decomposed, including functional and non-functional requirements, to construct a digital system requirement model, which is represented by a SysML requirement diagram.
[0018] Step S3 includes the following sub-steps: S31, Production Line Operation Activity Analysis: Based on the production line in the physical world, identify the work activities of the production line, including specific productive activities and supporting non-productive activities, construct a physical operation activity model, and represent it using SysML use case diagrams.
[0019] S32, Digital Twin Activity Recognition for Production Lines: Based on the digital system requirements model in S22, the physical operation activity model in S31, and the digital system structure model in S52, this paper describes the operation activities to be carried out by the digital twin of the production line and the specific implementation process of the operation activities, constructs a digital operation activity model, and represents it using SysML use case diagrams and activity diagrams.
[0020] Step S4 includes the following sub-steps: S41, Production Line Effectiveness Measurement Analysis: Based on production lines in the physical world, specific indicators for the production lines to complete their production tasks are identified, a physical effectiveness measurement model is constructed, and a graph is defined using SysML modules for representation.
[0021] S42, Construction of Effectiveness Measurement for Production Line Digital Twin: Based on the physical effectiveness measurement model in S41 and the digital system requirement model in S22, we design effectiveness measurement indicators for production line digital twins, construct a digital effectiveness measurement model, and represent it using a SysML module definition diagram.
[0022] Step S5 includes the following sub-steps: S51, Production Line System Structure Analysis: Based on the production line in the physical world, identify its main logical components, including module composition and interface interaction between modules, construct a physical system structure model, and represent it using SysML module definition diagrams and internal module diagrams.
[0023] S52, Digital Twin Structure Design for Production Lines: Based on the physical system structure model in S41, the digital operation activity model in S32, and the physical system structure model in S51, the logical modules and interaction interfaces of the production line digital twin are designed, the digital system structure model is constructed, and it is represented by SysML module definition diagram and internal module diagram.
[0024] Step S6 includes the following sub-steps: S61, Production Line Function Analysis: Based on the production line in the physical world, identify the functions of the production line module, including function description and function implementation logic, construct a physical system functional model, and represent it using SysML module definition diagrams and activity diagrams.
[0025] S62, Production Line Digital Twin Function Planning: Based on the digital system structure model in S52, the digital operation activity model in S32, the digital module component model in S72, and the physical system function model in S61, the detailed functions and functional logic required for each module of the production line digital twin system to complete its operation activities are defined, and a digital system function model is constructed, which is represented by SysML module definition diagrams and activity diagrams.
[0026] Step S7 includes the following sub-steps: S71, Production Line Component Analysis: Based on the production line in the physical world, identify the component composition and interface composition of the production line module, construct a physical module component model, and represent it using SysML module definition diagram and internal module diagram.
[0027] S72, Digital Twin Component Design for Production Lines: Based on the digital system functional model in S62, the digital system structural model in S42, and the physical module component model in S71, each digital module is decomposed into more specific components, classes, or services, and their interfaces and interactions are defined to construct a digital module component model, which is represented by SysML module definition diagrams and internal module diagrams.
[0028] Step S8 includes the following steps: S81, Production Line Parameter Analysis: Based on the production line in the physical world, identify the physical characteristics of the production line, capture the specific attributes, parameters and constraints of each component, construct a physical parameter model, and represent it using SysML module definition diagrams and parameter diagrams.
[0029] S82, Production Line Digital Twin Parameter Design: Based on the digital module component model in S72 and the physical parameter model in S81, we define various configuration parameters, algorithm parameters and performance constraints required for the production line digital twin, construct a digital parameter model, and represent it using SysML module definition diagrams and parameter diagrams.
[0030] In this invention, information transfer and traceability between the physical and digital domains are achieved through a data model-driven end-to-end traceability mechanism. In steps S1-S6 above, the key features of the physical model are extracted and formally defined as a structured data information model, which includes data information and data flow. This data information model, as an immutable, authoritative, and single source of implementation, strictly constrains and drives the design and construction of the corresponding digital twin model, thereby establishing a cross-domain, end-to-end traceability link.
[0031] The beneficial effects of this invention are: This method ensures functional consistency by starting from physical entities and using systematic reverse analysis to obtain requirements and design basis. It ensures that the final digital twin of the production line is a high-fidelity mapping of its physical counterpart in terms of function, structure, behavior and constraints, thus fundamentally solving the functional mismatch problem caused by function-oriented development. A complete cross-domain traceability chain has been established: by introducing a data information model as a bridge between the physical and digital domains, this method establishes a clear end-to-end, two-way traceability path from the actual needs, structure, and components of the production line to various design elements of the digital twin. Improved design quality and efficiency of digital twin lifecycle maintenance: The model-based systematic design process ensures the integrity of the system definition and avoids impractical functions or undefined constraints. When changes or upgrades occur in the production line, the impact of changes can be clearly analyzed through the model, and the changes are automatically propagated throughout the entire model system, greatly improving the efficiency and accuracy of digital twin system verification, validation, and lifecycle evolution. Attached Figure Description
[0032] The invention will now be further described with reference to the accompanying drawings.
[0033] Figure 1 This is a diagram illustrating the overall architecture of the modeling method of this invention. Figure 2 This is a flowchart illustrating the implementation of the scenario construction in step S1 of this invention. Figure 3 This is a flowchart illustrating the implementation of step S2, system requirement identification, in this invention. Figure 4 This is a flowchart illustrating the implementation of step S3, the activity analysis, in this invention. Figure 5 This is a flowchart illustrating the implementation of the validity measurement design in step S4 of this invention. Figure 6 This is a flowchart illustrating the implementation of the system module design in step S5 of this invention. Figure 7 This is a flowchart illustrating the implementation of step S6, system function planning, in this invention. Figure 8 This is a flowchart illustrating the implementation of the module component design in step S7 of this invention. Figure 9 This is a flowchart illustrating the implementation of step S8, system parameter design, in this invention. Figure 10 This is an end-to-end bidirectional traceability matrix in one embodiment of the present invention. Detailed Implementation
[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0035] Reference Figure 1 The MBSE-based production line digital twin system design method of the present invention includes the following steps: S1, Run Scenario Construction: Extract the physical operation scenarios of the production line to construct a physical operation scenario model of the production line, and construct a digital operation scenario model of the production line digital twin based on the physical operation scenario model.
[0036] S2, System Requirements Identification: Extract the system requirements that the physical production line meets to construct a physical system requirement model for the production line, and construct a digital twin digital operation scenario model for the production line based on the physical system requirement model, the digital operation scenario model, and stakeholder requirements.
[0037] S3, Activity Analysis: Extract the operational activities that the physical production line needs to perform to construct a physical operational activity model of the production line, and construct a digital operational activity model of the production line digital twin based on the physical operational activity model and the digital system requirement model.
[0038] S4, Validity Measurement Design: Extract the performance indicators that the physical production line meets to construct a physical effectiveness measurement model for the production line, and construct a digital effectiveness measurement model for the digital twin of the production line based on the physical effectiveness measurement model and the digital system requirement model.
[0039] S5, System Module Design: The physical production line's modular components are extracted to construct a physical system module model of the production line, and a digital system module model of the production line's digital twin is constructed based on the physical system module model and the digital operation activity model.
[0040] S6, System Function Planning: The system functions of the physical production line are extracted to construct a physical system function model of the production line. Based on the physical system function model, the digital operation activity model, the digital system module model, and the digital module component model, a digital system function model of the production line digital twin is constructed. S7, Modular Component Design: Extract the components of each module of the physical production line to construct a physical module component model of the production line, and construct a digital module component model of the production line digital twin based on the physical module component model, the digital system functional model, and the digital system module model.
[0041] S8, System Parameter Design: System parameters of each component in the physical production line are extracted to construct a physical system parameter model of the production line, and a digital system parameter model of the production line digital twin is constructed based on the physical system parameter model and the digital module component model.
[0042] Example A design method for a production line digital twin system based on MBSE includes the following steps: S1, Scenario Construction, refer to Figure 2 It includes the following steps: S11, Production Line Operation Scenario Analysis: Starting with the physical production line, this section analyzes the stakeholders directly related to the production line, the operating environment of the production line, and the target tasks that the production line needs to accomplish. Based on the above analysis, a physical operation scenario model is constructed using SysML use case diagrams.
[0043] S12, Production Line Digital Twin Scenario Construction: Based on the existing physical operation scenario model, through mapping and transformation, the stakeholders, operating environment and target tasks of the production line digital twin in the digital space are identified and defined. Finally, SysML use case diagrams are used to construct the digital operation scenario model.
[0044] S2, System Requirements Identification, Reference Figure 3 It includes the following steps: S21, Production Line Demand Analysis: Conduct demand analysis on the physical production line to clarify the functional requirements required to complete production tasks and the non-functional requirements to support its normal operation. Use SysML demand diagrams to construct a physical system demand model.
[0045] S22, Production Line Digital Twin Requirements Identification: Based on the digital operation scenario model and the top-level requirements of stakeholders, the target tasks are decomposed, and then functional requirements are matched with physical system requirements. The functional requirements that serve or enhance physical requirements and the non-functional requirements necessary to maintain the stable operation of the digital twin itself are output. The digital system requirements model is constructed using SysML requirement diagrams.
[0046] S3, Operational Activity Analysis, Reference Figure 4 It includes the following steps: S31, Production Line Operation Activity Analysis: Identify and distinguish between directly related productive activities and their auxiliary non-productive activities in the physical production line, and construct a physical operation activity model using SysML use case diagrams.
[0047] S32, Production Line Digital Twin Activity Identification: Based on the decomposition of the digital system requirements model and referring to the physical operation activity model, determine the various activities to be carried out to achieve the digital requirements. Use SysML use case diagrams to describe these activities, and map them to the digital system modules designed in step S5 to verify the logical correctness of the activity flow. Use SysML activity diagrams to construct the implementation mechanism of the activities, which together constitute the digital operation activity model.
[0048] S4, Validity Measurement Design, Reference Figure 5 It includes the following steps: S41, Production Line Effectiveness Measurement Analysis: This analysis measures various performance indicators used to measure the completion of physical production line tasks, and constructs a physical effectiveness measurement model using SysML module definition graphs.
[0049] S42, Construction of Effectiveness Measurement for Production Line Digital Twin: Based on the physical effectiveness measurement model and combined with the non-functional requirements in the digital system requirement model, a set of quantifiable and objective evaluation indicators is designed for the production line digital twin. The digital effectiveness measurement model is also constructed using SysML module definition graph.
[0050] S5, System Module Design, Refer to Figure 6 It includes the following steps: S51, Production Line System Structure Analysis: Based on the physical production line, we perform block component analysis and interface component analysis to obtain the modular composition of the physical production line and the logical interfaces between its modules. We then use SysML module definition diagrams and internal module diagrams to construct a physical system module model.
[0051] S52, Production Line Digital Twin Structure Design: Based on the digital operation activity model, functional clustering is performed, and referring to the division method of the physical system module model, the system logic modules and their interaction interfaces of the production line digital twin are designed. The digital system module model is constructed using SysML module definition diagram and internal module diagram.
[0052] S6, System Function Planning, Reference Figure 7 It includes the following steps: S61, Production Line Functional Analysis: Analyze the specific functions, performance indicators, and constraints of each module in the physical system, as well as the implementation mechanism of the functions. Use SysML module definition diagrams and activity diagrams to construct a functional model of the physical system.
[0053] S62, Production Line Digital Twin Function Planning: Within the framework of the digital system modules, each module is planned to have the specific functions required to complete digital operation activities, ensuring that there is a clear service relationship between digital functions and physical functions, and designing the functional mechanism based on the digital module component model in S7, using SysML module definition diagrams and activity diagrams to construct the digital system functional model.
[0054] S7, Modular Component Design, Reference Figure 8 It includes the following steps: S71, Production Line Component Analysis: Each logical module of the physical production line is further decomposed to identify its internal component composition and physical interface information. A physical module component model is constructed using SysML module definition diagrams and internal module diagrams.
[0055] S72, Production Line Digital Twin Component Design: Based on the digital system functional model, functional analysis is performed, and then the structure is refined based on the digital system structural model. Each digital module is refined into more specific components, classes or services, and the precise interfaces and interaction relationships between them are defined. The digital module component model is constructed using SysML module definition diagrams and internal module diagrams.
[0056] S8, System Parameter Design, Reference Figure 9 This includes the following steps: S81, Production Line Parameter Analysis: Collect and analyze the specific attributes, parameter values and physical constraints of each component in the physical production line, and construct a physical system parameter model using SysML module definition diagrams and parameter diagrams.
[0057] S82, Production Line Digital Twin Parameter Design: Based on the digital module component model and the parametric physical system parameter model, various configuration parameters, algorithm parameters and performance constraints are defined for the digital twin system. The digital system parameter model is constructed using SysML module definition diagrams and parameters.
[0058] In embodiments of the present invention, to achieve traceability from the physical domain to the digital domain, in each step S1-S6, the key features of the physical model are formalized into data information and data flow using SysML's "Block" and "flow" elements, and encapsulated into a structured data information model. This data model serves as the driving object, and the data information and data flow it contains strictly constrain the construction process of the corresponding digital model. It also demonstrates to developers the end-to-end traceability relationship between the physical and digital domains, as shown in the reference... Figure 10This demonstrates the end-to-end traceability established using the method of this invention. The rows and columns of the matrix represent various model elements in the system lifecycle, and the markers at the intersections of rows and columns indicate direct traceability relationships between elements supported by the data model. For example, this matrix can clearly show how the requirement for a digital twin of a production line is derived from the requirement of the physical production line, and which digital modules collaboratively realize this digital requirement.
[0059] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the present invention should still fall within the scope of the present invention.
Claims
1. A MBSE-based production line digital twin system design method, characterized in that, Comprising the following steps: S1, running scenario construction: extracting the running scenario of the physical production line to construct a physical running scenario model of the production line, and constructing a digital running scenario model of the production line digital twin based on the physical running scenario model; S2, system requirement identification: extracting the system requirements met by the physical production line to construct a physical system requirement model of the production line, and constructing a digital running scenario model of the production line digital twin based on the physical system requirement model, the digital running scenario model and the stakeholder requirements; S3, running activity analysis: extracting the running activities required to be performed by the physical production line to construct a physical running activity model of the production line, and constructing a digital running activity model of the production line digital twin based on the physical running activity model and the digital system requirement model; S4, effectiveness metric design: extracting the performance indicators met by the physical production line to construct a physical effectiveness metric model of the production line, and constructing a digital effectiveness metric model of the production line digital twin based on the physical effectiveness metric model and the digital system requirement model; S5, system module design: extracting the module composition of the physical production line to construct a physical system module model of the production line, and constructing a digital system module model of the production line digital twin based on the physical system module model and the digital running activity model; S6, system function planning: extracting the system functions possessed by the physical production line to construct a physical system function model of the production line, and constructing a digital system function model of the production line digital twin based on the physical system function model, the digital running activity model, the digital system module model and the digital module component model; S7, module component design: extracting the component composition of each module of the physical production line to construct a physical module component model of the production line, and constructing a digital module component model of the production line digital twin based on the physical module component model, the digital system function model and the digital system module model; S8, system parameter design: extracting the system parameters of each component of the physical production line to construct a physical system parameter model of the production line, and constructing a digital system parameter model of the production line digital twin based on the physical system parameter model and the digital module component model.
2. The MBSE-based production line digital twin system design method of claim 1, wherein, In the S1 to S6, after analyzing the physical production line to construct the physical model, further comprising: extracting the modeling features of the physical model, using SysML module definition diagram and activity diagram to construct a data information model carrying structured data information and data flow, and using the data information model to drive the construction of the corresponding digital model to realize the traceability from the physical domain to the digital domain.
3. The MBSE-based production line digital twin system design method of claim 1, wherein, In the S1, the model elements of the physical running scenario model and the digital running scenario model include stakeholders, running environment, production line / production line digital twin and target task.
4. The MBSE-based production line digital twin system design method of claim 1, wherein, In the S2, the model elements of the physical system requirement model include functional requirements representing the production tasks required to be performed by the production line and non-functional requirements guaranteeing the normal operation of the production line; The model elements of the digital system requirement model include functional requirements matched with the physical system requirements and non-functional requirements necessary to guarantee the operation of the production line digital twin itself.
5. The MBSE-based production line digital twin system design method of claim 1, wherein, In the S3, the digital operation activity model includes activity description and activity mechanism, wherein the logical flow of the activity mechanism needs to be verified by feedback of the digital system module model built in the S5.
6. The MBSE-based production line digital twin system design method of claim 1, wherein, In the S5, the physical / digital system structure model elements include module composition and interface composition.
7. The MBSE-based production line digital twin system design method of claim 1, wherein, In the S6, the digital system function model includes function description and function mechanism, wherein the logical flow of the function mechanism needs to be verified by feedback of the digital module component model built in the S7.
8. The MBSE-based production line digital twin system design method of claim 1, wherein, In the S7, the physical / digital module component model includes part composition and interface composition.
9. The MBSE-based production line digital twin system design method of claim 1, wherein, In the S8, the physical / digital parameter model includes parameter composition, attribute composition and constraint composition.