Modularly configured digital twinning construction testing device

Abstract

The invention relates to the technical field of digitization, and provides a modularly configured digital twinning construction testing device. Comprising a demand receiving analysis module, an entity modeling definition module, a component generation configuration module, a system library integration calling module, a digital model construction module, a simulation test module and a data collection analysis module, system automatic modeling and component generation are carried out, and then a complete digital twin model is constructed. The simulation test module simulates an actual operation scene evaluation model, after data collection and analysis, the model optimization iteration module optimizes model performance by using a machine learning algorithm, in the process, an operator can view a model construction and test result and adjust and optimize according to feedback, finally, efficient and accurate digital twin model construction and test are realized, and the test efficiency is improved. By means of the technical scheme, the problems that in the prior art, flexibility is insufficient, and simulation testing comprehensiveness is poor are solved.

Description

Technical Field

[0001] The present invention relates to the field of digital technology, and in particular to a modularly configured digital twin construction test device. Background Art

[0002] The modularly configured digital twin construction and testing device, as an innovative tool in the field of modern industry and intelligent manufacturing, is widely used in multiple links such as product design, production optimization, fault prediction, and operation and maintenance. Through highly integrated modular design, it can flexibly build a digital twin model to achieve accurate simulation and testing of physical entities or systems. In the product research and development stage, it helps designers to quickly verify the feasibility of the design plan and reduce development costs. In the production process, it optimizes the production process through real-time monitoring and data analysis, and improves production efficiency and product quality. In addition, the device also has powerful fault prediction capabilities, which provides strong support for the preventive maintenance of equipment and ensures the stable operation of the production line.

[0003] In the existing technology, the digital twin construction and testing devices lack an intuitive graphical operation interface, which makes the operator's demand input cumbersome and error-prone, making it difficult to ensure the accuracy and completeness of the data. In addition, the entity modeling process needs to rely on manual intervention and has a low degree of automation. Not only is the efficiency low, but it also cannot accurately reflect the complex properties and behavioral characteristics of physical entities. In addition, the component generation and configuration flexibility is insufficient to meet the diverse needs of users, which limits the adaptability and customizability of the digital twin model. The simulation test link also lacks comprehensive environmental simulation and real-time data analysis capabilities, making it difficult to accurately evaluate the accuracy and reliability of the model. Summary of the invention

[0004] The present invention proposes a digital twin construction and testing device with a modular configuration, which solves the problems of insufficient flexibility of related technical models and poor comprehensiveness of simulation tests.

[0005] The technical solution of the present invention is as follows:

[0006] It includes a demand receiving and parsing module, a physical modeling definition module, a component generation and configuration module, a system library integration and calling module, a digital model building module, a simulation and testing module, a data collection and analysis module, a model optimization and iteration module, and an operator interaction and feedback module. The output end of the demand receiving and parsing module is electrically connected to the input end of the physical modeling definition module, and the output end of the physical modeling definition module is electrically connected to the input ends of the component generation and configuration module and the system library integration and calling module respectively.

[0007] The component generation configuration module is electrically connected to the system library integration calling module, and the output ends of the component generation configuration module and the system library integration calling module are both electrically connected to the input end of the digital model construction module. The output end of the digital model construction module is respectively electrically connected to the input ends of the simulation test module and the data collection and analysis module, the simulation test module is electrically connected to the data collection and analysis module, and the output ends of the data collection and analysis module and the digital model construction module are both electrically connected to the input end of the model optimization iteration module, and the output end of the model optimization iteration module is electrically connected to the input end of the operator interaction feedback module.

[0008] Preferably, the demand receiving and parsing module is responsible for receiving the operator's physical entity information and functional requirements, converting the requirements into a standardized data format through a parsing algorithm, and providing input for subsequent modules. The entity modeling definition module is used to automatically build a digital model of the physical entity according to the output of the demand receiving and parsing module, and define the attribute information of the geometric dimensions and physical properties contained in the entity. The component generation configuration module is used to automatically generate components containing attribute data based on the attribute information of the entity model, and the operator configures and combines the components according to business requirements. The system library integration calling module is used to pre-build a system library according to the data processing system, simulation system and test system, and then automatically call the corresponding system from the system library to process the components and entity models according to business requirements.

[0009] Preferably, the digital model construction module is used to generate a complete digital twin model through entities, components and called systems, and supports the visual display and interactive operation of the model. The simulation test module is used to perform simulation testing on the constructed digital twin model, simulate the actual operation scenario, and verify the accuracy and reliability of the model. The data collection and analysis module is used to collect model operation data during the simulation test, and perform real-time analysis and processing to provide data support for model optimization.

[0010] Preferably, the model optimization iteration module is used to optimize and iterate the digital twin model based on the output results of the data collection and analysis module and introduce a machine learning algorithm to automatically learn the model operation rules to improve the performance and accuracy of the model. The operator interaction feedback module is used to provide an operator interaction interface, through which the operator can view the model building process, simulation test results and optimization iteration records, and receive operator feedback to improve system functions and performance.

[0011] Preferably, the system library integrated calling module includes a system library interface management module, a component matching and scheduling module, and an integrated testing and verification module, and the system library interface management module, the component matching and scheduling module, and the integrated testing and verification module are electrically connected in sequence.

[0012] Preferably, the system library interface management module is responsible for managing the interface of the system library, including interface registration, query, update and deletion operations; the component matching and scheduling module is used to match corresponding components from the system library according to the configured component set, and schedule them for integration; the integration testing and verification module is used to test and verify the integrated component set to ensure that they can collaborate correctly and meet the requirements.

[0013] Preferably, the digital model construction module includes a physical model mapping module, a behavioral model construction module and a visualization rendering module, and the physical model mapping module, the behavioral model construction module and the visualization rendering module are electrically connected in sequence.

[0014] Preferably, the physical model mapping module is used to map the entity model and attribute definition to the physical layer in the digital twin model to construct the physical model. The behavioral model construction module constructs the behavioral layer in the digital twin model based on the physical model to simulate the dynamic behavior and interaction of the entity. The visualization rendering module is used to visualize the data of the physical model and the behavioral model to generate an interactive digital twin model view.

[0015] The working principle and beneficial effects of the present invention are:

[0016] 1. In the present invention, through the setting of the system library integrated call module, the operator inputs the physical entity information through an intuitive graphical interface, the system automatically models and generates components, and then builds a complete digital twin model. The simulation test module simulates the actual operation scenario evaluation model. After data collection and analysis, the model optimization iteration module uses a machine learning algorithm to optimize the model performance. In this process, the operator can view the model construction and test results, and adjust and optimize according to feedback. Finally, efficient and accurate digital twin model construction and testing are achieved, enhancing the model's ability to be close to the actual physical system.

[0017] 2. In the present invention, through the setting of the system library integrated call module, the integrated call module efficiently manages data processing, simulation and test system library interfaces, and uses library files, service interfaces and APIs as the basis for building digital twins. According to needs, the module calls system library resources to improve system flexibility and response speed. The component matching and scheduling module is responsible for matching and scheduling components that meet the needs to ensure compatibility and collaboration between components. Subsequently, the integrated test and verification module tests and verifies the component integration effect to ensure that each component works together to achieve the expected functions, promptly discovers and repairs potential problems, and ensures the accuracy and reliability of the digital twin model, thereby optimizing the construction process and ensuring the high-quality completion of the model.

[0018] 3. In the present invention, the setting of the system library integrated call module is used, and the physical model mapping module maps the details of the physical entity to generate a high-precision digital model. Subsequently, the behavior model construction module constructs a behavior model that reflects the dynamic characteristics of the physical entity through simulation and dynamic modeling. The visualization rendering module uses graphics technology to present the three-dimensional form and interactive effects of the model. The three modules cooperate with each other, with the physical model as the basis, the behavior model adding actions, and the visualization rendering realizing the entity appearance, forming a closed-loop iteration. The user interaction feedback module collects opinions and guides model optimization to ensure that the digital twin model accurately reflects the physical system and provides simulation testing and decision support for operators. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0020] Figure 1 A system block diagram of a test device for building a digital twin of the modular configuration of the present invention;

[0021] Figure 2 It is a schematic diagram of the overall structure of the present invention;

[0022] Figure 3 It is a block diagram of the system library integration calling module of the present invention;

[0023] Figure 4 This is a block diagram of the digital model building module of the present invention.

[0024] In the figure: 1. Demand reception and analysis module; 2. Entity modeling definition module; 3. Component generation and configuration module; 4. System library integration and call module; 5. Digital model construction module; 6. Simulation test module; 7. Data collection and analysis module; 8. Model optimization and iteration module; 9. User interaction feedback module;

[0025] 41. System library interface management module; 42. Component matching and scheduling module; 43. Integration testing and verification module;

[0026] 51. Physical model mapping module; 52. Behavior model building module; 53. Visualization rendering module;

[0027] 10. Housing; 11. Display screen; 12. Protective cover; 13. Cabinet door. DETAILED DESCRIPTION

[0028] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] Example 1

[0030] like Figure 1 to Figure 4 As shown, this embodiment proposes a modularly configured digital twin construction and testing device, including a demand receiving and parsing module 1, an entity modeling definition module 2, a component generation and configuration module 3, a system library integration and calling module 4, a digital model construction module 5, a simulation test module 6, a data collection and analysis module 7, a model optimization and iteration module 8, and an operator interaction and feedback module 9. The output end of the demand receiving and parsing module 1 is electrically connected to the input end of the entity modeling definition module 2, and the output end of the entity modeling definition module 2 is electrically connected to the input end of the component generation and configuration module 3 and the system library integration and calling module 4, respectively. The component generation and configuration module Module 3 is electrically connected to the system library integration calling module 4, the output ends of the component generation configuration module 3 and the system library integration calling module 4 are both electrically connected to the input end of the digital model construction module 5, the output end of the digital model construction module 5 is respectively electrically connected to the input ends of the simulation test module 6 and the data collection and analysis module 7, the simulation test module 6 is electrically connected to the data collection and analysis module 7, the output ends of the data collection and analysis module 7 and the digital model construction module 5 are both electrically connected to the input end of the model optimization iteration module 8, and the output end of the model optimization iteration module 8 is electrically connected to the input end of the operator interaction feedback module 9.

[0031] like Figure 1 to Figure 4 As shown, the demand receiving and parsing module 1 is responsible for receiving the operator's physical entity information and functional requirements, converting the requirements into a standardized data format through a parsing algorithm to provide input for subsequent modules. The entity modeling definition module 2 is used to automatically build a digital model of the physical entity according to the output of the demand receiving and parsing module, and define the attribute information of the geometric dimensions and physical characteristics contained in the entity. The component generation configuration module 3 is used to automatically generate components containing attribute data based on the attribute information of the entity model. The operator configures and combines the components according to business requirements. The system library integration calling module 4 is used to pre-build a system library according to the data processing system, simulation system and test system, and then automatically call the corresponding system from the system library to process the components and entity models according to business requirements.

[0032] like Figure 1 to Figure 4As shown, the digital model construction module 5 is used to generate a complete digital twin model through entities, components and called systems, and supports the visual display and interactive operation of the model. The simulation test module 6 is used to perform simulation test on the constructed digital twin model, simulate the actual operation scenario, and verify the accuracy and reliability of the model. The data collection and analysis module 7 is used to collect model operation data during the simulation test, and perform real-time analysis and processing to provide data support for model optimization. The model optimization iteration module 8 is used to automatically learn the model operation rules according to the output results of the data collection and analysis module and the machine learning algorithm to optimize and iterate the digital twin model, thereby improving the performance and accuracy of the model. The operator interaction feedback module 9 is used to provide an operator interaction interface. The operator views the model construction process, simulation test results and optimization iteration records through the interaction interface, and receives operator feedback at the same time to improve system functions and performance.

[0033] In this embodiment, the operator starts the modularly configured digital twin construction and testing device, and controls the entire system through a computer, thereby coordinating and managing the operation of each module. First, the digital twin construction and testing device includes a shell 10, and the computer includes a display screen 11 and a system. The display screen 11 is installed on the shell 10, and the system is installed inside the shell. A protective cover plate 12 is rotatably installed on the shell 10, and a mouse and a keyboard are arranged inside the protective cover plate 12. Two symmetrically arranged cabinet doors 13 are rotatably installed on the shell 10. After the computer is started, the application program is automatically started. The application program includes software programs for realizing the functions of each module, demand reception and parsing software, entity modeling definition software, and component generation and configuration software. These software programs work together to jointly build and test the digital twin model.

[0034] In the modularly configured digital twin construction and testing device, the demand receiving and parsing module 1 provides an intuitive input platform through a built-in graphical operator interface. The operator inputs the size, material, and color information of the physical entity, as well as the functional requirements, in a form and graphically through the graphical operator interface. This interactive method simplifies the operator's operation process and improves the accuracy and completeness of the input data. The operator starts the process of initializing the device and then starts to build and test the digital twin model.

[0035] Furthermore, the entity modeling definition module 2 is mainly used to connect the demand with the digital reproduction of the physical world. When the parsed demand data is received, the automated modeling process is immediately started. This process mainly uses the three-dimensional modeling software to build a corresponding digital model according to the attribute information of the geometric dimensions and physical characteristics in the demand data. The constructed digital model can not only reflect the appearance of the physical entity, but also includes the internal physical properties and behavioral characteristics, which provides a basis for subsequent analysis and testing;

[0036] In addition, after the entity model is established, the component generation configuration module 3 generates components containing these data according to the attribute information of the entity model. These components are the basic units that constitute the digital model and carry specific functions and attributes. The operator then configures and combines these components accordingly through the graphical operator interface. Whether it is adjusting the parameter settings of the components or combining multiple components into a more complex structure, the operator can freely operate according to his actual needs. Through this flexibility and customizability, the digital twin model has achieved a closer demand for the actual physical system, and also laid a good foundation for subsequent simulation tests and optimization iterations;

[0037] Furthermore, after completing the entity modeling and component configuration, the system enters the digital model construction stage. The digital model construction module 5 receives the output from the entity modeling definition module 2 and the component generation configuration module 3, as well as the system resources provided by the system library integration call module 4. By integrating this information, a complete digital twin model is generated. This module can support the three-dimensional visualization display of the model, and the user can also rotate and scale the model through the graphical operator interface, and view the internal structure of the model, thereby enhancing the practical operator's experience and understanding;

[0038] Next, the simulation test module 6 starts working. This module uses the digital twin model generated by the digital model construction module 5 to simulate the actual operation scenario for simulation testing. By simulating different environmental conditions, workloads and external interference factors, the simulation test module 6 can comprehensively evaluate the accuracy and reliability of the model. Specifically, the data collection and analysis module 7 will collect various types of data generated by the model operation in real time, including state parameters, performance indicators and error logs, and perform preliminary analysis and processing;

[0039] After receiving the output results of the data collection and analysis module 7, the model optimization iteration module 8 optimizes and iterates the digital twin model through a machine learning algorithm. The module automatically learns the model operation rules, adjusts the algorithm parameters and structure, and iterates multiple times to improve the performance and accuracy of the model. The purpose is to make the digital twin model closer to the operating characteristics of the actual physical system and improve the model's predictive ability and application value. During the entire construction and testing process, the operator interactive feedback module can provide an intuitive interface, and the operator can directly view the model construction process, simulation test results, and optimization iteration records. The operator can adjust the model parameters and view detailed data reports through this interface, and provide feedback according to his or her needs. The system will make corresponding adjustments and optimizations based on the operator's feedback, thereby providing a more complete digital twin construction and testing service.

[0040] Finally, the module will also pay attention to operator feedback and adjustments. It will conduct interactive tests with operators through the graphical operator interface and collect feedback from operators on the model usage experience. Based on this feedback, the module will adjust and optimize the model to improve the operator experience and satisfaction. Through continuous self-checking and verification work, the integrity and correctness of the digital twin model are ensured, which also lays the foundation for subsequent simulation testing, data analysis and optimization iterations.

[0041] The operator interaction and feedback module provides an intuitive and easy-to-use graphical operator interface for operators, enhancing the interaction between operators and the system. The purpose is to allow operators to quickly view every step of model construction, from initial data input to the final model construction. All processes are clear at a glance. In addition, operators can also directly observe the results of simulation tests through this interface, including the changing trends of various parameters and the dynamic demonstration of simulation scenarios, so as to have a more comprehensive understanding of the performance and performance of the model;

[0042] In terms of interaction, operators can not only adjust the model's parameters, physical properties and boundary conditions through the operator interface, but also customize the simulation test scenarios and parameters as needed to meet different research and analysis needs. The high degree of customization enables operators to use digital twin models more flexibly. At the same time, the system also provides a detailed data reporting function, and operators can view the model's various data indicators, performance indicators and error logs at any time to conduct more in-depth analysis and optimization of the model.

[0043] Example 2

[0044] like Figure 1 to Figure 4 As shown, based on the same concept as the above-mentioned embodiment 1, this embodiment also proposes that the system library integration call module 4 includes a system library interface management module 41, a component matching and scheduling module 42 and an integration test and verification module 43. The system library interface management module 41, the component matching and scheduling module 42 and the integration test and verification module 43 are electrically connected in sequence. The system library interface management module 41 is responsible for managing the interface of the system library, including interface registration, query, update and deletion operations. The component matching and scheduling module 42 is used to match corresponding components from the system library according to the configured component set, and schedule them for integration. The integration test and verification module 43 is used to test and verify the integrated component set to ensure that they can collaborate correctly and meet the requirements.

[0045] In this embodiment, the system library integration calling module 4 mainly manages the system library interface management module 41. The database includes a data processing system, a simulation system and a test system. The above systems are the basis for supporting the operation of the entire digital twin environment. These systems exist in the form of library files, service interfaces and APIs, and are pre-installed in the computer system to provide support and resources for various business operations. When the system needs to process components or entity models, the system library integration calling module 4 will actively identify and automatically call the corresponding system and library files from the system library according to the needs issued by the operator, thereby improving the flexibility and response speed of the system and ensuring the smooth progress of the digital twin construction process;

[0046] During the digital twin construction process, the component matching and scheduling module 42 is responsible for matching components that meet current needs from the system library, and then scheduling and integrating these components according to business logic and rules. This process requires both high accuracy and efficiency, and needs to consider the compatibility and collaboration capabilities between components. As the components are successfully integrated, the integration test and verification module 43 will immediately enter to conduct comprehensive testing and verification of the integrated component set. The purpose is to ensure that the various components can work together correctly and jointly achieve the expected functions and performance goals. Through testing and verification, potential problems and defects can be discovered and repaired in a timely manner to ensure the accuracy and reliability of the digital twin model.

[0047] Example 3

[0048] like Figure 1 to Figure 4 As shown, based on the same concept as the above-mentioned embodiment 1, this embodiment also proposes that the digital model construction module 5 includes a physical model mapping module 51, a behavioral model construction module 52 and a visualization rendering module 53, including the physical model mapping module 51, the behavioral model construction module 52 and the visualization rendering module 53 are electrically connected in sequence, including the physical model mapping module 51 is used to map the entity model and attribute definition to the physical layer in the digital twin model, and construct the physical model, the behavioral model construction module 52 is based on the physical model, The behavior layer in the digital twin model is constructed to simulate the dynamic behavior and interaction of the entity, and the visualization rendering module 53 is used to visualize the data of the physical model and the behavior model to generate an interactive digital twin model view.

[0049] In this embodiment, the physical model mapping module 51 is the starting point of the digital twin construction process. First, it receives the detailed data of the entity modeling definition module 2, including the geometric dimensions, material properties and physical parameters of the physical entity. Then, the mapping algorithm is started inside the module to parse and convert the data structure of the entity model to ensure that all the details of the physical entity are reproduced in the digital twin environment. During the mapping process, the module will check the data multiple times to ensure that there are no errors, and optimize the data format to meet the subsequent processing requirements. Finally, the physical model mapping module 51 generates a highly accurate digital twin physical model;

[0050] Furthermore, after the physical model mapping module 51 is completed, the behavior model construction module 52 is immediately connected. This module first deeply analyzes the intrinsic properties and motion laws of the physical model, and uses simulation technology and dynamic modeling algorithms to start building the behavior layer in the digital twin model. During the construction process, the module simulates the dynamic response and interactive behavior of the physical entity under different environmental conditions, external stimuli and internal mechanisms to ensure the accuracy and reliability of the model. The behavior model construction module 52 will also perform multiple iterative tests, continuously adjust and optimize the model parameters until it can truly reflect the dynamic characteristics and interactive capabilities of the physical entity. Finally, a digital twin behavior model with rich dynamic behaviors and interactive capabilities is generated;

[0051] In addition, the visualization rendering module 53 is used to connect the digital twin model with the user interface. After receiving the data of the physical model and the behavior model, the graphics rendering engine is started to parse and convert the model data. Through the rendering algorithm and light and shadow processing technology, the module presents the three-dimensional form, texture material, dynamic behavior and interactive effect of the digital twin model. During the rendering process, the module also focuses on optimizing the rendering performance to ensure that users can perform interactive operations in a smooth environment.

[0052] It should be noted that in the digital twin model construction process, the physical model mapping module 51, the behavioral model construction module 52 and the visualization rendering module 53 work closely together. The physical model mapping module 51 first completes the construction of the physical model to provide an accurate data basis for the behavioral model construction module 52. The behavioral model construction module 52 further constructs a behavioral model with dynamic behavior and interactive capabilities based on the physical model. Finally, the visualization rendering module 53 visualizes the data of the physical model and the behavioral model to generate an intuitive digital twin model view for users to view and interact with. At the same time, the user interaction feedback module 9 collects feedback from operating users in this process, and feeds this information back to each module to guide the adjustment and optimization of the model. This closed-loop iterative process ensures that the digital twin model can continuously approach the operating state of the real physical system, providing users with more accurate and reliable simulation testing and optimization decision support.

[0053] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A modularly configured digital twin construction test device, characterized in that: The system comprises a demand receiving and parsing module (1), an entity modeling definition module (2), a component generation and configuration module (3), a system library integration and calling module (4), a digital model building module (5), a simulation and testing module (6), a data collection and analysis module (7), a model optimization and iteration module (8), and an operator interaction and feedback module (9), wherein the output end of the demand receiving and parsing module (1) is electrically connected to the input end of the entity modeling definition module (2), and the output end of the entity modeling definition module (2) is electrically connected to the input ends of the component generation and configuration module (3) and the system library integration and calling module (4) respectively; The component generation configuration module (3) is electrically connected to the system library integration calling module (4); the output ends of the component generation configuration module (3) and the system library integration calling module (4) are both electrically connected to the input end of the digital model construction module (5); the output end of the digital model construction module (5) is respectively electrically connected to the input ends of the simulation test module (6) and the data collection and analysis module (7); the simulation test module (6) is electrically connected to the data collection and analysis module (7); the output ends of the data collection and analysis module (7) and the digital model construction module (5) are both electrically connected to the input end of the model optimization iteration module (8); and the output end of the model optimization iteration module (8) is electrically connected to the input end of the operator interaction feedback module (9).

2. The digital twin construction test device with modular configuration according to claim 1, characterized in that: The demand receiving and parsing module (1) is responsible for receiving the operator's physical entity information and functional requirements, converting the requirements into a standardized data format through a parsing algorithm, and providing input for subsequent modules. The entity modeling definition module (2) is used to automatically build a digital model of the physical entity based on the output of the demand receiving and parsing module, and define the attribute information of the geometric dimensions and physical characteristics contained in the entity. The component generation configuration module (3) is used to automatically generate components containing attribute data based on the attribute information of the entity model. The operator configures and combines the components according to business requirements. The system library integration calling module (4) is used to pre-build a system library based on the data processing system, the simulation system and the test system, and then automatically call the corresponding system from the system library according to business requirements to process the components and entity models.

3. The modularly configured digital twin construction and testing device according to claim 1, characterized in that: The digital model construction module (5) is used to generate a complete digital twin model through entities, components and called systems, and supports visual display and interactive operation of the model. The simulation test module (6) is used to perform simulation tests on the constructed digital twin model, simulate actual operation scenarios, and verify the accuracy and reliability of the model. The data collection and analysis module (7) is used to collect model operation data during the simulation test process, and perform real-time analysis and processing to provide data support for model optimization.

4. The modularly configured digital twin construction and testing device according to claim 1, characterized in that: The model optimization iteration module (8) is used to optimize and iterate the digital twin model based on the output results of the data collection and analysis module and the introduction of a machine learning algorithm to automatically learn the model operation rules, thereby improving the performance and accuracy of the model. The operator interaction feedback module (9) is used to provide an operator interaction interface, through which the operator can view the model construction process, simulation test results, and optimization iteration records, and receive operator feedback to improve system functions and performance.

5. The modularly configured digital twin construction and testing device according to claim 1, characterized in that: The system library integrated calling module (4) comprises a system library interface management module (41), a component matching and scheduling module (42) and an integrated testing and verification module (43), and the system library interface management module (41), the component matching and scheduling module (42) and the integrated testing and verification module (43) are electrically connected in sequence.

6. The modularly configured digital twin construction and testing device according to claim 1, characterized in that: The system library interface management module (41) is responsible for managing the interface of the system library, including interface registration, query, update and deletion operations. The component matching and scheduling module (42) is used to match the corresponding components from the system library according to the configured component set and schedule them for integration. The integration test and verification module (43) is used to test and verify the integrated component set to ensure that they can collaborate correctly and meet the requirements.

7. The modularly configured digital twin construction and testing device according to claim 1, characterized in that: The digital model construction module (5) comprises a physical model mapping module (51), a behavioral model construction module (52) and a visualization rendering module (53), and the physical model mapping module (51), the behavioral model construction module (52) and the visualization rendering module (53) are electrically connected in sequence.

8. The modularly configured digital twin construction and testing device according to claim 1, characterized in that: The physical model mapping module (51) is used to map the entity model and attribute definition to the physical layer in the digital twin model to construct the physical model. The behavioral model construction module (52) constructs the behavioral layer in the digital twin model based on the physical model to simulate the dynamic behavior and interaction of the entity. The visualization rendering module (53) is used to visualize the data of the physical model and the behavioral model to generate an interactive digital twin model view.

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