Digital twin system for realizing data-driven design and manufacturing process fusion

Abstract

The invention discloses a digital twin system for realizing data-driven design and manufacturing process fusion, and relates to the technical field of intelligent manufacturing. According to the technical scheme of high integration, physical equipment in the physical world and the operation process of the physical equipment are digitized, and therefore more efficient production management and more intelligent manufacturing processes are achieved. The close cooperation among the components ensures that the system can respond to changes in real time, optimizes resource configuration, and improves the production efficiency and the product quality.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent manufacturing, and particularly relates to a digital twin system for realizing the integration of data-driven design and manufacturing processes. Background Art

[0002] The statements in this section only provide background information related to the present disclosure and may not constitute prior art.

[0003] Through its unique management features, intelligent manufacturing provides strong support and optimization tools for the production operation management and strategic planning of enterprises. It combines advanced technologies such as the Internet, big data, and artificial intelligence with advanced manufacturing technologies, not only promoting the innovation of the digital twin-driven manufacturing model, but also realizing the synergy effect of economies of scale and economies of scope, solving the contradiction between efficiency and flexibility in traditional manufacturing management, and promoting user demand-oriented management innovation, providing enterprises with a diversified scenario analysis platform for simulation and optimization.

[0004] As a bridge connecting the physical world and the virtual world, digital twin technology uses accurate data to map the entire life cycle of physical equipment and realizes the integration of multi-disciplinary, multi-physical quantity, multi-scale, and multi-probability simulations in a virtual environment. This technology originated in the aerospace field and has since been extended to multiple industries such as manufacturing, becoming an important means to improve the level of product design, manufacturing processes, and even the management of the entire engineering construction project. In the context of intelligent manufacturing, digital twin not only supports the real-time monitoring and predictive maintenance of equipment, but also helps factories conduct simulation before construction, maintain information interaction after completion, and continuously improve manufacturing processes.

[0005] For discrete manufacturing enterprises, existing production process monitoring and management information systems are often independent of each other and lack a linkage mechanism, which is particularly insufficient when dealing with emergencies.

[0006] Therefore, it is crucial to introduce a digital twin system for realizing the integration of data-driven design and manufacturing processes. This system can break information silos, enable effective communication and collaboration among subsystems, thereby improving production safety and efficiency and reducing potential risks and safety hazards.

[0007] In summary, digital twin technology has great potential in intelligent manufacturing, but to fully play its role, it is necessary to further deeply understand its concept, framework, and development methods to overcome current challenges and promote its wide application. Summary of the Invention

[0008] The object of the present invention is to provide a digital twin system that realizes the integration of data-driven design and manufacturing processes, aiming to break information silos, achieve efficient collaboration among systems, thereby improving safety and response efficiency during the production process, reducing accident retention phenomena, and reducing potential production safety hazards. This is in view of the problems in the prior art where single systems have a single function and there is a lack of effective linkage among systems, making it difficult to quickly associate systems and rely on manual judgment to implement emergency plans in case of emergencies.

[0009] The technical solution of the present invention is as follows:

[0010] A digital twin system that realizes the integration of data-driven design and manufacturing processes, comprising:

[0011] A physical entity unit, which is a bridge connecting the real world and the digital world and consists of multiple modules;

[0012] A digital twin platform, which receives data from the physical entity unit and converts it into a 3D model or simulation for simulating and predicting actual production behavior; it is also connected to a data analysis unit, a management terminal, and a data repository to form a closed-loop feedback system;

[0013] A data analysis unit, which interacts bidirectionally with the data repository for data processing, analysis, and storage;

[0014] A management terminal, which serves as a user interface through which all relevant information can be accessed and the production status can be monitored. At the same time, the management terminal receives suggestions from the data analysis unit and makes responses when necessary;

[0015] A data repository, which includes: a historical data repository and a management system standard database; among them, the management system standard database is used to input product quality characteristic control data and their corresponding index data, and associate corresponding pre-plan adjustment strategies through different characteristic control data.

[0016] Further, the physical entity unit includes:

[0017] A data acquisition module, which is responsible for collecting comprehensive production data, production schedule information, equipment parameters, production quality-related data, material management data, and production process data;

[0018] A GPS positioning module, which provides accurate location information services;

[0019] A sensing module, which is used to collect various parameters of the equipment operation environment and status in real time;

[0020] The 3D model of the physical device is created by 3D modeling software based on the data obtained by the data acquisition module and the data collected by the sensing module, and is used to reflect the real situation of the physical device;

[0021] The communication module ensures seamless communication between the physical entity unit and the digital twin platform.

[0022] Further, the communication module includes:

[0023] The network transmission channel supports multiple transmission protocols to adapt to different network environment requirements;

[0024] The communication module covers network routing, data communication interfaces, human-machine interaction interfaces, and cloud database access ports, and virtualizes physical resources through standardized networks and interfaces, enabling the physical entity information in the production workshop to be widely interconnected and mapped with the virtual space.

[0025] Further, the digital twin platform includes:

[0026] The twin digital model is a high-precision 3D simulation model constructed by integrating the design parameters of the physical device and the real-time production process parameters, which can dynamically map the device operation status and achieve real-time synchronization between the physical world and the virtual world;

[0027] The elimination module is used to screen and eliminate the auxiliary component information irrelevant to the process movement in the 3D model of the physical device, and only retain the key component data directly affecting the process;

[0028] The rendering module is used to perform high-fidelity visualization rendering on the process scene, enhance the optical properties of the material, and achieve parallel logical processing and graphics rendering through a multi-threaded mechanism;

[0029] The PLC control module realizes the two-way coordination between the physical device and the twin digital model through a programmable logic controller, receives sensor data in real time to drive the model movement, and at the same time feeds back the optimization instructions of the twin digital model to the physical device to form a closed-loop control;

[0030] The remote monitoring module supports cross-terminal access, real-time displays the 3D status of the device, the process progress, and the abnormal alarm information, and integrates a remote parameter adjustment interface, allowing operators to issue control commands through the management terminal.

[0031] Further, the data analysis unit includes:

[0032] Difference calculation module, which is used to calculate the difference between the monitored real-time data and the management system standard database, specifically including collecting process control data, calculating the standard deviation S and the average standard deviation X of the quality characteristic index values of each process;

[0033] Association matching module, which is used to associate and match the standard deviations of the quality characteristics of each process [S1...S2...Sk] with the index information in the management system standard database to form a normal matrix distribution;

[0034] Strategy generation module, which is used to adjust the strategy according to the associated plan of the matched index information, automatically generate an emergency plan and feedback the data to the management terminal.

[0035] Furthermore, the calculation formula of the standard deviation S is as follows:

[0036]

[0037] where x i represents a single observed value, is the average of all observed values, and n is the number of observed values.

[0038] Furthermore, the calculation formula of the average standard deviation X is as follows:

[0039]

[0040] where S j represents the standard deviation of the quality characteristic value of the jth process, and k is the total number of processes.

[0041] Furthermore, the construction method of the three-dimensional model of the physical equipment includes:

[0042] Step S1: Data acquisition; collect equipment design parameters and production process parameters through the sensing module and data acquisition module of the physical entity unit;

[0043] Step S2: Initial modeling; generate an initial three-dimensional model using 3D modeling software;

[0044] Step S3: Parameter integration; import the static equipment design parameters and dynamic production process parameters into the initial three-dimensional model;

[0045] Step S4: Map engine matching; associate the three-dimensional model imported in Step S3 with the multi-resolution map engine to generate a thumbnail of the process area.

[0046] Furthermore, the construction method of the digital twin model includes:

[0047] Step A: Data Deepening; On the basis of the three-dimensional model of the physical device, further integrate real-time high-frequency data;

[0048] Step B: Model Optimization; Filter out non-process-related auxiliary components through the elimination module and retain key moving parts;

[0049] Step C: Dynamic Rendering; Achieve high-fidelity materials and optical effects based on the rendering module;

[0050] Step D: Bidirectional Collaboration; Real-time interact with the physical device through the PLC control module.

[0051] Furthermore, the sensing module includes: a temperature and humidity sensor, a time sensor, a pressure sensor, a speed sensor, and a flow sensor.

[0052] Compared with the existing technology, the beneficial effects of the present invention are:

[0053] 1. By constructing a digital twin model, the present invention realizes the bidirectional data-driven collaborative movement between the physical device and the digital model. Specifically, the operating state of the physical device is real-time mapped onto the digital twin model, and at the same time, the simulation results of this model can also guide the actions of the physical device to ensure that the two can operate synchronously. This process not only accurately reflects the working conditions of the physical device, but also collects and analyzes the data generated during the production process, integrates big data according to the production logic relationship, and forms an integrated management framework. Under this framework, the system can real-time monitor production activities, simulate the operation processes of each system in the workshop, and provide automated operation suggestions based on the analysis of the simulation data. The system will compare the collected real-time data with the benchmark values in the standard database, calculate the differences, and automatically generate emergency plans according to the correlation between these differences and potential risk events to prevent accidents and reduce potential safety hazards.

[0054] 2. The present invention uses digital twin technology to enhance the intelligent management level of production equipment, improve the flexibility, response speed and safety of production, and effectively reduce the possibility of risk retention caused by abnormal situations.

[0055] 3. By creating a three-dimensional model of the physical device and combining with a multi-resolution map engine, the present invention realizes the high-precision simulation of the production process. Specifically, the three-dimensional model matches the map engine and can generate thumbnail images of each production process area. Users only need to select any process link in the three-dimensional model to directly enter the corresponding three-dimensional scene, which is convenient for viewing and adjusting the model structure.

[0056] 4. In the process of constructing the digital twin model, the present invention adopts an elimination module to optimize the model structure. This module retains the key component information containing process movements while removing the auxiliary components that do not participate in the actual process, thereby reducing the complexity of the model and improving the speed of virtual commissioning. In addition, in order to further enhance the realism of the 3D simulation and the performance of the system, rendering processing is performed on each process link in the digital twin model.

[0057] 5. The present invention not only improves the visualization effect and interactivity of the 3D model, but also significantly enhances the efficiency of the digital twin system in simulating and analyzing production capabilities, providing a more intuitive and efficient tool for operators and helping to improve the management level of the overall production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a schematic diagram of a digital twin system for realizing the integration of data-driven design and manufacturing processes. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0059] It should be noted that relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including the element.

[0060] The features and performance of the present invention will be further described in detail below in conjunction with the embodiments.

[0061] Embodiment 1

[0062] Please refer to Figure 1 , a digital twin system for realizing the integration of data-driven design and manufacturing processes, comprising:

[0063] A physical entity unit, which is a bridge connecting the real world and the digital world and consists of multiple modules;

[0064] A digital twin platform, which, as a core component, receives data from the physical entity unit and converts it into a 3D model or simulation for simulating and predicting actual production behavior; it is also connected to a data analysis unit, a management terminal and a data repository to form a closed-loop feedback system;

[0065] A data analysis unit, which interacts bidirectionally with the data repository for data processing, analysis, and storage. It not only receives data from the digital twin platform but also provides analysis results and decision-making support information to the management terminal.

[0066] A management terminal, which serves as a user interface. Managers can access all relevant information through the management terminal, monitor the production status. Meanwhile, the management terminal receives suggestions from the data analysis unit and responds when necessary.

[0067] A data repository, which includes: a historical data repository and a management system standard database. Among them, the management system standard database is used to input product quality characteristic control data and their corresponding index data, and associate corresponding plan adjustment strategies through different characteristic control data.

[0068] In summary, the digital twin method proposed in this embodiment aims to digitize the physical devices and their operation processes in the physical world through a highly integrated technical solution, thereby achieving more efficient production management and a more intelligent manufacturing process. The close cooperation among components ensures that the system can respond to changes in real time, optimize resource allocation, and improve production efficiency and product quality.

[0069] In this embodiment, specifically, the physical entity unit includes:

[0070] A data acquisition module, which is responsible for collecting comprehensive production data, production schedule information, equipment parameters, production quality-related data, material management data, and production process data to ensure a comprehensive understanding of the production process.

[0071] A GPS positioning module, which provides accurate location information services to help track and manage the location of production equipment.

[0072] A sensing module, which is used to collect various parameters of the equipment operation environment and status in real time. The sensing module includes: a temperature and humidity sensor, a time sensor, a pressure sensor, a speed sensor, and a flow sensor.

[0073] A three-dimensional model of the physical device, which is created through 3D modeling software based on the data obtained by the data acquisition module and the data collected by the sensing module, and is used to reflect the real situation of the physical device.

[0074] A communication module, which ensures seamless communication between the physical entity unit and the digital twin platform.

[0075] In this embodiment, specifically, the communication module includes:

[0076] A network transmission channel that supports multiple transmission protocols such as Local Area Network (LAN), WiFi, Zigbee, and 5G to meet the requirements of different network environments.

[0077] A communication module that includes a network router, a data communication interface, a human-machine interaction interface, and a cloud database access port. It virtualizes physical resources through standardized networks and interfaces, enabling the wide interconnection of physical entity information in the production workshop and its mutual mapping with the virtual space.

[0078] In this embodiment, specifically, the digital twin platform includes:

[0079] A twin digital model, which is a high-precision three-dimensional simulation model constructed by integrating the design parameters of physical devices and real-time production process parameters. It can dynamically map the operating state of the devices and achieve real-time synchronization between the physical world and the virtual world.

[0080] A removal module that is used to screen and remove the auxiliary component information irrelevant to the process movement in the three-dimensional model of the physical device, and only retain the key component data directly affecting the process.

[0081] A rendering module that is used to perform high-fidelity visualization rendering on the process scene, enhance the optical properties of the materials, and achieve parallel logical processing and graphics rendering through a multi-threaded mechanism.

[0082] A PLC control module that realizes the two-way coordination between the physical device and the twin digital model through a programmable logic controller. It receives sensor data in real time to drive the movement of the model, and at the same time feeds back the optimization instructions of the twin digital model to the physical device to form a closed-loop control.

[0083] A remote monitoring module that supports cross-terminal access, real-time displays the three-dimensional state of the device, the process progress, and the abnormal alarm information, and integrates a remote parameter adjustment interface, allowing operators to issue control commands through the management terminal.

[0084] In this embodiment, specifically, the data analysis unit includes:

[0085] A difference calculation module that is used to calculate the difference between the monitored real-time data and the management system standard database, specifically including collecting process control data, calculating the standard deviation S and the average standard deviation X of the quality characteristic index values of each process. The specific calculation steps are as follows:

[0086] Collect process control data: For each process, relevant quality characteristic index values need to be obtained; assuming there are k processes, then each process will have a set of quality characteristic index values a1, b1,..., bn;

[0087] Calculate the standard deviation: For the quality characteristic index values of each process, their standard deviation S needs to be calculated; the standard deviation is a measure of the dispersion of numerical values and reflects the fluctuation of these quality characteristic index values; the calculation formula for the standard deviation is:

[0088]

[0089] where x i represents a single observation value, is the average of all observation values, and n is the number of observation values;

[0090] Calculate the average standard deviation X: After obtaining the standard deviation S of the quality characteristic values of each process, the average of the standard deviations of these k processes is calculated to obtain the average standard deviation X; the calculation formula is as follows:

[0091]

[0092] where S j represents the standard deviation of the quality characteristic values of the jth process, and k is the total number of processes;

[0093] Summarize the results: Finally, an average standard deviation X will be obtained, which can represent the average fluctuation degree of all processes in terms of product quality characteristics. This value can be used to evaluate the stability between different processes and the consistency of the entire production process;

[0094] In this embodiment, it should be noted that in actual operation, specific data sets are required to perform the above calculations. If there are specific data about the processes, such as the quality characteristic index values (a1, b1,..., bn) in each process, the corresponding standard deviation and average standard deviation can be calculated according to the above method. If more detailed mathematical calculations or programming implementation assistance are needed, please provide specific numerical values or further problem descriptions;

[0095] An association matching module, which is used to associate and match the standard deviations of the quality characteristics of each process

S1...S2...Sk

S1...S2...Sk

a1, b1,...bn

[0096] A strategy generation module, which is used to adjust the strategy according to the matched index information and associated plans, automatically generate an emergency plan, and feedback the data to the management terminal; that is, the strategy generation module is used to match the associated plan adjustment strategy in the management system standard database according to the corresponding [a1, b1,..., bn] index information, generate an emergency plan, and timely feedback the data information to the management terminal.

[0097] In this embodiment, specifically, the method for constructing the three-dimensional model of the physical device includes:

[0098] Step S1: Data collection; collect the device design parameters and production process parameters through the sensing module and data acquisition module of the physical entity unit; specifically collect the specific data information of the device through the physical entity unit; this step involves two main types of data acquisition:

[0099] Design parameter information: covering the original design materials such as the design specifications, dimensions, and structural features of the device;

[0100] Production process parameter information: including the relevant parameters such as the operating status, performance indicators, and environmental conditions generated during the actual production process;

[0101] These data will provide a basis for constructing an accurate three-dimensional model in the subsequent steps, ensuring that the model can truly reflect the characteristics and behaviors of the physical device;

[0102] Step S2: Initial modeling; generate an initial three-dimensional model using 3D modeling software; specifically use professional 3D modeling software (such as 3Dmax or Maya) to create the initial three-dimensional models of various physical devices; in this step, determine and input the key physical data and attributes of the physical device, including but not limited to temperature, humidity, time, pressure, speed, and flow rate, etc., to ensure the accuracy and practicality of the model;

[0103] Step S3: Parameter integration; import the static device design parameters and dynamic production process parameters into the initial three-dimensional model; specifically integrate the collected physical device parameter information and the dynamic parameter information during the production process into the initial model, so as to generate a three-dimensional model of the physical device that reflects the actual situation;

[0104] Step S4: Map Engine Matching; Associate the 3D model imported in Step S3 with the multi-resolution map engine to generate a thumbnail of the process area. It should be noted that the development of the multi-resolution map engine is to build a multi-resolution map engine for high-precision simulation of each production process link. This engine can provide different levels of detailed views, ensuring that users can obtain the area thumbnails of each production process and supporting seamless switching from macro to micro; The matching of process simulation and the map engine is to establish a connection between the 3D model and the map engine, so that when any process link in the 3D model is selected, the system can automatically locate and display the corresponding specific process 3D scene, providing users with intuitive operation guidance and analysis tools; That is, by combining the production process simulation of the 3D model with an advanced map engine, the system can automatically generate area thumbnails corresponding to each production process. When users browse these thumbnails, they can intuitively understand the geographical location of each process and its relative layout. When users select any specific process link in the 3D model, the system will immediately locate and load the detailed 3D scene corresponding to that process. This function not only simplifies the process for users to navigate to specific processes, but also provides a more immersive interactive experience, enabling users to deeply explore and analyze the specific situation of each production step. This integration method not only improves the convenience and efficiency of user operations, but also enhances the understanding and control ability of the production process. Whether it is used for training new employees or optimizing existing production processes, this function can provide strong support to ensure that users can accurately simulate and evaluate actual production activities in a virtual environment.

[0105] In this embodiment, specifically, the construction method of the twin digital model includes:

[0106] Step A: Data Deepening; On the basis of the 3D model of the physical equipment, further integrate real-time high-frequency data; Among them, parameter information collection and input: First, import various parameter information of the physical equipment and all parameter information involved in the production process into the twin system. This includes but is not limited to various parameter adjustments of the equipment, the time required for each process, the movement trajectory, the pressure borne by the equipment components, the temperature and humidity information in the process environment, the running speed of the equipment components, and the material transfer flow rate, etc.;

[0107] Real-time data integration: Based on the above-collected information, build a twin digital model in the twin system that can reflect the real-time status of each production process. This model not only includes static physical properties, but also dynamically reflects the changes in the production process, such as time progress, movement path, pressure change, temperature and humidity conditions, speed change, and flow fluctuation, etc., ensuring a high degree of simulation of the actual production situation by the model;

[0108] Step B: Model Optimization; Filter out non-process-related auxiliary components through the elimination module and retain key moving parts; that is, when inputting the parameters of the equipment and the parameter information in the production process into the digital twin model, the elimination module focuses on retaining the key component information involved in the process movement in the 3D model of the physical equipment; this module will screen and exclude those auxiliary components that do not directly participate in the specific process steps to ensure that the digital twin model only contains elements that have a direct impact on the production process flow, thereby improving the accuracy and simulation efficiency of the model. This can ensure that the digital twin model can accurately reflect the functional characteristics of the physical equipment and simplify unnecessary details, making the simulation more efficient and focused;

[0109] Step C: Dynamic Rendering; Achieve high-fidelity materials and optical effects based on the rendering module; specifically including:

[0110] Select the rendering engine and technology: Select the Unreal engine as the underlying rendering engine to achieve high-quality visual effects. At the same time, use physically based rendering (PBR) technology for the material design of the process equipment to ensure that the objects in the virtual environment have realistic optical properties and surface textures;

[0111] Visualization of the process links: Conduct detailed rendering for each process link in the twin digital model, which not only enhances the realism of the model but also makes the positions and movement trajectories of each process component clearer and distinguishable. In this way, users can intuitively observe every detail in the production process, which helps improve the monitoring efficiency and decision-making accuracy;

[0112] Optimize the rendering performance: Introduce the rendering thread mechanism to enable the parallel execution of program logic processing and rendering content generation. This strategy effectively improves the system's response speed and operation efficiency, ensuring a smooth operation experience even when dealing with complex scenarios. In addition, it also supports more refined differentiation and management of the dynamic performance of each process component in the twin digital model, further enhancing the authenticity and interactivity of the simulation;

[0113] Step D: Bidirectional collaboration; real-time interaction through the PLC control module with physical devices; that is, using the PLC (Programmable Logic Controller) control module to operate the digital twin platform to achieve data interaction and synchronization between the physical device and the corresponding digital model. This process ensures that the data of the physical device can drive its digital twin model in real time, enabling the two to perform collaborative actions. At the same time, the digital twin model can also guide the actions of the physical device in reverse, forming a two-way interaction, thus achieving a highly consistent collaborative motion effect; through the above integration solution, a bidirectional collaborative motion mechanism between the physical device and the digital twin model is achieved; this mechanism allows different operation scenarios to be simulated and tested in a virtual environment, optimizes the working process of the physical device, and feeds back the optimized instructions to the actual device for execution, forming a closed-loop control system, greatly improving the intelligent level of production and resource utilization rate.

[0114] In this embodiment, to further enhance the flexibility and response speed of the system, the system also integrates a remote monitoring module, enabling operators to monitor and manage the status of physical devices in real time from a remote location. This not only improves the safety and efficiency of operations but also facilitates the timely discovery and resolution of potential problems, ensuring the smooth operation of the production process.

[0115] The above-described embodiments only represent the specific implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the protection scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the technical solution of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application.

[0116] This background art section is provided to generally present the context of the present invention. The work of the currently named inventors, to the extent described in this background art section, and aspects of the work that are not prior art as of the time of filing this application are neither expressly nor impliedly admitted to be prior art to the present invention.

Claims

1. A digital twin system that realizes the integration of data-driven design and manufacturing processes, characterized in that: include: A physical entity unit, which is a bridge connecting the real world and the digital world and is composed of multiple modules; A digital twin platform that receives data from physical entities and converts it into a three-dimensional model or simulation to simulate and predict actual production behavior; It is also connected with the data analysis unit, management terminal and data storage to form a closed-loop feedback system; A data analysis unit, which interacts bidirectionally with a data repository to perform data processing, analysis, and storage; A management terminal, which serves as a user interface, through which all relevant information can be accessed and production status can be monitored. The management terminal also receives suggestions from the data analysis unit and responds when necessary; The data repository includes: a historical data repository and a management system standard database; wherein the management system standard database is used to input product quality characteristic control data and its corresponding indicator data, and associate the corresponding plan adjustment strategy through different characteristic control data.

2. A digital twin system for realizing data-driven design and manufacturing process integration according to claim 1, characterized in that: The physical entity unit comprises: A data acquisition module, which is responsible for collecting comprehensive production data, planning and scheduling information, equipment parameters, production quality-related data, material management data, and production process data; A GPS positioning module, which provides accurate location information services; A sensor module, which is used to collect various parameters of the equipment's operating environment and status in real time; A three-dimensional model of the physical device, which is created by 3D modeling software based on the data acquired by the data acquisition module and the data collected by the sensor module, and is used to reflect the actual situation of the physical device; A communication module ensures seamless communication between the physical entity unit and the digital twin platform.

3. The digital twin system for realizing data-driven design and manufacturing process integration according to claim 2, characterized in that: The communication module comprises: Network transmission channel, which supports multiple transmission protocols to adapt to different network environment requirements; The communication module covers network routing, data communication interface, human-computer interaction interface and cloud database access port, and realizes the virtualization of physical resources through standardized networks and interfaces, so that the physical entity information in the production workshop can be widely interconnected and mapped with the virtual space.

4. The digital twin system for realizing data-driven design and manufacturing process integration according to claim 3, characterized in that: The digital twin platform includes: Twin digital model, which is a high-precision three-dimensional simulation model built by integrating the design parameters of the physical equipment and the real-time production process parameters. It can dynamically map the operation status of the equipment and achieve real-time synchronization between the physical world and the virtual world; A rejection module, which is used to screen and reject auxiliary component information irrelevant to process movement in the three-dimensional model of the physical equipment, and only retain key component data that directly affects the process; A rendering module, which is used to perform high-fidelity visual rendering of the process scene, enhance the optical properties of the material, and realize parallel logic processing and graphics rendering through a multi-threading mechanism; A PLC control module, which realizes two-way collaboration between the physical device and the twin digital model through a programmable logic controller, receives sensor data in real time to drive the model movement, and feeds back the twin digital model optimization instructions to the physical device to form a closed-loop control; The remote monitoring module can support cross-terminal access, display the three-dimensional status of the equipment, process progress and abnormal alarm information in real time, integrate a remote parameter adjustment interface, and allow operators to issue control commands through the management terminal.

5. The digital twin system for realizing data-driven design and manufacturing process integration according to claim 4, characterized in that: The data analysis unit comprises: A difference calculation module, which is used to perform difference calculation between the real-time monitoring data and the management system standard database, specifically including collecting process control data, calculating the standard deviation S and average standard deviation X of the quality characteristic index value of each process; An association matching module, wherein the association matching module is used to associate and match the standard deviation of the quality characteristic value of each process [S1...S2...Sk] with the indicator information in the management system standard database to form a normal matrix distribution; The strategy generation module is used to associate the plan adjustment strategy according to the matching indicator information, automatically generate the emergency plan and feed back the data to the management terminal.

6. The digital twin system for realizing data-driven design and manufacturing process integration according to claim 5, characterized in that: The calculation formula of the standard deviation S is as follows: Among them, x i represents a single observation, is the mean of all observations and n is the number of observations.

7. The digital twin system for realizing data-driven design and manufacturing process integration according to claim 5, characterized in that: The calculation formula of the average standard deviation X is as follows: Among them, S j represents the standard deviation of the quality characteristic value of the jth process, and k is the total number of processes.

8. The digital twin system for realizing data-driven design and manufacturing process integration according to claim 2, characterized in that: The method for constructing the three-dimensional model of the physical device includes: Step S1: data collection: collecting equipment design parameters and production process parameters through the sensor module and data acquisition module of the physical entity unit; Step S2: initial modeling: using 3D modeling software to generate an initial three-dimensional model; Step S3: parameter integration: importing static equipment design parameters and dynamic production process parameters into the initial three-dimensional model; Step S4: Map engine matching; associating the three-dimensional model imported in step S3 with the multi-resolution map engine to generate a thumbnail of the process area.

9. The digital twin system for realizing data-driven design and manufacturing process integration according to claim 4, characterized in that: The method for constructing the twin digital model comprises: Step A: Data deepening: Based on the 3D model of the physical equipment, further integrate real-time high-frequency data; Step B: Model optimization: filter out non-process-related auxiliary components by eliminating modules and retain key moving parts; Step C: Dynamic rendering; achieving high-fidelity materials and optical effects based on the rendering module; Step D: Two-way collaboration; real-time interaction with physical devices through the PLC control module.

10. The digital twin system for realizing data-driven design and manufacturing process integration according to claim 2, characterized in that: The sensor module includes: a temperature and humidity sensor, a time sensor, a pressure sensor, a speed sensor and a flow sensor.

Images