Data-driven digital twinning virtual model construction and entity synchronization method

Abstract

The invention relates to a data-driven digital twinning virtual model construction and entity synchronization method, which is used for constructing an intelligent production line digital twinning system by analyzing equipment, process flow and data interaction requirements of an actual production line and utilizing an existing five-dimensional model framework. Three-dimensional modeling is carried out on equipment, attributes of rigid bodies, collision bodies and the like, kinematic pairs and constraint conditions are set, physical and virtual mapping is completed, and a virtual model capable of reflecting characteristics of a production line is constructed. Then, an entity communication and data acquisition framework is built, production line equipment is accessed, and a twin database is built by utilizing Profinet or MODBUS TCP and other communication and an OPC UA server; and collecting and processing data according to a preset frequency and transmitting the data into a database. And finally, updating the state of the virtual model according to rules through a data driving engine, and sending an instruction by a human-computer interface through analysis and feedback, thereby realizing virtual-real synchronization and control of the production line, solving the traditional problems, improving the production efficiency and reducing the cost.

Description

Technical Field

[0001] The present invention relates to the technical field of digital twins, and in particular to a method for constructing a virtual model and synchronizing an entity of a digital twin driven by data. Background Art

[0002] With the continuous development of the manufacturing industry, traditional production line management and monitoring methods have been unable to meet the growing needs of production efficiency, quality control and cost optimization. In the traditional production environment, the monitoring and management of the actual production line usually rely on manual observation and decentralized automation systems, which have many limitations. On the one hand, the monitoring of the operating status of production line equipment and the production process is often isolated, and it is difficult to achieve efficient integration and collaborative analysis of data between different equipment, resulting in the inability to grasp the status and performance of the entire production process from a global perspective. On the other hand, during the production process, when problems arise or the production process needs to be optimized, it is usually only possible to use post-event data analysis and experience judgment, and it is difficult to dynamically adjust and optimize the production process in real time and accurately. At the same time, due to the complexity of the actual production process, physical testing and adjustment of the production line is costly and risky, which may lead to production interruptions, equipment damage and other problems. Moreover, traditional production lines have poor flexibility in dealing with rapid changes in products and processes, and the update of equipment and processes requires a long time and high costs.

[0003] The emergence of digital twin technology can be used to solve these problems. However, the existing digital twin technology still has some shortcomings. For example, the virtual models of some digital twin systems fail to accurately reflect the physical characteristics and operation logic of the actual production line, resulting in the simulation accuracy of the virtual model is not high enough, and it is impossible to accurately simulate the actual production process; in terms of data collection, there may be problems such as unscientific data collection frequency, untimely data transmission, and inaccurate data processing; in terms of entity synchronization, it is difficult to achieve real-time and reliable dynamic synchronization of physical and virtual states, and data inconsistency and synchronization deviation are prone to occur; in terms of system architecture, it often does not have good scalability. When new equipment needs to be added or the process needs to be updated, the entire system needs to be complexly reconstructed, which affects the stability and adaptability of the system. Summary of the invention

[0004] The main purpose of the present invention is to provide a data-driven method for constructing a virtual model and physical synchronization of digital twins, so as to achieve virtual-real synchronization and control of the production line, solve many problems of traditional production line management and monitoring, improve production efficiency and reduce costs.

[0005] To achieve the above object, the present invention provides a data-driven method for constructing a virtual model and synchronizing a physical digital twin, comprising the following steps: Analyze the actual production line, determine the equipment, process flow and data interaction requirements contained in the actual production line, and design and build a digital twin system for intelligent production lines based on the existing five-dimensional model framework. The digital twin system for intelligent production lines includes physical entities (PE), virtual entities (VE), services (Ss), twin data (DD) and connections (CN). The twin data is used as the core to collect manufacturing data and virtual simulation data. The service system performs real-time system monitoring and prediction of the current operating status of the actual production line and each device based on the virtual space; Carry out 3D modeling of the equipment in the actual production line, set the rigid body and collision body properties of the equipment, configure the corresponding kinematic pairs and constraints, clarify the specific position and speed parameters of each core component movement, map the physical equipment with the virtual model, and build a virtual model to describe the physical characteristics, operation logic and process of the actual production line; Based on the digital twin system and virtual model of the intelligent production line, a physical communication and data acquisition architecture is built to connect the production line equipment to the network nodes. The production line equipment includes sensors, PLCs and RFIDs. AGVs and industrial robots communicate with PLCs using Profinet or MODBUS TCP. At the same time, an integrated OPC UA server is embedded in the field equipment, PLCs, robots, and RFIDs to build a twin database for field data association and fusion. The operating data, status information, process parameters and environmental data of the equipment on the actual production line are collected in real time according to the predetermined sampling frequency, and the collected data is cleaned, verified and classified before being transmitted to the twin database; Based on the data-driven engine, data is received from the twin database, mapped to the corresponding components of the virtual model according to the predetermined mapping rules, and the physical state of the virtual model is updated. Through the data analysis in the twin database and the real-time feedback of the virtual model operation, control instructions are sent through the human-computer interaction interface to perform real-time mapping of the virtual and real production line manufacturing process and control of the entire product manufacturing process.

[0006] Furthermore, in the process of building the virtual model, the connection relationship and movement logic of each component of the equipment are refined, the movement simulation of each component is accurate to the change of each rotation angle and movement speed, and real-time adjustments are made according to the actual kinematic and dynamic characteristics.

[0007] Furthermore, the construction of the virtual model also includes the coordination of various devices through programming to simulate the actual production process according to the process logic consistent with the actual production line. The process logic includes the start, stop, operation sequence of the equipment, processing parameter settings and material transmission path.

[0008] Furthermore, the construction of the physical communication and data acquisition architecture also includes the design of a service management system, which uses a structured programming method to create an electrical control program based on S7-1200 using the LAD\SCL programming language. The electrical control program includes communication, data analysis modules, loading and unloading and processing flow control modules, and RFID read-write control and warehouse management modules.

[0009] Furthermore, the communication and data analysis modules of the electrical control program are used to monitor data anomalies in real time. When data fluctuations exceed a preset threshold, an alarm is automatically issued and abnormal data is recorded.

[0010] Furthermore, the loading and unloading and processing flow control module of the electrical control program not only realizes fully automatic flow control, but also reserves a manual intervention interface for rapid takeover control in special circumstances.

[0011] Furthermore, in the construction of the twin database, the configuration of the OPC UA server is optimized according to the number of field devices and data traffic, and a reasonable buffer and data update frequency are set.

[0012] Furthermore, a data acquisition module is set on the equipment of the actual production line, and the operation data, status information, process parameters and environmental data of the equipment are collected in real time according to a predetermined sampling frequency through the industrial Ethernet using respective private protocols.

[0013] Furthermore, in addition to basic operating instruction functions, the human-machine interface also provides data visualization display functions, which intuitively presents the key data and operating status of the production line in the form of charts or graphics.

[0014] Furthermore, the entire system is scalable. When adding new devices, the new devices can be integrated into the system through simple configuration and model update operations, without the need for large-scale reconstruction of the entire system.

[0015] The data-driven virtual model construction and entity synchronization method of digital twins provided by the present invention has the following beneficial effects: the present invention realizes all-round, high-fidelity monitoring and predictive maintenance of the production process by constructing an accurate virtual model, discovers problems in advance, and avoids production interruptions and quality problems. Multi-source data is collected through the twin database for data collection, processing and storage, which facilitates the real-time transmission and utilization of data and provides support for decision-making. Build consistent process logic, realize equipment collaboration, test and optimize processes in a virtual environment, and reduce costs and risks. Develop a data-driven engine and synchronization verification mechanism to ensure accurate and reliable virtual-real synchronization. The system can easily add new equipment and is simple to update, which is conducive to reducing costs and adapting to market changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1It is a flow chart of a method for constructing a virtual model and synchronizing a physical model of a digital twin driven by data in one embodiment of the present invention.

[0017] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0019] Reference Figure 1 , which is a flow chart of a method for constructing a virtual model and synchronizing a physical entity of a digital twin under data drive proposed by the present invention, comprising the following steps: S1, analyze the actual production line, determine the equipment, process flow and data interaction requirements contained in the actual production line, design and build a digital twin system for intelligent production lines based on the existing five-dimensional model framework. The digital twin system for intelligent production lines includes physical entities (PE), virtual entities (VE), services (Ss), twin data (DD) and connections (CN). The twin data is used as the core to collect manufacturing data and virtual simulation data. The service system performs real-time system monitoring and prediction of the current operating status of the actual production line and each device based on the virtual space; S2, 3D modeling of the equipment in the actual production line, setting the rigid body and collision body properties of the equipment, configuring the corresponding kinematic pairs and constraints, clarifying the specific position and speed parameters of each core component movement, mapping the physical equipment with the virtual model, and building a virtual model to describe the physical characteristics, operation logic and process of the actual production line; S3, based on the digital twin system and virtual model of the intelligent production line, build a physical communication and data acquisition architecture, and connect the production line equipment to the network node. The production line equipment includes sensors, PLC and RFID. AGV and industrial robots communicate with PLC using Profinet or MODBUS TCP. At the same time, an integrated OPC UA server is embedded in the field equipment, PLC, robot, and RFID to build a twin database with field data association and fusion; S4, collects the operation data, status information, process parameters and environmental data of the equipment on the actual production line in real time according to the predetermined sampling frequency, cleans, verifies and classifies the collected data, and then transmits it to the twin database; S5, based on the data-driven engine, receives data from the twin database, maps the data to the corresponding components of the virtual model according to the predetermined mapping rules, updates the physical state of the virtual model, analyzes the data in the twin database and provides real-time feedback on the operation of the virtual model, and uses the human-computer interaction interface to send control instructions to perform real-time mapping of the virtual and real production line manufacturing process and control of the entire product manufacturing process.

[0020] As described in step S1 above, a comprehensive analysis of the actual production line is the basis for building an effective digital twin system. It is necessary to identify the various types of equipment included in the production line, such as CNC lathes, machining centers, industrial robots, PLCs, sensors, AGV carts, stereoscopic warehouses, etc., and understand their respective functions and roles in the production process. Determining the process flow is to clarify the processing sequence, operation steps, and collaborative relationship between the products on the production line. For example, how the raw materials are processed into the final product through a series of processes, which equipment is required to participate in each link and their operating specifications. Clarify the data interaction requirements, that is, understand what data needs to be transmitted between devices, such as equipment operation status data, process parameter data, logistics information data, etc., as well as the flow and frequency of these data.

[0021] The digital twin system of intelligent production lines is designed based on the existing five-dimensional model framework, which can better meet the needs of information-physical data fusion and operator application. Among them, physical entities (PE) represent actual production equipment, etc.; virtual entities (VE) are digital modeling of physical entities; services (Ss) can monitor and predict the actual production line and equipment operation status based on virtual space, which is convenient for staff to view; twin data (DD) as the core can collect various types of manufacturing data and virtual simulation data, and realize the control, diagnosis and prediction of production lines and equipment through data model driving; connections (CN) ensure effective interaction between various components.

[0022] As described in step S2 above, three-dimensional modeling of the equipment in the actual production line is a key step in building a virtual model. Set the rigid body and collision body properties of the equipment, such as setting the rigid body properties for the spindle, tool holder, tool, workbench, etc. of the machine tool so that they have corresponding physical properties in the virtual environment, and the collision body properties are used to simulate the collision between equipment components to ensure that the physical behavior of the virtual model is consistent with reality. Configure the corresponding kinematic pairs and constraints, such as setting kinematic pairs in the X-axis, Y-axis, Z-axis and other directions of the machine tool, and define their position and speed parameters, clarify the specific rules of the movement of each core component, so that it can run in the virtual environment according to the motion logic of the actual equipment, thereby realizing the accurate mapping of physical equipment and virtual models, and building a virtual model that can accurately describe the physical characteristics, operation logic and process of the actual production line.

[0023] As described in step S3 above, building a physical communication and data collection architecture based on the digital twin system and virtual model of the intelligent production line is an important part of realizing data-driven. Network node access is performed on production line equipment, including sensors, PLCs, and RFIDs, to ensure that these devices can interact with the system.

[0024] Since AGVs and industrial robots usually communicate with PLCs using Profinet or MODBUS TCP, the connection configuration is performed in this way to ensure the stability and compatibility of data transmission.

[0025] The integrated OPC UA server is embedded in field devices, PLCs, robots, and RFIDs. The OPC UA standard specification has a unified address space and service model, which can store the data, alarms, events, and historical information of various devices in the address space of the server, making it easy to build a twin database that associates and integrates field data, and provides a unified platform architecture for subsequent data processing and applications.

[0026] As described in step S4 above, the operation data, status information, process parameters and environmental data of the equipment on the actual production line are collected in real time according to the predetermined sampling frequency in order to obtain comprehensive and timely production information. The predetermined sampling frequency needs to be reasonably set according to the characteristics of the production process and the importance of the data to ensure that changes in key data can be captured without causing excessive burden on the system due to excessive collection frequency. Cleaning, verifying and classifying the collected data are necessary steps to ensure data quality. Cleaning data can remove noise and erroneous data, verifying data to ensure its accuracy and completeness, and classification processing facilitates the subsequent storage of data in the corresponding location in the twin database to facilitate data management and application, and finally the processed data is transmitted to the twin database for system use.

[0027] As described in step S5 above, the data driven engine receives data from the twin database. The data driven engine is the core driving part of the entire system. It is responsible for accurately mapping the data to the corresponding components of the virtual model according to the predetermined mapping rules, thereby updating the physical state of the virtual model, so that the virtual model can reflect the operation of the actual production line in real time. Through the analysis of the data in the twin database and the real-time feedback of the operation of the virtual model, the control instructions are sent using the human-computer interaction interface. Data analysis can help discover problems and potential optimization points in the production process, and the operation feedback of the virtual model provides an intuitive display of the control effect. The human-computer interaction interface enables operators to easily send control instructions based on the analysis results and feedback information, thereby realizing the real-time mapping of the virtual and real production line manufacturing process, that is, ensuring that the state of the virtual production line is consistent with that of the actual production line, as well as effective control of the entire manufacturing process of the product, including production progress control, quality control, equipment maintenance management and other aspects.

[0028] In one embodiment, there is an intelligent manufacturing production line in an intelligent manufacturing production workshop, and the main equipment includes CNC lathes, machining centers, seven-axis industrial robots, S7-1200, HMI human-machine interaction interfaces, stereoscopic warehouses, and AGV carts. Prepare the corresponding hardware equipment, such as high-performance servers for running digital twin systems and storing data, network equipment to ensure stable communication between production line equipment and systems, and various sensors, data acquisition cards and other data acquisition devices. At the same time, install and configure the required software tools, such as NX MCD software for virtual model construction, TIA PORTAL software for control program design, and OPC UA related software components for data integration and communication.

[0029] The intelligent manufacturing production line in the embodiment is analyzed in detail. It is identified that the CNC lathe is used for cutting workpieces, the machining center can perform more complex precision machining, the seven-axis industrial robot is responsible for material handling and loading and unloading operations, the S7-1200 is used as the core controller to coordinate the operation of various equipment, the HMI human-machine interface is used for the interaction between operators and the production line, the stereoscopic warehouse stores raw materials and finished products, and the AGV trolley transports materials in the workshop. The process flow is determined as follows: the raw materials are first transported to the CNC lathe processing area by the AGV trolley, and the CNC lathe performs rough machining according to the preset program. After the machining is completed, the industrial robot transports it to the machining center for further fine machining, and then the robot transports it to the inspection area for quality inspection. Qualified products are transported to the stereoscopic warehouse for storage by the AGV trolley, and unqualified products enter the rework process. Clarify data interaction requirements. For example, CNC lathes need to transmit data such as tool speed, cutting depth, and workpiece processing dimensions during the processing process to the control system and digital twin system; industrial robots need to provide real-time feedback on their own position information, grasping status, motion trajectory and other data; AGV carts need to transmit data such as position, speed, and load status; sensors are responsible for collecting data such as ambient temperature, humidity, and equipment vibration and sending them to the system.

[0030] The digital twin system of intelligent production line is designed based on the five-dimensional model framework. In this system, physical entity (PE) covers all actual equipment on the production line; virtual entity (VE) builds a digital model corresponding to the physical entity through subsequent modeling steps; the service (Ss) system uses a visual interface to monitor the operating status of the production line and each equipment in real time, such as whether the equipment is operating normally, whether the production progress is in line with the plan, etc., and can perform certain predictive analysis, such as predicting the time when the equipment may fail based on the current operating parameters of the equipment; the twin data (DD) module collects manufacturing data and virtual simulation data from each device, such as historical processing data, equipment maintenance records, virtual debugging data, etc., which will serve as the key basis for driving system operation and decision-making; connection (CN) ensures close collaboration and data interaction between various parts through network communication and data interface.

[0031] In this embodiment, NX MCD software is used to perform three-dimensional modeling of the equipment on the production line. For CNC lathes, the models of its bed, spindle, tool holder, tool, workbench and other components are accurately constructed, and appropriate rigid body and collision body properties are set for these components. For example, the spindle is set as a rigid body so that it has stable physical properties during rotational motion, and collision body properties are set for the tool holder and tool to simulate collision during tool change and processing. Configure kinematic pairs and constraints. Linear kinematic pairs are set in the X-axis, Y-axis, and Z-axis directions of the CNC lathe, and the range of change of their position and speed is clarified according to the motion parameters of the actual equipment. For example, the stroke of the X-axis is set to 0-500mm and the speed range is set to 0-1000mm / s, and corresponding constraints are added to ensure the accuracy and stability of the motion. For industrial robots, rotary kinematic pairs are set according to their joint structures, and the range of motion and speed limit of each joint are accurately defined so that they can accurately simulate the actual motion trajectory in a virtual environment. Refine the connection relationship and motion logic of each component of the equipment. In the modeling process, the transmission relationship between the spindle and the tool holder of the CNC lathe, as well as the coordinated motion logic between the joints of the industrial robot, are accurately described. For example, when the industrial robot performs the action of grabbing a workpiece, each joint is programmed to move in a specific order and angle, and each rotation angle and movement speed change can be adjusted in real time according to the actual kinematic and dynamic characteristics, ensuring that the motion behavior of the virtual model is highly consistent with the actual equipment.

[0032] According to the process logic of the actual production line, the collaborative work between various devices is realized through programming. In the virtual model, according to the actual production process, the CNC lathe is set to perform processing according to the preset processing parameters after receiving the start signal, and send a signal to the industrial robot after the processing is completed. After receiving the signal, the industrial robot grabs the workpiece according to the predetermined path and posture and transports it to the machining center. After receiving the workpiece, the machining center automatically starts the machining program and a series of collaborative operations, so as to accurately simulate the actual production process.

[0033] Access the production line equipment to the network nodes. Assign independent network addresses to devices such as sensors, PLCs, and RFIDs, and connect them to the system through industrial Ethernet. For example, install temperature sensors, pressure sensors, and displacement sensors on CNC lathes, which send the collected data to PLCs through industrial Ethernet. Configure the communication between AGVs and industrial robots and PLCs. AGVs and industrial robots establish a stable communication connection with PLCs through Profinet or MODBUS TCP protocols. Ensure that the industrial robots can send their own status information and task execution progress to PLCs in a timely manner during the execution of tasks, and receive control instructions sent by PLCs; AGVs can transmit data such as position information, speed information, and load status to PLCs in real time so that PLCs can schedule and control them. Embed integrated OPC UA servers in field equipment, PLCs, robots, and RFIDs. Install and configure OPC UA server components in the control system of CNC lathes, the controller of industrial robots, and PLCs. Optimize the configuration of OPC UA servers according to the number of field equipment and data traffic, and set reasonable buffers and data update frequencies. For example, on machining center equipment with large data flow, a larger buffer is set to avoid data loss, and the data update frequency is set to 100ms to ensure the timeliness of data; on sensor equipment with relatively small data flow, the buffer is appropriately reduced and the data update frequency is extended to reduce the occupation of system resources. Through the OPC UA server, different types of equipment information and production data are converted into data that supports the OPC UA protocol, and a twin database with associated and fused field data is built to achieve unified management and sharing of data.

[0034] Design service management system. Use structured programming method to create electrical control program based on S7-1200 using LAD\SCL programming language. Among them, communication and data analysis modules monitor data anomalies in real time. For example, when the tool speed of the CNC lathe suddenly exceeds the normal range by 10%, an alarm is automatically issued and abnormal data is recorded, and the alarm information is sent to the HMI human-machine interaction interface and the terminal equipment of relevant staff; the loading and unloading and processing process control module not only realizes fully automatic process control, but also reserves a manual intervention interface. When debugging equipment or in special circumstances, operators can quickly take over control through the manual intervention interface to ensure the safety and flexibility of the production process; the RFID read-write control and warehouse management module realizes the identification and management of raw materials and finished products, and accurately records and tracks the entry and exit of materials through RFID technology to improve the efficiency and accuracy of warehouse management.

[0035] Set up data acquisition modules on the equipment of the actual production line. Install high-precision sensors on key parts of CNC lathes, such as speed sensors on the spindle, position sensors on the tool holder, and vibration sensors on the bed. These sensors use their own private protocols through industrial Ethernet to collect equipment operation data, status information, process parameters, and environmental data in real time according to a predetermined sampling frequency (such as the spindle speed sensor collects data every 10ms, and the tool holder position sensor collects data every 20ms). Clean, verify, and classify the collected data. Set data filtering rules in the data acquisition system to remove abnormal data caused by sensor failure or electromagnetic interference. For example, if the temperature data collected by a temperature sensor exceeds 50% of the normal operating temperature range of the equipment, the data is considered abnormal data and filtered. Ensure the accuracy and integrity of the data through data verification algorithms, such as checksum, parity check, and other methods. Then, according to the type and source of the data, the data is classified and stored in the corresponding table or data structure in the twin database to facilitate subsequent data query and application.

[0036] The data driven engine receives data from the twin database. The data driven engine accurately maps the data in the twin database to the corresponding components of the virtual model according to the predetermined mapping rules. For example, the real-time speed data of the CNC lathe is mapped to the speed attribute of the spindle in the virtual model, and the position data of the industrial robot is mapped to the joint position attribute of the robot in the virtual model, so as to update the physical state of the virtual model so that the virtual model can reflect the operation of the actual production line in real time. Through the analysis of the data in the twin database and the real-time feedback of the virtual model operation, the control instructions are sent using the human-computer interaction interface. The data analysis module analyzes the collected historical data and real-time data. For example, through the statistical analysis of the processing size data of the CNC lathe, it is found that the processing size has a trend of gradual deviation. The data driven engine adjusts the processing parameters of the virtual model according to the analysis results, and prompts the operator through the human-computer interaction interface that the tool of the CNC lathe may need to be replaced or adjusted. The operator can send control instructions on the human-computer interaction interface according to the prompts and the operation feedback of the virtual model, such as adjusting the processing parameters of the CNC lathe, starting the maintenance program of the industrial robot, etc. In addition to basic operating instruction functions, the human-computer interaction interface also provides data visualization display functions, which intuitively presents the key data and operating status of the production line in the form of charts or graphics. For example, a line chart is used to display the processing accuracy change trend of a CNC lathe over a period of time, and a 3D model is used to display the real-time motion trajectory of an industrial robot. This facilitates operators to fully understand the situation of the production line and make accurate decisions, thereby realizing real-time mapping of the virtual and real production line manufacturing process and effective management and control of the entire product manufacturing process.

[0037] After completing the above implementation steps, the entire system is fully tested and verified. By running different production tasks on the actual production line, observe the synchronization between the virtual model and the actual production line and the system's control effect on the production process. For example, when producing a batch of complex parts, check whether the virtual model can accurately reflect the actual operating status of CNC lathes, machining centers, and industrial robots, including equipment movement, machining processes, and material handling. At the same time, verify whether the system can promptly discover problems in the production process based on data analysis results, and provide effective solutions and control instructions through the human-computer interaction interface. After a period of operation and verification, ensure that the system can operate stably and achieve the expected goals of improving production efficiency and reducing production costs, such as a 20% increase in production efficiency and a 30% reduction in equipment failure rate.

[0038] In summary, the actual production line is analyzed to determine the equipment, process flow and data interaction requirements contained in the actual production line, and the digital twin system of the intelligent production line is designed and built based on the existing five-dimensional model framework; the equipment in the actual production line is three-dimensionally modeled, the rigid body and collision body properties of the equipment are set, the corresponding kinematic pairs and constraints are configured, the specific position and speed parameters of the movement of each core component are clarified, the physical equipment and the virtual model are mapped, and a virtual model is constructed to describe the physical characteristics, operation logic and process of the actual production line; based on the digital twin system and virtual model of the intelligent production line, a physical communication and data acquisition architecture is built, the network node access is carried out for the production line equipment, and the AGV and industrial robots communicate with the PLC using Profinet or MODBUS TCP. At the same time, OPC UA is embedded and integrated in the field equipment, PLC, robot and RFID. The server builds a twin database that associates and fuses on-site data; it collects the operating data, status information, process parameters and environmental data of the equipment on the actual production line in real time at a predetermined sampling frequency, and transmits the collected data to the twin database after cleaning, verification and classification; it receives data from the twin database based on the data-driven engine, maps the data to the corresponding components of the virtual model according to predetermined mapping rules, updates the physical state of the virtual model, and sends control instructions through the human-computer interaction interface through data analysis in the twin database and real-time feedback on the operation of the virtual model, so as to perform real-time mapping of the virtual and real production line manufacturing process and control of the entire product manufacturing process, so as to achieve virtual and real synchronization and control of the production line, solve many problems of traditional production line management and monitoring, and improve production efficiency and reduce costs.

[0039] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0040] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A data-driven method for constructing a virtual model and synchronizing a physical digital twin, characterized in that: The following steps are involved: Analyze the actual production line, determine the equipment, process flow and data interaction requirements contained in the actual production line, and design and build a digital twin system for intelligent production lines based on the existing five-dimensional model framework. The digital twin system for intelligent production lines includes physical entities (PE), virtual entities (VE), services (Ss), twin data (DD) and connections (CN). The twin data is used as the core to collect manufacturing data and virtual simulation data. The service system performs real-time system monitoring and prediction of the current operating status of the actual production line and each device based on the virtual space; Carry out 3D modeling of the equipment in the actual production line, set the rigid body and collision body properties of the equipment, configure the corresponding kinematic pairs and constraints, clarify the specific position and speed parameters of each core component movement, map the physical equipment with the virtual model, and build a virtual model to describe the physical characteristics, operation logic and process of the actual production line; Based on the digital twin system and virtual model of the intelligent production line, a physical communication and data acquisition architecture is built to connect the production line equipment to the network nodes. The production line equipment includes sensors, PLCs and RFIDs. AGVs and industrial robots communicate with PLCs using Profinet or MODBUS TCP. At the same time, an integrated OPC UA server is embedded in the field equipment, PLCs, robots, and RFIDs to build a twin database for field data association and fusion. The operating data, status information, process parameters and environmental data of the equipment on the actual production line are collected in real time according to the predetermined sampling frequency, and the collected data is cleaned, verified and classified before being transmitted to the twin database; Based on the data-driven engine, data is received from the twin database, mapped to the corresponding components of the virtual model according to the predetermined mapping rules, and the physical state of the virtual model is updated. Through the data analysis in the twin database and the real-time feedback of the virtual model operation, control instructions are sent through the human-computer interaction interface to perform real-time mapping of the virtual and real production line manufacturing process and control of the entire product manufacturing process.

2. The method for constructing a virtual model and synchronizing a physical model of a digital twin driven by data according to claim 1, characterized in that: In the process of building the virtual model, the connection relationship and movement logic of each component of the equipment are refined, the movement simulation of each component is accurate to the change of each rotation angle and movement speed, and real-time adjustments are made based on the actual kinematic and dynamic characteristics.

3. The method for constructing a virtual model and synchronizing a physical entity of a digital twin driven by data according to claim 1, characterized in that: The construction of the virtual model also includes the coordination of various devices through programming according to the process logic consistent with the actual production line to simulate the actual production process. The process logic includes the start, stop, operation sequence of the equipment, processing parameter settings and material transmission path.

4. The method for constructing a virtual model and synchronizing a physical entity of a data-driven digital twin according to claim 1, characterized in that: The construction of the physical communication and data acquisition architecture also includes the design of the service management system. The electrical control program is created based on S7-1200 using the LAD\SCL programming language using a structured programming method. The electrical control program includes communication, data analysis modules, loading and unloading and processing flow control modules, as well as RFID read-write control and warehouse management modules.

5. The method for constructing a virtual model and synchronizing a physical entity of a data-driven digital twin according to claim 4, characterized in that: The communication and data analysis modules of the electrical control program are used to monitor data anomalies in real time. When data fluctuations exceed a preset threshold, an alarm is automatically issued and abnormal data is recorded.

6. The method for constructing a virtual model and synchronizing a physical entity of a data-driven digital twin according to claim 4, characterized in that: The loading and unloading and processing flow control module of the electrical control program realizes fully automatic flow control and reserves a manual intervention interface for rapid takeover control in special circumstances.

7. The method for constructing a virtual model and synchronizing a physical entity of a data-driven digital twin according to claim 1, characterized in that: In the construction of the twin database, the configuration of the OPC UA server is optimized according to the number of field devices and data traffic, and a reasonable buffer and data update frequency are set.

8. The method for constructing a virtual model and synchronizing a physical entity of a data-driven digital twin according to claim 1, characterized in that: A data acquisition module is set up on the equipment on the actual production line, and the equipment's operating data, status information, process parameters and environmental data are collected in real time at a predetermined sampling frequency through industrial Ethernet using their own private protocols.

9. The method for constructing a virtual model and synchronizing a physical entity of a data-driven digital twin according to claim 1, characterized in that: In addition to basic operating instruction functions, the human-machine interface also provides data visualization display functions, which intuitively presents the key data and operating status of the production line in the form of charts or graphics.

10. The method for constructing a virtual model and synchronizing a physical entity of a data-driven digital twin according to claim 1, characterized in that: The entire system is scalable. When adding new devices, the new devices can be integrated into the system through simple configuration and model update operations, without the need for large-scale reconstruction of the entire system.

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