A production line unit modeling method for digital twin monitoring

Abstract

The application discloses a kind of production line unit modeling methods for digital twin monitoring, comprising the following steps: S1, establish production line scene data architecture;S2, establish warehouse production line system requirement diagram and monitoring application requirement diagram;S3, establish warehouse production line structure diagram, equipment structure diagram, value definition diagram, establish warehouse production line activity diagram, equipment activity diagram, equipment mathematical model;S4, establish the object node and variable of OPC UA information model;S5, establish equipment three-dimensional model in Solidwork, import three-dimensional simulation software VC in intermediate format STP;S6, define the operation flow of production line, the operation constraint relationship between each equipment;Define the independent and dependent relationship between each motion structure;S7, according to equipment mathematical model, establish corresponding data structure, determine the data source to collect data and update OPC UA information model.The application has good visualization and interactivity, and production line state feedback is timely, can effectively help manager to understand production line state, improve management efficiency.

Description

Technical Field

[0001] This invention relates to a production line unit modeling method for digital twin monitoring. Background Technology

[0002] Manufacturing is the mainstay of the real economy and the lifeline of a nation's economy. The level of development of manufacturing reflects a country's level of advancement. In recent years, the development of technologies such as the Internet of Things, cloud computing, and big data has driven the transformation of manufacturing, with manufacturing models gradually shifting from automated production to intelligent manufacturing. Digital twins are virtual models of physical entities created in the digital world using digital methods. They simulate the behavior and state of physical entities in the real environment through data-driven simulation and extend new functions to physical entities through data analysis and virtual-real interaction, playing a crucial role in the entire lifecycle of production and manufacturing.

[0003] In monitoring applications, the current visualization and control of production systems is mainly based on two-dimensional visualization and control, which focuses on data statistics and status parameter monitoring. However, integrating digital twin technology can further enhance its control capabilities and achieve three-dimensional visualization and control. Currently, the monitoring application of digital twin technology is still limited, and combining digital twin technology with monitoring is of research significance. The key to realizing digital twin technology lies in model construction, including three-dimensional models corresponding to entities, data models, and behavioral rule models.

[0004] Model-Driven System Engineering (MBSE) was defined by the International Association for Systems Engineering (IAS) in 2007. It is a model-driven system development process that uses models to represent the requirements, design, analysis, verification, and validation processes throughout the system's entire lifecycle. It is currently the most commonly used method in the aerospace and military industries for developing complex large-scale systems. This method differs from system design; instead, it focuses on digital twin monitoring, integrating MBSE concepts to model production lines and equipment, and using data planning based on system analysis to form data models of production lines and equipment.

[0005] Patent application number 202111356345.7 discloses a research and application of digital twin warehousing based on OPC UA, including collecting data information from corresponding monitoring devices within the warehousing system; transmitting the data information to an OPC UA server via Ethernet; the OPC UA server encoding the collected data and storing it in a database or sending it to an OPC UA client; and the OPC UA client requesting data from the OPC UA server and decoding the data. This method uses OPC UA for data transmission and provides data for warehousing digital twin monitoring applications. However, the method is limited to providing data to the twin model and does not address the needs analysis, object behavior analysis, or data modeling related to why this data is provided. Patent application No. 202210927756.5 discloses a digital twin method and system for welding robot workstations, including: creating a virtual twin workstation corresponding to a physical entity in the real physical space of the welding robot workstation using 3D modeling and simulation software; processing and saving the collected historical operating data of the welding robot workstation to an Internet of Things (IoT) platform; adjusting the operation of the virtual twin workstation to ensure consistency between the twin and the real workstation, and simulating the process by changing relevant parameters of the welding robot workstation when writing historical data, and obtaining a verified virtual twin workstation model; and inputting the real-time operating data collected by the IoT platform into the verified virtual twin workstation model for real-time monitoring of the welding robot workstation. This application creates and implements a digital twin of the welding robot using 3D and simulation software. However, this invention is only aimed at welding robots, while actual production line systems are generally more complex. Different equipment or the entire system have different data requirements, data sources, and data transmission and twin driving mechanisms. Therefore, this method is not applicable to the entire production line and digital workshop.

[0006] Production scenarios are complex, involving numerous hardware devices and software business systems. The data generated during the production process is multi-source and heterogeneous, characterized by large quantity, fragmentation, and low quality. Moreover, each production system is configured differently, with variations in business systems, automation levels, and equipment data sources. Therefore, most existing digital twin methods are geared towards specific equipment objects, resulting in relatively poor universality and reusability. In addition, most existing production line monitoring methods are two-dimensional statistical monitoring, which has relatively poor transparency and visibility. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the prior art and provide a production line unit modeling method for digital twin monitoring that has better visualization and interactivity, timely production line status feedback, and can effectively help managers understand the production line status and improve management efficiency.

[0008] The objective of this invention is achieved through the following technical solution: a production line unit modeling method for digital twin monitoring, comprising the following steps:

[0009] S1. Establish the production line scenario data architecture: Establish the corresponding MBSE model based on the production line layout, production line process, production line equipment, and software facilities in the production line scenario;

[0010] S2. Establish a warehouse production line system requirements diagram and a monitoring application requirements diagram; the functional requirements of the intelligent warehousing system include production information management, personnel information management, warehouse data storage, warehouse management, warehouse system monitoring, human-machine interface and warehouse hardware construction; the monitoring application requirements include equipment operation status monitoring, production task monitoring, warehouse personnel information monitoring, and inventory material monitoring.

[0011] S3. Establish warehouse production line structure diagram, equipment structure diagram, and value definition diagram; establish warehouse production line activity diagram, equipment activity diagram, and equipment mathematical model.

[0012] S4. Based on the XML file generated from the equipment structure diagram, establish the object nodes and variables of the OPC UA information model;

[0013] S5. Create a 3D model of the equipment in Solidworks and import it into the 3D simulation software VC in intermediate format STP.

[0014] S6. Based on the production line activity diagram, define the operation flow of the production line and the operation constraints between each piece of equipment in the 3D simulation software VC; define the independent and dependent relationships between each moving structure based on the equipment activity diagram; and establish the corresponding data-driven interface for value attributes.

[0015] S7. Establish a corresponding data structure based on the equipment mathematical model, collect data from the data source determined by the equipment structure diagram and update the OPC UA information model to realize real-time data-driven digital twin monitoring.

[0016] The specific implementation method of step S3 is as follows:

[0017] A production line structure diagram is established for the warehousing system, with the warehousing system as the main block, and hardware and software facilities as auxiliary blocks. Stacker cranes, inbound and outbound stations, racks, AGVs, and servers are auxiliary blocks for hardware facilities, while databases (MySQL, MES, WMS / WCS), real-time data caching (Redis), front-end web, 3D simulation software, and algorithm model libraries are auxiliary blocks for software facilities. A stacker crane structure diagram is also established, with inbound and outbound stations and WMS / WCS as external related actors, and horizontal travel mechanisms, loading platform lifting mechanisms, fork extension mechanisms, and electrical control systems as auxiliary blocks for the stacker crane. Corresponding value attributes are defined for the stacker crane, horizontal travel mechanism, loading platform lifting mechanism, and fork extension mechanism, and their behaviors are defined as movement, lifting, and extension.

[0018] Based on the intelligent warehousing production line operation process, a production line activity diagram is constructed. The warehousing system starts by issuing tasks from the MES, which are then parsed by the WMS / WCS system. Tasks are divided into inbound, outbound, and transfer. The tasks are executed sequentially, and the next task can only be executed after the current task is completed. Based on the operating characteristics of the stacker crane, a stacker crane activity diagram is constructed. The stacker crane has three-axis motion, which is divided into horizontal travel X-axis, loading platform lifting Y-axis, and fork extension Z-axis. The stacker crane activity diagram can reflect the sequential, parallel, and dependent relationships between its movements. The stacker crane operation process is divided into four parts: moving to the initial location, storing / retrieving goods, moving to the destination, and storing / retrieving goods.

[0019] The mathematical model of the equipment established in step S3 is as follows:

[0020] Equ ID ::=Eba+ExtS+ESS+Proc

[0021] Eba={EType,EO,EID,EAuto,EFal,ETID,ERtim,ERstat}

[0022] ExtS = {Min ID ,Ctr ID Mout ID}

[0023] ESS = {{SVal1,SVal2,…,SVal n1},{SVal1,SVal2,…,SVal n2},…,{SVal1,SVal2,…,SVal nn}},n1,n2,…,n n ∈R

[0024]

[0025] Equ ID The device is represented by its device number as a unique identifier; Eba refers to the device's basic data, including device category EType, device inherent attributes EO, device number EID, device operating mode EAuto, device fault code EFal, device task number ETID, device running time ERtim, and device operating status ERstat.

[0026] ExtS represents external systems related to the equipment, including material inputs (Min). ID Controller Ctr ID and material output Mout ID ;

[0027] ESS represents a sub-mechanism / system of equipment, where each mechanism / system has a corresponding value attribute SVal that reflects its specific operational characteristics;

[0028] Proc represents the process step, that is, the steps that the equipment can complete. The granularity of the task is at the step level. Each step corresponds to several equipment actions, and these actions form a set Beh, where... Each action Beh corresponds one-to-one with a set of sub-mechanisms / systems ESS. There are a maximum of n actions for a set of sub-mechanisms / systems.

[0029] The specific implementation method of step S4 is as follows: Based on the production line structure diagram and the stacker crane structure diagram, a corresponding virtual model is established in the digital twin application. Based on the production line activity diagram and the stacker crane activity diagram, production line process constraints and stacker crane behavior constraints are constructed in the digital twin application, and corresponding data-driven interfaces for value attributes are established. The stacker crane structure diagram is sent to the digital twin application in XML file format. The digital twin application parses the XML file and performs field matching of package name, corresponding components and value attributes. In this way, an OPC UA information model is generated in the OPC UA server. In the stacker crane's OPC UA information model, the stacker crane is the object node, and the running mode, task number, fault code, running time and running status are its variables, with values ​​obtained from Redis. The horizontal walking mechanism, the loading platform lifting mechanism and the fork extension mechanism are its constituent reference object nodes, and their value attributes are its variables, with values ​​obtained from Redis.

[0030] The beneficial effects of this invention are:

[0031] 1. Introducing the MBSE (Model-Based Systems Engineering) concept: Compared to traditional text-based system design patterns, MBSE-based system design patterns, due to their standardized language, avoid the design inconsistencies caused by varying textual expression abilities and different understandings of requirements in traditional design patterns. At the same time, the interconnectedness of various model diagrams in MBSE-based system design patterns makes requirements more traceable and allows for faster responses to changes in requirements, effectively helping system builders to grasp system requirements.

[0032] 2. SysML is used as the modeling language to model the production line system and equipment, conduct demand planning, and construct a simplified structural diagram to represent the key structures of the production line and equipment. A mathematical model of the equipment is established, and the equipment data is divided into four categories: self-data, external data, subsystem data, and behavioral data. The production task is decomposed into processes and then into the actions of each piece of equipment using the "task-process-behavior-data" model. The real-time operation data generated by the equipment subsystem drives the behavior of the digital twin equipment, and this serves as the driving logic to achieve an accurate mapping of the production process in the digital twin model.

[0033] 3. Enables real-time monitoring of digital twins, with good visualization and interactivity, and timely feedback on production line status, effectively helping managers understand the production line status and improve management efficiency. Attached Figure Description

[0034] Figure 1 This is a flowchart of the present invention;

[0035] Figure 2 Data architecture diagram for production scenarios;

[0036] Figure 3 Requirements diagram for the warehousing system;

[0037] Figure 4 Diagram showing the requirements for monitoring applications. Detailed Implementation

[0038] Definitions of abbreviations and key terms:

[0039] 1. WMS: Warehouse Management System. This system integrates functions such as batch management, material matching, inventory counting, quality inspection management, virtual warehouse management, and real-time inventory management through inbound and outbound operations, warehouse transfers, inventory transfers, and virtual warehouse management.

[0040] 2. WCS: Warehouse Control System. WCS is a management and control system layer between WMS and the underlying PLC. It interacts with WMS, receives tasks from WMS, and sends instructions to the underlying PLC to drive the automated equipment.

[0041] 3. MES: The MES system is a production information management system for the shop floor execution layer of manufacturing enterprises. It provides enterprises with management modules including manufacturing data management, planning and scheduling management, production scheduling management, inventory management, quality management, human resource management, work center / equipment management, tooling management, procurement management, cost management, project dashboard management, production process control, lower-level data integration and analysis, and upper-level data integration and decomposition, creating a solid, reliable, comprehensive and feasible manufacturing collaborative management platform for enterprises.

[0042] 4. Redis: A high-performance database with fast read and write speeds, but limited storage capacity.

[0043] 5. OPC UA: OPC Unified Architecture is a next-generation technology provided by the OPC Foundation that offers a secure, reliable, and vendor-independent TCP-based binary communication protocol to enable the transmission of raw data and pre-processed information from the manufacturing level to the production planning or ERP level.

[0044] 6. MBSE: Model-based systems engineering, which uses digital modeling to replace writing documents for system design. It transforms all the nouns, verbs, adjectives and parameters describing the system structure, function, performance and specifications in the design documents into digital model expressions.

[0045] 7. SysML: A general-purpose system architecture modeling language for systems engineering applications, which defines semantics for the structural model, behavioral model, requirement model and parameter model of a system.

[0046] 8. XML: Extensible Markup Language (XML), a subset of Standard Generalized Markup Language (SGML), can be used to mark up data and define data types. It is a source language that allows users to define their own markup languages. XML is a SGML with advantages such as good extensibility, separation of content and form, adherence to strict syntax requirements, and good value preservation.

[0047] This invention proposes a production line unit modeling method for digital twin monitoring. It introduces the Model-Based Systems Engineering (MBSE) concept, analyzes the production scenario data architecture and the behavioral characteristics of the production line and equipment, and uses SysML to establish production line system requirement diagrams, monitoring application requirement diagrams, production line structure diagrams, equipment structure diagrams, production line activity diagrams, and equipment activity diagrams, thus defining the data requirements and behavioral characteristics of the production line and equipment. Based on the equipment structure diagrams and equipment activity diagrams, a mathematical model of the equipment is established, dividing the equipment data into four parts: self-data, subsystem data, behavioral data, and external system data. Self-data refers to data based on the entire equipment; subsystem data consists of real-time data generated by each subsystem after dividing the equipment into multiple subsystems; behavioral data represents the actions the equipment can perform; and external system data comprises interaction data generated by other equipment or systems that interact with the equipment. Driven by a "task-process-behavior-data" logic, production tasks are broken down into several processes performed sequentially by several devices. Each process is further divided into several device actions completed sequentially by a single device. These actions are driven by real-time data generated by the device subsystem. A connection between data and the digital twin model is established through XML files and OPC UA, enabling real-time monitoring of the digital twin. The technical solution of this invention is further illustrated below using an intelligent warehousing production line as an example, in conjunction with the accompanying drawings.

[0048] like Figure 1 As shown, the present invention provides a production line unit modeling method for digital twin monitoring, comprising the following steps:

[0049] S1. Establish a Data Architecture for the Production Line Scenarios: Analyze the production line scenarios and equipment conditions. Smart warehousing related software includes MES, WMS / WCS, MySQL database, Redis real-time data cache, PLC software, front-end web applications, and 3D simulation software. Real-time data required by monitoring applications comes from the Redis real-time data cache. Smart warehousing related hardware includes AGVs, inbound platforms, stacker cranes, shelves, outbound platforms, and servers. Based on the production line layout, processes, equipment, and software infrastructure, establish a corresponding MBSE model. Based on the MBSE model, determine data requirements and data sources, including which production data and operational data need to be collected and from where. Data is retrieved from the infrastructure via APIs and stored persistently as structured, semi-structured, and unstructured data. Figure 2 As shown. Data-driven application services are divided into real-time driven and massive data driven. This invention mainly involves real-time driven, that is, constructing an OPC UA information model through an XML file obtained from the MBSE model. The data in this information model is updated in real time by data collected from the infrastructure through a monitoring mechanism, thereby achieving the purpose of real-time driven digital twin monitoring.

[0050] S2. Establish a requirements diagram for the warehousing production line system and a requirements diagram for monitoring applications, and determine the relevant software and hardware requirements and application requirements; the functional requirements of the intelligent warehousing system include production information management, personnel information management, warehousing data storage, warehouse management, warehousing system monitoring, human-machine interface, and warehousing hardware facility construction, such as... Figure 3 As shown; the monitoring application requirements include equipment operation status monitoring, production task monitoring, warehouse personnel information monitoring, and inventory material monitoring, while ensuring real-time and visualization of the monitoring, such as... Figure 4 As shown.

[0051] Based on the monitoring application requirements and the actual situation of the intelligent warehousing production line, establish the hardware and software structure diagram of the warehousing system and the structure diagram of the stacker crane, a key piece of equipment.

[0052] S3. Establish warehouse production line structure diagram, equipment structure diagram, and value definition diagram (define the data types required, including int data, float data, etc., and then separate the corresponding physical quantities for each data type, such as speed, displacement, temperature, etc., and then define the units for the physical quantities), and establish warehouse production line activity diagram, equipment activity diagram, and equipment mathematical model.

[0053] The specific implementation method is as follows: A production line structure diagram is established for the warehousing system, with the warehousing system as the main block, hardware and software facilities as auxiliary blocks, stacker cranes, inbound and outbound platforms, racks, AGVs, and servers as auxiliary blocks for hardware facilities, and databases (MySQL, MES, WMS / WCS, Redis for real-time data caching, front-end web, 3D simulation software, and algorithm model libraries) as auxiliary blocks for software facilities. A stacker crane structure diagram is established, with the stacker crane as the main block. Inbound and outbound platforms, and WMS / WCS are considered external related actors, while the horizontal walking mechanism, loading platform lifting mechanism, fork extension mechanism, and electrical control system are considered auxiliary blocks for the stacker crane. Corresponding value attributes are defined for the stacker crane, horizontal walking mechanism, loading platform lifting mechanism, and fork extension mechanism, and their behaviors are defined as movement, lifting, and extension.

[0054] Based on the intelligent warehousing production line operation process, a production line activity diagram is constructed. The warehousing system starts by issuing tasks from the MES, which are then parsed by the WMS / WCS system. Tasks are divided into inbound, outbound, and transfer. The tasks are executed sequentially, and the next task can only be executed after the current task is completed. Based on the operating characteristics of the stacker crane, a stacker crane activity diagram is constructed. The stacker crane has three-axis motion, which is divided into horizontal travel X-axis, loading platform lifting Y-axis, and fork extension Z-axis. The stacker crane activity diagram can reflect the sequential, parallel, and dependent relationships between its movements. The stacker crane operation process is divided into four parts: moving to the initial location, storing / retrieving goods, moving to the destination, and storing / retrieving goods.

[0055] The established mathematical model of the equipment is as follows:

[0056] Equ ID ::=Eba+ExtS+ESS+Proc

[0057] Eba={EType,EO,EID,EAuto,EFal,ETID,ERtim,ERstat}

[0058] ExtS = {Min ID ,Ctr ID Mout ID}

[0059] ESS = {{SVal1,SVal2,…,SVal n1},{SVal1,SVal2,…,SVal n2},…,{SVal1,SVal2,…,SVal nn}},n1,n2,…,n n ∈R

[0060]

[0061] Equ ID The device is represented by its device number as a unique identifier; Eba refers to the device's basic data, including device category EType, device inherent attributes EO, device number EID, device operating mode EAuto, device fault code EFal, device task number ETID, device running time ERtim, and device operating status ERstat.

[0062] ExtS represents external systems related to the equipment, including material inputs (Min). ID Controller Ctr ID and material output Mout ID ;

[0063] ESS represents a sub-mechanism / system of equipment, where each mechanism / system has a corresponding value attribute SVal that reflects its specific operational characteristics;

[0064] Proc represents the process step, that is, the steps that the equipment can complete. The granularity of the task is at the step level. Each step corresponds to several equipment actions, and these actions form a set Beh, where... Each action Beh corresponds one-to-one with a set of sub-mechanisms / systems ESS. There are a maximum of n actions for a set of sub-mechanisms / systems.

[0065] The following uses a stacker crane, a key piece of equipment, as an example to illustrate the mathematical model of the stacker crane: The equipment type EType of the stacker crane is logistics equipment; the specific values ​​of the other data in Eba are obtained from the relevant software systems on the production line; material input Min... ID For the inbound station, the controller Ctr ID For WCS / WMS, material output Mou ID The stacker crane submechanism / system ESS includes a horizontal travel mechanism, a loading platform lifting mechanism, and a fork extension mechanism. The median attribute SVal of the horizontal travel mechanism includes X-axis displacement, X-axis speed, X-axis start time, and X-axis stop time. The median attribute SVal of the loading platform lifting mechanism includes Y-axis displacement, Y-axis speed, Y-axis start time, and Y-axis stop time. The median attribute SVal of the fork extension mechanism includes Z-axis displacement, Z-axis speed, Z-axis start time, and Z-axis stop time. The stacker crane can perform two operations, Proc: moving to the destination point and storing / retrieving goods. The moving to the destination point operation includes two actions: X-axis movement and Y-axis movement. The storing / retrieving operation includes three actions: Z-axis movement, Y-axis movement, and Z-axis movement. The mathematical model of the stacker crane is as follows:

[0066] Equ 堆垛机 ::=Eba+ExtS+ESS+Proc

[0067] Eba = {Logistics Equipment, EO, EID, EAuto, EFal, ETID, ERtim, ERstat}

[0068] ExtS = {Inbound Station, WCS / WMS, Outbound Station}

[0069] ESS 堆垛机 ={Horizontal travel mechanism, loading platform lifting mechanism, fork extension mechanism, electrical control system}

[0070] ESS[0] = {X-axis displacement, X-axis velocity, X-axis start time, X-axis stop time}

[0071] ESS[1] = {Y-axis displacement, Y-axis velocity, Y-axis start time, Y-axis stop time}

[0072] ESS[2] = {Z-axis displacement, Z-axis velocity, Z-axis start time, Z-axis stop time}

[0073] Proc = {Move to destination, store / retrieve goods}

[0074] Proc[1] = {X-axis movement, Y-axis movement}

[0075] Proc[2] = {Z-axis movement, Y-axis movement, Z-axis movement}

[0076] S4. Based on the XML file generated from the stacker crane structure diagram, establish the object nodes and variables of the OPC UA information model. The specific implementation method is as follows: Based on the production line structure diagram and the stacker crane structure diagram, establish the corresponding virtual model in the digital twin application, and construct the production line process flow constraints and stacker crane behavior constraints in the digital twin application based on the production line activity diagram and the stacker crane activity diagram, and establish the corresponding data-driven interface for value attributes.

[0077] Export the equipment structure diagram as a corresponding XML file, parse the tree-like information structure in the XML file, and use the Package type "Stacker Crane" as an object node in the OPC UA information model. In the Class type, the Property types under the same name "Stacker Crane" are used as variables of the object node, including "Operating Mode" and "Task Number". The Class types with different names "Horizontal Walking Mechanism", "Loading Platform Lifting Mechanism", and "Fork Extension Mechanism" are used as reference object nodes, and the Property types under them are variables of the reference object node. The stacker crane structure diagram is sent to the digital twin application in XML file format. The digital twin application parses the XML file and matches the package name, corresponding components, and value attributes. This data is then mapped to an OPC UA information model in the OPC UA server. In the stacker crane's OPC UA information model, the stacker crane is the object node, and its variables are operating mode, task number, fault code, running time, and operating status, with values ​​retrieved from Redis. The horizontal walking mechanism, loading platform lifting mechanism, and fork extension mechanism are its component reference object nodes, with their value attributes as variables, also retrieved from Redis. Data establishes a driving relationship with the virtual model in the digital twin application through OPC UA, enabling real-time monitoring of the digital twin.

[0078] S5. Create 3D models of equipment such as racks, stacker cranes, inbound and outbound platforms in Solidworks, and import them into the 3D simulation software VC in intermediate format STP. First, create 3D models of stacker cranes, racks, and inbound / outbound platforms of the same size in the 3D modeling software Solidworks, and generate STP files to import into the 3D simulation engine VC to obtain the 3D models of the production line and equipment.

[0079] S6. Based on the production line activity diagram, define the production line operation flow in the 3D simulation software VC. The operational constraints between each piece of equipment are defined so that the stacker crane can only move after receiving materials at the receiving station for the inbound task. Based on the stacker crane structure diagram, divide the stacker crane into three moving parts in the 3D simulation engine: horizontal walking mechanism, loading platform lifting mechanism, and fork extension mechanism. Define the independence and dependence relationships between each moving structure according to the equipment activity diagram. The horizontal walking mechanism and the loading platform lifting mechanism can operate in parallel, while the fork extension mechanism can only operate after the former two have completed their operations. And establish the corresponding data-driven interface for value attributes.

[0080] S7. Establish a corresponding data structure based on the equipment mathematical model, collect data from the data source determined by the equipment structure diagram, and establish a corresponding data structure based on the equipment mathematical model to temporarily store the data collected from the data source. Then, update the variable values ​​in the OPC UA information model by monitoring the variables in this data structure to achieve real-time data-driven digital twin monitoring.

[0081] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. A production line unit modeling method for digital twin monitoring, characterized in that, Includes the following steps: S1. Establish the production line scenario data architecture: Establish the corresponding MBSE model based on the production line layout, production line process, production line equipment, and software facilities in the production line scenario; S2. Establish a warehouse production line system requirements diagram and a monitoring application requirements diagram; the functional requirements of the intelligent warehousing system include production information management, personnel information management, warehouse data storage, warehouse management, warehouse system monitoring, human-machine interface and warehouse hardware construction; the monitoring application requirements include equipment operation status monitoring, production task monitoring, warehouse personnel information monitoring, and inventory material monitoring. S3. Establish the warehouse production line structure diagram, equipment structure diagram, and value definition diagram; establish the warehouse production line activity diagram, equipment activity diagram, and equipment mathematical model; the equipment mathematical model is as follows: ； ； ； ； ； in The equipment is represented by its equipment number, which serves as a unique identifier. This refers to basic equipment data, including equipment category. Inherent properties of equipment Equipment number Equipment operation mode Equipment fault codes Equipment task number Equipment running time and equipment operating status ; This refers to external systems related to the equipment, including material inputs. Controller and material output ; This represents a sub-mechanism / system of equipment, where each mechanism / system has a corresponding value attribute. It reflects specific operational characteristics; This represents a technological process, specifically the steps that the equipment can perform. The granularity of task assignment is at the step level, with each step corresponding to several equipment actions, which form a set. ,in Representative actions With equipment sub-mechanisms / system sets One-to-one correspondence, with a maximum of n sub-organizations / systems, there are at most n actions; S4. Based on the XML file generated from the equipment structure diagram, establish the object nodes and variables of the OPC UA information model; S5. Create a 3D model of the equipment in Solidworks and import it into the 3D simulation software VC in intermediate format STP. S6. Define the production line's operation flow and the operational constraints between various devices in the 3D simulation software VC based on the production line activity diagram; define the independence and dependency relationships between various moving structures based on the equipment activity diagram. And establish corresponding data-driven interfaces for value attributes; S7. Establish a corresponding data structure based on the equipment mathematical model, collect data from the data source determined by the equipment structure diagram and update the OPC UA information model to realize real-time data-driven digital twin monitoring.

2. The production line unit modeling method for digital twin monitoring according to claim 1, characterized in that, The specific implementation method of step S3 is as follows: Establish a production line structure diagram for the warehousing system, with the warehousing system as the main block, hardware facilities and software facilities as auxiliary blocks of the warehousing system, stacker cranes, inbound and outbound stations, shelves, AGVs and servers as auxiliary blocks of hardware facilities, and database MySQL, MES system, WMS / WCS system, real-time data cache Redis, front-end web, 3D simulation software and algorithm model library as auxiliary blocks of software facilities. The stacker crane is used as the main block to establish the stacker crane structure diagram; the inbound platform, outbound platform, and WMS / WCS are used as external related actors; the horizontal travel mechanism, loading platform lifting mechanism, fork extension mechanism, and electrical control system are used as auxiliary blocks of the stacker crane. The stacker crane, horizontal travel mechanism, loading platform lifting mechanism, and fork extension mechanism have defined corresponding value attributes, and the horizontal travel mechanism, loading platform lifting mechanism, and fork extension mechanism have defined behaviors: movement, lifting, and extension. Based on the intelligent warehousing production line operation process, a production line activity diagram is constructed. The warehousing system starts by issuing tasks from MES, and then the WMS / WCS system parses the tasks. The tasks are divided into inbound, outbound and transfer. The tasks are in serial mode, and the next task can only be executed after the current task is completed. Based on the operating characteristics of the stacker crane, a stacker crane activity diagram is constructed. The stacker crane has three-axis motion, which is divided into horizontal travel X-axis, loading platform lifting Y-axis and fork extension Z-axis. The stacker crane activity diagram can reflect the serial, parallel and dependent relationships between its movements. The stacker crane operation process is divided into four parts: moving to the initial location, storing / retrieving goods, moving to the destination, and storing / retrieving goods.

3. The production line unit modeling method for digital twin monitoring according to claim 1, characterized in that, The specific implementation method of step S4 is as follows: Based on the production line structure diagram and the stacker crane structure diagram, a corresponding virtual model is established in the digital twin application. Based on the production line activity diagram and the stacker crane activity diagram, production line process constraints and stacker crane behavior constraints are constructed in the digital twin application, and corresponding data-driven interfaces for value attributes are established. The stacker crane structure diagram is sent to the digital twin application in XML file format. The digital twin application parses the XML file and performs field matching of package name, corresponding components, and value attributes. This is then mapped to generate an OPC UA information model in the OPC UA server. In the stacker crane's OPC UA information model, the stacker crane is the object node, and its variables are operating mode, task number, fault code, running time, and operating status, with values ​​obtained from Redis. The horizontal walking mechanism, loading platform lifting mechanism, and fork extension mechanism are its constituent reference object nodes, with their value attributes as variables, also obtained from Redis. Data establishes a driving connection with the virtual model in the digital twin application through OPC UA, realizing real-time monitoring of the digital twin.

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