CN114237170A - Assembly workshop virtual-real fusion operation and control method based on digital twinning technology - Google Patents
Assembly workshop virtual-real fusion operation and control method based on digital twinning technology Download PDFInfo
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Abstract
The invention discloses an assembly workshop virtual-real fusion operation and control method based on a digital twin technology, which is characterized in that various physical resources are packaged into software intelligent bodies by utilizing a packaging technology, and the intelligent bodies which correspond to physical entities and are expressed into digital models are used as interaction interfaces of a physical workshop and the digital twin workshop; meanwhile, a management and control platform management digital twin model for human operation is established; in a task allocation stage, an agent participates in decision making and updates a result to a digital twin model; in a workshop operation stage, intelligently sensing the state and the assembly data of the physical workshop in real time, updating a digital twin model, and making a real-time decision and controlling the operation of equipment and resources of the physical workshop; for the condition that people need to make decisions or process, the intelligent agent feeds back the event to workshop personnel in time through the management and control platform, and the personnel control the physical site through the digital twin model, so that the virtual and real fusion operation of the assembly workshop is realized, the intelligentization level of the workshop is improved, and the management and control efficiency is improved, thereby improving the production efficiency of the workshop.
Description
Technical Field
The invention belongs to the technical field of intelligent manufacturing, and particularly relates to a virtual-real fusion operation and control method for an assembly shop.
Background
The assembly is an important link in the production and manufacturing process, and takes the aircraft assembly as an example, and the aircraft assembly is mainly completed according to the design requirement. Because the aircraft assembly technology is difficult, complex and uncertain factors are more on site, and the management and control mainly depend on the experience of people, the digital technology needs to be deeply integrated into the aircraft assembly process in the assembly process management.
The digital twin technology is based on physical workshop entities, constructs a virtual workshop capable of truly reflecting the real state of the physical workshop, collects real-time data of the physical workshop and realizes mapping of the virtual workshop to the physical workshop. The digital twinning technology provides a way for improving the assembly shop management method.
The existing digital twin technology constructs a digital twin model containing all elements of a physical workshop, and updates the digital twin model by using collected data, but the automatic real-time updating of the data and the real-time synchronous operation of a virtual workshop and the physical workshop are difficult to realize; the feedback control effect of the digital twin model on the physical workshop is limited.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides an assembly workshop virtual-real fusion operation and control method based on a digital twin technology, various physical resources are packaged into software intelligent bodies by utilizing a packaging technology, and the intelligent bodies which correspond to physical entities and are expressed into digital models are used as interaction interfaces of a physical workshop and the digital twin workshop; meanwhile, a management and control platform management digital twin model for human operation is established; in a task allocation stage, an agent participates in decision making and updates a result to a digital twin model; in a workshop operation stage, intelligently sensing the state and the assembly data of the physical workshop in real time, updating a digital twin model, and making a real-time decision and controlling the operation of equipment and resources of the physical workshop; for the condition that people need to make decisions or process, the intelligent agent feeds back the event to workshop personnel in time through the management and control platform, and the personnel control the physical site through the digital twin model, so that the virtual and real fusion operation of the assembly workshop is realized, the intelligentization level of the workshop is improved, and the management and control efficiency is improved, thereby improving the production efficiency of the workshop.
The technical scheme adopted by the invention for solving the technical problem comprises the following steps:
step 1: the assembly shop virtual-real fusion model architecture comprises 3 parts, namely a physical shop model, a digital twin model and a control platform; the digital twin model comprises an intelligent body packaging module, a virtual workshop module and a data service module;
step 1-1: the physical workshop model comprises personnel, equipment, materials, tools, products and data acquisition equipment;
step 1-2: an agent encapsulation module in the digital twin model;
adopting an encapsulation technology to carry out intelligent body encapsulation on workshop production elements participating in the assembly process, and using the workshop production elements as an interaction and fusion interface of a physical workshop and a digital twin model; packaging workshop production elements into a task management intelligent agent, a task intelligent agent, a resource intelligent agent and an assembly station intelligent agent;
step 1-3: a virtual workshop module in the digital twin model;
firstly, constructing an intelligent production element three-dimensional model corresponding to a physical workshop, wherein the intelligent production element three-dimensional model comprises personnel attributes, equipment attributes, material attributes, tool attributes and product attributes; then modeling a digital twin assembly workshop, setting the attributes of all production elements in the established three-dimensional model, constructing a process model, planning the layout of the workshop according to the intelligent production element three-dimensional model and the process model, and performing virtual simulation operation of the twin workshop on the basis;
step 1-4: the data service module in the digital twin model comprises a database and a data server; the data in the digital twin model includes: personnel data, material data, tool data, product data, equipment data, assembly process data, assembly task data, scheduling data and assembly quality data;
the data stored in the database comprises real-time operation data, state data and historical data; continuously updating real-time operation data to ensure synchronous operation of the digital twin model and the physical workshop; the state data records the position information and the working state of workshop resources, and is updated when the state is changed or at intervals; the historical data is used for archiving assembly process data and process file data; the data server receives and issues instructions, calls database data, and connects the physical workshop with the digital twin model;
step 1-5: the management and control platform is a software platform for managing a physical workshop and a digital twin model, and is used for a user to manage the assembly workshop; the control platform is divided into 9 modules according to functions, and comprises a system management module, a task management module, a process management module, a plan management module, a production execution module, a resource management module, a simulation operation module, a quality management module and a monitoring and coordination processing module; each module function is realized by an intelligent body packaging module, a virtual workshop module and a data service module;
step 2: designing a process; a process model is built in the virtual workshop model by a technician through a control platform; the technical personnel formulate a detailed assembly process scheme by utilizing the BOM and the product three-dimensional model, construct a PPR structure tree containing product, process and resource information, and generate a digital assembly process file;
and step 3: the task management intelligent agent receives the assembly task through the management and control platform, audits and decomposes the task, sends an instruction for acquiring physical workshop information to each intelligent agent, each intelligent agent responds and feeds back the state information of the packaged physical object to the task management intelligent agent, and the fed-back physical object information comprises equipment, tools and material information of an assembly station, position inventory information of the tools, and inventory information of the materials;
and 4, step 4: the information of each agent is transmitted to the virtual workshop model, and the virtual workshop model is driven to perform virtual simulation; a user inquires task information and physical workshop information, completes layout planning according to a process model and performs virtual workshop simulation; the virtual workshop simulation is to simulate the virtual workshop in the digital twin model through simulation software;
and 5: the task management intelligent agent sends bidding information to the assembly station intelligent agent according to the current information, wherein the bidding information comprises an assembly task, required equipment and a tool; the assembly station intelligent body determines whether to participate in bidding according to the material type quantity, the tool type, the equipment type and state, the personnel arrangement, the station working state and the arranged task information of the assembly station; if so, sending bidding information to the task management agent, wherein the bidding information comprises the start time and cost of the task;
step 6: the task management intelligent agent analyzes the bidding information, selects the assembly station intelligent agent winning a bid according to the multi-objective optimization rule, confirms the assembly task arrangement, and updates the production plan result to the digital twin model and the related database by each intelligent agent;
and 7: the assembly station intelligent agent receives an assembly task from the task management intelligent agent and starts assembly preparation work; the method comprises the steps that assembly personnel check materials, tools and tool resources required by assembly, identity information and resource preparation conditions are fed back to a digital twin model through a control platform by means of RFID, and current assembly personnel and resource information of the digital twin model are updated, so that the digital twin model and a physical workshop are in an assembly preparation state at the same time; in the assembling preparation process, an assembling person sends a resource request to a resource intelligent agent through an assembling station intelligent agent, the resource intelligent agent prepares required resources, controls an AGV trolley to automatically send the resources to the assembling station according to a set path, and simultaneously updates the resource state in a digital twin model;
and 8: after the preparation work is finished, the assembly station starts assembly work; assembling according to the visual assembly process file by an assembler, reading assembly cautions, checking a detailed assembly process, starting first procedure assembly according to the steps, and monitoring the assembly process by an assembly station intelligent agent in real time;
and step 9: in the assembling operation and production operation process, the intelligent agent can sense assembling data in real time, and the obtained data comprises material information, personnel information and equipment position information read through the packaging interface; part positioning information and posture information collected by the numerical control positioner and the laser tracker; acquiring actual operation of personnel, equipment running state and working hour information in a sensor and manual feedback mode; and process problems, equipment failures, material shortage anomalies;
step 10: the intelligent agent updates the digital twin model in real time, automatically stores the sensed and acquired data into a database, and updates the virtual workshop model, so that the data interaction and the virtual-real fusion synchronous operation of the digital twin model and the physical assembly workshop are realized;
step 11: in the digital twin model, the virtual workshop model utilizes dynamically acquired field resources and assembly data to carry out visual real-time simulation, and synchronously displays the running state and progress of a physical workshop to managers;
step 12: in the assembling process of each station, the intelligent agents interact, cooperate and make decisions in real time; when a worker needs a material or a product in assembly operation, a request is sent to each resource intelligent agent through the task intelligent agent, the resource intelligent agents respond according to self states, the task intelligent agents select the resource intelligent agents to carry out material distribution tasks according to the shortest time principle, and decision results are stored in a database of the digital twin model; the resource intelligent agent simultaneously receives a plurality of part requests and responds according to the first-come-first-serve or task priority rule set by a workshop;
step 13: the resource intelligent agent controls the physical workshop according to the decision result, and sends the decision instruction to the physical workshop through the intelligent agent communication interface and runs through the PLC control equipment;
step 14: after the assembler finishes the first assembly procedure, the task agent feeds the assembly result back to the database, and the digital twin model updates the state of the first procedure; meanwhile, the intelligent agent stores the process data, the assembly process data, the scheduling data and the assembly quality data into a historical database to provide data support for the subsequent assembly process management;
step 15: the assembly personnel execute the subsequent assembly process according to the process file; similar to the first step of procedure, the intelligent agent senses the field data of the physical workshop in real time, updates the digital twin model, makes a decision in real time and controls the physical workshop to run according to the decision result in the assembly process until all assembly tasks are finished;
step 16: in the operation process of an assembly workshop, an intelligent agent can simultaneously feed back abnormal conditions and problems to a database and a control platform, and a manager checks the information of the assembly process through the control platform and records the assembly quality inspection data; monitoring the assembly site condition of the physical workshop in real time through the operation of the digital twin model, and issuing a control instruction to the physical workshop through the intelligent model; and coordinating and processing the conditions of material shortage, equipment failure and process problem abnormity through the control platform.
Further, the personnel include a technician, an administrator, and an assembler; the equipment comprises an AGV trolley, processing equipment, a numerical control positioner, a laser tracker and automatic drilling and riveting equipment; the materials comprise connecting pieces, wall plates, skins, stringers, partition frames and pipelines; the tool comprises an assembly fixture and an assembly clamp; the product comprises a complete machine, an assembly component, an assembly part and an assembly section part; the data acquisition equipment comprises an RFID, a sensor, an industrial personal computer, a PLC, measuring equipment, bar code reading equipment and voice input equipment.
Furthermore, the task management agent is a manager of the workshop tasks and is responsible for receiving, auditing and decomposing and distributing the tasks; packaging the assembly operation corresponding to each independent assembly process into a task agent, and taking charge of the management and execution of the task; packaging equipment, tools, materials and the like into a resource intelligent agent, and taking charge of managing and assembling resources and dispatching the resources to a required position; the assembly station intelligent bodies are managers of the assembly units, and each assembly station is an independent assembly station intelligent body and is responsible for receiving assembly tasks, monitoring the operation conditions of the stations and processing emergency events occurring on the stations.
Further, the communication among the agents is realized by adopting a blackboard mechanism and a message transmission mode; and message transmission is realized by adopting a TCP/IP network protocol and KQML or FIPA ACL language, and XML technology is used for message encapsulation.
Further, the digital twin assembly plant modeling was performed using Unity3D software.
Further, in the production element three-dimensional model, the personnel attributes comprise function information and task information, the equipment attributes comprise equipment states, equipment functions and processing capabilities, the material attributes comprise material information, inventory information, processing modes and process attributes, the tooling attributes comprise tooling states, inventory and tooling functions, and the product attributes comprise material information, quantity requirements and product functions.
The process model is constructed according to a product three-dimensional model to form a visual digital process file for guiding an actual assembly process;
the layout planning of the workshop comprises the positions and the placing sequence of equipment and tools, the transportation mode and the route of materials, the task arrangement of stations, the working flow and the movement route of personnel, and the detection and storage and the working hour information of products.
Further, the bid is developed at the production planning stage or in real time during the production process.
The invention has the following beneficial effects:
1. according to the method, on the basis of constructing a digital twin model for mapping a physical workshop, production elements of the physical workshop are encapsulated by adopting an intelligent body encapsulation technology, intelligent attributes are given to all the production elements, and the operation and control of the digital twin workshop are supported.
2. In the assembly task allocation stage, the intelligent agent autonomously decides and updates the digital twin model, so that the task allocation efficiency is improved, the personnel and resource information is updated again before assembly, and the synchronization of the digital twin model and the initial state of the physical workshop is ensured.
3. In the assembly operation stage, the intelligent agent performs data interaction with the physical workshop and the digital twin model, senses assembly data in real time, updates the digital twin model in real time, drives real-time simulation, and ensures that the digital twin model synchronously shows the operation state of the physical workshop.
4. The invention provides an intelligent agent real-time decision and control scheme, and the intelligent agent encapsulation interface is used for controlling the resources to be distributed to the appointed station, so that the resource distribution process is optimized, and the workshop production efficiency is improved.
5. On the basis of virtual-real fusion, management personnel and assembly personnel manage the conditions of an assembly site through a visual management and control platform and send instructions to a physical workshop through an intelligent body packaging interface of a digital twin model, so that the control of the assembly site and the coordination treatment of site abnormal conditions are realized.
Drawings
FIG. 1 is a schematic diagram of a model architecture according to the present invention.
FIG. 2 is a flow chart of a technique for constructing a virtual plant model according to the present invention.
FIG. 3 is a diagram illustrating an agent decision in the task allocation phase according to the present invention.
Fig. 4 is a schematic view illustrating the operation and control of the assembly process of the present invention.
Detailed Description
The invention is further illustrated with reference to the following figures and examples.
A virtual-real fusion operation and control method for an assembly workshop based on a digital twin technology is characterized in that a model architecture is divided into 3 parts, namely a physical workshop model, a digital twin model (comprising an intelligent body packaging module, a virtual workshop module and a data service module) and a control platform. The system specifically comprises the following 5 modules:
(1) the physical workshop model is characterized in that firstly, the composition of a physical assembly workshop is determined, and the physical assembly workshop comprises personnel, equipment, materials, tools, products and data acquisition equipment. The personnel include: a craftsman, an administrator, an assembler, etc.; the apparatus comprises: the system comprises an AGV trolley, processing equipment, a numerical control positioner, a laser tracker, automatic drilling and riveting equipment and the like; the material comprises: connecting pieces, wall plates, skins, stringers, bulkheads, pipelines and the like; the frock includes: assembling fixture, assembling jig, etc.; the product comprises the following components: complete machine, assembly subassembly, assembly part, assembly section spare. The data acquisition device includes: RFID, sensor, industrial computer, PLC, measuring equipment, bar code recognizing equipment, voice input equipment and the like.
(2) The intelligent body encapsulation module in the digital twin model encapsulates the physical objects and elements participating in the assembly process by adopting an encapsulation technology and serves as an interaction and fusion interface of a physical workshop and a digital twin workshop. And (4) packaging workshop production elements into four categories of task management agents, task agents, resource agents and assembly station agents. The task management intelligent agent is a manager of the workshop tasks and is responsible for receiving, auditing and decomposing and distributing the tasks; packaging the assembly operation corresponding to each independent assembly process into a task agent, and taking charge of the management and execution of the task; packaging equipment, tools, materials and the like into a resource intelligent agent, and taking charge of managing and assembling resources and dispatching the resources to a required position; the assembly station intelligent bodies are managers of the assembly units, and each assembly station is an independent assembly station intelligent body and is responsible for receiving assembly tasks, monitoring the operation conditions of the stations and processing emergency events occurring on the stations.
The communication between the intelligent agents is realized by adopting a blackboard mechanism and a message transmission mode, the message transmission is realized by adopting a TCP/IP network protocol and KQML or FIPA ACL language, and the XML technology is used for carrying out message encapsulation.
(3) As shown in fig. 2, a virtual workshop module in the digital twin model first constructs an intelligent production element three-dimensional model corresponding to a physical workshop, including personnel attributes, equipment attributes, material attributes, tool attributes, and product attributes. Adopting Unity3D software to carry out modeling on a digital twin assembly workshop, setting the attributes of all production elements in the established three-dimensional model, constructing a process model, carrying out workshop layout planning according to the three-dimensional model of the production elements and the process model, and carrying out virtual simulation operation on the twin workshop on the basis.
The method comprises the steps of setting attributes of all production elements, wherein the attributes of personnel comprise function information and task information, the attributes of equipment comprise equipment states, equipment functions and processing capabilities, the attributes of materials comprise material information, inventory information, processing modes and process attributes, the attributes of tools comprise tool states, inventory and tool functions, and the attributes of products comprise material information, quantity requirements and product functions.
The process model is to complete the construction of a product, process and resource (PPR) structure tree according to a product three-dimensional model to form a visual digital process file for guiding the actual assembly process. The layout planning of the workshop comprises the positions and the placing sequence of equipment and tools, the transportation mode and the route of materials, the task arrangement of stations, the working flow and the movement route of personnel, the detection and the storage of products and the working hour information.
(4) The data service module in the digital twin model comprises a database and a data server. The data includes: personnel data, material data, tooling data, product data, equipment data, assembly process data, assembly task data, scheduling data, assembly quality data and the like.
The data stored in the database are divided into real-time operation data, state data and historical data. Continuously updating real-time operation data to ensure synchronous operation of the digital twin workshop and the physical workshop; the state data records the position information, the working state and the like of workshop resources, and is updated when the state is changed or every short period of time; the historical data archives data such as assembly process data, process files and the like. The data server is mainly used for receiving and issuing instructions, calling database data and connecting the physical workshop with the digital twin model.
(5) The management and control platform is a software platform for managing the physical workshop and the digital twin model, and is used for a user to manage the assembly workshop. The control platform is divided into 9 modules according to functions, and comprises a system management module, a task management module, a process management module, a plan management module, a production execution module, a resource management module, a simulation operation module, a quality management module and a monitoring and coordination processing module. Each module function is realized by an intelligent agent model, a virtual workshop model and a database service, for example, a production execution module can be cooperated by a plurality of intelligent agents such as an assembly station intelligent agent, a task intelligent agent, a resource intelligent agent and the like, and is realized by interacting with the virtual workshop model and the database service, and a visual operation interface is provided for a user; during the actual operation of the system, a function menu which can be operated by a human is displayed.
The virtual-real fusion operation and control method of the assembly workshop based on the digital twin technology is implemented by taking an airplane final assembly pulsating production line as an embodiment, and the virtual-real fusion operation and control process of a physical workshop and a digital twin model is implemented by adopting the modules, wherein the operation process comprises the following steps:
(1) firstly, process design is carried out, and a craftsman constructs a process model in a virtual workshop model through a control platform. The method comprises the following steps that a craftsman performs assembly unit division by using a BOM and a three-dimensional model of the airplane, the airplane is divided into three parts, namely a front fuselage section, a middle fuselage section and a rear fuselage section, the parts are divided into sections and assemblies, and the parts are divided into parts at minimum; then, a process scheme is formulated, an assembly route is planned, an assembly process reference is determined, and positioning is carried out by an assembly fixture; and finally, determining the detailed content of the process step, completing the construction of the PPR structure tree, and generating a digital assembly process file.
(2) As shown in fig. 3, the task management agent receives an aircraft final assembly task through the management and control platform, checks and decomposes the task, and sends an instruction for acquiring physical workshop information to each agent, and each agent immediately responds and feeds back state information of the packaged physical object to the task management agent, where the fed-back physical object information includes equipment, tool, material information of an assembly station, position inventory information of the tool, and inventory information of the material.
(3) And the information of the intelligent agent is transmitted to the virtual workshop model, and the virtual workshop model is driven to perform virtual simulation. And (4) inquiring the task information and the physical workshop information by a user, finishing layout planning according to the process model, and performing virtual workshop simulation. The method is characterized in that an aircraft assembly pulsating production line is taken as an embodiment, the virtual workshop is simulated, simulation and optimization are carried out on the virtual workshop through simulation software in a digital twin model, the pulsating production line is planned, the workshop capacity is predicted, and the workshop station layout and the working procedure beat are optimized.
(4) The task management intelligent agent sends bidding information to the assembling station intelligent agent according to the current information, wherein the bidding information comprises station assembling tasks, required equipment, assembling type frames, assembling clamps and the like; the assembly station intelligent body determines whether to participate in bidding according to the information of the material type quantity, the assembly fixture type, the equipment type and state, the personnel arrangement, the station working state, the arranged task and the like of the assembly station; and if bidding is carried out, sending bidding information including task starting time, cost and the like to the task management agent.
(5) And the task management intelligent agent analyzes the bidding information, selects the assembly station intelligent agent winning a bid according to the multi-objective optimization rule, confirms the arrangement of the assembly tasks, and updates the production plan result to the digital twin model and the related database by each intelligent agent. And finally, determining four assembly stations to respectively complete the tasks of butt joint of the airplane body, installation of the guide pipe cable, system detection and airplane delivery.
(6) And the assembly station intelligent agent receives the body docking task from the task management intelligent agent and starts assembly preparation work. The assembly personnel check materials, assembly fixtures, assembly fixture frames and positioning and posture adjusting equipment required by assembly, the RFID is utilized to feed back identity information and resource preparation conditions to the digital twin model through the control platform, and the current assembly personnel and resource information of the digital twin model are updated, so that the digital twin model and the physical workshop are in an assembly preparation state at the same time. In the assembling preparation process, an assembling person can send a resource request to a resource intelligent body through an assembling station intelligent body, the resource intelligent body prepares resources such as a connecting piece, a wallboard, a skin, a stringer, a partition frame and a pipeline, the AGV is controlled to be automatically sent to the assembling station according to a set path, and meanwhile, the resource state in the digital twin model is updated.
(7) After the preparation work is finished, the assembling work is started at the butt joint station of the machine body. Assembling according to the visual assembly process file by an assembler, reading assembly cautions, checking a detailed assembly process, positioning and adjusting the body in the first procedure according to the steps, and monitoring the assembly process in real time by an assembly station intelligent agent.
(8) In the assembling operation and production operation process, the intelligent agent can sense the assembling data in real time. In the process of positioning and posture adjustment of the machine body, the numerical control positioner and the laser tracker collect positioning information and posture information of the machine body, and an assembler finishes positioning and posture adjustment work of the machine body according to the data, records working hour information and feeds back problems in the assembly process.
(9) As shown in fig. 4, the intelligent agent updates the digital twin model in real time, automatically stores the sensed and acquired data into the database, and updates the virtual workshop model, so that the data interaction and the virtual-real fusion synchronous operation of the digital twin model and the physical assembly workshop are realized.
(10) Inside the digital twin model, the virtual workshop model can utilize dynamically acquired field resources and assembly data to carry out visual real-time simulation, and synchronously show the process and progress of positioning and posture adjustment of the body in the physical workshop to managers.
(11) And in the assembling process of each station, the intelligent agents interact, cooperate and make decisions in real time. When a worker needs materials or products in assembly operation, a request is sent to each resource intelligent agent through the task intelligent agent, the resource intelligent agents respond according to self states, the task intelligent agents select the resource intelligent agents to carry out material distribution tasks according to the shortest time principle, and decision results are stored in a database of the digital twin model. The resource intelligence can receive a plurality of part requests at the same time and respond according to the rules set by the workshop, such as first-come first-serve, task priority and the like.
(12) And the resource intelligent body controls the physical workshop according to the decision result, the intelligent body sends the decision instruction to the physical workshop through the intelligent body communication interface and runs through control equipment such as a PLC (programmable logic controller), for example, an AGV (automatic guided vehicle) is controlled to dispatch the material to a specified assembly station, and therefore the actual assembly field is controlled.
(13) After the assembler finishes the first assembly procedure, the task agent feeds the assembly result back to the database, and the digital twin model updates the state of the first procedure. Meanwhile, the intelligent agent stores the process data, the assembly process data, the scheduling data and the assembly quality data into a historical database, and provides data support for the subsequent assembly process management.
(14) And the assembly personnel execute the subsequent assembly process of the station according to the process file. Similar to the first step of procedure, the intelligent agent senses the field data of the physical workshop in real time, updates the digital twin model, makes a decision in real time and controls the physical workshop to run according to the decision result in the assembly process; and after the assembly task of the first station is completed, sequentially completing assembly of the subsequent stations until the delivery work of the airplane is completed.
(15) As shown in fig. 4, in the operation process of the assembly shop, the intelligent agent can simultaneously feed back abnormal conditions and problems to the database and the control platform, and managers check the assembly process information through the control platform and record the assembly quality inspection data; monitoring the assembly site condition of the physical workshop in real time through the operation of the digital twin model, and issuing a control instruction to the physical workshop through the intelligent model; abnormal conditions such as material shortage, equipment failure, process problems and the like can be coordinated and processed through the control platform.
Claims (7)
1. A virtual-real fusion operation and control method for an assembly shop based on a digital twinning technology is characterized by comprising the following steps:
step 1: the assembly shop virtual-real fusion model architecture comprises 3 parts, namely a physical shop model, a digital twin model and a control platform; the digital twin model comprises an intelligent body packaging module, a virtual workshop module and a data service module;
step 1-1: the physical workshop model comprises personnel, equipment, materials, tools, products and data acquisition equipment;
step 1-2: an agent encapsulation module in the digital twin model;
adopting an encapsulation technology to carry out intelligent body encapsulation on workshop production elements participating in the assembly process, and using the workshop production elements as an interaction and fusion interface of a physical workshop and a digital twin model; packaging workshop production elements into a task management intelligent agent, a task intelligent agent, a resource intelligent agent and an assembly station intelligent agent;
step 1-3: a virtual workshop module in the digital twin model;
firstly, constructing an intelligent production element three-dimensional model corresponding to a physical workshop, wherein the intelligent production element three-dimensional model comprises personnel attributes, equipment attributes, material attributes, tool attributes and product attributes; then modeling a digital twin assembly workshop, setting the attributes of all production elements in the established three-dimensional model, constructing a process model, planning the layout of the workshop according to the intelligent production element three-dimensional model and the process model, and performing virtual simulation operation of the twin workshop on the basis;
step 1-4: the data service module in the digital twin model comprises a database and a data server; the data in the digital twin model includes: personnel data, material data, tool data, product data, equipment data, assembly process data, assembly task data, scheduling data and assembly quality data;
the data stored in the database comprises real-time operation data, state data and historical data; continuously updating real-time operation data to ensure synchronous operation of the digital twin model and the physical workshop; the state data records the position information and the working state of workshop resources, and is updated when the state is changed or at intervals; the historical data is used for archiving assembly process data and process file data; the data server receives and issues instructions, calls database data, and connects the physical workshop with the digital twin model;
step 1-5: the management and control platform is a software platform for managing a physical workshop and a digital twin model, and is used for a user to manage the assembly workshop; the control platform is divided into 9 modules according to functions, and comprises a system management module, a task management module, a process management module, a plan management module, a production execution module, a resource management module, a simulation operation module, a quality management module and a monitoring and coordination processing module; each module function is realized by an intelligent body packaging module, a virtual workshop module and a data service module;
step 2: designing a process; a process model is built in the virtual workshop model by a technician through a control platform; the technical personnel formulate a detailed assembly process scheme by utilizing the BOM and the product three-dimensional model, construct a PPR structure tree containing product, process and resource information, and generate a digital assembly process file;
and step 3: the task management intelligent agent receives the assembly task through the management and control platform, audits and decomposes the task, sends an instruction for acquiring physical workshop information to each intelligent agent, each intelligent agent responds and feeds back the state information of the packaged physical object to the task management intelligent agent, and the fed-back physical object information comprises equipment, tools and material information of an assembly station, position inventory information of the tools, and inventory information of the materials;
and 4, step 4: the information of each agent is transmitted to the virtual workshop model, and the virtual workshop model is driven to perform virtual simulation; a user inquires task information and physical workshop information, completes layout planning according to a process model and performs virtual workshop simulation; the virtual workshop simulation is to simulate the virtual workshop in the digital twin model through simulation software;
and 5: the task management intelligent agent sends bidding information to the assembly station intelligent agent according to the current information, wherein the bidding information comprises an assembly task, required equipment and a tool; the assembly station intelligent body determines whether to participate in bidding according to the material type quantity, the tool type, the equipment type and state, the personnel arrangement, the station working state and the arranged task information of the assembly station; if so, sending bidding information to the task management agent, wherein the bidding information comprises the start time and cost of the task;
step 6: the task management intelligent agent analyzes the bidding information, selects the assembly station intelligent agent winning a bid according to the multi-objective optimization rule, confirms the assembly task arrangement, and updates the production plan result to the digital twin model and the related database by each intelligent agent;
and 7: the assembly station intelligent agent receives an assembly task from the task management intelligent agent and starts assembly preparation work; the method comprises the steps that assembly personnel check materials, tools and tool resources required by assembly, identity information and resource preparation conditions are fed back to a digital twin model through a control platform by means of RFID, and current assembly personnel and resource information of the digital twin model are updated, so that the digital twin model and a physical workshop are in an assembly preparation state at the same time; in the assembling preparation process, an assembling person sends a resource request to a resource intelligent agent through an assembling station intelligent agent, the resource intelligent agent prepares required resources, controls an AGV trolley to automatically send the resources to the assembling station according to a set path, and simultaneously updates the resource state in a digital twin model;
and 8: after the preparation work is finished, the assembly station starts assembly work; assembling according to the visual assembly process file by an assembler, reading assembly cautions, checking a detailed assembly process, starting first procedure assembly according to the steps, and monitoring the assembly process by an assembly station intelligent agent in real time;
and step 9: in the assembling operation and production operation process, the intelligent agent can sense assembling data in real time, and the obtained data comprises material information, personnel information and equipment position information read through the packaging interface; part positioning information and posture information collected by the numerical control positioner and the laser tracker; acquiring actual operation of personnel, equipment running state and working hour information in a sensor and manual feedback mode; and process problems, equipment failures, material shortage anomalies;
step 10: the intelligent agent updates the digital twin model in real time, automatically stores the sensed and acquired data into a database, and updates the virtual workshop model, so that the data interaction and the virtual-real fusion synchronous operation of the digital twin model and the physical assembly workshop are realized;
step 11: in the digital twin model, the virtual workshop model utilizes dynamically acquired field resources and assembly data to carry out visual real-time simulation, and synchronously displays the running state and progress of a physical workshop to managers;
step 12: in the assembling process of each station, the intelligent agents interact, cooperate and make decisions in real time; when a worker needs a material or a product in assembly operation, a request is sent to each resource intelligent agent through the task intelligent agent, the resource intelligent agents respond according to self states, the task intelligent agents select the resource intelligent agents to carry out material distribution tasks according to the shortest time principle, and decision results are stored in a database of the digital twin model; the resource intelligent agent simultaneously receives a plurality of part requests and responds according to the first-come-first-serve or task priority rule set by a workshop;
step 13: the resource intelligent agent controls the physical workshop according to the decision result, and sends the decision instruction to the physical workshop through the intelligent agent communication interface and runs through the PLC control equipment;
step 14: after the assembler finishes the first assembly procedure, the task agent feeds the assembly result back to the database, and the digital twin model updates the state of the first procedure; meanwhile, the intelligent agent stores the process data, the assembly process data, the scheduling data and the assembly quality data into a historical database to provide data support for the subsequent assembly process management;
step 15: the assembly personnel execute the subsequent assembly process according to the process file; similar to the first step of procedure, the intelligent agent senses the field data of the physical workshop in real time, updates the digital twin model, makes a decision in real time and controls the physical workshop to run according to the decision result in the assembly process until all assembly tasks are finished;
step 16: in the operation process of an assembly workshop, an intelligent agent can simultaneously feed back abnormal conditions and problems to a database and a control platform, and a manager checks the information of the assembly process through the control platform and records the assembly quality inspection data; monitoring the assembly site condition of the physical workshop in real time through the operation of the digital twin model, and issuing a control instruction to the physical workshop through the intelligent model; and coordinating and processing the conditions of material shortage, equipment failure and process problem abnormity through the control platform.
2. The assembly shop virtual-real fusion operation and control method based on the digital twin technology as claimed in claim 1, wherein the personnel comprise a technician, an administrator and an assembler; the equipment comprises an AGV trolley, processing equipment, a numerical control positioner, a laser tracker and automatic drilling and riveting equipment; the materials comprise connecting pieces, wall plates, skins, stringers, partition frames and pipelines; the tool comprises an assembly fixture and an assembly clamp; the product comprises a complete machine, an assembly component, an assembly part and an assembly section part; the data acquisition equipment comprises an RFID, a sensor, an industrial personal computer, a PLC, measuring equipment, bar code reading equipment and voice input equipment.
3. The assembly shop virtual-real fusion operation and control method based on the digital twin technology as claimed in claim 1, wherein the task management agent is a manager of a shop task and is responsible for task reception, task review, task decomposition and distribution; packaging the assembly operation corresponding to each independent assembly process into a task agent, and taking charge of the management and execution of the task; packaging equipment, tools, materials and the like into a resource intelligent agent, and taking charge of managing and assembling resources and dispatching the resources to a required position; the assembly station intelligent bodies are managers of the assembly units, and each assembly station is an independent assembly station intelligent body and is responsible for receiving assembly tasks, monitoring the operation conditions of the stations and processing emergency events occurring on the stations.
4. The assembly shop virtual-real fusion operation and control method based on the digital twin technology according to claim 1, wherein communication between the agents is implemented by a blackboard mechanism and message transmission; and message transmission is realized by adopting a TCP/IP network protocol and KQML or FIPA ACL language, and XML technology is used for message encapsulation.
5. The assembly shop virtual-real fusion operation and control method based on the digital twin technology as claimed in claim 1, wherein the modeling of the digital twin assembly shop is performed by adopting Unity3D software.
6. The assembly shop virtual-real fusion operation and control method based on the digital twin technology as claimed in claim 1, wherein in the production element three-dimensional model, personnel attributes include function information and task information, equipment attributes include equipment state, equipment function and processing capability, material attributes include material information, inventory information, processing mode and process attributes, tool attributes include tool state, inventory and tool function, and product attributes include material information, quantity demand and product function;
the process model is constructed according to a product three-dimensional model to form a visual digital process file for guiding an actual assembly process;
the layout planning of the workshop comprises the positions and the placing sequence of equipment and tools, the transportation mode and the route of materials, the task arrangement of stations, the working flow and the movement route of personnel, and the detection and storage and the working hour information of products.
7. The assembly shop virtual-real fusion operation and control method based on the digital twin technology as claimed in claim 1, wherein the tender is developed at a production planning stage or in real time during a production process.
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