CN113093680A - FIMS system architecture design method based on digital twin technology - Google Patents
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- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
- G05B19/41885—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by modeling, simulation of the manufacturing system
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Abstract
The invention discloses a FIMS system architecture design method based on a digital twin technology, which solves the problems that a multisource heterogeneous FMS system is easy to generate data islands and a traditional FMS system is not flexible enough and cannot be dynamically adjusted in real time, and has the technical scheme key points of designing a novel digital twin flexible intelligent manufacturing system architecture, establishing a virtual digital twin model, establishing a FIMS digital twin data acquisition system, establishing a FIMS processing production system, establishing a RMFS logistics system in the flexible intelligent manufacturing system, establishing a three-dimensional warehouse system, establishing a digital twin-based workshop remote virtual monitoring system and establishing a digital twin predictive maintenance and fault analysis system. Helping the factory better cope with new problems and challenges of the full life cycle.
Description
Technical Field
The invention relates to the technical field of digital twinning, in particular to a FIMS system architecture design method based on a digital twinning technology.
Background
As the mass production era is gradually being replaced by production that is adaptive to the dynamic changes of the market, the viability and competitiveness of a manufacturing automation system depends to a large extent on its ability to produce different varieties of products with lower cost and higher quality within a very short development period, and flexibility has taken a considerable place. When building a traditional FMS, higher flexibility and greater automation are always desired, but with the increased cost, the difficulty of the technology is increased.
The traditional mechanical production line is only suitable for large-batch rigid production, and has the advantages of single variety, unstable processing quality, low precision, poorer independent development and innovation capability of products and less personalized difference caused by standardized production. The machine has high repeatability, is only suitable for mass production of the same product, and the mass production causes the difficulty of small-quantity production. Since the machine operation requires parameter adjustment to achieve a certain operation environment, if the production is small, huge resource consumption will be caused.
Conventional FMS systems are generally not flexible enough, and often implement device networking based on a dedicated network and implement device data acquisition and monitoring based on customized codes, and when a flexible manufacturing system needs to be adjusted, customers are often limited by whether a supplier can provide timely, high-quality, and appropriate-cost services, which may limit the flexibility of the flexible manufacturing system itself.
Disclosure of Invention
The invention aims to provide a FIMS system architecture design method based on a digital twin technology, which can provide theoretical guidance in the processes of product research and development, design, prediction, manufacture, test, maintenance and the like on the premise of saving time cost and economic cost and help a factory to better deal with new problems and challenges of a full life cycle.
The technical purpose of the invention is realized by the following technical scheme:
a FIMS design method based on a digital twin technology comprises the following steps:
a FIMS system architecture design method based on a digital twin technology comprises the following steps:
s1, designing and providing a novel digital twin flexible intelligent manufacturing system architecture for a full life cycle, wherein the architecture comprises a manufacturing, running and managing MES system, a DT system, an FMS system, an IOT-Enabler system, bottom hardware equipment and the like;
s2, according to the structure of an actual workshop, the workshop objects are mainly divided into types such as a processing production line, an RMFS logistics transport line, a stereoscopic warehouse, a movable object AGV and the like, and a virtual digital twin model corresponding to the real physical workshop is established;
s3, establishing a FIMS digital twin data acquisition system, wherein in order to realize data interaction between models and interaction of external real-time data, a communication control signal interface is established for the twin model according to an operation logic interaction signal and driving data;
s4, establishing a FIMS processing production system;
s5, establishing an RMFS logistics system in the flexible intelligent manufacturing system;
s6, establishing a stereoscopic warehouse system;
s7, establishing a workshop remote virtual monitoring system based on the digital twin:
and S8, establishing a digital twin-based predictive maintenance and fault analysis system.
Preferably, step S2 specifically includes:
s201, carrying out real mapping according to a physical entity, and establishing a related mathematical twin model corresponding to a physical world in matlab software;
s202, establishing a three-dimensional model of a physical entity by using Demo 3D, importing the model into a Plant Simulation platform, and selectively carrying out lightweight processing on the model to reduce display pressure during operation;
s203, acquiring a corresponding twin mechanical model according to the mathematical twin model, setting object attribute parameters and operation logic parameters in a parameterized mode, and adjusting optimization parameters to enable twin mechanical model generated data to be matched with an actually measured data result;
s204, setting the movable component parts in the three-dimensional model as movable drawing objects, further editing the action paths of the movable drawing components, and associating the component animations to form a complete action;
s205, converting production logistics rules and strategies in an actual workshop system into simulation operation logics, writing in an object Method to drive the movable entity to run inside the digital twin workshop, and realizing parameterization setting of related rule strategies.
Preferably, the construction of the prediction model specifically comprises:
defining a prediction model: DTOPER＝(OPMODEL,OPINTERFACEAFCE,OPSERVICE,)；
Wherein OPMODELBeing a three-dimensional model of a person, OPINTERFACEAFCEFor a person position/motion data interface, OPSERVICEServing production monitoring;
simulating fault factors to detect the equipment to be predicted and outputting a simulation result; and extracting training data for machine learning through the simulation result, deploying an algorithm and outputting the result.
Preferably, step S3 specifically includes:
s301, acquiring parameter data required by a mathematical twin model through distributed sensors such as temperature, pressure and vibration, planning each workshop element of a workshop, and establishing virtual digital mapping of the workshop element;
s302, establishing connection between the digital space and field real-time data through an internal UA client, preprocessing according to acquired parameter data, and writing feedback data of a production field into inverse control while mapping a physical entity by a driving model;
s303, building a rapid and reliable information transmission network, wherein information interaction is realized mainly based on synchronous read/write, asynchronous read/write and subscription modes of an OPC protocol, system state information is safely and real-timely transmitted to an upper computer, and data transmission between an OPC UA Server and an OPC UA Client is realized;
s304, designing a UA server application architecture, wherein the UA server application architecture comprises a communication network architecture physical layer consisting of an industrial robot, a PLC, a sensor, an industrial personal computer, a cable and a network cable device; a data link layer for data transmission via an Ethernet protocol; a network layer and a transport layer using a TCP/IP protocol; using OPC UA protocol as application layer of data protocol;
s305, connecting the server with the switch and further connecting the server with an industrial personal computer through the Ethernet, and setting the IP of the industrial personal computer to be in the same network segment with the server according to the IP address of the server; and the data communication module of the digital twin system is connected with the URL of the server by acquiring the local IP.
Preferably, step S4 specifically includes:
s401, triggering an event of reading data in a digital twin system in a subscription mode, and transmitting production order information and workpiece attributes by a scheduling system of a manufacturing operation management system;
s402, outputting the required workpieces by the stereoscopic warehouse, putting the workpieces into an annular conveying belt by an RMFS (remote message service platform) AGV (automatic guided vehicle), and conveying to achieve the purpose that each functional module is processed and assembled;
s403, judging whether the robot works or not by performing visual analysis on the materials;
and S404, detecting CCD non-contact images, and assembling, wherein when no raw material is provided for the stereoscopic warehouse, the raw material is supplied by the material supplementing unit.
Preferably, step S5 specifically includes:
s501, processing orders in the twin data center, converting corresponding production tasks into task data, transmitting the task data to the digital twin system for subsequent operation, and storing the task data in a digital twin data center library;
s502, in the twin system, an initial scheduling scheme is produced according to task data and real-time running data, the scheduling scheme is simulated by means of a virtual simulation system, an optimal scheduling scheme is determined according to a simulation result, and simulation result data are transmitted to a twin data center;
and S503, generating a corresponding instruction according to the received simulation result data in the twin data center, and transmitting the instruction to the physical system. And the physical system reads the instruction transmitted by the data center to guide the operation of personnel and the operation of the AGV.
Preferably, step S6 specifically includes:
s601, the RMFS logistics system conveys AGV running data and scheduling cache data to a twin data center, and in the twin data center, instructions are transmitted to the AGV according to data read by a warehouse system;
s602, the order processing method comprises the steps of distributing work stations for orders through order integration, determining whether to supplement goods according to the order conditions, generating corresponding supplement tasks if the supplement goods are supplemented, splitting the corresponding pick tasks according to the work stations, and determining the selected inventory tasks according to system requirements. Determining tasks of replenishment, picking and checking to generate task data;
s603, when the production line needs to supplement materials, the task data is uploaded to a twin database, and the RMFS logistics system reads the instruction transmitted by the data center and responds to the warehouse;
and S604, when the production line needs to receive materials, the task data is uploaded to a twin database, and the RMFS reads the instruction transmitted by the data center and responds to the warehouse.
Preferably, step S7 specifically includes:
s701, the twin model realizes on-site synchronous mapping under the drive of real-time data of the production line, and the production condition of the production line is reflected in real time;
s702, remote operation and maintenance installation of various devices is met through remote control software, and bidirectional transmission, remote diagnosis, remote configuration and CMD diversified control are realized;
s703, performing all-around and multi-angle monitoring and visualization service on the production activities through data storage, data statistics and data analysis of the production process by the data center.
Preferably, step S8 specifically includes:
s801, PHM comprises fault prediction and remote diagnosis;
s802, transmitting the real-time data acquired by the intelligent sensor to a twin data center;
s803, signal processing: preprocessing and feature extraction of data
S804, state detection: judging the threshold value by using fuzzy logic;
and S805, comparing the actual data with the predicted data, performing health assessment, detecting whether the data is abnormal, and performing data fusion, fault analysis and final fault maintenance if the data is abnormal.
In conclusion, the invention has the following beneficial effects:
the digital twinning technology is used, FMS and IMS are combined to provide a novel FIMS framework design method, the digital twinning technology is used for automatically collecting and transmitting information and automatically diagnosing faults in the running process, and the fault self-elimination and self-maintenance are realized, so that the intelligent manufacturing system can be automatically optimized and adapt to various complex environments; with the maturity and application of industrial robot technology, FIMS absorbs application practice experience, and the overall structure adopts modular, generalized, functional, software and hardware function compatible and extensible design technology.
Drawings
FIG. 1 is a schematic flow chart of a FIMS system architecture design method based on digital twinning technology according to the present invention;
FIG. 2 is a schematic block diagram of the FIMS system architecture design method based on the digital twin technology provided in the present invention;
FIG. 3 is a data communication network architecture diagram of an information communication system based on the FIMS system architecture design method of the digital twin technology provided by the present invention;
FIG. 4 is a digital twin key technology block diagram of a FIMS system architecture design method based on the digital twin technology provided by the present invention;
FIG. 5 is a diagram of a digital twin system architecture of the FIMS system architecture design method based on the digital twin technology provided in the present invention;
FIG. 6 is a diagram of a data transmission system of a FIMS system architecture design method based on digital twinning technique according to the present invention;
FIG. 7 is a block diagram of an RMFS logistics system of the FIMS system architecture design method based on the digital twin technology provided by the invention;
FIG. 8 is a production process flow chart of a FIMS system architecture design method based on the digital twinning technology provided by the invention;
FIG. 9 is a transportation flow chart of a FIMS system architecture design method based on the digital twin technology provided by the present invention;
FIG. 10 is a product warehousing flowchart of a FIMS system architecture design method based on digital twinning technology provided in the present invention;
FIG. 11 is a flow chart of the product ex-warehouse of the FIMS system architecture design method based on the digital twin technology provided by the present invention;
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
With the acceptance of the society on various products and medium and small-batch products, the requirements on short production period and low manufacturing cost are increased, and the manual operation is reduced to the minimum. Meanwhile, the digital twin technology is used for automatically collecting and transmitting information in the operation process, automatically diagnosing faults, automatically removing the faults and automatically maintaining the faults, and the characteristic enables the intelligent manufacturing system to be self-optimized and adapt to various complex environments.
The flexibility, degree of automation and production of FIMS is directly dependent on its system architecture. The structure also affects the integration among all parts of the FIMS, the matching of a computer communication network, the matching and function arrangement of all control equipment and subsystems in the FIMS and the like, and if the system structure is unreasonable, the cost of the FIMS is greatly increased, the system is huge and swollen, and the expected effect cannot be achieved, so the design of the FIMS system structure is well solved.
The novel flexible manufacturing system meeting the application requirements of modern manufacturing enterprises has the four characteristics of platform integration, flexibility matching, virtual-real fusion and man-machine cooperation, and solves the problems of data islands, high construction threshold, difficulty in improvement and the like caused by the traditional flexible manufacturing system. The novel FIMS framework can provide strong and powerful support for manufacturing enterprises to build advanced flexible manufacturing factories which integrate full manufacturing business into a whole, and are flexible, efficient, transparent and intelligent.
According to one or more embodiments, a FIMS system architecture design method based on digital twin technology is disclosed, as shown in fig. 1, including the following steps:
s1, designing and providing a novel digital twin flexible intelligent manufacturing system architecture facing a full life cycle, as shown in FIG. 2, wherein the architecture comprises a manufacturing, running and managing MES system, a DT system, an FMS system, an IOT-Enabler system, bottom layer hardware equipment and the like;
s2, according to the structure of an actual workshop, the workshop objects are mainly divided into types such as a processing production line, an RMFS logistics transport line, a stereoscopic warehouse, a movable object AGV and the like, and a virtual digital twin model corresponding to the real physical workshop is established;
s3, establishing a FIMS digital twin data acquisition system, wherein in order to realize data interaction between models and interaction of external real-time data, a communication control signal interface is established for the twin model according to an operation logic interaction signal and driving data;
s4, establishing a FIMS processing production system;
s5, establishing an RMFS logistics system in the flexible intelligent manufacturing system;
s6, establishing a stereoscopic warehouse system;
s7, establishing a workshop remote virtual monitoring system based on the digital twin:
and S8, establishing a digital twin-based predictive maintenance and fault analysis system.
Step S2 specifically includes:
s201, carrying out real mapping according to a physical entity, and establishing a related mathematical twin model corresponding to a physical world in matlab software;
s202, establishing a three-dimensional model of a physical entity by using Demo 3D, importing the model into a Plant Simulation platform, and selectively carrying out lightweight processing on the model to reduce display pressure during operation;
s203, acquiring a corresponding twin mechanical model according to the mathematical twin model, setting object attribute parameters and operation logic parameters in a parameterized mode, and adjusting optimization parameters to enable twin mechanical model generated data to be matched with an actually measured data result;
s204, setting the movable component parts in the three-dimensional model as movable drawing objects, further editing the action paths of the movable drawing components, and associating the component animations to form a complete action;
s205, converting production logistics rules and strategies in an actual workshop system into simulation operation logics, writing in an object Method to drive the movable entity to run inside the digital twin workshop, and realizing parameterization setting of related rule strategies.
The construction of the prediction model specifically comprises the following steps:
defining a prediction model: DTOPER＝(OPMODEL,OPINTERFACEAFCE,OPSERVICE,)；
Wherein OPMODELBeing a three-dimensional model of a person, OPINTERFACEAFCEFor a person position/motion data interface, OPSERVICEServing production monitoring;
simulating fault factors to detect the equipment to be predicted and outputting a simulation result; and extracting training data for machine learning through the simulation result, deploying an algorithm and outputting the result.
Step S3 specifically includes:
s301, as shown in FIG. 5, acquiring parameter data required by a mathematical twin model through distributed sensors such as temperature, pressure, vibration and the like, planning each workshop element of a workshop, and establishing a virtual digital mapping of the workshop element;
s302, as shown in fig. 6, the digital space is connected to the real-time data of the production site through the internal UA client, and performs preprocessing according to the acquired parameter data, and writes back control on the feedback data of the production site while mapping the physical entity by the driving model;
s303, building a rapid and reliable information transmission network, wherein information interaction is realized mainly based on synchronous read/write, asynchronous read/write and subscription modes of an OPC protocol, system state information is safely and real-timely transmitted to an upper computer, and data transmission between an OPC UAServer and an OPC UA Client is realized;
s304, designing a UA server application architecture, as shown in FIG. 3, including a communication network architecture physical layer composed of an industrial robot, a PLC, a sensor, an industrial personal computer, a cable and a network cable device; a data link layer for data transmission via an Ethernet protocol; a network layer and a transport layer using a TCP/IP protocol; using OPC UA protocol as application layer of data protocol;
s305, connecting the server with the switch and further connecting the server with an industrial personal computer through the Ethernet, and setting the IP of the industrial personal computer to be in the same network segment with the server according to the IP address of the server; and the data communication module of the digital twin system is connected with the URL of the server by acquiring the local IP.
As shown in fig. 7, step S4 specifically includes:
s401, triggering an event of reading data in a digital twin system in a subscription mode, and transmitting production order information and workpiece attributes by a scheduling system of a manufacturing operation management system;
s402, outputting the required workpieces by the stereoscopic warehouse, putting the workpieces into an annular conveying belt by an RMFS (remote message service platform) AGV (automatic guided vehicle), and conveying to achieve the purpose that each functional module is processed and assembled;
s403, judging whether the robot works or not by performing visual analysis on the materials;
and S404, detecting CCD non-contact images, and assembling, wherein when no raw material is provided for the stereoscopic warehouse, the raw material is supplied by the material supplementing unit.
Step S5 specifically includes:
s501, as shown in FIG. 8, in the twin data center, processing orders, converting corresponding production tasks into task data, transmitting the task data to the digital twin system for subsequent operation, and storing the task data in a digital twin data center library;
s502, in the twin system, an initial scheduling scheme is produced according to task data and real-time running data, the scheduling scheme is simulated by means of a virtual simulation system, an optimal scheduling scheme is determined according to a simulation result, and simulation result data are transmitted to a twin data center;
and S503, generating a corresponding instruction according to the received simulation result data in the twin data center, and transmitting the instruction to the physical system. And the physical system reads the instruction transmitted by the data center to guide the operation of personnel and the operation of the AGV.
Step S6 specifically includes:
s601, the RMFS logistics system conveys AGV running data and scheduling cache data to a twin data center, and in the twin data center, instructions are transmitted to the AGV according to data read by a warehouse system;
s602, as shown in fig. 9, the order processing method includes allocating workstations to orders through order integration, determining whether to replenish the orders according to the order conditions, splitting the orders into corresponding picking tasks according to the workstations if corresponding replenishment tasks are generated by replenishing the orders, and determining the picked checking tasks according to system requirements. Determining tasks of replenishment, picking and checking to generate task data;
s603, as shown in figure 10, when the production line needs to supplement materials, the task data is uploaded to a twin database, and the RMFS logistics system reads the instruction transmitted by the data center and responds to the warehouse;
s604, as shown in FIG. 11, when the production line needs to receive material, the task data is uploaded to the twin database, and the RMFS reads the instruction transmitted by the data center, so as to respond to the warehouse.
Step S7 specifically includes:
s701, the twin model realizes on-site synchronous mapping under the drive of real-time data of the production line, and the production condition of the production line is reflected in real time;
s702, remote operation and maintenance installation of various devices is met through remote control software, and bidirectional transmission, remote diagnosis, remote configuration and CMD diversified control are realized;
s703, performing all-around and multi-angle monitoring and visualization service on the production activities through data storage, data statistics and data analysis of the production process by the data center.
Step S8 specifically includes:
s801, PHM comprises fault prediction and remote diagnosis;
s802, transmitting the real-time data acquired by the intelligent sensor to a twin data center;
s803, signal processing: preprocessing and feature extraction of data
S804, state detection: judging the threshold value by using fuzzy logic;
and S805, comparing the actual data with the predicted data, performing health assessment, detecting whether the data is abnormal, and performing data fusion, fault analysis and final fault maintenance if the data is abnormal.
The digital twin technology is shown in fig. 4 and comprises key technologies such as physical entities, virtual entities, twin data, connection and integration, services and the like. The digital twin model comprises a physical entity modeling module, a sensor module, a virtual twin module, a data communication module and a functional module as shown in figure 5. The physical entity layer is used as the basis of the digital twin system and mainly comprises a production equipment entity, a data acquisition and transmission functional part entity and an operator entity. The production equipment entity mainly comprises entities such as an industrial robot, a robot electrical control cabinet, a sliding rail type carrying gantry, a product workpiece and the like; the data acquisition and transmission functional part entity mainly comprises an industrial personal computer, a spherical camera, a Programmable Logic Controller (PLC), gateway equipment such as a router and the like, and display equipment such as a wireless large screen and the like; the operator entity is an equipment operation manager. The twin model layer is used as the core of the industrial robot digital twin system and mainly comprises a digital model and twin data.
The digital model is an entity mapping for the industrial robot production process, and truly reflects the position, behavior and state characteristics of physical entities such as robots, workpieces and the like in the production process. The twin data refers to data generated by physical entities such as robots in production activities and fusion derivative data generated after the data is fed back to the digital model by the functional application layer. The twin model formed by combining the two parts is a digital construction of FIMS, the reconstruction of physical space production activities is completed through the constructed virtual digital space, the simulation and iterative optimization of the production activities are performed on the model in the virtual space, and then the decision of the physical space production activities is re-determined, as shown in FIG. 6, the model is a relational graph of various elements of the digital twin of the supply chain. The functional application layer serves as a service of the FIMS digital twin system, and provides services such as monitoring and optimization of a production process, maintenance of physical entity layer equipment and the like according to virtual-real interaction of the physical entity layer and the twin model layer and twin data acquired by data acquisition.
By the framework, integration with information systems such as PLM, ERP and the like is realized, and the problem of data isolated island caused by independent construction of FMS is avoided; the existing production planning, production execution, storage management, logistics execution, equipment management, data acquisition and monitoring function modules of the FIMS can be fully utilized, repeated construction is avoided, and investment of an FMS system is greatly reduced; most importantly, the novel FIMS platform can well support continuous improvement of various production modes to flexible production.
Conventional FMS systems often implement device networking based on a dedicated network and device data acquisition and monitoring based on customized codes, and when a flexible manufacturing system needs to be adjusted, customers are often limited by whether suppliers can provide timely, high-quality, and appropriate-cost services, which may limit the flexibility of the flexible manufacturing system itself. The data acquisition and monitoring layer of the flexible manufacturing system is constructed based on a mature industrial Internet of things platform, and meanwhile, the Internet of things platform provides better openness, flexibility and expansibility and can be adjusted quickly when adjustment is needed. The invention provides a method for supporting the access of a mainstream PLC, an intelligent terminal and intelligent equipment, which can extend and collect a communication protocol by self, can solve the problem by configuration modeling when a production line is adjusted or new hardware is introduced, can not be limited by a solidified code compiled for the specific equipment by a traditional FMS system, and can quickly meet the requirement of continuously improving data collection and monitoring in flexible production.
The FIMS is a powerful tool for researching FMS planning design, production scheduling and operation management, and is an optimal way for solving manufacturing complexity. Through computer modeling and simulation analysis, the static and dynamic performance of the flexible manufacturing system can be fully predicted in the planning and design stages, so that the problems in the aspects of system layout, configuration, regulation and control strategies can be found as early as possible, and system design decision can be made more quickly and better
The present embodiment is only for explaining the present invention, and it is not limited to the present invention, and those skilled in the art can make modifications of the present embodiment without inventive contribution as needed after reading the present specification, but all of them are protected by patent law within the scope of the claims of the present invention.
Claims (9)
1. A FIMS system architecture design method based on a digital twin technology is characterized by comprising the following steps:
s1, designing and providing a novel digital twin flexible intelligent manufacturing system architecture facing a full life cycle, wherein the architecture comprises a manufacturing, running and managing MES system, a DT system, an FMS system, an IOT-Enabler system and bottom hardware equipment;
s2, according to the structure of an actual workshop, the workshop objects are mainly divided into processing production lines, RMFS logistics transport lines, stereoscopic warehouses and movable object AGV types, and a virtual digital twin model corresponding to a real physical workshop is established;
s3, establishing a FIMS digital twin data acquisition system, wherein in order to realize data interaction between models and interaction of external real-time data, a communication control signal interface is established for the twin model according to an operation logic interaction signal and driving data;
s4, establishing a FIMS processing production system;
s5, establishing an RMFS logistics system in the flexible intelligent manufacturing system;
s6, establishing a stereoscopic warehouse system;
s7, establishing a workshop remote virtual monitoring system based on the digital twin:
and S8, establishing a digital twin-based predictive maintenance and fault analysis system.
2. The FIMS system architecture design method based on the digital twin technology as claimed in claim 1, wherein: step S2 specifically includes:
s201, carrying out real mapping according to a physical entity, and establishing a related mathematical twin model corresponding to a physical world in matlab software;
s202, establishing a three-dimensional model of a physical entity by using Demo 3D, importing the model into a Plant Simulation platform, and selectively carrying out lightweight processing on the model to reduce display pressure during operation;
s203, acquiring a corresponding twin mechanical model according to the mathematical twin model, setting object attribute parameters and operation logic parameters in a parameterized mode, and adjusting optimization parameters to enable twin mechanical model generated data to be matched with an actually measured data result;
s204, setting the movable component parts in the three-dimensional model as movable drawing objects, further editing the action paths of the movable drawing components, and associating the component animations to form a complete action;
s205, converting production logistics rules and strategies in an actual workshop system into simulation operation logics, writing in an object Method to drive the movable entity to run inside the digital twin workshop, and realizing parameterization setting of related rule strategies.
3. The FIMS system architecture design method based on the digital twin technology as claimed in claim 1, wherein the construction of the prediction model specifically comprises:
defining a prediction model: DTOPER＝(OPMODEL,OPINTERFACEAFCE,OPSERVICE,)；
Wherein OPMODELBeing a three-dimensional model of a person, OPINTERFACEAFCEFor a person position/motion data interface, OPSERVICEServing production monitoring;
simulating fault factors to detect the equipment to be predicted and outputting a simulation result; and extracting training data for machine learning through the simulation result, deploying an algorithm and outputting the result.
4. The FIMS system architecture design method based on the digital twin technology as claimed in claim 1, wherein the step S3 specifically includes:
s301, acquiring parameter data required by a mathematical twin model through temperature, pressure and vibration distributed sensors, planning each workshop element of a workshop, and establishing virtual digital mapping of the workshop element;
s302, establishing connection between the digital space and field real-time data through an internal UA client, preprocessing according to acquired parameter data, and writing feedback data of a production field into inverse control while mapping a physical entity by a driving model;
s303, building a rapid and reliable information transmission network, wherein information interaction is realized mainly based on synchronous read/write, asynchronous read/write and subscription modes of an OPC protocol, system state information is safely and real-timely transmitted to an upper computer, and data transmission between an OPC UA Server and an OPC UA Client is realized;
s304, designing a UA server application architecture, wherein the UA server application architecture comprises a communication network architecture physical layer consisting of an industrial robot, a PLC, a sensor, an industrial personal computer, a cable and a network cable device; a data link layer for data transmission via an Ethernet protocol; a network layer and a transport layer using a TCP/IP protocol; using OPC UA protocol as application layer of data protocol;
s305, connecting the server with the switch and further connecting the server with an industrial personal computer through the Ethernet, and setting the IP of the industrial personal computer to be in the same network segment with the server according to the IP address of the server; and the data communication module of the digital twin system is connected with the URL of the server by acquiring the local IP.
5. The FIMS system architecture design method based on the digital twin technology as claimed in claim 1, wherein the step S4 specifically includes:
s401, triggering an event of reading data in a digital twin system in a subscription mode, and transmitting production order information and workpiece attributes by a scheduling system of a manufacturing operation management system;
s402, outputting the required workpieces by the stereoscopic warehouse, putting the workpieces into an annular conveying belt by an RMFS (remote message service platform) AGV (automatic guided vehicle), and conveying to achieve the purpose that each functional module is processed and assembled;
s403, judging whether the robot works or not by performing visual analysis on the materials;
and S404, detecting CCD non-contact images, and assembling, wherein when no raw material is provided for the stereoscopic warehouse, the raw material is supplied by the material supplementing unit.
6. The FIMS system architecture design method based on the digital twin technology as claimed in claim 1, wherein the step S5 specifically includes:
s501, processing orders in the twin data center, converting corresponding production tasks into task data, transmitting the task data to the digital twin system for subsequent operation, and storing the task data in a digital twin data center library;
s502, in the twin system, an initial scheduling scheme is produced according to task data and real-time running data, the scheduling scheme is simulated by means of a virtual simulation system, an optimal scheduling scheme is determined according to a simulation result, and simulation result data are transmitted to a twin data center;
and S503, generating a corresponding instruction according to the received simulation result data in the twin data center, and transmitting the instruction to the physical system. And the physical system reads the instruction transmitted by the data center to guide the operation of personnel and the operation of the AGV.
7. The FIMS system architecture design method based on the digital twin technology as claimed in claim 1, wherein the step S6 specifically includes:
s601, the RMFS logistics system conveys AGV running data and scheduling cache data to a twin data center, and in the twin data center, instructions are transmitted to the AGV according to data read by a warehouse system;
s602, the order processing method comprises the steps of distributing work stations for orders through order integration, determining whether to supplement goods according to the order conditions, generating corresponding supplement tasks if the supplement goods are supplemented, splitting the corresponding pick tasks according to the work stations, and determining the selected inventory tasks according to system requirements. Determining tasks of replenishment, picking and checking to generate task data;
s603, when the production line needs to supplement materials, the task data is uploaded to a twin database, and the RMFS logistics system reads the instruction transmitted by the data center and responds to the warehouse;
and S604, when the production line needs to receive materials, the task data is uploaded to a twin database, and the RMFS reads the instruction transmitted by the data center and responds to the warehouse.
8. The FIMS system architecture design method based on the digital twin technology as claimed in claim 1, wherein the step S7 specifically includes:
s701, the twin model realizes on-site synchronous mapping under the drive of real-time data of the production line, and the production condition of the production line is reflected in real time;
s702, remote operation and maintenance installation of various devices is met through remote control software, and bidirectional transmission, remote diagnosis, remote configuration and CMD diversified control are realized;
s703, performing all-around and multi-angle monitoring and visualization service on the production activities through data storage, data statistics and data analysis of the production process by the data center.
9. The FIMS system architecture design method based on the digital twin technology as claimed in claim 1, wherein the step S8 specifically includes:
s801, PHM comprises fault prediction and remote diagnosis;
s802, transmitting the real-time data acquired by the intelligent sensor to a twin data center;
s803, signal processing: preprocessing and feature extraction of data
S804, state detection: judging the threshold value by using fuzzy logic;
and S805, comparing the actual data with the predicted data, performing health assessment, detecting whether the data is abnormal, and performing data fusion, fault analysis and final fault maintenance if the data is abnormal.
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|---|---|---|---|---|
| CN113515098A (en) * | 2021-07-30 | 2021-10-19 | 青岛海尔工业智能研究院有限公司 | Digital twin system of logistics robot |
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107832497A (en) * | 2017-10-17 | 2018-03-23 | 广东工业大学 | A kind of intelligent workshop fast custom design method and system |
| CN111353225A (en) * | 2020-02-26 | 2020-06-30 | 洛阳中科晶上智能装备科技有限公司 | Method for performing predictive maintenance by using digital twin and application |
| CN111857065A (en) * | 2020-06-08 | 2020-10-30 | 北京邮电大学 | Intelligent production system and method based on edge calculation and digital twinning |
| CN112256751A (en) * | 2020-10-10 | 2021-01-22 | 天津航天机电设备研究所 | Warehouse logistics visualization system based on twin data and construction method thereof |
| CN112327780A (en) * | 2020-11-16 | 2021-02-05 | 中国电子科技集团公司第二十九研究所 | Digital twin system construction method and architecture of electronic equipment test production line |
-
2021
- 2021-04-07 CN CN202110371675.7A patent/CN113093680A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107832497A (en) * | 2017-10-17 | 2018-03-23 | 广东工业大学 | A kind of intelligent workshop fast custom design method and system |
| CN111353225A (en) * | 2020-02-26 | 2020-06-30 | 洛阳中科晶上智能装备科技有限公司 | Method for performing predictive maintenance by using digital twin and application |
| CN111857065A (en) * | 2020-06-08 | 2020-10-30 | 北京邮电大学 | Intelligent production system and method based on edge calculation and digital twinning |
| CN112256751A (en) * | 2020-10-10 | 2021-01-22 | 天津航天机电设备研究所 | Warehouse logistics visualization system based on twin data and construction method thereof |
| CN112327780A (en) * | 2020-11-16 | 2021-02-05 | 中国电子科技集团公司第二十九研究所 | Digital twin system construction method and architecture of electronic equipment test production line |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023006080A1 (en) * | 2021-07-30 | 2023-02-02 | 卡奥斯工业智能研究院(青岛)有限公司 | Logistics robot digital twin system |
| CN113515098A (en) * | 2021-07-30 | 2021-10-19 | 青岛海尔工业智能研究院有限公司 | Digital twin system of logistics robot |
| WO2023024476A1 (en) * | 2021-08-25 | 2023-03-02 | 中国矿业大学 | Digital twin drive-based autonomous driving system and method for monorail crane |
| CN113992864A (en) * | 2021-10-20 | 2022-01-28 | 中国电信股份有限公司 | AGV visual navigation system, method, device, electronic equipment and medium |
| CN114077235A (en) * | 2021-11-18 | 2022-02-22 | 四川启睿克科技有限公司 | Equipment predictive maintenance system and method based on digital twin technology |
| CN114137917A (en) * | 2021-11-19 | 2022-03-04 | 北京京东乾石科技有限公司 | Device control method, device, electronic device, system and storage medium |
| CN114137921B (en) * | 2021-11-24 | 2023-12-19 | 晋江海纳机械有限公司 | Real-time allocation system and allocation method for intelligent production workshop of sanitary equipment |
| CN114137921A (en) * | 2021-11-24 | 2022-03-04 | 晋江海纳机械有限公司 | Real-time allocation system and allocation method for intelligent production workshop of sanitary equipment |
| CN114384881A (en) * | 2022-01-12 | 2022-04-22 | 哈尔滨工业大学 | Workshop logistics monitoring and simulation system and method based on digital twins |
| CN114578770A (en) * | 2022-02-28 | 2022-06-03 | 安徽工程大学 | Digital twin formula intelligence production line system |
| CN117311301A (en) * | 2023-11-30 | 2023-12-29 | 宁德时代新能源科技股份有限公司 | AGV scheduling policy verification method and device and electronic equipment |
| CN117311301B (en) * | 2023-11-30 | 2024-04-12 | 宁德时代新能源科技股份有限公司 | AGV scheduling policy verification method and device and electronic equipment |
| CN117475041A (en) * | 2023-12-28 | 2024-01-30 | 湖南视觉伟业智能科技有限公司 | Digital twin shore bridge simulation method based on RCMS |
| CN117475041B (en) * | 2023-12-28 | 2024-03-29 | 湖南视觉伟业智能科技有限公司 | Digital twin shore bridge simulation method based on RCMS |
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