WO2022000286A1 - 一种生产系统的控制方法及其装置 - Google Patents

一种生产系统的控制方法及其装置 Download PDF

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Publication number
WO2022000286A1
WO2022000286A1 PCT/CN2020/099350 CN2020099350W WO2022000286A1 WO 2022000286 A1 WO2022000286 A1 WO 2022000286A1 CN 2020099350 W CN2020099350 W CN 2020099350W WO 2022000286 A1 WO2022000286 A1 WO 2022000286A1
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Prior art keywords
control
production line
controller
cloud
digital twin
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PCT/CN2020/099350
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English (en)
French (fr)
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闻博
范顺杰
徐云龙
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西门子股份公司
西门子（中国）有限公司
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Priority to PCT/CN2020/099350 priority Critical patent/WO2022000286A1/zh
Publication of WO2022000286A1 publication Critical patent/WO2022000286A1/zh

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • 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] or computer integrated manufacturing [CIM]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Definitions

  • the invention mainly relates to the field of industrial control, in particular to a control method of a production system and a device thereof.
  • Digital twin technology refers to the use of digital technology to describe and model physical objects, and generate a digital image in the virtual space that is exactly the same as the physical object in the physical space. Objects are interacting in real-time, enabling evaluation and prediction of physical physical objects.
  • digital mirror can realize more complex operations.
  • users can interact with production systems remotely (such as offices) through digital mirroring, which can significantly improve production efficiency.
  • the existing digital twin can only be used to evaluate, warn, diagnose, monitor and model the production system, and cannot control the actual production process of the actual production system.
  • the present invention provides a control method and a device for a production system. Users can realize remote control of the production system in the cloud, so as to improve the controllability and safety of the production system.
  • the present invention proposes a control method for a production system, the production system includes a production line, a controller and a high-speed communication interface, the control method includes: acquiring data information of the production line from the high-speed communication interface and the control logic of the controller; generate a digital twin of the production line in the cloud according to the data information of the production line, and configure a simulation controller according to the control logic of the controller; accept user input from the human-machine interface of the cloud control instructions, the control instructions are issued by the user based on the digital twin of the production line and the simulation controller; the control instructions are obtained from the high-speed communication interface, and the controller executes the control instructions according to the control instructions Production line control.
  • the digital twin of the production line is generated in the cloud by transmitting data information through the high-speed communication interface. Users can check the real-time status of the production line, avoiding the state deviation caused by communication delay. Users can also send control commands through the high-speed communication interface, realizing the The remote control of the production system improves the controllability and safety of the production system.
  • the production system further includes an industrial robot
  • the control method further includes: acquiring data information of the industrial robot from the high-speed communication interface, and storing the data at the location according to the data information of the industrial robot.
  • the cloud generates a digital twin of an industrial robot. To this end, a digital twin can also be generated in the cloud for a production line equipped with industrial robots, further improving the controllability and safety of the production system.
  • the control method includes: accepting a control instruction input by a user from a human-machine interface on the cloud, and the controller assigns an operation task to a designated industrial robot for independent operation according to the control instruction .
  • the controller assigns work tasks to designated industrial robots to work individually, the accuracy of production system control can be improved.
  • the control method includes: accepting a control instruction input by a user from a human-machine interface on the cloud, and the controller assigns an operation task to a plurality of industrial robots for cooperative operation according to the control instruction .
  • the controller assigns an operation task to a plurality of industrial robots for cooperative operation according to the control instruction .
  • the control method includes: the controller further generates an on-site instruction according to the operating condition of the production line, and when the on-site instruction is inconsistent with the control instruction, the controller executes the on-site instructions. Therefore, when the on-site instruction and the control instruction are inconsistent, the controller executes the on-site instruction, taking into account the safety of the control of the production system.
  • the control method includes: establishing a machine learning model for the digital twin of the production line, so that the digital twin of the production line has a learning function through the machine learning model.
  • a machine learning model is established for the production line digital twin so that it has the learning function, which can expand the control function and improve the intelligence of the control.
  • the present invention also provides a control device for a production system, the production system includes a production line, a controller and a high-speed communication interface, the control device includes: an acquisition unit, which acquires data information of the production line and a high-speed communication interface from the high-speed communication interface.
  • the control logic of the controller the generating unit, which generates a digital twin of the production line in the cloud according to the data information of the production line, and configures a simulation controller according to the control logic of the controller; the instruction receiving unit accepts the user from the cloud
  • the control instruction input by the human-machine interface, the control instruction is issued by the user based on the digital twin of the production line and the simulation controller; the control unit obtains the control instruction from the high-speed communication interface, and is controlled by the control unit.
  • the controller controls the production line according to the control instruction.
  • the production system further includes an industrial robot, the acquiring unit acquires data information of the industrial robot from the high-speed communication interface, and the generating unit generates data in the industrial robot according to the data information of the industrial robot.
  • the cloud generates digital twins of industrial robots.
  • control device includes: accepting a control instruction input by a user from a human-machine interface on the cloud, and the controller assigns a work task to a designated industrial robot to work alone according to the control instruction .
  • control device includes: accepting a control instruction input by a user from a human-machine interface on the cloud, and the controller assigns an operation task to a plurality of industrial robots for cooperative operation according to the control instruction .
  • control device includes: the controller further generates an on-site instruction according to the operating condition of the production line, and when the on-site instruction is inconsistent with the control instruction, the controller executes the on-site instructions.
  • control device establishes a machine learning model for the digital twin of the production line, so that the digital twin of the production line has a learning function through the machine learning model.
  • the present invention also provides an electronic device comprising a processor, a memory and instructions stored in the memory, wherein the instructions, when executed by the processor, implement the method as described above.
  • the present invention also proposes a computer-readable storage medium having stored thereon computer instructions which, when executed, perform the method according to the above.
  • FIG. 1 is a schematic diagram of an industrial control system according to an embodiment of the present invention.
  • FIG. 2 is a flowchart of a control method of a production system according to an embodiment of the present invention
  • FIG. 3 is a schematic diagram of a control device of a production system according to an embodiment of the present invention.
  • FIG. 1 is a schematic diagram of an industrial control system 100 according to an embodiment of the present invention.
  • the industrial control system 100 includes a production system 110 and a virtual system 120 connected to the production system 110 .
  • a digital mirror image of the production system 110 may be generated and stored in the virtual system 120 .
  • the production system 110 is a real space including a production line 111 and a controller 112 connected to the production line 111 .
  • the production line 111 may include production equipment, production equipment, materials, workpieces, etc.
  • the controller 112 is connected to the production line 111 and controls the production line 111 .
  • the controller 112 may be a programmable logic controller (PLC) or the like.
  • the production system 100 may further include an industrial robot 113, and the industrial robot 113 may be arranged near the production line 111 for providing production functions or assisting production functions.
  • a production line includes a plurality of processes, and one or more industrial robots may be arranged on each process, and these industrial robots may form an industrial robot cluster.
  • the industrial robot may include robotic arms, joints, and motors, which may be controlled by the controller 112 .
  • the virtual system 120 is a virtual space, and the virtual system 120 may be located in the cloud, including the production line digital twin 121 and the simulation controller 122 .
  • the production line digital twin 121 is a digital mirror image of the production line 111
  • the simulation controller 122 is a digital mirror image of the controller 112 .
  • the virtual system 120 may further include an industrial robot digital twin 123 , which is a digital twin of the industrial robot 113 .
  • FIG. 2 is a flowchart of a control method 200 of a production system according to an embodiment of the present invention.
  • the control method 200 may be implemented on the industrial control system 100 shown in FIG. 1 .
  • the production system includes a production line, a controller and a high-speed communication interface, as shown in Figure 2, the control method includes:
  • step S210 the data information of the production line and the control logic of the controller are acquired from the high-speed communication interface.
  • the production line 111 may include data information such as production equipment, production equipment, materials, the position, posture, speed, and state of the workpiece. This step acquires the data information of the production line 111.
  • the controller 112 connects to Go to the production line 111 and use the control logic to control the production line 111 , for example, stop production when the ambient temperature reaches a warning value, this step also acquires these control logics of the controller 112 .
  • the data information of the production line 111 and the control logic of the controller 112 can be obtained through the high-speed communication interface, and the high-speed communication interface can send the data information of the production line 111 and the control logic of the controller 112 to the virtual system 120 in the cloud at high speed to reduce the delay, Improve data transfer speed and reliability.
  • the high-speed communication interface may be a communication interface supporting 5G communication protocol, Wi-Fi communication protocol, and LAN communication protocol.
  • step S220 a digital twin of the production line is generated in the cloud according to the data information of the production line, and a simulation controller is configured according to the control logic of the controller.
  • the production line digital twin 121 can be generated in the cloud according to the production equipment, production equipment, materials, position, posture, speed, state and other data information of the production line 111 .
  • the neural network model corresponding to the digital twin of the production line can be trained using the data information of the production line, or the digital twin of the production line can be generated using the data information of the production line combined with the mechanism model, where the data information of the production line is used to calibrate or calibrate the parameters of the mechanism model.
  • the data information of the production line 111 can be converted into intermediate data for building a three-dimensional model, and then a three-dimensional model can be built using the intermediate data, and the three-dimensional model can be rendered by means of coloring, etc.
  • a real-time production line digital twin 121 that is consistent with data information such as position, pose, speed, and state.
  • a simulation controller 122 is also configured according to the control logic of the controller 112 , that is, the simulation controller 122 is a digital image of the controller 112 in the cloud, and the simulation controller 122 has the same control logic as the controller 112 .
  • the production system 110 further includes the industrial robot 113
  • step 220 may further include: acquiring data information of the industrial robot 113 from the high-speed communication interface, and generating a digital twin of the industrial robot in the cloud according to the data information of the industrial robot 113 123.
  • the industrial robot 113 may include data information such as the size and mass of the robotic arm, the position, speed and acceleration of the joint, and the rotational speed, torque and current of the motor.
  • the data information of the industrial robot 113 can be converted into intermediate data for establishing a three-dimensional model, and then a three-dimensional model can be built using the intermediate data, and the three-dimensional model can be rendered by means of coloring, etc., so as to generate a size and the size of the mechanical arm of the industrial robot 113.
  • Mass, joint position, speed and acceleration, motor speed, torque and current are consistent with the real-time industrial robot digital twin123.
  • the method may further include: establishing a machine learning model for the digital twin of the production line, so that the digital twin of the production line has a learning function through the machine learning model.
  • the machine learning model may be a neural network model, and the neural network model may be a Recurrent Neural Network (RNN), a Convolutional Neural Network (CNN), or the like.
  • RNN Recurrent Neural Network
  • CNN Convolutional Neural Network
  • a machine learning model is established for the production line digital twin so that it has the learning function, which can expand the control function and improve the intelligence of the control.
  • a machine learning model can also be established for the industrial robot digital twin, so that the industrial robot digital twin has a learning function through the machine learning model.
  • Step S230 accepting the control instruction input by the user from the human-machine interface in the cloud, and the control instruction is issued by the user based on the digital twin of the production line and the simulation controller.
  • the production line digital twin 121 is a real-time digital mirror of the production line 111
  • the simulation controller 122 is a real-time digital mirror of the controller 112.
  • Users can monitor and inspect the production line 111 through the production line digital twin 121 and the simulation controller 122 in the cloud.
  • the user can also input control commands through a human-machine interface (HMI) in the cloud to control the production line 111. So far, the user can monitor and control the production line 111 through the cloud.
  • HMI human-machine interface
  • the human-machine interface may present the production line digital twin 121 and the simulation controller 122 , and may receive control instructions input by the user after browsing the production line digital twin 121 and the simulation controller 122 .
  • the user finds that the load of a certain process is too high, and the simulation controller 122 is used to simulate the reduction of the load of the process, so that the entire production system can run stably at high speed.
  • the user inputs control instructions according to the simulation results,
  • the control command is used to reduce the load of the process.
  • the human-machine interface may be a touch screen, virtual reality (VR), or the like.
  • the human-machine interface can also present the industrial robot digital twin 123.
  • the user After browsing the production line digital twin 121, the simulation controller 122, and the industrial robot digital twin 123, the user can input control instructions through the human-machine interface, so as to realize the control instruction of the production system. 110 controls.
  • step S240 a control instruction is obtained from the high-speed communication interface, and the controller controls the production line according to the control instruction.
  • control command input by the user is sent to the controller 122 through the high-speed communication interface, and the controller 122 parses and recognizes the control command,
  • control instructions input by the user from the human-machine interface in the cloud can be accepted, and the controller assigns the work tasks to the designated industrial robots to work individually according to the control instructions. For example, if the control instruction is to speed up the process of a certain process, the controller 112 parses the control instruction and assigns the task to the industrial robot corresponding to the process. The industrial robot can increase the speed or torque of the motor to realize the process of The process is accelerated. To this end, by assigning work tasks to designated industrial robots to work individually, the accuracy of production system control can be improved.
  • control instructions input by the user from the human-machine interface in the cloud may be accepted, and the controller assigns the operation tasks to multiple industrial robots for coordinated operation according to the control instructions.
  • the controller 112 parses the control instruction and assigns the task to the industrial robot corresponding to each process. The process of production is accelerated. To this end, by assigning work tasks to multiple industrial robots to work together, the efficiency of production system control can be improved.
  • the controller 112 can also generate on-site instructions according to the operating conditions of the production line, and when the on-site instructions and the control instructions are inconsistent, the controller executes the on-site instructions.
  • the control command received by the controller 112 is to speed up the progress of a certain process, but the controller 112 receives the information of a sudden failure of the process, and generates an on-site command to stop the process.
  • the on-site command is inconsistent with the control command, the controller 112 Executes the on-site command, that is, stops the process. Therefore, when the on-site instruction and the control instruction are inconsistent, the controller executes the on-site instruction, taking into account the safety of the control of the production system.
  • the first example is a computer numerical control machine tool (CNC machine).
  • CNC machine computer numerical control machine tool
  • Multiple industrial robots are set up in the environment of the computer numerical control machine tool.
  • the digital twin of the computer numerical control machine tool and the industrial robot is generated in the cloud.
  • the user can display the computer numerical control through the human-machine interface.
  • Digital twins of machine tools and industrial robots inspect production processes. Under normal conditions, the computer numerical control machine tool is automatically controlled by the controller.
  • the user can input a control instruction to designate a single robot to perform replenishment, and the controller 112 controls the designated single robot to perform replenishment operations according to the control instruction.
  • the second example is Green house cultivation.
  • Multiple industrial robots are set up in the greenhouse cultivation environment.
  • the greenhouse and industrial robots generate digital twins in the cloud. Users can use the human-machine interface to display the greenhouse and industrial robots
  • the digital twin checks the production process. When a plant needs to be harvested, the user can input a control instruction for harvesting the plant, and the controller 112 parses the control instruction to control a plurality of robots to cooperate in the harvesting operation.
  • This embodiment of the present invention provides a control method for a production system, which transmits data information through a high-speed communication interface to generate a digital twin of the production line in the cloud.
  • the control command can also be sent through the high-speed communication interface, which realizes the remote control of the production system and improves the controllability and safety of the production system.
  • FIG. 3 is a schematic diagram of a control device 300 of a production system according to an embodiment of the present invention.
  • the production system includes a production line, a controller and a high-speed communication interface.
  • the control device 300 includes:
  • the acquisition unit 310 obtains the data information of the production line and the control logic of the controller from the high-speed communication interface; the generation unit 320 generates a digital twin of the production line in the cloud according to the data information of the production line, and configures a simulation controller according to the control logic of the controller; the instruction The receiving unit 330 accepts the control instructions input by the user from the man-machine interface of the cloud, and the control instructions are issued by the user based on the digital twin of the production line and the simulation controller; Production line control.
  • the production system further includes an industrial robot, the acquiring unit 310 acquires data information of the industrial robot from the high-speed communication interface, and the generating unit generates a digital twin of the industrial robot in the cloud according to the data information of the industrial robot.
  • control device 300 includes: accepting a control instruction input by a user from a human-machine interface in the cloud, and the controller assigns the operation task to a designated industrial robot to operate independently according to the control instruction.
  • control device 300 includes: accepting a control instruction input by a user from a human-machine interface in the cloud, and the controller assigns a job task to a plurality of industrial robots to work together according to the control instruction.
  • control device 300 includes: the controller also generates on-site instructions according to the operating conditions of the production line, and when the on-site instructions and the control instructions are inconsistent, the controller executes the on-site instructions.
  • control device 300 establishes a machine learning model for the digital twin of the production line, so that the digital twin of the production line has a learning function through the machine learning model.
  • control device 300 of the production system For the implementation manner and specific process of the control device 300 of the production system, reference may be made to the control method 200 of the production system, which will not be repeated here.
  • the present invention also provides an electronic device, comprising a processor, a memory and instructions stored in the memory, wherein the above method is implemented when the instructions are executed by the processor.
  • the present invention also proposes a computer-readable storage medium on which computer instructions are stored, which when executed, perform the method according to the above.
  • aspects of the methods and apparatus of the present invention may be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software.
  • the above hardware or software may be referred to as a "data block”, “module”, “engine”, “unit”, “component” or “system”.
  • the processor may be one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DAPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors , controller, microcontroller, microprocessor, or a combination thereof.
  • aspects of the present invention may be embodied as a computer product comprising computer readable program code embodied in one or more computer readable media.
  • computer readable media may include, but are not limited to, magnetic storage devices (eg, hard disks, floppy disks, magnetic tapes%), optical disks (eg, compact discs (CDs), digital versatile discs (DVDs)%), smart cards and flash memory devices (eg cards, sticks, key drives).
  • magnetic storage devices eg, hard disks, floppy disks, magnetic tapes
  • optical disks eg, compact discs (CDs), digital versatile discs (DVDs)
  • smart cards and flash memory devices eg cards, sticks, key drives.
  • a computer-readable medium may contain a propagated data signal with the computer program code embodied therein, for example, on baseband or as part of a carrier wave.
  • the propagating signal may take a variety of manifestations, including electromagnetic, optical, etc., or a suitable combination.
  • a computer-readable medium can be any computer-readable medium other than a computer-readable storage medium that can communicate, propagate, or transmit a program for use by being coupled to an instruction execution system, apparatus, or device.
  • Program code on a computer readable medium may be propagated by any suitable medium, including radio, cable, fiber optic cable, radio frequency signal, or the like, or a combination of any of the foregoing.

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Abstract

本发明提出了一种生产系统的控制方法及其装置，所述生产系统包括生产线、控制器和高速通信接口，所述控制方法包括：从所述高速通信接口获取所述生产线的数据信息和所述控制器的控制逻辑；根据所述生产线的数据信息在云端生成生产线数字孪生，以及根据所述控制器的控制逻辑配置一仿真控制器；接受用户从所述云端的人机接口输入的控制指令，所述控制指令由所述用户基于所述生产线数字孪生和所述仿真控制器发出；从所述高速通信接口获取所述控制指令，由所述控制器根据所述控制指令对所述生产线进行控制。与现有技术相比，用户在云端可以实现对生产系统的远程控制，提升了生产系统的操控性和安全性。

Description

一种生产系统的控制方法及其装置 技术领域
本发明主要涉及工业控制领域，尤其涉及一种生产系统的控制方法及其装置。
背景技术
数字孪生技术(Digital Twin，DT)是指利用数字技术对物理实体对象进行描述和建模，在虚拟空间中生成一个与物理空间中的物理实体对象完全一样的数字镜像，使得数字镜像和物理实体对象处于实时交互中，从而实现对物理实体对象的评估和预测。
随着数字孪生技术准确度的提升，数字镜像可以实现更多复杂的操作。例如对于生产系统，区别于传统的现场方式，用户可以远程(例如办公室)通过数字镜像与生产系统进行交互，可以显著提升生产效率。然而，现有的数字孪生仅能用于对生产系统进行评估、预警、诊断、监控和建模，无法对实际的生产系统的实际生产过程进行控制。
发明内容
为了解决上述技术问题，本发明提供一种生产系统的控制方法及其装置，用户在云端可以实现对生产系统的远程控制，以提升生产系统的操控性和安全性。
为实现上述目的，本发明提出了一种生产系统的控制方法，所述生产系统包括生产线、控制器和高速通信接口，所述控制方法包括：从所述高速通信接口获取所述生产线的数据信息和所述控制器的控制逻辑；根据所述生产线的数据信息在云端生成生产线数字孪生，以及根据所述控制器的控制逻辑配置一仿真控制器；接受用户从所述云端的人机接口输入的控制指令，所述控制指令由所述用户基于所述生产线数字孪生和所述仿真控制器发出；从所述高速通信接口获取所述控制指令，由所述控制器根据所述控制指令对所述生产线进行控制。为此，通过高速通信接口传输数据信息在云端生成生产线的数字孪生，用户可以检查生产线的实时状态，避免了通信延时导致的状态偏差，用户还可以通过高速通信接口发送控制指令，实现了对生产系统的远程控制，提高了生产系统的操控性和安全性。
在本发明的一实施例中，所述生产系统还包括工业机器人，所述控制方法还包括：从所述高速通信接口获取所述工业机器人的数据信息，根据所述工业机器人的数据信息在所述云端生成工业机器人数字孪生。为此，还可以为配置有工业机器人的生产线在云端生成数字孪生，进一步提高了生产系统的操控性和安全性。
在本发明的一实施例中，所述控制方法包括：接受用户从所述云端的人机接口输入的 控制指令，所述控制器根据所述控制指令将作业任务分配给指定的工业机器人单独作业。为此，通过将作业任务分配给指定的工业机器人单独作业，可以提升生产系统控制的准确性。
在本发明的一实施例中，所述控制方法包括：接受用户从所述云端的人机接口输入的控制指令，所述控制器根据所述控制指令将作业任务分配给多个工业机器人协同作业。为此，通过将作业任务分配给多个工业机器人协同作业，可以提升生产系统控制的效率。
在本发明的一实施例中，所述控制方法包括：所述控制器还根据所述生产线的运行状况生成现场指令，在所述现场指令与所述控制指令不一致时，所述控制器执行所述现场指令。为此，在现场指令与控制指令不一致时，控制器执行现场指令，兼顾了生产系统控制的安全性。
在本发明的一实施例中，所述控制方法包括：为所述生产线数字孪生建立机器学习模型，以使所述生产线数字孪生通过所述机器学习模型具备学习功能。为此，在云端生成生产线数字孪生之后，为生产线数字孪生建立机器学习模型，使其具备学习功能，可以扩展控制功能以及提高控制的智能性。
本发明还提出了一种生产系统的控制装置，所述生产系统包括生产线、控制器和高速通信接口，所述控制装置包括：获取单元，从所述高速通信接口获取所述生产线的数据信息和所述控制器的控制逻辑；生成单元，根据所述生产线的数据信息在云端生成生产线数字孪生，以及根据所述控制器的控制逻辑配置一仿真控制器；指令接收单元，接受用户从所述云端的人机接口输入的控制指令，所述控制指令由所述用户基于所述生产线数字孪生和所述仿真控制器发出；控制单元，从所述高速通信接口获取所述控制指令，由所述控制器根据所述控制指令对所述生产线进行控制。
在本发明的一实施例中，所述生产系统还包括工业机器人，所述获取单元从所述高速通信接口获取所述工业机器人的数据信息，所述生成单元根据所述工业机器人的数据信息在云端生成工业机器人数字孪生。
在本发明的一实施例中，所述控制装置包括：接受用户从所述云端的人机接口输入的控制指令，所述控制器根据所述控制指令将作业任务分配给指定的工业机器人单独作业。
在本发明的一实施例中，所述控制装置包括：接受用户从所述云端的人机接口输入的控制指令，所述控制器根据所述控制指令将作业任务分配给多个工业机器人协同作业。
在本发明的一实施例中，所述控制装置包括：所述控制器还根据所述生产线的运行状况生成现场指令，在所述现场指令与所述控制指令不一致时，所述控制器执行所述现场指令。
在本发明的一实施例中，所述控制装置为所述生产线数字孪生建立机器学习模型，以使所述生产线数字孪生通过所述机器学习模型具备学习功能。
本发明还提出了一种电子设备，包括处理器、存储器和存储在所述存储器中的指令，其中所述指令被所述处理器执行时实现如上所述的方法。
本发明还提出了一种计算机可读存储介质，其上存储有计算机指令，所述计算机指令在被运行时执行根据如上所述的方法。
附图说明
以下附图仅旨在于对本发明做示意性说明和解释，并不限定本发明的范围。其中，
图1是根据本发明的一实施例的一种工业控制系统的示意图；
图2是根据本发明的一实施例的一种生产系统的控制方法的流程图；
图3是根据本发明的一实施例的一种生产系统的控制装置的示意图。
附图标记说明
100 工业控制系统
110 生产系统
111 生产线
112 控制器
113 工业机器人
120 虚拟系统
121 生产线数字孪生
122 仿真控制器
123 工业机器人数字孪生
200 生产系统的控制方法
S210-S240 步骤
300 生产系统的控制装置
310 获取单元
320 生成单元
330 指令接收单元
340 控制单元
具体实施方式
为了对本发明的技术特征、目的和效果有更加清楚的理解，现对照附图说明本发明的具体实施方式。
在下面的描述中阐述了很多具体细节以便于充分理解本发明，但是本发明还可以采用其它不同于在此描述的其它方式来实施，因此本发明不受下面公开的具体实施例的限制。
如本申请和权利要求书中所示，除非上下文明确提示例外情形，“一”、“一个”、“一种”和/或“该”等词并非特指单数，也可包括复数。一般说来，术语“包括”与“包含”仅提示包括已明确标识的步骤和元素，而这些步骤和元素不构成一个排它性的罗列，方法或者设备也可能包含其他的步骤或元素。
图1是根据本发明的一实施例的一种工业控制系统100的示意图。如图1所示，工业控制系统100包括生产系统110和连接至生产系统110的虚拟系统120。虚拟系统120中可以生成和存储生产系统110的数字镜像。
生产系统110为真实空间，包括生产线111和连接至生产线111的控制器112。生产线111可以包括生产设备、生产设备、物料、工件等，控制器112连接至生产线111并对生产线111进行控制。控制器112可以是可编程逻辑控制器(PLC)等。可选地，生产系统100还可以包括工业机器人113，工业机器人113可以布置在生产线111附近，用于提供生产功能或辅助生产功能。示例性地，生产线包括多个工序，每个工序上可以布置一个或多个工业机器人，这些工业机器人可以组成工业机器人集群。优选地，工业机器人可以包括机械臂、关节和电机，这些机械臂、关节和电机可以借由控制器112进行控制。
虚拟系统120为虚拟空间，虚拟系统120可以位于云端，包括生产线数字孪生121和仿真控制器122。生产线数字孪生121是生产线111的数字镜像，仿真控制器122是控制器112的数字镜像。相应地，虚拟系统120还可以包括工业机器人数字孪生123，工业机器人数字孪生123是工业机器人113的数字孪生。
图2是根据本发明的一实施例的一种生产系统的控制方法200的流程图。该控制方法200可以在图1所示的工业控制系统100上实施。生产系统包括生产线、控制器和高速通信接口，如图2所示，该控制方法包括：
步骤S210，从高速通信接口获取生产线的数据信息和控制器的控制逻辑。
在本发明的实施例中，生产线111可以包括生产设备、生产设备、物料、工件的位置、位姿、速度、状态等数据信息，此步骤获取生产线111的这些数据信息，此外，控制器112连接至生产线111并使用控制逻辑对生产线111进行控制，例如，在环境温度到达警戒值时停止生产，此步骤还获取控制器112的这些控制逻辑。
生产线111的数据信息和控制器112的控制逻辑可以通过高速通信接口获取，高速通 信接口可以将生产线111的数据信息和控制器112的控制逻辑高速发送至云端的虚拟系统120，以降低延时，提高数据传输速度和可靠性。优选地，高速通信接口可以是支持5G通信协议、Wi-Fi通信协议、LAN通信协议的通信接口。
步骤S220，根据生产线的数据信息在云端生成生产线数字孪生，以及根据控制器的控制逻辑配置一仿真控制器。
在此步骤中，可以根据生产线111的生产设备、生产设备、物料、工件的位置、位姿、速度、状态等数据信息，在云端生成生产线数字孪生121。可以使用生产线的数据信息训练生成生产线数字孪生相应的神经网络模型，也可以使用生产线的数据信息结合机理模型生成生产线数字孪生，其中，生产线的数据信息用于标定或者校准机理模型的参数。可以将生产线111的数据信息转换成用于建立三维模型的中间数据，然后使用中间数据建立三维模型，并通过着色等方式对三维模型进行渲染，从而生成与生产设备、生产设备、物料、工件的位置、位姿、速度、状态等数据信息相一致的实时的生产线数字孪生121。
此步骤还根据控制器112的控制逻辑配置一仿真控制器122，即仿真控制器122是控制器112在云端的数字镜像，且仿真控制器122具有与控制器112相同的控制逻辑。
在一种可选的情况下，生产系统110还包括工业机器人113，步骤220还可以包括：从高速通信接口获取工业机器人113的数据信息，根据工业机器人113的数据信息在云端生成工业机器人数字孪生123。工业机器人113可以包括机械臂的尺寸和质量，关节的位置、速度和加速度，电机的转速、力矩和电流等数据信息。
可以将工业机器人113的数据信息转换成用于建立三维模型的中间数据，然后使用中间数据建立三维模型，并通过着色等方式对三维模型进行渲染，从而生成与工业机器人113的机械臂的尺寸和质量，关节的位置、速度和加速度，电机的转速、力矩和电流等数据信息相一致的实时的工业机器人数字孪生123。
在一种可选的情况下，步骤220之后还可以包括：为生产线数字孪生建立机器学习模型，以使生产线数字孪生通过机器学习模型具备学习功能。机器学习模型可以是神经网络模型，神经网络模型可以是递归神经网络(Recurrent Neural Network，RNN)、卷积神经网络(Convolutional Neural Network，CNN)等。为此，在云端生成生产线数字孪生之后，为生产线数字孪生建立机器学习模型，使其具备学习功能，可以扩展控制功能以及提高控制的智能性。可选地，还可以为工业机器人数字孪生建立机器学习模型，以使工业机器人数字孪生通过机器学习模型具备学习功能。
步骤S230，接受用户从云端的人机接口输入的控制指令，控制指令由用户基于生产线数字孪生和仿真控制器发出。
生产线数字孪生121是生产线111的实时数字镜像，仿真控制器122是控制器112的实时数字镜像，用户可以通过云端的生产线数字孪生121和仿真控制器122实现对生产线111的监督和检查。此外，用户还可以通过云端的人机接口(Human Machine Interface，HMI)输入控制指令，以实现对生产线111的控制，至此，用户通过云端即可实现对生产线111的监督和控制。
人机接口可以呈现生产线数字孪生121和仿真控制器122，并且可以接收用户在浏览生产线数字孪生121和仿真控制器122之后输入的控制指令。例如，用户在浏览生产线数字孪生121之后，发现某一工序负荷过高，通过仿真控制器122对降低该工序的负荷进行仿真，整个生产系统可以高速稳定运行，用户根据该仿真结果输入控制指令，该控制指令用于降低该工序的负荷。示例性地，人机接口可以是触摸屏、虚拟现实(VR)等。
可选地，人机接口还可以呈现工业机器人数字孪生123，用户在浏览生产线数字孪生121、仿真控制器122和工业机器人数字孪生123之后可以通过人机接口输入的控制指令，以实现对生产系统110的控制。
步骤S240，从高速通信接口获取控制指令，由控制器根据控制指令对生产线进行控制。
在此步骤中，用户输入的控制指令通过高速通信接口发送至控制器122，控制器122对控制指令进行解析和识别，
在一些实施例中，可以接受用户从云端的人机接口输入的控制指令，控制器根据控制指令将作业任务分配给指定的工业机器人单独作业。例如，控制指令为加快某一工序的进程，控制器112对控制指令进行解析之后将该作业任务分配给该工序对应的工业机器人，工业机器人可以增加电机的转速或力矩，以实现对该工序的进程加速。为此，通过将作业任务分配给指定的工业机器人单独作业，可以提升生产系统控制的准确性。
在另一些实施例中，可以接受用户从云端的人机接口输入的控制指令，控制器根据控制指令将作业任务分配给多个工业机器人协同作业。例如，控制指令为加快整个生产的进程，控制器112对控制指令进行解析之后将该作业任务分配给各工序对应的工业机器人，多个工业机器人可以协同增加电机的转速或力矩，以实现对整个生产的进程加速。为此，通过将作业任务分配给多个工业机器人协同作业，可以提升生产系统控制的效率。
控制器112还可以根据生产线的运行状况生成现场指令，在现场指令与控制指令不一致时，控制器执行现场指令。例如，控制器112接收到的控制指令为加快某一工序的进程，然而控制器112接收到该工序突发故障的信息，生成了停止该工序的现场指令，现场指令与控制指令不一致，控制器112执行现场指令，即停止该工序。为此，在现场指令与控制指令不一致时，控制器执行现场指令，兼顾了生产系统控制的安全性。
下面提供根据本发明的实施例中的生产系统的控制方法的两个示例。
第一个示例为计算机数控机床(CNC machine)，计算机数控机床的环境中设置有多个工业机器人，计算机数控机床和工业机器人在云端生成有数字孪生，用户可以借由人机接口展示的计算机数控机床和工业机器人的数字孪生检查生产过程。正常状态下，计算机数控机床由控制器自动控制。在一些特殊的情况下，例如需要补给时，用户可以输入控制指令，指定单个机器人进行补给，控制器112根据该控制指令控制指定的单个机器人进行补给操作。
第二个示例为温室种植(Green house cultivation)，温室种植的环境中设置有多个工业机器人，温室和工业机器人在云端生成有数字孪生，用户可以借由人机接口展示的温室和工业机器人的数字孪生检查生产过程。在需要收获种植物时，用户可以输入收获种植物的控制指令，控制器112解析该控制指令控制多个机器人协同进行收获作业。
在此使用了流程图用来说明根据本申请的实施例的方法所执行的操作。应当理解的是，前面的操作不一定按照顺序来精确地执行。相反，可以按照倒序或同时处理各种步骤。同时，或将其他操作添加到这些过程中，或从这些过程移除某一步或数步操作。
本发明的该实施例提供了一种生产系统的控制方法，通过高速通信接口传输数据信息在云端生成生产线的数字孪生，用户可以检查生产线的实时状态，避免了通信延时导致的状态偏差，用户还可以通过高速通信接口发送控制指令，实现了对生产系统的远程控制，提高了生产系统的操控性和安全性。
图3是根据本发明的一实施例的一种生产系统的控制装置300的示意图。生产系统包括生产线、控制器和高速通信接口，如图3所示，该控制装置300包括：
获取单元310，从高速通信接口获取生产线的数据信息和控制器的控制逻辑；生成单元320，根据生产线的数据信息在云端生成生产线数字孪生，以及根据控制器的控制逻辑配置一仿真控制器；指令接收单元330，接受用户从云端的人机接口输入的控制指令，控制指令由用户基于生产线数字孪生和仿真控制器发出；控制单元340，从高速通信接口获取控制指令，由控制器根据控制指令对生产线进行控制。
在一种可选的情况下，生产系统还包括工业机器人，获取单元310从高速通信接口获取工业机器人的数据信息，生成单元根据工业机器人的数据信息在云端生成工业机器人数字孪生。
在一种可选的情况下，控制装置300包括：接受用户从云端的人机接口输入的控制指令，控制器根据控制指令将作业任务分配给指定的工业机器人单独作业。
在一种可选的情况下，控制装置300包括：接受用户从云端的人机接口输入的控制指 令，控制器根据控制指令将作业任务分配给多个工业机器人协同作业。
在一种可选的情况下，控制装置300包括：控制器还根据生产线的运行状况生成现场指令，在现场指令与控制指令不一致时，控制器执行现场指令。
在一种可选的情况下，控制装置300为生产线数字孪生建立机器学习模型，以使生产线数字孪生通过机器学习模型具备学习功能。
生产系统的控制装置300的实现方式和具体过程可参考生产系统的控制方法200，此处不再赘述。
本发明还提出一种电子设备，包括处理器、存储器和存储在存储器中的指令，其中指令被处理器执行时实现如上的方法。
本发明还提出一种计算机可读存储介质，其上存储有计算机指令，计算机指令在被运行时执行根据如上的方法。
本发明的方法和装置的一些方面可以完全由硬件执行、可以完全由软件(包括固件、常驻软件、微码等)执行、也可以由硬件和软件组合执行。以上硬件或软件均可被称为“数据块”、“模块”、“引擎”、“单元”、“组件”或“系统”。处理器可以是一个或多个专用集成电路(ASIC)、数字信号处理器(DSP)、数字信号处理器件(DAPD)、可编程逻辑器件(PLD)、现场可编程门阵列(FPGA)、处理器、控制器、微控制器、微处理器或者其组合。此外，本发明的各方面可能表现为位于一个或多个计算机可读介质中的计算机产品，该产品包括计算机可读程序编码。例如，计算机可读介质可包括，但不限于，磁性存储设备(例如，硬盘、软盘、磁带……)、光盘(例如，压缩盘(CD)、数字多功能盘(DVD)……)、智能卡以及闪存设备(例如，卡、棒、键驱动器……)。
计算机可读介质可能包含一个内含有计算机程序编码的传播数据信号，例如在基带上或作为载波的一部分。该传播信号可能有多种表现形式，包括电磁形式、光形式等等、或合适的组合形式。计算机可读介质可以是除计算机可读存储介质之外的任何计算机可读介质，该介质可以通过连接至一个指令执行系统、装置或设备以实现通讯、传播或传输供使用的程序。位于计算机可读介质上的程序编码可以通过任何合适的介质进行传播，包括无线电、电缆、光纤电缆、射频信号、或类似介质、或任何上述介质的组合。
应当理解，虽然本说明书是按照各个实施例描述的，但并非每个实施例仅包含一个独立的技术方案，说明书的这种叙述方式仅仅是为清楚起见，本领域技术人员应当将说明书作为一个整体，各实施例中的技术方案也可以经适当组合，形成本领域技术人员可以理解的其他实施方式。
以上所述仅为本发明示意性的具体实施方式，并非用以限定本发明的范围。任何本领域的技术人员，在不脱离本发明的构思和原则的前提下所作的等同变化、修改与结合，均应属于本发明保护的范围。

Claims (14)

  1. 一种生产系统的控制方法(200)，所述生产系统包括生产线、控制器和高速通信接口，所述控制方法(200)包括：
    从所述高速通信接口获取所述生产线的数据信息和所述控制器的控制逻辑(210)；
    根据所述生产线的数据信息在云端生成生产线数字孪生，以及根据所述控制器的控制逻辑配置一仿真控制器(220)；
    接受用户从所述云端的人机接口输入的控制指令，所述控制指令由所述用户基于所述生产线数字孪生和所述仿真控制器发出(230)；
    从所述高速通信接口获取所述控制指令，由所述控制器根据所述控制指令对所述生产线进行控制(240)。
  2. 根据权利要求1所述的控制方法(200)，其特征在于，所述生产系统还包括工业机器人，所述控制方法(200)还包括：从所述高速通信接口获取所述工业机器人的数据信息，根据所述工业机器人的数据信息在所述云端生成工业机器人数字孪生。
  3. 根据权利要求2所述的控制方法(200)，其特征在于，所述控制方法(200)包括：接受用户从所述云端的人机接口输入的控制指令，所述控制器根据所述控制指令将作业任务分配给指定的工业机器人单独作业。
  4. 根据权利要求2所述的控制方法(200)，其特征在于，所述控制方法(200)包括：接受用户从所述云端的人机接口输入的控制指令，所述控制器根据所述控制指令将作业任务分配给多个工业机器人协同作业。
  5. 根据权利要求1所述的控制方法(200)，其特征在于，所述控制方法(200)包括：所述控制器还根据所述生产线的运行状况生成现场指令，在所述现场指令与所述控制指令不一致时，所述控制器执行所述现场指令。
  6. 根据权利要求1所述的控制方法(200)，其特征在于，所述控制方法(200)包括：为所述生产线数字孪生建立机器学习模型，以使所述生产线数字孪生通过所述机器学习模型具备学习功能。
  7. 一种生产系统的控制装置(300)，所述生产系统包括生产线、控制器和高速通信接口，所述控制装置包括：
    获取单元(310)，从所述高速通信接口获取所述生产线的数据信息和所述控制器的控制逻辑；
    生成单元(320)，根据所述生产线的数据信息在云端生成生产线数字孪生，以及根据 所述控制器的控制逻辑一配置仿真控制器；
    指令接收单元(330)，接受用户从所述云端的人机接口输入的控制指令，所述控制指令由所述用户基于所述生产线数字孪生和所述仿真控制器发出；
    控制单元(340)，从所述高速通信接口获取所述控制指令，由所述控制器根据所述控制指令对所述生产线进行控制。
  8. 根据权利要求7所述的控制装置(300)，其特征在于，所述生产系统还包括工业机器人，所述获取单元(310)从所述高速通信接口获取所述工业机器人的数据信息，所述生成单元(320)根据所述工业机器人的数据信息在云端生成工业机器人数字孪生。
  9. 根据权利要求8所述的控制装置(300)，其特征在于，所述控制装置(300)包括：接受用户从所述云端的人机接口输入的控制指令，所述控制器根据所述控制指令将作业任务分配给指定的工业机器人单独作业。
  10. 根据权利要求8所述的控制装置(300)，其特征在于，所述控制装置(300)包括：接受用户从所述云端的人机接口输入的控制指令，所述控制器根据所述控制指令将作业任务分配给多个工业机器人协同作业。
  11. 根据权利要求7所述的控制装置(300)，其特征在于，所述控制装置(300)包括：所述控制器还根据所述生产线的运行状况生成现场指令，在所述现场指令与所述控制指令不一致时，所述控制器执行所述现场指令。
  12. 根据权利要求7所述的控制装置(300)，其特征在于，所述控制装置(300)为所述生产线数字孪生建立机器学习模型，以使所述生产线数字孪生通过所述机器学习模型具备学习功能。
  13. 一种电子设备，包括处理器、存储器和存储在所述存储器中的指令，其中所述指令被所述处理器执行时实现如权利要求1-6任一项所述的方法。
  14. 一种计算机可读存储介质，其上存储有计算机指令，所述计算机指令在被运行时执行根据权利要求1-6中任一项所述的方法。
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