Patent Publication Number: US-2021181695-A1

Title: Artificial intelligence computing device, control method and apparatus, engineer station, and industrial automation system

Description:
PRIORITY STATEMENT 
     This application is the national phase under 35 U.S.C. § 371 of PCT International Application No. PCT/CN2018/101973 which has an International filing date of Aug. 23, 2018, which designated the United States of America, the entire contents of each of which are hereby incorporated herein by reference. 
    
    
     FIELD 
     Embodiments of the invention generally relate to the field of industrial automation, and in particular to an artificial intelligence (AI) computing device, a control method and apparatus, an engineer station (ES), and an industrial automation system. 
     BACKGROUND 
     After a long period of continuous development of the autonomous technology of industrial production, it is now possible to use computer technology to control industrial production processes. An industrial automatic control system gathers, analyzes, and organizes various parameters in industrial production collected by sensors through industrial control computers for information management and automatic control. At present, the implementation of intelligent control in industrial automation systems has become a trend in the field of industrial automation. In order to realize intelligence in the control chain, the challenge is that traditional industrial controllers cannot provide sufficient computing capacity and there is no flexible solution to add AI to the control system. 
     SUMMARY 
     The embodiments of this application propose an AI computing device, a control method and apparatus, an engineer station, and an industrial automation system. At least one embodiment is used to implement closed-loop control with AI in an industrial automation system and to improve the control capability and the control efficiency of the automation system. 
     The AI computing device of at least one embodiment of the present application, applied to an industrial automation system, may comprise: a backplane, a communication component, and a computing component. 
     In at least one embodiment, the backplane comprises a backplane bus and a field bus interface, wherein the backplane bus is connected to the communication component and the computing component, and the field bus interface can be connected to and communicate with a field bus of an industrial automation system through the field bus interface; the industrial automation system comprises at least one controller; 
     the communication component performs data interchange between the controller and the computing component; and 
     the computing component receives data sent by the controller through the communication component, analyzes the data by use of an embedded AI computing architecture, and sends the analysis result to the controller by use of the communication component. 
     It can be seen that the AI computing device of at least one embodiment has a field bus interface, which can be directly connected to the field bus of an industrial automation system to provide plug-and-play intelligent control functions and enhances the processing capacity of the control system. At the same time, the AI computing device is directly connected to the field bus, which facilitates real-time intelligent closed-loop control and improves the control efficiency of the system. 
     The control method of at least one embodiment of the present application, applied to a controller in an industrial automation system, may comprise: 
     obtaining data in the industrial automation system; 
     sending the data to an artificial intelligence (AI) computing device connected to a field bus; and 
     receiving, through the field bus, an analysis result obtained by analyzing the data by the AI computing device, and providing the analysis result to a decision-making apparatus in the controller, so that the decision-making apparatus generates a control instruction for automation control. 
     It can be seen that the control method of each embodiment analyzes data by use of the AI computing device connected to the field bus, and uses the analysis result to generate automatic control instructions, so as to improve the processing capacity of the controller and realize real-time intelligent closed-loop control without replacing existing equipment or limitation by the processing capacity of existing equipment. 
     The control apparatus of at least one embodiment of the present application, applied to a controller in an industrial automation system, may comprise: 
     a production data obtaining unit, configured to obtain data in the industrial automation system; 
     a task sending unit, configured to send the data to a first computing component in an artificial intelligence (AI) computing device connected to a field bus; and 
     a result collecting unit, configured to receive, by use of the field bus, an analysis result sent by the AI computing device, wherein the analysis result is obtained by analyzing the data by the first computing component; and to provide the analysis result to a decision-making apparatus in the controller, so that the decision-making apparatus generates a control instruction for automation control. 
     It can be seen that the control apparatus of at least one embodiment analyzes data by use of the AI computing device connected to the field bus, and uses the analysis result to generate automatic control instructions, so as to improve the processing capacity of the controller and realize real-time intelligent closed-loop control without replacing existing equipment or limitation by the processing capacity of existing equipment. 
     The engineer station of at least one embodiment of the present application, connected to a field bus of an industrial automation system, may comprise a processor and a memory, wherein the memory comprises machine readable instructions, and the instructions are executable by the processor for: 
     providing a device configuration interface; 
     receiving device configuration information from the device configuration interface, wherein the device configuration information comprises an identifier of an AI computing device and a computing component identifier of at least one computing component in the AI computing device, and the AI computing device is connected to the field bus by use of a field bus interface; and sending the device configuration information to a controller of the industrial automation system, so that the controller communicates with the AI computing device by use of the device configuration information; and 
     obtaining a control logic corresponding to the AI computing device, receiving, from the device configuration interface, control configuration information for the AI computing device, wherein the control configuration information comprises a computing parameter of a first computing component in the at least one computing component; loading the control configuration information into the control logic, and loading the control logic into the controller, wherein the control logic is used for enabling the controller to configure the first computing component; sending data in the industrial automation network to the first computing component for analysis, and obtaining an analysis result fed back by the first computing component. 
     It can be seen that the engineer station of each embodiment can configure the controller through the field bus, so as to realize the communication between the controller and the AI computing device for real-time intelligent closed-loop control in the industrial automation system. 
     The industrial automation system of at least one embodiment of the present application may comprise: an engineer station (ES), a controller, production equipment and an AI computing device, and a field bus connecting the devices;
         the ES is configured to:
           provide a device configuration interface, receive configuration information from the device configuration interface, wherein the configuration information comprises information about the AI computing device, and load the configuration information into the controller; and   obtain a control logic corresponding to the AI computing device, and load the control logic into the controller;   
           the controller is configured to:
           execute the control logic, obtain values of a plurality of production parameters in the industrial automation system, and send the values of the plurality of production parameters to the AI computing device; and receive an analysis result sent by the AI computing device; and   generate a control instruction for the production equipment based on the analysis result; and   
           the AI computing device is configured to:
           receive, by use of the field bus, the values of the plurality of production parameters sent by the controller, analyze the values of the plurality of production parameters to obtain an analysis result, and send the analysis result to the controller by use of the field bus.   
               

     It can be seen that the industrial automation system of each embodiment improves the control capability of the system by use of the AI computing device connected to the field bus, and at the same time, it can also realize real-time intelligent closed-loop control. 
     The embodiments of the present application also provide a computer readable storage medium, storing machine readable instructions, wherein the machine readable instructions can enable a processor to perform the control method of the embodiments. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The preferred embodiments of the present application will be described in detail below with reference to the drawings, so that those skilled in the art will better understand the above and other features and advantages of the present application. In the drawings: 
         FIG. 1  illustrates the industrial automation system of an embodiment of the present application; 
         FIG. 2  illustrates the AI computing device of an embodiment of the present application; 
         FIG. 3  illustrates the logic of internal data communication of the AI computing device of an embodiment of the present application; 
         FIG. 4  illustrates the logic of internal data processing of the AI computing device of an embodiment of the present application; 
         FIG. 5  illustrates a state machine of the AI computing architecture of an embodiment of the present application; 
         FIG. 6  illustrates a control method of an embodiment of the present application; 
         FIG. 7  illustrates a control apparatus of an embodiment of the present application; 
         FIG. 8  illustrates an engineer station of an embodiment of the present application; and 
         FIG. 9  illustrates a production process of an embodiment of the present application. 
     
    
    
     In the drawings, the following symbols are used: 
     
       
         
           
               
               
             
               
                   
               
               
                 No. 
                 Meaning 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
            
               
                 10 
                 Industrial automation system 
               
               
                 20 
                 AI computing device 
               
               
                 21 
                 Backplane 
               
               
                 211 
                 Backplane bus 
               
               
                 212 
                 Field bus interface 
               
               
                 22 
                 Communication component 
               
               
                 221 
                 Periodic data 
               
               
                 222 
                 Aperiodic data 
               
               
                 223 
                 Diagnostic service data 
               
               
                 23 
                 Computing component 
               
               
                 231 
                 AI computing architecture 
               
               
                 232 
                 Configuration data storage 
               
               
                   
                 module 
               
               
                 233 
                 Training data storage module 
               
               
                 235 
                 Status switching module 
               
               
                 26 
                 Record data 
               
               
                 27 
                 IO channel 
               
               
                 271 
                 First transmission channel 
               
               
                 272 
                 Second transmission channel 
               
               
                 30 
                 Engineer station 
               
               
                 31 
                 Control logic 
               
               
                 32 
                 Processor 
               
               
                 33 
                 Memory 
               
               
                 34 
                 Communication device 
               
               
                 35 
                 Operating system 
               
               
                 36 
                 Network communication module 
               
               
                 37 
                 Management module 
               
               
                 371 
                 Interface module 
               
               
                 372 
                 Device configuration module 
               
               
                 373 
                 Control configuration module 
               
               
                 40 
                 Controller 
               
               
                 41 
                 Control apparatus 
               
               
                 411 
                 Production data obtaining unit 
               
               
                 412 
                 Task sending unit 
               
               
                 413 
                 Result collecting unit 
               
               
                 414 
                 Configuration unit 
               
               
                 415 
                 Training unit 
               
               
                 42 
                 Decision-making apparatus 
               
               
                 60 
                 Field bus 
               
               
                 S1 
                 Initialization complete 
               
               
                 S2 
                 Preparing for training 
               
               
                 S3 
                 Training 
               
               
                 S4 
                 Training error 
               
               
                 S5 
                 Preparing for operation 
               
               
                 S6 
                 Operating 
               
               
                 S7 
                 Operation error 
               
               
                 S61-S63 
                 Steps 
               
               
                 K1, K2, K3,  
                 Parameters 
               
               
                 V, T, Q1  
                   
               
               
                 and Q2 
               
               
                   
               
            
           
         
       
     
     DETAILED DESCRIPTION OF THE EXAMPLE EMBODIMENTS 
     The following example embodiments will further illustrate the present application in detail in order to clarify its purpose, technical solution and advantages. 
     In order to overcome the limitation of the controller&#39;s computing capacity on the control mechanism of industrial automation systems, the embodiments of the present application propose to add an AI computing device with the artificial intelligence computing capability to the control loop of industrial automation systems, thereby enhancing the system&#39;s autonomous control capability and realizing intelligence in the control chain.  FIG. 1  illustrates the industrial automation system of the embodiments of the present application. The industrial automation system  10  is an industrial production system that uses automatic control and automatic regulating devices to replace manually operated machines and machine systems for manufacturing and production. As shown in  FIG. 1 , the system  10  may comprise: an AI computing device  20 , an engineer station (ES)  30 , a controller  40 , and a field bus  60  connecting these devices. 
     The field bus  60 , also known as the industrial data bus, is used to implement digital communication among field devices such as the controller  40 , data acquisition equipment (not shown), and actuators (not shown) at an industrial production site, and information transfer between these field devices and advanced control systems (such as ES  30 ). The field bus  60  may be implemented with a certain field bus technology, such as ProfiBus, InterBus, Controller Area Network (CAN) bus, and Addressable Remote Transducer (HART) bus. 
     The controller  40  may be one or more industrial controllers, such as programmable logic (PLC) controllers, PC bus industrial computer (IPC) controllers, distributed control system (DCS) controllers, field bus control system (FCS) controllers, computer numerical control (CNC) controllers, etc. The controller  40  can communicate with various data acquisition equipment (such as intelligent instruments, sensors, etc.) and actuators (such as current regulating valves, voltage regulating valves, feed valves, etc.) on the production site, and monitor and control the production processes of production equipment. Production equipment (not shown) refers to a collection of one or more equipment used for industrial processing and manufacturing, such as machine tools, lathes, assembly line equipment, etc. When the production equipment is a program-controlled equipment with a field bus interface, the controller  40  can also communicate with the production equipment through the field bus  60 , collect process information of the production equipment, and monitor and control the production process of the production equipment. 
     The controller  40  can obtain various parameter values of the production equipment (hereinafter also referred to as production parameter values and production process data), and control the operation of the production equipment according to these production parameter values. The production parameters may be any parameters related to the production process of the production equipment, such as voltage, current, motor speed, feeding speed of raw materials, etc. The values of production parameters may be obtained from data acquisition equipment or from program-controlled production equipment. The controller  40  can also regulate the values of production parameters by sending control instructions to actuators. An actuator can receive the control signal of the controller  40  and perform corresponding regulation actions to change the value of a production parameter. 
     The AI computing device  20  is a plug-and-play device with the AI computing capability, which can be connected to a field bus through a field bus interface. The AI computing device  20  can receive, by use of the field bus  60 , the values of a plurality of production parameters sent by the control apparatus  41  in the controller  40 , analyze the values of the plurality of production parameters to obtain an analysis result, and send the analysis result to the controller  40  by use of the field bus  60 , so that the decision-making apparatus  42  in the controller  40  can generate control instructions for automatic control based on the analysis result. In some embodiments, the system  10  can be connected to a plurality of AI computing devices  20 . The plurality of AI computing devices may include devices with different AI functions, or may include several identical devices to provide redundant backup or load sharing. 
     In order to enable the controller  40  to identify and use the AI computing device  20 , the controller  40  may be configured for the AI computing device  20  at the ES  30 . 
     The ES  30  refers to the industrial process monitoring and management equipment used by industrial process control engineers. It can configure the controller  40  in the industrial control system so that the controller  40  can communicate with other equipment on the production site, perform data processing, and make control decisions to monitor and control the production process. 
     The ES  30  may provide a device configuration interface, receive configuration information about the AI computing device from the device configuration interface, and load the configuration information into the controller  40 . The controller  40  uses the configuration information to communicate with the AI computing device  20 . 
     In some embodiments, the device configuration interface of ES  30  may comprise a human-machine interaction interface. ES  30  can display the configuration and operation of each device in the system  10  through the human-machine interaction interface, and display the configuration interface through the human-machine interaction interface. In response to the operator&#39;s operation on the configuration interface in the human-machine interaction interface, the ES  30  can complete various device configuration operations, such as device addition, deletion, adding device configuration information, modifying device configuration information, deleting device configuration information, etc. The configuration information received from the device configuration interface may comprise the text information (such as the ID and address of the AI computing device, etc.) entered by the operator in the ES  30 , configuration files obtained from a path entered or selected by the operator through the device configuration interface, control logic, etc. A path entered or selected by the operator may be a storage path in the built-in storage device of the ES  30 , a path in an external extended storage device of the ES  30 , or a location on the network (such as a URL, etc.). In some embodiments, the configuration information may comprise the ID of the AI computing device  20  (e.g., a media access control (MAC) address, device name, etc.), so that the controller  40  can communicate with the AI computing device  20 . In some embodiments, the configuration information may also comprise information about a plurality of production parameters required by the AI computing device  20  to perform the AI computing process, so that the controller  40  can provide the AI computing device  20  with the values of these production parameters collected by sensors. 
     In some embodiments, the ES  30  may also obtain the control logic  31  corresponding to the AI computing device  20 , and load the control logic  31  to the controller  40 . For example, the ES  30  can obtain the control logic  31  from a path entered or selected by the operator through the device configuration interface. A path entered or selected by the operator may be a storage path in the built-in storage device of the ES  30 , a path in an external extended storage device of the ES  30 , or a location on the network (such as a URL, etc.). The ES  30  can also configure and edit the functions of the control logic  31  through the interface provided by the control logic  31 . The controller  40  controls the AI computing device  20  by executing the control logic  31 . The above-mentioned information about the plurality of production parameters may also be loaded into the control logic  31  through the interface provided by the control logic  31 . The controller  40  obtains the values of these production parameters by executing the control logic  31  and provides them to the AI computing device  20 . In some embodiments, the AI computing device  20  may comprise a communication component  22  and one or more computing components  23 . The AI computing device  20  may comprise a plurality of computing components  23  with different AI functions, or a plurality of computing components with the same function. The computing components  23  with different AI functions can correspond to different control logics  31 , and the computing components  23  with different AI functions can be controlled by the same control logic  31 . 
     The industrial automation system of each embodiment has the plug-and-play intelligent control function and better processing capacity of the control system without the need to replace existing devices or limitation by the processing capacity of existing devices by use of an AI computing device with a field bus interface. At the same time, the AI computing device is directly connected to the field bus, and production process data are analyzed and processes directly on site, which facilitates real-time intelligent closed-loop control and improves the control efficiency. 
     In order to realize plug and play, the AI computing device  20  comprises a physical interface that can be connected to a field bus, and supports industrial communication protocols used by industrial automation systems for communication. The following takes the implementation of an AI computing device  20  as an example for illustration.  FIG. 2  illustrates the AI computing device of the embodiments of the present application. As shown in  FIG. 2 , the AI computing device  20  may comprise: a backplane  21 , a communication component  22 , and a computing component  23 ; 
     The backplane  21  comprises a backplane bus  211  and a field bus interface  212 . The backplane bus  211  is used to connect the communication component  22  and the computing component  23 . The field bus interface  212  can be connected to and communicate with a field bus  60  through the field bus interface. The field bus interface  212  is an interface that conforms to the field bus technology used by the field bus  60 , such as ProfiBus, InterBus, CAN, HART, etc. The backplane bus  211  may use any bus technology, such as a bus technology designed required, or an existing data transmission bus. For example, the backplane bus interface provided by the backplane bus  211  may be a low-voltage differential signaling interface, an S 422  interface, an RS485 interface, etc. 
     The communication component  22  performs data interchange between the controller  40  and the computing component  23 . 
     The computing component  23  comprises an AI computing architecture. In some embodiments, the AI computing architecture may comprise an AI computing logic, such as machine learning logic, and neural network algorithm. In some embodiments, the AI computing architecture may also comprise special hardware with high computing performance customized for AI computing (such as GPU, FPGA, ASIC, neural network processor, etc.). The computing component  23  can receive data sent by the controller  40  through the communication component  22 , analyze the data by use of an embedded AI computing architecture, and send the analysis result to the controller  40  by use of the communication component  22 . 
     The AI computing device  20  of each embodiment has a field bus interface, and thus can provide plug-and-play intelligent control functions in industrial automation systems and enhance the processing capacity of the control system. At the same time, the AI computing device analyzes and processes production process data on the production site, which facilitates real-time intelligent closed-loop control and improves the control efficiency. 
     In some embodiments, in order to realize the communication between the AI computing device  20  and the controller, the communication component  22  may communicate with the field bus  60  by use of a packet format defined by an industrial communication protocol (such as PROFINET, EtherCat, etc.). The communication component  22  can use the packet format defined by the industrial communication protocol to parse a packet sent by the controller  40 , and send the content of the packet obtained from the parsing to the computing component  23 . The communication component  22  can also use the packet format to encapsulate the feedback data sent by the computing component  23 , and send the encapsulated packet to the controller  40 . Through the communication with the controller  40  by use of existing industrial communication protocol packets, it is easy to implement the AI computing device  20  directly in an existing industrial communication network without modifying the network. In some embodiments, the communication component may be implemented as FPGA, ASIC, integrated circuits, industrial communication chips, etc. 
     The AI computing device  20  may comprise one or more computing components  23 . When a computing component  23  is provided in the AI computing device  20 , the communication component  22  can forward data between the computing component and the controller  40 . When the AI computing device  20  comprises a plurality of computing components  23 , the communication component  22  can use the computing component identifier of each computing component  23  to realize the communication between each computing component  23  and the controller  40 . In some embodiments, the communication component may receive a packet conforming to the industrial communication protocol sent by the controller  40  through the field bus interface  212  (in order to distinguish it from the packet sent by the AI computing device  20 , it will be referred to as the first packet hereinafter). Specifically, the first packet comprises a header and a payload. The payload comprises one or more identifiers of the computing components  23  (hereinafter referred to as computing component identifiers) and the packet data or content corresponding to each computing component identifier. The computing component identifier of the computing component  23  is used to distinguish each computing component  23  in the AI computing device  20 . It may be a name, a serial number, etc., a device identification code configured in the computing component  23  during production, or another identifier assigned by the controller  40 . The communication component  22  can parse the identifier of the computing component  23  and the packet data corresponding to the identifier from the first packet according to the packet format defined by the industrial communication protocol, and send the packet data to the computing component  23  corresponding to the identifier through the backplane bus  211 . The communication component  22  can also receive feedback data from the first computing component  23  (the first computing component  23  is one computing component of a plurality of computing components  23 ) through the backplane bus  211 , combine the feedback data with the computing component identifier of the first computing component  23  as the packet data to generate a packet in the packet format defined by the industrial communication protocol (in order to distinguish it from the packet sent by the controller  40 , it is referred to as the second packet hereinafter), and send the second packet to the controller  40  through the field bus interface  212 . Specifically, the feedback data is the result of processing the message data by the computing component  23 , which is used to enable the controller  40  to obtain the status of the computing component  23 , analysis results, etc., so as to generate control decisions for the AI computing device  20  or other equipment (such as production equipment, actuators, etc.). For example, when the packet data is configuration information, the feedback data may be a confirmation packet that the configuration is complete; when the packet data is a training instruction, the feedback data may be the result of the training; when the packet data is production process data, the feedback data may be the analysis result of the production process data. In this way, by adding the identifier of the computing component  23  in communication, the controller  40  can communicate with multiple computing components  23  in the AI computing device  20 . A single AI computing device  20  may comprise s plurality of computing components  23 , which greatly improves the computing capacity and computing capability of a single AI computing device  20 . 
     In some embodiments, the data interchanged between the computing component  23  and the controller  40  may be divided into a plurality of types. For example, the data may be divided into a plurality of types depending on the priority, transmission time requirement, etc. The communication component can process the data according to a preset processing strategy corresponding to the type of the received data. Preset processing strategies may comprise the strategy for the data processing sequence, the strategy for the data transmission sequence, the strategy for the data transmission method, etc. In some embodiments, different types of logical transmission channels may be preset for different types of data. The communication component  22  can determine the type of data according to the data channel identifier corresponding to the data in the first packet sent by the controller  40 , and process the data according to the processing strategy corresponding to the type; obtain the data type provided by the first computing component  23  through the backplane bus  211 , and send the second packet corresponding to the feedback data to the controller  40  through the field bus interface  212  by use of the transmission channel preset to be corresponding with the type of the feedback data. 
       FIG. 3  illustrates the internal data communication between the computing component  23  and the communication component  22  of the AI computing device  20  of the embodiments of the present application. As shown in  FIG. 3 , one or more computing components  23  in the AI computing device  20  can send feedback data and its type to the communication component  22 . The communication component  22  maps the feedback data to different data channels according to the type of the feedback data, and transmits it to the field bus  60  through the field bus interface  212 . In this example, the types of the feedback data may comprise periodic data  221 , aperiodic data  222 , diagnostic service data  223 , etc. 
       FIG. 4  illustrates the data communication between the computing component  23  of the AI computing device  20  and the controller  40  through different logical channels in the embodiments of the present application. As shown in  FIG. 4 , the logical channels between the AI computing device  20  and the controller  40  (hereinafter also referred to as the IC channel  27 ) are classified as a first transmission channel  271  and a second transmission channel  272 . The first transmission channel  271  is an aperiodic transmission channel, i.e., its transmission timing is not periodic and it transmits data only when needed. The second transmission channel  272  is a periodic transmission channel that transmits data at a fixed time interval. The first transmission channel  271  and the second transmission channel  272  can be distinguished by their channel identifiers. 
     In  FIG. 4 , the computing component  23  can interchange record data with the controller  40  through the first transmission channel  271 . Record data may be data of low importance or low requirement for real-time communication, such as configuration information (such as configuration information of the structure of the AI computing architecture, configuration information of the input parameters and output parameters of the AI computing architecture  231 , etc.), training data, etc. The computing component  23  can store configuration information in the built-in configuration data storage module  232 , and store training data in the training data storage module  233 . In some cases, the computing component can send the configuration information in the built-in configuration data storage module  232  to the controller  40  through the first transmission channel  271 , and the controller can adjust the computing architecture of other modules according to the configuration information. 
     In  FIG. 4 , the computing component  23  can also interchange control data with the controller  40  through the second transmission channel  272 . For example, the control data may comprise a state switching instruction for the AI computing architecture  231  sent by the controller  40 , production process data to be analyzed sent by the controller  40 , a status report of the AI computing architecture  231  sent by the computing component  23 , etc. 
     The computing component  23  may comprise a status switching module  235  for changing the operating status of the AI computing architecture  231  according to the status switching instruction sent by the controller  40 . For example, the status switching module  235  may use the second transmission channel  272  to send the current operating status of the AI computing architecture  231  to the controller  40  through the communication component  22 , receive a status switching instruction sent by the controller  40  through the communication component  22 , and switch the AI computing architecture  231  from a first operating status to a second operating status. For example, the status switching module  235  may send the status of the AI computing architecture  231  to the controller  40  through the second transmission channel  272  by use of the initial status word (ISW), and switch the status of the AI computing architecture  231  according to the operation control word (OCW) sent by the controller  40 . 
     The state switching module  235  may also input data corresponding to the current operating status provided by the controller ( 40 ) into the AI computing architecture ( 231 ) according to the current operating status of the AI computing architecture ( 231 ). For example, when the AI computing architecture  231  is in the training status, the status switching module  235  may input the training data sent by the controller  40  through the first transmission channel  271  and stored in the training data storage module  233  into the AI computing architecture  231  for training the AI computing architecture  231 . For example, when the AI computing architecture  231  is in the operating status, the status switching module  235  may input the data to be analyzed periodically sent by the controller  40  through the second transmission channel  272  into the AI computing architecture  231  so that the AI computing architecture  231  may output the analysis result. The analysis result generated by the AI computing architecture  231  may also be fed back to the controller  40  through the second transmission channel  272 . 
     In this way, by use of different transmission channels to transmit different types of data, the data response capability and processing efficiency of the AI computing device  20  are improved. 
     One or more AI computing devices  20  may be provided in the system  10 , and each AI computing device  20  may comprise one or more computing components  23 , and different computing components  23  may have AI computing architectures  231  with different functions. The functions that the AI computing architecture  231  can implement may include, but are not limited to, online parameter optimization, process monitoring, fault diagnosis, etc. 
     For example, when the AI computing architecture  231  comprises an optimization computation process for online optimization, the AI computing architecture  231  can perform a preset optimization computation process to optimize a plurality of production parameters in the production process data, and output the recommended values of a plurality of production parameters. In some examples, the computing module  23  may also convert the recommended values into production adjustment recommendation identifiable by the controller  40  as analysis result. Production adjustment recommendation comprises the recommended value of at least one of the plurality of production parameters. In some examples, the processing strategy for the recommended values can be preset. For example, a threshold can be preset, and when the difference between the recommended value and the actual value of a parameter is no greater than the threshold, the adjustment recommendation for the parameter can be excluded from the analysis result. This avoids frequent and unnecessary changes to production parameters. 
     For another example, when the AI computing architecture  231  comprises a parameter measuring process for status monitoring, the AI computing architecture  231  can perform a preset parameter measuring process to measure a plurality of production parameters in the production process data, and output the result of the parameter measurement, which indicates whether the plurality of production parameters are normal. In some examples, the computing module  23  may also convert the parameter measurement result into a status monitoring report identifiable by the controller  40  as analysis result, and the status monitoring report comprises information for indicating whether the status of the production equipment is normal. 
     For yet another example, when the AI computing architecture  231  comprises a fault diagnosis process, the AI computing architecture  231  can perform a preset fault diagnosis process, whereby the values of a plurality of production parameters in the production process data are used to perform fault diagnosis and the fault diagnosis result is output. The fault diagnosis result comprises information about a component (e.g., collector, actuator, production equipment, etc.) in the system  10 . In some examples, the computing module  23  may also convert the fault diagnosis result into a fault report identifiable by the controller  40  as analysis result, and the fault report comprises information about components. 
     By adopting the AI computing architecture  231  with different functions, the control capability of industrial automation systems can be improved. 
     In some embodiments, the computing component  23  may also comprise a configuration unit (not shown) for configuring the AI computing architecture  231 . The configuration unit may receive the configuration parameters sent by the controller  40  through the communication component  22 , wherein the configuration parameters comprise a plurality attribute values of the AI computing architecture  231 ; and set the values of a plurality of attributes corresponding to the AI computing architecture  231  as a plurality attribute values in the configuration parameters. For example, the plurality of attribute values may comprise structural attribute values, and the configuration unit may use the structural attribute values in the configuration parameters to set the components and connection methods of the AI computing architecture  231 , such as the hierarchical structure of the neural network in the AI computing architecture  231 . Different AI functions and different production processes have different computing requirements. The ES  30  can configure the structure information of the required AI computing architecture to the controller  40 , and the controller  40  configures the corresponding computing components  23 . For another example, the plurality of attribute values may comprise a first parameter and a second parameter, and the first parameter is one or more production parameters of the production equipment. The configuration unit may set the first parameter as an input parameter of the AI computing architecture  231 ; and set the second parameter as an output parameter of the AI computing architecture  231 . Different production processes have different production parameters. The ES  30  can configure the information of the production parameters to be used to the controller  40 , and the controller configures the corresponding computing components  23 . The configuration unit can also save the configuration parameters in the configuration data storage module  232 . An open configuration interface of the computing component  23  expands the application scope of the AI computing device  20 , and the problem of excessively high costs in the development of special AI computing devices  20  for different scenarios can be solved. 
     In some embodiments, the status switching module  235  can also use the state machine mechanism to manage the operating status of the AI computing architecture  231 .  FIG. 5  illustrates a state machine of the AI computing architecture of the embodiments of the present application. As shown in  FIG. 5 , the AI computing architecture  231  may have 7 statuses, namely initialization complete (S 1 ), preparing for training (S 2 ), training (S 3 ), training error (S 4 ), preparing for operation (S 5 ), operating (S 6 ), and operation error (S 7 ). Operation refers to the process whereby the AI computing architecture  231  uses a trained model to analyze production process data. Accordingly, the controller  40  can use six control words to control the status transition of the AI computing architecture  231 . The six control words are implemented by six signals. For example, 6-bit information may be used to represent the six types of control words, with each bit representing one instruction. For example, bit  1  means preparing for training (transition from S 1  to S 2 ), bit  2  means initiating operation (transition from S 1  to S 5 ), bit  3  means starting training (transition from S 2  to S 3 ), bit  4  means starting operation (transition from S 5  to S 6 ), bit  5  means ending training (transition from S 2 /S 3  to S 1 ), bit  6  means ending operation (transition from S 6 /S 5  to S 1 ). Setting the bit corresponding to an instruction indicates that the AI computing architecture  231  is required to perform the status transition operation corresponding to the instruction. When the AI computing architecture  231  enters the error status of S 4  or S 7 , it can directly enter S 1  after reporting the status S 4  or S 7  to the controller  40  without waiting for an instruction from the controller  40 . With a state machine to perform status management of the AI computing architecture  231 , the operation control of the AI computing architecture  231  is more standardized and management is easier. 
     The controller  40  can also use the state machine of the computing component  23  to synchronize the parallel tasks of a plurality of computing components  23 . For example, the controller  40  allocates computing tasks to a plurality of computing components  23 , and each computing component  23  will set the status bit to S 1  after completing the computation. Then the controller  40  only needs to wait for all the computing components  23  to return to S 1  before determining that the computing components  23  are currently synchronized and ready for next cycle of operation. 
     In some embodiments, the computing component  23  may also comprise an energy saving unit (not shown) for changing the working mode of the computing component  23  according to an instruction of the controller  40 . The energy saving unit may receive a first instruction sent by the controller  40  through the communication component  22 , and enter the low power consumption mode according to the first instruction; receive a second instruction sent by the controller  40  through the communication component  22 , and exit the low power consumption mode according to the second instruction. For example, when the PROFINET protocol is used, the controller  40  will send all devices a PROFIenergy instruction for them to sleep or wake up when the automation system enters or exits the low power consumption mode. The computing component  23  can enter or exit the low power consumption mode according to the PROFIenergy instruction sent by the controller  40 . This can reduce the energy consumption of the AI computing device  20  when the automation system is operating in the low power consumption mode. 
     In some embodiments, the backplane  21  may comprise a plurality of slots, and the computing component  23  is connected to the backplane bus  211  through the slots as a pluggable expansion card. In this way, when the computing capacity of the AI computing device  20  needs to be expanded, a new computing component  23  can be connected through the slots. When a computing component  23  needs to be replaced (for example, an existing computing component  23  for parameter optimization needs to be replaced with a computing component  23  for fault diagnosis), the existing computing component  23  may be removed from the slot, and a new computing component  23  may be inserted. This pluggable design can easily expand the computing capability of the AI computing device  20  or change the AI functions of the AI computing device  20 . 
     The following describes how the controller  40  controls the AI computing device  20 .  FIG. 6  is a flow chart of a control method of the embodiments of the present application. The method can be implemented by the control apparatus  41  in the controller  40 . The method may comprise the following steps. 
     Step S 61 , obtaining data in the industrial automation system. 
     Step S 62 , sending the data through the field bus  60  of the industrial automation system to the AI computing device connected to the field bus  60 . 
     Step S 63 , receiving, through the field bus  60 , an analysis result obtained by analyzing the data by the AI computing device  20 , and providing the analysis result to a decision-making apparatus  42  in the controller  40 , so that the decision-making apparatus generates a control instruction for the production equipment. 
     In the control method of each embodiment, by use of an AI computing device with a field bus interface to analyze production process data, it is possible to implement intelligent closed-loop control in an industrial automation system and improve control efficiency. 
     In some embodiments, in order to determine the production parameters that need to be provided to the AI computing device  20 , the control apparatus  41  may set a plurality of input parameters in the preset first configuration information as a plurality of production parameters, and obtain the values of the plurality of production parameters from the data provided by the data acquisition equipment in the industrial automation system, which will be used as production process data. In some embodiments, the control apparatus  41  may obtain the values of a plurality of production parameters from the data provided by the data acquisition equipment in the previous time period as the production process data at an interval of a preset length of time. By periodically sending the production process data to the AI computing device  20  for analysis, the analysis result of the current production process can be obtained in a relatively timely manner, which facilitates timely adjustment of production parameters and improves production efficiency. 
     In some embodiments, when sending the production process data to the AI computing device  20 , the control apparatus  41  may obtain the information of a first computing component  23  in the AI computing device  20  from the information of a preset second configuration; send the production process data to the first computing component  23  in the first AI computing device  20 . By obtaining the information of the computing component  23  from the configuration information, it is convenient and flexible to adaptively adjust the control apparatus  41  simply through updating the configuration information when the computing component  23  is replaced or another component is added. 
     When the AI computing device  20  comprises a plurality of computing components  23 , the control apparatus  41  needs to obtain the information of these computing components for the communication with each computing component. In some embodiments, the control apparatus  41  may obtain the in formation of a second configuration, in which information of a plurality of computing components is recorded. In some embodiments, the control apparatus  41  may obtain the information of a second configuration, in which information of a plurality of computing components is recorded. 
     In order to achieve load balancing, the control apparatus  41  may obtain load information of a plurality of computing components  23  from at least one AI computing device  20 ; select one computing component  23  from the plurality of computing components  23  as the first computing component  23  according to the load information, and set the AI computing device  20  to which the computing component  23  belongs as the AI computing device  20 . For example, the computing component  23  may upload its load information (such as computing load, processor temperature, computing bandwidth (number of operations completed per second), etc.) to the controller  40  through identification and maintenance (I&amp;M) information. The controller  40  can allocate a computation task to the computing component  23  according to a load balancing algorithm (for example, the packing algorithm, etc.), and send the production process data corresponding to the computation task. With a plurality of computing components  23  for load sharing, it is possible to ensure smooth operation of the control system and improve operation efficiency even when the number of computation tasks is high. The method for allocating computation tasks depends on the AI algorithm used. For example, a plurality of computing components  23  can process different parameters, or process data in different time periods, or the same data set in parallel, etc. 
     In some embodiments, the control apparatus  41  may use periodic data to input normalized production process data to the computing component  23  at a fixed cycle, and obtain normalized output data output from the computing component  23 . Different structures of the computing components  23  may generate different amounts of input data and output data. The packet length (such as 16 bytes, 64 bytes, etc.) used by each computing component  23  may be specified in the configuration information of the control apparatus  41 . The configuration information can be provided to the control apparatus  41  by the ES  30  through, for example, configuration files, device description files, etc. For example, the configuration file can be a GSDML file, which can comprise information about the manufacturer, communication ports, modules and sub-modules, alarm diagnosis, etc. Through configuration of the packet length supported by each computing component  23 , the data communication between the control apparatus  41  and each computing component  23  can be implemented efficiently and flexibly. 
     In some embodiments, different computing components may have the AI computing architectures  231  with different functions, and the output data of the AI computing architectures  231  with different functions are also different, so are the analysis results provided to the decision-making apparatus. 
     For example, when the AI computing architecture  231  comprises an optimization computation process for online optimization, the AI computing architecture  231  optimizes a plurality of production parameters. The control apparatus  41  can obtain the recommended values of a plurality of production parameters output from the AI computing architecture  231 , and convert the recommended values into production adjustment recommendations that can be identified by the decision apparatus  42  as analysis result provided to the decision-making apparatus  42 . The production adjustment recommendations comprise the recommended value of at least one of the plurality of production parameters. 
     For another example, when the AI computing architecture  231  comprises a parameter measuring process for status monitoring, the AI computing architecture  231  measures the values of a plurality of production parameters. The control apparatus  41  can obtain the parameter measurement result output from the AI computing architecture  231 , wherein the parameter measurement result indicates whether the values of the plurality of production parameters are normal; and convert the parameter measurement result into a status monitoring report that can be identified by the decision-making apparatus  42  as a report provided to the decision-making apparatus  42  as the analysis result, wherein the status monitoring report comprises information used to indicate whether the status of the production equipment is normal. 
     For yet another example, when the AI computing architecture  231  comprises a fault diagnosis process, the AI computing architecture  231  uses the values of a plurality of production parameters to perform fault diagnosis. The control apparatus  41  can obtain the fault diagnosis result output from the AI computing architecture  231 , wherein the fault diagnosis result comprises information about one component of the production equipment; and convert the fault diagnosis result into a fault diagnosis report that can be identified by the decision-making apparatus  42  as a report provided to the decision-making apparatus  42  as the analysis result, wherein the fault diagnosis report comprises information about the component. 
     The conversion of AI computation results to analysis results in the control apparatus  41  can simplify the implementation of the computing component  23 . Moreover, since the control process of the control apparatus  41  can be adjusted through configuration and programming, the adjustment is relatively easy when the control apparatus  41  processes the output data from the AI computing architecture. 
     In some embodiments, when the parameters of the computing component need to be configured, the control apparatus  41  may obtain the configuration parameters of a first computing component  23  from preset third configuration information, wherein the configuration parameters comprise a plurality of attribute values of the AI computing architecture used by the first computing component  23 ; send the configuration parameters to the first computing component  23 , so that the first computing component  23  sets the values of a plurality of attribute values of the AI computing architecture to the plurality of attribute values. The plurality of attribute values of the AI computing architecture may comprise structural attribute values, or input and output parameters, etc. 
     In some embodiments, the training data of the AI computing architecture can also be obtained from the configuration information. For example, the control apparatus  41  may obtain training data from preset fourth configuration information, and send the training data to the first computing component  23 , so that the first computing component  23  can use the training data to train the AI computing architecture in the first computing component  23 . 
     The control method of each embodiment can be implemented by the control apparatus  41  provided in the controller  40 .  FIG. 7  illustrates a control apparatus of the embodiments of the present application. As shown in  FIG. 7 , the control apparatus  41  may comprise: a production data acquiring unit  411 , a task sending unit  412 , and a result collecting unit  413 . 
     The production data acquiring unit  411  can acquire the production process data of the production equipment in the industrial automation system. The production process data comprises the values of a plurality of production parameters of the production equipment. 
     The task sending unit  412  can send the production process data to the first computing component  23  in the AI computing device connected to the field bus  60  through the field bus  60  of the industrial automation system. 
     The result collecting unit  413  can receive the analysis result sent by the AI computing device  20  through the field bus  60 , wherein the analysis result is obtained from analysis of the production process of the production equipment by the first computing component  23  using the production process data; and provide the analysis result to the decision-making apparatus in the controller  40  so that the decision-making apparatus generates control instructions for the production equipment. 
     In some embodiments, the control apparatus may further comprise a configuration unit  414 . The configuration unit  414  obtains the configuration parameters of the first computing component  23  from the preset third configuration information, wherein the configuration parameters comprise a plurality of attribute values of the AI computing architecture of the first computing component  23 ; and sends the configuration parameters to the first computing component  23 , so as to set a plurality of attribute values of the AI computing architecture to the plurality of attribute values. 
     In some embodiments, the control apparatus may further comprise a training unit  415 . The training unit  415  may obtain training data from preset fourth configuration information, and send the training data to the first computing component  23 , so that the first computing component  23  can use the training data to train the AI computing architecture. 
     The control method of the embodiment of the present application may also be implemented as software code. The software code may be stored in a computer readable storage medium. The software code may be machine-readable instructions that comply with the standards for industrial control programming languages (such as IEC 61131-3). In some embodiments, the software code may be read by the ES  30  from a remote storage device, a local storage device, or a removable storage device (such as a compact disc, flash memory, etc.), and then loaded to the controller  40  after configuration. The controller  40  will execute the code to implement the above control method. 
     In each embodiment, the ES  30  can provide the configuration information and control logic required to control the AI computing device  20  to the controller  40 , so as to enable the controller  40  to control the AI computing device  20 .  FIG. 8  illustrates an ES of the embodiments of the present application. As shown in  FIG. 5 , the ES  30  may comprise: a processor  32 , a memory  33 , and a communication device  34 . The communication device  34  is used to enable the ES  30  to communicate with other devices on the network. The memory  33  may comprise a management module  37 . 
     The management module  37  comprises an interface module  371 , a device configuration module  372 , and a control configuration module  373 . 
     The interface module  371  can provide device configuration interfaces. 
     The device configuration module  372  may receive device configuration information from a device configuration interface, wherein the device configuration information comprises the identifier of the AI computing device and the identifier information of at least one computing component  23  in the AI computing device  20 ; send the device configuration information to the controller  40  of the industrial automation system, so that the controller  40  uses the device configuration information to communicate with the AI computing device  20 . 
     The control configuration module  373  may obtain the control logic  31  corresponding to the AI computing device  20 , and receive control configuration information for the AI computing device  20  from a device configuration interface, wherein the control configuration information comprises the computation parameters of the first computing component  23  of the at least one computing component  23 ; load the control configuration information into the control logic  31 , and load the control logic  31  into the controller  40 . The control logic can enable the controller  40  to configure the first computing component  23 , send the production process data of the production equipment in the industrial automation network to the first computing component  23  for analysis, and obtain the analysis result fed back from the first computing component  23 . 
     The interface module  371 , the device configuration module  372 , and the control configuration module  373  can be implemented by machine-readable instructions. 
     In some embodiments, the memory  33  may also comprise an operating system  35  and a network communication component  36 . 
     In some embodiments, the device configuration module  372  may receive the channel information of the first computing component  23  from a device configuration interface, wherein the channel information comprises the identifier of one or more channels; send the channel information to the controller  40  so that the controller  40  can send the data to the first computing component  23  based on channel corresponding to the data type. 
     In some embodiments, the control configuration module  373  may receive architecture information of the first computing component  23  from a device configuration interface, wherein the architecture information comprises the components and connection methods of the AI computing architecture in the first computing component  23 , and load the architecture information as control configuration information to the control logic; receive the input parameters and output parameters of the AI computing architecture of the first computing component  23  from a device configuration interface, and load the input parameters and output parameters as control configuration information into the control logic; receive the training data of the first computing component  23  from a device configuration interface, and load the training data as control configuration information into the control logic. 
     The following is an example of the control of an industrial production process to facilitate understanding of the control mechanism of industrial automation systems in each embodiment. This is a simple example, while other embodiments may involve more equipment and production parameters.  FIG. 9  illustrates a production process of the embodiments of the present application. As shown in  FIG. 9 , the controller  40  controls the flow of raw materials through three servo valves K 1 , K 2 , and K 3 , controls the stirring speed through the motor speed V, and controls the temperature T through the set point of a thermostat. The output parameters of the production process are quality Q 1  and output Q 2  which can be measured by sensors. 
     In some embodiments, the controller  40  may send multiple sets of sample values of the production parameters K 1 , K 2 , K 3 , V, T, Q 1 , and Q 2  to the computing component  23  with the parameter optimization function, and configure K 1 , K 2 , K 3 , V, and T as the input parameters of the AI computing architecture and Q 1  and Q 2  as the output parameters of the AI computing architecture. The computing component  23  can train the AI computing architecture through a neural network to obtain the neural network model corresponding to the production process. After the training, the controller  40  sends the actually collected values of the production parameters K 1 , K 2 , K 3 , V, T, Q 1 , and Q 2  to the computing component  23 . Based on the model obtained through training, the AI computing architecture of the computing component  23  can obtain a set of recommended values for the production parameters K 1 , K 2 , K 3 , V, and T, whereby the quality Q 1  and output Q 2  can be optimized. 
     In some embodiments, the controller  40  may send multiple sets of sample values of the production parameters K 1 , K 2 , K 3 , V, T, Q 1 , and Q 2  to the computing component  23  with the parameter measuring function, and configure K 1 , K 2 , K 3 , V, and T as the input parameters of the AI computing architecture and Q 1  and Q 2  as the output parameters of the AI computing architecture. The computing component  23  can train the AI computing architecture through a neural network to obtain the neural network model corresponding to the production process. After the training, the controller  40  sends the actually collected values of the production parameters K 1 , K 2 , K 3 , V, T, Q 1 , and Q 2  to the computing component  23 . Based on the model obtained from the training, the AI computing architecture of the computing component  23  can obtain the estimated values of Q 1  and Q 2  corresponding to the actual values of K 1 , K 2 , K 3 , V, and T. When the actual values of Q 1  and Q 2  do not match the estimated values, a test result indicating abnormal parameter values will be output. 
     In some embodiments, the controller  40  may also send the values of the production parameters K 1 , K 2 , K 3 , V, T, Q 1 , and Q 2  collected during a fault to the computing component  23  with the fault diagnosis function. The AI computing architecture obtained by the computing component  23  through training can obtain the probability of failure of each actuator. If the probability of K 2  failure is the greatest, it can output a fault diagnosis result indicating failure of the K 2  servo valve. 
     It can be seen that, with the plug-and-play AI computing device  20  of the embodiments of the present application, closed-loop control with artificial intelligence can be conveniently implemented in existing industrial automation control systems, thereby improving production efficiency. 
     The above are only the preferred embodiments of the present application, and are not intended to limit the present application. Any modification, equivalent replacement and improvement made without departing from the motivation and principle of the present application shall be included in its scope.