Industrial automation system

Abstract

The embodiment of the invention provides an industrial automation system. The industrial automation system comprises an edge layer and an equipment layer, wherein the equipment layer comprises one or more pieces of industrial equipment deployed on a production line system, and sensors and actuators deployed on the one or more pieces of industrial equipment, the sensors are used for collecting monitoring data of the industrial equipment, and the actuators are used for executing control actions of the industrial equipment; the edge layer comprises a plurality of edge control units, and the edge control units acquire monitoring data of the one or more industrial devices and adjust actuators of the one or more industrial devices according to the monitoring data to execute control actions; wherein the plurality of edge control units are in communication connection through a self-organizing network or a point-to-point network.

Description

Technical Field

[0001] The present application relates to the field of industrial automation technology, and in particular to an industrial automation system. Background Art

[0002] Traditional industrial automation systems typically rely on a centralized control architecture, where a central controller monitors and coordinates all devices. While this architecture has been widely used for decades, as factories expand and the number of devices proliferates, centralized control faces a range of challenges, including increased latency, single points of failure, and limited scalability. Summary of the Invention

[0003] An embodiment of the present application provides an industrial automation system that combines distributed autonomous collaborative technology and edge computing, and can achieve efficient and reliable automation control in complex industrial environments.

[0004] To this end, the following technical solutions are provided in the embodiments of the present application:

[0005] An industrial automation system includes an edge layer and a device layer; wherein the device layer includes one or more industrial devices deployed on a production line system and sensors and actuators deployed on the one or more industrial devices, the sensors are used to collect monitoring data of the industrial devices, and the actuators are used to execute control actions of the industrial devices; the edge layer includes multiple edge control units, the edge control units are used to obtain monitoring data of the one or more industrial devices, and adjust the actuators of the one or more industrial devices to execute control actions based on the monitoring data; wherein the multiple edge control units are connected to each other through a self-organizing network or a point-to-point network.

[0006] In this embodiment, the distributed autonomous collaborative industrial automation system of the present application combines decentralized software and hardware design with edge computing technology, adopts a layered structure, and each layer communicates through the network to achieve distributed task collaboration and control of the entire system. Specifically, the overall system architecture adopts a distributed architecture, which consists of a device layer and an edge layer. The nodes at each layer have the ability to make autonomous decisions and execute tasks. They collaborate with each other through self-organizing networks (SON) or point-to-point P2P network communications. The control functions of the central controller are dispersed to edge nodes and device nodes to achieve complete decentralization.

[0007] Through the distributed control architecture, each device and node can dynamically join or exit the system according to demand without affecting the global task allocation and resource utilization. Each node performs autonomous task scheduling based on the current load and computing power, reducing the burden on the central unit and ensuring that the system can be flexibly expanded as the number of devices increases. Through a distributed task scheduling mechanism, the present application can dynamically allocate tasks and schedule resources between each industrial access point IAP and the edge control unit ECU based on the current device status and regional load, achieving true load balancing and ensuring maximum utilization of system resources. Under a decentralized architecture, the resource allocation and scheduling of each device can be more flexible and efficient. Through self-organizing networks (SON) and point-to-point communication (P2P), the present invention solves the problem of insufficient cross-regional collaboration between devices in the prior art. Devices can directly exchange data and collaborate on tasks through the P2P network, avoiding the intermediary role of the central node and achieving more efficient collaborative work and resource utilization.

[0008] As a possible implementation, the industrial automation system also includes a coordination management layer, which includes one or more industrial access points, which are signal-connected to multiple edge control units and are used to obtain operational data from the edge control units. Based on this operational data, the industrial access points adjust the control tasks and resource allocation of the edge control units to ensure continuous system operation. The operational data is cleaned and filtered based on the monitoring data; the control tasks indicate the specific operations for performing production tasks on the industrial equipment at the device layer; and the resource allocation indicates the dynamic allocation of computing resources, network bandwidth, or power to different industrial equipment based on the importance and priority of the production tasks.

[0009] As one possible implementation, the coordination management layer also includes a server. The server generates a predictive global task maintenance strategy based on the aggregated data uploaded by the one or more industrial access points, thereby coordinating the system's global tasks. The aggregated data includes operational data of one or more industrial devices, which can be used to indicate the real-time operating data and operating status of the industrial devices. The predictive global task maintenance strategy coordinates and optimizes scheduled maintenance operations for the industrial devices based on the real-time data and operating status of the industrial devices.

[0010] In this implementation, the coordination management layer consists of multiple IAPs (Industrial Access Points) and servers for global management and optimization. This application uses a distributed task scheduling mechanism to dynamically allocate tasks and schedule resources between each IAP and edge control unit (ECU) based on the current device status and regional load, achieving true load balancing and ensuring maximum utilization of system resources. Under a decentralized architecture, resource allocation and scheduling for each device can be more flexible and efficient.

[0011] As a possible implementation, the device layer also includes a programmable logic controller, which is deployed on the industrial equipment and is used to receive the monitoring data collected by the sensor and issue control instructions to the actuator based on the monitoring data, thereby controlling the actuator to perform control actions of the industrial equipment.

[0012] In this embodiment, a PLC (Programmable Logic Controller) is used as the core device to receive sensor data and control the actions of the actuators. The PLC in this application works in conjunction with a distributed edge control unit (ECU). The PLC performs preliminary processing of complex real-time control tasks and can upload some data to the edge control unit (ECU), which performs higher-level task scheduling and collaborative control.

[0013] As a possible implementation, the edge control unit includes a local task scheduling module, a data acquisition and preprocessing module, and an edge control module; wherein the data acquisition and preprocessing module is used to receive monitoring data acquired by sensors and perform data cleaning and / or compression to obtain preprocessed data; the edge control module issues control instructions to the device layer based on the preprocessed data; in response to detecting a failure of any one of the edge control units, the local task scheduling module configures another edge control unit to coordinate and determine a replacement node for redeploying the edge control unit that has failed, so as to achieve task collaboration among multiple edge control units.

[0014] As a possible implementation method, the edge control unit also includes a self-organizing network module, which is used to connect multiple edge control units so that the multiple edge control units self-organize into a distributed network with multi-point connections in the network, thereby realizing decentralized autonomous collaboration.

[0015] As a possible implementation, the industrial access point includes a global task scheduling module, a cross-region collaboration module, and a real-time monitoring module. The global task scheduling module is used to analyze operational data from multiple edge control units, optimize global tasks, and allocate resources, including: adjusting the execution order of multiple production tasks based on the importance, urgency, or impact on overall production of the production line system, and evenly distributing different production tasks based on the computing power of the edge control unit and the current workload; for each industrial access point, corresponding resources are allocated according to the priority of the production task, the required computing power, memory, and storage requirements; the cross-region collaboration module is responsible for cross-region collaboration with other industrial access points, coordinating the execution of multiple production tasks by sharing operational data; the cross-region collaboration indicates industrial access points in different geographical locations or different logical divisions; the real-time monitoring module is used to monitor and provide feedback on the operating status of the entire system.

[0016] As a possible implementation, the actuator includes at least one of a motor, a hydraulic system, a robotic arm, a fan, a speed regulating board, a relay, a switch controller, a current regulator, and a PWM modulator.

[0017] As a possible implementation, the sensor includes at least one of a temperature sensor, a pressure sensor, and an acceleration sensor.

[0018] In this embodiment, sensors, actuators, and industrial equipment, as production machines, are essential components of the production system and are the origin of device data generation.

[0019] As a possible implementation method, the industrial access point and the edge control unit are connected by signals using any one of 3G communication network, 4G communication network, 5G communication network and industrial Ethernet.

[0020] In this implementation, the ECU communicates with the upper-layer IAP via industrial Ethernet or 5G networks to report status and receive global task instructions, ensuring that devices in different areas work together.

[0021] As a possible implementation method, a distributed data synchronization protocol and a distributed consensus mechanism are deployed between each node in the collaborative management layer, the edge layer and the device layer.

[0022] In this implementation, the present application ensures data consistency and real-time synchronization among multiple nodes by introducing the Distributed Data Synchronization Protocol (DDSP) and the Distributed Consensus Mechanism (DCM). During task allocation and collaboration, each node can ensure data consistency through the consensus mechanism, avoiding data asynchrony issues caused by distributed networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 : shows a schematic diagram of the architecture of an industrial automation system provided by an embodiment of the present application;

[0024] Figure 2 : shows a schematic diagram of the software and hardware structure of an industrial automation system provided by an embodiment of the present application;

[0025] Figure 3 Shown in FIG is a schematic diagram of an application scenario of an industrial automation system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0027] In the description of the embodiments of the present application, words such as "exemplary," "for example," or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary," "for example," or "for example" in the embodiments of the present application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "for example," or "for example" is intended to present the relevant concepts in a concrete manner.

[0028] In the description of the embodiments of this application, the term "and / or" is simply a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent the following three situations: A exists alone, B exists alone, and A and B exist at the same time. In addition, unless otherwise specified, the term "plurality" means two or more. For example, "multiple systems" refers to two or more systems, and "multiple terminals" refers to two or more terminals.

[0029] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly identifying the technical features being referred to. Thus, features specified as "first" or "second" may explicitly or implicitly include one or more of such features. The terms "including," "comprising," "having," and their variations all mean "including but not limited to," unless otherwise specifically emphasized.

[0030] In the description of the embodiments of the present application, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it can be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict.

[0031] In the description of the embodiments of the present application, the terms "first\second\third, etc." or module A, module B, module C, etc. are only used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that the specific order or sequence can be interchanged where permitted so that the embodiments of the present application described here can be implemented in an order other than that illustrated or described here.

[0032] In the description of the embodiments of the present application, the numbers representing the steps, such as S101, S102, etc., do not necessarily mean that the steps must be executed in this manner. If permitted, the order of the previous and next steps can be interchanged, or they can be executed simultaneously.

[0033] Related terms involved in the embodiments of this application:

[0034] Edge Control Unit (ECU): An edge control unit (ECU) is a control device installed on a terminal device or sensor, responsible for local data collection, processing, and decision-making. The ECU possesses a certain level of computing power, enabling real-time monitoring and control of the device without relying on a central control unit. This enables devices at the edge node to have preliminary autonomous scheduling capabilities.

[0035] Industrial Access Point (IAP): An IAP is a regional coordination center responsible for multiple devices or edge control units (ECUs) within a system. IAPs not only act as data communication relays but also possess powerful computing capabilities, responsible for task distribution, device coordination, and load balancing within a local area. Multiple IAPs can communicate with each other, achieving decentralized control.

[0036] Decentralized core network (DCN): A decentralized core network consists of multiple interconnected industrial access points (IAPs). This distributed network enables data synchronization and task collaboration across devices and regions. Rather than relying on a single central control center, the DCN dynamically adjusts task allocation through distributed algorithms to achieve global optimization.

[0037] Self-organizing network (SON): A self-organizing network is a distributed network architecture in which network nodes automatically build and adjust the network topology based on their current state and the presence of neighboring nodes. SON technology enables connections between devices and control units to flexibly adapt to actual needs, eliminating single points of failure.

[0038] Distributed consensus mechanism (DCM): A distributed consensus mechanism is a protocol used to reach consensus among multiple distributed nodes. In this invention, each industrial access point (IAP) uses a distributed consensus algorithm to make collaborative decisions on task allocation and data processing, ensuring system consistency without central control.

[0039] Fault tolerance mechanism (FTM): FTM is a design that ensures that the system can continue to operate normally when some devices or nodes fail. Through the cooperation between IAP and ECU in the distributed network, the tasks of the failed node can be automatically taken over by the neighboring node, ensuring the continuity of task execution and data processing.

[0040] Peer-to-peer (P2P) networking is a decentralized communication architecture where each node can act as both a client and a server. All devices, ECUs, and IAPs are connected via a P2P network, allowing data to be transferred directly between nodes without requiring a central control unit. This improves system robustness and fault tolerance.

[0041] Decentralized architecture (DA): A decentralized architecture is a design approach that does not rely on a single central control unit. In this architecture, each node in the system has independent decision-making capabilities and can achieve autonomous execution and collaborative work without central coordination.

[0042] Distributed Data Synchronization Protocol (DDSP): A distributed data synchronization protocol is a communication protocol used to maintain data consistency and integrity across multiple devices or nodes. Through DDSP, IAPs and ECUs can synchronize device status, task progress, and real-time data, ensuring global coordination and task consistency.

[0043] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0044] Traditional industrial automation systems typically rely on a central controller to perform control tasks and manage devices. While this centralized architecture was common in industrial environments in the past, the limitations of centralized control systems have become increasingly apparent as industrial production scales and becomes more complex. For example, as the number of devices increases, the central controller may be unable to process the overwhelming amount of data, resulting in degraded system performance. Furthermore, a failure in the central controller can cause the entire factory production line to shut down.

[0045] To address the shortcomings of centralized control systems, the concept of edge computing has entered the industrial automation field. By decentralizing data processing and task distribution to "edge" nodes close to the devices, edge computing reduces latency and improves the system's real-time responsiveness. In these edge computing industrial control systems, edge nodes (such as local controllers or smart gateways) assume some data processing and task scheduling responsibilities, decentralizing some computation and decision-making to nodes close to the devices, thereby reducing latency. However, current edge computing systems still mostly rely on one or more central nodes for global control and management. Coordination between edge nodes often requires scheduling by a central controller, making it impossible to independently achieve cross-node collaborative tasks. In other words, while existing decentralized edge computing industrial control systems decentralize some tasks, they are still based on a centralized management framework, with a limited degree of decentralization. These systems are often limited to distributed hardware or simple task distribution, and still fail to achieve true decentralized control. In particular, they lack autonomous collaboration, dynamic decision-making, and fault tolerance among multiple nodes.

[0046] In order to meet these challenges, the embodiments of the present application propose a completely decentralized industrial automation system architecture, which combines distributed autonomous collaborative technology and edge computing, and can achieve efficient and reliable automated control in complex industrial environments. Specifically, the embodiments of the present application provide a distributed autonomous collaborative automation system, including an edge control unit (ECU) and an industrial access point (IAP), wherein the ECU is an autonomous control node with independent computing, control, storage, and communication capabilities; the IAP is responsible for managing multiple ECUs within its coverage area and collaborating with other IAPs through a distributed consensus protocol. The ECU and IAP can communicate directly through wireless or wired networks to form a distributed network without a central control console. The IAP has an edge computing unit that can locally process tasks or data uploaded by the ECU connected to it. The system ensures data consistency and task collaboration of all nodes in the system through a distributed architecture. When an ECU or IAP in the system fails, other nodes can automatically reallocate tasks and resources to ensure the continuous operation of the system.

[0047] Next, the architecture of an industrial automation system provided in an embodiment of the present application is introduced.

[0048] Figure 1 The diagram in FIG. 1 is a schematic diagram of an industrial automation system provided by an embodiment of the present application. Figure 1 As shown, the architecture of the industrial automation system includes one or more edge control units (ECUs) and one or more industrial access points (IAPs). The one or more edge control units (ECUs) and one or more industrial access points (IAPs) can be directly or indirectly connected to each other via a wired or wireless network for data transmission, although this embodiment of the present application is not limited thereto.

[0049] In one embodiment, the edge control unit ECU may include ECU-1, ECU-2, ECU-3 and ECU-4, etc. The number of edge control unit ECUs may be more or less, and the embodiments of the present application do not limit this. It should be noted that the edge control unit ECU is an autonomous control node of the system, which has independent computing, control, storage, and communication capabilities, can autonomously perform local tasks and communicate directly with other ECUs. The edge control unit ECU is able to complete part of the control tasks independently without relying on support from a remote server or the cloud. This autonomy enables the ECU to perform real-time control and decision-making on devices or systems in a local area, reducing dependence on the central server and improving the real-time and reliability of the system.

[0050] For example, ECU-1 includes a CPU, a storage unit, a communication module, and an I / O interface. The CPU in ECU-1 is responsible for executing control instructions and performing tasks such as data analysis and processing. This enables it to make decisions based on input data and execute necessary control actions. For example, the ECU might adjust a robot's trajectory or the speed of a production line based on real-time data provided by sensors.

[0051] The storage unit of the ECU can be a memory for temporarily storing data, such as a cache, dynamic random access memory (DRAM), or static random access memory (SRAM), and is used to store data that needs to be temporarily stored during the operation of the automation system for access or operation by the processor or other hardware. The storage unit can store data corresponding to the control tasks to be executed by the automation system, data corresponding to the control tasks that have not been executed in the interrupted control tasks, intermediate data during the processing process, processed data, and other cached data. In the embodiment of the present application, the storage unit can also store the operating system, control program, and system log.

[0052] I / O interfaces are key components for ECUs to interact with external devices, such as sensors and actuators. Sensors transmit environmental data to the ECU via input signals, while actuators execute control commands issued by the ECU via output signals. For example, a temperature sensor transmits temperature data to the ECU, which then adjusts the fan or heater status according to pre-set control logic.

[0053] The communication module supports multiple communication protocols for communicating with other ECUs and IAPs for real-time data exchange and collaborative work. The communication module can be a wired transmission interface, such as the Compute Express Link (CXL) interface, the Peripheral Component Interconnect Express (PCIe) interface, or the Universal Serial Bus (USB) interface. The communication interface can be a wireless transmission interface, such as a Bluetooth (BT) module, a Wireless Fidelity (WI-FI) module, or a wireless communication module. The communication module can use protocols such as Wi-Fi, Ethernet, Modbus, CAN, DDSP, SON, or a P2P network communication protocol to ensure that the ECU can effectively communicate and transmit data with other devices in the system.

[0054] ECUs can perform local tasks such as collecting sensor data, executing control algorithms, adjusting actuators, and recording system status. ECUs can also communicate with other ECUs to share data or coordinate work. For example, in an automated factory, multiple ECUs can collaborate to complete complex production tasks. Because ECUs have autonomous control capabilities, they can respond directly to local sensor data without waiting for feedback from remote servers, significantly reducing latency and improving the system's real-time responsiveness. Even if part of the communication network fails, the ECU can still independently execute control tasks, reducing the system's risk of single points of failure. ECUs support flexible communication with other devices or systems and can be expanded based on demand. For example, when a new device needs to be added, only a new ECU needs to be added and integrated with the other devices through a communication protocol.

[0055] It is understandable that each ECU automatically performs control tasks based on the status of local sensors or preset rules. If the task needs to span multiple ECUs or requires more resources, the ECU can request assistance through the IAP.

[0056] In one embodiment, the industrial access point IAP may include IAP-1, IAP-2, and IAP-3, etc. The number of industrial access points IAP may be more or less, and the embodiments of the present application are not limited to this. It should be noted that the IAP is responsible for managing multiple ECUs within its coverage, coordinating the communications of these ECUs, maintaining the stability of the local network, and having edge computing capabilities. One of the core functions of the IAP is to manage multiple ECUs within its coverage. This includes monitoring the operating status of the ECU, assigning tasks, scheduling resources, and ensuring the normal operation of each ECU. The IAP acts as a bridge connecting the ECU and a wider network (such as the cloud or upper-level system) to ensure that each ECU can run and communicate smoothly in the system. It is understandable that each IAP has the same permissions and functions and can independently execute and coordinate tasks.

[0057] For example, an IAP coordinates communications between multiple ECUs. This involves more than just physical connections; it also involves data routing, communication protocol conversion, and message dispatching. The IAP ensures that these ECUs can collaborate, execute cross-ECU tasks, and share data, ensuring that devices within the system can operate collaboratively and avoid conflicts and communication interference. Furthermore, the IAP must ensure the stability of the local network it manages (i.e., the network between its connected ECUs and other devices). This means ensuring network connectivity reliability, monitoring key parameters such as network load, latency, and bandwidth, automatically handling potential network failures, and minimizing communication interruptions and data loss. The IAP is not only a communication and management node; it can also possess computing capabilities, known as edge computing. The IAP can locally process tasks or data uploaded by the ECU, avoiding all data transmission to remote servers or the cloud. This reduces latency and improves real-time responsiveness. Furthermore, the IAP can collaborate with other IAPs as needed to share computing load and provide more efficient resource utilization.

[0058] Exemplarily, IAP-1 mainly includes a distributed database, a computing unit, and a communication module.

[0059] Among them, the communication module of the IAP supports wireless (such as Wi-Fi, LoRa, Zigbee, etc.) and wired (such as Ethernet, industrial Ethernet, etc.) communication protocols. Multiple IAPs are interconnected through wired or wireless networks through the communication module to form a distributed network, for example, a distributed network architecture such as a self-organizing network SON type. In addition, the communication module allows the IAP to establish a communication link with the connected ECU and other IAPs. For example, communicating with ECU: The IAP connects to multiple ECUs through wired or wireless communications to transmit control instructions, data, status information, etc. Communicating with other IAPs: In order to ensure that multiple IAPs in the system can work together, IAPs also need to communicate, exchange information, and coordinate work. In one embodiment, this communication can be achieved through a high-speed wired connection (such as Ethernet) to ensure the stability and low latency of data transmission. The communication module can use Wi-Fi, Ethernet, Modbus, CAN, DDSP, SON or P2P network communication protocols to achieve communication with ECUs, other IAPs and upper-level control systems (such as SCADA (supervisory control and data acquisition) and cloud platforms).

[0060] It should be noted that each ECU is connected to one or more IAPs via a wireless network or a wired network. Exemplarily, the above-mentioned wired network or wireless network can use standard communication technologies and / or protocols, including but not limited to any combination of local area network (LAN), metropolitan area network (MAN), wide area network (WAN), mobile, wired network, private network or virtual private network. In this way, the IAP coordinates the communication and task collaboration between them according to the needs and status of the ECUs. In this embodiment, data and instructions can be transmitted between ECUs through the IAP, or direct point-to-point communication can be carried out to achieve more efficient collaboration.

[0061] For example, communication connections are established between the IAP-1 communication module and the IAP-2 / IAP-3 communication module, the IAP-1 / 2 / 3 communication module and the ECU-1 / 2 / 3 communication module, and the ECU-1 communication module and the ECU-2 / 3 communication module via a self-organizing network (SON) or point-to-point communication (P2P). By introducing the self-organizing network (SON) and point-to-point (P2P) communication protocols, this embodiment achieves cross-node collaboration and direct communication between devices. Devices can autonomously coordinate tasks through the P2P network, avoiding communication bottlenecks that rely on central control. IAP improves system stability and reliability by coordinating ECU communication and task scheduling. Even if some IAPs fail, other IAPs can continue to operate normally.

[0062] The edge computing unit built into the IAP is able to process tasks or data uploaded from the ECU to which it is connected. For example, the ECU may upload real-time sensor data (temperature, pressure, speed, etc.), and the IAP can analyze, process, and make decisions locally, and then feed the results back to the ECU or other devices without sending all the data to the cloud. The IAP computing unit can also assign different computing tasks to connected ECUs or other IAPs based on its own computing power and network conditions. For example, when an ECU is unable to handle certain complex tasks, the IAP computing unit can help it perform these tasks or assign the tasks to other IAPs for processing. Through edge computing, the IAP is able to process some computing tasks locally, reducing the need to transmit large amounts of data to the cloud, thereby reducing communication delays and bandwidth pressure, and enabling the system to achieve faster response.

[0063] In one embodiment, the edge computing unit in the IAP can be composed of an embedded processor (such as an ARM processor or Intel's X86 processor) capable of supporting lightweight computing tasks. The IAP implements tasks scheduling, data processing, real-time decision-making, and other functions by deploying an embedded operating system (such as Linux or RTOS). Through edge computing, the IAP can process tasks locally, reducing the need to transmit data to the cloud or remote servers, thereby improving the system's real-time responsiveness.

[0064] The IAP contains a distributed database, primarily used to store data within the local network. This data includes real-time sensor data from various ECUs, system operating status, task logs, and other information. This distributed database ensures redundant data storage, improves data reliability, and enables data sharing between different IAPs. The distributed database within the IAP not only stores local data but also enables data sharing with other IAPs. In certain scenarios, when an IAP needs to know the status of another IAP or ECU, it can obtain relevant data from other IAPs. For example, if data from one ECU experiences an anomaly, the IAP can query other IAPs for status data from similar ECUs for diagnostic and coordinated processing.

[0065] In one embodiment, the database within the IAP uses a distributed database system (such as SQLite, InfluxDB, Cassandra, etc.) to handle large amounts of data storage and query requirements. The database on each IAP node can store data independently and can also share necessary information with other IAP nodes.

[0066] In this implementation, the IAP features intelligent task scheduling, deciding whether to execute tasks locally or forward them to other IAPs or the cloud based on real-time status and resource availability. This mechanism ensures efficient system operation by optimizing computing resource utilization and network bandwidth. The Industrial Access Point (IAP) serves as a core management and communication hub in industrial automation systems. Through powerful communication modules, edge computing units, a distributed database, and intelligent scheduling mechanisms, it coordinates and manages multiple ECUs, maintains local network stability, and improves system real-time performance and reliability. Through this distributed architecture, the IAP helps industrial systems achieve efficient and flexible operation and data processing capabilities.

[0067] In one embodiment, each ECU and IAP within the system also has dynamic decision-making capabilities. Specifically, during operation, each ECU and IAP within the system can autonomously determine the current task load and status and reallocate tasks and resources as needed to optimize system performance and respond to changing needs. Specifically, each ECU or IAP monitors its own workload (e.g., computing tasks, communication load, etc.) and resource usage (e.g., CPU utilization, memory usage, etc.). When a node's load is too high, it can assess whether to offload some tasks to other nodes to avoid system overload and performance bottlenecks. When a node's load is too high or its resources are insufficient, other nodes in the system will take over these tasks as needed. For example, if a task handled by an ECU is too complex or consumes too many resources, it can transfer the task to another ECU with a lighter load and more sufficient resources. This makes task allocation in the system more flexible and efficient. Thus, by designing a distributed task scheduling and resource allocation mechanism, each node dynamically allocates tasks based on its own load and status, improving system flexibility and resource utilization. In a decentralized architecture, task allocation can be autonomously adjusted based on the node's real-time status, avoiding resource waste and task overload.

[0068] Alternatively, when an ECU or IAP fails, other nodes within the system can automatically detect the problem and initiate fault-tolerance mechanisms, reallocating tasks and resources to ensure continued system operation. This ensures high system reliability, enabling the entire system to continue executing tasks stably even if some nodes fail. For example, each node (such as an ECU or IAP) can monitor the health of other nodes. By monitoring various operating parameters (such as CPU status, memory usage, and data transfer), the system can detect in real time whether a node is experiencing a failure or an anomaly. If a node fails to respond to tasks or exhibits an abnormal state, the system will immediately issue an alert. Furthermore, if an ECU fails, the system may automatically transfer its tasks to other healthy ECUs. This includes not only task migration but also data backup and dynamic adjustment of network topology. Furthermore, after a failure occurs, the system must quickly resume normal operation. This recovery process includes reinitializing the failed node, reloading tasks, and reallocating resources. This mechanism allows the system to quickly recover to a sustainable state after a node failure.

[0069] Therefore, the industrial automation system provided in the embodiment of the present application introduces a distributed autonomous collaboration mechanism, so that each device, industrial access point (IAP) and edge control unit (ECU) can independently complete data processing and task scheduling, eliminating dependence on the central controller, realizing true decentralized control, and avoiding the risk of single point failure.

[0070] It can be understood that the industrial automation system described in the embodiment of the present application is for the purpose of more clearly illustrating the technical solution of the embodiment of the present application, and does not constitute a limitation on the technical solution provided by the embodiment of the present application. Those skilled in the art will know that with the evolution of the system architecture and the emergence of new business scenarios, the technical solution provided in the embodiment of the present application is also applicable to similar technical problems.

[0071] To facilitate understanding of the technical solution of the present application, the software architecture of the industrial automation system of the embodiment of the present application is introduced in detail below.

[0072] Figure 2 The figure shows a schematic diagram of the hardware and software structure of an industrial automation system provided by an embodiment of the present application. Figure 2 As shown, the overall architecture of the industrial automation system adopts a distributed architecture, which combines decentralized software and hardware design with edge computing technology and adopts a layered structure. The layers communicate through the network, for example, by setting up a communication gateway to achieve distributed task collaboration and control of the entire system through the communication gateway. For example, the industrial automation system architecture based on distributed autonomous collaboration can be divided into a collaborative management layer, an edge layer, and a device layer from the perspective of software and hardware. Among them, each layer of the collaborative management layer, edge layer, and device layer has collaborative management nodes, edge nodes, and device nodes for performing its own functions. The nodes in each layer have the ability to make autonomous decisions and execute tasks, and collaborate with each other through self-organizing networks (SON) or P2P network communications. The central controller is dispersed to the edge nodes and device nodes to achieve complete decentralization.

[0073] For example, the device layer is the bottom layer, which is mainly composed of industrial equipment, PLC (programmable logic controller), sensors and actuators, and is responsible for on-site data acquisition and equipment control. The edge control layer is mainly composed of edge control units ECU, which have local processing, control and distributed collaboration functions. Optionally, the edge control unit ECU is deployed with a local task scheduling module, a data acquisition and preprocessing module, an edge control module and a self-organizing network module. The collaborative management layer is mainly composed of multiple industrial access points IAP and servers for global management and optimization. Optionally, the industrial access point IAP is deployed with a global task scheduling module, a cross-regional collaboration module and a real-time monitoring module.

[0074] In addition, in this embodiment, a distributed data synchronization protocol (DDSP) and a distributed consensus mechanism (DCM) are deployed between each node in each layer of the collaborative management layer, edge layer and device layer. Specifically, each node (IAP, ECU, etc.) implements data exchange through the DDSP protocol. Each node needs to maintain a local data copy and synchronize information with other nodes. For example, the edge control unit (ECU) obtains the real-time device status and synchronizes it to other nodes to ensure data consistency. When the device status changes, the relevant event will be triggered and broadcast in the system through the DDSP protocol. All relevant nodes receive the event information and make necessary responses to ensure the consistency of the system status. DDSP can help store all synchronized data in a central database or decentralized distributed storage to ensure consistency when sharing information between devices.

[0075] When the system allocates tasks, it uses a DCM protocol (such as Raft or Paxos) to ensure consensus among all participating IAP nodes. This ensures that all nodes make consistent decisions regarding task execution order, resource scheduling, and other aspects. When multiple nodes need to access shared data, the DCM protocol helps ensure that all nodes reach consensus on data access. For example, task scheduling decisions are made consistently across multiple IAP nodes to avoid conflicts and inconsistencies. The DCM protocol helps the system achieve fault tolerance. Even if some nodes fail, the system can still recover through the consensus algorithm, ensuring that task execution is not affected.

[0076] In this way, the Distributed Data Synchronization Protocol (DDSP) and the Distributed Consensus Mechanism (DCM) ensure data consistency and real-time synchronization across multiple nodes. During task allocation and collaboration, each node can ensure data consistency through the consensus mechanism, avoiding data asynchrony issues caused by distributed networks.

[0077] In one embodiment, the industrial equipment in the equipment layer, as hardware equipment, can be a production machine on each production line system in the factory. It is a necessary component of the production system and the origin of the equipment monitoring data. Therefore, various sensors and / or actuators are arranged on the industrial equipment. The sensors collect real-time monitoring data (such as position, pressure, load and temperature) and transmit it to the PLC. The PLC generates control execution instructions based on the real-time monitoring data and feeds them back to the actuator. The actuator performs various operations according to the PLC control execution instructions, that is, the actuator is used to execute the control actions required by the industrial equipment in accordance with the production tasks predetermined by the production line system, where the production tasks include production goals or production requirements. It should be noted that, here, "control action" refers to the physical operation or adjustment of the industrial equipment by the actuator according to the instruction or control signal. These control actions are the key to the realization of automated operation and task execution of industrial equipment on the production line in the equipment layer. Specifically, control actions can include the following types:

[0078] Position control: Actuators adjust the position of a device to accomplish some action. For example, in a robotic arm, actuators might use motors to adjust the position of the arm, enabling it to grasp, move, or place an object.

[0079] Speed Control: Controlling the speed or rotational velocity of a device, such as conveyor belt speed control or motor speed regulation. The actuator receives a control signal and adjusts the motor's speed to control the device's movement.

[0080] Control of environmental variables such as temperature, pressure, and flow: These variables regulate the operating environment of industrial equipment, such as by adjusting temperature, pressure, or flow through actuators such as valves, heaters, and coolers. For example, actuators in air conditioning systems control the flow or temperature of cooling air to maintain desired environmental conditions.

[0081] Switching operation: Actuators may start or stop certain devices to complete operations. For example, they can control the start and stop of a mechanical component, or open or close a valve or circuit switch.

[0082] Material handling control: Controlling actuators to operate equipment such as hoppers, grippers, and conveyors to distribute, transfer, or process materials. For example, actuators might control automated equipment to move or sort materials to a specific location.

[0083] For example, in an assembly robot on a production line, actuator control actions might include grasping parts, installing components, and tightening screws. In packaging equipment, actuators might be used to seal boxes, cut packaging materials, and adjust the speed of the packaging machine. Control actions are the specific operations performed by the actuator on industrial equipment. These actions are typically performed based on monitoring data collected by sensors or instructions from a higher-level PLC or edge control unit. Their purpose is to ensure that the equipment or system operates according to predetermined goals and requirements.

[0084] In this implementation, the PLC not only serves as the core device for receiving sensor data and controlling actuators, but also collaborates with distributed edge control units (ECUs). For example, the PLC performs preliminary processing of complex real-time control tasks while simultaneously uploading some data to the edge control unit ECU, which then performs higher-level task scheduling and collaborative control.

[0085] Specifically, in a control system, the PLC is responsible for acquiring monitoring data from various sensors (such as temperature, position, and pressure sensors) for industrial equipment. Based on this data, it controls actuators (such as motors and pneumatic devices) to execute control actions corresponding to production tasks. The PLC is responsible for real-time decision-making and control, and all sensor data and actuator instructions are processed directly by the PLC. For example, at a certain workstation on a production line, the PLC can control the movement of a robotic arm to ensure precise assembly operations. The PLC can also handle some low-level tasks, such as switch control and simple logical judgment. Furthermore, the PLC uploads some of the collected data (such as temperature, pressure, and workstation status) to distributed edge control units (ECUs). ECUs possess greater computing power and are responsible for high-level task scheduling, data analysis, and coordinated control. Specifically, the ECUs analyze monitoring data from multiple PLCs and clean and filter it to obtain operational data for industrial equipment. This operational data indicates the real-time operating data and status of the industrial equipment, enabling the edge control unit (ECU) to identify the overall operational status of the production line and make higher-level decisions. For example, the ECU can adjust the control tasks of production scheduling according to the working status of different workstations, optimize resource allocation, predict equipment failures and perform maintenance in advance.

[0086] It is understandable that in this embodiment, monitoring data refers to the original monitoring data of industrial equipment collected by sensors, which reflects the real-time status of industrial equipment. These data include various physical quantities such as temperature, pressure, speed, current, vibration, etc., which are used to describe the current situation of equipment and production processes. Operation data is the data after the monitoring data has been cleaned and filtered, and is used in the system to describe the real-time data and operating status of equipment, control units or the entire production system. Unlike monitoring data, operation data generally removes some irrelevant or noise data, performs necessary calculations (such as average, maximum, minimum, trend analysis, etc.), and converts it into indicators that can provide support for system control and decision-making. For example, operation data may reflect the operating efficiency, failure probability, load conditions, etc. of a certain equipment, rather than just a single sensor reading.

[0087] Furthermore, control tasks instruct specific production operations on industrial equipment at the device layer. These tasks, issued by the edge control unit (ECU) at the coordination management layer, direct the industrial equipment within the area to perform a specific control function. For example, a control task might be adjusting the speed of a production line, starting or stopping a piece of equipment, or adjusting temperature or pressure set points. Control tasks are adjusted based on operational data to optimize the operating status of industrial equipment, ensuring a stable and efficient production process.

[0088] Resource allocation refers to the reasonable allocation of limited resources in the system (such as computing power, storage space, bandwidth, operator time, energy, etc.) to different industrial equipment to ensure that the system can operate efficiently and stably. In the industrial automation system provided in this embodiment, resource allocation indicates the dynamic allocation of computing resources, network bandwidth or electricity to different industrial equipment based on the importance and priority of production tasks. For example, in the production process, some production tasks may require more computing resources to process complex control algorithms, while other production tasks may require fewer resources and only require simple monitoring and control. The goal of resource allocation is to ensure that each industrial equipment part in the production process can obtain the required resources while avoiding waste or shortage of resources to ensure the continuous operation of the production system. For example, if an industrial equipment has an abnormality, it may be necessary to dynamically adjust resource allocation to allocate more computing and network bandwidth to fault detection and repair, while reducing resource consumption of other industrial equipment.

[0089] In one embodiment, consider a production line with multiple workstations (such as welding, assembly, and packaging). Each workstation has a programmable logic controller (PLC) responsible for collecting sensor data and controlling actuators at that workstation. For example, at a welding station, the PLC receives data from a temperature sensor and adjusts the welding current in real time to ensure welding quality. Simultaneously, the PLC also collects other data from the welding process and uploads this data to the edge control unit (ECU).

[0090] The ECU receives monitoring data from the PLCs at multiple workstations and analyzes it. For example, the ECU can monitor the production status of all workstations, providing real-time insights into each workstation's progress and determining whether adjustments to the production sequence or resource allocation are necessary. If equipment at a workstation malfunctions and requires repair, the ECU can immediately adjust tasks, minimizing production line downtime. If a workstation is overloaded, the ECU can also reassign some tasks to less-loaded stations, ensuring a balanced load across the entire production line and improving production efficiency.

[0091] Imagine a PLC at a workstation fails and cannot continue normal operation. The ECU can promptly detect this problem and dispatch tasks to PLCs at other workstations, ensuring the production line is not affected. Furthermore, the ECU can analyze operating data from all workstations to identify potential equipment failures or production bottlenecks, and issue maintenance or adjustment recommendations in advance, thereby reducing production downtime and equipment loss.

[0092] In this distributed automation control system, the PLC and edge control unit (ECU) work together, each responsible for different tasks. The PLC handles real-time control and simple logical judgment, while the ECU handles data analysis, task scheduling, and overall coordinated control. This collaboration makes the entire production system more flexible and intelligent, and improves efficiency, fault tolerance, and scalability.

[0093] It is understandable that one edge control unit ECU can be connected to one or more device nodes, and one or more device nodes connected to the same edge control unit ECU constitute an area. Figure 2 As shown in Figure 1, the device layer has five device nodes. The first three device nodes are connected to an edge control unit (ECU) in the edge layer, and the remaining two device nodes are connected to another edge control unit (ECU) in the edge layer. The five device nodes are divided into two areas: Area 1 and Area 2.

[0094] The edge control unit ECU is located at the edge layer and is connected to the PLC and sensors at the device layer. It is responsible for data processing, preliminary decision-making, task allocation, and collaboration between devices. The ECU communicates directly with other edge control unit ECUs or industrial access points (IAPs) through a self-organizing network (SON) to achieve true distributed control. For example, the connection between the edge control unit ECU and the PLC: Through the industrial network, the ECU receives monitoring data from the PLC and sends control instructions to it. The connection between the edge control unit ECU and other ECUs: The ECU establishes a full mesh connection through the SON self-organizing network module to achieve decentralized collaboration and dynamically optimize control tasks. The connection between the edge control unit ECU and the IAP: Through industrial Ethernet or 5G network, the ECU communicates with the upper-layer IAP to report status and receive global task instructions.

[0095] For example, the main hardware of the edge control unit ECU includes: processor: high-performance processor supports real-time computing and distributed task processing; communication interface: industrial Ethernet, 5G interface, supports communication with PLC, IAP and other ECUs; storage module: used to store local data and control logic. For details, please refer to the above Figure 1 The description will not be repeated here.

[0096] In one embodiment, the data acquisition and preprocessing module divided by the edge control unit ECU from a software perspective can receive sensor data from the PLC and perform preprocessing, such as data cleaning and compression. The edge control module is responsible for real-time control of the device layer and makes control decisions based on the preprocessed data. The self-organizing network (SON) module allows multiple ECUs to self-organize into a distributed network with multi-point connections in the network to achieve decentralized autonomous collaboration. Each ECU can dynamically select the optimal path for communication and task allocation based on the load and resource status. Local task scheduling module: Multiple ECUs can share data and control instructions through the self-organizing network (SON) module to collaboratively complete complex tasks without relying on a central controller.

[0097] Consider a scenario where an automated production line has multiple workstations. Each workstation collects production data through sensors and performs basic control via a PLC. To improve the efficiency and flexibility of the entire production system, edge control units (ECUs) and self-organizing network (SON) technologies are employed to collaboratively complete production tasks.

[0098] Specifically, the PLC at each workstation is responsible for acquiring real-time monitoring data from connected sensors. For example, at a workstation, the PLC might acquire temperature data from a temperature sensor and pressure data from a pressure sensor. The data acquisition and preprocessing module of the edge control unit (ECU) receives this sensor data from the PLC and then performs further preprocessing. These preprocessing operations may include:

[0099] Data cleaning: Remove outliers or noisy data to ensure the quality of the data received. For example, there may be extreme temperature readings due to sensor failure, and the ECU can filter this data.

[0100] Data compression: compresses large amounts of real-time data into smaller data packets for more efficient transmission and reduces network burden.

[0101] The preprocessed data is then sent to the edge control module, which is responsible for real-time control of various industrial equipment operations. For example, the edge control unit can determine whether to activate the cooling system or adjust the position of the robotic arm based on temperature and pressure data. Based on this processed data and real-time feedback, the edge control module makes rapid control decisions. This enables the ECU to intelligently adjust the equipment's operating state based on current operating conditions without waiting for instructions from the central server.

[0102] Decentralized network architecture: Utilizing the Self-Organizing Network (SON) module, multiple ECUs can self-organize to form a distributed network without a central controller. For example, ECUs on multiple workstations can automatically detect each other and form an interconnected network. This eliminates the need for a single central controller, allowing each ECU to independently complete its tasks while also collaborating with other ECUs.

[0103] Dynamic path selection and task allocation: Each ECU in the network can select the optimal communication path based on its load and resource availability. For example, when a particular ECU's computing resources are limited, it can delegate some tasks to other ECUs to ensure continued progress. Communication paths within the network can also be dynamically adjusted based on network status to improve data transmission efficiency.

[0104] The local task scheduling module enables task collaboration. Specifically, multiple ECUs can share data and control instructions through the SON module. For example, if the ECU at a certain workstation requires additional material input, it can send a request to ECUs at other workstations to coordinate the scheduling of production resources and achieve load balancing on the production line. Because the entire control system utilizes a distributed architecture, each ECU can independently schedule local tasks. Even if an ECU fails, the other ECUs can continue to operate and collaboratively complete the entire production task through the network, without relying on a central controller. This decentralized design enhances the system's robustness and flexibility.

[0105] Specific examples:

[0106] On an automated automobile assembly line, each workstation's ECU controls the equipment at that station. Imagine a temperature sensor at one assembly station detects a temperature that's too high, exceeding a safe range. The data acquisition and preprocessing module cleans the temperature data received by the ECU from the PLC, confirms the temperature is indeed too high, and compresses it before sending it to other ECUs.

[0107] The edge control module is responsible for real-time control of the equipment layer, combining preprocessed data to make control decisions. For example, based on detection data, the ECU decides to activate the cooling device and adjust the equipment's operating speed, and transmits this decision to the ECU at the relevant workstation.

[0108] Self-organizing network module: During the production process, ECUs at multiple workstations form a self-organizing network through the SON module. If the processing power of an ECU at a workstation is limited, it can offload some data analysis tasks to other ECUs with lighter loads, ensuring efficient system operation.

[0109] Local task scheduling module: If the ECU at a certain station needs more raw materials or components, it can request assistance from the ECUs at other stations to ensure that the tasks on the production line can proceed smoothly.

[0110] Through this collaborative working mechanism, the entire production system achieves efficient distributed control without relying on a central controller, which enhances the system's flexibility, scalability and fault tolerance.

[0111] The Industrial Access Point (IAP) of the system management layer is responsible for coordinating the work of multiple ECUs, performing regional task scheduling and cross-regional collaboration. Through the IAP, the collaborative management layer can achieve global optimization. Main hardware components: Processor: supports large-scale data processing and global task scheduling. Communication interface: an interface for communicating with ECUs, other IAPs, and the cloud. For example, the IAP connects to multiple ECUs through the SON network to coordinate tasks and receive device status feedback. IAPs are connected to each other through industrial Ethernet or 5G networks to ensure that devices in different regions work together. The IAP uploads data to the cloud server and receives global optimization strategies from the cloud. Please refer to the above description here and will not repeat it here.

[0112] In one embodiment, the global task scheduling module, separated from the software modules of the industrial access point (IAP), analyzes operational data from each ECU, optimizes global tasks, and allocates resources. The cross-region collaboration module is responsible for cross-region collaboration with other IAPs, coordinating the execution of multiple production tasks by sharing operational data.

[0113] Specifically, optimizing global tasks involves rationally adjusting and scheduling production tasks within the production line system based on operational data provided by multiple edge control units to maximize overall efficiency. For example, optimizing global tasks can include adjusting the execution order of multiple production tasks within the production line system based on their importance, urgency, or impact on overall production. For example, some tasks may be maintenance tasks for critical equipment, which have a high priority and should be executed first.

[0114] At the same time, different production tasks are evenly distributed based on the computing power of the edge control unit and the current workload to avoid overloading of industrial equipment in certain areas or idle industrial equipment in certain areas, thereby improving the overall operating efficiency of the system.

[0115] Alternatively, depending on the nature of the production task, a larger production task can be split into smaller tasks and executed on multiple units respectively, or multiple smaller tasks can be combined for processing to reduce communication overhead and improve processing efficiency.

[0116] Resource allocation primarily refers to how to rationally arrange computing, storage, bandwidth, and other resources to ensure efficient production task execution. Optionally, resource allocation can be optimized through real-time monitoring of system resource usage. If resources in a particular area or device are limited, resource allocation can be dynamically adjusted to ensure sufficient resources for critical tasks. For each industrial access point, resources are allocated based on the production task's priority, required computing power, memory, and storage requirements. For example, tasks requiring large-scale data processing can be allocated more computing resources. This ensures that data transmission bandwidth and latency are minimized to avoid inefficient task execution due to network congestion or insufficient resources.

[0117] Among them, cross-regional collaboration indicates industrial access points divided by different geographical locations or different logics. Specifically, geographical location areas: different factories, workshops, and production lines may be located in different locations, and these locations may be called different "areas." For example, Factory A, Factory B, Workshop 1, Workshop 2, etc., each factory or workshop can be considered a "region." System functional area: In some possible scenarios, areas can also refer to different functional parts of the system. For example, one area may focus on equipment monitoring, while another area focuses on data analysis and storage.

[0118] Cross-regional collaboration means that industrial access points in different regions cooperate with each other to coordinate the flow of resources, tasks, and data to achieve efficient operation and optimization of the entire system. For example, an access point in Factory A may need to obtain data or share resources with an access point in Factory B.

[0119] The real-time monitoring module is used to monitor and provide feedback on the operating status of the entire system. A key component of the industrial access point in the system, the real-time monitoring module is used to continuously track the operating status of the entire industrial system and provide feedback on any anomalies.

[0120] Specifically, the real-time monitoring module uses IoT (Internet of Things) sensor technology to collect and transmit various monitoring data from industrial automation systems. Sensors can monitor the operating status of industrial equipment, edge control units, and industrial access points in real time, such as temperature, pressure, vibration, speed, and current. Through the IoT, the data collected by the sensors is transmitted in real time to the industrial access points for further processing and analysis.

[0121] For example, in an industrial production environment, temperature sensors can monitor the operating temperature of machinery, and vibration sensors can detect abnormal vibrations in industrial equipment. Through IoT technology, this data is transmitted in real time to industrial access points for analysis to determine whether the equipment is in normal working order.

[0122] In industrial automation, real-time monitoring modules can leverage industrial protocols for data transmission. For example, Modbus and OPC facilitate data exchange between industrial equipment and industrial automation systems. It's important to note that Modbus is an open protocol for communication between electronic devices, commonly used in industrial control systems, supporting real-time acquisition and feedback of device data. The OPC protocol is primarily used in industrial automation and manufacturing systems, supporting data exchange and sharing between different devices and software.

[0123] For example, through the Modbus protocol, the real-time monitoring module can obtain real-time data from sensors, such as the status of the production line, the start and stop status of the equipment, etc.

[0124] In one possible implementation, the real-time monitoring module implements data collection and real-time analysis through RTU (Remote Terminal Unit) and SCADA (Supervisory Control and Data Acquisition). In industrial automation, RTU and SCADA are key technologies for achieving real-time monitoring and feedback. RTU is responsible for collecting on-site equipment data and transmitting it to the SCADA system. The SCADA system is responsible for centralized management and real-time analysis of data, providing real-time feedback to operators. It should be noted that RTU (Remote Terminal Unit): RTU is a device used to collect on-site equipment status information and transmit the collected data to a central monitoring system (such as SCADA). SCADA (Supervisory Control and Data Acquisition): The SCADA system is used to centrally monitor the operating status of industrial systems, display the operating status of each device in real time, and provide feedback and alarms as needed. For example, in a water treatment plant or power system, the SCADA system monitors the operating status of various types of equipment (such as water pumps and transformers) through RTU devices, and automatically generates feedback or alarm information based on the equipment status.

[0125] The specific implementation method may include the following steps:

[0126] Data collection

[0127] Collecting real-time monitoring data from sensors on industrial equipment: The real-time monitoring module collects data from various industrial access points and industrial equipment. This data can include equipment operating status, production line progress, and environmental sensor readings.

[0128] Data Analysis

[0129] Real-time analysis and processing: The real-time monitoring module needs to analyze the collected data in real time and use algorithms (such as data filtering, predictive models, machine learning, etc.) to evaluate the health status of the system and detect whether there are faults, performance degradation or potential problems.

[0130] Anomaly detection: By setting thresholds or using adaptive algorithms, the system can detect anomalies in system operation, such as excessively high equipment temperature, reduced production efficiency, and abnormal sensor data.

[0131] Feedback mechanism

[0132] Real-time feedback: Once an anomaly or potential problem is detected, the monitoring module immediately alerts the operator or other system components. For example, a fault warning may be displayed on the operator's interface, or an emergency shutdown may be automatically triggered.

[0133] Decision support: The real-time monitoring system can also support the decision-making of the industrial access point's superiors. For example, the industrial access point may automatically adjust production line tasks or dispatch equipment for urgent maintenance based on real-time data feedback.

[0134] Automated responses: In some cases, the system can take automated actions directly based on feedback, such as automatically adjusting equipment parameters, starting backup equipment, or adjusting task priorities.

[0135] Through such a monitoring and feedback mechanism, industrial access points can identify and respond to problems in a timely manner, ensuring the stability and efficiency of the overall production process.

[0136] Consider a large manufacturing plant with multiple zones, each containing multiple devices and workstations. Each workstation's equipment is managed by an edge control unit (ECU). To achieve efficient resource allocation, task scheduling, and cross-zone collaboration, the factory deploys industrial access points (IAPs) to coordinate the operation of the entire production system.

[0137] The Industrial Access Point (IAP) is responsible for coordinating and scheduling the work of ECUs in each area. The global task scheduling module optimizes production tasks by analyzing data from various ECUs and dynamically allocates resources based on task requirements. For example, after receiving the working status, equipment load and production progress data from different ECUs, the IAP can calculate which tasks have the highest priority and allocate resources (such as machines, labor, and raw materials) to the priority tasks. For example, suppose there is an assembly station on the production line that needs more robotic arms to increase production speed, and the robotic arm of another station fails, the IAP will allocate the available robotic arm resources from other stations to this assembly station to ensure the efficient operation of the entire production line.

[0138] IAPs in different areas are connected via Industrial Ethernet or 5G networks, enabling cross-regional collaboration. The cross-regional collaboration module is responsible for communicating and collaborating with other IAPs, coordinating task execution and data sharing between areas. Although the equipment in each area is managed by its own ECU, the collaboration of IAPs enables seamless production processes across the entire factory. For example, suppose the production process in one area encounters a bottleneck, resulting in slower production progress in that area. In this case, the IAP will work with IAPs in other areas to allocate some tasks or resources to other areas to help alleviate the bottleneck and ensure that the production goals of the entire factory are successfully achieved.

[0139] The real-time monitoring module is responsible for monitoring and providing feedback on the status of the entire production system. This includes the production status of each area, equipment operation status, and task execution progress. The IAP can adjust and optimize production based on this real-time data to ensure smooth system operation. For example, if the real-time monitoring module detects that the temperature in a certain area is too high, which may cause equipment damage, the IAP will use real-time data to determine the root cause of the problem (such as a cooling system failure) and respond promptly, such as dispatching maintenance personnel or adjusting production tasks to prevent equipment damage from affecting production progress.

[0140] Industrial access points (IAPs) play a crucial role in this factory. Through the global task scheduling module, IAPs analyze real-time data from various regions to optimize global tasks and allocate resources. Through the cross-region collaboration module, IAPs in different regions collaborate to ensure smooth task execution. And through the real-time monitoring module, IAPs track and monitor system operating status in real time, predicting and preventing potential problems and ensuring the stability and efficiency of the production system.

[0141] In another scenario, consider two production areas within a factory: one for assembly and the other for testing. An equipment failure on the assembly line causes a production delay in that area. The IAP, through the cross-area collaboration module, promptly learns of this situation and reallocates some resources from the test line to the assembly line to compensate for the production shortfall. Simultaneously, the real-time monitoring module detects that the assembly line's temperature is too high, prompting the IAP to promptly adjust the cooling system to prevent equipment damage. The IAP coordinates task scheduling, resource allocation, and status monitoring throughout the system, ensuring smooth production flow.

[0142] The central server (DC Server) is responsible for data storage and high-level task optimization. Specifically, the server performs global coordination and generates a predictive maintenance strategy based on the aggregated data uploaded by one or more industrial access points. The aggregated data includes operating data of one or more industrial devices, which can be used to indicate the real-time data and operating status of the industrial devices. The predictive maintenance strategy coordinates and optimizes the scheduled maintenance operations of the industrial devices based on the real-time data and operating status of the industrial devices. In this embodiment, a "global optimization module" is added to the existing server. The global optimization module analyzes the data aggregated by the IAP and optimizes the global task strategy.

[0143] For example, a server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud business libraries, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, security services, and big data and artificial intelligence platforms.

[0144] It's important to note that a global maintenance strategy refers to a comprehensive maintenance plan and measures across devices, units, and regions within a large industrial automation system to ensure the stability, efficiency, and continuous operation of the entire system. It considers all components of the system and their interrelationships, focusing on the overall system's operational status rather than targeting local issues in a single device or region.

[0145] Specifically, the global maintenance strategy has the following meanings:

[0146] Cross-system and cross-regional considerations: A global maintenance strategy addresses the entire system, not just single devices or local issues. It comprehensively considers the operating status of all components (e.g., equipment, edge control units, production lines, etc.). This strategy, based on a holistic analysis of all equipment and system status, ensures coordinated operation of all components and prevents local issues from escalating into system-wide failures.

[0147] Predictive maintenance: By aggregating data from multiple industrial access points, the server can analyze the data, using predictive models to proactively identify potential failures or issues and generate maintenance strategies. This strategy differs from traditional scheduled maintenance in that it takes proactive action based on forecasts of equipment and system operating trends. For example, if a piece of equipment is about to fail, the server can use predictive models to identify it early and develop a repair or replacement plan, thus preventing the impact of a sudden equipment failure on the entire production process.

[0148] Dynamic Adjustment and Optimization: Global maintenance strategies are dynamic, adjusting periodically or as needed based on real-time data and operational status of each component in the system. This strategy flexibly responds to equipment failures, changes in production tasks, or external environmental impacts, optimizing the allocation of maintenance resources. For example, if certain equipment fails due to aging or unusually high malfunctions, the system may automatically adjust its strategy to prioritize maintenance of these devices to avoid impacting the entire production line.

[0149] Global Task and Resource Coordination: Global maintenance strategies also involve the overall coordination of tasks and resources. The server aggregates data and analyzes the priorities and resource requirements of all tasks in the system to optimally schedule maintenance work. For example, certain critical equipment requires maintenance during specific time periods to minimize disruption to production. Through global strategies, maintenance work can be completed without disrupting other production tasks, ensuring efficient operation of the entire system.

[0150] Multi-level execution plans: Global maintenance strategies typically develop detailed maintenance plans based on the needs of different levels (e.g., equipment, production line, factory, etc.). Based on the aggregated data, the server may generate a global strategy for maintenance, from specific equipment-level operations to the entire production line, including which equipment requires maintenance priority, when maintenance should be performed, and how to allocate manpower, material resources, and time.

[0151] A global maintenance strategy is a comprehensive plan that coordinates and optimizes maintenance activities across an entire industrial system. By analyzing operational data from multiple devices and systems and leveraging the concept of predictive maintenance, it proactively plans and adjusts maintenance tasks to ensure efficient and stable system operation and minimize the impact of unexpected failures on production.

[0152] In this way, the industrial automation system provided by the embodiment of the present application introduces a decentralized self-organizing network and a distributed consensus mechanism, so that each device, edge control unit (ECU) and industrial access point (IAP) can autonomously process tasks, allocate resources and work together without relying on a central controller, thereby achieving high reliability of the entire system and eliminating the risk of single point failure. Through a distributed control architecture, each device and node can dynamically join or exit the system according to demand without affecting the global task allocation and resource utilization. Each node performs autonomous task scheduling based on the current load and computing power, reducing the burden on the central unit and ensuring that the system can be flexibly expanded as the number of devices increases. Through a distributed task scheduling mechanism, each IAP and ECU can perform dynamic task allocation and resource scheduling based on the current device status and regional load, achieving true load balancing and ensuring maximum utilization of system resources. Under a decentralized architecture, the resource allocation and scheduling of each device can be more flexible and efficient. Through a self-organizing network (SON) and point-to-point communication (P2P), the present invention solves the problem of insufficient cross-regional collaboration between devices in the prior art. Devices can directly exchange data and collaborate on tasks through the P2P network, avoiding the intermediary role of the central node and achieving more efficient collaborative work and resource utilization. The embodiment of the present application introduces the Distributed Data Synchronization Protocol (DDSP) and the Distributed Consensus Mechanism (DCM) to ensure data consistency and real-time synchronization among multiple nodes. During the task allocation and collaboration process, each node can ensure data consistency through the consensus mechanism, avoiding data asynchrony problems caused by the distributed network.

[0153] The embodiments of the present application fundamentally solve the technical problems of existing industrial automation systems in terms of reliability, scalability, task scheduling, equipment collaboration and data consistency through an innovative distributed autonomous collaborative architecture design.

[0154] The above is an introduction to the hardware and software architecture of the industrial automation system provided by the embodiments of this application. Based on the above, the technical solution of this application is described in detail below using specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0155] Example 1

[0156] Figure 3 FIG. 1 is a schematic diagram of an application scenario of an industrial automation system provided by an embodiment of the present application. Figure 3 As shown:

[0157] System architecture and deployment:

[0158] Device layer

[0159] On each production line, multiple sensors and actuators are installed, including:

[0160] Sensors: such as temperature sensors, pressure sensors, acceleration sensors, etc., to monitor the operating status of the equipment in real time.

[0161] Actuators: Such as motors, hydraulic systems, robotic arms, etc., perform operations.

[0162] PLC (Programmable Logic Controller): Connected to sensors and actuators, responsible for data acquisition and real-time control of equipment.

[0163] Edge Control Layer

[0164] Each production unit is equipped with an ECU (Edge Control Unit), which is located at each key node of the production line. ECU functions:

[0165] Data preprocessing: Obtain data from PLC and perform preliminary filtering and anomaly detection.

[0166] Edge control: Local real-time decision-making, such as automatically cooling the device when the temperature is too high, to avoid over-reliance on central system instructions.

[0167] Self-Organizing Network (SON) module: This allows multiple ECUs at different locations on the production line to communicate with each other, coordinate actions, and optimize workflows. For example, if a piece of equipment in a production unit fails, other ECUs can collaborate to complete the task, avoiding production interruptions.

[0168] Fault prediction and maintenance module: Combines sensor data to monitor and predict equipment health in real time.

[0169] Collaborative management

[0170] Deploy multiple IAPs (Industrial Access Points) throughout the factory’s production areas:

[0171] The IAP receives data from multiple ECUs and performs scheduling across production units. For example, the IAP can dynamically adjust the workload of a production unit based on production demand, or automatically redirect workflow to other lines if a problem occurs on a production line.

[0172] IAP communicates with DC to provide long-term data storage, analysis, and system optimization capabilities.

[0173] Specific control process

[0174] Production line startup and initial setup:

[0175] When the production line starts, each ECU obtains the initial equipment status from the PLC and communicates with adjacent ECUs to form a self-organizing network.

[0176] IAP receives production tasks from the central system, breaks down the tasks into different ECUs, and sends instructions to each production unit.

[0177] Distributed collaborative production control:

[0178] During the production process, sensors monitor the equipment status in real time, such as temperature and pressure, and the data is transmitted to the PLC and further to the ECU.

[0179] The ECU makes local control decisions based on preset control logic. For example, if the temperature of a device exceeds the preset range during the production process, the ECU will immediately adjust the device's operating parameters and even suspend operations for self-maintenance if necessary.

[0180] If the ECU of a production unit detects a device failure, it can communicate with other ECUs through the SON module to distribute the workload to other units, ensuring that the overall operation of the production line is not affected.

[0181] Collaborative management and optimization:

[0182] IAP performs overall scheduling and optimization based on the operating data of different production units. For example, if a piece of equipment is overloaded, IAP can instruct other production units to increase output to ensure global balance.

[0183] When the fault prediction and maintenance module detects a potential equipment failure, it will send an alert to the IAP and cloud systems in advance to initiate the predictive maintenance process.

[0184] Recovery of self-organizing networks:

[0185] If a network failure or communication anomaly occurs in an ECU, the SON module can automatically adjust the network topology through other ECUs to ensure that the entire production line will not be shut down due to the failure of a single node.

[0186] System operation effects and innovations

[0187] Decentralized control and collaborative optimization: Through the combination of ECU and SON, production line control and management no longer rely on a central control room, providing greater flexibility and fault tolerance. Production tasks can be automatically distributed across different production units, enabling distributed, autonomous, and collaborative control.

[0188] Improved response speed and system fault tolerance: Compared to traditional systems that rely on central control, ECUs make local, real-time decisions through edge computing, significantly reducing response time. If a device fails, other ECUs can automatically take over, ensuring uninterrupted production.

[0189] This implementation demonstrates the application of a distributed autonomous collaborative industrial automation system in a smart factory production line. Through the collaboration of ECUs, IAPs, and SON, distributed collaborative optimization is achieved across the device layer, edge control layer, and collaborative management layer. This system offers enhanced flexibility, fault tolerance, and responsiveness, enabling decentralized autonomous control and intelligent optimization in a variety of industrial scenarios.

[0190] For example, in one scenario, the above industrial automation system can be applied to the automated control of complex manufacturing processes:

[0191] Scenario Overview: In an automobile manufacturing plant, the production line requires an efficient and stable automated control system to ensure the coordination and continuity of each process. Traditional centralized control systems often suffer from single-point failures that can bring the entire production line to a standstill. This distributed autonomous collaborative industrial automation system completely eliminates this risk.

[0192] Implementation plan:

[0193] Production line layout and system architecture:

[0194] Production line: This production line consists of multiple workstations, including welding, assembly, painting, quality inspection and other processes. Each process has its own specific machines and sensors.

[0195] Edge Control Unit (ECU) Deployment: Each workstation is equipped with an ECU responsible for controlling the machines and sensors at that workstation. For example, the ECU for the welding process controls the welding robot and welding temperature sensor, while the ECU for the assembly process controls the robotic arm and position sensor. These ECUs operate independently and can autonomously perform tasks specific to the corresponding process, such as temperature regulation, robotic arm motion control, and position calibration.

[0196] Industrial Access Point (IAP) deployment: IAPs are deployed at regular intervals along the production line. Each IAP covers several adjacent workstations and coordinates data exchange and task collaboration among them. Equipped with edge computing capabilities, IAPs can process data uploaded by ECUs in real time and perform local optimization adjustments based on the overall production line situation.

[0197] Task execution and collaboration mechanism:

[0198] Autonomous task execution:

[0199] When a welding robot starts working, its ECU detects the welding temperature and welding position through sensors and automatically adjusts the welding parameters to ensure welding quality.

[0200] If the welding process is completed and the assembly instructions need to be passed to the ECU of the next process, the ECU of the welding process will pass the relevant data and control instructions to the ECU of the assembly process through IAP.

[0201] Dynamic task scheduling:

[0202] If a robot arm at a workstation fails, the ECU at that workstation will upload the fault information to the IAP. The IAP will immediately coordinate with a nearby backup robot arm, whose ECU will take over the workstation's tasks, ensuring uninterrupted production lines.

[0203] After the fault is recovered, IAP can reallocate tasks based on the current production status and production plan to restore the optimal production process.

[0204] Production optimization and fault tolerance:

[0205] Production Optimization: IAP uses edge computing to analyze the workload and production efficiency of each workstation in real time, dynamically adjusting production line operating parameters. For example, if it detects that the temperature of a welding process deviates from the optimal range, the IAP will instruct the welding ECU to make adjustments to ensure welding quality.

[0206] Fault-tolerance mechanism: When an ECU or IAP fails, the system immediately selects a new IAP or ECU to take over the task through a distributed consensus mechanism. Other ECUs and IAPs complete task transfer and fault recovery without central control intervention, ensuring continuous and stable operation of the production line.

[0207] For example, in one scenario, the above industrial automation system can be applied to distributed data collection and analysis:

[0208] Scenario Overview:

[0209] In a widely distributed industrial park, real-time data collection and analysis are crucial for production decision-making. The distributed autonomous collaborative industrial automation system of this invention uses distributed networks and edge computing to ensure that data is processed locally and shared in a timely manner, achieving efficient data collection and analysis.

[0210] Implementation plan:

[0211] Industrial park layout and system architecture

[0212] Park layout: The industrial park contains multiple dispersed production workshops and storage areas. Each area has several monitoring points for environmental monitoring, equipment status monitoring, etc.

[0213] Edge Control Unit (ECU) Deployment: Each monitoring point is equipped with an ECU responsible for local environmental data collection and equipment status monitoring. For example, temperature and humidity sensors, machine vibration sensors, and energy consumption sensors within the workshop all collect data through the ECU. The ECU performs preliminary processing on the collected data, such as filtering out abnormal data, converting data formats, and compressing data.

[0214] Industrial Access Point (IAP) deployment: Multiple IAPs are deployed in each workshop. Each IAP manages several ECUs and is responsible for data aggregation and preliminary analysis. The IAP stores data in a local distributed database and synchronizes it with other IAPs or a central server (if available) as needed.

[0215] Data collection and processing mechanism

[0216] Autonomous data collection: When sensor data shows anomalies (e.g., excessive temperature or abnormal machine vibration), the ECU immediately processes the data and transmits it to the IAP. The IAP further analyzes the data, determining whether the anomaly meets alarm criteria and deciding whether to take action (e.g., adjusting the machine's operating status or initiating a maintenance request).

[0217] Data collaboration and sharing: IAPs in different workshops are connected via wireless networks, forming a distributed data processing network. After processing local data, an IAP in one workshop can share its analysis results with IAPs in other workshops. If an IAP in one workshop requires more data to support decision-making, it can obtain relevant data from IAPs in other workshops through the distributed database. For example, an IAP in the warehouse can obtain real-time production data from IAPs in the production workshop to optimize warehouse management.

[0218] Data optimization and fault tolerance:

[0219] Data Optimization: IAP uses edge computing to conduct in-depth analysis of real-time data and identify potential optimization opportunities. For example, IAP can identify abnormal energy consumption of certain devices and promptly adjust their operating parameters to reduce energy consumption. Distributed data storage and processing can also reduce bandwidth consumption and improve data transmission efficiency.

[0220] Fault-tolerance mechanism: If an IAP fails, the ECU it manages will automatically connect to a nearby IAP to continue uploading data and receiving commands. Once the failed IAP recovers, the system will automatically synchronize data and tasks back to the original IAP, resuming normal operation.

[0221] The implementation examples of this invention achieve decentralized management of smart factory production lines through distributed autonomous collaborative control, breaking through the limitations of existing technologies. The following are the main improvements and corresponding beneficial effects of this invention compared with existing technologies:

[0222] Improvement 1: Decentralized distributed control architecture: The system of the present invention has no central control console. All control and decision-making are completed independently by the ECU and IAP, eliminating the risk of single point failure.

[0223] Traditional industrial automation systems rely on a central control room for centralized management. The system's control and decision-making power is highly centralized, resulting in slow response speeds and the system is prone to crashes due to failures in the central controller or network bottlenecks.

[0224] Improvements of the present invention: The present invention adopts distributed autonomous collaborative control, utilizes ECU edge computing and self-organizing network (SON), to achieve independent control and collaborative operation of each production unit. Beneficial effects brought about:

[0225] 1. Improved response speed: Since control decisions are processed locally by the ECU, dependence on the central system is reduced, and equipment failures or adjustments can be responded to quickly.

[0226] 2. Improve fault tolerance: Even if a problem occurs in the central controller, ECUs can still work together through SON to ensure that the production line is not interrupted.

[0227] Improvement 2: Distributed communication and collaboration enabled by self-organizing networks (SON)

[0228] Existing systems rely on fixed network topologies and centralized communication architectures. If a node in the communication network fails, it may cause system failure or large-scale communication interruption. The present invention uses self-organizing network (SON) technology to allow multiple ECUs to automatically form a distributed network. Each ECU in the system can dynamically adjust the network path to achieve full mesh connection and avoid single point failure. Through the autonomous collaborative mechanism, the system can flexibly respond to changes in the industrial environment and has a high degree of scalability. New ECUs or IAPs can be added at any time as needed. Beneficial effects:

[0229] 1. Enhanced system stability: Through the dynamic network adjustment of the SON module, the failure of a node in the communication network will not affect the normal operation of other nodes, and the system has high fault tolerance.

[0230] 2. Strong scalability: When new equipment or new production units are added to the system, SON can automatically adjust the network topology to achieve rapid deployment and expansion.

[0231] Improvement point 3: Local decision-making and collaboration capabilities of the edge control layer

[0232] Under a centralized control architecture, all control decisions rely on a central controller, and the operations of lower-level devices need to wait for instruction transmission and cannot be adjusted autonomously based on local conditions. In this invention, each ECU is equipped with an edge control module that can make local decisions based on field data and collaborate with other ECUs through SON to complete complex tasks. For example, when a problem occurs with local equipment on a production line, the ECU can automatically adjust the production process based on the actual situation. The system uses a distributed consensus protocol and fault-tolerant mechanism to ensure that even in the event of a local node failure, the system can still maintain normal operation, bringing beneficial effects:

[0233] 1. Improve production efficiency: Local decision-making can respond to emergencies on the production line more quickly and accurately, avoiding the delay of waiting for central instructions.

[0234] 2. Reduce production interruptions: When a problem occurs in one part of the production line, other ECUs can quickly coordinate and adjust production tasks to avoid the entire production line from stopping.

[0235] Improvement point 4: IAP cross-regional collaboration and global optimization capabilities

[0236] In traditional systems, each production unit is managed relatively independently, lacking cross-regional collaborative management and global optimization capabilities. This can easily lead to uneven resource allocation and overall low efficiency. The IAP in this invention integrates data from multiple ECUs to take charge of global task scheduling and cross-regional collaborative operations. It can dynamically adjust the task load of different production units to achieve the optimal allocation of production resources across the entire factory. Beneficial effects brought about:

[0237] 1. Optimize resource allocation: IAP can monitor the status of each production unit in real time, dynamically adjust the task load of each unit, achieve global resource optimization, and avoid equipment idleness or overload.

[0238] 2. Improve overall efficiency: IAP can flexibly allocate production tasks to different production units through cross-regional scheduling, thereby improving the production efficiency of the entire factory.

[0239] By deploying edge computing units in IAP, the system can perform processing close to the data source, improve real-time response capabilities, and reduce dependence on cloud resources.

[0240] Finally, it should be noted that the above embodiments are merely illustrative of the technical solutions of the present application. Those skilled in the art should understand that, although the present application has been described in detail with reference to the aforementioned embodiments, the technical solutions described in the aforementioned embodiments may be modified or some of the technical features thereof may be replaced with equivalents. However, such modifications or replacements do not deviate from the spirit and scope of the technical solutions in the various embodiments of the present application.

Claims

1. An industrial automation system, characterized in that: Includes edge layer and device layer; among them, The device layer includes one or more industrial devices deployed on the production line system and sensors and actuators deployed on the one or more industrial devices, wherein the sensors are used to collect monitoring data of the industrial devices, and the actuators are used to execute control actions of the industrial devices; The edge layer includes multiple edge control units, which are used to obtain monitoring data of the one or more industrial devices and adjust the actuators of the one or more industrial devices according to the monitoring data to perform control actions; wherein, the multiple edge control units are connected to each other through a self-organizing network or a point-to-point network.

2. The system according to claim 1, wherein: The industrial automation system also includes a coordination management layer, which includes one or more industrial access points, which are connected to multiple edge control units and are used to obtain operating data of the edge control units and adjust the control tasks and resource allocation of the edge control units based on the operating data; wherein the operating data is data obtained after cleaning and filtering the monitoring data; the control task indicates the specific operation of performing production tasks on the industrial equipment in the equipment layer; and the resource allocation indicates the dynamic allocation of computing resources, network bandwidth or power to different industrial equipment based on the importance and priority of the production task.

3. The system according to claim 1, wherein: The coordination management layer also includes a server, which generates a predictive maintenance strategy based on the aggregated data uploaded by the one or more industrial access points; wherein the aggregated data includes operating data of one or more industrial devices, and the operating data can be used to indicate real-time data and operating status of the industrial devices; the predictive maintenance strategy coordinates and optimizes the scheduled maintenance operations of the industrial devices based on the real-time data and operating status of the industrial devices.

4. The system according to any one of claims 1 to 3, characterized in that: The edge control unit includes a local task scheduling module, a data acquisition and preprocessing module and an edge control module; wherein, The data acquisition and preprocessing module is used to receive the monitoring data acquired by the sensor and perform data cleaning and / or compression to obtain preprocessed data; The edge control module sends a control instruction to the device layer based on the pre-processed data; In response to detecting a failure of any one of the edge control units, the local task scheduling module configures another edge control unit to coordinate and determine a replacement node for redeploying the failed edge control unit to achieve task collaboration among multiple edge control units.

5. The system according to claim 4, characterized in that The edge control unit further includes a self-organizing network module, which is used to connect multiple edge control units so that the multiple edge control units are self-organized into a distributed network with multiple points of connection in the network.

6. The system according to any one of claims 2 to 5, characterized in that: The industrial access point includes a global task scheduling module, a cross-regional collaboration module and a real-time monitoring module; wherein, The global task scheduling module is used to analyze the operating data from multiple edge control units, optimize global tasks and allocate resources, including: adjusting the execution order of multiple production tasks based on the importance, urgency or impact on overall production of the production line system, and evenly distributing different production tasks based on the computing power of the edge control unit and the current workload; for each industrial access point, allocating corresponding resources according to the priority of the production task, the required computing power, memory, and storage requirements; The cross-region collaboration module is responsible for cross-region collaboration with other industrial access points, coordinating the execution of multiple production tasks by sharing operation data; wherein, cross-region collaboration indicates industrial access points in different geographical locations or different logical divisions; The real-time monitoring module is used to monitor and provide feedback on the operating status of the entire system.

7. The system according to any one of claims 1 to 6, characterized in that: The actuator includes at least one of a motor, a hydraulic system, a mechanical arm, a fan, a speed regulating board, a relay, a switch controller, a current regulator and a PWM modulator.

8. The system according to any one of claims 1 to 7, characterized in that: The sensor includes at least one of a temperature sensor, a pressure sensor, and an acceleration sensor.

9. The system according to any one of claims 1 to 8, characterized in that: The industrial access point and the edge control unit are connected by signals using any one of 3G communication network, 4G communication network, 5G communication network and industrial Ethernet.

10. The system according to claim 9, characterized in that A distributed data synchronization protocol and a distributed consensus mechanism are deployed between each node in the collaborative management layer, edge layer and device layer.

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