An industrial network-based control system cloud-edge commissioning method

Abstract

The application discloses a kind of control system cloud edge commissioning methods based on industrial network, comprising: using the large bandwidth of 5G network, low latency and network slice capacity, unified management in cloud multiple control system commissioning tasks;Each physical control system is registered to cloud by the unique device identification that it is independently generated in its built-in lightweight debugging agent module;Planning debugging channel, allow multiple low-priority control system to share channel to improve resource efficiency, high real-time control system then monopolizes channel to guarantee performance;All commissioning data carry device identification transmission, each control system built-in debugging agent module strictly checks device identification, only handles the instruction that matches this system, realizes the on-demand acquisition of multiple control system state information, accurate distribution and collaborative diagnosis, significantly improve the concurrency capability of remote debugging, resource utilization and system reliability.And adapt to the remote maintenance, efficient commissioning and real-time response demand under the multi-device collaborative scenario in industrial automation.

Description

Technical Field

[0001] This invention relates to the field of industrial automation control technology, and specifically to a cloud-edge debugging method for control systems based on industrial networks. Background Technology

[0002] In industrial automation control systems, the control system, as the core control unit, is widely used in various control tasks in industrial settings, including logic operations, sequential control, counting, and timing. With the rapid advancement of Industry 4.0 and smart manufacturing, industrial settings are placing higher demands on the remoteness, intelligence, and real-time performance of control systems. While some solutions attempt to introduce remote access technology, 5G networks, with their network slicing capabilities, lay the communication foundation for building an efficient cloud-edge collaborative debugging system. Through 5G slicing, dedicated logical channels can be allocated for debugging operations, achieving isolation from production operations.

[0003] Traditional control system commissioning methods rely on on-site deployment and manual configuration, resulting in long commissioning cycles and low efficiency. This is especially true in multi-device collaboration and large-scale industrial systems, where slow response times, difficult system maintenance, and a lack of flexibility are more likely to occur. Existing remote commissioning methods are still largely based on traditional Ethernet or 4G networks, which suffer from bandwidth limitations, large latency fluctuations, and weak concurrency capabilities. More importantly, when a cloud platform needs to serve multiple edge control systems simultaneously, allocating independent communication channels to each system fails to fully utilize 5G networks, leading to resource waste. Therefore, current industrial control systems lack a cloud-edge commissioning mechanism for multiple real physical control systems. It is unclear whether multiple devices can share channels, and no secure routing and access control policies based on device identification have been established, resulting in the underutilization of 5G capabilities. Summary of the Invention

[0004] The purpose of this invention is to provide a cloud-edge debugging method for control systems based on industrial networks. By constructing a device registration mechanism and a device isolation connection system, combined with a differentiated channel resource configuration strategy, the method ensures the communication isolation and priority processing capability of multi-device debugging. At the same time, it introduces a built-in debugging agent module and a dynamic channel adjustment mechanism in the control system to ensure that debugging tasks are executed efficiently and reliably under limited network resources, thereby solving the problems mentioned in the background art.

[0005] The present invention provides the following technical solution: a cloud-edge debugging method for a control system based on an industrial network, comprising the following operational steps:

[0006] Step S1: Generate a globally unique device identifier using the communication module built into the edge control system, and upload key information to the cloud debugging platform for registration; key information includes: device identifier of the edge control system, device type, load status and network reachability.

[0007] Preferably, the globally unique device identifier is generated as a globally unique string or numeric ID that is bound to the MAC address, serial number, or IP address of the control system.

[0008] Preferably, the specific implementation process includes: after the edge control system starts, it sends a registration request to the cloud through the integrated communication module; generates a globally unique identifier based on the inherent hardware characteristics (MAC address / serial number) of the edge control system; and uploads key information such as device identifier, type, load status and network reachability to the cloud; the edge control system periodically sends heartbeat signals, and the cloud dynamically updates the list of online devices.

[0009] Step S2: The cloud platform receives the multi-device debugging request initiated by the user, parses the target device set, and generates a debugging instruction package carrying device identifiers.

[0010] Preferably, the specific implementation includes: identifying one or more target control systems selected by the user and extracting the device identifiers corresponding to the target control systems; binding the debugging operations with the device identifiers corresponding to the target control systems to form structured instructions; verifying whether the user has the authority to perform debugging operations on the target control systems; and allocating the debugging tasks to three priority levels—high, medium, and low—based on the urgency and real-time requirements of the debugging content.

[0011] Step S3: The target control system receives the debugging command, activates the built-in lightweight debugging agent module to verify the legality of the debugging command, and prepares the debugging environment for the target control system.

[0012] Preferably, the specific implementation includes: verifying the reliability and timeliness of the debugging command source; the debugging agent module built into the edge control system performs a single verification on the debugging task start command issued by the cloud, comparing whether the device identifier in the command is consistent with the inherent identifier of the target control system; if it is determined that they are inconsistent, the command is immediately discarded, a "device identifier mismatch" error code is returned to the cloud, and the current debugging activation process is terminated; if it is determined that they are consistent, the debugging service of the target control system is activated and data interaction is prepared; then the computing and communication resources in the target control system are evaluated to see if they meet the set requirements, and execution is suspended when the load exceeds the set threshold.

[0013] Step S4: Based on the real-time requirements of the debugging tasks corresponding to the debugging instructions, divide the debugging tasks into three priority levels: high, medium, and low, and plan the debugging logic channel through a high-bandwidth, low-latency communication network.

[0014] Preferably, the debugging logic channel includes: an exclusive channel for carrying high-priority debugging tasks; and a shared channel for carrying multiple medium- and low-priority debugging tasks, which are scheduled for transmission in priority order within the shared channel.

[0015] Preferably, the channel is dynamically adjusted, that is, when the load of the shared channel exceeds a set threshold, the system automatically migrates some debugging tasks to other channels or upgrades them to exclusive channels.

[0016] Step S5: The built-in debugging agent module of the control system uses the debugging data carrying the device identifier during transmission to perform device identifier verification.

[0017] Preferably, the format of the debugging data includes a triple: device identifier, variable address, and data value. The debugging agent verifies the device identifier based on this triple and only processes instructions that match the target control system.

[0018] Preferably, it also includes an access control mechanism: the cloud platform restricts the range of control system devices that it can operate based on user roles and authorization policies to prevent unauthorized access.

[0019] Step S6: The debug agent collects relevant debug data according to the instructions of the current debug session, compresses and identifies it according to the task priority, and uploads it to the cloud using the planned channel. The cloud routes the data to the corresponding virtual debug session according to the device identifier.

[0020] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0021] (1) This invention supports core functions such as remote variable monitoring, breakpoint control, parameter configuration and debugging context acquisition by constructing a debugging channel between the cloud and multiple edge control systems.

[0022] (2) In this invention, users can initiate multi-device debugging tasks in a unified manner on the cloud platform. Each edge control system completes autonomous registration and instruction processing through its built-in lightweight debugging agent module, and ensures accurate data verification based on device identification. The system intelligently plans network debugging channels according to the priority and real-time level of the debugging tasks, allowing multiple low-priority debugging tasks (such as periodic log collection) to share the same channel to improve resource efficiency, while high real-time debugging tasks (such as breakpoint triggering and real-time variable monitoring) are allocated exclusive channels to ensure performance.

[0023] (3) This invention constructs a device registration mechanism and a device isolation connection system, combined with a differentiated channel resource configuration strategy, to ensure the communication isolation and priority processing capabilities of multi-device debugging. At the same time, it introduces a built-in debugging agent module and a dynamic channel adjustment mechanism in the control system to ensure that the debugging task is executed efficiently and reliably under limited network resources. This not only significantly improves the debugging concurrency capability and resource utilization in multi-control system scenarios, but also provides reliable support for the remote debugging of large-scale physical equipment clusters in intelligent manufacturing. Attached Figure Description

[0024] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0025] Figure 1 This is a flowchart illustrating the steps of the cloud-edge debugging method provided in this embodiment of the invention;

[0026] Figure 2 This is a schematic diagram of the architecture layout of the cloud-edge debugging method provided in an embodiment of the present invention;

[0027] Figure 3 This is a schematic diagram of the registration and discovery process of the edge control system provided in an embodiment of the present invention;

[0028] Figure 4 This is a flowchart illustrating the debugging task distribution mechanism provided in an embodiment of the present invention;

[0029] Figure 5 This is a flowchart illustrating the trial agent activation process provided in an embodiment of the present invention;

[0030] Figure 6 This is a flowchart illustrating the debugging channel planning process provided in an embodiment of the present invention;

[0031] Figure 7 This is a schematic diagram of the debugging connection establishment process provided in an embodiment of the present invention;

[0032] Figure 8 This is a schematic diagram of the debugging data acquisition and uploading process provided in an embodiment of the present invention. Detailed Implementation

[0033] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] Combination Figures 1-2 As shown, the present invention provides a technical solution: a cloud-edge debugging method for a control system based on an industrial network, comprising the following operational steps:

[0035] Step S1: Registration and discovery of edge control systems.

[0036] In this embodiment, when each edge control system of the present invention starts up or accesses the network, it autonomously generates a globally unique device identifier through its built-in communication module, and actively reports its own type, communication capabilities, debugging support information and network status to the cloud debugging platform to complete the registration.

[0037] For example, in combination Figure 3 As shown, the specific implementation process includes:

[0038] Step S101: Device self-registration: After the edge control system is started, it sends a registration request to the cloud through the integrated communication module;

[0039] Step S102: Device Identifier Generation: Generate a globally unique identifier based on the inherent hardware characteristics (MAC address / serial number) of the edge control system;

[0040] Step S103: Registration Information Reporting: Upload key information such as device identification, type, load status, and network reachability to the cloud;

[0041] Step S104: Status Heartbeat Maintenance: The edge control system periodically sends heartbeat signals, and the cloud dynamically updates the list of online devices.

[0042] Step S2: Debug task distribution.

[0043] In this embodiment, the cloud platform receives a multi-device debugging request initiated by the user, parses the target device set, and generates a debugging instruction package carrying device identifiers. The system simultaneously verifies user permissions to ensure that the user can only operate the control system within their authorized scope, preventing unauthorized access.

[0044] For example, in combination Figure 4 As shown, the specific implementation process includes:

[0045] Step S201: Multi-target task parsing: The system identifies one or more target control systems selected by the user and extracts the corresponding device identifiers;

[0046] Step S202: Instruction encapsulation: Bind the debugging operation with the target device identifier to form a structured instruction;

[0047] Step S203: Permission Verification: Verify whether the user has permission to perform debugging operations on the target control system;

[0048] Step S204: Task Priority Marking: Based on the urgency and real-time requirements of the debugging content, assign tasks to three priority levels: high, medium, and low. Real-time requirements are determined by the type of debugging operation. For example, interactive operations such as breakpoint triggering and single-step execution are high-priority tasks, periodic variable monitoring is a medium-priority task, and log collection is a low-priority task.

[0049] Step S3: Activate the debug agent module.

[0050] In this embodiment, after the target control system receives the debugging command, its built-in lightweight debugging agent module is activated to verify the legality of the command and prepare the debugging environment for the control system.

[0051] For example, in combination Figure 5 As shown, the specific implementation steps include:

[0052] Step S301: Instruction legality verification: Check the credibility and timeliness of the instruction source to prevent illegal or expired instructions from being executed;

[0053] Step S302: Debugging task instruction verification: The debugging agent module built into the edge control system performs a single verification on the debugging task start instruction (including target device identifier, debugging operation type and priority mark) sent from the cloud: compare whether the device identifier in the instruction is consistent with the inherent identifier of this control system; if they are inconsistent, immediately discard the instruction, return the "device identifier mismatch" error code to the cloud, and terminate the current debugging activation process to avoid invalid occupation of system resources;

[0054] Step S303: Enable Debugging Interface: Activate the debugging service of this control system and prepare for data interaction;

[0055] Step S304: Resource occupancy assessment: Determine whether the computing and communication resources within the current control system are sufficient. If the load is too high, execution will be temporarily suspended.

[0056] Step S4: Debug channel planning.

[0057] In this embodiment, based on the real-time requirements of the debugging tasks, the system divides the tasks into three priority levels: high, medium, and low. It then requests logical channels with differentiated quality of service (QoS) through a high-bandwidth, low-latency communication network. High-priority debugging tasks are allocated dedicated channels; medium- and low-priority debugging tasks are merged into a shared channel and scheduled for transmission according to priority within the shared channel. Simultaneously, the system requests corresponding logical slices from the high-bandwidth, low-latency communication network based on task priority—high-priority tasks are associated with low-latency, high-reliability slices, while medium- and low-priority tasks are associated with regular slices, thus achieving logical isolation between debugging and production services.

[0058] For example, in combination Figure 6 As shown, the specific implementation process includes:

[0059] Step S401: Channel type definition: Preset two types of channel templates: exclusive and shared. Exclusive channels are used exclusively for high-priority debugging tasks, while shared channels are used to carry multiple medium- and low-priority debugging tasks.

[0060] Step S402: Grouping and scheduling debugging tasks: Group low-to-medium priority debugging tasks (which may come from different control systems) with similar priority levels into the same shared channel group, and schedule and transmit them within the shared channel according to the principle that medium priority is superior to low priority.

[0061] Step S403: Logical slice association: Based on the priority level of the debugging task, apply for a matching quality of service logical slice from the high-bandwidth low-latency communication network: high-priority tasks are associated with low-latency high-reliability slices, and medium- and low-priority tasks are associated with regular slices.

[0062] Step S404: Dynamic Channel Adjustment: The system monitors the load status of each channel in real time. When the load of a shared channel exceeds a preset level, it automatically migrates some debugging tasks to a backup channel or upgrades them to a dedicated channel to ensure the real-time performance and reliability of the tasks.

[0063] Step S5: Debug connection establishment.

[0064] In this embodiment, all debugging data carries the device identifier during transmission. The debugging agent module built into the control system strictly verifies the device identifier and only processes instructions that match the system. Non-target instructions are discarded, fundamentally prohibiting cross-device access.

[0065] For example, in combination Figure 7 As shown, the specific implementation process includes:

[0066] Step S501: Data format definition: Debugging data adopts a unified format, including device identifier, operation address and data content;

[0067] Step S502: Real-time verification of debug data stream. During the debug session, the built-in debug agent module performs real-time device identification verification on each transmitted debug data packet (including interactive data such as variable read / write requests, breakpoint status reports, and register snapshots). If the data packet identifier does not match the identifier of this control system, the data packet is immediately discarded, and the abnormal event (including timestamp, source IP, and device identifier) ​​is recorded in the local security log, realizing dynamic isolation and security auditing throughout the session.

[0068] Step S503: Encrypted transmission: Enable end-to-end encryption on the debugging channel to ensure communication security;

[0069] Step S504: Session Isolation: Each control system's debugging session is always independent, and the key is bound to the session's lifecycle to prevent cross-access.

[0070] Step S6: Debug data acquisition and uploading.

[0071] In this embodiment, the debugging agent collects relevant debugging data according to the instructions of the current debugging session (such as breakpoint settings and variable monitoring lists), prioritizing tasks. High-priority data is pushed in real time, while low-priority data is polled periodically. The collected data is compressed and labeled at the edge, and then uploaded to the cloud through an allocated channel. The cloud distributes the data to the corresponding user's debugging interface based on the device identifier, supporting concurrent debugging of different control system devices.

[0072] For example, in combination Figure 8 As shown, the specific implementation process includes:

[0073] Step S601: Priority-driven acquisition: Dynamically adjust the acquisition strategy according to the priority level of the debugging task: high-priority events (such as breakpoint triggering) are immediately triggered for acquisition, medium-priority tasks (such as variable monitoring) are acquired periodically, and low-priority tasks (such as logs) are polled as needed.

[0074] Step S602: Multi-control system commissioning data aggregation: On the shared commissioning channel, edge nodes encapsulate commissioning data from multiple control systems according to a predefined frame format. Each frame contains a unique device identifier (such as a serial number), a commissioning session ID, a timestamp, and a compressed commissioning payload. After receiving the data, the cloud accurately routes it to the corresponding user's commissioning session based on the frame header information, ensuring that commissioning data from multiple devices are not mixed up.

[0075] Step S603: Debug data preprocessing: Lightweight compression and standardized encapsulation of the acquired debug context (such as register snapshots and call stacks) to reduce transmission overhead;

[0076] Step S604: Data Upload and Confirmation: Upload data using a reliable transmission mechanism and wait for cloud confirmation to ensure complete delivery of information.

[0077] This invention constructs a debugging channel between the cloud and multiple edge control systems, supporting core functions such as remote variable monitoring, breakpoint control, parameter configuration, and debugging context acquisition. Through this invention, users can initiate multi-device debugging tasks uniformly on the cloud platform. At the edge, a lightweight agent completes device identification and command distribution, ensuring accurate data routing based on device identifiers. The system intelligently plans network debugging channels according to the priority and real-time level of the debugging tasks, allowing multiple low-priority debugging tasks (such as periodic log collection) to share the same channel to improve resource efficiency, while high-real-time debugging tasks (such as breakpoint triggering and real-time variable monitoring) are allocated exclusive channels to ensure performance. The device registration mechanism and device isolation connection system constructed in this invention, combined with a differentiated channel resource configuration strategy, ensure communication isolation and priority processing capabilities for multi-device debugging. Simultaneously, the introduction of edge agents and dynamic channel adjustment mechanisms ensures efficient and reliable execution of debugging tasks under limited network resources. This technology not only significantly improves the debugging concurrency capability and resource utilization in multi-control system scenarios but also provides reliable support for the remote debugging of large-scale physical equipment clusters in intelligent manufacturing.

[0078] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0079] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A cloud-edge debugging method for a control system based on an industrial network, characterized in that: The following steps are included: Step S1: Use the communication module built into the edge control system to generate a globally unique device identifier and upload the key information to the cloud debugging platform for registration; The key information includes: device identification, device type, load status, and network reachability of the edge control system; Step S2: The cloud platform receives the multi-device debugging request initiated by the user, parses the target device set, and generates a debugging instruction package carrying device identifiers; Step S3: The target control system receives the debugging command, activates the built-in lightweight debugging agent module to verify the legality of the debugging command, and prepares the debugging environment for the target control system. Step S4: Based on the real-time requirements of the debugging tasks corresponding to the debugging instructions, divide the debugging tasks into three priority levels: high, medium, and low, and plan the debugging logic channels through a high-bandwidth, low-latency communication network. Step S5: The built-in debugging agent module of the control system uses the debugging data carrying the device identifier during transmission to perform device identifier verification; Step S6: The debug agent collects relevant debug data according to the instructions of the current debug session, compresses and identifies it according to the task priority, and uploads it to the cloud using the planned channel. The cloud routes the data to the corresponding virtual debug session according to the device identifier.

2. The cloud-edge debugging method for a control system based on an industrial network according to claim 1, characterized in that: In step S1, a globally unique device identifier is generated as a globally unique string or numeric ID that is bound to the MAC address, serial number, or IP address of the control system.

3. The cloud-edge debugging method for a control system based on an industrial network according to claim 1, characterized in that: The implementation of step S2 includes: Identify one or more target control systems selected by the user and extract the device identifiers corresponding to the target control systems; The debugging operation is bound to the device identifier corresponding to the target control system to form a structured instruction; Verify whether the user has permission to perform debugging operations on the target control system; Based on the urgency and real-time requirements of the debugging content, debugging tasks are assigned to three priority levels: high, medium, and low.

4. The cloud-edge debugging method for a control system based on an industrial network according to claim 1, characterized in that: The real-time requirements of the debugging content are determined by the type of debugging operation, which includes interactive operation, periodic variable monitoring operation, and log collection operation. Interactive operations are high-priority debugging tasks, periodic variable monitoring operations are medium-priority debugging tasks, and log collection operations are low-priority debugging tasks.

5. The cloud-edge debugging method for a control system based on an industrial network according to claim 1, characterized in that: The implementation of step S3 includes: Verify the reliability and timeliness of the debugging commands; The built-in debugging agent module of the edge control system performs a single verification on the debugging task start command sent from the cloud, comparing whether the device identifier in the command is consistent with the inherent identifier of the target control system. If an inconsistency is detected, the instruction is immediately discarded, a "device identifier mismatch" error code is returned to the cloud, and the current debugging and activation process is terminated. When the results are consistent, the debugging service of the target control system is activated to prepare for data interaction. Reassess whether the computing and communication resources within the target control system meet the set requirements, and suspend execution when the load exceeds the set threshold.

6. The cloud-edge debugging method for a control system based on an industrial network according to claim 1, characterized in that: The cloud-based debugging task start command includes: target device identifier, debugging operation type, and priority flag.

7. The cloud-edge debugging method for a control system based on an industrial network according to claim 1, characterized in that: Step S4, debugging the logic channel, includes: Dedicated channels are used to carry high-priority debugging tasks; A shared channel is used to carry multiple low-to-medium priority debugging tasks, and the transmission is scheduled in order of priority within the shared channel.

8. The cloud-edge debugging method for a control system based on an industrial network according to claim 1, characterized in that: Step S4 also includes: dynamic channel adjustment. When the load of a shared channel exceeds a set threshold, the system automatically migrates some debugging tasks to other channels or upgrades them to exclusive channels.

9. The cloud-edge debugging method for a control system based on an industrial network according to claim 1, characterized in that: In step S5, the format of the debugging data includes a triplet: device identifier, variable address, and data value. The debugging agent verifies the device identifier based on this triplet and only processes instructions that match the target control system.

10. The cloud-edge debugging method for a control system based on an industrial network according to claim 1, characterized in that: Step S5 also includes an access control mechanism: the cloud platform restricts the range of control system devices that it can operate based on user roles and authorization policies to prevent unauthorized access.

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