Edge computing remote control PLC method, device, equipment and storage medium

Abstract

The present invention relates to the field of PLC control technology, and in particular to a method, device, equipment and storage medium for remotely controlling PLC by edge computing. The method comprises: determining the complexity, real-time requirements and network conditions of PLC control tasks on a production line; determining the optimal allocation ratio of edge and cloud computing resources based on the complexity, real-time requirements and network conditions of the PLC control tasks; dynamically adjusting the allocation of edge computing and cloud computing resources based on the optimal allocation ratio; processing the PLC control tasks based on the edge and cloud after resource allocation, and outputting control operations to the PLC. The present invention can effectively solve the challenges of edge computing in processing real-time requirements and complex tasks by dynamically adjusting the allocation of edge computing and cloud computing resources.

Description

Technical Field

[0001] The present invention relates to the field of PLC control technology, and in particular to a method, device, equipment and storage medium for remotely controlling a PLC using edge computing. Background Art

[0002] Edge computing is a technology that moves data processing and analysis functions from traditional centralized computing to a location close to the data source. In the industrial field, edge computing is widely used to improve real-time performance and response speed, especially in remote control PLC (programmable logic controller). One of its advantages is high real-time performance, because data can be processed locally on the edge device without having to wait to be sent to a remote server for processing. This is very important for industrial automation applications that require fast response. However, edge computing also has some limitations, especially when processing complex data structures or large data volumes, because edge devices usually have small storage and computing capabilities.

[0003] The above contents are only used to assist in understanding the technical solution of the present invention and do not constitute an admission that the above contents are prior art. Summary of the invention

[0004] The main purpose of the present invention is to provide a method, device, equipment and storage medium for edge computing remote control PLC, aiming to solve the technical problems of the existing edge computing remote control PLC in processing data structure, which is relatively simple and small in size.

[0005] To achieve the above object, the present invention provides a method for remotely controlling a PLC by edge computing, the method comprising:

[0006] Determine the complexity, real-time requirements and network conditions of the PLC control tasks on the production line. The complexity of the PLC control tasks includes the complexity of the control logic, the diversity of the control tasks and the complexity of the data processing. The real-time requirements include the response time requirements of the control tasks, the frequency requirements of the control tasks for data updates and the timeliness requirements of fault handling. The network conditions include network bandwidth and delay, network stability and data security requirements;

[0007] Determine the optimal allocation ratio of edge and cloud computing resources based on the complexity, real-time requirements and network conditions of the PLC control task;

[0008] Dynamically adjust the allocation of edge computing and cloud computing resources based on the optimal allocation ratio;

[0009] The PLC control task is processed on the edge and cloud after resource allocation, and the control operation is output to the PLC.

[0010] Optionally, determining the optimal allocation ratio of edge and cloud computing resources based on the complexity of the PLC control task, real-time requirements, and network conditions includes:

[0011] Splitting the PLC control task into multiple subtasks based on the complexity, real-time requirements and network conditions of the PLC control task;

[0012] The optimal allocation ratio of edge and cloud computing resources is determined based on the characteristics and resource requirements of the subtasks.

[0013] Optionally, determining the optimal allocation ratio of edge computing resources and cloud computing resources based on the characteristics and resource requirements of the subtasks includes:

[0014] Determine the corresponding subtasks that the PLC node and its expansion module can undertake based on the characteristics and resource requirements of the subtasks;

[0015] Determine the order of task execution based on the corresponding subtasks that the PLC node and its expansion module can undertake;

[0016] The optimal allocation ratio of edge and cloud computing resources is determined based on the task execution order.

[0017] Optionally, the PLC control task is split into multiple subtasks based on the complexity, real-time requirements and network conditions of the PLC control task, including:

[0018] Based on the complexity, real-time requirements and network conditions of the PLC control task, the PLC control task is divided into multiple functional modules according to function or logic;

[0019] Determine the dependencies and data flow relationships between functional modules;

[0020] A plurality of subtasks are determined based on the dependency relationship and data flow relationship between the functional modules.

[0021] Optionally, the edge end and the cloud end processing the PLC control task based on resource allocation and outputting the control operation to the PLC further include:

[0022] Obtaining historical data and real-time monitoring data of the PLC control task;

[0023] Predicting PLC system failures based on the historical data and real-time monitoring data, and generating automatic adjustment control instructions before failures occur;

[0024] Accordingly, the edge end and the cloud end after resource allocation process the PLC control task and output the control operation to the PLC, including:

[0025] Based on the automatic adjustment control instructions, the edge end after resource allocation and the cloud end process the PLC control task, and output the control operation to the PLC.

[0026] Optionally, the processing of the PLC control task by the edge end and the cloud end after the automatic adjustment of the control instruction and resource allocation, and outputting the control operation to the PLC, includes:

[0027] Based on the automatic adjustment control instruction, the edge end after resource allocation and the cloud end process the PLC control task, and generate a remote control instruction, the remote control instruction uses a 64-bit digital signature to encrypt and verify the data;

[0028] The remote control instruction is output to the PLC, wherein the remote control instruction includes a PLC control operation.

[0029] Optionally, the outputting of the remote control instruction to the PLC, the remote control instruction including before the PLC control operation, further includes:

[0030] Determining biometric data of a user and performing user identity verification based on the biometric data;

[0031] After the user identity verification is passed, the step of outputting the remote control instruction to the PLC is performed.

[0032] In addition, to achieve the above object, the present invention also provides an edge computing remote control PLC device, the edge computing remote control PLC device comprising:

[0033] Task determination module, used to determine the complexity, real-time requirements and network status of PLC control tasks on the production line;

[0034] A ratio calculation module, used to determine the optimal allocation ratio of edge and cloud computing resources based on the complexity, real-time requirements and network conditions of the PLC control task;

[0035] A resource allocation module, used to dynamically adjust the allocation of edge computing and cloud computing resources based on the optimal allocation ratio;

[0036] The control output module is used to process the PLC control task based on the edge and cloud after resource allocation, and output the control operation to the PLC.

[0037] In addition, to achieve the above-mentioned objectives, the present invention also provides an edge computing remote control PLC device, the device comprising: a memory, a processor, and an edge computing remote control PLC program stored in the memory and executable on the processor, the edge computing remote control PLC program being configured to implement the steps of the edge computing remote control PLC method as described in any one of the above texts.

[0038] In addition, to achieve the above-mentioned objectives, the present invention also provides a storage medium, on which an edge computing remote control PLC program is stored. When the edge computing remote control PLC program is executed by a processor, the steps of the edge computing remote control PLC method as described in any one of the above items are implemented.

[0039] The present invention provides a method for remotely controlling PLC by edge computing, the method comprising: determining the complexity, real-time requirements and network conditions of PLC control tasks on a production line; determining the optimal allocation ratio of edge and cloud computing resources based on the complexity, real-time requirements and network conditions of the PLC control tasks; dynamically adjusting the allocation of edge computing and cloud computing resources based on the optimal allocation ratio; processing the PLC control tasks based on the edge and cloud after resource allocation, and outputting control operations to the PLC. The present invention can effectively solve the challenges of edge computing in processing real-time requirements and complex tasks by dynamically adjusting the allocation of edge computing and cloud computing resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a schematic diagram of the device structure of the edge computing remote control PLC of the hardware operating environment involved in the embodiment of the present invention;

[0041] Figure 2 A schematic diagram of a flow chart of a first embodiment of a method for remotely controlling a PLC using edge computing according to the present invention;

[0042] Figure 3 A schematic diagram of a second embodiment of the method for remotely controlling a PLC using edge computing according to the present invention;

[0043] Figure 4 A schematic diagram of a flow chart of a third embodiment of a method for remotely controlling a PLC using edge computing according to the present invention;

[0044] Figure 5 This is a structural block diagram of the first embodiment of the device for edge computing remote control of PLC of the present invention.

[0045] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0046] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0047] Reference Figure 1 , Figure 1 A schematic diagram of the structure of an edge computing remote control PLC device in the hardware operating environment involved in an embodiment of the present invention.

[0048] like Figure 1 As shown, the edge computing remote control PLC device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The wired interface of the user interface 1003 may be a USB interface in the present invention. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (WIreless-FIdelity, WI-FI) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM) memory, or a stable memory (Non-volatile Memory, NVM), such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0049] Those skilled in the art will understand that Figure 1 The structure shown in does not constitute a limitation on the edge computing remote control PLC device, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0050] like Figure 1 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a program for edge computing remote control of the PLC.

[0051] exist Figure 1 In the edge computing remote control PLC device shown, the network interface 1004 is mainly used to connect to the background server and communicate data with the background server; the user interface 1003 is mainly used to connect peripheral devices; the edge computing remote control PLC device calls the edge computing remote control PLC program stored in the memory 1005 through the processor 1001, and executes the edge computing remote control PLC method provided in an embodiment of the present invention.

[0052] Based on the above hardware structure, an embodiment of the method of edge computing remote control PLC of the present invention is proposed.

[0053] Reference Figure 2 , Figure 2 The present invention provides a flow chart of the first embodiment of the method for remotely controlling PLC by edge computing according to the present invention, and proposes the first embodiment of the method for remotely controlling PLC by edge computing according to the present invention.

[0054] In a first embodiment, the method for edge computing remotely controlling a PLC comprises the following steps:

[0055] S10: Determine the complexity, real-time requirements and network conditions of the PLC control tasks on the production line. The complexity of the PLC control tasks includes the complexity of the control logic, the diversity of the control tasks and the complexity of the data processing. The real-time requirements include the response time requirements of the control tasks, the frequency requirements of the control tasks for data updates and the timeliness requirements of fault handling. The network conditions include network bandwidth and delay, network stability and data security requirements.

[0056] It should be noted that the executor of this embodiment can be a comprehensive edge computing remote control PLC system, which can determine the optimal allocation ratio of edge and cloud computing resources, dynamically adjust the allocation of edge computing and cloud computing resources based on the optimal allocation ratio, process the PLC control tasks based on the edge and cloud after resource allocation, and output control operations to the PLC function, or other electronic devices that can realize the above functions. This embodiment does not limit this.

[0057] It should be noted that complexity analysis refers to the evaluation of the complexity of PLC control tasks, and the following aspects can be considered: The complexity of control logic: including the complexity of logic flow, the number of conditional branches, etc. The diversity of control tasks: whether it involves the coordinated control of multiple devices or the switching of multiple processes. The complexity of data processing: whether complex data processing or algorithm calculations are required. Specifically, by analyzing the PLC program or observing and recording the actual operation of the production line, using professional industrial automation software tools for analysis, such as using PLC programming software or diagnostic tools in industrial control systems, the complexity of the control task can be accurately evaluated, which provides a basis for subsequent resource allocation and ensures that the system can meet the needs of the control task.

[0058] Real-time demand analysis refers to examining the real-time requirements of PLC control tasks, including the following aspects: Response time requirements of control tasks: that is, the reaction time limit from input to output. Data update frequency requirements: The frequency requirements of data updates for control tasks, such as how many times per second the data needs to be updated. Timeliness requirements for fault handling: How quickly fault conditions need to be handled. Specifically, by communicating with production line operators or referring to relevant standards and specifications, the real-time requirements of control tasks are understood, thereby clarifying the real-time requirements, providing guidance for subsequent resource allocation and system design, and ensuring that the system can respond to the needs of control tasks in a timely manner.

[0059] Network status assessment refers to analyzing the network environment of the production line, including the following aspects: Network bandwidth and latency: whether the network bandwidth is sufficient and whether the latency is stable. Network stability: whether the network is susceptible to interference or interruption. Data security requirements: security requirements for data transmission, whether encrypted transmission is required. Specifically, through network monitoring tools or communication with network administrators, data and information on network status are obtained, so as to understand the impact of network status on the system, provide a basis for subsequent resource allocation and system design, and ensure that the system can operate stably and meet security requirements.

[0060] S20: Determine the optimal allocation ratio of edge and cloud computing resources based on the complexity, real-time requirements and network conditions of the PLC control task.

[0061] It should be noted that a comprehensive assessment is conducted on complexity, real-time requirements, and network conditions, and the degree of impact of these factors on edge computing and cloud computing resource requirements is comprehensively considered. Based on the results of the comprehensive assessment, the resource allocation ratio between the edge and the cloud is determined when processing PLC control tasks. This includes determining the proportional relationship between the amount of tasks processed by the edge and the amount of tasks processed by the cloud. Design a resource allocation strategy, including rules and algorithms for determining whether to allocate tasks to the edge or cloud based on the complexity and real-time requirements of different control tasks. Monitor the load conditions, network conditions, and real-time requirements of PLC control tasks on the edge and the cloud in real time, and dynamically adjust the resource allocation ratio based on the real-time situation. Periodically evaluate and optimize the resource allocation ratio, and adjust the resource allocation strategy based on the actual operating conditions and performance indicators to improve the overall efficiency and performance of the system.

[0062] S30: Dynamically adjust the allocation of edge computing and cloud computing resources based on the optimal allocation ratio.

[0063] It should be noted that monitoring tools or systems are used to monitor the utilization, load, and network latency of edge and cloud computing resources in real time. Analyze whether the real-time requirements of PLC control tasks on the production line have changed, such as whether the response time requirements of tasks have increased or decreased. Adjust the allocation ratio of edge and cloud computing resources based on the real-time monitoring of resource utilization and changes in the real-time requirements of PLC control tasks. If the edge resource utilization is high and the real-time requirements are high, increase the allocation ratio of edge resources; if the cloud resource utilization is high or the real-time requirements are reduced, reduce the allocation ratio of edge resources. According to the adjusted resource allocation ratio, implement the resource allocation strategy to allocate the PLC control tasks to the appropriate computing nodes for processing. Monitor the performance indicators of the adjusted system, including real-time indicators, resource utilization, etc., and evaluate whether the adjustment effect meets expectations. Periodically evaluate the adjustment effect, and further optimize the resource allocation strategy based on the actual operation and performance indicators to improve the stability and performance of the system.

[0064] S40: Process the PLC control task based on the edge and cloud after resource allocation, and output the control operation to the PLC.

[0065] It should be noted that the edge is responsible for processing simple PLC control tasks with high real-time requirements, such as tasks with strict response time requirements but simple logic. Specifically, the PLC control program is deployed on the edge, the control logic is executed through edge computing resources, and the results are fed back to the PLC, thereby reducing the communication delay with the cloud, improving the system response speed, and ensuring the timely execution of control tasks with high real-time requirements.

[0066] The cloud is responsible for handling PLC control tasks with high complexity or low real-time requirements, such as tasks with complex data processing and complex logic but low real-time requirements. Specifically, high-performance computing resources are deployed in the cloud, and complex control logic and data processing are executed through the cloud computing platform, and the results are fed back to the PLC, thereby utilizing the powerful computing power of the cloud, being able to handle more complex control tasks, and improving the overall intelligence level and processing efficiency of the system.

[0067] Regardless of whether the control task is executed on the edge or in the cloud, the result must eventually be output to the PLC to achieve control of the production line equipment. Specifically, through the network communication protocol, the processing results are sent to the corresponding PLC controller to trigger the corresponding control action, thereby ensuring the system's real-time control of the production line and ensuring the smooth progress of the production process. Through the implementation of the above steps, the system can make full use of the computing resources of the edge and the cloud to achieve flexible processing of different types of PLC control tasks, thereby improving the intelligence level and overall efficiency of the production line.

[0068] The present embodiment provides a method for remotely controlling a PLC using edge computing, the method comprising: determining the complexity, real-time requirements, and network conditions of a PLC control task on a production line; determining the optimal allocation ratio of edge and cloud computing resources based on the complexity, real-time requirements, and network conditions of the PLC control task; dynamically adjusting the allocation of edge computing and cloud computing resources based on the optimal allocation ratio; processing the PLC control task based on the edge and cloud after resource allocation, and outputting control operations to the PLC. The present embodiment can effectively solve the challenges of edge computing in processing real-time requirements and complex tasks by dynamically adjusting the allocation of edge computing and cloud computing resources.

[0069] Reference Figure 3 , Figure 3 The flowchart of the second embodiment of the method for remotely controlling PLC by edge computing of the present invention is as follows. In the second embodiment, the optimal allocation ratio of edge and cloud computing resources is determined based on the complexity, real-time requirements and network conditions of the PLC control task, including:

[0070] S201: splitting the PLC control task into multiple subtasks based on the complexity, real-time requirements and network conditions of the PLC control task;

[0071] S202: Determine the optimal allocation ratio of edge and cloud computing resources based on the characteristics and resource requirements of the subtask.

[0072] It should be noted that, according to the complexity of the control task, the real-time requirements and the network status, the original PLC control task is divided into multiple subtasks, each with different characteristics and resource requirements. The characteristics and resource requirements of each subtask are evaluated, including the requirements for computing resources, memory, bandwidth, etc., as well as the degree of real-time requirements. The optimal resource allocation ratio is determined by comprehensively considering the characteristics and resource requirements of each subtask, as well as the supply of computing resources on the edge and cloud. For example, tasks that require low latency and high real-time performance can be allocated to the edge first; tasks that require a large amount of computing resources can be allocated to the cloud. According to the real-time monitored network status and resource utilization, the resource allocation ratio is dynamically adjusted to adapt to environmental changes and the real-time requirements of the task. By monitoring indicators such as network latency, edge and cloud resource utilization, resource allocation is adjusted in a timely manner to ensure that the system can operate effectively in different environments.

[0073] It should be understood that splitting tasks allows the system to execute multiple subtasks simultaneously, thereby improving the parallelism and processing efficiency of the system. By breaking down tasks into smaller subtasks, the system can more flexibly adapt to different environments and resource constraints, as well as respond to different real-time requirements. Different subtasks may have different characteristics and resource requirements. By splitting tasks, computing resources can be allocated more accurately, so that each subtask can get the optimal resource configuration, thereby improving resource utilization. Splitting tasks can reduce the complexity of a single task and reduce the possibility of system errors. At the same time, even if a subtask fails, other subtasks can still continue to execute, improving the fault tolerance of the system.

[0074] Furthermore, in this embodiment, determining the optimal allocation ratio of edge and cloud computing resources based on the characteristics and resource requirements of the subtasks includes:

[0075] Determine the corresponding subtasks that the PLC node and its expansion module can undertake based on the characteristics and resource requirements of the subtasks;

[0076] Determine the order of task execution based on the corresponding subtasks that the PLC node and its expansion module can undertake;

[0077] The optimal allocation ratio of edge and cloud computing resources is determined based on the task execution order.

[0078] It should be noted that, according to the characteristics and resource requirements of each subtask, the computing power, storage capacity and other resources of the PLC node and its expansion modules are evaluated to determine the corresponding subtasks it can undertake. This analysis will help to reasonably allocate tasks between the edge and the cloud, make full use of resources, and improve system efficiency. Based on the subtasks undertaken by the PLC node and its expansion modules, determine the execution order of these subtasks to ensure that the dependencies between tasks are met. Determining the appropriate task execution order helps avoid resource waste and improve the overall performance of the system. Combined with the task execution order, the optimal edge and cloud computing resource allocation ratio is determined according to the real-time and computing resource requirements of each subtask. Through reasonable resource allocation, the performance of the system can be optimized to meet the needs of each subtask. Implementing these steps can effectively manage and allocate computing resources on the edge and cloud to ensure that the system can operate efficiently when processing complex PLC control tasks, improve production efficiency and system stability.

[0079] Furthermore, in this embodiment, the PLC control task is split into multiple subtasks based on the complexity, real-time requirements and network conditions of the PLC control task, including:

[0080] Based on the complexity, real-time requirements and network conditions of the PLC control task, the PLC control task is divided into multiple functional modules according to function or logic;

[0081] Determine the dependencies and data flow relationships between functional modules;

[0082] A plurality of subtasks are determined based on the dependency relationship and data flow relationship between the functional modules.

[0083] It should be noted that, according to the complexity of the task, real-time requirements and network conditions, the original PLC control task is divided into multiple functional modules according to function or logic, and each module is responsible for performing a specific task or function. The dependencies between the functional modules are analyzed, that is, which modules need to be executed before which modules can be executed, and how the data flows between these modules. Based on the dependencies of the functional modules and the data flow relationship, multiple subtasks are determined. Each subtask is a collection of related functional modules, which can be executed independently or depend on the output of other subtasks.

[0084] In the specific implementation, by analyzing the functions and logic of the tasks, the original PLC control tasks are split into different functional modules, such as input processing, logical operations, output control, etc. The dependency analysis is performed on each functional module to determine which modules must be executed after other modules are executed, and how data flows between modules. According to the dependency and data flow relationships, multiple subtasks are determined, each subtask contains a set of related functional modules, and the relationship and order between subtasks are ensured to be reasonable. The effect of these steps is to optimize the system's task execution process, reduce the complexity of the system, and improve the flexibility and efficiency of task execution, so that the system can better cope with the complexity of PLC control tasks, real-time requirements and changes in network conditions.

[0085] It should be understood that the determination of dependencies helps determine the execution order of functional modules. Knowing which modules need to be executed after other modules can help the system schedule tasks reasonably and ensure that tasks are executed in the correct order, thereby avoiding problems such as deadlock or data competition. By understanding the data flow relationship, the resource requirements of each functional module can be better analyzed, so that resources can be allocated more effectively. For example, modules that require more computing resources can be allocated to higher-performance devices, or modules with higher real-time requirements can be prioritized. Understanding the dependencies and data flow relationships between functional modules can help quickly locate and solve problems in the system. When a fault occurs, the root cause of the fault can be found by tracing the data flow path, and measures can be taken quickly to repair it. Clear dependencies and data flow relationships help system designers and maintainers better understand the relationship between system structure and functional modules. This makes the design, expansion and maintenance of the system more concise and efficient.

[0086] Reference Figure 4 , Figure 4 The flowchart of the third embodiment of the method for remotely controlling PLC by edge computing of the present invention is as follows. In the third embodiment, the edge end and the cloud end after resource allocation process the PLC control task and output the control operation to the PLC, and further includes:

[0087] S01: Acquire historical data and real-time monitoring data of the PLC control task;

[0088] S02: predicting a fault of the PLC system based on the historical data and the real-time monitoring data, and generating an automatic adjustment control instruction before the fault occurs;

[0089] Accordingly, the edge end and the cloud end after resource allocation process the PLC control task and output the control operation to the PLC, including:

[0090] S40': Processing the PLC control task based on the automatic adjustment control instruction, the edge end after resource allocation, and the cloud end, and outputting the control operation to the PLC.

[0091] It should be noted that at the edge and cloud, mechanisms need to be established to obtain historical data and real-time monitoring data of PLC control tasks. This may involve the configuration and management of components such as data acquisition equipment, sensors, and data storage systems. Based on historical data and real-time monitoring data, various machine learning or statistical analysis techniques can be used to predict possible failures of the PLC system. This includes establishing fault prediction models, monitoring abnormal patterns and trends, etc. When a possible failure of the PLC system is predicted, the system needs to generate automatic adjustment control instructions to cope with the failure. This may involve adjusting parameters, switching working modes, reconfiguring control strategies, etc. At the edge and cloud, the PLC control tasks are processed according to the generated automatic adjustment control instructions. This may include operations such as reallocating resources, adjusting task priorities, and modifying control logic.

[0092] In the specific implementation, configure data acquisition equipment and storage systems to ensure that historical data and real-time monitoring data of PLC control tasks can be obtained and stored. Use historical data to train fault prediction models, or use real-time monitoring data to monitor abnormal patterns and trends to predict possible faults in the PLC system. According to the fault prediction results, generate automatic adjustment control instructions, including adjusting parameters, switching modes, etc. According to the automatically generated control instructions, process the PLC control tasks to ensure that the system automatically adjusts the control before a fault occurs, ensuring the stability and reliability of the system.

[0093] Further, in this embodiment, the edge end and the cloud end processing the PLC control task based on the automatic adjustment control instruction and resource allocation, and outputting the control operation to the PLC, include:

[0094] Based on the automatic adjustment control instruction, the edge end after resource allocation and the cloud end process the PLC control task, and generate a remote control instruction, the remote control instruction uses a 64-bit digital signature to encrypt and verify the data;

[0095] The remote control instruction is output to the PLC, wherein the remote control instruction includes a PLC control operation.

[0096] It should be noted that at the edge and in the cloud, PLC control tasks are processed based on automatically generated control instructions and resource allocation. This may involve re-adjusting task priorities, allocating computing resources, etc. Based on the processed PLC control tasks, remote control instructions are generated. These instructions may include control commands, parameter settings, etc., in order to remotely control the operating status of the PLC device. In order to ensure the security and integrity of the instructions, the generated remote control instructions are encrypted and verified using a 64-bit digital signature to prevent the instructions from being tampered with or forged. The encrypted and verified remote control instructions are transmitted to the PLC device to implement remote control operations on the PLC, including control operations and parameter settings.

[0097] In the specific implementation, the PLC control task is processed according to the automatically generated control instructions and resource allocation to ensure the accuracy and stability of task execution. According to the processed PLC control task, the remote control instruction is generated, including the required control operation and parameter setting. The generated remote control instruction is encrypted and verified using a 64-bit digital signature to ensure the security and integrity of the instruction. The encrypted remote control instruction is transmitted to the PLC device to realize the remote control operation of the PLC.

[0098] Further, in this embodiment, the outputting of the remote control instruction to the PLC, the remote control instruction including before the PLC control operation, further includes:

[0099] Determining biometric data of a user and performing user identity verification based on the biometric data;

[0100] After the user identity verification is passed, the step of outputting the remote control instruction to the PLC is performed.

[0101] It should be noted that biometric data usually includes fingerprints, irises, facial recognition, etc. In this case, the system needs to collect and extract the user's biometric data for subsequent identity verification. Using biometric data for user identity verification may involve comparing and matching biometric data to ensure the accuracy and uniqueness of the user's identity. Only after the user's identity verification is passed can the system execute the operation of outputting remote control instructions to the PLC, ensuring that only authenticated users can remotely control the PLC.

[0102] In the specific implementation, biometric devices are used to collect the user's biometric data, such as fingerprints, irises, etc., to ensure the accuracy and integrity of the data. The collected biometric data is compared and matched with the user's biometric data stored in the system to verify the user's identity. After the user's identity is authenticated, the step of outputting remote control instructions to the PLC is executed to ensure that these operations are performed by authenticated users, thereby improving the security and credibility of the system. The effect of these steps is to enhance the security and protection of the system, prevent unauthorized users from remotely controlling the PLC, improve the system's user identity authentication mechanism and access control capabilities, and protect the security of PLC devices and related systems.

[0103] In addition, an embodiment of the present invention also proposes a storage medium, on which a program for edge computing remote control of PLC is stored. When the program for edge computing remote control of PLC is executed by a processor, the steps of the method for edge computing remote control of PLC as described above are implemented.

[0104] In addition, refer to Figure 5 The embodiment of the present invention further provides an edge computing remote control PLC device, the edge computing remote control PLC device comprising:

[0105] The task determination module 10 is used to determine the complexity, real-time requirements and network status of the PLC control task on the production line;

[0106] A ratio calculation module 20, used to determine the optimal allocation ratio of edge and cloud computing resources based on the complexity, real-time requirements and network conditions of the PLC control task;

[0107] A resource allocation module 30, configured to dynamically adjust the allocation of edge computing and cloud computing resources based on the optimal allocation ratio;

[0108] The control output module 40 is used to process the PLC control task based on the edge and the cloud after resource allocation, and output the control operation to the PLC.

[0109] Other embodiments or specific implementation methods of the edge computing remote control PLC device described in the present invention can refer to the above-mentioned method embodiments and will not be repeated here.

[0110] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0111] The serial numbers of the embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments. In a unit claim that lists several means, several of these means may be embodied by the same hardware item. The use of the words first, second, and third, etc. does not indicate any order and these words may be interpreted as names.

[0112] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory image (ROM) / random access memory (RAM), a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal user device (which can be a mobile phone, a computer, a server, an air conditioner, or a network user device, etc.) to execute the methods described in each embodiment of the present invention.

[0113] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for remotely controlling PLC by edge computing, characterized in that: The method comprises: Determine the complexity, real-time requirements and network conditions of the PLC control tasks on the production line. The complexity of the PLC control tasks includes the complexity of the control logic, the diversity of the control tasks and the complexity of the data processing. The real-time requirements include the response time requirements of the control tasks, the frequency requirements of the control tasks for data updates and the timeliness requirements of fault handling. The network conditions include network bandwidth and delay, network stability and data security requirements; Determine the optimal allocation ratio of edge and cloud computing resources based on the complexity, real-time requirements and network conditions of the PLC control task; Dynamically adjust the allocation of edge computing and cloud computing resources based on the optimal allocation ratio; Processing the PLC control task based on the edge and cloud after resource allocation, and outputting the control operation to the PLC; Determine the optimal allocation ratio of edge and cloud computing resources based on the complexity of PLC control tasks, real-time requirements, and network conditions, including: Splitting the PLC control task into multiple subtasks based on the complexity, real-time requirements and network conditions of the PLC control task; Determine the optimal allocation ratio of edge and cloud computing resources based on the characteristics and resource requirements of the subtasks; Determining the optimal allocation ratio of edge and cloud computing resources based on the characteristics and resource requirements of the subtasks includes: Determine the corresponding subtasks that the PLC node and its expansion module can undertake based on the characteristics and resource requirements of the subtasks; Determine the order of task execution based on the corresponding subtasks that the PLC node and its expansion module can undertake; Determine the optimal allocation ratio of edge and cloud computing resources based on the task execution order; The PLC control task is split into multiple subtasks based on the complexity, real-time requirements and network conditions of the PLC control task, including: Based on the complexity, real-time requirements and network conditions of the PLC control task, the PLC control task is divided into multiple functional modules according to function or logic; Determine the dependencies and data flow relationships between functional modules; A plurality of subtasks are determined based on the dependency relationship and data flow relationship between the functional modules.

2. The method for remotely controlling PLC by edge computing as claimed in claim 1, characterized in that: The edge end and the cloud end after resource allocation process the PLC control task and output the control operation to the PLC, and also include: Obtaining historical data and real-time monitoring data of the PLC control task; Predicting PLC system failures based on the historical data and real-time monitoring data, and generating automatic adjustment control instructions before failures occur; Accordingly, the edge end and the cloud end after resource allocation process the PLC control task and output the control operation to the PLC, including: Based on the automatic adjustment control instructions, the edge end after resource allocation and the cloud end process the PLC control task, and output the control operation to the PLC.

3. The method for remotely controlling PLC by edge computing as claimed in claim 2, characterized in that: The automatic adjustment control instruction, the edge end after resource allocation and the cloud end process the PLC control task, and output the control operation to the PLC, including: Based on the automatic adjustment control instruction, the edge end after resource allocation and the cloud end process the PLC control task, and generate a remote control instruction, the remote control instruction uses a 64-bit digital signature to encrypt and verify the data; The remote control instruction is output to the PLC, wherein the remote control instruction includes a PLC control operation.

4. The method for remotely controlling PLC by edge computing as claimed in claim 3, characterized in that: The outputting of the remote control instruction to the PLC, wherein the remote control instruction includes before the PLC control operation, further includes: Determining biometric data of a user and performing user identity verification based on the biometric data; After the user identity verification is passed, the step of outputting the remote control instruction to the PLC is performed.

5. An edge computing remote control PLC device, characterized in that: The edge computing remote control PLC device comprises: A task determination module is used to determine the complexity, real-time requirements and network conditions of the PLC control tasks on the production line. The complexity of the PLC control tasks includes the complexity of the control logic, the diversity of the control tasks and the complexity of the data processing. The real-time requirements include the response time requirements of the control tasks, the frequency requirements of the control tasks for data updates and the timeliness requirements of fault processing. The network conditions include network bandwidth and delay, network stability and data security requirements. A ratio calculation module, used to determine the optimal allocation ratio of edge and cloud computing resources based on the complexity, real-time requirements and network conditions of the PLC control task; A resource allocation module, used to dynamically adjust the allocation of edge computing and cloud computing resources based on the optimal allocation ratio; A control output module, used to process the PLC control task based on the edge end and the cloud after resource allocation, and output the control operation to the PLC; Determine the optimal allocation ratio of edge and cloud computing resources based on the complexity of PLC control tasks, real-time requirements, and network conditions, including: Splitting the PLC control task into multiple subtasks based on the complexity, real-time requirements and network conditions of the PLC control task; Determine the optimal allocation ratio of edge and cloud computing resources based on the characteristics and resource requirements of the subtasks; Determining the optimal allocation ratio of edge and cloud computing resources based on the characteristics and resource requirements of the subtasks includes: Determine the corresponding subtasks that the PLC node and its expansion module can undertake based on the characteristics and resource requirements of the subtasks; Determine the order of task execution based on the corresponding subtasks that the PLC node and its expansion module can undertake; Determine the optimal allocation ratio of edge and cloud computing resources based on the task execution order; The PLC control task is split into multiple subtasks based on the complexity, real-time requirements and network conditions of the PLC control task, including: Based on the complexity, real-time requirements and network conditions of the PLC control task, the PLC control task is divided into multiple functional modules according to function or logic; Determine the dependencies and data flow relationships between functional modules; A plurality of subtasks are determined based on the dependency relationship and data flow relationship between the functional modules.

6. An edge computing remote control PLC device, characterized in that: The device includes: a memory, a processor, and an edge computing remote control PLC program stored in the memory and executable on the processor, wherein the edge computing remote control PLC program is configured to implement the steps of the edge computing remote control PLC method as described in any one of claims 1 to 4.

7. A storage medium, characterized in that: The storage medium stores an edge computing remote control PLC program, and when the edge computing remote control PLC program is executed by the processor, the steps of the edge computing remote control PLC method as described in any one of claims 1 to 4 are implemented.

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