Industrial Internet of Things-oriented AloT cloud side intelligent control method and system

Abstract

The invention provides an AloT cloud side intelligent control method and system oriented to the industrial Internet of Things, and relates to the technical field of cloud side intelligent control, and the method comprises the steps: creating a cloud-side-side collaborative architecture; performing adaptation analysis on the intelligent manufacturing task according to the equipment performance and the equipment function, and establishing a first adaptation constraint; performing task decomposition of the intelligent manufacturing task by using the dynamic task decomposition channel, and creating M sub-tasks; performing task competition game analysis of the terminal device on the M sub-tasks by using the cloud center, and establishing a second adaptation constraint; and carrying out balance analysis on the first adaptation constraint and the second adaptation constraint to generate an intelligent execution scheme of the terminal equipment of the intelligent manufacturing task. According to the method and the device, the technical goal of intelligent task scheduling and optimal allocation under a cloud-side-end cooperative computing architecture can be realized, and the technical effects of improving task execution efficiency, reducing computing delay, optimizing the computing resource utilization rate and enhancing data security are achieved.

Description

Technical Field

[0001] The present application relates to the field of cloud-edge intelligent control technology, and in particular to an AloT cloud-edge intelligent control method and system for the Industrial Internet of Things. Background Art

[0002] In the context of the development of intelligent manufacturing and industrial Internet of Things, how to efficiently allocate and execute complex computing tasks has become a key issue. Traditional task scheduling methods usually rely on cloud computing centers for unified management and allocation. However, this model has certain limitations, especially when facing complex and changeable computing needs in the industrial Internet of Things environment. A single cloud processing method often leads to uneven distribution of computing resources, inefficient task execution, and slow system response. Therefore, the cloud-edge-end collaborative computing architecture has gradually become an important technical direction to solve this problem. It can combine the powerful computing power of the cloud, the real-time processing capability of edge computing, and the distributed execution capability of terminal devices to optimize task scheduling and improve computing efficiency.

[0003] At present, the existing cloud computing model mainly relies on a centralized computing architecture, that is, all computing tasks need to be uploaded to the remote cloud for processing, and then the calculation results are returned to the terminal device. Although this model can provide powerful computing power, it also has some obvious defects. First, since cloud computing needs to transmit data through the network, the traditional cloud computing model is often difficult to meet the tasks with high real-time requirements in the industrial Internet of Things scenario. For example, in intelligent manufacturing systems, some key tasks such as equipment fault detection and production process optimization need to complete calculations and decisions in a very short time, and the high latency of the traditional cloud computing model may cause the system to respond untimely, thereby affecting production efficiency. Secondly, with the continuous increase in the number of industrial Internet of Things devices, the load of data transmission is also increasing. The traditional cloud computing model is easily limited by network bandwidth, resulting in data transmission congestion, thereby affecting the efficiency of task execution. In addition, a single cloud computing model may also face data security and privacy issues. For example, for some sensitive production data, if all are uploaded to the cloud for processing, the risk of data leakage may increase.

[0004] To sum up, there are technical problems in the existing technology that the cloud computing model relies on a centralized computing architecture, which results in computing tasks needing to be transmitted remotely, causing high latency, limited network bandwidth, and data security risks, further affecting the needs for real-time, high efficiency, and data privacy in the industrial Internet of Things scenario, thereby reducing production efficiency and system stability. Summary of the invention

[0005] The purpose of this application is to provide an AloT cloud-edge intelligent control method and system for the Industrial Internet of Things, so as to solve the technical problems in the prior art that the cloud computing model relies on a centralized computing architecture, which results in the need for remote transmission of computing tasks, thereby causing high latency, limited network bandwidth and data security risks, further affecting the requirements for real-time, high efficiency and data privacy in the Industrial Internet of Things scenario, thereby reducing production efficiency and system stability.

[0006] In view of the above problems, the present application provides an AloT cloud-edge intelligent control method and system for industrial Internet of Things.

[0007] In the first aspect, the present application provides an AloT cloud-edge-end intelligent control method for the industrial Internet of Things, which is implemented through the AloT cloud-edge-end intelligent control system for the industrial Internet of Things, including: creating a cloud-edge-end collaborative architecture, the cloud-edge-end collaborative architecture includes a cloud center, an edge computing node and a terminal device, and obtaining intelligent manufacturing tasks from the cloud center; obtaining the device performance and device functions of the terminal device at the edge computing node, performing adaptation analysis of the intelligent manufacturing task according to the device performance and device functions, and establishing a first adaptation constraint; activating the dynamic task decomposition channel of the cloud center, using the dynamic task decomposition channel to perform task decomposition of the intelligent manufacturing task, and creating M subtasks; using the cloud center to perform task competition game analysis of the terminal device on the M subtasks, and establishing a second adaptation constraint; performing a balance analysis on the first adaptation constraint and the second adaptation constraint, and generating an intelligent execution plan for the terminal device of the intelligent manufacturing task.

[0008] In the second aspect, the present application also provides an AloT cloud-edge-end intelligent control system for industrial Internet of Things, which is used to execute the AloT cloud-edge-end intelligent control method for industrial Internet of Things as described in the first aspect, including: a collaborative architecture creation module, used to create a cloud-edge-end collaborative architecture, the cloud-edge-end collaborative architecture includes a cloud center, an edge computing node and a terminal device, and obtains intelligent manufacturing tasks from the cloud center; an adaptation analysis module, used to obtain the device performance and device functions of the terminal device at the edge computing node, perform adaptation analysis of the intelligent manufacturing task according to the device performance and device functions, and establish a first adaptation constraint; a task decomposition module, used to activate the dynamic task decomposition channel of the cloud center, use the dynamic task decomposition channel to perform task decomposition of the intelligent manufacturing task, and create M subtasks; a competition game analysis module, used to use the cloud center to perform task competition game analysis of the terminal device on M subtasks, and establish a second adaptation constraint; a solution generation module, used to perform a balance analysis on the first adaptation constraint and the second adaptation constraint, and generate an intelligent execution solution for the terminal device of the intelligent manufacturing task.

[0009] The technical solution provided in this application has at least the following technical effects or advantages: by realizing the technical goal of intelligent task scheduling and optimized allocation under the cloud-edge-end collaborative computing architecture, the technical effects of improving task execution efficiency, reducing computing latency, optimizing computing resource utilization, and enhancing data security are achieved.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0012] Figure 1 This is a flow chart of the AloT cloud-edge-end intelligent control method for the industrial Internet of Things in this application; Figure 2 This is a schematic diagram of the structure of the AloT cloud-edge intelligent control system for the industrial Internet of Things in this application.

[0013] Explanation of the accompanying drawings: collaborative architecture creation module 11, adaptation analysis module 12, task decomposition module 13, competition game analysis module 14, solution generation module 15. DETAILED DESCRIPTION

[0014] This application provides an AloT cloud-edge-end intelligent control method and system for the industrial Internet of Things, which solves the technical problem in the prior art that the cloud computing model relies on a centralized computing architecture, resulting in the need for remote transmission of computing tasks, which leads to high latency, limited network bandwidth, and data security risks, further affecting the demand for real-time, high efficiency, and data privacy in the industrial Internet of Things scenario, thereby reducing production efficiency and system stability. The technical goal of achieving intelligent task scheduling and optimized allocation under the cloud-edge-end collaborative computing architecture is achieved, achieving the technical effects of improving task execution efficiency, reducing computing latency, optimizing computing resource utilization, and enhancing data security.

[0015] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0016] For example, please refer to the attached Figure 1 The present application provides an AloT cloud-edge-end intelligent control method for the industrial Internet of Things, which is applied to the AloT cloud-edge-end intelligent control system for the industrial Internet of Things, and specifically includes the following steps: S1: Create a cloud-edge-end collaborative architecture, which includes a cloud center, edge computing nodes and terminal devices, and obtain smart manufacturing tasks from the cloud center.

[0017] Specifically, a cloud-edge-end collaborative architecture is created to build a system architecture consisting of three parts: cloud, edge computing, and terminal devices. The cloud center is responsible for processing and storing large amounts of data. Among them, relevant task instructions are generated based on the real-time collected environmental data and device data, and then the tasks are sent to edge computing nodes or terminal devices. Edge computing nodes are responsible for data processing and analysis closer to the terminal devices, while terminal devices are responsible for interacting with the real environment and performing actual tasks. Through the collaborative work of the cloud, edge, and end, smooth data transmission and efficient execution of intelligent decisions can be achieved.

[0018] S2: Obtain device performance and device functions of the terminal device at the edge computing node, perform adaptation analysis of the intelligent manufacturing task according to the device performance and device functions, and establish a first adaptation constraint.

[0019] Specifically, the device performance and device functions of the terminal device are obtained at the edge computing node. Device performance refers to hardware indicators such as the terminal device's processing power, computing speed, and memory, while device functions refer to specific tasks that the terminal device can complete, such as detection, monitoring, and control.

[0020] Next, the adaptation analysis of the intelligent manufacturing task is carried out according to the equipment performance and equipment functions to evaluate whether the terminal equipment is competent for the current intelligent manufacturing task. Intelligent manufacturing tasks include automatic control, data collection, process optimization, etc. According to the analysis results, certain constraints are set for the matching between equipment performance and functions and intelligent manufacturing tasks, and the first adaptation constraint is established. The first adaptation constraint ensures that the performance and functions of the equipment are consistent with the task requirements, avoiding waste of resources and task execution failure.

[0021] S3: Activate the dynamic task decomposition channel of the cloud center, use the dynamic task decomposition channel to decompose the intelligent manufacturing task, and create M subtasks.

[0022] Specifically, the dynamic task decomposition channel of the cloud center is activated. The dynamic task decomposition channel is a mechanism for task splitting and scheduling, which can flexibly adjust the task decomposition strategy according to the real-time computing needs and device status of the system. For example, in some cases, the task may need to be subdivided into multiple small tasks for parallel processing, while in the case of fewer computing resources, the number of task splits may need to be reduced to adapt to computing power. Therefore, activating the dynamic task decomposition channel of the cloud center can make task decomposition more flexible and intelligent, and improve overall computing efficiency.

[0023] Next, the dynamic task decomposition channel is used to decompose the intelligent manufacturing tasks, which means that with the support of the cloud center, the intelligent manufacturing tasks are decomposed into smaller subtasks through the dynamic task decomposition channel. The task decomposition process needs to consider multiple factors, such as the computing requirements of the task, the computing power of the equipment, the stability of network communication, etc. For example, a large machine vision inspection task may be decomposed into multiple subtasks, each of which is responsible for different inspection areas or different feature extraction operations, so that multiple computing devices can be used for parallel processing to improve the inspection efficiency. In addition, the dynamic task decomposition channel can adjust the task decomposition strategy according to the real-time computing load. For example, when computing resources are sufficient, a more refined task splitting can be adopted to improve the computing parallelism; when resources are limited, the task splitting can be reduced to reduce the communication and scheduling costs.

[0024] Finally, after the task decomposition is completed, M executable subtasks are generated. The value of M depends on the complexity of the task, the availability of computing resources, and the way the task is executed. For example, if an intelligent manufacturing task involves complex data processing and there are sufficient computing resources, M may be large, such as 50 subtasks, each of which is executed on a different computing device; if computing resources are limited, M may be small, such as only 10 subtasks, to reduce competition for computing resources. Through reasonable task decomposition, computing efficiency can be improved, the waste of computing resources can be reduced, and tasks can be completed within the specified time.

[0025] S4: Utilize the cloud center to perform task competition game analysis on the M subtasks by the terminal device, and establish a second adaptation constraint.

[0026] Specifically, M subtasks are multiple executable tasks created through the dynamic task decomposition channel, and each task may have different computing requirements and execution priorities. Task competition game analysis is a computational method based on game theory that aims to solve the problem of multiple tasks competing for limited computing resources. For example, when multiple subtasks need to be assigned to different terminal devices, the computing power, current load, and task adaptability of each device will affect the results of task scheduling. Through task competition game analysis, an optimal task allocation strategy can be found to maximize task execution efficiency and maximize device resource utilization.

[0027] Then, after the task competition game analysis is completed, a more reasonable task allocation constraint is formulated based on the calculation results as the second adaptation constraint to ensure that the task is reasonably allocated to the most suitable terminal device. If the computing power of a terminal device is low, the task may be avoided from being assigned to the device, thereby reducing the possibility of computing failure or execution delay. The second adaptation constraint comprehensively considers the results of the task competition game analysis, making task allocation more intelligent and efficient. For example, in the first adaptation constraint, a task may be considered suitable for execution by multiple devices, while in the second adaptation constraint, the optimal device is finally selected to perform the task based on the task competition situation and the real-time computing load of the device.

[0028] S5: Perform a balance analysis on the first adaptation constraint and the second adaptation constraint to generate an intelligent execution plan for the terminal device of the intelligent manufacturing task.

[0029] Specifically, the first adaptation constraint and the second adaptation constraint are comprehensively compared to ensure the rationality of task allocation. The goal of the balance analysis is to find the optimal compromise between the two, so that the task can not only match the appropriate device, but also be dynamically optimized according to the real-time computing status. For example, if the first adaptation constraint believes that a certain device is suitable for performing high-computing tasks, but the second adaptation constraint finds that the current load of the device is too high, it may choose to assign the task to a device with a lower load but slightly lower computing power to improve the overall execution efficiency. Balance analysis usually involves multiple optimization parameters, such as computing resource utilization, task execution time, communication stability, and task completion rate. Finally, based on the results of the balance analysis, an intelligent execution plan for the terminal device of the intelligent manufacturing task is generated. The intelligent execution plan is an optimized task scheduling plan that can reasonably allocate tasks based on the computing power, task requirements, real-time load conditions, and historical execution performance of the device.

[0030] Furthermore, the present application also includes: activating the task competition game analysis function of the cloud center, executing the task competition game analysis of M subtasks, the game characteristics of the competition game analysis function include device adaptability characteristics, historical performance consistency characteristics, communication stability characteristics, and data fidelity assurance characteristics, and the task competition game analysis function performs game compensation through inter-task synergy factors.

[0031] Specifically, the task competition game analysis function is as follows: ;in, Representation subtask Resources on terminal equipment The competitive game loss on Characterization Task The dynamic priority weight of Characterizes the device adaptability factor, i.e., the device adaptability feature, which is used to measure the terminal device resources Subtask The adaptability Characterizing terminal equipment resources The historical performance consistency, that is, the historical performance consistency feature, Characterize the communication stability factor, that is, the communication stability feature, Characterize the data fidelity assurance factor, that is, the data fidelity assurance feature, Characterize the synergy factor between tasks, are the weight factors of device adaptability, historical performance consistency, communication stability, and data fidelity assurance, respectively. is the loss weight factor of the coordination factor between tasks. Start the task competition game analysis function of the cloud center. Task competition game analysis is a method based on game theory to solve the problem of multiple tasks competing for limited resources. The analysis function is used to calculate and evaluate the competition relationship between tasks in order to optimize the task allocation strategy.

[0032] Then, perform task competition game analysis on M subtasks to determine how these subtasks can reasonably use terminal device resources to avoid excessive resource competition that leads to system performance degradation. M represents the number of tasks to be performed. A subtask refers to a small-scale calculation or operation after a complete task is split, and each subtask requires a certain amount of computing resources to complete.

[0033] Next, Representation subtask Resources on terminal equipment The competitive game loss on the ,is used to measure the efficiency loss caused by multiple subtasks competing for the ,resources of the terminal device. When multiple tasks compete for the same computing resources, ,delays, task failures, or decreased computing resource utilization may occur.

[0034] Characterization Task The dynamic priority weight refers to a variable used to measure the priority of different tasks. Dynamic priority means that the priority of tasks will be adjusted as time or environment changes. For example, the priority of urgent tasks may rise, while tasks of low importance may be postponed to ensure the reasonable allocation of resources.

[0035] at the same time, The adaptability factor of the device is used to measure the adaptability of the terminal device resources to subtasks, indicating the ability of the device to adapt to different task requirements. For example, a high-performance computing device may be able to handle multiple complex tasks at the same time, while a low-power device may only be able to perform simple calculations. The higher this factor is, the stronger the device's ability to adapt to different tasks.

[0036] Characterizing terminal equipment resources Historical performance consistency refers to a variable used to measure whether the performance of a terminal device was stable during its past operation. If the fluctuations in parameters such as computing speed and response time of a device are small in different time periods, it means that its historical performance consistency is high, which means that tasks can be performed more reliably.

[0037] The communication stability factor is used to measure the communication quality between the terminal device and the cloud or other devices. If the communication stability factor is high, it means that the network connection is stable, the data transmission delay is low, and data loss or transmission interruption is not likely to occur, thus ensuring the normal execution of the task.

[0038] The data fidelity factor is a parameter used to measure whether the data can maintain integrity and accuracy during transmission and processing. If the data fidelity factor is high, it means that the data is less disturbed during transmission, the error is lower, and the reliability of the final calculation result is higher.

[0039] Characterizes the inter-task synergy factor, which measures the degree of collaboration between multiple tasks. When the synergy factor is high, different tasks can efficiently share resources, reduce resource conflicts, and improve the overall efficiency of the system. For example, on a manufacturing production line, different tasks can share sensor data to optimize the entire production process.

[0040] They are the weight factors for device adaptability, historical performance consistency, communication stability, and data fidelity assurance, which refer to the weight values ​​used to adjust the influence of the above parameters on the final calculation. Different application scenarios may require different weights. For example, in a system with high real-time requirements, the weight factor of communication stability may be greater, while in data analysis tasks, the weight factor of data fidelity may account for a higher proportion. In addition, the loss weight factor of the collaborative factor between tasks It is used to measure the impact of the degree of coordination between tasks on the overall loss. If the task coordination ability is insufficient, the resource utilization efficiency will be reduced. Therefore, it is necessary to set the corresponding loss weight factor for optimization.

[0041] The inter-task coordination factor is calculated as follows: ;in, Characterize the resources in the terminal equipment The total number of subtasks scheduled on Representation subtask With another subtask The collaborative similarity of Belongs to terminal device resources The set of subtasks scheduled on the task.

[0042] First, the inter-task coordination factor is a parameter used to measure the coordination of multiple subtasks when they are executed on the terminal device resources. It can reflect the degree of mutual influence between tasks. The purpose of calculating the inter-task coordination factor is to optimize task scheduling so that multiple tasks can efficiently share resources, reduce resource waste, and improve overall execution efficiency.

[0043] in, Characterize the resources in the terminal equipment The total number of subtasks scheduled on a terminal device refers to the number of subtasks running simultaneously on a specific terminal device. Terminal device resources include limited resources such as computing power, storage space, and network bandwidth. When multiple subtasks are running simultaneously, they need to be properly scheduled to ensure maximum resource utilization. For example, if a terminal device can process five subtasks at the same time, but only three subtasks are actually running, it means that resources are not fully utilized. If the subtasks running exceed its processing capacity, it may cause computing delays or failures.

[0044] Representation subtask With another subtask The collaborative similarity of means that this variable is used to measure how closely two subtasks work together during execution. If two subtasks can share computational results or data inputs, their collaborative similarity is high. For example, in an image processing task, if one subtask is responsible for image segmentation and the other is responsible for object detection, they can share preprocessed data, thereby reducing repeated computations and improving execution efficiency. On the contrary, if two subtasks require independent data processing and have a low collaborative similarity, the degree of resource sharing between them is low, which may increase computational costs.

[0045] Belongs to terminal device resources The set of subtasks scheduled on the system means that the subtasks involved in calculating the coordination factor between tasks must be part of the task set currently running on the terminal device. The subtask set refers to all tasks scheduled to run on a terminal device, not all tasks in the entire system. For example, if a terminal device is running four subtasks and the entire system has a total of ten subtasks, then only these four tasks need to be considered when calculating the coordination factor, not all ten tasks. This restriction can ensure the targeted calculation and make task scheduling more accurate.

[0046] Furthermore, the present application also includes: evaluating the computing capability of the terminal device according to the device performance of the terminal device, and establishing a weak computing device performance protection identifier; performing a load analysis of the terminal device within a preset period range, and establishing a high load identifier; using the weak computing device performance protection identifier and the high load identifier to perform balanced compensation for task competition game analysis, and establishing a second adaptation constraint based on the balanced compensation result.

[0047] Specifically, computing power evaluation refers to evaluating the ability of terminal devices to process tasks based on their performance parameters in order to reasonably allocate computing tasks. For example, if a device has strong computing power, complex tasks can be assigned, while devices with weak computing power can only perform simple tasks, thereby improving the overall system operation efficiency.

[0048] Next, a performance protection flag for weak computing devices is established, which means that if some terminal devices have low computing power, a special flag will be set for them to prevent these devices from taking on computing tasks with excessive loads. Weak computing devices usually refer to devices with low processor performance, small memory, or limited power consumption, such as small sensor nodes or low-power embedded devices. The role of the protection flag is to limit the task allocation of such devices to ensure that they do not suffer performance degradation or even downtime due to overload computing.

[0049] Then, the operating load of the terminal device is regularly evaluated at the set time interval. The preset period range refers to the time window set by technical personnel in this field, such as a load test every ten minutes or every hour. Load analysis mainly focuses on indicators such as the device's CPU occupancy rate, memory usage rate, and data throughput to determine the current working status of the device. If a device is in a high-load state for a long time, it may affect the stability of the system, so measures need to be taken to optimize the scheduling.

[0050] Next, a high-load flag is established. If a terminal device is detected to be continuously under high load during the load analysis process, a high-load flag is assigned to it to alert the task scheduling system and avoid further assigning computing tasks to the device to prevent resource exhaustion or task execution failure. For example, if the CPU usage of a device remains at 90% for a long time, the device may no longer be able to take on new computing tasks, and it will be marked as busy through the high-load flag, thereby optimizing the task allocation strategy.

[0051] Furthermore, balanced compensation for task competition game analysis using performance protection flags and high-load flags for weak computing devices means comprehensively considering the computing power and current load of the device to optimize the task allocation plan. Task competition game analysis is a computational method based on game theory that aims to solve the problem of multiple tasks competing for limited computing resources. Balanced compensation means ensuring that the computing resources of different devices are optimally utilized by reasonably adjusting the allocation of tasks, so that neither devices with weak computing power nor high-load devices are overloaded. For example, if a weak computing device originally needs to perform five tasks, but due to insufficient computing power, the allocation plan can be adjusted to allow devices with stronger computing power to take over two of the tasks, thereby achieving balanced compensation.

[0052] Finally, the second adaptation constraint is established based on the equilibrium compensation result, which means that after the task competition game analysis is completed, the matching relationship between tasks and devices is further optimized based on the equilibrium compensation result. The role of the second adaptation constraint is to ensure that the task allocation is more reasonable and avoid resource waste or computing bottlenecks. For example, if a device is considered suitable for performing a task in the last adaptation analysis, but the adaptability of the device changes due to the subsequent load increase, the second adaptation constraint will re-evaluate the task allocation and adjust the resource usage strategy to maintain the stability and efficiency of the system.

[0053] Furthermore, the present application also includes: uploading the time constraints, computational complexity constraints, computational requirement constraints, and task association constraints of the intelligent manufacturing task; obtaining the highest computing power and the lowest computing power of the terminal device, and establishing granularity decomposition constraints based on the highest computing power and the lowest computing power; performing task decomposition of the intelligent manufacturing task based on the granularity decomposition constraints, the time constraints, computational complexity constraints, computational requirement constraints, and task association constraints to create M subtasks.

[0054] Specifically, upload the time constraints, computational complexity constraints, computational demand constraints, and task association constraints of smart manufacturing tasks for subsequent analysis and processing. Smart manufacturing tasks refer to calculations or operations that need to be performed in the industrial production process, such as production scheduling, equipment control, data analysis, etc. Time constraints mean that tasks must be completed within a specified time, for example, the processing of a certain part must be completed within 20 minutes, otherwise it will affect the entire production process. Computational complexity constraints are a measure of the computing resources required for a task, which determines how much computing power is required to complete the task. For example, complex artificial intelligence model reasoning requires high computational complexity, while simple temperature monitoring tasks have low computational complexity. Computational demand constraints refer to the computing resources required for a task, including CPU, memory, storage, etc. For example, a task may require high-performance GPU computing, while another task only requires basic numerical operations. Task association constraints refer to the interdependence between different tasks. For example, the output of a task may be the input of another task, so they must be executed in a certain order.

[0055] Next, obtaining the highest and lowest computing capabilities of the terminal devices means testing the computing capabilities of all available terminal devices. The highest computing capability refers to the capability of the device with the strongest computing performance, such as a high-performance computing server or an industrial-grade edge computing node. The lowest computing capability refers to the device with the weakest computing capability in the system, such as a low-power sensor or embedded device. The purpose of obtaining these two extreme values ​​is to evaluate the overall computing capability range of the system and provide a basis for subsequent task decomposition.

[0056] Then, according to the highest computing power and the lowest computing power, the granularity decomposition constraint is established, which means that the decomposition granularity of the task is set according to the range of the computing power of the device. The granularity decomposition constraint determines how many subtasks the task is split into and the amount of computation for each subtask. If the computing power range is large, indicating that strong computing devices are available, the task can be split into finer granularities so that multiple devices can process in parallel and improve overall efficiency. For example, if a task requires 100 units of computing power, and the device with the highest computing power can process 50 units per second, while the device with the lowest computing power can only process 5 units per second, then the system may split the task into multiple subtasks of different sizes and assign them to devices with different computing powers to achieve optimal computing scheduling.

[0057] Finally, the intelligent manufacturing task is decomposed according to the granularity decomposition constraint, time constraint, computational complexity constraint, computational demand constraint, and task association constraint to create M subtasks. The intelligent manufacturing task is split into M subtasks so that they can be more reasonably assigned to different terminal devices for execution. Task decomposition is an important means for intelligent manufacturing systems to optimize the utilization of computing resources. For example, a large data analysis task may be split into multiple small tasks for parallel computing, which are executed by different devices, thereby speeding up the calculation. If the time constraint is tight, the task may be assigned to devices with high computing power first; if the computing demand is high, the task assignment to devices with low computing power may be minimized to ensure the smooth completion of the task. Table 1 is a record of the most recent intelligent manufacturing task and terminal device computing power data.

[0058] Table 1: The most recent intelligent manufacturing task and terminal equipment computing power data record

[0059] Furthermore, the present application also includes: monitoring the execution of subtasks on the terminal device and establishing execution monitoring results; performing execution delay analysis on the subtasks based on the execution monitoring results and establishing execution delay identification; establishing optimization constraints based on the execution delay identification, and optimizing the execution plan with the optimization constraints.

[0060] Specifically, the execution of subtasks is monitored on the terminal device. Execution monitoring refers to real-time tracking and recording of the execution of subtasks by the terminal device, such as monitoring the execution time of the task, computing resource consumption, data transmission delay and other key indicators. These monitoring data are collated and stored to establish the execution monitoring results for subsequent analysis. For example, if the CPU usage of a terminal device is always close to 100% when executing a subtask, while the CPU usage of another device executing the same task is only 50%, then this difference will be recorded to determine whether the task allocation strategy needs to be adjusted.

[0061] Next, based on the execution monitoring results, the execution delay analysis of the subtask is performed, the execution delay mark is established, and the execution efficiency of the task is analyzed using the execution monitoring data. Execution delay refers to the delay that occurs during the execution of a subtask, such as the task execution time exceeds expectations, the task execution progress lags behind the scheduling plan, etc. Execution delays may be caused by a variety of factors, such as insufficient computing resources, network communication delays, or unreasonable task scheduling. The execution delay mark is a label for these task delays and is used to optimize the task scheduling strategy. For example, if a terminal device causes task delays due to excessive load, an execution delay mark can be assigned to the device to remind the task scheduling system to reduce the task allocation to the device.

[0062] Then, optimization constraints are established based on the execution delay flags, and the execution plan is optimized based on the optimization constraints. Optimization constraints refer to the rules for optimizing task allocation based on execution delay flags to ensure that tasks can be executed more efficiently. For example, if a device is judged to be overloaded due to an execution delay flag, it can be restricted from receiving new tasks, or some tasks can be reallocated to devices with lower loads. Optimization refers to adjusting the entire task execution plan based on optimization constraints to improve task execution efficiency. For example, if it is found that some devices have long-term execution delays while other devices still have computing margins, the optimized plan may reallocate tasks to shorten the overall execution time and improve the utilization of computing resources.

[0063] Furthermore, the present application also includes: performing a synchronous execution impact analysis of the linked tasks according to the delay identifier, and generating a delay time constraint according to the impact analysis result; obtaining the task requirement constraint of the delay subtask according to the delay identifier, and performing succession optimization of the terminal device according to the delay time constraint and the task requirement constraint, so as to optimize the execution plan based on the succession optimization result.

[0064] Specifically, the synchronous execution impact analysis of linked tasks is performed based on the hysteresis flag. The hysteresis flag is used to identify subtasks whose execution time exceeds expectations. Linked tasks refer to multiple interdependent tasks. For example, the output of one task may be the input of another task. Therefore, if a task is delayed, it may affect the execution of subsequent tasks. Synchronous execution impact analysis refers to the evaluation of such impact. For example, if a task is expected to be completed within 10 minutes, but it actually takes 15 minutes to execute, then the downstream tasks that depend on this task will also be affected.

[0065] Generating delay time constraints based on the impact analysis results means setting new time constraints. For example, if the execution delay of a task affects the overall production process, the time windows of other tasks may be adjusted to reduce the overall delay impact.

[0066] Next, the task requirement constraints of the delayed subtasks are obtained according to the delay identifier, and the terminal device is optimized for succession according to the delay time constraint and the task requirement constraint. Delayed subtasks refer to tasks that affect the overall progress due to execution delays, and the task requirement constraints refer to the computing resources, storage space, network bandwidth, etc. required for these tasks when they are executed. For example, if a subtask requires high computing power, but the computing power of the assigned device is low, resulting in task delay, then its computing resource requirement constraints can be adjusted to better match the appropriate computing resources in the succession optimization process. Succession optimization refers to finding terminal devices that are more suitable for executing these delayed tasks. Possible strategies include transferring tasks to devices with stronger computing power, reducing the computational complexity of tasks, or adjusting the task scheduling order to reduce the overall delay. For example, if a device is delayed due to insufficient computing power, and another device is currently under a low load, the task can be reallocated to the latter to speed up the execution progress.

[0067] Finally, optimization of the execution plan based on the successor optimization result means that after the successor optimization is completed, the overall execution strategy is optimized according to the new task allocation plan. The goal of optimization is to ensure that all tasks can be completed in the shortest time while maximizing the utilization of computing resources. For example, in an intelligent manufacturing environment, if a task is delayed due to excessive equipment load, the successor optimization can be used to find equipment with lower load and reallocate tasks, so that the overall execution time is shortened, thereby optimizing production efficiency.

[0068] Furthermore, the present application also includes: determining whether the optimization adaptation value of the terminal device that has completed the subtask execution meets the preset threshold; if the optimization adaptation value of the terminal device that has completed the subtask execution meets the preset threshold, the terminal device with the highest optimization adaptation value is output as the replacement optimization result.

[0069] Specifically, a terminal device that has completed the execution of a subtask refers to a computing node that has completed a subtask, just completing the current task, and may or may not have subsequent tasks. Determine whether the optimal adaptation value of the terminal device that has completed the execution of the subtask meets the preset threshold. The optimal adaptation value is an indicator used to measure the quality of task execution of the terminal device, which may include a comprehensive score of multiple factors such as task execution time, computing resource utilization, and energy efficiency. The preset threshold is a set minimum standard used to determine whether the execution performance of the device meets the requirements. For example, if the full score of the optimal adaptation value is 100%, and the preset threshold is set to 70%, only devices with an optimal adaptation value greater than or equal to 70% will be considered qualified execution devices. The process of determining whether the optimal adaptation value meets the preset threshold can help screen out devices with better performance and avoid assigning tasks to devices with poor execution capabilities.

[0070] Next, if the optimization adaptation value of the terminal device that has completed the subtask meets the preset threshold, the terminal device with the highest optimization adaptation value will be output as the successor optimization result. If the optimization adaptation value of a terminal device meets the minimum requirement set by the system, the device with the highest optimization adaptation value will be selected from all devices that meet the requirements as the best candidate for successor task execution. For example, suppose three terminal devices have completed the subtask and obtained optimization adaptation values ​​of 75%, 85% and 90% respectively, and the preset threshold is 70%, then the terminal device corresponding to the 90% with the highest optimization adaptation value will be selected as the final successor optimization result. The output of the successor optimization result can be used for subsequent task scheduling. For example, if there is a new subtask to be executed, it can be assigned to the device with the high adaptation value first to ensure more efficient execution.

[0071] Furthermore, the present application also includes: if the optimization adaptation value of the terminal device that has completed the subtask cannot meet the preset threshold, then the interruption loss of the terminal device that has not completed the subtask is calculated; after compensating the optimization adaptation value of the terminal device that has not completed the subtask according to the interruption loss, the replacement optimization result is reconstructed.

[0072] Specifically, if the optimal adaptation value of the terminal device that has completed the subtask cannot meet the preset threshold, the interruption loss of the terminal device that has not completed the subtask is calculated. The optimal adaptation value is an indicator to measure the quality of the device's task execution. If the value is lower than the preset threshold set by the system, it means that the execution performance of the device is not ideal, and there may be problems such as low computing efficiency, long task execution time, or low resource utilization. In this case, the interruption loss of the terminal device that has not completed the subtask is calculated. The terminal device that has not completed the subtask refers to the device that failed to complete the task, such as the device whose task was interrupted due to insufficient computing power, network delays, or system failures. Interruption loss is an indicator to measure the negative impact of the incomplete task, which may include waste of computing resources, extended task execution time, and chain reactions to other tasks.

[0073] Next, after compensating the optimal adaptation value of the terminal device whose subtask has not been completed according to the interruption loss, the succession optimization result is reconstructed. For the device that has not completed the task, the optimal adaptation value is adjusted according to its interruption loss to compensate for the impact of the unfinished task. For example, if a device fails to complete a task due to insufficient computing resources, but its communication stability and energy efficiency are high, it may be given a certain optimal adaptation value compensation to more fairly measure its comprehensive capabilities. The compensated optimal adaptation value can be used to optimize task scheduling to ensure that suitable devices can be matched more reasonably when tasks are reallocated. Reconstructing the succession optimization result means that after the compensation adjustment, the best task succession plan will be recalculated, and the most suitable device will be selected to perform the unfinished task.

[0074] To sum up, the AloT cloud-edge-end intelligent control method for industrial Internet of Things provided in this application has the following technical effects: by realizing the technical goals of intelligent task scheduling and optimal allocation under the cloud-edge-end collaborative computing architecture, the technical effects of improving task execution efficiency, reducing computing delays, optimizing computing resource utilization, and enhancing data security are achieved.

[0075] Embodiment 2: Based on the same inventive concept as the AloT cloud-edge-end intelligent control method for industrial Internet of Things in the aforementioned embodiment, this application also provides an AloT cloud-edge-end intelligent control system for industrial Internet of Things. Please refer to the attached Figure 2 , including: a collaborative architecture creation module 11, used to create a cloud-edge-end collaborative architecture, the cloud-edge-end collaborative architecture includes a cloud center, an edge computing node and a terminal device, and obtains the intelligent manufacturing task from the cloud center; an adaptation analysis module 12, used to obtain the device performance and device function of the terminal device at the edge computing node, perform adaptation analysis of the intelligent manufacturing task according to the device performance and device function, and establish a first adaptation constraint; a task decomposition module 13, used to activate the dynamic task decomposition channel of the cloud center, use the dynamic task decomposition channel to perform task decomposition of the intelligent manufacturing task, and create M subtasks; a competition game analysis module 14, used to use the cloud center to perform task competition game analysis of the terminal device on the M subtasks, and establish a second adaptation constraint; a solution generation module 15, used to perform a balance analysis on the first adaptation constraint and the second adaptation constraint, and generate an intelligent execution solution for the terminal device of the intelligent manufacturing task.

[0076] Furthermore, the AloT cloud-edge intelligent control system for industrial Internet of Things is also used to: activate the task competition game analysis function of the cloud center, execute task competition game analysis of M subtasks, the game characteristics of the competition game analysis function include equipment adaptability characteristics, historical performance consistency characteristics, communication stability characteristics, and data fidelity assurance characteristics, and the task competition game analysis function performs game compensation through collaborative factors between tasks.

[0077] Furthermore, the AloT cloud-edge intelligent control system for industrial Internet of Things is also used to: upload the time constraints, computational complexity constraints, computational requirement constraints, and task association constraints of the intelligent manufacturing tasks; obtain the highest computing power and the lowest computing power of the terminal device, and establish granularity decomposition constraints based on the highest computing power and the lowest computing power; perform task decomposition of the intelligent manufacturing tasks based on the granularity decomposition constraints, the time constraints, computational complexity constraints, computational requirement constraints, and task association constraints to create M subtasks.

[0078] Furthermore, the AloT cloud-edge intelligent control system for industrial Internet of Things is also used to: monitor the execution of subtasks on the terminal device and establish execution monitoring results; perform execution delay analysis of subtasks based on the execution monitoring results and establish execution delay identification; establish optimization constraints based on the execution delay identification, and optimize the execution plan with the optimization constraints.

[0079] Furthermore, the AloT cloud-edge intelligent control system for industrial Internet of Things is also used to: perform impact analysis on the synchronous execution of linkage tasks according to the hysteresis identifier, and generate hysteresis time constraints according to the impact analysis results; obtain task requirement constraints of hysteresis subtasks according to the hysteresis identifier, and perform succession optimization of terminal equipment according to the hysteresis time constraints and the task requirement constraints, so as to optimize the execution plan of the succession optimization results.

[0080] Furthermore, the AloT cloud-edge intelligent control system for industrial Internet of Things is also used to: determine whether the optimization adaptation value of the terminal device that has completed the subtask execution meets the preset threshold; if the optimization adaptation value of the terminal device that has completed the subtask execution meets the preset threshold, the terminal device with the highest optimization adaptation value will be output as the successor to the optimization result.

[0081] Furthermore, the AloT cloud-edge intelligent control system for industrial Internet of Things is also used to: if the optimal adaptation value of the terminal device that has completed the subtask cannot meet the preset threshold, calculate the interruption loss of the terminal device that has not completed the subtask; after compensating the optimal adaptation value of the terminal device that has not completed the subtask according to the interruption loss, reconstruct the successor optimization result.

[0082] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The AloT cloud-edge intelligent control method for industrial Internet of Things and the specific examples in the aforementioned embodiment one are also applicable to the AloT cloud-edge intelligent control system for industrial Internet of Things in this embodiment. Through the aforementioned detailed description of the AloT cloud-edge intelligent control method for industrial Internet of Things, technical personnel in this field can clearly understand the AloT cloud-edge intelligent control system for industrial Internet of Things in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here.

[0083] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0084] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. AloT cloud-edge-end intelligent control method for industrial Internet of Things, characterized in that: include: Creating a cloud-edge-end collaborative architecture, which includes a cloud center, edge computing nodes, and terminal devices, and obtaining smart manufacturing tasks from the cloud center; Acquire device performance and device function of the terminal device at the edge computing node, perform adaptation analysis of the intelligent manufacturing task according to the device performance and device function, and establish a first adaptation constraint; Activate the dynamic task decomposition channel of the cloud center, use the dynamic task decomposition channel to decompose the intelligent manufacturing task, and create M subtasks; Using the cloud center to perform task competition game analysis of the terminal devices on the M subtasks, and establish a second adaptation constraint; A balance analysis is performed on the first adaptation constraint and the second adaptation constraint to generate an intelligent execution plan for the terminal device of the intelligent manufacturing task.

2. The AloT cloud-edge-end intelligent control method for industrial Internet of Things according to claim 1 is characterized in that: The using the cloud center to perform task competition game analysis of the terminal device on the M subtasks includes: Activate the task competition game analysis function of the cloud center and perform task competition game analysis of M subtasks. The game characteristics of the competition game analysis function include device adaptability characteristics, historical performance consistency characteristics, communication stability characteristics, and data fidelity assurance characteristics. The task competition game analysis function performs game compensation through collaborative factors between tasks.

3. The AloT cloud-edge-end intelligent control method for industrial Internet of Things as claimed in claim 1 is characterized in that: The using the cloud center to perform task competition game analysis of the terminal device on the M subtasks also includes: Evaluate the computing capability of the terminal device according to the device performance of the terminal device, and establish a weak computing device performance protection mark; Perform load analysis on the terminal device within a preset period and establish a high load identification; The weak computing device performance protection identifier and the high load identifier are used to perform balanced compensation for task competition game analysis, and a second adaptation constraint is established according to the balanced compensation result.

4. The AloT cloud-edge-end intelligent control method for industrial Internet of Things as claimed in claim 1 is characterized in that: The step of utilizing the dynamic task decomposition channel to decompose the intelligent manufacturing task and create M subtasks includes: Upload the time constraints, computational complexity constraints, computational demand constraints, and task association constraints of the intelligent manufacturing task; Acquire the highest computing capability and the lowest computing capability of the terminal device, and establish a granularity decomposition constraint according to the highest computing capability and the lowest computing capability; The intelligent manufacturing task is decomposed according to the granularity decomposition constraint, the time constraint, the computational complexity constraint, the computational requirement constraint, and the task association constraint to create M subtasks.

5. The AloT cloud-edge-end intelligent control method for industrial Internet of Things according to claim 1 is characterized in that: After the intelligent execution scheme of the terminal device of the intelligent manufacturing task is generated, it includes: Performing execution monitoring of the subtasks on the terminal device and establishing execution monitoring results; Performing execution delay analysis of the subtask based on the execution monitoring result and establishing an execution delay indicator; An optimization constraint is established according to the execution delay identifier, and the execution scheme is optimized with the optimization constraint.

6. The AloT cloud-edge-end intelligent control method for industrial Internet of Things as claimed in claim 5 is characterized in that: The step of establishing an optimization constraint according to the execution delay identifier and optimizing the execution scheme with the optimization constraint includes: Performing a synchronous execution impact analysis of the linkage tasks according to the hysteresis identifier, and generating a hysteresis time constraint according to the impact analysis result; The task requirement constraint of the hysteresis subtask is obtained according to the hysteresis identifier, and the succession optimization of the terminal device is performed according to the hysteresis time constraint and the task requirement constraint, so as to optimize the succession optimization result execution plan.

7. The AloT cloud-edge-end intelligent control method for industrial Internet of Things as claimed in claim 6 is characterized in that: The performing the optimal replacement of the terminal device according to the hysteresis time constraint and the task requirement constraint also includes: Determine whether the optimal adaptation value of the terminal device after the subtask is completed meets the preset threshold; If the optimization adaptation value of the terminal device that has completed the subtask execution meets the preset threshold, the terminal device with the highest optimization adaptation value is output as the replacement optimization result.

8. The AloT cloud-edge-end intelligent control method for industrial Internet of Things as claimed in claim 7 is characterized in that: The step of determining whether the optimal adaptation value of the terminal device after the subtask is completed satisfies a preset threshold value also includes: If the optimal adaptation value of the terminal device that has completed the subtask cannot meet the preset threshold, then the interruption loss of the terminal device that has not completed the subtask is calculated; After compensating the optimal adaptation value of the terminal device whose subtask has not been completed according to the interruption loss, the replacement optimal result is reconstructed.

9. AloT cloud-edge-end intelligent control system for industrial Internet of Things, characterized by: The steps for implementing the AloT cloud-edge-end intelligent control method for industrial Internet of Things as described in any one of claims 1 to 8 include: A collaborative architecture creation module is used to create a cloud-edge-end collaborative architecture, which includes a cloud center, edge computing nodes, and terminal devices, and obtains intelligent manufacturing tasks from the cloud center; An adaptation analysis module, used to obtain device performance and device functions of a terminal device at an edge computing node, perform adaptation analysis on the intelligent manufacturing task according to the device performance and device functions, and establish a first adaptation constraint; A task decomposition module, used to activate a dynamic task decomposition channel of a cloud center, use the dynamic task decomposition channel to decompose the intelligent manufacturing task, and create M subtasks; A competition game analysis module, used to use the cloud center to perform task competition game analysis of the terminal device on the M subtasks and establish a second adaptation constraint; A solution generation module is used to perform a balance analysis on the first adaptation constraint and the second adaptation constraint to generate an intelligent execution solution for the terminal device of the intelligent manufacturing task.

Images