Task processing method, system and device based on Internet of Things edge computing and medium

Abstract

The invention relates to a task processing method, system and device based on Internet of Things edge computing and a medium, and belongs to the technical field of computing task processing, and the task processing method based on Internet of Things edge computing comprises the following steps: obtaining attribute data of a task; processing the attribute data of the task and calculating a task priority judgment value; evaluating the priority degree of the task based on the task priority judgment value; acquiring state data of the system, and calculating a system resource use value based on the state data of the system; and performing dynamic adjustment based on the priority degree of the task and the resource use value of the system. According to the method, the task execution sequence and the resource allocation are dynamically adjusted, so that the high-priority task can be preferentially processed, and the response speed of the system is improved; by monitoring the resource use condition of the system in real time and performing dynamic adjustment according to task requirements, limited computing resources can be utilized more effectively, and the performance and efficiency of the whole system are improved.

Description

Technical Field

[0001] The present disclosure belongs to the technical field of computing task processing, and particularly relates to a task processing method, system, device, and medium based on Internet of Things (IoT) edge computing. Background Art

[0002] With the rapid development of IoT technology, it has indeed brought unprecedented device connections and data generation volumes, greatly promoting the progress in fields such as smart cities, industrial automation, and smart homes.

[0003] However, when facing such a large amount of data, traditional IoT systems often encounter problems such as high data transmission latency and low system response speed. Edge computing technology can effectively alleviate these problems by pushing computing resources to the network edge, but there are still certain deficiencies in aspects such as edge computing resource management and achieving dynamic scheduling and optimization of tasks.

[0004] Therefore, it is necessary to provide a new task processing method, system, device, and medium based on IoT edge computing to solve the above technical problems. Summary of the Invention

[0005] The purpose of the present disclosure is to provide a task processing method, system, device, and medium based on IoT edge computing to solve the above problems.

[0006] The present disclosure achieves the above purpose through the following technical solutions:

[0007] A task processing method based on IoT edge computing includes the following steps:

[0008] Obtain the attribute data of the task;

[0009] Process the attribute data of the task and calculate the task priority judgment value;

[0010] Evaluate the priority of the task based on the task priority judgment value;

[0011] Obtain the status data of the system, and calculate the system resource usage value based on the status data of the system;

[0012] Perform dynamic adjustment based on the priority of the task and the resource usage value of the system.

[0013] As a further optimization scheme of the present disclosure, the attribute data of the task includes the processing time requirement value, the task execution frequency, and the maximum delay time value of the task.

[0014] As a further optimization scheme of the present disclosure, processing the attribute data of the task and calculating the task priority judgment value includes:

[0015] During a preset monitoring period, perform a ratio process on the processing time requirement value and the monitoring period to obtain a processing time requirement ratio SJ;

[0016] Compare the maximum delay value of the task with the monitoring period to obtain a delay time ratio YC;

[0017] Mark the reciprocal of the task execution frequency as the task execution frequency PL;

[0018] Calculate a task priority judgment value YX based on the processing time requirement ratio SJ, the delay time ratio YC, and the task execution frequency PL.

[0019] As a further optimization solution of the present disclosure, calculating the task priority judgment value YX based on the processing time requirement ratio SJ, the delay time ratio YC, and the task execution frequency PL includes:

[0020] Substitute the processing time requirement ratio SJ, the delay time ratio YC, and the task execution frequency PL into a calculation formula to calculate the task priority judgment value YX. The calculation formula is as follows:

[0021]

[0022] Where s1, s2, and s3 are preset proportionality coefficients.

[0023] As a further optimization solution of the present disclosure, evaluating the priority level of a task based on the obtained task priority judgment value includes:

[0024] Set a task priority judgment threshold, compare the task priority judgment threshold with the task priority judgment value, and classify the priority level of the task;

[0025] As a further optimization solution of the present disclosure, obtaining the status data of the system and calculating the system resource usage value based on the status data of the system includes:

[0026] The status data of the system includes the CPU usage rate CP, the memory occupancy rate NC, and the network bandwidth usage rate KD;

[0027] Substitute the CPU usage rate CP, the memory occupancy rate NC, and the network bandwidth usage rate KD into a calculation formula to calculate the system resource usage value YS. The formula is as follows:

[0028]

[0029] Where a1, a2, and a3 are preset proportionality coefficients.

[0030] As a further optimization solution of the present disclosure, the dynamic adjustment based on the priority of the task and the resource usage value of the system includes:

[0031] Substitute the priority judgment value of the task and the resource usage value of the system into the trained scheduling policy model, sort the tasks in the task list according to the output scheduling policy value, and give priority to executing high-priority tasks.

[0032] A task processing system based on Internet of Things edge computing includes:

[0033] A data acquisition module for acquiring attribute data of a task;

[0034] A task priority judgment value calculation module for processing the attribute data of the task and calculating a task priority judgment value;

[0035] A task priority evaluation module for evaluating the priority of a task based on the task priority judgment value;

[0036] A system resource usage value calculation module for acquiring the status data of the system and calculating the system resource usage value based on the status data of the system;

[0037] A dynamic adjustment module for performing dynamic adjustment based on the priority of the task and the resource usage value of the system.

[0038] An electronic device includes a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete communication with each other through the communication bus;

[0039] The memory is used for storing computer programs;

[0040] The processor is used for executing the programs stored in the memory to implement a task processing method based on Internet of Things edge computing.

[0041] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements a task processing method based on Internet of Things edge computing.

[0042] The beneficial effects of the present disclosure are as follows:

[0043] Since data processing and calculation are both performed on edge devices in the present disclosure, the time for data to be transmitted from edge devices to the cloud or data centers is reduced, thereby reducing the overall data transmission delay;

[0044] By dynamically adjusting the task execution order and resource allocation, high-priority tasks can be preferentially processed, thereby improving the response speed of the system;

[0045] By monitoring the system resource usage in real time and making dynamic adjustments according to the task requirements, it is possible to more effectively utilize the limited computing resources and improve the performance and efficiency of the overall system. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a flowchart of the method in an embodiment of the present disclosure;

[0047] Figure 2 is a block diagram of the system structure in an embodiment of the present disclosure;

[0048] Figure 3 is a block diagram of the device structure in an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] The following further describes the present application in detail with reference to the accompanying drawings. It is necessary to point out here that the following specific embodiments are only used to further illustrate the present application and should not be construed as limiting the protection scope of the present application. Those skilled in the art can make some non-essential improvements and adjustments to the present application according to the above application content.

[0050] As Figure 1 shown, a task processing method based on Internet of Things edge computing includes the following steps:

[0051] Obtain the attribute data of the task;

[0052] Process the attribute data of the task and calculate the task priority judgment value;

[0053] Evaluate the priority of the task based on the task priority judgment value;

[0054] Obtain the status data of the system and calculate the system resource usage value based on the status data of the system;

[0055] Make dynamic adjustments based on the priority of the task and the resource usage value of the system.

[0056] In this embodiment, it specifically includes:

[0057] S1: Obtain the attribute data of the task;

[0058] In a specific embodiment, the attribute data of the task includes the data volume size, the processing time requirement value, and the task execution frequency;

[0059] The attribute data of the task may also include the estimated resource consumption of the task, the complexity value of the task, and the maximum delay time value of the task;

[0060] S2: Process the obtained attribute data of the task and calculate the task priority judgment value;

[0061] In a specific embodiment, a preset monitoring period is set, a processing time requirement value is obtained, and the processing time requirement value is ratio-processed with the monitoring period to obtain a processing time requirement ratio SJ;

[0062] The maximum delay time value of the task is obtained, and the maximum delay value of the task is compared with the monitoring period to obtain a delay time ratio YC;

[0063] The task execution frequency is obtained, and the reciprocal of the task execution frequency is obtained and marked as the task execution frequency PL;

[0064] The processing time requirement ratio SJ, the delay time ratio YC, and the task execution frequency PL are substituted into the formula The task priority judgment value YX is calculated, where s1, s2, and s3 are preset proportionality coefficients.

[0065] S3: Based on the obtained task priority judgment value, evaluate the priority of the task;

[0066] In a specific embodiment, a task priority judgment threshold is set, the task priority judgment threshold is compared with the task priority judgment value, and the priority of the task is classified, for example, into three levels: high, medium, and low.

[0067] S4: Obtain the status data of the system and calculate the system resource usage value;

[0068] In a specific embodiment, the system status data includes the CPU usage rate, the memory occupancy rate, and the network bandwidth usage rate. The system status data here is only an example and is not limited thereto;

[0069] Among them, the status data of the system is obtained in real time through a system monitoring tool or an API interface;

[0070] The CPU usage rate CP, the memory occupancy rate NC, and the network bandwidth usage rate KD are substituted into the formula The resource usage value YS is calculated, where a1, a2, and a3 are preset proportionality coefficients;

[0071] By setting a resource usage threshold, the resource usage value is compared with the resource usage threshold to evaluate the performance status of the system.

[0072] S5: Perform dynamic adjustment based on the priority of the task and the resource usage value of the system;

[0073] In a specific embodiment, train a scheduling policy model:

[0074] By extracting multiple groups of data from historical data, each group of data includes a task priority judgment value, a resource usage value, and a scheduling policy value, where the scheduling policy value is the task completion time;

[0075] Select the polynomial regression model DD = m * YX + n * YS + p for training, and divide the extracted historical data into a training array and a validation array;

[0076] Substitute the calculated task priority judgment value and resource usage value into the trained scheduling policy model, sort the tasks in the task list according to the output scheduling policy value, give priority to executing high-priority tasks, and improve the task execution efficiency as much as possible when the system resources permit.

[0077] Calculate the task priority judgment value through the attribute data of the task, and evaluate the priority of the task based on the task priority judgment value. The multi-dimensional comprehensive calculation can better reflect the accuracy of the task priority, and the tasks to be processed can be understood. Based on the evaluated level classification, the tasks with higher priority can be processed;

[0078] At the same time, obtain the system status data to calculate the system resource usage value, and perform dynamic scheduling of tasks based on the task priority and the system resource usage value to ensure that high-priority tasks are given priority to be processed when the resources are sufficient or the resource reallocation can meet their needs, which not only improves the system response speed but also realizes the efficient utilization of resources and load balancing;

[0079] Closely combine the task priority evaluation with the dynamic allocation of system resources to form a closed-loop collaborative mechanism. This mechanism can dynamically adjust the execution order of tasks and resource allocation according to the task attributes and the real-time state of the system, thus realizing the intelligence and automation of task scheduling.

[0080] As Figure 2 shown, the embodiments of the present disclosure provide a task processing system based on Internet of Things edge computing, including:

[0081] A data acquisition module for acquiring the attribute data of the task;

[0082] A task priority judgment value calculation module for processing the attribute data of the task and calculating the task priority judgment value;

[0083] A task priority evaluation module for evaluating the priority of the task based on the task priority judgment value;

[0084] A system resource usage value calculation module for obtaining the system status data and calculating the system resource usage value based on the system status data;

[0085] A dynamic adjustment module for performing dynamic adjustment based on the priority of the task and the system resource usage value.

[0086] For the implementation process of the functions and roles of each module in the above system, please refer to the implementation process of the corresponding steps in the above method for details, which will not be elaborated here.

[0087] For the system embodiment, since it basically corresponds to the method embodiment, the relevant parts can be referred to the partial description of the method embodiment. The system embodiment described above is only illustrative. The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the present disclosure solution. Those of ordinary skill in the art can understand and implement it without creative work.

[0088] In the above embodiments, any multiple of all the modules can be combined and implemented in one module, or any one of the modules can be split into multiple modules. Or, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. At least one of all the modules can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on chip, a system on substrate, a system on package, an application specific integrated circuit (ASIC), or can be implemented by any other reasonable way of integrating or packaging circuits, etc., in hardware or firmware, or implemented in any one of the three implementation ways of software, hardware, and firmware, or in an appropriate combination of any several of them. Or, at least one of all the modules can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding functions can be executed.

[0089] See Figure 3 , the electronic device provided by the embodiment of the present disclosure includes a processor 1110, a communication interface 1120, a memory 1130, and a communication bus 1140. Among them, the processor 1110, the communication interface 1120, and the memory 1130 communicate with each other through the communication bus 1140;

[0090] The memory 1130 is used to store computer programs;

[0091] When the processor 1110 is used to execute the program stored on the memory 1130, it implements the task processing method based on Internet of Things edge computing as shown below.

[0092] The communication bus 1140 described above may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus 1140 may be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience in representation, only a thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0093] The communication interface 1120 is used for communication between the above-mentioned electronic device and other devices.

[0094] The memory 1130 may include a Random Access Memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. Optionally, the memory 1130 may also be at least one storage device located far from the aforementioned processor 1110.

[0095] The processor 1110 described above may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0096] Embodiments of the present disclosure also provide a computer-readable storage medium. A computer program is stored on the above-mentioned computer-readable storage medium, and when the computer program is executed by a processor, the task processing method based on Internet of Things edge computing as described above is implemented.

[0097] The computer-readable storage medium may be included in the device / device described in the above embodiments; it may also exist separately without being assembled into the device / device. The above-mentioned computer-readable storage medium carries one or more programs, and when the one or more programs are executed, the task processing method based on Internet of Things edge computing according to the embodiments of the present disclosure is implemented.

[0098] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, it may include but is not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In the present disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program, and the program can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0099] The above-described embodiments merely represent several implementation manners of the present disclosure, and the description thereof is relatively specific and detailed. However, it should not be construed as a limitation on the patent scope of the present disclosure. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present disclosure, several modifications and improvements can still be made, and these all belong to the protection scope of the present disclosure.

Claims

1. A task processing method based on edge computing of the Internet of Things, characterized in that: The following steps are involved: Get the attribute data of the task; Processing the attribute data of the task and calculating the task priority judgment value; Based on the task priority judgment value, evaluating the priority of the task; Acquire system status data, and calculate system resource usage values ​​based on the system status data; Dynamic adjustment is performed based on the priority of the task and the resource usage value of the system.

2. According to claim 1, a task processing method based on edge computing of the Internet of Things is characterized in that: The attribute data of the task includes a processing time requirement value, a task execution frequency, and a maximum delay time value of the task.

3. The task processing method based on edge computing of the Internet of Things according to claim 2 is characterized in that: Processing the attribute data of the task and calculating the task priority judgment value includes: Preset a monitoring period, perform ratio processing on the processing time requirement value and the monitoring period to obtain a processing time requirement ratio SJ; Compare the maximum delay value of the task with the monitoring period to obtain a delay time ratio YC; The inverse of the task execution frequency is marked as the task execution frequency PL; The task priority determination value YX is calculated based on the processing time requirement ratio SJ, the delay time ratio YC, and the task execution frequency PL.

4. The task processing method based on edge computing of the Internet of Things according to claim 3 is characterized in that: The task priority judgment value YX calculated based on the processing time requirement ratio SJ, the delay time ratio YC and the task execution frequency PL includes: Substitute the processing time requirement ratio SJ, the delay time ratio YC and the task execution frequency PL into the calculation formula to obtain the task priority judgment value YX, and the calculation formula is as follows: Among them, s1, s2, and s3 are preset proportional coefficients.

5. The task processing method based on edge computing of the Internet of Things according to claim 1 is characterized in that: Based on the obtained task priority judgment value, the priority of the task is evaluated including: Set a task priority judgment threshold, compare the task priority judgment threshold with the task priority judgment value, and classify the priority of the tasks.

6. The task processing method based on edge computing of the Internet of Things according to claim 1 is characterized in that: Acquiring system status data, and calculating system resource usage values ​​based on the system status data includes: The system status data includes CPU usage CP, memory occupancy NC, and network bandwidth usage KD; Substitute the CPU usage rate CP, the memory occupancy rate NC and the network bandwidth usage rate KD into the calculation formula to calculate the system resource usage value YS, which is as follows: Among them, a1, a2, and a3 are preset proportional coefficients.

7. The task processing method based on edge computing of the Internet of Things according to claim 1 is characterized in that: The dynamic adjustment based on the priority of the task and the resource usage value of the system includes: The priority judgment value of the task and the resource usage value of the system are substituted into the trained scheduling strategy model, and the tasks in the task list are sorted according to the output scheduling strategy value, and high-priority tasks are executed first.

8. A task processing system based on edge computing of the Internet of Things, characterized in that: include: Data acquisition module, used to obtain attribute data of tasks; A task priority judgment value calculation module, used for processing the attribute data of the task and calculating the task priority judgment value; A task priority evaluation module, which evaluates the priority of the task based on the task priority judgment value; A system resource usage value calculation module, which obtains system status data and calculates the system resource usage value based on the system status data; The dynamic adjustment module is used to perform dynamic adjustment based on the priority of the task and the resource usage value of the system.

9. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; Memory, for storing computer programs; A processor is used to execute the program stored in the memory to implement the task processing method based on edge computing of the Internet of Things described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the task processing method based on edge computing of the Internet of Things according to any one of claims 1 to 7 is implemented.

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