Application interaction method and system based on cloud edge collaborative edge cluster system

Abstract

The invention discloses an application interaction method, system and device based on a cloud edge collaborative edge cluster system and a medium, and belongs to the technical field of system application. The level of the system application task is determined from two dimensions of time required for processing the system application task and the emergency degree of the system application task, so that the level determination of the system application task is more scientific; meanwhile, weight calculation is carried out on the time required for processing the system application task and the emergency degree of the system application task through a subjective and objective combination method, so that the accuracy of weight determination is improved, and better cloud edge interaction can be realized.

Description

Technical Field

[0001] This invention relates to the field of system application technology, specifically to an application interaction method, system, device, and medium based on a cloud-edge collaborative edge cluster system. Background Technology

[0002] The cloud-edge collaborative system is a novel application system combining cloud computing and edge computing technologies. It aims to improve the operational efficiency, stability, and economy of power systems. By establishing efficient collaboration between the cloud computing platform and edge computing nodes, it enables real-time monitoring, optimized scheduling, and intelligent decision-making in the power system. The cloud possesses powerful data storage and computing capabilities, while edge devices, due to their closer physical location, achieve low-latency real-time responses. This combination significantly improves data processing speed and decision-making efficiency in the power environment. An edge cluster is a collection of computing resources deployed at edge nodes within the system application for real-time data processing and control tasks. These edge nodes, located close to the data source, can quickly respond to local events, reduce data transmission latency, and improve the system's real-time performance and reliability. Through collaborative work with the cloud system, the edge cluster achieves efficient monitoring and optimized scheduling of the power system.

[0003] In existing technologies, there are schemes for interaction between system application systems and edge clusters based on lightweight cloud-edge interaction models. For example, Chinese invention patent (CN 118972404A) discloses an edge cluster access method and system in a cloud-edge collaborative system application system. The edge cluster connects to the cloud system, and the edge cluster and the cloud system interact with each other, as well as with other business systems, to exchange models, data, and operation and maintenance management information. The edge cluster also connects to an edge gateway, and the edge gateway and the edge cluster interact with each other to exchange models, data, status, and operation and maintenance management information. However, the above scheme does not integrate edge cluster access with scheduling application tasks, resulting in a lack of scientific rigor in edge cluster access. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, this invention provides an application interaction method based on a cloud-edge collaborative edge cluster system to solve the problems existing in the prior art.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: an application interaction method based on a cloud-edge collaborative edge cluster system, comprising,

[0007] The level of system application tasks is determined by a lightweight cloud-edge interaction model;

[0008] The order of application interactions is determined based on the system application task level to improve cloud-edge interaction efficiency.

[0009] As a preferred embodiment of the application interaction method based on a cloud-edge collaborative edge cluster system described in this invention, the lightweight cloud-edge interaction model includes two levels: urgent tasks and non-urgent tasks.

[0010] As a preferred embodiment of the application interaction method based on a cloud-edge collaborative edge cluster system described in this invention, the lightweight cloud-edge interaction model further includes calculating the time required to process system application tasks based on the load capacity, average computing power, scheduling load, and average transmission rate of the edge cluster.

[0011] The urgency parameter of the task is obtained based on the time required to process the system application task, and the system application tasks are classified into levels, assigned level coefficients, and the level coefficients are calculated using a combination of subjective and objective methods.

[0012] As a preferred embodiment of the application interaction method based on a cloud-edge collaborative edge cluster system described in this invention, the subjective-objective combination method includes:

[0013] The first value of the grade coefficient is determined by expert scoring;

[0014] The second value of the rank coefficient is determined using hierarchical analysis;

[0015] The value of the grade coefficient is determined based on the first value and the second value.

[0016] As a preferred embodiment of the application interaction method based on a cloud-edge collaborative edge cluster system described in this invention, the first value includes: selecting experts to assign weights to the grade coefficients respectively, and taking the average as the first value of the grade coefficients.

[0017] As a preferred embodiment of the application interaction method based on a cloud-edge collaborative edge cluster system described in this invention, the second value includes scoring evaluation indicators and creating a judgment matrix. The judgment matrix is ​​constructed based on the relative importance of different evaluation indicators, including the time required to process system application tasks and the urgency of system application tasks.

[0018] As a preferred embodiment of the application interaction method based on a cloud-edge collaborative edge cluster system described in this invention, the determination of the order of application interactions includes:

[0019] If the system application task level is an urgent task, the cloud server will specify the edge cluster to perform task priority calculation based on the computing power of the edge cluster.

[0020] If the system application task is classified as a non-urgent task, then the task calculation will be postponed.

[0021] Another objective of this invention is to provide an application interaction system based on a cloud-edge collaborative edge cluster system.

[0022] To solve the above technical problems, the present invention provides the following technical solution: an application interaction system based on a cloud-edge collaborative edge cluster system, comprising: a system application task level determination module, used to determine the level of system application tasks through a lightweight cloud-edge interaction model;

[0023] The edge cluster computing module is used to determine the order of application interactions based on the application task level of the system, thereby improving the efficiency of cloud-edge interaction.

[0024] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, characterized in that the processor executes the computer program to implement the steps of the application interaction method based on a cloud-edge collaborative edge cluster system.

[0025] The present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of the application interaction method based on a cloud-edge collaborative edge cluster system.

[0026] The beneficial effects of this invention are as follows: By establishing a lightweight cloud-edge interaction model, this invention determines the level of system application tasks from two dimensions: the time required to process system application tasks and the urgency of system application tasks, making the determination of system application task levels more scientific.

[0027] Meanwhile, by using a combination of subjective and objective methods to calculate the weights of the time required to process system application tasks and the urgency of the system application tasks, the accuracy of weight determination is improved, thereby enabling better cloud-edge interaction. Attached Figure Description

[0028] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 The present invention provides an overall flowchart of an application interaction method based on a cloud-edge collaborative edge cluster system, which is an embodiment of the present invention. Detailed Implementation

[0030] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0031] Example 1, referring to Figure 1 This is one embodiment of the present invention, which provides an application interaction method based on a cloud-edge collaborative edge cluster system, including:

[0032] S1: Determine the level of system application tasks through a lightweight cloud-edge interaction model;

[0033] In power systems, various application task requests are continuously submitted to cloud servers or edge clusters for processing. If requests are processed according to submission time, high-priority tasks may not be processed in a timely manner, resulting in a poor user experience. Therefore, in this embodiment, a scheduling task level model is first established to assign levels to various system application tasks.

[0034] The lightweight cloud-edge interaction model is divided into two levels: urgent tasks and non-urgent tasks.

[0035] Specifically, the lightweight cloud-edge interaction model is as follows:

[0036]

[0037] K = a × T + b × U + c × E

[0038] Where T is the time required to process system application tasks, L is the load capacity of the edge cluster, C is the average computing power of the edge cluster, E is the scheduling load of system application tasks, R is the average transmission rate of application tasks, U is the urgency parameter of system application tasks, and T max The maximum time to process system application tasks, K is the level score of the system application task, and a, b, and c are level coefficients;

[0039] In this embodiment, the values ​​of the coefficients are determined by assignment, i.e., a is 0.5, b is 0.3, and c is 0.2;

[0040] At the same time, the subjective and objective combination method can also be used to calculate the values ​​of a, b and c;

[0041] Specifically, the calculation of the values ​​of a, b, and c using the subjective-objective combination method is as follows:

[0042] Sa: The first value of a, b and c is determined by an expert scoring method;

[0043] In an embodiment of the present invention, eight experts are selected to assign weights to a, b, and c respectively, and then the average value is taken as the first value of a, b, and c.

[0044] In an optional embodiment, the first value can be a fixed weight combination pre-set according to the type of task in the power industry, such as fault detection, data acquisition, and user requests. For high-urgent tasks (such as fault alarms): a = 0.6, b = 0.2, c = 0.2; for ordinary tasks (such as data synchronization): a = 0.3, b = 0.4, c = 0.3, to more evenly consider the load L and computing power C.

[0045] When a new task arrives, first determine its type, then directly apply the corresponding coefficients without the need for expert intervention.

[0046] In another optional embodiment, the first value can also be 500 typical task samples processed in the past 3 months extracted from the power system dispatch log. The actual processing delay of each task is analyzed by back regression with each parameter. The influence weight of each parameter on the task delay is automatically fitted by the machine learning model. The weights are normalized and used as the initial values ​​of a, b, c (for example, a = 0.52, b = 0.28, c = 0.20). The weight coefficients are dynamically updated once a month.

[0047] Sb: The second value of a, b and c is determined using the analytic hierarchy process (AHP);

[0048] Specifically, the method of using the analytic hierarchy process (AHP) to determine the second values ​​of a, b, and c is as follows:

[0049] Sb1: Score the evaluation indicators to create a judgment matrix A;

[0050] The judgment matrix is: A = (a ij ), where a ij To evaluate the relative importance of evaluation index i and evaluation index j;

[0051]

[0052] The evaluation indicators include: the time required to process system application tasks and the urgency of system application tasks.

[0053] Sb2: Calculate the second values ​​of a, b, and c using the arithmetic mean method;

[0054] In this process, each column of the judgment matrix is ​​normalized, then the normalized columns are summed, and finally the summation result is divided by the order of the judgment matrix to obtain the second values ​​of a, b, and c.

[0055] Sc: Determine the values ​​of a, b, and c based on the first values ​​of a, b, and c and the second values ​​of a, b, and c;

[0056] The values ​​of a, b, and c are obtained by averaging the first values ​​of a, b, and c and the second values ​​of a, b, and c, respectively.

[0057] In this step, the level of the system application task is determined based on the magnitude of K. By establishing the aforementioned system application task level model, the level of the system application task is determined from two dimensions: the time required to process the task and the urgency of the task, making the level determination more scientific. Simultaneously, by using a combination of subjective and objective methods to calculate the weights for the time required to process the task and the urgency of the task, the accuracy of the weight determination is improved.

[0058] S2: Determine the order of application interactions based on the system application task level to improve cloud-edge interaction efficiency;

[0059] In this step, S2 specifically means: if the system application task level is an urgent task, then the cloud server specifies the edge cluster to perform task priority calculation based on the computing power of the edge cluster; if the system application task level is a non-urgent task, then the task calculation is postponed.

[0060] Specifically, if the system application task is classified as an urgent task, the cloud server will prioritize task computation by assigning the edge cluster to the edge cluster based on its computing power.

[0061] Sa: The edge cluster reports its computing power status to the cloud server;

[0062] Specifically, the computing power status includes CPU utilization, memory utilization, and network latency, etc.

[0063] Sb: The cloud server calculates the available computing power of the edge cluster based on the reported computing power status of the edge cluster;

[0064] The weighted scoring method is used to calculate the available computing power status of the edge cluster.

[0065] Specifically, the calculation of the available computing power status of the edge cluster using the weighted scoring method is as follows:

[0066] First, assign weights to CPU utilization, memory utilization, and network latency;

[0067] Among them, CPU utilization, memory utilization, and network latency are weighted. cpu w memory w network w cpu =0.7, w memory =0.2, w network =0.1;

[0068] Then, a comprehensive score of the available computing power status of the edge cluster is calculated based on the weights of CPU utilization, memory utilization, and network latency.

[0069] The formula for calculating the overall score of its available computing power status is as follows:

[0070] Score = w cpu ×CPU score +W memory ×Memory score w network Network score ;

[0071] In the formula, Score is the comprehensive score of the available computing power status of the edge cluster, and CPU... score For CPU score, Memory score For memory scores, Network score The score is for network latency;

[0072] Sc: The cloud server assigns the edge cluster to perform task computation based on the comprehensive score of the edge cluster;

[0073] In this step, the weighted scoring model is a multi-factor decision-making model that selects the optimal solution by assigning weights to different factors and calculating a comprehensive score. In system application tasks, the weighted scoring model is used to evaluate the computing power status of the edge cluster, comprehensively considering factors such as CPU utilization, memory utilization, and network latency, thereby improving the scientific nature of edge cluster operations.

[0074] Example 2 is an embodiment of the present invention, which provides an application interaction system based on a cloud-edge collaborative edge cluster system, including:

[0075] The system application task level determination module is used to determine the level of system application tasks through a lightweight cloud-edge interaction model.

[0076] The edge cluster computing module is used to determine the order of application interactions based on the application task level of the system, thereby improving the efficiency of cloud-edge interaction.

[0077] This embodiment also provides an electronic device applicable to an application interaction method based on a cloud-edge collaborative edge cluster system, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the application interaction method based on a cloud-edge collaborative edge cluster system as proposed in the above embodiment.

[0078] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements an application interaction method based on a cloud-edge collaborative edge cluster system as proposed in the above embodiment.

[0079] The storage medium proposed in this embodiment and the application interaction method for implementing a cloud-edge collaborative edge cluster system proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0080] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0081] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An application interaction method based on a cloud-edge collaborative edge cluster system, characterized in that: include, The level of system application tasks is determined by a lightweight cloud-edge interaction model; The order of application interactions is determined based on the system application task level to improve cloud-edge interaction efficiency.

2. The application interaction method based on a cloud-edge collaborative edge cluster system as described in claim 1, characterized in that: The lightweight cloud-edge interaction model includes two levels: urgent tasks and non-urgent tasks.

3. The application interaction method based on a cloud-edge collaborative edge cluster system as described in claim 2, characterized in that: The lightweight cloud-edge interaction model also includes calculating the time required to process system application tasks based on the load capacity, average computing power, scheduling load, and average transmission rate of the edge cluster. The urgency parameter of the task is obtained based on the time required to process the system application task, and the system application tasks are classified into levels, assigned level coefficients, and the level coefficients are calculated using a combination of subjective and objective methods.

4. The application interaction method based on a cloud-edge collaborative edge cluster system as described in claim 3, characterized in that: The subjective-objective combination method includes... The first value of the grade coefficient is determined by expert scoring; The second value of the rank coefficient is determined using hierarchical analysis; The value of the grade coefficient is determined based on the first value and the second value.

5. The application interaction method based on a cloud-edge collaborative edge cluster system as described in claim 4, characterized in that: The first value includes selecting experts to assign weights to the grade coefficients and taking the average as the first value of the grade coefficients.

6. The application interaction method based on a cloud-edge collaborative edge cluster system as described in claim 5, characterized in that: The second value includes scoring the evaluation indicators and creating a judgment matrix. The judgment matrix is ​​constructed by the relative importance of different evaluation indicators, including the time required to process system application tasks and the urgency of system application tasks.

7. The application interaction method based on a cloud-edge collaborative edge cluster system as described in claim 6, characterized in that: Determining the order of application interactions includes, If the system application task level is an urgent task, the cloud server will specify the edge cluster to perform task priority calculation based on the computing power of the edge cluster. If the system application task is classified as a non-urgent task, then the task calculation will be postponed.

8. An application interaction system based on a cloud-edge collaborative edge cluster system, using the application interaction method for a cloud-edge collaborative edge cluster system as described in any one of claims 1 to 7, characterized in that, include: The system application task level determination module is used to determine the level of system application tasks through a lightweight cloud-edge interaction model. The edge cluster computing module is used to determine the order of application interactions based on the application task level of the system, thereby improving the efficiency of cloud-edge interaction.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the application interaction method based on a cloud-edge collaborative edge cluster system as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the application interaction method based on the cloud-edge collaborative edge cluster system according to any one of claims 1 to 7.

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