Edge computing terminal resource configuration method and device, electronic equipment and storage medium

Abstract

This invention discloses a method, apparatus, electronic device, and storage medium for configuring edge computing terminal resources. The method includes receiving historical data on microgrid control service demands; determining a kernel density model for the microgrid control services based on the historical data; calculating a terminal resource configuration model based on the kernel density model; and obtaining the resource configuration amount for the microgrid edge computing terminal based on the terminal resource configuration model. This invention solves the problems of blind configuration, redundant configuration, and on-demand configuration of deterministic service volumes by receiving historical data on microgrid control service demands, determining a kernel density model based on the historical data, and then further calculating a terminal resource configuration model based on the kernel density model. This improves the utilization rate of computing resources, realizes the distribution of concurrent service volumes in the microgrid, and enhances the microgrid's ability to adapt to concurrent service demands.

Description

Technical Field

[0001] The embodiments of the present invention relate to the software field, and more particularly to an edge computing terminal resource configuration method, apparatus, electronic device, and storage medium. Background Technology

[0002] With the integration of distributed power sources, distributed energy storage, and intelligent power distribution devices into microgrids, the types and number of microgrid control services are showing a continuous increasing trend, posing a severe challenge to the concurrent processing capabilities of microgrid control services. Therefore, researching edge computing resource allocation methods for microgrids is of great significance.

[0003] In existing technologies, computing resource allocation methods often employ blind allocation, redundant allocation, or on-demand allocation of deterministic business volumes. When the amount of computing resources allocated is insufficient, it will lead to the inability to meet the real-time and reliability requirements of microgrid control services. When the amount of computing resources allocated is redundant, it will lead to low utilization of microgrid edge computing resources, wasted space, and high costs, making it difficult to adapt to the development trend of massive access objects and diversified microgrid control services in the context of new power systems. Summary of the Invention

[0004] This invention provides an edge computing terminal resource allocation method, apparatus, electronic device, and storage medium to enable microgrids to adapt to concurrent service demands.

[0005] In a first aspect, embodiments of the present invention provide an edge computing terminal resource configuration method, the method comprising: receiving historical data of microgrid control service requirements; determining a kernel density model of microgrid control services based on the historical data of service requirements; calculating a terminal resource configuration model based on the kernel density model; and obtaining the resource configuration amount of the microgrid edge computing terminal based on the terminal resource configuration model.

[0006] Secondly, embodiments of the present invention also provide an edge computing terminal resource configuration device, the device comprising: a data receiving module for receiving historical data of microgrid control service requirements; a model determination module for determining a kernel density model of microgrid control services based on the historical data of service requirements; and a resource configuration module for calculating a terminal resource configuration model based on the kernel density model and obtaining resource configuration quantities.

[0007] Thirdly, embodiments of the present invention also provide an electronic device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the edge computing terminal resource configuration method as described in any of the embodiments of the present invention.

[0008] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the edge computing terminal resource configuration method as described in any of the embodiments of the present invention.

[0009] This invention addresses the problems of blind configuration, redundant configuration, and on-demand configuration of deterministic business volumes by receiving historical data on microgrid control service demands; determining the kernel density model of microgrid control services based on the historical data; calculating the terminal resource configuration model based on the kernel density model; and obtaining the resource configuration amount of microgrid edge computing terminals based on the terminal resource configuration model. This improves the utilization rate of computing resources, realizes the distribution of concurrent business volumes in the microgrid, and enhances the ability of the microgrid to adapt to concurrent business demands. Attached Figure Description

[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a flowchart of an edge computing terminal resource configuration method provided in an embodiment of the present invention;

[0012] Figure 2 This is a microservice execution logic diagram of the microgrid control service provided in this embodiment of the invention;

[0013] Figure 3 This is an edge computing terminal model for microgrid control services provided in this embodiment of the invention;

[0014] Figure 4 This is another flowchart of the edge computing terminal resource configuration method provided in this embodiment of the invention;

[0015] Figure 5 This is a flowchart of a microgrid edge computing terminal resource allocation method based on kernel density estimation provided in this embodiment of the invention;

[0016] Figure 6 This is a schematic diagram of the edge computing terminal resource configuration device provided in an embodiment of the present invention;

[0017] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0018] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0019] Furthermore, in the embodiments of the present invention, terms such as "optional" or "exemplary" are used to indicate that they are examples, illustrations, or descriptions. Any embodiment or design that is described as "optional" or "exemplary" in the embodiments of the present invention should not be construed as being more preferred or advantageous than other embodiments or design options. Specifically, the use of terms such as "optional" or "exemplary" is intended to present the relevant concepts in a specific manner.

[0020] Figure 1 This is a flowchart of an edge computing terminal resource configuration method provided in an embodiment of the present invention. This embodiment is applicable to the configuration of edge computing terminal resources in microgrids. The method can be executed by an edge terminal resource configuration device, which can be implemented in software and / or hardware. In a specific embodiment, the device can be integrated into an electronic device. The following embodiments will illustrate this using the integration of the edge computing terminal resource configuration device into an electronic device as an example. (Refer to...) Figure 1 The method of this invention specifically includes the following steps:

[0021] S110: Receive historical data on microgrid control service requirements.

[0022] A microgrid is an autonomous system capable of self-control, protection, and management. As a complete power system, it relies on its own control and management to achieve functions such as power balance control, system operation optimization, fault detection and protection, and power quality management. Microgrid control services include functional microservices and system microservices; among which, functional microservices include analytical microservices, computational microservices, and control microservices.

[0023] Furthermore, the microgrid control services of parsing-type microservices include Sampled Value (SV) message parsing microservices, Generic Object Oriented Substation Event (GOSSE) message parsing microservices, environmental quantity parsing microservices, and video quantity message parsing microservices, etc.; the microgrid control services of computing-type microservices include vector calculation microservices, frequency calculation microservices, harmonic calculation microservices, RMS value calculation microservices, and power calculation microservices, etc.; the microgrid control services of control-type microservices include electric vehicle charging control microservices, photovoltaic grid connection management and control microservices, and interruptible load control microservices, etc.; the microgrid control services of system-type microservices include microservice management microservices, container management microservices, resource configuration microservices, and log management microservices, etc., which are not limited in this embodiment.

[0024] Specifically, Figure 2 This is a microservice execution logic diagram of microgrid control services provided in this embodiment of the invention. The microgrid control services are composed of the above microservices, and each microservice has an execution logic relationship. For example... Figure 2 As shown, to implement microgrid control services, the following microservices must first be executed: SV message parsing, GOSSE message parsing, environmental quantity parsing, and video quantity message parsing. These microservices execute in parallel. Next, the following microservices must be executed: vector calculation, frequency calculation, harmonic calculation, RMS value calculation, and power calculation. These microservices also execute in parallel. Finally, the following microservices must be executed: electric vehicle charging control, photovoltaic grid connection management and control, and interruptible load control. These microservices also execute in parallel.

[0025] Historical data refers to data left over from the operation of the microgrid system, and its volume increases with the increase of system operating time. Historical data mainly includes the microgrid control business volume at various times within a certain period of time, the number of microgrid control microservices at various times within a certain period of time, etc. This embodiment does not limit this.

[0026] Specifically, when microgrid resource allocation is required, the system requests the microgrid control service volume at various times and the number of microgrid control microservices at various times within a certain time period from the microgrid control service requirements, and obtains the required historical data from the microgrid's historical database.

[0027] S120. Determine the kernel density model for microgrid control services based on historical data of business needs.

[0028] Among them, the kernel density model is used to quantitatively characterize the concurrent service volume of microgrids.

[0029] Specifically, after receiving historical data on microgrid control service requirements, the number of microgrid control services within each unit time interval in the historical data is determined, the total number of unit time intervals within the time period is counted, and the kernel density model f(x,h) of the microgrid control services is determined. Where x is the volume of microgrid control services, and a i K represents the number of microgrid control services within the i-th unit time interval, N represents the total number of unit time intervals within the statistical period, and K represents the total number of unit time intervals within the statistical period. h Here, h is the kernel function, and h is the bandwidth window.

[0030] S130. Calculate the terminal resource configuration model based on the kernel density model, and obtain the resource configuration amount of the microgrid edge computing terminal based on the terminal resource configuration model.

[0031] Among them, the terminal resource configuration model is a method for configuring terminal resources of microgrid edge computing based on kernel density model. It solves the drawbacks of existing blind configuration, redundant configuration and on-demand configuration of deterministic business volume, and improves the ability of microgrid to adapt to concurrent business needs.

[0032] Figure 3 This is an edge computing terminal model for microgrid control services provided in this embodiment of the invention; such as... Figure 3 The diagram illustrates a model of a microgrid edge computing terminal. This model includes containers, functional microservices, eth0, veth0, and a Docker0 bridge data interaction channel. Specifically, eth0 is a physical network interface card (NIC); veth0 is a virtual NIC, existing in pairs; and Docker0 is a virtual bridge, which can be understood as a virtual switch used to connect the networks within the docker0 containers. Containers provide the runtime environment for functional microservices, which support business logic. eth0 and veth0 serve as data interaction interfaces, and the Docker0 bridge data interaction channel provides a pathway for data exchange between different containers, achieving hardware and software decoupling and data / application separation, thereby improving the utilization of computing resources.

[0033] Specifically, after calculating the terminal resource allocation model based on the kernel density model, the microgrid-controlled services are calculated using the microgrid edge computing terminal resource allocation method to determine the risk level of the microgrid-controlled services and further determine the resource allocation amount.

[0034] The technical solution of this invention receives historical data on microgrid control service demands, determines a kernel density model for the microgrid control services based on this data, calculates a terminal resource configuration model based on the kernel density model, and obtains the resource allocation of the microgrid edge computing terminals based on the terminal resource configuration model. Building upon the above embodiments, by receiving historical data on microgrid control service demands, determining a kernel density model based on this data, and then further calculating a terminal resource configuration model based on the kernel density model, the invention solves the problems of blind configuration, redundant configuration, and on-demand configuration of deterministic service volumes. This improves the utilization rate of computing resources, enables the distribution of concurrent service volumes in the microgrid, and enhances the microgrid's ability to adapt to concurrent service demands.

[0035] Figure 4 This is another flowchart of the edge computing terminal resource configuration method provided in this embodiment of the invention. Based on the above embodiment, the kernel density model of microgrid control services is determined according to historical data of business needs, the terminal resource configuration model is calculated according to the kernel density model, and the resource configuration amount of the microgrid edge computing terminal is obtained based on the terminal resource configuration model for further optimization. The method specifically includes the following steps:

[0036] S410: Receive historical data on microgrid control service requirements.

[0037] Specifically, when microgrid resource allocation is required, the system requests the microgrid control service volume at various times and the number of microgrid control microservices at various times within a certain time period from the microgrid control service requirements, and obtains the required historical data from the microgrid's historical database.

[0038] S420. Determine the number of microgrid control services per unit time interval based on historical data.

[0039] The number of microgrid control services per unit time interval refers to the different microgrid control service volumes that exist in different time intervals in historical data.

[0040] Specifically, based on historical data in the historical database, the number of specific services controlled by the microgrid within each time interval is determined according to the setting method of equal time intervals.

[0041] S430. Determine the kernel density model of microgrid control services by calculating the number of microgrid control services per unit time interval.

[0042] Specifically, after determining the number of microgrid control services per unit time interval by analyzing the different microgrid control service volumes within different time intervals in historical data, the kernel density model is determined according to the following formula. Where x is the volume of microgrid control services, and ai K represents the number of microgrid control services within the i-th unit time interval, N represents the total number of unit time intervals within the statistical period, and K represents the total number of unit time intervals within the statistical period. h Here, h is the kernel function, and h is the bandwidth window.

[0043] S440. Calculate the terminal resource mismatch risk index based on the kernel density model.

[0044] Among them, the terminal resource mismatch risk index refers to the standard for judging whether the allocation of terminal resources is reasonable during the process of allocating terminal resources, which can balance the concurrency of microgrid services and the rationality of terminal resource calculation.

[0045] Specifically, in determining the kernel density model for microgrid control operations Then, the terminal resource mismatch risk index y is calculated according to the following formula;

[0046] S450. Calculate the load model of microgrid control services based on the terminal resource mismatch risk index, and determine the concurrency of microgrid control services based on the load model.

[0047] Specifically, the load model for microgrid control services includes the concurrent resource requirements L of the central processing unit (CPU) of the microgrid edge computing terminal. cpu (t) and concurrent memory resource requirements L ram (t); further, Where j = 1, 2, 3, ..., p represent the identifiers of the microservices in the microgrid, and c j,cpu It is the CPU resource requirement for microgrid control services, c j,ram This refers to the memory resource requirements for microgrid control services, where ε is a step function and t is the memory resource requirement for microgrid control services. j,start Let d be the start execution time of the j-th microservice in the microgrid control business. j Let β be the data size of the j-th microservice in the microgrid control business. j Let F be the computational complexity coefficient of the j-th microservice in the microgrid control business. -1 It is the inverse function of the cumulative kernel density model f(x,h) for microgrid control operations.

[0048] Specifically, in the computing terminal resource mismatch risk index Subsequently, the concurrency of microgrid control services with different risk levels is determined based on the terminal resource mismatch risk index and the inverse function of the kernel density model f(x,h).

[0049] S460. Determine the terminal resource configuration model based on the concurrency of microgrid control services, and obtain the resource configuration amount of the microgrid edge computing terminal based on the terminal resource configuration model.

[0050] The terminal resource configuration model includes R cpu =max(L cpu (t)) and R ram =max(L ram (t)); further, R cpu R is the amount of terminal resource configuration corresponding to CPU resource requirements. ram This refers to the amount of terminal resources required to meet memory resource needs.

[0051] Specifically, after determining the concurrency of microgrid control services, the terminal resource allocation model R is determined based on the concurrency of microgrid control services. cpu =max(L cpu (t)) and R ram =max(L ram (t)) and finally obtain the resource allocation of the microgrid edge computing terminal with different risk levels.

[0052] Based on the above embodiments, further, Figure 5 This is a flowchart of a microgrid edge computing terminal resource allocation method based on kernel density estimation provided in this embodiment of the invention, as follows: Figure 5 As shown, firstly, historical data on microgrid control service requirements are received. Kernel density estimation is applied to the historical data to obtain the kernel density model of the microgrid control service. The technical variable m = 1 is set, where m is the risk level variable in terminal resource allocation. Then, the resource mismatch risk level is calculated according to the gradient of equal time intervals, with the upper limit of risk being M. The concurrency of the microgrid control service at the m-th risk level is obtained through the inverse function of the kernel density model. Finally, the terminal resource allocation amount at the m-th risk level is obtained using the edge computing terminal resource allocation model based on the kernel density model. It is determined whether the risk m is less than or equal to the upper limit of risk M. If it is less than or equal to M, the concurrency and terminal resource allocation amount are calculated in a loop. If it is not less than or equal to M, the terminal resource allocation amount is output.

[0053] The technical solution of this invention involves receiving historical data on microgrid control service requirements, determining the number of microgrid control services within a unit time interval based on the historical data, then calculating and determining the kernel density model of the microgrid control services based on the number of microgrid control services within a unit time interval, calculating the terminal resource mismatch risk index based on the kernel density model, calculating the load model of the microgrid control services based on the terminal resource mismatch risk index, determining the concurrency of the microgrid control services based on the load model, finally determining the terminal resource configuration model based on the concurrency of the microgrid control services, and obtaining the resource configuration amount of the microgrid edge computing terminal based on the terminal resource configuration model. In the above technical solution, the terminal resource mismatch risk index is calculated based on the kernel density model, the load model of microgrid control services is calculated based on the terminal resource mismatch risk index, the concurrency of microgrid control services is determined based on the load model, and finally the terminal resource configuration model is determined through the concurrency. Based on the terminal resource configuration model, the resource configuration of microgrid edge computing terminals is obtained, which effectively solves the computing resource requirements of microgrid control services at a certain computing resource mismatch risk level, solves the drawbacks of existing blind configuration, redundant configuration, and on-demand configuration of deterministic business volume, and improves the ability of microgrid to adapt to concurrent business needs.

[0054] Figure 6 This is a schematic diagram of the edge computing terminal resource configuration device provided in an embodiment of the present invention. As can be seen from the diagram, the device includes: a data receiving module 610, a model determination module 620, and a resource configuration module 630.

[0055] The data receiving module 610 is used to receive historical data for microgrid control business requirements.

[0056] The model determination module 620 is used to determine the kernel density model of microgrid control business based on historical data of business needs.

[0057] The resource configuration module 630 is used to calculate the terminal resource configuration model based on the kernel density model, and to obtain the resource configuration amount of the microgrid edge computing terminal based on the terminal resource configuration model.

[0058] Optionally, microgrid control services include: functional microservices and system microservices; among which, functional microservices include parsing microservices, computing microservices and control microservices.

[0059] Optionally, the model determination module 620 is specifically used to: determine the number of microgrid control services within a unit time interval based on historical data; and calculate and determine the kernel density model of the microgrid control services based on the number of microgrid control services within a unit time interval.

[0060] Optionally, the resource configuration module 630 is specifically used for: calculating the terminal resource mismatch risk index based on the kernel density model; calculating the load model of the microgrid control service based on the terminal resource mismatch risk index, and determining the concurrency of the microgrid control service based on the load model; determining the terminal resource configuration model based on the concurrency of the microgrid control service, and obtaining the resource configuration amount of the microgrid edge computing terminal based on the terminal resource configuration model.

[0061] Optionally, the resource configuration module 630 is specifically used to: determine the kernel density model f(x,h) according to the following formula; Where x is the volume of microgrid control services, and a i K represents the number of microgrid control services within the i-th unit time interval, N represents the total number of unit time intervals within the statistical period, and K represents the total number of unit time intervals within the statistical period. h Let h be the kernel function and h be the bandwidth window; calculate the terminal resource mismatch risk index y according to the following formula;

[0062] Optionally, the load model includes the concurrent CPU resource requirements L of the microgrid edge computing terminals. cpu (t) and concurrent memory resource requirements L ram (t); where, Where j = 1, 2, 3, ..., p represent the identifiers of the microservices in the microgrid, and c j,cpu It is the CPU resource requirement for microgrid control services, c j,ram This refers to the memory resource requirements for microgrid control services, where ε is a step function and t is the memory resource requirement for microgrid control services. j,start Let d be the start execution time of the j-th microservice in the microgrid control business. j Let β be the data size of the j-th microservice in the microgrid control business. j Let F be the computational complexity coefficient of the j-th microservice in the microgrid control business. -1 It is the inverse function of the cumulative kernel density model f(x,h) for microgrid control operations.

[0063] Optionally, the terminal resource configuration model includes R cpu =max(L cpu (t)) and R ram =max(L ram (t)); where R cpu R is the amount of terminal resource configuration corresponding to CPU resource requirements. ram This refers to the amount of terminal resources required to meet memory resource needs.

[0064] The edge computing terminal resource configuration device provided in this embodiment of the invention can execute the edge computing terminal resource configuration method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0065] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention, such as... Figure 7 As shown, the electronic device includes a processor 710, a memory 720, an input device 730, and an output device 740; the number of processors 710 in the electronic device can be one or more. Figure 7 Taking a processor 710 as an example; the processor 710, memory 720, input device 730, and output device 740 in the electronic device can be connected via a bus or other means. Figure 7 Taking the example of a connection between China and Israel via a bus.

[0066] The memory 720, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as program instructions / modules corresponding to an edge computing terminal resource configuration method in this embodiment of the invention (e.g., the data receiving module 610, model determination module 620, and resource configuration module 630 in an edge computing terminal resource configuration device). The processor 710 executes various functional applications and data processing of the electronic device by running the software programs, instructions, and modules stored in the memory 720, thereby implementing the aforementioned edge computing terminal resource configuration method.

[0067] The memory 720 may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a given function; the data storage area may store data created based on terminal usage. Furthermore, the memory 720 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 720 may further include memory remotely located relative to the processor 710, which can be connected to the electronic device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0068] Input device 730 can be used to receive input digital or character information, and to generate key signal inputs related to user settings and function control of the electronic device. Output device 740 may include display devices such as a display screen.

[0069] This invention also provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform an edge computing terminal resource configuration method, the method comprising:

[0070] Receive historical data on microgrid control service requirements;

[0071] Determine the kernel density model for microgrid control operations based on historical data related to business needs;

[0072] The terminal resource configuration model is calculated based on the kernel density model, and the resource configuration amount of the microgrid edge computing terminal is obtained based on the terminal resource configuration model.

[0073] Of course, the computer-executable instructions provided in the embodiments of the present invention are not limited to the above-described method operations, but can also execute related operations in the edge computing terminal resource configuration method provided in any embodiment of the present invention.

[0074] 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 described in the various embodiments of the present invention.

[0075] It is worth noting that in the above embodiments of the edge computing terminal resource configuration device, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the scope of protection of the present invention.

[0076] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A method for configuring resources for an edge computing terminal, characterized in that, The method includes: Receive historical data on microgrid control service requirements; the historical data includes the microgrid control service volume at each moment within a time period and the number of microgrid control microservices at each moment within a time period. The kernel density model for microgrid control services is determined based on historical data of the aforementioned business requirements; The terminal resource configuration model is calculated based on the kernel density model, and the resource configuration amount of the microgrid edge computing terminal is obtained based on the terminal resource configuration model. The microgrid control services include: functional microservices and system microservices; wherein the functional microservices include parsing microservices, computing microservices and control microservices. The process of determining the kernel density model for microgrid control services based on historical data of the business requirements includes: The number of microgrid control services per unit time interval is determined based on the historical data. The kernel density model of the microgrid control service is determined by calculating the number of microgrid control services within the unit time interval; The step of calculating the terminal resource configuration model based on the kernel density model and obtaining the resource configuration amount of the microgrid edge computing terminal based on the terminal resource configuration model includes: Calculate the terminal resource mismatch risk index based on the kernel density model; The load model of microgrid control services is calculated based on the terminal resource mismatch risk index, and the concurrency of microgrid control services is determined based on the load model. The terminal resource configuration model is determined based on the concurrency of the microgrid control services, and the resource configuration amount of the microgrid edge computing terminal is obtained based on the terminal resource configuration model.

2. The method according to claim 1, characterized in that, The kernel density model is determined according to the following formula. ; ； in, This refers to the service volume of the microgrid control service. For the first The number of microgrid control services within a unit time interval This represents the total number of time intervals within the statistical period. For kernel function, For bandwidth window; The terminal resource mismatch risk index y is calculated according to the following formula; 。 3. The method according to claim 2, characterized in that, The load model includes the concurrent CPU resource requirements of the microgrid edge computing terminals. and memory resource demand concurrency ; Among them, the The ; Where j=1,2,3,…p represent the identifiers of the microservices in the microgrid. This refers to the CPU resource requirements of the microgrid control service. This refers to the memory resource requirements for the microgrid control services. It is a step function. Let j be the start execution time of the j-th microservice in the microgrid control service. Let j be the data size of the j-th microservice in the microgrid control service. Let be the computational complexity coefficient of the j-th microservice in the microgrid control service. The cumulative kernel density model for the microgrid control services The inverse function of .

4. The method according to claim 3, characterized in that, The terminal resource allocation model includes and ; in, This refers to the amount of terminal resources configured corresponding to the CPU resource requirements. This refers to the amount of terminal resources configured corresponding to the aforementioned memory resource requirements.

5. An edge computing terminal resource allocation device, characterized in that, include: The data receiving module is used to receive historical data on microgrid control service requirements; the historical data includes the microgrid control service volume at each moment within a time period and the number of microgrid control microservices at each moment within a time period. The model determination module is used to determine the kernel density model of the microgrid control service based on historical data of the business requirements. The resource configuration module is used to calculate the terminal resource configuration model according to the kernel density model, and to obtain the resource configuration amount of the microgrid edge computing terminal based on the terminal resource configuration model. The microgrid control services include: functional microservices and system microservices; wherein the functional microservices include parsing microservices, computing microservices and control microservices. The model determination module is specifically used for: determining the number of microgrid control services within a unit time interval based on the historical data; and calculating and determining the kernel density model of the microgrid control services based on the number of microgrid control services within the unit time interval. The resource configuration module is specifically used for: calculating the terminal resource mismatch risk index according to the kernel density model; calculating the load model of microgrid control services according to the terminal resource mismatch risk index; determining the concurrency of microgrid control services based on the load model; determining the terminal resource configuration model according to the concurrency of microgrid control services; and obtaining the resource configuration amount of microgrid edge computing terminals based on the terminal resource configuration model.

6. An electronic device, characterized in that, The electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the program, implements the edge computing terminal resource configuration method as described in any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the edge computing terminal resource configuration method as described in any one of claims 1 to 4.

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