Method and device for optimizing communication delay between edge charging facilities and power distribution network

Abstract

The application relates to a communication delay optimization method and device for edge charging facilities and a power distribution network, wherein the method comprises the following steps: determining a division scheme of edge charging facility management nodes according to system node distribution, so as to determine node clusters of the system; establishing a double-layer model of edge charging facility deployment and service distribution with edge computing capacity considering service association; solving the double-layer model by using a preset algorithm, and distributing an optimal solution obtained as the edge charging facility deployment and service distribution result, so as to realize the optimality of system service processing real-time performance. The application can improve system data processing real-time performance and computing resource utilization.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric vehicle charging, in particular to a communication delay optimization method and device for edge charging facilities and power distribution networks. BACKGROUND

[0002] In the power distribution network, the number of intelligent devices is huge, and the amount of information to be processed is growing explosively. Therefore, effectively reducing the processing delay of edge data has become one of the problems to be solved. At present, the application of edge computing technology in the power distribution network exists in the form of "edge-edge interaction" and "edge-end interaction", and the electric vehicle charging facilities with edge computing capability have a certain business processing independence. The location, control area and business of the charging facilities directly affect the data transmission characteristics and processing characteristics of the system. Therefore, in the system data architecture of multiple charging facilities cooperative processing, reasonable deployment of charging facilities is of great significance to improve the real-time performance of system data processing and the utilization rate of computing resources. SUMMARY

[0003] The technical problem to be solved by the present application is to provide a communication delay optimization method and device for edge charging facilities and power distribution networks, which can improve the real-time performance of system data processing and the utilization rate of computing resources.

[0004] The technical solution adopted by the present application to solve the technical problem is: providing a communication delay optimization method for edge charging facilities and power distribution networks, comprising the following steps:

[0005] determining a division scheme of edge charging facility control nodes according to the distribution of system nodes to determine the node clusters of the system;

[0006] establishing a double-layer model of edge charging facility deployment and business allocation with edge computing capability considering business association, the double-layer model comprising an outer layer model and an inner layer model, the outer layer model solving the allocation of system application services among edge charging facilities with the minimum total processing delay as the target; the inner layer model solving the deployment of edge charging facilities in node clusters with the minimum transmission delay as the target based on the division scheme;

[0007] solving the double-layer model by using a preset algorithm, and distributing the optimal solution obtained as the edge charging facility deployment and business allocation result to realize the optimization of system business processing real-time performance.

[0008] The division scheme of edge charging facility control nodes according to the distribution of system nodes specifically comprises:

[0009] determining the average number of nodes controlled by edge charging facilities according to the number of edge charging facilities and the number of data nodes;

[0010] Optionally, the data nodes with the average number of nodes are selected to form node clusters, and the sum of the relative distances of the nodes in each node cluster is calculated;

[0011] The sum of the relative distances of the nodes in each node cluster is sorted to obtain a sequence A;

[0012] The data nodes with the average number of nodes closest to each other in a set direction are selected to form new node clusters, and the sum of the relative distances of the nodes in each new node cluster is calculated, and the maximum value is selected, and the position m of the maximum value in the sequence A is determined;

[0013] The node clusters without common nodes in the first m node clusters in the sequence A are determined, the sum of the relative distances of the nodes in each combination is calculated, and the combination with the minimum sum value is taken as the union of the node clusters of the system.

[0014] When the average number of nodes controlled by the edge charging facility is determined according to the number of edge charging setting and the number of data nodes, the average number of nodes controlled by the edge charging facility is determined by The average number of nodes controlled by the edge charging facility is determined, wherein h e is the number of data nodes controlled by the edge charging facility e, n is the number of data nodes, K is the number of edge charging facilities, P int is the integer part calculated, P mod is the decimal part calculated; when P mod ≠0, and when there are [K×(1-P mod )] edge charging facilities, the number of data nodes controlled by the edge charging facility e is P int ; when there are (K×P mod ) edge charging facilities, the number of data nodes controlled by the edge charging facility e is (P int +1).

[0015] The sum of the relative distances of the nodes in the node cluster is calculated by , wherein L emin is the sum of the relative distances of the nodes in the node cluster; l ie is the distance between the data node i and the edge charging facility e; (x i , y i ) and (x e , y e ) are the position coordinates of the data node i and the edge charging facility e, respectively.

[0016] The objective function of the outer model is: minD sum =D t +D c , and the objective function of the inner model is: minD c ; wherein D sum is the total processing delay; D tis the total computation delay, D c is the total communication delay, i is the data node number; a is the service number; e, e' are the edge charging facility numbers; t ae is the computation delay generated when the edge charging facility e processes the service a; d ie and d ee′ are the communication delay between the data node i and the edge charging facility e and the communication delay between the edge charging facility e and the edge charging facility e', respectively.

[0017] The computation delay generated when the edge charging facility e processes the service a is calculated by t ae ∝ w a , where w a is the load generated by the service a in the edge charging facility, ζ ae is a variable, ζ ae = 0 indicates that the edge charging facility e does not complete the processing task of the service a, and ζ ae = 1 indicates that the edge charging facility e completes the processing task of the service a, f ae is the processing frequency of the edge charging facility e for the service a, and t0 is the computation delay generated when the unit processing frequency f0 processes the unit load w0.

[0018] The communication delay between the data node i and the edge charging facility e is calculated by , and the communication delay between the edge charging facility e and the edge charging facility e' is calculated by , where d0 is the delay generated when the basic data unit u0 is transmitted over a unit distance at the set data transmission rate R0, u ie is the transmission data volume between the data node i and the edge charging facility e, R ie is the transmission efficiency between the data node i and the edge charging facility e, B ie is the communication bandwidth between the data node i and the edge charging facility e, is the signal-to-noise ratio of the information transmission rate, ζ ie is a variable, ζ ae = 0 indicates that the edge charging facility e does not complete the processing task of the node i, and ζ ae = 1 indicates that the edge charging facility e completes the processing task of the node i, u ee′ and R ee′ are the transmission data volume and the transmission efficiency between the edge charging facility e and the edge charging facility e', respectively.

[0019] The technical scheme adopted by the present application to solve its technical problems is: a communication delay optimization device for an edge charging facility and a power distribution network is provided, comprising:

[0020] A division scheme determination module is configured to determine a division scheme of edge charging facility management and control nodes according to system node distribution, so as to determine each node cluster of the system.

[0021] A double-layer model establishment module is configured to establish a double-layer model of edge charging facility deployment and service allocation with edge computing capability considering business association, wherein the double-layer model comprises an outer layer model and an inner layer model, the outer layer model is configured to solve the allocation of system application services among edge charging facilities with the minimum total delay as the target, and the inner layer model is configured to solve the deployment of the edge charging facilities in the node cluster with the minimum transmission delay as the target based on the division scheme.

[0022] An allocation module is configured to solve the double-layer model by using a preset algorithm, and allocate the optimal solution obtained as the edge charging facility deployment and service allocation result, so as to realize the optimality of system service processing real-time performance.

[0023] The technical scheme adopted by the present application to solve its technical problems is: an electronic device is provided, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the edge charging facility and power distribution network communication delay optimization method.

[0024] The technical scheme adopted by the present application to solve its technical problems is: a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by a processor to implement the steps of the edge charging facility and power distribution network communication delay optimization method.

[0025] Advantages

[0026] Compared with the prior art, the present application has the following advantages and positive effects: the present application fully considers the influence of node position difference on the deployment scheme, so that the deployment scheme and the business arrangement scheme are more reasonable. The present application jointly considers the charging facility deployment and the service allocation, improves the service processing performance, and has strong feasibility. The present application considers the calculation delay and the communication delay, and the analysis result has high accuracy. BRIEF DESCRIPTION OF DRAWINGS

[0027] Figure 1 is a flowchart of the edge charging facility and power distribution network communication delay optimization method of the first embodiment of the present application;

[0028] Figure 2 is a flowchart of the system node cluster division in the first embodiment of the present application;

[0029] Figure 3 is a flow chart for solving the double-layer model in the first embodiment of the present application;

[0030] Figure 4 is a structural diagram of communication delay optimization of the edge charging facility and the power distribution network in the second embodiment of the present application. DETAILED DESCRIPTION

[0031] The present application will be further described below in connection with specific embodiments. It should be understood that these embodiments are only used to illustrate the present application and not used to limit the scope of the present application. Furthermore, it should be understood that after reading the content taught by the present application, those skilled in the art can make various modifications or changes to the present application, and these equivalent forms also fall within the scope defined by the appended claims of the present application.

[0032] The first embodiment of the present application relates to a communication delay optimization method of an edge charging facility and a power distribution network, as shown in Figure 1 , comprising the following steps:

[0033] determining a division scheme of the edge charging facility management node according to the system node distribution, so as to determine the node cluster of the system;

[0034] establishing a double-layer model of the edge charging facility deployment and service allocation with edge computing considering business association, the double-layer model comprising an outer layer model and an inner layer model, the outer layer model solving the allocation of the system application service among the edge charging facilities with the minimum total delay as the target; the inner layer model solving the deployment of the edge charging facilities in the node cluster with the minimum transmission delay as the target based on the division scheme;

[0035] solving the double-layer model by using a preset algorithm, and distributing the optimal solution obtained as the edge charging facility deployment and service allocation result, so as to realize the optimization of the system service processing real-time performance.

[0036] The present embodiment takes the electric vehicle charging facility as an example, the communication topology structure of the system comprises a data analysis device, a collection device and an instruction execution terminal, the data analysis device refers to the electric vehicle charging facility with edge computing capability, the collection device and the instruction execution terminal respectively refer to the data node (datapoints, DP) for realizing data collection in the power distribution network and the physical device for realizing instruction operation function. In the information processing process, the data information of the DP is uploaded to the electric vehicle charging facility, the electric vehicle charging facility realizes the processing of the received data according to the system allocated service, and the processing result information is communicated and shared with other electric vehicle charging facilities, while the electric vehicle charging facility issues processing instructions to the execution terminal and completes it by the execution terminal, and the execution terminal returns the processed result to the electric vehicle charging facility.

[0037] The business information processed by the electric vehicle charging facility with edge computing capability includes two types of basic business and application service, the basic business refers to the processing task that the electric vehicle charging facility needs to complete for each DP within the control range, and the application service refers to some advanced application services after the basic business is completed. The data processing task of the application service of the system is completed by an electric vehicle charging facility, so each application service is deployed on an electric vehicle charging facility. The basic business that each electric vehicle charging facility needs to perform can be processed in parallel, and the processing result is input to the next business according to the business processing logic.

[0038] Based on the communication topology structure of the above system, the division scheme of the electric vehicle charging facility control node can be determined according to the system node distribution, and the DP controlled by the electric vehicle charging facility e is referred to as a node cluster S e The union set of each node cluster of the system is S sum , specifically:

[0039] The premise of realizing the deployment of the electric vehicle charging facility in the node cluster is to divide the DP, and the main basis for the division is the number and position of the DP controlled by the electric vehicle charging facility. Let the number of nodes controlled by the electric vehicle charging facility e be h e , and the average number of nodes controlled by the charging facility is determined according to the number of electric vehicle charging facilities K and the number of DPs n e :

[0040]

[0041] Among them, P int is the integer part calculated, P mod is the decimal part calculated; when P mod ≠0, and when there are [K×(1-P mod )] electric vehicle charging facilities, the number of data nodes controlled by the electric vehicle charging facility e is P int ; when there are (K×P mod ) electric vehicle charging facilities, the number of data nodes controlled by the electric vehicle charging facility e is (P int +1).

[0042] As shown in Figure 2 , after determining the number of nodes in the node cluster, h e nodes are randomly taken to form a node cluster, and the sum of the relative distances of the nodes of each node cluster L emin is calculated. The sum of the relative distances of the nodes of the node cluster L emin is sorted from small to large to form a sequence A, and then the nearest h eThe nodes form new node clusters, and the sum of the relative distances of the nodes in each new node cluster is calculated, the maximum sum of the relative distances of the nodes in the new node cluster appears at position m in the sequence A, combinations of node clusters that do not contain common nodes in the first m node clusters are determined, the sum of the relative distances of the nodes in each combination is calculated, and the combination with the minimum sum is the union of the node clusters in the system, which is S sum .

[0043] The sum of the relative distances of the nodes in a node cluster L emin is calculated in the following manner:

[0044]

[0045] The sum of the relative distances of the nodes in a node cluster L emin is calculated in the following manner: ie is the distance between data node i and electric vehicle charging facility e; (x i , y i ) and (x e , y e ) are the position coordinates of data node i and electric vehicle charging facility e, respectively.

[0046] According to the above business processed by the electric vehicle charging facility, a double-layer model of electric vehicle charging facility deployment and business distribution with edge computing capability considering business association can be established, wherein the outer-layer model solves the distribution of system application services among the charging facilities with the minimum total delay as the target; and the inner-layer model solves the deployment of electric vehicle charging facilities in node clusters with the minimum transmission delay as the target.

[0047] For each charging facility deployment scheme in the inner-layer model, the outer-layer model can calculate an optimal application service distribution scheme in real time, and the outer-layer model determines the application service distribution scheme with the minimum total delay and the corresponding charging facility deployment after traversing all distribution schemes.

[0048] The outer-layer model aims to optimize the system data processing delay:

[0049] minD sum =D t +D c

[0050]

[0051]

[0052] D sum is the total processing delay; D t is the total calculation delay; and D ct represents the total communication delay; i represents the data node number; a represents the service number; e, e′ represent the electric vehicle charging facility numbers; t represents the total communication delay. ae For calculation delay, d represents the calculation delay incurred when electric vehicle charging facility e processes service a; ie and d ee′ These represent the communication delay between data node i and electric vehicle charging facility e, and the communication delay between electric vehicle charging facility e and electric vehicle charging facility e′, respectively.

[0053] DP service data processing response time includes the data processing time t of the service application model in the charging facility. ae The delay is mainly related to the processing frequency and data processing load allocated to the task by the charging facility.

[0054] Within the charging facility, task processing consumes CPU resources, and the required processing time is characterized by the number of CPU clock cycles elapsed. When processing service a at a unit processing frequency f0, the clock cycles required for service a are w. a That is, the load generated by service a within the charging facility is w. a The processing frequency f of service a at the same time. ae This represents the CPU resources acquired by service a. To better optimize charging facility deployment and service allocation, a method is proposed that utilize unit computational delay t0 to represent t. ae To simplify, t0 is the calculation delay generated when a unit processing frequency f0 processes a unit load w0. Therefore, when f ae Calculate the delay t given the time. ae It is a multiple of t0, and its dynamic change relationship is with w. a Same, t ae The expression is as follows:

[0055]

[0056] Among them, w a and f ae The load and frequency generated by the business of electric vehicle charging facility e; ζ ae As a variable, when ζ ae =0 indicates that the electric vehicle charging facility e has not completed the processing task of business a. When ζ = 0, it means that the electric vehicle charging facility e has not completed the processing task of business a. ae =1 indicates that the electric vehicle charging facility e has completed the processing task of business a.

[0057] The transmission delay caused by data in a communication network is positively correlated with the amount of data transmitted and negatively correlated with the transmission rate. Therefore, the transmission delay d between data node i and electric vehicle charging facility e is... ie As shown below. ieThe basic data unit u0 is composed of multiple basic data units u0, and the delay d0 generated by the basic data unit u0 when transmitting a unit distance at a certain data transmission rate R0. When the transmission rate is constant, the transmission delay can be simplified as a multiple of the unit transmission delay d0, d ee′ The communication delay between charging facilities in distributed deployment is calculated in the same way as d ie .

[0058]

[0059]

[0060]

[0061] Wherein, u ie and R ie are the transmission amount and transmission efficiency between data node i and electric vehicle charging facility e; u ee′ and R ee′ are the transmission data amount and transmission efficiency between electric vehicle charging facility e and electric vehicle charging facility e'; B ie is the communication bandwidth between data node i and electric vehicle charging facility e; is the signal-to-noise ratio of information transmission rate; ζ ie is a variable, ζ ae =0 indicates that the electric vehicle charging facility e does not complete the processing task of node i, and ζ ae =1 indicates that the electric vehicle charging facility e completes the processing task of node i.

[0062] In the system communication architecture of the embodiment, the relative position relationship between the electric vehicle charging facility and the data nodes in the node cluster and other charging facilities in the system directly affects the data transmission delay of the node cluster. The inner model in the embodiment is used to realize the deployment of the charging facility in the node cluster, so as to minimize the system business processing delay. According to the description of the communication delay in the outer model, the objective function of the inner model is as follows:

[0063] minD c

[0064] The double-layer model is solved by using a preset algorithm, and the optimal solution obtained is used as the deployment and business allocation result of the electric vehicle charging facility to realize the optimization of system business processing real-time. The embodiment can use CPLEX to solve the double-layer optimization problem. In the system business processing cost analysis process, the nodes controlled by the electric vehicle charging facility and the selection of the deployment nodes need to be considered, as well as the business types processed by the charging facilities in cooperation. On the basis of realizing the division of the node cluster, the double-layer model is equivalent to solving ζ ae and ζ ie , that is, ζ aeand zeta ie Set as the decision variable of CPLEX. Figure 3 As shown in the figure, first, an initial electric vehicle charging facility deployment scheme and an initial service allocation scheme are given; then, the inner model is solved, specifically: the electric vehicle charging facility deployment and service allocation scheme are transmitted to the inner model, the communication delay is calculated and the electric vehicle charging facility deployment scheme is optimized, and the charging facility deployment scheme and the communication delay are transmitted to the outer model; then in the outer model, the total delay is calculated, it is judged whether the total delay is the minimum, if yes, the electric vehicle charging facility deployment and service allocation result is output, if not, the service allocation result is optimized; then the electric vehicle charging facility deployment scheme and the service allocation scheme are updated, and are sent into the inner model again for solving until the total delay reaches the minimum.

[0065] It can be found that the present application fully considers the influence of node position difference on the deployment scheme, so that the deployment scheme and the service arrangement scheme are more reasonable. The present application jointly considers the charging facility deployment and service allocation, improves the service processing performance, and has strong feasibility. The present application comprehensively considers the calculation delay and the communication delay, and the analysis result has high accuracy.

[0066] The second embodiment of the present application relates to an edge charging facility and power distribution network communication delay optimization device, as shown in the figure, comprising: Figure 4

[0067] The division scheme determination module is used for determining the division scheme of the edge charging facility management and control node according to the system node distribution, so as to determine each node cluster of the system.

[0068] The double-layer model establishment module is used for establishing a double-layer model of edge charging facility deployment and service allocation with edge computing capability considering service association, the double-layer model comprises an outer model and an inner model, the outer model solves the allocation of system application services among each edge charging facility with the minimum total delay as the target; and the inner model solves the deployment of the edge charging facility in the node cluster with the minimum transmission delay as the target based on the division scheme.

[0069] The allocation module is used for solving the double-layer model by using a preset algorithm, and allocating the optimal solution obtained as the edge charging facility deployment and service allocation result, so as to realize the optimality of system service processing real-time performance.

[0070] The division scheme determination module comprises:

[0071] The first determination unit is used for determining the average number of nodes managed by the edge charging facility according to the number of edge charging facilities and the number of data nodes.

[0072] The first calculation unit is used for selecting a node cluster composed of data nodes of the average number of nodes at random, and calculating the sum of the relative distances of the nodes of each node cluster.​

[0073] a sorting unit, configured to sort the sum of node relative distances of the node clusters to obtain a sequence A;

[0074] a second determining unit, configured to select data nodes with the average number of nodes in a set direction in sequence to form new node clusters, and calculate the sum of node relative distances of each new node cluster, select a maximum value, and determine a position m of the maximum value in the sequence A;

[0075] a second calculating unit, configured to determine node clusters without common nodes in the first m node clusters in the sequence A, sum the sum of node relative distances of each combination, and take the combination with the minimum sum value as the union of the node clusters of the system.

[0076] the first determining unit determines the average number of nodes controlled by the edge charging facility by D c = D t + D c , and the objective function of the inner layer model is minD c ; wherein D sum is a total processing delay; D t is a total calculation delay, D c is a total communication delay, e is the number of data nodes controlled by the edge charging facility e, n is the number of data nodes, K is the number of edge charging facilities, P int is an integer part calculated, P mod is a decimal part calculated; when P mod ≠ 0, and when there are [K × (1-P mod )] edge charging facilities, the number of data nodes controlled by the edge charging facility e is P int ; when there are (K × P mod ) edge charging facilities, the number of data nodes controlled by the edge charging facility e is (P int + 1).

[0077] the first calculating unit calculates L emin by , wherein L emin is the sum of node relative distances of the node clusters; l ie is the distance between the data node i and the edge charging facility e; (x i , y i ) and (x e , y e ) are the position coordinates of the data node i and the edge charging facility e respectively.

[0078] the objective function of the outer layer model established by the double-layer model establishing module is minD sum = D t + D c , and the objective function of the inner layer model is minD c ; wherein D sum is a total processing delay; D t is a total calculation delay, D c is a total communication delay, i is the data node number; a is the service number; e, e' are the edge charging facility numbers; t ae represents the computing delay generated when the edge charging facility e processes the service a; d ie and d ee′ are the communication delay between the data node i and the edge charging facility e and the communication delay between the edge charging facility e and the edge charging facility e', respectively.

[0079] The computing delay generated when the edge charging facility e processes the service a is calculated by t ae ∝ w a , wherein w a is the load generated by the service a in the edge charging facility, ζ ae is a variable, ζ ae = 0 indicates that the edge charging facility e does not complete the processing task of the service a, and ζ ae = 1 indicates that the edge charging facility e completes the processing task of the service a, f ae is the processing frequency of the edge charging facility e for the service a, and t0 is the computing delay generated when the unit processing frequency f0 processes the unit load w0.

[0080] The communication delay between the data node i and the edge charging facility e is calculated by , and the communication delay between the edge charging facility e and the edge charging facility e' is calculated by , wherein d0 is the delay generated when the basic data unit u0 is transmitted over a unit distance at the set data transmission rate R0, u ie is the transmission data volume between the data node i and the edge charging facility e, R ie is the transmission efficiency between the data node i and the edge charging facility e, B ie is the communication bandwidth between the data node i and the edge charging facility e, is the signal-to-noise ratio of the information transmission rate, ζ ie is a variable, ζ ae = 0 indicates that the edge charging facility e does not complete the processing task of the node i, and ζ ae = 1 indicates that the edge charging facility e completes the processing task of the node i, u ee′ and R ee′ are the transmission data volume and the transmission efficiency between the edge charging facility e and the edge charging facility e', respectively.

[0081] The third embodiment of the present application relates to an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the communication delay optimization method between the edge charging facility and the power distribution network of the first embodiment when executing the computer program.

[0082] The fourth embodiment of the present application relates to a computer readable storage medium, which stores a computer program, wherein the computer program implements the steps of the communication delay optimization method between the edge charging facility and the power distribution network of the first embodiment when executed by a processor.

[0083] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) containing computer-usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0084] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus generate a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks Figure 1 The means for implementing the functions specified in one or more flows and / or blocks.

[0085] These computer program instructions can also be stored in a computer-readable memory capable of guiding the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product comprising instruction means, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks Figure 1 The means for implementing the functions specified in one or more flows and / or blocks.

[0086] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 one or more flowcharts and / or blocks

[0087] Although preferred embodiments of the application have been described herein, it will be apparent to those skilled in the art that various modifications can be made within the scope of the application without departing from the spirit of the application. Accordingly, it is intended that all such possible modifications be included within the scope of the application as claimed.

[0088] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described herein.

Claims

1. A method for optimizing communication delay between edge charging facilities and power distribution networks, characterized in that, The method comprises the following steps: According to the system node distribution, the division scheme of the edge charging facility management and control node is determined to determine the node cluster of the system; wherein, the division scheme of the edge charging facility management and control node is determined according to the system node distribution, and specifically comprises: According to the number of edge charging facilities and the number of data nodes, the average number of nodes of the edge charging facility management and control is determined; Arbitrarily select the data nodes of the average node number to form a node cluster, and calculate the sum of the node relative distances of each node cluster; Sort the sum of the node relative distances of the node cluster to obtain a sequence A; Select the nearest data nodes of the average node number in a set direction to form a new node cluster, and calculate the sum of the node relative distances of each new node cluster, and select the maximum value, and determine the position m of the maximum value in the sequence A; Determine the node cluster without common nodes in the first m node clusters in the sequence A, sum the node relative distances of each combination, and the combination with the minimum sum value is the union of each node cluster of the system; A double-layer model considering business association and having edge computing capability is established for the deployment and business distribution of the edge charging facility, the double-layer model comprises an outer model and an inner model, the outer model solves the distribution of the system application service among the edge charging facilities with the minimum total delay as the target; the inner model solves the deployment of the edge charging facility in the node cluster with the minimum total communication delay as the target based on the division scheme; The double-layer model is solved by using a preset algorithm, and the optimal solution obtained is used as the deployment and business distribution result of the edge charging facility to realize the optimization of the real-time performance of the system business processing.

2. The method of claim 1, wherein the communication delay between the edge charging facility and the power distribution network is optimized by, The average node number of the edge charging facility management and control is determined according to the edge charging facility charging parameter quantity and the data node quantity, and the average node number of the edge charging facility management and control is determined through determining the average node number of the edge charging facility management and control, wherein h e is the data node quantity of the edge charging facility e, n is the number of data nodes, K is the number of edge charging facilities, P int is the integer part calculated, P mod is the decimal part calculated; when P mod ≠0, and when there are [K×(1-P mod )] edge charging facilities, the data node quantity of the edge charging facility e is P int ; when there are (K×P mod ) edge charging facilities, the data node quantity of the edge charging facility e is (P int +1).

3. The method of claim 1, wherein the communication delay between the edge charging facility and the power distribution network is optimized by, The sum of the relative distances of the nodes of the node cluster is calculated by L emin is the sum of the relative distances of the nodes of the node cluster; l ie is the distance between the data node i and the edge charging facility e; (x i , y i ) and (x e , y e ) are the position coordinates of the data node i and the edge charging facility e, respectively, and S e is the node cluster, the nodes in which are data nodes controlled by the edge charging facility e.

4. The method of claim 1, wherein the method further comprises: The objective function of the outer layer model is min D sum = D t + D c , and the objective function of the inner layer model is min D c ; wherein D sum is the total processing delay; D t is the total calculation delay, D c is the total communication delay, i is the data node number; a is the service number; e, e' are the edge charging facility numbers; t ae denotes the computation latency incurred when edge charging facility e processes service a; d ie and d ee′ are the communication latency between data node i and edge charging facility e and the communication latency between edge charging facility e and edge charging facility e', respectively.

5. The method of claim 4, wherein the communication delay between the edge charging facility and the power distribution network is optimized by, The computing delay generated by the edge charging facility e when processing the service a is passed through t ae ∝w a is calculated, wherein w a is the load generated by the service a in the edge charging facility, ζ ae is a variable, when ζ ae = 0, it indicates that the edge charging facility e does not complete the processing task of the service a, when ζ ae = 1, it indicates that the edge charging facility e completes the processing task of the service a, f ae is the processing frequency of the edge charging facility e processing the service a, and t0 is the computing delay generated when processing a unit load w0 with a unit processing frequency f0.

6. The method of claim 4, wherein the communication delay between the edge charging facility and the power distribution network is optimized by, The communication delay between the data node i and the edge charging facility e is calculated by d ie ∝u ie The communication delay between the edge charging facility e and the edge charging facility e' is calculated by where d0 is the delay generated by the basic data unit u0 when it is transmitted over a unit distance at the set data transmission rate R0, u ie is the transmission data volume between the data node i and the edge charging facility e, R ie is the transmission efficiency between the data node i and the edge charging facility e, B ie is the communication bandwidth between the data node i and the edge charging facility e, is the signal-to-noise ratio of the information transmission rate, ζ ie is a variable, which represents that the edge charging facility e does not complete the processing task of the node i when ζ ae = 0, and represents that the edge charging facility e completes the processing task of the node i when ζ ae = 1, u ee′ and R ee′ are the transmission data volume and the transmission efficiency of the edge charging facility e and the edge charging facility e', respectively.

7. A communication delay optimization device for edge charging facilities and power distribution networks, characterized in that, Comprise: The division scheme determination module is used for determining the division scheme of the edge charging facility management and control node according to the system node distribution to determine each node cluster of the system; The division scheme determination module comprises: The first determination unit is used for determining the average number of nodes of the edge charging facility management and control according to the number of edge charging facilities and the number of data nodes; The first calculation unit is used for arbitrarily selecting the data nodes of the average node number to form a node cluster, and calculating the sum of the node relative distances of each node cluster; The sorting unit is used for sorting the sum of the node relative distances of the node cluster to obtain a sequence A; The second determination unit is used for selecting the nearest data nodes of the average node number in a set direction to form a new node cluster, and calculating the sum of the node relative distances of each new node cluster, and selecting the maximum value, and determining the position m of the maximum value in the sequence A; The second calculation unit is used for determining the node cluster without common nodes in the first m node clusters in the sequence A, summing the node relative distances of each combination, and the combination with the minimum sum value is the union of each node cluster of the system; The double-layer model establishment module is configured to establish a double-layer model of edge charging facility deployment and service allocation with edge computing capability considering service association, the double-layer model comprising an outer-layer model and an inner-layer model, the outer-layer model solving allocation of system application services among edge charging facilities with a target of minimizing total delay, and the inner-layer model solving deployment of the edge charging facilities in a node cluster based on the allocation scheme with a target of minimizing total communication delay; the allocation module is configured to solve the double-layer model by using a preset algorithm, and allocate an optimal solution obtained as a result of edge charging facility deployment and service allocation, so as to achieve optimal system service processing real-time performance.

8. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the steps of the method for optimizing communication delay between the edge charging facility and the power distribution network according to any one of claims 1-6.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method for optimizing communication delay between the edge charging facility and the power distribution network according to any one of claims 1-6.

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