CN113676357A - Decision method for edge data processing in power internet of things and application thereof - Google Patents

Decision method for edge data processing in power internet of things and application thereof Download PDF

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CN113676357A
CN113676357A CN202111002096.1A CN202111002096A CN113676357A CN 113676357 A CN113676357 A CN 113676357A CN 202111002096 A CN202111002096 A CN 202111002096A CN 113676357 A CN113676357 A CN 113676357A
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service
data
processing
edge server
edge
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CN113676357B (en
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陆旭
聂伟峰
陈影
张海全
慕春芳
孙毅
胡亚杰
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North China Electric Power University
State Grid Eastern Inner Mongolia Power Co Ltd
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State Grid Eastern Inner Mongolia Power Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0803—Configuration setting
    • H04L41/0823—Configuration setting characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability
    • H04L41/083—Configuration setting characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability for increasing network speed
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y10/00—Economic sectors
    • G16Y10/35—Utilities, e.g. electricity, gas or water
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/142—Network analysis or design using statistical or mathematical methods
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L47/00—Traffic control in data switching networks
    • H04L47/50—Queue scheduling
    • H04L47/62—Queue scheduling characterised by scheduling criteria
    • H04L47/625—Queue scheduling characterised by scheduling criteria for service slots or service orders
    • H04L47/6275—Queue scheduling characterised by scheduling criteria for service slots or service orders based on priority
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks

Abstract

The decision method for processing edge data in the power Internet of things and the application thereof comprise the following steps: setting the importance level of each service accessed by the edge server: setting a set of electric power services borne by an electric power communication network as M, wherein the importance level of the electric power service M belonging to the M is divided into 4 levels according to the time delay sensitivity degree, and the importance level of the service M is recorded as km，(kmK is not more than K and K is 4); constructing a service processing depth model according to the service attributes and the network topology: selecting service data volume S for data processing by using edge server ccBinary variable

Description

Decision method for edge data processing in power internet of things and application thereof
Technical Field
The invention belongs to the field of electric power internet of things edge data processing, and particularly relates to a differentiated service data flow processing decision method based on edge calculation and application thereof.
Background
With the continuous development of the power internet of things, the service types and the data scale in the power internet of things are also expanded, more and more sensor terminals are deployed in the sensing layer of the power internet of things, and sensing data are uploaded to the cloud platform. However, the data collected and uploaded by the sensor terminal are easily subject to redundancy, errors, loss and other problems due to low energy consumption, insufficient processing capacity and the like of the sensor terminal.
In response to this situation, in recent years, a number of documents have proposed using edge computing techniques to overcome the above-mentioned problems by preprocessing the data before uploading it. Their basic idea can be summarized as boosting the grid floor processing capacity by introducing edge servers or edge gateways. Ronhong et al published an intelligent measurement terminal data processing method based on edge computing and MapReduce in Intelligent electric journal 2020, volume 48, and proposed a method for improving management capability of data blocks of the Internet of things of electric power by using edge nodes. Chen research and published on volume six of IEEE Transactions on Cloud Computing in 2018, article MapReduce scheduling for offline-constrained jobs in heterologous Computing systems, in an attempt to improve data mining and processing capabilities of the edge layer of the power grid using edge servers. Bai Yi Yang et al, published on volume 46 of the journal 2020, a text of cloud-edge intelligence, an edge computing method for power system operation control and the current application and prospect thereof, and proposed a new mode for improving the fast scheduling and reliable operation capability of a power system by using edge computing. After the introduction of the edge computing technology, the power system can be regarded as that the computing and processing capacity of the system main station is moved down to the edge layer of the network. In the power internet of things, the data processing process of the edge computing mode is beneficial to reducing the data volume of the uploaded data, so that the communication pressure of the network is reduced.
However, although the above work improves the data calculation efficiency of the power communication service, a new problem is brought when the data is preprocessed by using the edge calculation. Compared with the cloud center, the computing power of the edge computing server is very limited, which may cause that a large processing delay overhead is required when the edge computing server performs data processing, and the size of the processing delay is generally in positive correlation with the size of the data amount processed by the edge computing server. Moreover, for some delay-sensitive services, if the raw data is preprocessed, the processing delay caused by the preprocessing may affect the execution of the service.
In addition, chinese patent application, application No. CN2021100901205, publication No.: CN112764835A discloses an electric power thing networking sensing equipment configuration micro-service system and method based on edge calculation, including: the system comprises an edge internet of things agent architecture, a cloud service center and terminal equipment, wherein the edge internet of things agent architecture processes terminal equipment data and performs computing task cooperative processing between the cloud service center and the terminal equipment according to the priority of the terminal equipment data; the edge internet of things agent architecture comprises edge internet of things agent hardware and edge internet of things agent software, wherein the edge internet of things agent software is a micro-service module architecture which is built on the basis of an EdgeX fountain edge computing open source platform by using a Linux operating system and a Docker container engine; the core micro-service module comprises a strategy control micro-service module, a strategy execution micro-service module, a data maintenance micro-service module and a command operation micro-service module. Application No. CN2021100079494, publication No.: CN112835691A discloses an edge data processing method and system for Internet of things communication, which comprises the following steps: acquiring computing parameters of each edge computing device through the cloud computing center, wherein the computing parameters comprise computing capacity and processing time delay; acquiring the request task from the intelligent terminal through the cloud computing center, wherein the request task comprises data computing amount, time requirement, emergency degree and corresponding physical addresses of the collectors, and the emergency degree comprises emergency, urgent and general; and analyzing the request task and the computing parameters through the cloud computing center to obtain the edge computing equipment which is most matched with the request task. However, the foregoing prior art cannot solve the problems that the delay in the edge service processing process is reduced and the efficiency and performance of the network are improved when the power internet of things implements part of services.
Disclosure of Invention
In order to realize the balance of time delay effect of part of services and reduction of transmission pressure of the power communication network, the basic idea of the invention is that the edge server selectively processes data of part of services, thereby reducing the transmission pressure of the power internet of things on the premise of optimizing the time delay effect of all services as far as possible. In order to meet the requirements, the invention provides a decision method for processing edge data in an electric power internet of things, which reduces the transmission pressure of a communication network, optimizes the network performance and improves the communication quality on the premise of optimizing the time delay of all services as far as possible, and the technical scheme is as follows:
the differentiated service data flow processing decision method based on edge calculation is characterized by comprising the following steps:
s10, setting each service importance level accessed by the edge server: the method comprises the steps that the set of electric power services borne by an electric power communication network under the application scene of the method is set to be M, the importance level of the electric power service M belonging to the M is divided into 4 levels according to the time delay sensitivity degree, and the importance level of the service M is recorded as km，(kmK is not more than K and K is 4);
s20, constructing a service processing depth model according to the service attributes and the network topology: selecting service data volume S for data processing by using edge server ccBinary variable
Figure BDA0003235980420000031
Constructing a service processing depth model by using the data base compression ratio delta and the information attribute set A;
and S30, solving the optimal data processing decision scheme, and setting the processing decision of each edge server for each service.
The invention also discloses a differentiated service data flow processing decision method based on edge calculation, which is applied to the power communication network.
The invention has the beneficial effects that: the invention discloses a differentiated service data flow processing decision method based on edge calculation in an electric power Internet of things. The change situation of the data volume processed by the edge server is quantified through an edge Internet of things proxy data processing model, and then the service experiment model is used for analyzing the time delay influence of data processing on the service. This changes the traffic weighted delay and minimization problem into a binary decision problem for the edge server to selectively handle traffic. This problem can be solved by using intelligent optimization algorithms. The invention can reduce the time delay in the edge service processing process and improve the efficiency and the performance of the network.
Drawings
Fig. 1 is a flowchart of a differentiated service data traffic processing decision method based on edge calculation according to the present invention;
FIG. 2 is a flowchart of a process for solving optimal data using an intelligent optimization algorithm according to the present invention;
fig. 3 is a performance comparison diagram of a differentiated service data traffic processing decision method based on edge calculation according to the present invention.
Detailed Description
The present invention is explained in further detail below with reference to the drawings and the specific embodiments, but it should be understood that the scope of the present invention is not limited to the specific embodiments.
Fig. 1 is a schematic flow chart of a differentiated service data traffic processing decision method based on edge calculation according to the present invention. The method specifically comprises the following steps:
s10, setting sidesAnd (4) each service importance level accessed by the edge server. The set of power services carried by a power communication network of an input algorithm is set as M, wherein the importance level of the power service M belonging to the M (hereinafter referred to as the service for short) is divided into 4 levels according to the time delay sensitivity (the level is classified and is more than s, hundred ms and ten ms), and the importance level of the service M is recorded as km，(kmK is not more than K and K is 4);
the dividing of the service importance level in S10 includes the following steps:
in the power internet of things, each terminal may be applied to different services, and service data collected by the terminal may be uploaded to an edge server accessed by the terminal in a burst or at a fixed period. The edge server is responsible for aggregating and uploading data from the terminal, and determining whether to process the data according to specific time delay conditions.
The communication network topology of the power internet of things is characterized by using an undirected graph N ═ (U, C, R, E). Wherein, U is a terminal set, C is an edge server set, R is a cloud routing set, and E is a network link set. Collection
Figure BDA0003235980420000041
And characterizing the terminal U belonging to the access edge server C belonging to the C, wherein the number of the terminals in the set is UMc. Edge server c exploits computing power βcFor data processing of terminal data information within a coverage area, wherein the computing power betacExpressed in GHz by the CPU operating frequency of the computer. Since the data volume uploaded by the terminal at a time is usually not large (for example, the data volume uploaded by the pole switch remote signaling sensor at a time is only a few bytes), the process of uploading data to the edge server by the terminal can be regarded as being completed instantly. Edge server c and cloud routing r accessed by edge server c1Inter-aggregation link
Figure BDA0003235980420000042
And cloud routing r1And r2Inter-forwarding link
Figure BDA0003235980420000043
Respectively of
Figure BDA0003235980420000044
And
Figure BDA0003235980420000045
the set of the electric power services carried by the electric power communication network is M, wherein the importance level of the electric power service M belonging to the M (which is hereinafter referred to as the service for short) is divided into 4 levels according to the time delay sensitivity (the level is classified and is more than s, hundred ms and ten ms), wherein the importance level of the service M is marked as km，(kmK ≤ and K ═ 4).
And S20, constructing a service processing depth model according to the service attributes and the network topology. Selecting service data volume S for data processing by using edge server ccBinary variable
Figure BDA0003235980420000046
And (3) constructing a service processing depth model shown in the formula (3) by using the data base compression ratio delta and the information attribute set A. Constructing a service processing depth model according to the service attributes and the network topology:
using binary variables respectively
Figure BDA0003235980420000047
And an information attribute set a to characterize all information attributes (of | a | types) included in the service M and | M | types of services served by the terminal u. Therefore, the information attributes owned by the service m and the values of the information attributes can be respectively represented by the binary information attribute set Am＝(a1,..,ai,..a|A|) And a set of attribute values Bm＝(b1,..,bi,..b|A|) To characterize. In which the binary variable aiData characterizing a service m includes an attribute a of 1iAnd the value of the information attribute is bi。
For each terminal u, the data information it transmits may be characterized as a row vector
Figure BDA0003235980420000051
Since there may be multiple terminals serving the same service in the edge server, the data information of the service m in the edge server c may be used as a matrix
Figure BDA0003235980420000052
To indicate. Matrix array
Figure BDA0003235980420000053
Has the dimension of
Figure BDA0003235980420000054
Wherein
Figure BDA0003235980420000055
Is the number of terminals serving the service m within the coverage of the edge server c. Similarly, the matrix DM can be used when the edge server c receives the data information of all servicescIs represented and matrix DM iscHas a dimension of
Figure BDA0003235980420000056
For any service m on the edge server c, the time delay comprises two parts, namely processing time delay and forwarding time delay. Due to the computing power β of the edge server ccThere is a limit, so all the traffic on the edge server c that selects to process data needs to wait for a processing delay
Figure BDA0003235980420000057
To avoid processing delay
Figure BDA0003235980420000058
If the size is too large, the edge server c can choose to perform data processing only on the data of part of the services. Processing latency of edge server c
Figure BDA0003235980420000059
The definition formula is:
Figure BDA00032359804200000510
wherein, deltacAnd
Figure BDA00032359804200000511
respectively edge server c processing delay
Figure BDA00032359804200000512
And the two parameters are both the inherent property of the edge server and the size S of the received datacIs irrelevant. Basic processing delay
Figure BDA00032359804200000513
It is a necessary delay overhead for the edge server c to initiate the data processing process, and the delay is determined by the hardware configuration of the edge server. Since the edge server c can select to process data only for part of the services, the edge server c selects the service data volume S for data processingcCan be characterized as a data matrix DMcI.e.:
Figure BDA00032359804200000514
wherein, binary variables
Figure BDA00032359804200000515
Is a decision variable for the edge server c to perform data processing on the service m.
Figure BDA00032359804200000516
And the characterization edge server c processes the data of the service m.
The invention refers to the ratio of the data volume before and after data processing as the data processing depth phi of the edge server cc. However due to the set of information attributes a of the different services mmComposed of different information attributes, and therefore the data processing of different services on the edge server c
Figure BDA0003235980420000061
As well as different. The invention defines the data processing depth phi of the edge server ccComprises the following steps:
Figure BDA0003235980420000062
where Δ is the data base compression rate, related to the effect of the software function itself used to process the service.
And S30, solving an optimal data processing decision scheme by applying an intelligent optimization algorithm, and setting processing decisions (processing or direct uploading without processing) of each edge server to each service.
Because the edge server c may only process data of part of the service m, the edge server c will successively establish two data packets
Figure BDA0003235980420000063
And
Figure BDA0003235980420000064
to load the processed and unprocessed traffic data, respectively. Wherein the service data is not processed
Figure BDA0003235980420000065
Without waiting for processing delay
Figure BDA0003235980420000066
But can be directly encapsulated in a data packet
Figure BDA0003235980420000067
And sends the data to a system main station through the power Internet of things
Figure BDA0003235980420000068
Otherwise, the service data processed by the data processing
Figure BDA0003235980420000069
When it is required to wait for processingDelay time
Figure BDA00032359804200000610
And then the packet can be packed and sent to the system master station. According to the processing decision of the edge server c on each service m, the data volume of two data packets of the edge server c
Figure BDA00032359804200000611
And
Figure BDA00032359804200000612
respectively as follows:
Figure BDA00032359804200000613
Figure BDA00032359804200000614
wherein
Figure BDA00032359804200000615
And
Figure BDA00032359804200000616
and respectively loading the processed and unprocessed service data.
After the edge server c completes the encapsulation of the two data packets, the data packet obtained according to the specific routing rule is forwarded to the system master station d0The routing path of (1). Two data packets of the same edge server c are agreed in the invention
Figure BDA00032359804200000617
And
Figure BDA00032359804200000618
have the same forwarding routes. At the same time, if a certain section of link of network layer
Figure BDA00032359804200000619
Are simultaneously selected as a pluralityForwarding path of edge server c, link
Figure BDA00032359804200000620
Divide its bandwidth amount equally
Figure BDA00032359804200000621
To each edge server c. In order to characterize the routing relation of the data packet of the edge server, the invention uses binary variable
Figure BDA00032359804200000622
To characterize whether the data packet of the edge server c passes through the link
Figure BDA0003235980420000071
The forwarding delay of the two data packets
Figure BDA0003235980420000072
And
Figure BDA0003235980420000073
are respectively defined as:
Figure BDA0003235980420000074
Figure BDA0003235980420000075
wherein the content of the first and second substances,
Figure BDA0003235980420000076
the data packet shares the link with the edge server when being forwarded in the network layer
Figure BDA0003235980420000077
The number of edge servers.
In summary, for the service m, the delay consists of two parts, namely forwarding delay and processing delay. Therefore, the service delay is defined as:
Figure BDA0003235980420000078
the optimization goal of the invention is to minimize the weighted delay sum of all services, where the delay weight coefficient of a service is the importance level k of each servicem. In equation (8), the delay size of the traffic m on the edge server c
Figure BDA0003235980420000079
Depending on whether the data processing is done or not. Therefore, the objective function of the service delay optimization problem belongs to an integer programming problem, as shown in formula (9):
Figure BDA00032359804200000710
equation (9) shows whether the edge server c performs data processing on the service m or not under the condition that the objective of time delay weighted sum minimization is considered
Figure BDA00032359804200000711
The binary decision problem of (2), the corresponding constraints of the binary integer program are:
Figure BDA0003235980420000081
Figure BDA0003235980420000082
Figure BDA0003235980420000083
wherein constraints (10) - (12) are binary variable constraints of a binary integer program. Wherein, binary variables
Figure BDA0003235980420000084
And if the terminal u serves the service m, selecting the service m, wherein the value is 1, and otherwise, selecting the service m is 0. Binary variable
Figure BDA0003235980420000085
To characterize whether the data packet of the edge agent c passes through the link
Figure BDA0003235980420000086
If passing through
Figure BDA0003235980420000087
The value is 1, otherwise 0.
The intelligent optimization algorithm used for solving the service delay optimization problem is a spider monkey algorithm, and the steps are as follows:
an initial population of N individuals is first generated, where N is determined based on the number of incoming power services. Wherein each individual is a C-dimensional vector, and the ith individual is recorded as SMic，SMicIs equal to 1
Figure BDA0003235980420000088
Its meaning is a potential solution to characterize the objective function. Its initial position is determined by the following equation:
SMij＝SMminj+U(0,1)*(SMmaxj-SMminj) (13)
wherein U (0,1) produces a random number of 0-1 and SMmaxjAnd SMminjThen the given upper and lower bounds of the individual jth dimension location information, respectively.
Then each individual needs to be optimized by learning the local leader position information of the group to which the individual belongs and a random identical group of individual position information:
SMnewij＝SMij+U(0,1)*(LLkj-SMij)+U(-1,1)*(SMrj-SMij) (14)
wherein LLkjRepresents the individual SMiIn the kth group, the j-dimension position information of the local leader; and SMrjThen indicate the task in the group kFree individual SMrThe j-th dimension position information. And simultaneously, taking the optimal solution corresponding to the currently obtained optimal objective function value as the current global leader of the optimal solution. The position update formula of each individual at this time is as follows:
SMnewij＝SMij+U(0,1)*(GLj-SMij)+U(-1,1)*(SMrj-SMij) (15)
in the formula, GLjJ-th dimension location information representing a current global leader. The individual fitness value characterizes the relative superiority of the individual in the current population, and is defined by the formula:
Figure BDA0003235980420000091
the location update probability of the individual at this time is:
Figure BDA0003235980420000092
and selecting the individual with the maximum fitness in the current population as a new global leader, and if the individual with the maximum fitness in the current population is the same as the new global leader, adding 1 to a global leader counter GlobalLeaderCount.
And selecting the individual with the maximum fitness in each group as a local leader in each group, and if the local leader is the same as the previous round of selection, adding 1 to a local leader counter.
If the local leader counter localreadercount of a certain group has not reached the given local leader count threshold localreaderlimit, the individuals within the group update their location information using the following equation:
SMnewij＝SMij+U(0,1)*(GLj-SMij)+U(0,1)*(SMij-LLkj) (18)
if the count value of the global leader counter globallerdercount reaches a given global leader count threshold globallerderlimit, the whole population needs to be split into more than one population. If the population number in the population reaches the preset maximum population number MG at the momentThe individuals are gathered into a group, and the position of the global leader represents the approximate optimal solution, namely the approximate optimal value of the formula (9) is obtained
Figure BDA0003235980420000093
And meanwhile, outputting the positions of the spider monkeys, namely the processing decision results corresponding to each power service.
The specific process involves: -
S31, first, according to the input data of the network topology N ═ U, C, R, E, the service importance level, the power service information attribute set a, and the basic compression ratio Δ, a time delay weighted and minimized objective function shown in formula (9) is constructed, and the specific steps of constructing the objective function include:
the edge server c may only process data of part of the services m, and the edge server c successively establishes two data packets
Figure BDA0003235980420000094
And
Figure BDA0003235980420000095
to load the processed and unprocessed traffic data, respectively. Wherein the service data is not processed
Figure BDA0003235980420000096
Without waiting for processing delay
Figure BDA0003235980420000097
But can be directly encapsulated in a data packet
Figure BDA0003235980420000098
And sends the data to a system main station through the power Internet of things
Figure BDA0003235980420000099
Otherwise, the service data processed by the data processing
Figure BDA00032359804200000910
It will wait for the processing delay
Figure BDA00032359804200000911
And then the packet can be packed and sent to the system master station. According to the processing decision of the edge server c on each service m, the data volume of two data packets of the edge server c
Figure BDA00032359804200000912
And
Figure BDA00032359804200000913
respectively as follows:
Figure BDA0003235980420000101
Figure BDA0003235980420000102
wherein
Figure BDA0003235980420000103
And
Figure BDA0003235980420000104
and respectively loading the processed and unprocessed service data.
After the edge server c completes the encapsulation of the two data packets, the data packet obtained according to the specific routing rule is forwarded to the system master station d0The routing path of (1). Two data packets of the same edge server c are agreed in the invention
Figure BDA0003235980420000105
And
Figure BDA0003235980420000106
have the same forwarding routes. At the same time, if a certain section of link of network layer
Figure BDA0003235980420000107
Is simultaneously selected as a plurality of edge clothesForwarding path of server c, then link
Figure BDA0003235980420000108
Divide its bandwidth amount equally
Figure BDA0003235980420000109
To each edge server c. In order to characterize the routing relation of the data packet of the edge server, the invention uses binary variable
Figure BDA00032359804200001010
To characterize whether the data packet of the edge server c passes through the link
Figure BDA00032359804200001011
The forwarding delay of the two data packets
Figure BDA00032359804200001012
And
Figure BDA00032359804200001013
are respectively defined as:
Figure BDA00032359804200001014
Figure BDA00032359804200001015
wherein the content of the first and second substances,
Figure BDA00032359804200001016
the data packet shares the link with the edge server when being forwarded in the network layer
Figure BDA00032359804200001017
The number of edge servers.
In summary, for the service m, the delay consists of two parts, namely forwarding delay and processing delay. Therefore, the service delay is defined as:
Figure BDA00032359804200001018
the optimization goal of the invention is to minimize the weighted delay sum of all services, where the delay weight coefficient of a service is the importance level k of each servicem. In equation (8), the delay size of the traffic m on the edge server c
Figure BDA0003235980420000111
Depending on whether the data processing is done or not. Therefore, the objective function of the service delay optimization problem belongs to an integer programming problem, as shown in formula (9):
Figure BDA0003235980420000112
equation (9) shows whether the edge server c performs data processing on the service m or not under the condition that the objective of time delay weighted sum minimization is considered
Figure BDA0003235980420000113
The binary decision problem of (2), the corresponding constraints of the binary integer program are:
Figure BDA0003235980420000114
Figure BDA0003235980420000115
Figure BDA0003235980420000116
wherein constraints (10) - (12) are binary variable constraints of a binary integer program. Wherein, binary variables
Figure BDA0003235980420000117
And if the terminal u serves the service m, selecting the service m, wherein the value is 1, and otherwise, selecting the service m is 0. Binary variable
Figure BDA0003235980420000118
To characterize whether the data packet of the edge agent c passes through the link
Figure BDA0003235980420000119
If passing through
Figure BDA00032359804200001110
The value is 1, otherwise 0. S32, an initial population of N individuals is generated, where N is determined according to the number of incoming power traffic. Wherein each individual is a C-dimensional vector, and the ith individual is recorded as SMic，SMicIs equal to 1
Figure BDA00032359804200001111
Its meaning is a potential solution to characterize the objective function. The initial position is determined according to equation (13);
s33, each individual needs to update by learning the local leader location information of its belonging group and randomly selecting the same group of individual location information according to the probabilities of equations (16) - (17), and meanwhile, the current global leader is the optimal solution corresponding to the currently obtained optimal objective function value.
And S34, selecting the individual with the maximum fitness in the current population as a new global leader, and if the individual is the same as the previous round, adding 1 to a global leader counter GloballenderCount.
And S35, selecting the individual with the maximum fitness in each group as a local leader in each group, and if the local leader is the same as the previous round of selection, adding 1 to a local leader counter.
S36, if the local leader counter localreadercount of a certain group has not reached the given local leader count threshold localreaderlimit, the individuals in the group update their location information using equation (18):
s37, if the global leader counter GloballederCount countsIf the value reaches the given global leader count threshold GlobalLeaderLimit, the whole population needs to be split into more than one population, i.e. the step S33 is returned. Otherwise, all the individuals are aggregated into a group, and the algorithm execution is exited at S37, wherein the position of the global leader represents the approximate optimal solution, namely, the approximate optimal value of the acquisition formula (9)
Figure BDA0003235980420000121
And meanwhile, outputting the positions of the spider monkeys, namely the processing decision results corresponding to each power service.
Such as: one possible data processing decision scheme is the following table, with a final weighted delay sum of 892.69 ms.
Figure BDA0003235980420000122
In the corresponding edge server data processing decision scheme shown in the above table, the frequency of the edge server deciding to perform data processing on the four power services is 73.33%, 60% and 53.33%, respectively. The method of the invention effectively reduces the time delay in the edge service processing process and improves the efficiency and the performance of the network.
The invention discloses a differentiated service data flow processing decision method based on edge calculation in an electric power Internet of things. The change situation of the data volume processed by the edge server is quantified through an edge Internet of things proxy data processing model, and then the service experiment model is used for analyzing the time delay influence of data processing on the service. This changes the traffic weighted delay and minimization problem into a binary decision problem for the edge server to selectively handle traffic. This problem can be solved by using intelligent optimization algorithms. The invention can reduce the time delay in the edge service processing process and improve the efficiency and the performance of the network.

Claims (5)

1. The differentiated service data flow processing decision method based on edge calculation is characterized by comprising the following steps: the method comprises the following steps:
s10, setting edge server accessThe service importance level of (2): setting a set of electric power services borne by an electric power communication network as M, wherein the importance level of the electric power service M belonging to the M is divided into 4 levels according to the time delay sensitivity degree, and the importance level of the service M is recorded as km，(kmK is not more than K and K is 4);
s20, constructing a service processing depth model according to the service attributes and the network topology: selecting service data volume S for data processing by using edge server ccBinary variable
Figure FDA0003235980410000011
Constructing a service processing depth model by using the data base compression ratio delta and the information attribute set A;
and S30, solving the optimal data processing decision scheme, and setting the processing decision of each edge server for each service.
2. The differentiated service data traffic processing decision method based on edge computing according to claim 1, characterized by: the S10 further includes the following: in the power internet of things, each terminal may be applied to different services, and service data collected by the terminal may be uploaded to an edge server accessed by the terminal in a burst or fixed period; the edge server is responsible for aggregating and uploading data from the terminal, and determining whether to process the data according to specific time delay conditions.
3. The differentiated service data traffic processing decision method based on edge computing according to claim 1, characterized by: the S20 further includes the following: selecting service data volume S for data processing by using edge server ccBinary variable
Figure FDA0003235980410000012
Constructing a service processing depth model by using the data base compression ratio delta and the information attribute set A; and constructing a service processing depth model according to the service attributes and the network topology.
4. The differentiated service data traffic processing decision method based on edge computing according to claim 1, characterized by: the S30 further includes the following:
s31, firstly, according to the input network topology structure N ═ U, C, R, E, the service importance level, the power service information attribute set A, the basic compression ratio delta and other data, constructing a time delay weighting and minimizing objective function;
s32, generating an initial population consisting of N individuals, wherein N is determined according to the number of input power services;
s33, each individual needs to randomly select the position information of the same group of individuals to update by learning the position information of the local leader of the group to which the individual belongs, and simultaneously, the optimal solution corresponding to the currently obtained optimal objective function value is used as the current global leader of the individual;
s34, selecting the individual with the maximum fitness in the current population as a new global leader, and if the individual is the same as the previous round, adding 1 to a global leader counter GloballenderCount;
s35, selecting the individual with the maximum fitness in each group as a local leader in each group, and if the selection is the same as the selection in the previous round, adding 1 to a local leader counter;
s36, if the local leader counter localreadercount of a certain group has not reached the given local leader count threshold localreaderlimit, the individuals in the group update their location information;
s37, if the count value of the global leader counter GloballeaderCount reaches a given global leader count threshold GloballeaderLimit, the whole population needs to be split into one more population, namely, the step S33 is returned; otherwise, all individuals are aggregated into a group, the algorithm execution is exited at S37, the position of the global leader represents an approximate optimal solution, and the positions of spider monkeys are output at the same time, namely the processing decision results corresponding to each power service are output.
5. The differentiated service data traffic processing decision method based on edge computing according to any one of claims 1 to 4 is applied to a power communication network.
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