CN116132998A - Urban edge server deployment method based on intersection centrality - Google Patents

Urban edge server deployment method based on intersection centrality Download PDF

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CN116132998A
CN116132998A CN202310322605.1A CN202310322605A CN116132998A CN 116132998 A CN116132998 A CN 116132998A CN 202310322605 A CN202310322605 A CN 202310322605A CN 116132998 A CN116132998 A CN 116132998A
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intersection
urban
centrality
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intersections
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CN116132998B (en
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简雯欣
马勇
江兴鸿
谢麒麟
牛新增
夏云霓
刘志全
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Jiangxi Normal University
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Abstract

The invention discloses a city edge server deployment method based on intersection centrality, which comprises the following steps: obtaining urban intersection data, calculating intersection centrality and normalizing; calculating the intersection priority of the urban intersection according to the intersection center; clustering the urban intersections by adopting a K-means++ algorithm to obtain a plurality of intersection clustering areas; calculating the average value of the intersection priorities of all urban intersections in the intersection clustering area so as to obtain area priorities, and distributing the number of edge servers according to the area priorities; and selecting a proper urban intersection deployment edge server from the intersection clustering area by adopting a preference method. According to the invention, the urban intersection is used as the deployment position of the edge server, so that the range of edge computing service is enlarged; and the base station centrality is used as an important index for intersection selection so as to improve the effect of the edge server deployment scheme.

Description

Urban edge server deployment method based on intersection centrality
Technical Field
The invention relates to the field of edge server deployment, in particular to an urban edge server deployment method based on intersection centrality.
Background
Next generation mobile applications in smart cities, including assisted autopilot, high-dimensional data preprocessing, require complex data processing and fast information exchange. One practical way to address these needs is to employ an edge computing paradigm of a 5G architecture to place storage, computing, and network resources at the network edge, i.e., closer to the end user. Any metropolitan area requires the provision of multiple edge nodes to support the intended use of the vehicle network in future 5G scenarios. In the case of a limited budget, how to effectively deploy a limited number of edge nodes in a city scenario is called an edge node placement problem.
Firstly, traffic light equipment is arranged at the urban intersection, so that deployment space is provided for an edge server, and deployment cost is saved; secondly, vehicles stay at the urban intersections for a period of time frequently, so that time is provided for task calculation, and therefore, the edge servers deployed at the urban intersections can provide services for the vehicles; finally, the track data of the vehicles are associated with the urban roads, so that the edge servers deployed at the urban intersections can reduce the complexity of task migration prediction tasks to improve the accuracy of task migration, when the edge servers are deployed at the urban intersections, the priority degree of the intersections is only considered from the angle of the intersection centers, and the intersection centers of the urban intersections in developed areas are higher than those of the urban intersections in less developed areas, so that the edge servers are deployed at a few developed areas, and the aggregation effect is generated.
The bulletin number is CN113347267B, the name is MEC server deployment method in the mobile edge cloud computing network, and modeling and analyzing various factors influencing deployment efficiency are provided to obtain a calculation formula of deployment efficiency. The values of the various parameters needed to obtain the calculated deployment efficiency are then utilized. Then, according to a calculation formula of the deployment efficiency and values of all parameters, calculating an optimal deployment decision by using a three-layer optimization method; thereby deploying MEC servers and computing resource amounts on base stations in the city.
The bulletin number is CN110972152B, and the name is a city space 5G base station site selection optimization method considering the signal blocking effect, which proposes to optimize the number and the space layout of communication base stations on the premise of considering the blocking effect of a city building on communication signals, thereby realizing the maximization of reasonably controlling the number of the base stations and the signal coverage range thereof.
The two patents are used for deploying the edge servers or the base stations in cities, and aim to maximize signal coverage and improve deployment efficiency from factors such as quantity.
Disclosure of Invention
The invention provides an urban edge server deployment method based on intersection centrality, which can solve the problem of aggregation effect easily occurring when edge servers are deployed based on intersection centrality.
In order to solve the technical problems, the invention adopts a technical scheme that: a city edge server deployment method based on intersection centrality comprises the following steps:
s1: obtaining urban intersection data, calculating intersection centrality and normalizing; the city intersection data includes: intersection longitude and latitude, intersection position information, vehicle track data and base station longitude and latitude;
s2: calculating the intersection priority of the urban intersection according to the intersection center;
s3: clustering the urban intersections by adopting a K-means++ algorithm according to the longitude and latitude of the intersections to obtain a plurality of intersection clustering areas;
s4: calculating the intersection priority mean value of all urban intersections in the intersection clustering area;
s5: calculating the region importance degree of the intersection clustering region according to the intersection priority mean value, and distributing the number of edge servers according to the region importance degree;
s6: and selecting a proper urban intersection deployment edge server from the intersection clustering area by adopting a preference method.
Further, the intersection centrality includes: dynamic centrality and static centrality;
the dynamic centrality refers to traffic density of urban intersections;
the static centrality comprises: connection centrality, intermediary centrality, and base station centrality.
Further, the traffic density refers to traffic flow at urban intersections at early peak time, and the calculation formula is as follows:
Figure SMS_1
；
wherein ,
Figure SMS_3
refers to city intersection set->
Figure SMS_5
Middle->
Figure SMS_7
Urban intersections->
Figure SMS_4
Refers to urban intersections at early peak time>
Figure SMS_6
Traffic density of->
Figure SMS_8
Refers to the approach of urban intersections in early peak time>
Figure SMS_9
Vehicle number of>
Figure SMS_2
Refers to the total amount of vehicles passing through all urban intersections at the early peak.
Further, the connection centrality refers to the number of urban intersections directly connected with the urban intersections, and the calculation formula is as follows:
Figure SMS_10
；
wherein ,
Figure SMS_11
refers to urban intersections +.>
Figure SMS_12
Is (are) connected with center degree, ">
Figure SMS_13
Refers to urban intersections +.>
Figure SMS_14
The number of connecting edges with other urban intersections, +.>
Figure SMS_15
Refers to the number of urban intersections;
the mediating center degree refers to the number of times that the urban intersection is used as a node in the shortest path, and the calculation formula is as follows:
Figure SMS_16
；
wherein ,
Figure SMS_17
refers to urban intersections +.>
Figure SMS_21
Middle center of->
Figure SMS_23
Refers to urban intersections +.>
Figure SMS_19
and />
Figure SMS_22
The number of shortest paths between, +.>
Figure SMS_24
Representing urban intersections +.>
Figure SMS_25
and />
Figure SMS_18
The city crossing is crossed>
Figure SMS_20
Is the shortest path number of (a);
the base station centrality refers to the number of base stations within the range of 500 meters of an urban intersection, and the calculation formula is as follows:
Figure SMS_26
；
wherein ,
Figure SMS_27
refers to urban intersections +.>
Figure SMS_28
Base station centrality, & gt>
Figure SMS_29
Representing urban intersections +.>
Figure SMS_30
500m range of base station number,/->
Figure SMS_31
Representing the number of base stations within 500m of all city intersections.
Further, the normalization, the calculation formula is:
Figure SMS_32
；
wherein ,
Figure SMS_33
refer to source data +.>
Figure SMS_34
Data after normalization, ++>
Figure SMS_35
Refers to the minimum value in the source data, < +.>
Figure SMS_36
Refers to the maximum value in the source data.
Further, the intersection priority of the urban intersection is calculated, each item of normalized data is used as source data, weights are given and overlapped, and a calculation formula is as follows:
Figure SMS_37
；/>
wherein ,
Figure SMS_39
refers to urban intersections +.>
Figure SMS_44
Crossing priority,/->
Figure SMS_48
Refers to urban intersections +.>
Figure SMS_38
Is used for the connection centrality of the (c) and (d),
Figure SMS_42
refers to urban intersections +.>
Figure SMS_45
Middle center of->
Figure SMS_49
Refers to urban intersections +.>
Figure SMS_41
Base station centrality, & gt>
Figure SMS_47
Refers to early heightUrban intersections at peak time->
Figure SMS_51
Traffic density of->
Figure SMS_52
、/>
Figure SMS_40
、/>
Figure SMS_43
and />
Figure SMS_46
The weights of the connection centrality, the intermediate centrality, the base station centrality and the traffic density are positive values and +.>
Figure SMS_50
。
Further, the calculating the average value of the intersection priorities of all urban intersections in the intersection clustering area comprises the following calculation formula:
Figure SMS_53
；
wherein ,
Figure SMS_54
refers to->
Figure SMS_55
An intersection priority average value of each intersection clustering area; />
Figure SMS_56
The total urban intersections in the current intersection clustering area are referred; />
Figure SMS_57
Refers to the +.>
Figure SMS_58
Intersection priority of individual city intersections.
Further, the calculating area importance degree of the intersection clustering area is calculated according to the following calculation formula:
Figure SMS_59
；
wherein ,
Figure SMS_60
refers to->
Figure SMS_61
Regional importance degree of individual intersection clustering regions, +.>
Figure SMS_62
Refers to the total quantity of the road-mouth clustering areas, +.>
Figure SMS_63
Refers to->
Figure SMS_64
Intersection priority mean value of individual intersection clustering areas, < ->
Figure SMS_65
Refers to->
Figure SMS_66
An intersection priority average value of each intersection clustering area;
the step of distributing the number of the edge servers according to the region importance degree is to distribute 40% of the edge servers to each intersection clustering region on average; the remaining 60% of the edge servers are assigned according to the regional importance level.
Further, the remaining 60% of edge servers are distributed according to the regional importance degree, and the calculation formula is as follows:
Figure SMS_67
；
wherein ,
Figure SMS_68
refers to subsequent allocation in the road-opening cluster region iEdge server number of->
Figure SMS_69
Refers to the total number of edge servers.
Further, the selecting an appropriate urban intersection deployment edge server from the intersection clustering area by adopting the optimization method comprises the following steps:
s6-1, acquiring the number of the distributed edge servers and the number of urban intersections in an intersection clustering area;
s6-2, selecting an urban intersection deployment edge server with highest intersection priority;
s6-3, deleting the urban intersections of the deployed edge servers;
s6-4, deleting the urban intersections covered by the deployed edge servers;
and S6-5, repeating the steps S6-1 to S6-4 until one of the number of the allocated edge servers or the number of the urban intersections in the intersection clustering area is zero.
The beneficial effects of the invention are as follows:
1. under urban environment, the intersection is used as the deployment position of the edge server for the first time, so that the range of the edge computing service is enlarged;
2. considering a base station deployment strategy in urban environment, taking the number of base stations as an index for measuring the importance degree of an area, providing a concept of the center degree of the base stations of the intersections, and taking the concept as an important index for selecting the intersections of the cities so as to improve the effect of an edge server deployment scheme;
3. dividing urban areas by using a K-means++ algorithm, distributing a fixed number of edge servers for each intersection clustering area, and distributing redundant edge servers according to the area priority, so that the problem that the limited edge servers are deployed in a few areas of the city to cause other areas not to be provided with edge services is avoided;
4. in the same area, a plurality of deployment servers are easy to generate aggregation effect, and the urban intersections with high priority are selected as deployment intersections by adopting a preference method, so that the repetition of the service range is avoided.
Drawings
FIG. 1 is a flow chart of a city edge server deployment method based on intersection centrality.
FIG. 2 is a diagram of an urban intersection of a method for deploying urban edge servers based on intersection centrality.
FIG. 3 is a city intersection cluster map of a city edge server deployment method based on intersection centrality.
Fig. 4 is a deployment effect diagram of an edge server of a city road junction based on a road junction centrality city edge server deployment method.
Detailed Description
The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby making clear and defining the scope of the present invention.
Referring to fig. 1, 2, 3 and 4, an embodiment of the present invention includes:
a city edge server deployment method based on intersection centrality comprises the following steps:
s1: obtaining urban intersection data, calculating intersection centrality and normalizing; the city intersection data includes: intersection longitude and latitude, intersection position information, vehicle track data and base station longitude and latitude;
s2: calculating the intersection priority of the urban intersection according to the intersection center;
s3: clustering the urban intersections by adopting a K-means++ algorithm according to the longitude and latitude of the intersections to obtain a plurality of intersection clustering areas;
s4: calculating the intersection priority mean value of all urban intersections in the intersection clustering area;
s5: calculating the region importance degree of the intersection clustering region according to the intersection priority mean value, and distributing the number of edge servers according to the region importance degree;
s6: and selecting a proper urban intersection deployment edge server from the intersection clustering area by adopting a preference method.
As shown in FIG. 2, according to the urban intersection data, an urban intersection map of an urban edge server deployment method based on intersection centrality is obtained.
The K-means++ algorithm comprises the following steps:
s3-1: determining the area number of the intersection clustering areas according to the number of the edge servers;
s3-2: randomly selecting an urban intersection as a clustering center of the intersection clustering area;
s3-3: calculating the distance between each city intersection corresponding to each non-clustering center and each clustering center;
s3-4: selecting the maximum value of the distance as a new clustering center;
s3-5: repeating the step S3-2, the step S3-3 and the step S3-4, and finding out clustering centers of k intersection clustering areas;
s3-6: for urban intersections with non-clustering centers, calculating the distance from the urban intersections to the clustering centers, selecting the clustering center with the smallest distance, and classifying the urban intersections into an intersection clustering area;
the method comprises the steps of determining the area number of the intersection clustering area according to the number of edge servers, wherein a calculation formula is as follows:
Figure SMS_70
；
where n refers to the total number of edge servers, and k refers to the number of areas of the road-opening cluster area.
As shown in fig. 3, the 1236 urban intersections of fig. 2 are divided into 7 intersection cluster areas by a K-means++ algorithm.
Further, the intersection centrality includes: dynamic centrality and static centrality;
the dynamic centrality refers to traffic density of urban intersections;
the static centrality comprises: connection centrality, intermediary centrality, and base station centrality.
Further, the traffic density refers to traffic flow at urban intersections at early peak time, and the calculation formula is as follows:
Figure SMS_71
；
wherein ,
Figure SMS_74
refers to city intersection set->
Figure SMS_75
Middle->
Figure SMS_77
Urban intersections->
Figure SMS_72
Refers to urban intersections at early peak time>
Figure SMS_76
Traffic density of->
Figure SMS_78
Refers to the approach of urban intersections in early peak time>
Figure SMS_79
Vehicle number of>
Figure SMS_73
Refers to the total amount of vehicles passing through all urban intersections at the early peak.
Further, the connection centrality refers to the number of urban intersections directly connected with the urban intersections, and the calculation formula is as follows:
Figure SMS_80
；
wherein ,
Figure SMS_81
refers to urban intersections +.>
Figure SMS_82
Is (are) connected with center degree, ">
Figure SMS_83
Refers to urban intersections +.>
Figure SMS_84
The number of connecting edges with other urban intersections, +.>
Figure SMS_85
Refers to the number of urban intersections;
the mediating center degree refers to the number of times that the urban intersection is used as a node in the shortest path, and the calculation formula is as follows:
Figure SMS_86
；
wherein ,
Figure SMS_89
refers to urban intersections +.>
Figure SMS_91
Middle center of->
Figure SMS_93
Refers to urban intersections +.>
Figure SMS_88
and />
Figure SMS_92
The number of shortest paths between, +.>
Figure SMS_94
Representing urban intersections +.>
Figure SMS_95
and />
Figure SMS_87
The city crossing is crossed>
Figure SMS_90
Is the shortest path number of (a);
the base station centrality refers to the number of base stations within the range of 500 meters of an urban intersection, and the calculation formula is as follows:
Figure SMS_96
；
wherein ,
Figure SMS_97
refers to urban intersections +.>
Figure SMS_98
Base station centrality, & gt>
Figure SMS_99
Representing urban intersections +.>
Figure SMS_100
Base station number within a certain range, < > a>
Figure SMS_101
Indicating the number of base stations within a certain range for all city intersections.
Further, the normalization, the calculation formula is:
Figure SMS_102
；
wherein ,
Figure SMS_103
refer to source data +.>
Figure SMS_104
Data after normalization, ++>
Figure SMS_105
Refers to the minimum value in the source data, < +.>
Figure SMS_106
Refers to the maximum value in the source data.
Further, the intersection priority of the urban intersection is calculated, each item of normalized data is used as source data, weights are given and overlapped, and a calculation formula is as follows:
Figure SMS_107
；
wherein ,
Figure SMS_109
refers to urban intersections +.>
Figure SMS_112
Crossing priority,/->
Figure SMS_116
Refers to urban intersections +.>
Figure SMS_110
Is used for the connection centrality of the (c) and (d),
Figure SMS_114
refers to urban intersections +.>
Figure SMS_118
Middle center of->
Figure SMS_121
Refers to urban intersections +.>
Figure SMS_108
Base station centrality, & gt>
Figure SMS_115
Refers to urban intersections at early peak time>
Figure SMS_119
Traffic density of->
Figure SMS_122
、/>
Figure SMS_111
、/>
Figure SMS_113
and />
Figure SMS_117
The weights of the connection centrality, the intermediate centrality, the base station centrality and the traffic density are positive values and +.>
Figure SMS_120
。
Further, the calculating the average value of the intersection priorities of all urban intersections in the intersection clustering area comprises the following calculation formula:
Figure SMS_123
；
wherein ,
Figure SMS_124
refers to->
Figure SMS_125
An intersection priority average value of each intersection clustering area; />
Figure SMS_126
The total urban intersections in the current intersection clustering area are referred; />
Figure SMS_127
Refers to the +.>
Figure SMS_128
Intersection priority of individual city intersections.
Further, the calculating area importance degree of the intersection clustering area is calculated according to the following calculation formula:
Figure SMS_129
；
wherein ,
Figure SMS_130
refers to->
Figure SMS_131
Regional importance degree of individual intersection clustering regions, +.>
Figure SMS_132
Refers to the total quantity of the road-mouth clustering areas, +.>
Figure SMS_133
Refers to->
Figure SMS_134
Intersection priority mean value of individual intersection clustering areas, < ->
Figure SMS_135
Refers to->
Figure SMS_136
An intersection priority average value of each intersection clustering area;
the step of distributing the number of the edge servers according to the region importance degree is to distribute 40% of the edge servers to each intersection clustering region on average; the remaining 60% of the edge servers are assigned according to the regional importance level.
Further, the remaining 60% of edge servers are distributed according to the regional importance degree, and the calculation formula is as follows:
Figure SMS_137
；
wherein ,
Figure SMS_138
refers to the number of edge servers allocated subsequently in the road-mouth cluster area i, +.>
Figure SMS_139
Refers to the total number of edge servers.
Further, the selecting an appropriate urban intersection deployment edge server from the intersection clustering area by adopting the optimization method comprises the following steps:
s6-1, acquiring the number of the distributed edge servers and the number of urban intersections in an intersection clustering area;
s6-2, selecting an urban intersection deployment edge server with highest intersection priority;
s6-3, deleting the urban intersections of the deployed edge servers;
s6-4, deleting the urban intersections covered by the deployed edge servers;
and S6-5, repeating the steps S6-1 to S6-4 until one of the number of the allocated edge servers or the number of the urban intersections in the intersection clustering area is zero.
Through the mode, 1236 urban intersections are divided into 7 intersection clustering areas by adopting a K-means++ algorithm, and 250 edge servers are deployed in the 7 intersection clustering areas; each intersection clustering area is distributed to 14 edge servers in advance; calculating intersection priority according to intersection centrality, accumulating to obtain area priority of intersection clustering areas, and distributing the number of the left 152 edge servers according to the area priority; according to the number of the edge servers, the number of the urban intersections and the intersection priorities of the urban intersections which are distributed, an optimization method is adopted to deploy the edge servers; the edge server deployment effect is shown in fig. 4.
The foregoing description is only illustrative of the present invention and is not intended to limit the scope of the invention, and all equivalent structures or equivalent processes or direct or indirect application in other related technical fields are included in the scope of the present invention.

Claims (10)

1. The city edge server deployment method based on the intersection centrality is characterized by comprising the following steps:
s1: obtaining urban intersection data, calculating intersection centrality and normalizing; the city intersection data includes: intersection longitude and latitude, intersection position information, vehicle track data and base station longitude and latitude;
s2: calculating the intersection priority of the urban intersection according to the intersection center;
s3: clustering the urban intersections by adopting a K-means++ algorithm according to the longitude and latitude of the intersections to obtain a plurality of intersection clustering areas;
s4: calculating the intersection priority mean value of all urban intersections in the intersection clustering area;
s5: calculating the region importance degree of the intersection clustering region according to the intersection priority mean value, and distributing the number of edge servers according to the region importance degree;
s6: and selecting a proper urban intersection deployment edge server from the intersection clustering area by adopting a preference method.
2. The urban edge server deployment method based on intersection centrality according to claim 1, wherein the intersection centrality comprises: dynamic centrality and static centrality;
the dynamic centrality refers to traffic density of urban intersections;
the static centrality comprises: connection centrality, intermediary centrality, and base station centrality.
3. The urban edge server deployment method based on intersection centrality as claimed in claim 2, wherein the traffic density is traffic flow of urban intersections at early peak time, and the calculation formula is:
Figure QLYQS_1
；
wherein ,
Figure QLYQS_2
refers to city intersection set->
Figure QLYQS_6
Middle->
Figure QLYQS_8
Urban intersections->
Figure QLYQS_4
Refers to urban intersections at early peak time>
Figure QLYQS_5
Traffic density of->
Figure QLYQS_7
Refers toUrban intersections in early peak time>
Figure QLYQS_9
Vehicle number of>
Figure QLYQS_3
Refers to the total amount of vehicles passing through all urban intersections at the early peak.
4. The urban edge server deployment method based on intersection centrality as claimed in claim 2, wherein the connection centrality refers to the number of urban intersections directly connected with the urban intersections, and the calculation formula is as follows:
Figure QLYQS_10
；
wherein ,
Figure QLYQS_11
refers to urban intersections +.>
Figure QLYQS_12
Is (are) connected with center degree, ">
Figure QLYQS_13
Refers to urban intersections +.>
Figure QLYQS_14
The number of connecting edges with other urban intersections, +.>
Figure QLYQS_15
Refers to the number of urban intersections;
the mediating center degree refers to the number of times that the urban intersection is used as a node in the shortest path, and the calculation formula is as follows:
Figure QLYQS_16
；
wherein ,
Figure QLYQS_18
refers to urban intersections +.>
Figure QLYQS_21
Middle center of->
Figure QLYQS_23
Refers to urban intersections +.>
Figure QLYQS_17
and />
Figure QLYQS_22
The number of shortest paths between, +.>
Figure QLYQS_24
Representing urban intersections +.>
Figure QLYQS_25
and />
Figure QLYQS_19
The city crossing is crossed>
Figure QLYQS_20
Is the shortest path number of (a);
the base station centrality refers to the number of base stations within the range of 500 meters of an urban intersection, and the calculation formula is as follows:
Figure QLYQS_26
；
wherein ,
Figure QLYQS_27
refers to urban intersections +.>
Figure QLYQS_28
Base station centrality, & gt>
Figure QLYQS_29
Representing urban intersections +.>
Figure QLYQS_30
500m range of base station number,/->
Figure QLYQS_31
Representing the number of base stations within 500m for all city intersections.
5. The urban edge server deployment method based on intersection centrality according to claim 1, wherein the normalization and calculation formula is:
Figure QLYQS_32
；
wherein ,
Figure QLYQS_33
refer to source data +.>
Figure QLYQS_34
Data after normalization, ++>
Figure QLYQS_35
Refers to the minimum value in the source data, < +.>
Figure QLYQS_36
Refers to the maximum value in the source data.
6. The urban edge server deployment method based on the intersection centrality of claim 1, wherein the calculating intersection priority of the urban intersection is obtained by taking normalized data as source data, weighting and overlapping, and the calculating formula is as follows:
Figure QLYQS_37
；
wherein ,
Figure QLYQS_39
refers to urban intersections +.>
Figure QLYQS_43
Crossing priority,/->
Figure QLYQS_47
Refers to urban intersections +.>
Figure QLYQS_41
Is used for the connection centrality of the (c) and (d),
Figure QLYQS_45
refers to urban intersections +.>
Figure QLYQS_49
Middle center of->
Figure QLYQS_52
Refers to urban intersections +.>
Figure QLYQS_38
Base station centrality, & gt>
Figure QLYQS_42
Refers to urban intersections at early peak time>
Figure QLYQS_46
Traffic density of->
Figure QLYQS_50
、/>
Figure QLYQS_40
、/>
Figure QLYQS_44
and />
Figure QLYQS_48
The weights of the connection centrality, the intermediate centrality, the base station centrality and the traffic density are positive values and +.>
Figure QLYQS_51
。
7. The urban edge server deployment method based on intersection centrality according to claim 1, wherein the calculating the intersection priority mean value of all urban intersections in the intersection clustering area is as follows:
Figure QLYQS_53
；
wherein ,
Figure QLYQS_54
refers to->
Figure QLYQS_55
An intersection priority average value of each intersection clustering area; />
Figure QLYQS_56
The total urban intersections in the current intersection clustering area are referred; />
Figure QLYQS_57
Refers to the +.>
Figure QLYQS_58
Intersection priority of individual city intersections.
8. The urban edge server deployment method based on intersection centrality according to claim 1, wherein the calculating the regional importance degree of the intersection clustering region comprises the following calculation formula:
Figure QLYQS_59
；
wherein ,
Figure QLYQS_60
refers to->
Figure QLYQS_61
Regional importance degree of individual intersection clustering regions, +.>
Figure QLYQS_62
Refers to the total quantity of the road-mouth clustering areas, +.>
Figure QLYQS_63
Refers to->
Figure QLYQS_64
Intersection priority mean value of individual intersection clustering areas, < ->
Figure QLYQS_65
Refers to->
Figure QLYQS_66
An intersection priority average value of each intersection clustering area;
the step of distributing the number of the edge servers according to the region importance degree is to distribute 40% of the edge servers to each intersection clustering region on average; the remaining 60% of the edge servers are assigned according to the regional importance level.
9. The urban edge server deployment method based on intersection centrality according to claim 8, wherein the remaining 60% of edge servers are distributed according to regional importance degrees, and a calculation formula is as follows:
Figure QLYQS_67
；
Figure QLYQS_68
refers to the number of edge servers allocated subsequently in the road-mouth cluster area i, +.>
Figure QLYQS_69
Refers to the total number of edge servers.
10. The urban edge server deployment method based on intersection centrality according to claim 1, wherein the selecting an appropriate urban intersection deployment edge server from the intersection clustering area by adopting the preference method comprises:
s6-1, acquiring the number of the distributed edge servers and the number of urban intersections in an intersection clustering area;
s6-2, selecting an urban intersection deployment edge server with highest intersection priority;
s6-3, deleting the urban intersections of the deployed edge servers;
s6-4, deleting the urban intersections covered by the deployed edge servers;
and S6-5, repeating the steps S6-1 to S6-4 until one of the number of the allocated edge servers or the number of the urban intersections in the intersection clustering area is zero.
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