CN110661649B - Power communication network resource allocation method - Google Patents

Power communication network resource allocation method Download PDF

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CN110661649B
CN110661649B CN201910835962.1A CN201910835962A CN110661649B CN 110661649 B CN110661649 B CN 110661649B CN 201910835962 A CN201910835962 A CN 201910835962A CN 110661649 B CN110661649 B CN 110661649B
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service provider
service
communication network
power communication
resource allocation
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CN110661649A (en
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高强
田志峰
郑启文
黄儒雅
王曦
保剑
宋旅宁
杨旸
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Shenzhen Power Supply Bureau Co Ltd
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Shenzhen Power Supply Bureau 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
    • H04L41/50—Network service management, e.g. ensuring proper service fulfilment according to agreements
    • H04L41/5041—Network service management, e.g. ensuring proper service fulfilment according to agreements characterised by the time relationship between creation and deployment of a service
    • 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
    • H04L41/0893—Assignment of logical groups to network elements
    • 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
    • H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
    • 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
    • H04L41/50—Network service management, e.g. ensuring proper service fulfilment according to agreements
    • H04L41/5041—Network service management, e.g. ensuring proper service fulfilment according to agreements characterised by the time relationship between creation and deployment of a service
    • H04L41/5051—Service on demand, e.g. definition and deployment of services in real time
    • 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
    • H04L67/50—Network services
    • H04L67/51—Discovery or management thereof, e.g. service location protocol [SLP] or web services

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Abstract

The invention provides a power communication network resource allocation method, which comprises the following steps: step S1, constructing a non-cooperative game model for power communication network resource allocation; step S2, assigning values to each parameter in the model, and initializing the service capability of each service provider; step S3, solving and obtaining the service ability of the service provider by using a Jacobian iterative formula; and step S4, performing power communication network resource allocation based on the service capability of the service provider. The invention provides the power communication network resource allocation method based on the competitive game theory under complete information, verifies that the resource allocation of the power communication network obtains Nash balance in the aspect of service capacity under the competitive environment of a self-built service provider and a third-party service provider, and reduces the capital investment of network resources when the third-party service provider is introduced, thereby improving the benefit of the service provider.

Description

Power communication network resource allocation method
Technical Field
The invention relates to the field of resource management of a power communication network, in particular to a power communication network resource allocation method.
Background
With the rapid development of smart grid services, smart grids have increasingly greater demands on power communication networks. In order to meet the requirements of smart grid services, power companies generally adopt two ways of self-establishing a power communication network and renting a third-party communication network to solve the problem. Currently, most electric power companies adopt an internal performance system, and each electric power branch company is responsible for self-establishing an electric power communication network in each region, then providing the electric power communication network for an electric power service department for use and performing performance assessment. For convenience of description, the present invention refers to a power branch company of a Self-established power communication network as a Self-established power communication network Service Provider (SSP), and refers to a company of a Third-party communication network Service Provider (TSP).
In order to meet the demand of power business, how to select a self-established power communication network and lease a third-party communication network for a power company has become an urgent problem to be solved. In the prior art, the betweenness characteristic of the shortest path of the complex network topology is analyzed, and the importance of the nodes in the complex network is evaluated, so that important resources in the network are better allocated to service demands with higher importance. In the second prior art, aiming at the problem of unbalanced distribution when the power optical fiber network resources are newly built, an improved genetic algorithm is adopted to plan the optical transmission network, so that the reliable construction of the power optical network is realized with the least resource investment. In the third prior art, a self-adaptive power communication network resource allocation mechanism is realized by a dynamic planning method aiming at the QoS priority of each smart grid service, so that the satisfaction of power users and the resource income of a power communication network are improved. In the prior art, a multi-party game theory is used as a basis, the QoS requirements of all parties of power resource demands are met, a resource allocation mechanism with maximized resource utility is constructed, Nash balance can be obtained by the allocation mechanism, and the problem of QoS-constrained power communication network resource allocation is solved well. In the prior art, a network resource allocation problem is modeled into an auction problem in the economic field, a resource allocation model formed by a resource demand party, a resource provider and a resource allocation center is constructed, a distributed network resource auction mechanism is constructed based on a VCG mechanism, and the utilization rate of network resources and social benefits of auction parties are improved well.
As can be seen from the analysis of the results of the prior art, most of the related studies only consider the competition between SSPs or TSPs, but the related studies involve less competition between SSPs and TSPs. Therefore, it is important to establish a competitive game model to study the competitive relationship between the SSPs and the TSPs.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a method for allocating power communication network resources, so as to reduce the capital investment of network resources and improve the income of network service providers.
In order to solve the above technical problem, the present invention provides a power communication network resource allocation method, including:
step S1, constructing a non-cooperative game model of power communication network resource allocation, wherein participants of the model comprise a power communication network demand party, a self-established power communication network service provider and a third-party power communication network service provider, and the power communication network demand party provides resource requests to the self-established power communication network service provider and the third-party power communication network service provider;
step S2, assigning values to each parameter in the model, and initializing the service capability of each service provider;
step S3, using Jacobi iterative formula to solve and obtain the service ability of the service provider;
and step S4, performing power communication network resource allocation based on the service capability of the service provider.
Preferably, the step S1 specifically includes:
step S11, calculating the operation cost of each service provider in the kth competition according to the receiving rate and the service capacity of the electric power communication network of each service provider;
step S12, calculating the utility of each service provider in the kth competition according to the operation cost of each service provider and the service price of the power communication network;
step S13, calculating the income of each service provider in the kth competition according to the utility and market share of each service provider;
and step S14, constructing an objective function for solving the service provider profit maximization problem under the Nash equilibrium problem by taking the profit maximization as a competitive game objective.
Preferably, the service providers are the ith self-built electric power communication network service provider SSPi and the jth third-party electric power communication network service provider TSPj, and the service capability of the service provider SSPi is
Figure BDA0002192166240000021
The service capability of the service provider TSPj is
Figure BDA0002192166240000022
And
Figure BDA0002192166240000023
subject to the constraint of
Figure BDA0002192166240000024
Preferably, the calculation method of the operation cost of each service provider in the kth round of competition is as follows:
Figure BDA0002192166240000025
wherein the content of the first and second substances,
Figure BDA0002192166240000026
for the operating cost of the service provider SSPi in the kth round of competition,
Figure BDA0002192166240000027
the operation cost of the service provider TSPj in the k-th round of competition, q is the receiving rate of the power communication network of the service provider,
Figure BDA0002192166240000028
respectively representing the service capability coefficients of the respective service providers,
Figure BDA0002192166240000031
respectively, a network cost coefficient is represented,
Figure BDA0002192166240000032
preferably, the utility of each facilitator in the kth round of competition is calculated in the following manner:
Figure BDA0002192166240000033
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0002192166240000034
for the utility of the facilitator SSPi in the kth round of competition,
Figure BDA0002192166240000035
for the utility of the facilitator TSPj in the kth round of competition, P s Serving price, P, for electric power network of SSPi as service provider in kth round of competition t And (5) serving the price for the power communication network of the service provider TSPj in the k-th competition.
Preferably, the calculation method of the profit of each service provider in the kth round of competition is as follows:
Figure BDA0002192166240000036
the constraint condition is
Figure BDA0002192166240000037
Wherein the content of the first and second substances,
Figure BDA0002192166240000038
for the benefit of the facilitator SSPi in the kth round of competition,
Figure BDA0002192166240000039
for the benefit of the facilitator TSPj in the kth round of competition,
Figure BDA00021921662400000310
being the market share of the service provider SSPi,
Figure BDA00021921662400000311
lambda is the sum of the market size of the demand of the power communication network, which is the market share of the service provider TSPj.
Preferably, the objective function is:
Figure BDA00021921662400000312
the total number of the service providers is r, and when the total number of the self-built power communication network service providers is m, the total number of the third-party power communication network service providers is r-m.
Preferably, in the step S2, the service capability of each service provider is initialized
Figure BDA00021921662400000313
Figure BDA00021921662400000314
Wherein i belongs to {1, 2.. eta., m }, and j belongs to {1, 2.. eta., r-m }.
Preferably, in step S3, the optimal service capability of each service provider is calculated as follows:
Figure BDA00021921662400000315
(u s,i ) * for optimal service capability of the service provider SSPi, (u) t,j ) * The optimal service capability of the service provider TSPj;
using jacobian transformation to service capabilities
Figure BDA00021921662400000316
The reaction function of (a) is transformed to obtain a jacobian iterative formula:
Figure BDA0002192166240000041
the service capabilities of the facilitator are calculated using the jacobian iteration formula,if the service capability calculated in the k-th round
Figure BDA0002192166240000042
Satisfy the convergence condition
Figure BDA0002192166240000043
Figure BDA0002192166240000044
Then at this time
Figure BDA0002192166240000045
Is the optimum service capability of the service provider, i.e.
Figure BDA0002192166240000046
Otherwise, performing the (k + 1) th iteration.
Preferably, the step S4 is specifically to calculate the market share of each service provider to realize resource allocation based on the service capability of the service provider, where the market share of each service provider is calculated as follows:
Figure BDA0002192166240000047
wherein, a i Is the service factor of the service provider SSPi,
Figure BDA0002192166240000048
serving coefficient for service provider TSPj, b ij As an alternative factor to the service provider SSPi,
Figure BDA0002192166240000049
are alternative coefficients for the facilitator TSPj.
The embodiment of the invention has the advantages that through constructing and solving the model of the power communication network resource allocation, the power communication network resource allocation obtains Nash balance in service capacity under three competitive environments, and when a third-party service provider is introduced, the requirement of a user can be met through lower service capacity, so that the capital investment of network resources is reduced and the benefit of the network service provider is improved on the premise of obtaining the same benefit.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart illustrating a method for allocating resources of an electrical power communication network according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a power communication network resource allocation model according to an embodiment of the present invention.
FIG. 3 is a schematic diagram illustrating service capability comparison under the competitive environment among SSPs according to the embodiment of the present invention.
FIG. 4 is a diagram illustrating a comparison of service capabilities in a contention environment among TSPs according to an embodiment of the present invention.
FIG. 5 is a schematic diagram illustrating a comparison of service capabilities in a competitive environment between SSPs and TSPs according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments refers to the accompanying drawings, which are included to illustrate specific embodiments in which the invention may be practiced.
Referring to fig. 1, an embodiment of the invention provides a method for allocating power communication network resources, including:
step S1, constructing a non-cooperative game model for power communication network resource allocation, wherein participants of the model comprise a power communication network demand side, a self-built power communication network service provider and a third-party power communication network service provider;
step S2, assigning values to each parameter in the model, and initializing the service capability of each service provider;
step S3, solving and obtaining the service ability of the service provider by using a Jacobian iterative formula;
and step S4, performing power communication network resource allocation based on the service capability of the service provider.
In step S1, the power communication network resource allocation model includes three participating entities, i.e., SSPs, TSPs, and DSs, as shown in fig. 2. The SSPs and the TSPs are responsible for providing power communication network resources, DSs (Demand side of power communication network, DS) is a power communication network Demand side, and DSs makes a resource request of the power communication network to the SSPs and the TSPs. To implement the power communication network resource allocation, DSs may make a resource request to multiple SSPs, TSPs. SSPs and TSPs feed back service quality and price to DSs which provides resource requests according to DSs provided resource requests and own resource conditions. DSs one or more SSPs and TSPs are selected according to the service quality and the price to provide the power communication network service for the SSPs and TSPs, and the payment is paid for supporting the normal operation of SSPs and TSPs companies. Considering that the SSPs and the TSPs compete for the benefit of respective companies, the invention models the resource allocation problem of the power communication network into a non-cooperative game model to analyze the competition problem between the SSPs and the TSPs.
The total amount of power communication network demand of the power communication network demand side DSs is expressed by M. Lambda is used for demand quantity of power communication network of mth power communication network demand side DSm m Indicate, therefore, use
Figure BDA0002192166240000051
Representing the sum of market sizes of the demand of the power communication network.
The connectivity of the service provider's power communication network refers to the probability that a power service request is successfully received by the service provider, and is denoted by q. The information quantity transmitted by the ith self-built electric power communication network service provider SSPi in the kth round of competition in unit time is used
Figure BDA0002192166240000061
Represents, a service capability called SSPi; information quantity usage transmitted by jth third-party power communication network service provider TSPj in unit time
Figure BDA0002192166240000062
Indicating the service capabilities, referred to as TSPj,
Figure BDA0002192166240000063
and
Figure BDA0002192166240000064
subject to the constraint of
Figure BDA0002192166240000065
Since the service capacity is directly related to the resource investment of a service provider in building a power communication network and is a key factor influencing the service quality and the service price, the invention uses s,i ) * ，(u t,j ) * Indicating the optimal service capabilities of SSPi, TSPj.
The step S1 specifically includes:
step S11, calculating the operation cost of each service provider in the kth competition according to the receiving rate and the service capacity of the electric power communication network of each service provider;
specifically, based on the receiving rate q and the service capability of the electric power communication network of a service provider
Figure BDA0002192166240000066
Solving the operating costs of SSPi and TSPj in the kth round of competition by using the formula (1)
Figure BDA0002192166240000067
Figure BDA0002192166240000068
Wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0002192166240000069
a service capability coefficient is represented by a coefficient of the service capability,
Figure BDA00021921662400000610
representing the network cost factor. Since when the user of the electric power communication network leases the resource of TSPj, TSPj also needs to lease the interface resource of SSPi to provide service for the electric power communication service, therefore,
Figure BDA00021921662400000611
step S12, calculating the utility of each service provider in the kth competition according to the operation cost of each service provider and the service price of the power communication network;
specifically, based on the above analysis, the utility of SSPi, TSPj in the kth round of competition is calculated using equation (2):
Figure BDA00021921662400000612
wherein the content of the first and second substances,
Figure BDA00021921662400000613
respectively, the effects of SSPi, TSPj, P s 、P t Respectively, the service prices of the power communication networks provided by the SSPi and the TSPj.
Every service provider in the electric power communication network market tries to reduce price or improve service quality to attract more electric power communication network users, and the maximization of the utility and the profit is pursued, therefore, the invention adopts a non-cooperative game model under complete information to analyze the resource allocation problem between SSPi and TSPj. Assuming that there are a total of r service providers, when the total number of SSPs is m, the total number of TSPs is r-m.
According to the economic theory, the better the service quality is, the larger the market share is under the condition of a certain price. The market share of SSPi, TSPj in the kth round of competition is calculated using equation (3):
Figure BDA0002192166240000071
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0002192166240000072
denotes the market share, a, of SSPi, TSPj, respectively i 、
Figure BDA0002192166240000073
Representing service coefficients of SSPi, TSPj, respectively, b ij 、
Figure BDA0002192166240000074
Representing alternative coefficients of SSPi, TSPj, respectively.
Step S13, calculating the income of each service provider in the kth competition according to the utility and market share of each service provider;
in particular, revenue usage by the facilitators SSPi, TSPj in the kth round of competition
Figure BDA0002192166240000075
Expressed using the formula (4) with the constraint of
Figure BDA0002192166240000076
Figure BDA0002192166240000077
Step S14, constructing an objective function for solving the service provider profit maximization problem under the Nash equilibrium problem by taking the profit maximization as a competitive game objective;
specifically, in order to realize the competitive game goal of maximizing the benefits of the SSPi and the TSPj, under the Nash (Nash) balance problem, the objective function (i.e. the service capability) of the service provider benefit maximization problem needs to be solved
Figure BDA0002192166240000078
And earnings
Figure BDA0002192166240000079
Reaction function of (c) is as shown in equation (5):
Figure BDA00021921662400000710
step S2 performs network environment initialization and service capability initialization.
First, the parameters in the resource allocation model are allocatedAssigning and secondly initializing the service capabilities, i.e. assigning initial values to the service capabilities of each facilitator
Figure BDA00021921662400000711
Wherein i belongs to {1, 2.. eta., m }, and j belongs to {1, 2.. eta., r-m }.
Step S3 first solves for service capability using a jacobian iterative formula:
slave service capability
Figure BDA00021921662400000712
And earnings
Figure BDA00021921662400000713
The reaction function (i.e. formula (5)) of (A) shows the optimal service capability of the service provider
Figure BDA00021921662400000714
Can be calculated using equation (6):
Figure BDA00021921662400000715
using Jacobian transformations to service capabilities
Figure BDA00021921662400000716
The reaction function of (2) is transformed to obtain a jacobian iterative formula (7):
Figure BDA0002192166240000081
the service capability of the service provider is calculated by using a Jacobi iterative formula (7), the service capability of the service provider is continuously updated, and the service capabilities of the SSPi and the TSPj calculated in the kth round are respectively
Figure BDA0002192166240000082
Then, the iteration is executed until the service capability meets the convergence condition, and if the convergence strip is met during the judgmentPiece
Figure BDA0002192166240000083
The iterative computation of the optimal service capacity ends when
Figure BDA0002192166240000084
Is the optimal service capacity, i.e.
Figure BDA0002192166240000085
Otherwise, performing the (k + 1) th iteration.
Step S4 is specifically to calculate market share of each service provider by using formula (3) based on service capability of the converged service provider, thereby implementing resource allocation.
In order to verify the performance of the resource allocation method provided by the present invention, the present embodiment adopts MATLAB to perform experiments (data in the experiments are all results of normalization processing), and verifies resource allocation under three environments, namely, competition between SSPs, competition between TSPs, and competition between SSPs and TSPs. The parameters used in the experiment were as follows: p s ＝1，
Figure BDA0002192166240000086
P t ＝0.8，
Figure BDA0002192166240000087
q＝0.8，
Figure BDA0002192166240000088
Figure BDA0002192166240000089
Competition among SSPs was simulated using SSP1 and SSP2, and SSP1 and SSP2 parameters were set to a 1 ＝2.5，a 2 The experimental results are shown in fig. 3, 2.9. The competition between TSPs is simulated by TSP1 and TSP2, and parameters of TSP1 and TSP2 are set as
Figure BDA00021921662400000810
The results of the experiment are shown in FIG. 4. The competition between SSPs and TSPs is simulated by SSP1 and TSP1, and the parameters of SSP1 and TSP1 are set as a 1 ＝2.5，
Figure BDA00021921662400000811
b 11 ＝0.5，
Figure BDA00021921662400000812
The results of the experiment are shown in FIG. 5.
The experimental results are analyzed from the two aspects of Nash equilibrium condition and service capacity under three competitive environments as follows: (1) in the nash equilibrium under three competitive environments, from fig. 3 to fig. 5, the curve fluctuates before the server obtains the unique nash equilibrium, which indicates that there is a fierce competitive game between the servers before reaching a steady state, but as the iteration number increases, the service capability of the server gradually approaches a steady value, i.e. the uniqueness of the nash equilibrium is proved. (2) In the aspect of comparison of service capacity under three competitive environments, when only the self-service provider is provided in fig. 3, the service capacity is low; in fig. 4, the service capability is higher when only the third-party service provider is present, which indicates that the third-party service provider can provide better service quality than the self-established service provider. When the self-building service provider and the third-party service provider participate in competition at the same time, the service capacity is reduced, and the requirement of the user can be met through lower service capacity when the third party is introduced, so that the capital investment of network resources is reduced on the premise of obtaining the same income, and the income of the network service provider is improved.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it is therefore to be understood that the invention is not limited by the scope of the appended claims.

Claims (9)

1. A power communication network resource allocation method is characterized by comprising the following steps:
step S1, constructing a non-cooperative game model of power communication network resource allocation, wherein participants of the model comprise a power communication network demand party, a self-established power communication network service provider and a third-party power communication network service provider, and the power communication network demand party provides resource requests to the self-established power communication network service provider and the third-party power communication network service provider;
step S2, assigning values to each parameter in the model, and initializing the service capability of each service provider;
step S3, solving and obtaining the service ability of the service provider by using a Jacobian iterative formula;
step S4, power communication network resource allocation is carried out based on the service capability of the service provider;
the step S1 specifically includes:
step S11, calculating the operation cost of each service provider in the kth competition according to the receiving rate and the service capacity of the electric power communication network of each service provider;
step S12, calculating the utility of each service provider in the kth competition according to the operation cost of each service provider and the service price of the power communication network;
step S13, calculating the income of each service provider in the kth competition according to the utility and market share of each service provider;
and step S14, constructing an objective function for solving the service provider profit maximization problem under the Nash equilibrium problem by taking the profit maximization as a competitive game objective.
2. The method according to claim 1, wherein the service providers are an ith self-established electric power communication network service provider SSPi and a jth third-party electric power communication network service provider TSPj, and the service capabilities of the service providers SSPi are
Figure FDA0003544576140000011
The service capability of the service provider TSPj is
Figure FDA0003544576140000012
And
Figure FDA0003544576140000013
subject to the constraint of
Figure FDA0003544576140000014
3. The power communication network resource allocation method according to claim 2, wherein the calculation manner of the operation cost of each service provider in the kth round of competition is as follows:
Figure FDA0003544576140000015
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003544576140000016
for the operating cost of the service provider SSPi in the kth competition,
Figure FDA0003544576140000017
the operation cost of the service provider TSPj in the k-th round of competition, q is the receiving rate of the power communication network of the service provider,
Figure FDA0003544576140000018
respectively representing the service capability coefficients of the respective service providers,
Figure FDA0003544576140000021
respectively, a network cost coefficient is represented,
Figure FDA0003544576140000022
4. the power communication network resource allocation method according to claim 3, wherein the utility of each service provider in the kth round of competition is calculated in a manner that:
Figure FDA0003544576140000023
wherein the content of the first and second substances,
Figure FDA0003544576140000024
for the utility of the facilitator SSPi in the kth round of competition,
Figure FDA0003544576140000025
for the utility of the facilitator TSPj in the kth round of competition, P s Price, P, for the electric power network service of the SSPi in the k-th competition t And (5) serving the price of the power communication network of the service provider TSPj in the k-th round of competition.
5. The power communication network resource allocation method according to claim 4, wherein the profit of each service provider in the kth round of competition is calculated by:
Figure FDA0003544576140000026
the constraint condition is
Figure FDA0003544576140000027
Wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003544576140000028
for the benefit of the facilitator SSPi in the kth round of competition,
Figure FDA0003544576140000029
for the revenue of the facilitator TSPj in the k-th round of competition,
Figure FDA00035445761400000210
being the market share of the service provider SSPi,
Figure FDA00035445761400000211
lambda is the sum of the market size of the demand of the power communication network, which is the market share of the service provider TSPj.
6. The power communication network resource allocation method according to claim 5, wherein the objective function is:
Figure FDA00035445761400000212
the total number of the service providers is r, and when the total number of the self-built power communication network service providers is m, the total number of the third-party power communication network service providers is r-m.
7. The power communication network resource allocation method according to claim 6, wherein in step S2, the service capability of each service provider is initialized
Figure FDA00035445761400000213
Figure FDA00035445761400000214
Wherein, i belongs to {1,2,. eta., m }, and j belongs to {1,2,. eta., r-m }.
8. The power communication network resource allocation method according to claim 6, wherein in step S3, the calculation method of the optimal service capacity of each service provider is:
Figure FDA00035445761400000215
(u s,i ) * for optimal service capability of the service provider SSPi, (u) t,j ) * The optimal service capability of the service provider TSPj;
using Jacobian transformations to service capabilities
Figure FDA0003544576140000031
The reaction function of (a) is transformed to obtain a jacobian iterative formula:
Figure FDA0003544576140000032
wherein, a i Is the service factor of the service provider SSPi,
Figure FDA0003544576140000033
service factor of the service provider TSPj, b ij As an alternative factor to the service provider SSPi,
Figure FDA0003544576140000034
alternative coefficients for the facilitator TSPj;
calculating the service capability of the service provider by using the Jacobi iterative formula if the service capability calculated in the k round
Figure FDA0003544576140000035
Satisfy the convergence condition
Figure FDA0003544576140000036
or
Figure FDA0003544576140000037
Figure FDA0003544576140000038
Figure FDA0003544576140000039
or ukt, j is more than or equal to 1, then
Figure FDA00035445761400000310
Is the optimum service capability of the service provider, i.e.
Figure FDA00035445761400000311
Otherwise, performing the (k + 1) th iteration.
9. The power communication network resource allocation method according to claim 5 or 8, wherein the step S4 is specifically to calculate the market share of each service provider based on the service capability of the service provider to realize resource allocation, and the market share of each service provider is calculated as follows:
Figure FDA00035445761400000312
wherein, a i Being the service factor of the service provider SSPi,
Figure FDA00035445761400000313
service factor of the service provider TSPj, b ij As an alternative factor to the service provider SSPi,
Figure FDA00035445761400000314
as an alternative factor for the facilitator TSPj.
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