CN114205830B - Smart grid communication network planning method, system, equipment and storage medium - Google Patents

Smart grid communication network planning method, system, equipment and storage medium Download PDF

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CN114205830B
CN114205830B CN202111432457.6A CN202111432457A CN114205830B CN 114205830 B CN114205830 B CN 114205830B CN 202111432457 A CN202111432457 A CN 202111432457A CN 114205830 B CN114205830 B CN 114205830B
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base station
objective function
planned
deployment scheme
base stations
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CN114205830A (en
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江璟
辛培哲
王玉东
贺立新
侯宾
张勇
刘思革
李军
韩柳
肖志宏
刘文轩
王浩
董茵
梁毅
韩震焘
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STATE GRID LIAONING ECONOMIC TECHNIQUE INSTITUTE
State Grid Economic And Technological Research Institute Co LtdB412 State Grid Office
State Grid Corp of China SGCC
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State Grid Economic And Technological Research Institute Co LtdB412 State Grid Office
State Grid Corp of China SGCC
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/18—Network planning tools
    • G—PHYSICS
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    • G—PHYSICS
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    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/12—Computing arrangements based on biological models using genetic models
    • G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • H04W64/003—Locating users or terminals or network equipment for network management purposes, e.g. mobility management locating network equipment
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

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Abstract

The invention relates to a smart grid communication network planning method, a system, equipment and a storage medium, which are characterized by comprising the following steps: acquiring base station measurement data of a base station in a region to be planned; determining a pareto optimal solution set of base stations in an area to be planned according to the acquired base station measurement data, wherein the pareto optimal solution set comprises the number of candidate base stations and a position information deployment scheme; according to the pareto optimal solution set of the base stations in the area to be planned, the optimal base station number and the optimal position information deployment scheme in the area to be planned are determined, and the optimal communication network base station deployment scheme can be obtained, so that the resource is utilized maximally, and the method and the device can be widely applied to the field of intelligent power grids.

Description

Smart grid communication network planning method, system, equipment and storage medium
Technical Field
The invention relates to the field of smart grids, in particular to a smart grid communication network planning method, a smart grid communication network planning system, smart grid communication network planning equipment and a smart grid communication network storage medium.
Background
The power communication network is an important component of the smart grid, and is a basic condition for realizing the smart grid. The construction goal of the power communication network is to utilize an economic, reasonable, advanced and mature communication technology, meet the requirements of the smart power grid in each development stage on the power communication network, support flexible access of various services, provide a plug and play power communication guarantee for a power intelligent system or equipment, and provide an information interaction communication channel for power users and distributed energy sources.
In the current construction process of the power distribution communication network in China, the power distribution business in the core area is mostly accessed by optical fibers, the communication mode is single, and the optical fibers are limited by factors such as environment, economy and the like and cannot be used on a large scale. Wireless network access is the best method for locations where the geographic environment is complex and maintenance is difficult. Currently, the wireless public network and the wireless private network are developed rapidly, the coverage of multiple wireless networks at the same position is quite common, the main wireless access comprises 5G, NB-IoT, 2G/3G/4G, lora, zigbee, UWB, WIFI and the like, but cannot cover all power service scene requirements in a single communication mode, for example, in a network only supporting micro-power wireless, the cost of realizing communication is quite high when meeting an installation environment with a basement. In the case where multiple heterogeneous networks are available at the same time, intelligent selection becomes a key to ensure the quality of communication service.
However, the existing communication network planning method cannot determine the optimal number of base stations and location information deployment scheme.
Disclosure of Invention
In view of the above problems, the present invention aims to provide a smart grid communication network planning method, system, device and storage medium, which can determine the optimal number of base stations and a location information deployment scheme.
In order to achieve the above purpose, the present invention adopts the following technical scheme: in a first aspect, a smart grid communication network planning method is provided, including:
acquiring base station measurement data of a base station in a region to be planned;
determining a pareto optimal solution set of base stations in an area to be planned according to the acquired base station measurement data, wherein the pareto optimal solution set comprises the number of candidate base stations and a position information deployment scheme;
and determining the optimal number of the base stations in the area to be planned and the position information deployment scheme according to the pareto optimal solution set of the base stations in the area to be planned.
Further, the base station measurement data comprises field actual measurement data, coverage capability data, base station evaluation coefficients and single base station cost coefficients, and the field actual measurement data comprises a terminal set, a base station set to be selected, a terminal signal threshold value and a terminal rate requirement.
Further, the determining, according to the acquired base station measurement data, the pareto optimal solution set of the base stations in the area to be planned includes:
performing gene mapping on a pre-established base station deployment scheme to obtain a population of the base station deployment scheme;
establishing a multi-target base station deployment optimization model;
setting parameters, termination conditions and winning conditions of a genetic algorithm;
and determining the pareto optimal solution set of the base stations in the area to be planned according to the acquired base station measurement data by adopting a genetic algorithm based on the established multi-target base station deployment optimization model, the set parameters, the set termination conditions and the set winning conditions and the obtained population of the base station deployment scheme.
Further, the performing gene mapping on the base station deployment scheme to obtain a population of the base station deployment scheme includes:
performing gene mapping on a pre-established base station deployment scheme to obtain a genome g= (c) of the corresponding base station deployment scheme 1 ,c 2 ,...,c l ) Wherein c i Is chromosome, and c i ＝(x i ,y i ) I=1, 2, l, l is the number of base stations in the base station deployment scenario, each chromosome represents the location of a certain base station in the corresponding base station deployment scenario, (x i ,y i ) Coordinates of the base station position in a digital map;
generating individuals corresponding to each base station deployment scheme according to the obtained genome, and forming a plurality of generated individuals into a population of the base station deployment scheme.
Further, the establishing the multi-target base station deployment optimization model includes:
setting a coverage objective function, a capacity objective function, a performance demand objective function and a cost objective function;
according to the set coverage objective function, capacity objective function, performance demand objective function and cost objective function, a multi-objective base station deployment optimization model is established, wherein the objective function is as follows:
f m ＝min(f 1 ,f 2 ,f 3 ,f 4 )
wherein f 1 To minimize the coverage objective function; f (f) 2 As a capacity objective function; f (f) 3 Is a performance demand objective function; f (f) 4 As a cost objective function.
Further, the coverage objective function cov (T) is:
wherein R is a terminal set; t is a base station set; covered (r) is the coverage function; r is a terminal;
the capacity target function Δcap (T) is:
wherein t is a base station; s is S t Is tau t ＝(p t ,c t ,s t ) E type of bandwidth provided by base station T, τ t For the base station to be selected in the base station set, p t Is the power factor of the base station t; c t Is a cost factor of the base station t; s is(s) t Is the capacity factor of the base station t; delta r Rate requirements for terminal r; θ r A signal threshold value for terminal r; s is S r,t Signal strength for terminal r covered by base station t;
the performance requirement objective function request (T) is:
wherein N is RB Total resources provided for the base station; n (N) t The demand of the user for the base station t to be selected is set;
the cost objective function cost (T) is:
wherein AC t Is the cost coefficient of the base station t; k is the sequence number M of the base station to be selected and is the set of the base stations to be selected; z i To determine whether the base station to be selected is assigned a parameter.
Further, the determining, according to the pareto optimal solution set of the base stations in the area to be planned, the optimal base station number and the position information deployment scheme in the area to be planned includes:
adopting a fuzzy theory to preliminarily obtain an optimal candidate solution in the pareto optimal solution set;
determining the total number of base stations of the best candidate solution according to the reliability coefficient alpha;
each base station of the best candidate solution corresponds to a geographic position coordinate, and a position information deployment scheme is determined according to the selected base station.
In a second aspect, a smart grid communication network planning system is provided, including:
the data acquisition module is used for acquiring base station measurement data of the base stations in the area to be planned;
the genetic algorithm module is used for determining the pareto optimal solution set of the base stations in the area to be planned according to the acquired base station measurement data, and comprises the number of candidate base stations and a position information deployment scheme;
the deployment scheme determining module is used for determining the optimal number of the base stations in the area to be planned and the position information deployment scheme according to the pareto optimal solution set of the base stations in the area to be planned.
In a third aspect, a processing device is provided, including computer program instructions, where the computer program instructions, when executed by the processing device, are configured to implement steps corresponding to the smart grid communication network planning method described above.
In a fourth aspect, a computer readable storage medium is provided, where the computer readable storage medium stores computer program instructions, where the computer program instructions are executed by a processor to implement steps corresponding to the smart grid communication network planning method described above.
Due to the adoption of the technical scheme, the invention has the following advantages: according to the invention, a multi-objective optimized NSGA-II genetic algorithm is introduced, a multi-objective base station deployment objective function is established, an optimal allocation solution of the objective function is sought, an optimal base station number and position information deployment scheme is obtained, the maximum utilization of resources is realized, and the multi-objective base station deployment objective function can be widely applied to the field of intelligent power grids.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Like parts are designated with like reference numerals throughout the drawings. In the drawings:
fig. 1 is a schematic flow chart according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
It is to be understood that the terminology used herein is for the purpose of describing particular example embodiments only, and is not intended to be limiting. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. The terms "comprises," "comprising," "includes," "including," and "having" are inclusive and therefore specify the presence of stated features, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and/or groups thereof. The method steps, processes, and operations described herein are not to be construed as necessarily requiring their performance in the particular order described or illustrated, unless an order of performance is explicitly stated. It should also be appreciated that additional or alternative steps may be used.
In the prior art, the influence of performance indexes and reliability indexes on network planning cannot be comprehensively considered when network deployment is carried out; the method has no pertinence of electric power service, can not meet the requirements of different electric power service scenes on various network indexes, and can not introduce the requirements into network planning; when the base station planning deployment is performed, the effect of four aspects of cost, performance requirement, coverage and capacity on the result cannot be considered simultaneously. Based on the above problems, in the deployment planning process of the wireless network base station, the method, the system, the device and the storage medium for planning the smart grid communication network provided by the embodiment of the invention adopt the multi-objective genetic algorithm NSGA-II to solve the base station planning mathematical model in the wireless network planning, build the multi-objective base station deployment optimization model, take the minimum construction cost, the minimum performance requirement difference, the maximum coverage area and the minimum capacity difference as objective functions, search the optimal allocation solution of four targets, determine the optimal communication network base station deployment scheme and realize the maximum utilization of resources.
The following describes the genetic algorithm NSGA-II used in the examples of the present invention.
The genetic algorithm NSGA-II is a rapid non-dominant multi-objective optimization algorithm with elite retention strategy, and is a multi-objective optimization algorithm based on Pareto optimal solution (Pareto optimal solution is also called Pareto efficiency and refers to an ideal state of resource allocation). The terms appearing in the algorithm include:
fast non-dominant ordering: the population is layered according to the quality of the individuals and is divided into a plurality of different front faces.
Crowding distance: the degree of dispersion of individuals in the population is described, and the degree of crowding can ensure the diversity of the population. To maintain diversity of the population, it is necessary to compare the degree of congestion, and for individuals on the same front face, the degree of congestion is preferably selected to be small so as to ensure diversity of the individuals.
Elite strategy: will father population P t And offspring seedGroup Q t Combining into a new population with the size of 2N, and comparing Q t Performing rapid non-dominant ranking and crowding calculation to select top N individuals into population P t+1 . If all individuals on a same front join, this would result in the population upper limit being exceeded.
Solving the multi-objective optimization problem is to find the optimal solution that enables each objective function to reach as large (or as small) as possible. The solving process is to find the optimal combination meeting the limiting condition, namely Pareto solution, from all the undetermined variable parameters. The set consisting of Pareto optimal solutions is called Pareto optimal solution set.
Pareto solution is defined as: in the decision space, the objective function value F (x) = (F) of x 1 (x),f 2 (x),...,f m (x) If it is a globally optimal solution, then x is called Pareto optimal solution in the decision space. For multi-objective functions, the set of Pareto optimal solutions is referred to as the Pareto optimal solution set.
Example 1
As shown in fig. 1, the present embodiment provides a smart grid communication network planning method, which includes the following steps:
1) And acquiring base station measurement data of the base stations in the area to be planned.
Specifically, the base station measurement data includes field measurement data, coverage capability data, base station evaluation coefficients, and single base station cost coefficients AC t 。
More specifically, the field measured data includes a terminal set R, a set of base stations to be selected M, a single candidate base station capacity (i.e., the maximum bandwidth that can be provided), a terminal signal threshold θ, a signal strength of the terminal, and a terminal rate requirement δ.
More specifically, the coverage capability data is derived from a technical document or actual measurement, including a single base station coverage radius R t And a base station rate S.
More specifically, the base station evaluation coefficients may be set according to an actual technical document, and the specific process will not be described in detail herein.
2) And determining the pareto optimal solution set of the base stations in the area to be planned according to the acquired base station measurement data by adopting a genetic algorithm, wherein the method specifically comprises the following steps of:
2.1 Gene mapping is carried out on a pre-established base station deployment scheme to obtain a population of the base station deployment scheme:
2.1.1 Performing gene mapping on a pre-established base station deployment scheme to obtain a genome corresponding to the base station deployment scheme, wherein a gene mapping result is the genome.
Specifically, the genome is g= (c) 1 ,c 2 ,...,c l ) Wherein the genome is used to represent the number and location of base stations within the base station deployment scenario, c i Is chromosome, and c i ＝(x i ，y i ) I=1, 2, l, l is the length of the chromosomes, i.e. the number of base stations in the base station deployment scenario, each chromosome representing the position of a certain base station in the corresponding base station deployment scenario, (x i ，y i ) For the coordinates of the base station location in the digital map, if c i = (Null ), this chromosome is empty and does not represent the base station position. One genome includes several chromosomes, each chromosome representing the position of a corresponding one of the base stations, and how many chromosomes one genome represents how many base stations (and the position information of the base stations) are present.
2.1.2 According to the obtained genome, generating individuals corresponding to each base station deployment scheme, and forming a population by the generated individuals. Because each individual has only one genome, each individual in the population represents only one base station arrangement.
2.2 A multi-target base station deployment optimization model is established.
2.2.1 A coverage objective function, a capacity objective function, a performance demand objective function, and a cost objective function are set.
Specifically, in the coverage objective function, each terminal r can be covered by one or more base stations t, namely:
wherein covered (r) is a coverage function;a logical operation symbol, which indicates that the base station t exists in the base station set; t is a base station set; s is S r，t Signal strength for terminal r covered by base station t; θ r Is the signal threshold of terminal r.
The coverage objective function cov (T) of the base station in the area to be planned is:
wherein R is a terminal set.
Specifically, in the objective function of capacity, the network capacity is estimated using the data rate. On the area PG to be planned, its power, cost and capacity (bandwidth) are considered for the network, i.e. τ= (p, c, s) ∈t, where τ is a capacity function representation of the area to be planned; p is a power factor; c is a cost factor; s is a capacity factor; the base station set T is:
T＝{t＝(φ t ，τ t )|φ t ∈PG，τ t ∈TP} (3)
wherein phi is t A terminal to be covered of the area to be planned; τ t The method comprises the steps of selecting a base station to be selected from a base station set; TP is a set of types of base stations, and tp=1 indicates that there is only one network, |tp|>1 indicates that there are multiple networks in the area to be planned.
The objective of optimizing the network capacity is to minimize the difference between the base station bandwidth and the sum of all terminal rate requirements, so the capacity objective function Δcap (T) of the base stations in the area to be planned is:
wherein S is t Is tau t ＝(p t ，c t ，s t ) E the bandwidth provided by base station T of type T p t Is the power factor of the base station t; c t Is a cost factor of the base station t; s is(s) t Is the capacity factor of the base station t; θ r A signal threshold value for terminal r; delta r Is the rate requirement of terminal r.
Specifically, in the objective function of the performance requirement, the resources provided by the base station need to be able to meet the QoS requirements, i.e. the performance requirement, of all users. The goal of optimizing QoS requirements is to minimize the provision of resource N for the base station RB And the sum of all user demands.
Therefore, the performance requirement objective function request (T) of the base station in the area to be planned is:
wherein N is RB Total resources provided for the base station; n (N) t Is the demand of the user for the base station t, and:
wherein L is the length of a service transmission data packet, and the unit is Bit; v t Is the service transmission delay;is the rate of the traffic data packet, and:
wherein B is the bandwidth of service transmission;an average signal-to-noise ratio for each terminal.
Specifically, in the objective function of cost, the total cost is the sum of the costs of all deployed base stations. Minimizing the total cost amounts to minimizing the number of base stations deployed.
Therefore, the cost objective function cost (T) of the base station in the area to be planned is:
wherein AC t Is the cost coefficient of the base station t; k is the sequence number of the base station to be selected; m is a base station set to be selected; z i To determine whether the base station to be selected is assigned a parameter, and z i A value of 0 or 1, when the base station to be selected is allocated, then z i ＝1。
2.1.2 According to the set coverage objective function, capacity objective function, performance demand objective function and cost objective function, determining an optimized objective function of a genetic algorithm to obtain a multi-objective base station deployment optimization model.
In particular, the objective function of maximum coverage is converted into minimization of uncovered area, i.e. f 1 =1-cov (T). Optimization objective function f of genetic algorithm m Expressed as four objective functions, namely, a minimized coverage objective function, a capacity objective function, a performance demand objective function, and a cost objective function:
f m ＝min(f 1 ，f 2 ，f 3 ，f 4 ) (9)
wherein:
f 1 ＝1-cov(T) (10)
f 2 ＝Δcop(T) (11)
f 3 ＝reque(T) (12)
f 4 ＝cost(T) (13)
2.3 Parameters of the genetic algorithm including race scale, algebra, crossover probability, and mutation probability, termination conditions, and winning conditions are set.
Specifically, the parameters of the genetic algorithm may be set to the ethnic scale N p =1000, genetics algebra N gen =30, cross probability P cross Probability of variation P =0.9 mut ＝1/30。
Specifically, the termination condition is a propagation algebra threshold, i.e. the number of iterations, which can be set to N gen ＝30。
Specifically, the winning condition may be set such that, before ranking based on the winning concept according to the objective function value, if one of the two individuals does not satisfy the constraint condition, the individual satisfying the constraint condition wins; if neither individual satisfies the constraint, it is less than which individual exceeds the constraint, and the individual with the smaller value wins the partial weighted scalar sum of the objective function value exceeding the constraint.
2.4 The method comprises the following steps of) adopting a genetic algorithm, based on an established multi-target base station deployment optimization model, set parameters, termination conditions and winning conditions, and a population of an obtained base station deployment scheme, determining a pareto optimal solution set of base stations in a to-be-planned area according to acquired base station measurement data, wherein the pareto optimal solution set comprises the number of the to-be-selected base stations and a position information deployment scheme, and the specific process is as follows:
2.4.1 Initializing parameters of a genetic algorithm and randomly generating a set of parameters of size N p Initial population P of t I.e., parent population.
2.4.2 For parent population P t Non-dominant ordering is performed.
Specifically, the parent population P is evaluated using the four objective functions described above t For the parent population P t All individuals in the population are ranked in Pareto level (non-dominant level) to obtain a parent population P t Is a dominant number n p Wherein, level 1 is optimal, level 2 is suboptimal, and so on; individual n of the first non-dominant layer p ＝0。
2.4.3 Calculating the congestion degree n based on the non-dominant ranking result d 。
Specifically, let the congestion degree n d =0, N e 1,2,..n. According to the objective function f m Ordering individuals in the same non-dominant layer, two boundaries I after ordering d And N d The congestion degree of (2) is infinity, the congestion degree n d The method comprises the following steps:
wherein f m (i+1) ranking individual iA target function value; f (f) m (i-1) ranking the last objective function value for individual i;as an objective function f m Is the maximum value of (2); />As an objective function f m Is a minimum of (2).
2.4.4 Based on the non-dominant ranking results, conducting a bid selection.
Specifically, binary competitive competition selection is adopted, two individuals are selected randomly, the individuals with high non-dominant grades are selected to enter the next generation population, and the individuals with high crowdedness are selected when the grades are the same.
2.4.5 Cross variation.
Specifically, using simulated binary crossover and polynomial variation, crossover variation yields N with the same population size p Offspring population Q t And evaluating the offspring population Q for each objective function t Each of the individuals within.
2.4.6 Generating a new population and using the new population as a parent population P of t+1 generation t+1 。
Specifically, the parent population P t And offspring population Q t Mixing to form a new population R t For a new population R t Performing non-dominant ranking and congestion degree calculation, and selecting a new population R according to the non-dominant ranking and congestion degree calculation result t Creating a parent population P for the t+1 generation t+1 。
2.4.7 Step 2.4.2) is entered until a maximum number of genetics or individual difference threshold (e.g. 10) is reached -5 ) And obtaining a Pareto optimal solution set, namely, the optimal solution set of minimum coverage and optimal capacity, performance requirement and cost of the base station in the area to be planned. If the Pareto optimal solution is not obtained, the method goes to the step 2.4.3) to recalculate the congestion degree n d And (5) until the optimization is finished.
3) According to the pareto optimal solution set of the base stations in the area to be planned, determining the optimal number of the base stations in the area to be planned and a position information deployment scheme, wherein the deployment scheme specifically comprises the following steps:
3.1 A fuzzy theory is adopted to initially obtain the best candidate solution in the pareto optimal solution set.
Specifically, an objective function f corresponding to each solution in the pareto front m Related membership function ρ m，s The method comprises the following steps:
wherein f m.max And f m.min Respectively the objective function f m Maximum and minimum of (2).
For each non-dominant solution s, a normalized membership function ρ s The method comprises the following steps:
wherein M and N obj The total number of non-dominant solutions and the total number of objective functions, respectively.
Thus, the best candidate solution is the normalized membership function ρ s The position information deployment scheme with the maximum value preliminarily obtains the optimal number N of base stations in the area to be planned 0 And a location information deployment scheme.
3.2 Because of the large difference of the requirements of different services on safety and reliability, the reliability coefficient alpha is introduced, the redundancy quantity of the base stations is increased to ensure the effective transmission of data, and the total number N of the base stations with the best candidate solution is as follows:
N＝(1+α)N 0 (17)
3.3 Each base station of the best candidate solution corresponds to a geographic location coordinate, and a location information deployment scheme is determined according to the selected base station.
Example 2
The embodiment provides a smart grid communication network planning system, which comprises:
the data acquisition module is used for acquiring base station measurement data of the base stations in the area to be planned.
And the genetic algorithm module is used for determining a Pareto optimal solution set of the base stations in the area to be planned according to the acquired base station measurement data by adopting a genetic algorithm, and comprises the number of the base stations to be selected and a position information deployment scheme.
The deployment scheme determining module is used for determining the optimal number of the base stations in the area to be planned and the position information deployment scheme according to the Pareto optimal solution set of the base stations in the area to be planned.
In a preferred embodiment, the base station measurement data includes field measured data including a set of terminals, a set of candidate base stations, a single candidate base station capacity, a terminal signal threshold, a signal strength of the terminal, and a terminal rate requirement, coverage capability data, a base station evaluation coefficient, and a single base station cost coefficient.
In a preferred embodiment, the genetic algorithm module is specifically processed as follows:
and carrying out gene mapping on the pre-established base station deployment scheme to obtain a population of the base station deployment scheme.
And establishing a multi-target base station deployment optimization model.
Setting parameters, termination conditions and winning conditions of the genetic algorithm.
And determining the pareto optimal solution set of the base stations in the area to be planned according to the acquired base station measurement data by adopting a genetic algorithm based on the established multi-target base station deployment optimization model, the set parameters, the set termination conditions and the set winning conditions and the obtained population of the base station deployment scheme.
In a preferred embodiment, the genetic mapping is performed on a pre-established base station deployment scheme to obtain a population of base station deployment schemes, including:
performing gene mapping on a pre-established base station deployment scheme to obtain a genome g= (c) of the corresponding base station deployment scheme 1 ，c 2 ，...,c l ) Wherein c i Is chromosome, and c i ＝(x i ,y i ) I=1, 2,.. l is the number of base stations in the base station deployment scenario, each chromosome represents a corresponding base station deployment scenarioThe location of a base station (x) i ,y i ) Coordinates of the base station position in a digital map;
generating individuals corresponding to each base station deployment scheme according to the obtained genome, and forming a plurality of generated individuals into a population of the base station deployment scheme.
In a preferred embodiment, the specific processing procedure of the deployment scenario determination module is:
and (5) preliminarily obtaining the best candidate solution in the pareto optimal solution set by adopting a fuzzy theory.
And determining the total number of the base stations of the best candidate solution according to the reliability coefficient alpha.
Each base station of the best candidate solution corresponds to a geographic position coordinate, and a position information deployment scheme is determined according to the selected base station.
Example 3
The present embodiment provides a processing device corresponding to the smart grid communication network planning method provided in the present embodiment 1, where the processing device may be a processing device for a client, for example, a mobile phone, a notebook computer, a tablet computer, a desktop computer, etc., so as to execute the method in embodiment 1.
The processing device comprises a processor, a memory, a communication interface and a bus, wherein the processor, the memory and the communication interface are connected through the bus so as to complete communication among each other. The memory stores a computer program that can be run on the processing device, and when the processing device runs the computer program, the smart grid communication network planning method provided in this embodiment 1 is executed.
In some implementations, the memory may be high-speed random access memory (RAM: random Access Memory), and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
In other implementations, the processor may be a Central Processing Unit (CPU), a Digital Signal Processor (DSP), or other general-purpose processor, which is not limited herein.
Example 4
The present embodiment provides a computer program product corresponding to the smart grid communication network planning method provided in the present embodiment 1, and the computer program product may include a computer readable storage medium having computer readable program instructions for executing the smart grid communication network planning method described in the present embodiment 1.
The computer readable storage medium may be a tangible device that retains and stores instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination of the preceding.
The foregoing embodiments are only for illustrating the present invention, wherein the structures, connection modes, manufacturing processes, etc. of the components may be changed, and all equivalent changes and modifications performed on the basis of the technical solutions of the present invention should not be excluded from the protection scope of the present invention.

Claims (7)

1. A smart grid communication network planning method, comprising:
acquiring base station measurement data of a base station in a region to be planned;
determining a pareto optimal solution set of base stations in an area to be planned according to the acquired base station measurement data, wherein the pareto optimal solution set comprises the number of candidate base stations and a position information deployment scheme;
determining the optimal number of base stations in the area to be planned and a position information deployment scheme according to the pareto optimal solution set of the base stations in the area to be planned;
the determining the pareto optimal solution set of the base stations in the area to be planned according to the acquired base station measurement data comprises the following steps:
performing gene mapping on a pre-established base station deployment scheme to obtain a population of the base station deployment scheme;
establishing a multi-target base station deployment optimization model;
setting parameters, termination conditions and winning conditions of a genetic algorithm;
adopting a genetic algorithm, and determining a pareto optimal solution set of base stations in a region to be planned according to the acquired base station measurement data based on the established multi-target base station deployment optimization model, the set parameters, the set termination conditions and the set winning conditions and the obtained population of the base station deployment scheme;
the establishing the multi-target base station deployment optimization model comprises the following steps:
setting a coverage objective function, a capacity objective function, a performance demand objective function and a cost objective function;
according to the set coverage objective function, capacity objective function, performance demand objective function and cost objective function, a multi-objective base station deployment optimization model is established, wherein the objective function is as follows:
f m ＝min(f 1 ,f 2 ,f 3 ,f 4 )
wherein f 1 To minimize the coverage objective function; f (f) 2 As a capacity objective function; f (f) 3 Is a performance demand objective function; f (f) 4 Is a cost objective function;
the coverage objective function cov (T) is:
wherein R is a terminal set; t is a base station set; covered (r) is the coverage function; r is a terminal;
the capacity target function Δcap (T) is:
wherein t is a base station; s is S t Is tau t ＝(p t ,c t ,s t ) E type of bandwidth provided by base station T, τ t For the base station to be selected in the base station set, p t Is the power factor of the base station t; c t Is a cost factor of the base station t; s is(s) t Is the capacity factor of the base station t; delta r Rate requirements for terminal r; θ r A signal threshold value for terminal r; s is S r,t Signal strength for terminal r covered by base station t;
the performance requirement objective function request (T) is:
wherein N is RB Total resources provided for the base station; n (N) t The demand of the user for the base station t;
the cost objective function cost (T) is:
wherein AC t Is the cost coefficient of the base station t; k is the sequence number of the base station to be selected; m is a base station set to be selected; z i To determine whether the base station to be selected is assigned a parameter.
2. A smart grid communication network planning method as claimed in claim 1, wherein the base station measurement data includes field measurement data, coverage capability data, base station evaluation coefficients and single base station cost coefficients, the field measurement data including a set of terminals, a set of candidate base stations, a single candidate base station capacity, a terminal signal threshold, a signal strength of a terminal and a terminal rate requirement.
3. The smart grid communication network planning method of claim 1, wherein the performing gene mapping on the base station deployment scheme to obtain the population of the base station deployment scheme includes:
performing gene mapping on a pre-established base station deployment scheme to obtain a genome g= (c) of the corresponding base station deployment scheme 1 ,c 2 ,...,c l ) Wherein c i Is chromosome, and c i ＝(x i ,y i ) I=1, 2, l, l is the number of base stations in the base station deployment scenario, each chromosome represents the location of a certain base station in the corresponding base station deployment scenario, (x i ,y i ) Is the base station bitCoordinates placed in the digital map;
generating individuals corresponding to each base station deployment scheme according to the obtained genome, and forming a plurality of generated individuals into a population of the base station deployment scheme.
4. The smart grid communication network planning method according to claim 1, wherein the determining the optimal base station number and the optimal location information deployment scheme in the area to be planned according to the pareto optimal solution set of the base stations in the area to be planned includes:
adopting a fuzzy theory to preliminarily obtain an optimal candidate solution in the pareto optimal solution set;
determining the total number of base stations of the best candidate solution according to the reliability coefficient alpha;
each base station of the best candidate solution corresponds to a geographic position coordinate, and a position information deployment scheme is determined according to the selected base station.
5. A smart grid communication network planning system, comprising:
the data acquisition module is used for acquiring base station measurement data of the base stations in the area to be planned;
the genetic algorithm module is used for determining the pareto optimal solution set of the base stations in the area to be planned according to the acquired base station measurement data, and comprises the number of candidate base stations and a position information deployment scheme;
the deployment scheme determining module is used for determining the optimal number of the base stations in the area to be planned and the position information deployment scheme according to the pareto optimal solution set of the base stations in the area to be planned;
the determining the pareto optimal solution set of the base stations in the area to be planned according to the acquired base station measurement data comprises the following steps:
performing gene mapping on a pre-established base station deployment scheme to obtain a population of the base station deployment scheme;
establishing a multi-target base station deployment optimization model;
setting parameters, termination conditions and winning conditions of a genetic algorithm;
adopting a genetic algorithm, and determining a pareto optimal solution set of base stations in a region to be planned according to the acquired base station measurement data based on the established multi-target base station deployment optimization model, the set parameters, the set termination conditions and the set winning conditions and the obtained population of the base station deployment scheme;
the establishing the multi-target base station deployment optimization model comprises the following steps:
setting a coverage objective function, a capacity objective function, a performance demand objective function and a cost objective function;
according to the set coverage objective function, capacity objective function, performance demand objective function and cost objective function, a multi-objective base station deployment optimization model is established, wherein the objective function is as follows:
f m ＝min(f 1 ,f 2 ,f 3 ,f 4 )
wherein f 1 To minimize the coverage objective function; f (f) 2 As a capacity objective function; f (f) 3 Is a performance demand objective function; f (f) 4 Is a cost objective function;
the coverage objective function cov (T) is:
wherein R is a terminal set; t is a base station set; covered (r) is the coverage function; r is a terminal;
the capacity target function Δcap (T) is:
wherein t is a base station; s is S t Is tau t ＝(p t ,c t ,s t ) E type of bandwidth provided by base station T, τ t For the base station to be selected in the base station set, p t Is the power factor of the base station t; c t Is a cost factor of the base station t; s is(s) t Is the capacity factor of the base station t; delta r Rate requirements for terminal r; θ r A signal threshold value for terminal r; s is S r,t Signal strength for terminal r covered by base station t;
the performance requirement objective function request (T) is:
wherein N is RB Total resources provided for the base station; n (N) t The demand of the user for the base station t;
the cost objective function cos T (T) is:
wherein AC t Is the cost coefficient of the base station t; k is the sequence number of the base station to be selected; m is a base station set to be selected; z i To determine whether the base station to be selected is assigned a parameter.
6. A processing device, characterized by comprising a processor, a memory, a communication interface and a bus, wherein the processor, the memory and the communication interface are connected through the bus, a computer program capable of running on the processing device is stored in the memory, and the computer program is used for realizing the steps corresponding to the smart grid communication network planning method according to any one of claims 1-4 when being executed by the processing device.
7. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon computer program instructions, wherein the computer program instructions, when executed by a processor, are for implementing the steps corresponding to the smart grid communication network planning method of any one of claims 1-4.
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