CN110650482B - Base station equipment planarization optimization layout method based on gridding small-area principle and genetic algorithm - Google Patents
Base station equipment planarization optimization layout method based on gridding small-area principle and genetic algorithm Download PDFInfo
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Abstract
The invention discloses a base station equipment planarization optimization layout method based on a gridding small-area principle and a genetic algorithm. The method provides a method for dividing a plane covered area by gridding, approximately calculating the covered area by using a small area principle of differential chemistry, and simultaneously optimizing the spatial distribution of base station equipment in the plane covered area by using a genetic algorithm, so as to solve the problems of low covering efficiency, more coverage cross ranges, equipment resource waste and the like caused by mainly depending on an empirical method during equipment layout in the prior related field. The method comprises the steps of dividing and positioning coordinates of a covered area through gridding, and determining a possible coordinate position of an equipment installation point; simulating the area of the signal coverage cross region of the approximate computing equipment by adopting a Monte Carlo method; calculating the signal coverage area of the edge area by adopting a differential principle small-area approximation method; and optimizing the installation number and the installation positions of the equipment by utilizing a genetic algorithm, thereby realizing the optimal cost ratio between the maximization of the signal coverage area of the plane area and the installation number of the equipment.
Description
Technical Field
The invention relates to the field of planar area optimization layout, in particular to a base station equipment planarization optimization layout method based on a gridding small-area principle and a genetic algorithm.
Background
In recent years, with rapid development and popularization of technologies such as a 5G technology and smart security, the arrangement of base station (5G base station, panoramic camera base station, etc.) facilities is becoming more and more widespread. Traditional arrangement of indoor or garden WiFi, cameras and other base stations is often planned and laid out by designers according to experience, and the method has the problems of low coverage efficiency, large coverage cross range, waste of equipment resources and the like.
Disclosure of Invention
In order to effectively improve the arrangement efficiency of the base station, reduce the proportion of the coverage cross range, improve the utilization efficiency of equipment and reduce the overall construction cost of a project, the invention provides a base station equipment planarization optimization layout method based on a gridding small-area principle and a genetic algorithm by combining the existing machine learning and computer aided design methods, so as to solve the problems of low coverage efficiency, more coverage cross ranges, equipment resource waste and the like caused by mainly depending on an experience method when equipment layout is in the current planar area optimization layout field. The technical scheme adopted by the invention is as follows:
a base station equipment planarization optimization layout method based on a gridding small-area principle and a genetic algorithm is characterized by comprising the following steps:
step (1): the layout area is gridded, assuming that the layout area is as indicated by the shaded area in fig. 1. The method specifically comprises the following substeps:
(1.1) respectively taking the lowest end and the leftmost end of the layout area as an x axis and a y axis of a grid coordinate system;
(1.2) adopting unit length e as a grid length for the grid;
(1.3) establishing a boundary coordinate matrix of the layout area
Wherein num border The serial number of the intersection point of the boundary of the layout area and the grid; x is the number of border The horizontal coordinate of the intersection point of the boundary of the layout area and the grid is taken as the horizontal coordinate; y is border Vertical direction of intersection point of layout region boundary and gridCoordinates; according to the characteristics of grid coordinates, num border ∈N，x border ,y border ∈R + ；
Determining the points where the base station equipment can be installed according to the actual situation, wherein the coverage range of the points is 360 degrees; the coverage radius is the unit length of a grid of integral multiple, R is nxe, and N is determined to be a natural number fixed value N belonging to a natural number set according to the actual capacity of the equipment; after gridding, determining the device coordinate matrix of these regions
Wherein
Assuming that the points can only be located at the intersection positions of the grids; selecting the intersection point position which is not at the intersection point position and is closest to the intersection point position as the coordinate position of the intersection point position;
and (3): and (3) solving the number m and the positions of the installed base station equipment by adopting a genetic algorithm to realize optimization.
The method specifically comprises the following substeps:
(3.1) generating an initial number of base station devices, corresponding to the "possible" installation of base station devices
Generating an "actual" device coordinate location point matrix B location . The method comprises the following specific steps:
I. randomly generating the number of installed base station devices
And m belongs to N;
II. from
Within the range, m positive integers (z) are randomly selected 1 ,z 2 ,…,z m ) As the initial point position of the selected installation equipment;
from matrix
In (2), look up the serial number and (z) 1 ,z 2 ,…,z m ) The coordinate positions of each row corresponding to each integer can be confirmed, and after all m positions are selected, an 'actual' equipment coordinate position point matrix B is generated location 。
(3.2) generating an initial population. Generating M (usually M > 10000) positive integers (z) 1 ,z 2 ,…,z m ) And binary coded (z) 1 ,z 2 ,…,z m ) 2 (2 in the upper right hand notation represents binary), determining an initial population;
and (3.3) determining an adaptive value function Fitness, calculating the individual Fitness of the M chromosomes and carrying out optimization screening. The adaptation value function is the total area S covered by the equipment sum The ratio to the number of devices m, i.e. the average coverage of a single device, is the widest. The adaptive value calculation process adopts (z) 1 ,z 2 … zm) corresponding to the "actual" device coordinate location point matrix B location . According to the algorithm of 'calculating the area of the cross area' and the algorithm of 'approximating a small area', judging whether two devices cover the cross area and whether the device coverage area exceeds the boundary of the layout area, calculating the area according to the algorithm of 'calculating the area of the cross area' and the algorithm of 'approximating a small area', and expressing an adaptive value function as follows:
namely:
for some practical needs, sometimes the number of devices may not be considered, and only the coverage rate of the devices to a specific area is required, in this case, the fitness function may be selected as:
setting the screening proportion of 0 to 10 percent, abandoning the smaller chromosome after calculating the adaptive value according to the screening proportion, and replacing the chromosome with the higher adaptive value.
The algorithm for "area determination of intersection region" mentioned above is as follows:
(1) calculating an "actual" device coordinate location point matrix B location ＝[num x num y num ] num×3 The distance between the centers of any two points in num belongs to N is as follows:
i, j represent the serial numbers of any two device locations (i.e., the serial numbers of any two devices).
(2) If Dis i,j If < 2R, it indicates that the coverage areas of the two devices are crossed, and the actual coverage areas of the two devices are:
wherein the content of the first and second substances,
is the area of the intersection of the two circles; s i,j The method can be simulated by using a Monte Carlo method, and the flow chart of the algorithm is shown in FIG. 2.
The "small area approximation" algorithm mentioned above is as follows:
(1) and determining the coverage area of the base station equipment. Suppose that a matrix B of coordinates points is determined from the "actual" base station device location location The coordinate vector of the position of any actual installation device is extracted as A location ＝(i x i y i ) I ∈ num, where i is the location number of any device (i.e., the serial number of the base station device), (x) i ,y i ) Is its coordinate position; according to the characteristics of the grid coordinate system, the base station equipment can cover in the directions of the x axis and the y axisMaximum distance of cover is [ x ] i -R x i +R]And [ y i -R y i +R]；
(2) And calculating the coordinates of the intersection point of the coverage circle of the base station equipment and the grid coordinate system. And calculating the coordinates of the intersection point of each transverse axis in the grid and the equipment coverage circle. According to the characteristics of the grid coordinate system, along the y-axis direction, the intersection point of the device point I and the ordinate of the grid coordinate system is
(according to the actual capability of the equipment, determining N as a natural number fixed value N belonging to a natural number set, belonging to N), correspondingly, determining the intersection point of the I equipment point and the abscissa of a grid coordinate system as
(3) And judging the position of the coverage area of the base station equipment beyond the layout boundary, namely whether the coverage area exceeds the left side or the right side relative to the grid y coordinate axis. Query matrix B border Whether or not all data of column 3 of (1) exist
If it is not
Then judge
Is right-side over, or
Left-side over;
(4) actual effective coverage area S of computing device i i . Because each mesh of the grid coordinate systemThe cell areas are all unit areas, the height is only one unit height e, and the small area approximation method is adopted, and the width is only considered and is regarded as a small area trapezoid, so the area S i Can be expressed as:
(3.4) setting the crossover probability 0 < P cro And (3) carrying out gene crossing operation on individual chromosomes, wherein the gene crossing operation is carried out according to the gene number of the individual chromosomes. For two z's in the "higher" M "chromosomes" of the above fitness value i ,z j The positions (i ≠ j) are crossed (i.e., substituted for each other).
(3.5) setting the mutation probability 0 < P mut < 1 >, the "M" chromosomes "with higher fitness value are genetically altered (i.e., with probability P) mut Randomly replacing one of the devices).
And (3.6) outputting the calculation result. Setting iteration times T and an iteration threshold value U; when the iteration number T is reached, selecting each coordinate position corresponding to the chromosome with the highest adaptive value calculation as the optimal deployment position of each device corresponding to the device number m, and taking the result of the adaptive value calculation as the coverage result in the optimal position; or, if the Fitness value Fitness of a certain 'chromosome' is more than or equal to U, each coordinate position corresponding to the 'chromosome' is taken as the optimal equipment deployment position corresponding to the equipment number m, and the result of the Fitness value calculation is taken as the coverage result in the optimal position.
And (4): the comparison method determines the optimum installation number m of the equipment. The above genetic algorithm determines a set of "optimized" equipment numbers m opt And the location of the device
Then, selecting a group of quantity m and positions for installing the base station equipment according to the step 3 to carry out optimization calculation, comparing the adaptive values corresponding to the two groups of optimal solutions, and selecting m corresponding to the result with a larger adaptive value opt As a "global optimization result" m entire-opt Then its corresponding device coordinate location is alsoGlobally optimal deployment location
In addition, m can be judged by two comparisons opt Whether it is getting bigger or getting smaller, if getting bigger is found, m should be larger than m at the next selection opt Is selected, otherwise, is smaller than m opt So that the global optimization result m can be quickly positioned entire-opt The number of times and the amount of calculation are reduced, and a better effect is achieved.
The method comprises the steps of dividing a plane into areas (layout areas) to be deployed by utilizing gridding, approximately calculating the coverage area by utilizing a small area principle of differential chemistry, and simultaneously optimizing the spatial distribution of base station equipment (such as a 5G network base station, a WiFi base station, a camera monitoring base station and the like) in the covered area of the plane by utilizing a genetic algorithm; coordinates of a covered area are divided and positioned through gridding, and a possible equipment installation point coordinate position is determined; simulating the area of the signal coverage cross region of the approximate computing equipment by adopting a Monte Carlo method; calculating the signal coverage area of the edge area by adopting a differential principle and a small-area approximation method; and optimizing the installation number and the installation positions of the equipment by utilizing a genetic algorithm, thereby realizing the optimal cost ratio between the maximization of the signal coverage area of the plane area and the installation number of the equipment.
Drawings
FIG. 1 is a schematic diagram of a covered area (layout area) network;
FIG. 2 is a flowchart of an algorithm for solving the area of an intersecting circle by Monte Carlo;
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The invention provides a base station equipment planarization optimization layout method based on a gridding small area principle and a genetic algorithm, and provides a method for dividing a covered area (layout area) of a plane by gridding and approximately calculating the coverage area by using a differential small area principle, and simultaneously optimizing the spatial distribution of base station equipment (such as a network base station, a camera monitoring and the like) in the covered area of the plane by using the genetic algorithm, so as to solve the problems of low coverage efficiency, more coverage cross range, equipment resource waste and the like caused by mainly depending on an experience method during equipment layout in the related field at present. The method comprises the steps of dividing and positioning coordinates of a covered area through gridding, and determining a possible coordinate position of an equipment installation point; simulating the area of the signal coverage cross region of the approximate computing equipment by adopting a Monte Carlo method; calculating the signal coverage area of the edge area by adopting a differential principle and a small-area approximation method; and optimizing the installation number and the installation positions of the equipment by utilizing a genetic algorithm, thereby realizing the optimal cost ratio between the maximization of the signal coverage area of the plane area and the installation number of the equipment. To illustrate the effects of the present invention, the following is a detailed description of the process of the present invention:
step (1): the layout area is gridded, assuming that the layout area is as indicated by the shaded area in fig. 1. The method specifically comprises the following substeps:
(1.1) respectively taking the lowest end and the leftmost end of the layout area (shown in figure 1) as an x-axis and a y-axis of a grid coordinate system;
(1.2) the grid used for gridding adopts a unit length e as a grid length (as shown in FIG. 1);
(1.3) establishing a boundary coordinate matrix of the layout area
Wherein num border The serial number of the intersection point of the boundary of the layout area and the grid; x is the number of border The horizontal coordinate of the intersection point of the boundary of the layout area and the grid is taken as the horizontal coordinate; y is border The vertical coordinate of the intersection point of the boundary of the layout area and the grid is taken as the vertical coordinate; according to the characteristics of grid coordinates, num border ∈N，x border ,y border ∈R + ；
Determining a point where base station equipment is possibly installed according to actual conditions, wherein the base station equipment can be a network base station or a camera and the like, and the coverage range of the base station equipment is 360 degrees; the coverage radius is the integral multiple of the unit length of the grid, R is N multiplied by e, and N is determined as a natural number fixed value N belonging to a natural number set according to the actual capability of the base station equipment. After gridding, determining the coordinate matrix of the base station equipment in the areas
Wherein
Assuming that the points can only be located at the intersection positions of the grids; selecting the intersection point position which is not at the intersection point position and is closest to the intersection point position as the coordinate position of the intersection point position;
and (3): and (3) solving the number m and the positions of the installed base station equipment by adopting a genetic algorithm to realize optimization.
The method specifically comprises the following substeps:
(3.1) generating a coordinate position matrix of the initial number of base station devices corresponding to the "possible" installation base station devices
Generating an "actual" device coordinate position matrix B location . The method comprises the following specific steps:
I. randomly generating the number of installed base station devices
And m is an element of N;
II. from
Within the range, m positive integers (z) are randomly selected 1 ,z 2 ,…,z m ) As the selected initial point location for installing the base station equipment;
slave matrix
In (1), look up the serial number and (z) 1 ,z 2 ,…,z m ) The coordinate positions of each row corresponding to each integer can be confirmed, and after all m positions are selected, an 'actual' equipment coordinate position point matrix B is generated location 。
(3.2) generating an initial population. Generating M (usually M > 10000) positive integers (z) 1 ,z 2 ,…,z m ) And binary coded (z) 1 ,z 2 ,…,z m ) 2 (2 in the upper right hand notation represents binary), determining an initial population;
and (3.3) determining an adaptive value function Fitness, calculating the individual Fitness of the M chromosomes and carrying out optimization screening. The adaptive value function is the total coverage area S of the base station equipment sum The ratio of the number m of base station devices, i.e. the average coverage of a single base station device, is the widest. The adaptive value calculation process adopts (z) 1 ,z 2 ,…,z m ) Corresponding 'actual' base station equipment coordinate position point matrix B location . According to the algorithm of 'calculating the area of the cross area' and the algorithm of 'approximating a small area', whether two base station devices cover the cross area and whether the coverage area of the base station devices exceeds the boundary of the layout area is judged, and the area is calculated according to the algorithm of 'calculating the area of the cross area' and the algorithm of 'approximating a small area', so that the adaptive value function can be expressed as:
namely:
for some practical needs, sometimes the number of devices may not be considered, and only the coverage rate of the base station device to a specific area is required, in this case, the fitness function may be selected as:
setting the screening proportion of 0 to 10 percent, abandoning the smaller chromosome after calculating the adaptive value according to the screening proportion, and replacing the chromosome with the higher adaptive value.
The algorithm for "area determination of intersection region" mentioned above is as follows:
(1) calculating the coordinate position point matrix B of the 'actual' base station equipment location ＝[num x num y num ] num×3 The distance between the centers of any two points in num belongs to N is as follows:
i, j represent the serial numbers of any two device locations (i.e., the serial numbers of any two devices).
(2) If Dis i,j If < 2R, it indicates that the coverage areas of the two devices are crossed, and the actual coverage areas of the two devices are:
wherein the content of the first and second substances,
is the area of the intersection of the two circles; s i,j The method can be simulated by using a Monte Carlo method, and the flow chart of the algorithm is shown in FIG. 2.
The "small area approximation" algorithm mentioned above is as follows:
(1) a device coverage is determined. Suppose that a matrix B of "actual" device coordinate points is derived location The coordinate vector of the position of any actual installation device is extracted as A location ＝(i x i y i ) I ∈ num, where i is the location number of any device (i.e., the serial number of this device), (x) i ,y i ) Is its coordinate position; root of herbaceous plantAccording to the characteristics of the grid coordinate system, the maximum distance covered by the equipment in the directions of the x axis and the y axis is [ x [ ] i -R x i +R]And [ y i -R y i +R]；
(2) The computing device covers the coordinates of the intersection of the circle and the grid coordinate system. And calculating the coordinates of the intersection point of each transverse axis in the grid and the equipment coverage circle. According to the characteristics of the grid coordinate system, along the y-axis direction, the intersection point of the device point I and the ordinate of the grid coordinate system is
(according to the actual capability of the equipment, determining N as a natural number fixed value N belonging to a natural number set, belonging to N), correspondingly, determining the intersection point of the I equipment point and the abscissa of a grid coordinate system as
(3) A determination is made as to where the device coverage is beyond the layout boundary, i.e., left-side or right-side beyond with respect to the grid y coordinate axis. Query matrix B border Whether or not all data of column 3 of (1) exist
If it is not
Then judge
Is right-side over, or
Left-side over;
(4) the actual of computing device iEffective coverage area S i . Because each grid area of the grid coordinate system is a unit area, the height of each grid area is only one unit height e, and the method of small area approximation is adopted, only the width of each grid area is considered and the grid area is regarded as a small area trapezoid, so the area S is i Can be expressed as:
(3.4) setting the crossover probability 0 < P cro And (3) carrying out gene crossing operation on individual chromosomes, wherein the gene crossing operation is carried out according to the gene number of the individual chromosomes. For two z's in the "higher" M "chromosomes" of the above fitness value i ,z j The positions (i ≠ j) are crossed (i.e., substituted for each other).
(3.5) setting the mutation probability 0 < P mut < 1 >, the "M" chromosomes "with higher fitness value are genetically altered (i.e., with probability P) mut Randomly replacing one of the devices).
And (3.6) outputting the calculation result. Setting iteration times T and an iteration threshold value U; when the iteration number T is reached, selecting each coordinate position corresponding to the chromosome with the highest adaptive value calculation as the optimal deployment position of each device corresponding to the device number m, and taking the result of the adaptive value calculation as the coverage result in the optimal position; or, if the Fitness value Fitness of a certain 'chromosome' is more than or equal to U, each coordinate position corresponding to the 'chromosome' is taken as the optimal equipment deployment position corresponding to the equipment number m, and the result of the Fitness value calculation is taken as the coverage result in the optimal position.
And (4): the comparison method determines the optimum installation number m of the equipment. The above genetic algorithm determines a set of "optimized" equipment numbers m opt And the location of the device
Then, selecting a group of quantity m and positions for installing the base station equipment according to the step 3 to carry out optimization calculation, comparing the adaptive values corresponding to the two groups of optimal solutions, and selecting m corresponding to the result with a larger adaptive value opt As a "global optimization result" m entire-opt Then its corresponding device coordinate location is also the globally optimal deployment location
In addition, m can be judged by two comparisons opt Whether it is getting bigger or getting smaller, if getting bigger is found, m should be larger than m at the next selection opt Is selected, otherwise, is smaller than m opt So that the global optimization result m can be quickly positioned entire-opt The number of times and the amount of calculation are reduced, and a better effect is achieved.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (3)
1. A base station equipment planarization optimization layout method based on a gridding small-area principle and a genetic algorithm is characterized by comprising the following steps:
step (1): the gridding layout area specifically comprises the following substeps:
(1.1) respectively taking the lowest end and the leftmost end of the layout area as an x axis and a y axis of a grid coordinate system;
(1.2) adopting unit length e as a grid length for the grid;
(1.3) establishing a boundary coordinate matrix of the layout area
Wherein num border The serial number of the intersection point of the boundary of the layout area and the grid; x is the number of border The horizontal coordinate of the intersection point of the boundary of the layout area and the grid is taken as the horizontal coordinate; y is border The vertical coordinate of the intersection point of the boundary of the layout area and the grid is taken as the vertical coordinate; according to the characteristics of grid coordinates, num border ∈N，x border ,y border ∈R + ；
Step (2) determining the points where the base station equipment is possibly installed according to the actual situation,
the coverage area of the base station equipment is 360 degrees; covering unit length of grid with integral multiple radius, where R is nxe, determining N as a natural number constant value N belonging to a natural number set according to actual capacity of equipment, determining base station equipment position coordinate point matrix of these areas after gridding
Wherein
Assuming that the points can only be located at the intersection positions of the grids; selecting the intersection point position which is not at the intersection point position and is closest to the intersection point position as the coordinate position of the intersection point position;
and (3): solving the number m and the positions of the installed base station equipment by adopting a genetic algorithm to realize optimization; the method specifically comprises the following substeps:
(3.1) generating the initial number of the base station equipment, and corresponding to the coordinate point matrix of the position where the base station equipment can be installed
Generating a matrix B of actual device location coordinates points location (ii) a The method comprises the following specific steps:
I. randomly generating the number of installed base station devices
And m is an element of N;
II. from
Within the range, m positive integers (z) are randomly selected 1 ,z 2 ,…,z m ) As the initial point position of the selected installation equipment;
slave matrix
In (1), look up the serial number and (z) 1 ,z 2 ,…,z m ) The coordinate positions of each row corresponding to each integer can be confirmed, and after all m positions are selected, a coordinate position point matrix B of the actual equipment is generated location ；
(3.2) generating an initial population; generating M positive integers (z) 1 ,z 2 ,…,z m ) Is the initial "chromosome" of (i.e. binary code (z) 1 ,z 2 ,…,z m ) 2 The upper 2 of the right label represents binary system, and the initial population is determined;
(3.3) determining an adaptive value function Fitness, calculating the individual Fitness of the M chromosomes, and performing optimization screening; the adaptation value function is the total area S covered by the equipment sum The ratio of the number m of the base station devices, namely the average coverage area of a single base station device is widest; the adaptive value calculation process adopts (z) 1 ,z 2 ,…,z m ) Corresponding actual base station equipment position coordinate point matrix B location (ii) a According to the algorithm of 'calculating the area of the cross area' and the algorithm of 'approximating a small area', whether two base station devices cover the cross area and whether the coverage area of the base station devices exceeds the boundary of the layout area is judged, and the area is calculated according to the algorithm of 'calculating the area of the cross area' and the algorithm of 'approximating a small area', so that the adaptive value function can be expressed as:
namely:
or, regardless of the number of devices, only the coverage rate of the base station device to a specific area is required, and in this case, the fitness function is selected as:
setting the screening proportion of 0 to 10 percent, abandoning the smaller chromosomes after the adaptive value is calculated according to the screening proportion, and replacing the chromosomes with higher adaptive values;
(3.4) setting the crossover probability 0 < P cro < 1, performing gene crossover operation on each somatic chromosome; for two z of the M "chromosomes" with higher fitness values i ,z j (i ≠ j) is crossed;
(3.5) setting the mutation probability 0 < P mut If < 1 >, the M chromosomes with high fitness are subjected to gene variation, namely with the probability P mut Randomly replacing a binary code group of one representative base station device;
(3.6) outputting a calculation result;
setting iteration times T and an iteration threshold value U; when the iteration number T is reached, selecting each coordinate position corresponding to the chromosome with the highest adaptive value calculation as the optimal deployment position of each device corresponding to the device number m, and taking the result of the adaptive value calculation as the coverage result in the optimal position; or, if the Fitness value Fitness of a certain 'chromosome' is more than or equal to U, each coordinate position corresponding to the 'chromosome' is taken as the optimal equipment deployment position corresponding to the equipment number m, and the result of the Fitness value calculation is taken as the coverage result in the optimal position;
and (4): determining the optimal installation number m of the base station equipment by a comparison method;
the above genetic algorithm determines a set of "optimized" equipment numbers m opt And the location of the device
Then, selecting a group of quantity m and positions for installing the base station equipment according to the step 3 to carry out optimization calculation, comparing the adaptive values corresponding to the two groups of optimal solutions, and selecting m corresponding to the result with a larger adaptive value opt As global optimization result m entire-opt Then its corresponding base station device coordinatesLocation, i.e., globally optimal deployment location
M can also be judged by two comparisons opt Whether it is getting bigger or getting smaller, if getting bigger is found, m should be larger than m at the next selection opt Is selected, otherwise, is smaller than m opt So that the global optimization result m can be quickly positioned entire -opt The number of times and the amount of calculation are reduced, and a better effect is achieved.
2. The method according to claim 1, wherein the specific algorithm of the area calculation of the intersection region in the step (3) is as follows:
(1) calculating a coordinate position point matrix B of the actual base station equipment location ＝[num x num y num ] num×3 The distance between the centers of any two points in num belongs to N is as follows:
i, j represents the serial numbers of any two base station equipment positions;
(2) if Dis i,j If < 2R, it indicates that the coverage areas of the two base station devices intersect, and the actual coverage areas of the two base station devices are:
wherein the content of the first and second substances,
is the area of the intersection of the two circles; s i,j Can be obtained by Monte Carlo method simulation.
3. The method of claim 1, wherein the specific method of the "small area approximation" algorithm in step (3) is as follows:
(1) determining the coverage area of base station equipment;
suppose a matrix of point coordinates B from the actual base station device location location The coordinate vector of the position of any actual installation device is extracted as A location ＝(i x i y i ) I belongs to num, wherein i is the position number of any base station equipment, (x) i ,y i ) Is its coordinate position; according to the characteristics of the grid coordinate system, the maximum distance covered by the base station equipment in the directions of the x axis and the y axis is [ x [ ] i -R x i +R]And [ y i -R y i +R]；
(2) Calculating the coordinates of the intersection point of the circle covered by the equipment and the grid coordinate system;
calculating the intersection point coordinates of each transverse axis and the equipment coverage circle in the grid; according to the characteristics of the grid coordinate system, along the y-axis direction, the intersection point of the device point I and the ordinate of the grid coordinate system is
According to the actual capability of the equipment, determining N as a natural number fixed value N belonging to a natural number set, and correspondingly, obtaining the intersection point of the I equipment point and the abscissa of a grid coordinate system as
(3) Judging the position of the coverage area of the base station equipment beyond the layout boundary, namely whether the left side exceeds or the right side exceeds relative to a grid y coordinate axis; query matrix B border Whether all the data of column 3 of (1) exist or not
If it is not
Then judge
Is right-side over, or
Left-side over;
(4) actual effective coverage area S of computing device i i ；
Area S i Expressed as:
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