CN115134815B - Wireless AP deployment optimization method, system, equipment and storage medium - Google Patents
Wireless AP deployment optimization method, system, equipment and storage medium Download PDFInfo
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Abstract
The application discloses a wireless AP deployment optimization method, a system, equipment and a storage medium, comprising the following steps: space information of a three-dimensional space and position information of each terminal are obtained, the space information, the position information and the number of wireless APs are subjected to partition clustering processing to obtain an initial solution of the position of the wireless AP, a large-step-size population and a small-step-size population are generated based on the initial solution, the initial solution is subjected to optimizing processing through a pre-built large-step-size population optimizing algorithm and a pre-built small-step-size population optimizing algorithm to obtain an optimizing result of the large-step-size population and an optimizing result of the small-step-size population, objective function values corresponding to the optimizing result are calculated respectively, an optimal solution of the current iteration is determined based on the objective function values, and if the optimal solution is detected to be an effective solution and the current iteration times reach a preset maximum iteration times, the optimal solution is used as a target deployment position of the wireless AP. The method and the device solve the technical problems of long deployment time and low efficiency of the AP.
Description
Technical Field
The present disclosure relates to the field of wireless local area networks, and in particular, to a wireless AP deployment optimization method, system, device, and storage medium.
Background
With rapid development of wireless technology, wireless local area networks are widely used in various fields due to high efficiency, flexibility and low cost of networking. In the networking of the industrial internet of things, a wireless access point (Wireless Access Point, wireless AP for short) is the most commonly used device when constructing a small wireless local area network.
At present, the deployment of the wireless AP is usually carried out through a traditional genetic algorithm, the cross operation is needed when the population is updated in the genetic algorithm, and two chromosomes are selected by using a roulette algorithm to carry out segment splicing on the generation of each new chromosome, so that the calculation complexity of the process of generating the new population at last iteration is high, and the wireless AP deployment efficiency is low and the time is long.
Disclosure of Invention
The main purpose of the application is to provide a wireless AP deployment optimization method, a system, equipment and a storage medium, which aim to solve the technical problems of longer deployment time and lower efficiency of a wireless access point AP in the prior art.
In order to achieve the above object, the present application provides a wireless AP deployment optimization method, which includes:
acquiring space information of a three-dimensional space and position information of each terminal in the three-dimensional space, and determining the number of wireless APs;
carrying out partition clustering processing on the space information, the position information and the wireless AP quantity to obtain an initial solution of the wireless AP position;
generating a large-step-length population and a small-step-length population based on the initial solution of the wireless AP position;
carrying out optimizing treatment on the initial solution of the wireless AP position through a pre-constructed large-step-size population optimizing algorithm and a small-step-size population optimizing algorithm to obtain an optimizing result of the large-step-size population and an optimizing result of the small-step-size population;
respectively calculating an objective function value corresponding to the optimizing result of the large-step-length population and the optimizing result of the small-step-length population, and determining an optimal solution of the current iteration based on the objective function value;
and if the optimal solution is detected to be an effective solution and the current iteration number reaches the preset maximum iteration number, the optimal solution is used as the target deployment position of the wireless AP.
The application also provides a wireless AP deployment optimization system, which is a virtual system, and includes:
the acquisition module is used for acquiring the space information of the three-dimensional space and the position information of each terminal in the three-dimensional space and determining the number of wireless APs;
the partition clustering module is used for performing partition clustering processing on the space information, the position information and the wireless AP quantity to obtain an initial solution of the wireless AP position;
the population generation module is used for generating a large-step-length population and a small-step-length population based on the initial solution of the wireless AP position;
the optimizing module is used for optimizing the initial solution of the wireless AP position through a pre-constructed large-step-size population optimizing algorithm and a small-step-size population optimizing algorithm to obtain an optimizing result of the large-step-size population and an optimizing result of the small-step-size population;
the optimal solution calculation module is used for respectively calculating an objective function value corresponding to the optimizing result of the large-step-length population and the optimizing result of the small-step-length population, and determining an optimal solution of the current iteration based on the objective function value;
the deployment position determining module is used for taking the optimal solution as a target deployment position of the wireless AP if the optimal solution is detected to be an effective solution and the current iteration number reaches the preset maximum iteration number.
The application also provides a wireless AP deployment optimization device, which is an entity device, and includes: the wireless AP deployment optimization system comprises a memory, a processor and a wireless AP deployment optimization program stored on the memory, wherein the wireless AP deployment optimization program is executed by the processor to realize the steps of the wireless AP deployment optimization method.
The application also provides a storage medium, which is a computer readable storage medium, wherein the computer readable storage medium stores a wireless AP deployment optimization program, and the wireless AP deployment optimization program is executed by a processor to realize the steps of the wireless AP deployment optimization method.
The method comprises the steps of firstly obtaining space information of a three-dimensional space and position information of each terminal in the three-dimensional space, determining the number of wireless APs, further carrying out partition clustering processing on the space information, the position information and the number of the wireless APs to obtain an initial solution of a wireless AP position, further, generating a large-step-size population and a small-step-size population based on the initial solution of the wireless AP position, carrying out optimizing processing on the initial solution of the wireless AP position through a pre-constructed large-step-size population optimizing algorithm and a small-step-size population optimizing algorithm to obtain optimizing results of the large-step-size population and optimizing results of the small-step-size population, further respectively calculating target function values corresponding to the optimizing results of the large-step-size population and optimizing results of the small-step-size population, and further, determining an optimal solution of a current iteration based on the target function values, further, if the optimal solution is detected to be an effective solution, and the current optimal solution is a preset maximum number of times, carrying out optimizing processing on the basis of the initial solution, carrying out random access to the wireless access point-based on the wireless AP position, carrying out random access iteration algorithm, carrying out random access based on the iterative algorithm, and carrying out the random access algorithm, and carrying out the iterative processing on the wireless access based on the initial solution, thereby improving the wireless access point-based on the wireless access algorithm.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the description of the embodiments or the prior art will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
Fig. 1 is a schematic flow chart of a first embodiment of a wireless AP deployment optimization method of the present application;
fig. 2 is a schematic flow chart of a second embodiment of the wireless AP deployment optimization method of the present application;
fig. 3 is a schematic flow chart of a third embodiment of a wireless AP deployment optimization method according to the present application;
fig. 4 is a schematic diagram comparing the time consumed by the wireless AP deployment optimization method of the present application with the conventional genetic algorithm at the same iteration number;
FIG. 5 is a schematic diagram showing the comparison of the wireless AP deployment optimization method and the conventional genetic algorithm to obtain objective function values by calculation at the same iteration times;
fig. 6 is a schematic structural diagram of a wireless AP deployment optimization device of a hardware running environment according to an embodiment of the present application;
fig. 7 is a schematic diagram of functional modules of the wireless AP deployment optimizing apparatus of the present application.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
An embodiment of the present application provides a wireless AP deployment optimization method, in a first embodiment of the wireless AP deployment optimization method of the present application, referring to fig. 1, the wireless AP deployment optimization method includes:
step S10, acquiring space information of a three-dimensional space and position information of each terminal in the three-dimensional space, and determining the number of wireless APs;
in this embodiment, it should be noted that the spatial information includes x-axis information, y-axis information, and z-axis information of a three-dimensional space, that is, sizes in x, y, and z-axis directions in the three-dimensional space, for example, 50m×50m×12m in the three-dimensional space, 50m in the x-axis direction, 50m in the y-axis direction, and 12m in the z-axis direction, and further, the position information is a coordinate position of each terminal in the three-dimensional space.
Step S20, carrying out partition clustering processing on the space information, the position information and the wireless AP quantity to obtain an initial solution of the wireless AP position;
in this embodiment, it should be noted that the initial solution characterizes the initial positions of the wireless APs.
Specifically, the size proportion of the x-axis information and the y-axis information corresponding to the x-direction and the y-direction in the three-dimensional space is determined, and then the space information is partitioned based on the size proportion relation and the number of wireless APs to obtain a plurality of area blocks, and it is to be noted that the number of the area blocks divided in the x-direction and the y-direction is respectively:
wherein ceil () represents a rounding-up, g x Representing x-axis information, g y Represents y-axis information, na represents the number of wireless APs, N x Indicates the number of area blocks in the x-axis direction, N y The number of the area blocks in the y-axis direction is represented, and further, the sizes of the area blocks in the x-axis direction and the y-axis direction are respectively:
where Δx denotes a size in the x direction, Δy denotes a size in the y direction, floor () denotes a rounding down.
Further, calculating the central position of each regional block on the three-dimensional space, and taking the central position of each regional block as a target seed, wherein the number of the target seeds in the initial population is equal to the number of the regional blocks, and further forming the initial population based on each target seed, and further, based on the position information of each terminal andcalculating the distance between each terminal and each target seed respectively at the center positions corresponding to the target seeds, classifying the terminals into corresponding point groups in the target seeds closest to the terminal, and obtaining point groups corresponding to each target seed, wherein the point groups comprise at least one terminal, calculating the centroid of each point group respectively, and taking the centroid corresponding to each point group as a new target seed, for example, assuming that the positions of the terminals of the point groups with the size Ng are { (x) 1 ,y 1 ,z 1 ),(x 2 ,y 2 ,z 2 ),…,(x Ng ,y Ng ,z Ng ) The centroid of the point group is:
further, judging whether the positions of the new target seeds and the target seeds corresponding to the corresponding point groups are the same, if not, returning to the execution step based on the new target seeds: and respectively calculating the distance between the position of each terminal and each target seed, so as to iterate to obtain a new point group, and if the new point group is the same, taking the position corresponding to each new target seed as an initial solution of the wireless AP position.
Step S30, generating a large-step-length population and a small-step-length population based on an initial solution of the wireless AP position;
in this embodiment, specifically, the calculated initial solution is replicated to obtain a large-step population and a small-step population, where the population sizes of the large-step population and the small-step population may be different or the same, for example, the initial solution is replicated to Nb and Ns shares, where Nb and Ns are the population sizes of the large-step population and the small-step population, respectively.
Step S40, optimizing the initial solution of the wireless AP position through a pre-constructed large-step-size population optimizing algorithm and a small-step-size population optimizing algorithm to obtain an optimizing result of the large-step-size population and an optimizing result of the small-step-size population;
in the present embodiment, specifically, the optimization limits in the x, y, z axis directions are first determined. Further, the specific flow of the large-step population optimization is as follows: for each wireless AP position in the large-step-size population, determining a range of the large-step-size population in the x-axis direction based on the optimizing limit in the x-axis direction and the x-axis information, determining a range of the large-step-size population in the y-axis direction based on the optimizing limit in the y-axis direction and the y-axis information, determining a range of the large-step-size population in the z-axis direction based on the optimizing limit in the z-axis direction and the z-axis information, dividing the range of the large-step-size population in the x-axis direction, the range of the large-step-size population in the y-axis direction and the range of the large-step-size population in the z-axis direction, randomly generating corresponding random numbers respectively, and further superposing the corresponding random numbers to three-dimensional coordinates in the corresponding directions of the wireless AP positions, so that new wireless AP positions are obtained, and ensuring that the new wireless AP positions do not cross the range through modulo operation.
The specific flow of the small step size population optimization is as follows: for each wireless AP position in the small step size population, determining the interval range of the small step size population in the x, y and z axis directions based on the wireless AP position and the optimizing limit in the x, y and z axis directions, further randomly generating random numbers corresponding to the x, y and z axis directions based on the interval range in the x, y and z axis directions, further adding the random numbers into three-dimensional coordinate information corresponding to the wireless AP position, thereby obtaining a new wireless AP position, and ensuring that the new wireless AP position is not out of range through modulo operation.
Step S50, calculating objective function values corresponding to the optimizing result of the large-step-size population and the optimizing result of the small-step-size population respectively, and determining an optimal solution of the current iteration based on the objective function values;
and step S60, if the optimal solution is detected to be an effective solution and the current iteration number reaches the preset maximum iteration number, the optimal solution is used as the target deployment position of the wireless AP.
In this embodiment, the signal corresponding to the wireless AP whose effective solution indicates the optimal solution can cover all terminals. Specifically, after determining the optimal solution of the current iteration, if the optimal solution is detected to be an effective solution and the current iteration number reaches a preset maximum iteration number, the optimal solution is used as a target deployment position of the wireless AP, otherwise, if the optimal solution is detected to be an ineffective solution or the current iteration number does not reach the preset maximum iteration number, the optimal solution is copied to generate a new large-step population and a small-step population, so that all individuals are moved to the current optimal solution to update the population, the whole population iteration update process is simple and quick, the efficiency of wireless AP deployment is improved, the optimal solution is used as an initial solution, and the execution steps are returned: and carrying out optimizing treatment on the initial solution of the wireless AP position through a pre-constructed large-step-size population optimizing algorithm and a small-step-size population optimizing algorithm to obtain an optimizing result of the large-step-size population and an optimizing result of the small-step-size population.
According to the method, spatial information of a three-dimensional space and position information of each terminal in the three-dimensional space are obtained, the number of wireless APs is determined, then the spatial information, the position information and the number of the wireless APs are subjected to partition clustering processing to obtain an initial solution of the wireless AP position, further, a large-step population and a small-step population are generated based on the initial solution of the wireless AP position, the initial solution of the wireless AP position is subjected to optimizing processing through a pre-built large-step population optimizing algorithm and a small-step population optimizing algorithm, the optimizing result of the large-step population and the optimizing result of the small-step population are obtained, further, the optimizing result of the large-step population and a target function value corresponding to the optimizing result of the small-step population are calculated respectively, the optimal solution of the current iteration is determined based on the target function value, further, if the optimal solution is detected to be an effective solution, the current iteration number reaches the preset maximum iteration number, the optimal solution is used as the target deployment position of the wireless AP, the wireless AP position is obtained based on the partition optimizing algorithm, the random iteration is carried out, the wireless access point is obtained through the random iteration is obtained, and the wireless access is carried out, and the wireless access is performed through the random iteration method, and the wireless iteration is carried out according to the random iteration method, and the wireless iteration is further, the wireless access is achieved.
Further, referring to fig. 2, based on the first embodiment of the present application, in another embodiment of the present application, the step of performing the optimizing process on the initial solution of the wireless AP location by using a pre-constructed large-step population optimizing algorithm and a small-step population optimizing algorithm to obtain the optimizing result of the large-step population and the optimizing result of the small-step population includes:
step A10, calculating optimization limits in the x, y and z axis directions in a three-dimensional space based on the z axis information of the space information and the size of each region block in the x, y axis directions;
step A20, determining interval ranges of the large-step-size population and the small-step-size population in the x, y and z axis directions respectively according to the optimizing limit in the x, y and z axis directions and the space information for each wireless AP position in the initial solution of the wireless AP position;
step A30, generating random numbers in the x, y and z axis directions respectively in the interval ranges in the x, y and z axis directions;
and step A40, superposing the random numbers in the x, y and z axis directions on the wireless AP position to obtain the optimizing result of the large-step-size population and the optimizing result of the small-step-size population.
In this embodiment, specifically, optimization limits in the x, y, and z axis directions are determined, where the optimization limits in the x, y, and z axis directions are:
wherein Deltax and Deltay are the sizes of each regional block in the x and y directions, g z Representing z-axis information, x STEP Represents the optimization limit in the x-axis direction, y STEP Represents the optimization limit in the y-axis direction, z STEP Indicating the optimization limit in the z-axis direction.
The specific method for optimizing the large-step population comprises the following steps: for each AP position of each solution, at [ x ] STEP ,g x ]、[y STEP ,g y ]、[z STEP ,g z ]Generating a random number in the interval range, adding the random number to the coordinate value of the corresponding direction of the AP position, ensuring that the new wireless AP position does not cross the boundary through modulo operation, and taking each new wireless AP position as the optimizing result.
The specific method for optimizing the small-step-length population comprises the following steps: for each AP position (x, y, z) of each solution, at [ -a x ,b x ]、[-a y ,b y ]、[-a z ,b z ]Generating a random number in the interval range, adding the random number to the coordinate value of the corresponding direction of the wireless AP position, ensuring that the new wireless AP position does not cross the boundary through modulo operation, and taking each new wireless AP position as the optimizing result, wherein the method comprises the following steps:
a x ＝min(x,x STEP )
b x ＝min(g x -1-x,x STEP )
a y ＝min(y,y STEP )
b y ＝min(g y -1-y,y STEP )
a z ＝min(z,z STEP )
b z ＝min(g z -1-z,z STEP )
according to the scheme, the method and the device realize that the optimizing process is carried out on each wireless AP position according to the optimizing modes of different step sizes based on the optimizing limit and the space information, namely, the random search is carried out in a simple mixed step size random moving mode during iterative optimizing, so that the optimized position is rapidly determined, the iterative optimization is further carried out based on the optimized position, and the efficiency of wireless AP deployment is improved.
Further, referring to fig. 3, based on the first embodiment of the present application, in another embodiment of the present application, the step of calculating objective function values corresponding to the optimizing result of the large-step population and the optimizing result of the small-step population, respectively, and determining the optimal solution of the current iteration based on the objective function values includes:
step B10, calculating signal attenuation values between each wireless AP and each terminal of the optimizing result through a pre-constructed channel model aiming at the optimizing result of the large-step-size population and each optimizing result of the small-step-size population;
in this embodiment, it should be noted that, the channel model is constructed based on path loss and penetration loss of wireless signals in a field geographic environment, and for each of the optimizing result of the large-step population and the optimizing result of the small-step population, the signal attenuation value between each wireless AP and each terminal of the optimizing result is calculated through a pre-constructed channel model, where the channel model is as follows:
wherein d0 is Euclidean distance between the transmitting end (wireless AP) and the reference point, 1m is usually taken, PL (d 0) is a signal strength attenuation value actually measured by the wireless AP and the reference point, n is a path loss coefficient, d i,j For the euclidean distance between the wireless AP and the terminal,c, the penetration loss of the barrier i,j,k Representing slave wireless APs i Whether the link to terminal j passes by an obstacle or not, the value is 1 if it passes by, and 0 if it does not pass by.
Step B20, determining the received signal strength of each terminal corresponding to each wireless AP based on the preset transmitted signal strength and the corresponding signal attenuation value of the wireless AP;
in this embodiment, it should be noted that, the signal strength of the wireless AP transmission signal is known, and the signal attenuation value corresponding to each terminal and the wireless AP transmission signal strength corresponding to each wireless AP is calculated based on the preset transmission signal strength of the wireless AP, for example, P i,j ＝P t -PL i,j Wherein P is t To preset the transmitted signal strength, PL i,j Is the signal attenuation value.
Step B30, associating each terminal with the wireless AP corresponding to the strongest received signal strength to calculate the normalized throughput of each wireless AP in each optimizing result;
step B40, summing the normalized throughput of the wireless AP in each optimizing result to obtain the objective function value of each optimizing result;
and step B50, selecting an optimizing result corresponding to the maximum objective function value as an optimal solution of the current iteration.
In this embodiment, specifically, each terminal is associated with a wireless AP corresponding to the strongest received signal strength, where,P i,j is AP i After each terminal is associated with a wireless AP, the normalized throughput corresponding to each wireless AP can be calculated, so as to obtain the normalized throughput of each wireless AP in each optimizing result, for example, for a BSS with the number of terminals n, the normalized throughput of the wireless AP is calculated according to the Bianchi model as follows:
wherein P is tr ＝1-(1-τ) n Indicating the probability that at least one node is attempting to transmit,the probability of successful transmission on the premise of node transmission is represented, the probability of station transmission packet and the probability of collision when station transmission are respectively represented by τ and p, and the simultaneous solution is carried out by the following equation.
Wherein sigma is the time length of one standard time slot specified in IEEE 802.11 protocol, T s And T c Respectively, the time consumed by the node to successfully transmit data and the time consumed by the node to fail to transmit data.
Further, for each of the optimizing results: summing the normalized throughput of each wireless AP in the optimizing results to obtain an objective function value of the optimizing results, and further selecting the optimizing result corresponding to the maximum objective function value from the objective function values of the optimizing results as the optimal solution of the current iteration.
According to the method and the device for optimizing the wireless AP, through the scheme, the channel model constructed based on the path loss and the penetration loss of the wireless signals in the geographic environment is achieved, the signal attenuation value between each wireless AP and each terminal is calculated, and accordingly, each terminal is associated with the wireless AP corresponding to the strongest received signal strength based on the signal attenuation value, normalized throughput of the wireless AP is calculated, the problem is quantified by taking the overall throughput of the network as a target, and the channel model, the optimization problem model and the like are built, so that the optimizing result corresponding to the maximum overall throughput is selected to serve as the optimal solution of the current iteration, energy consumption of the wireless AP is saved, and network deployment cost is reduced.
Further, in the specific embodiment of the present application, considering the three-dimensional space with the size of 50m×50m×12m, the random generation of 80 terminal positions, and the situation that the AP number is 8, when the population size is set to 60, the deployment is performed by using the wireless AP deployment optimization method of the present application and the deployment are performed by using the conventional genetic algorithm, referring to fig. 4, fig. 4 is a schematic diagram comparing the time consumed by the wireless AP deployment optimization method of the present application and the conventional genetic algorithm in the same iteration number, and referring to fig. 5, fig. 5 is a schematic diagram comparing the wireless AP deployment optimization method of the present application and the conventional genetic algorithm in the same iteration number to obtain the objective function value, wherein the network throughput represents the objective function value, and based on fig. 4 and fig. 5, the network throughput obtained by solving is relatively close to the genetic algorithm in the case of the same iteration number, and the operation time is far less than the conventional genetic algorithm.
Referring to fig. 6, fig. 6 is a schematic structural diagram of a wireless AP deployment optimizing device of a hardware running environment according to an embodiment of the present application.
As shown in fig. 6, the wireless AP deployment optimization device may include: a processor 1001, such as a CPU, memory 1005, and a communication bus 1002. Wherein a communication bus 1002 is used to enable connected communication between the processor 1001 and a memory 1005. The memory 1005 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
Optionally, the wireless AP deployment optimization device may further include a rectangular user interface, a network interface, a camera, an RF (Radio Frequency) circuit, a sensor, an audio circuit, a WiFi module, and so on. The rectangular user interface may include a Display screen (Display), an input sub-module such as a Keyboard (Keyboard), and the optional rectangular user interface may also include a standard wired interface, a wireless interface. The network interface may optionally include a standard wired interface, a wireless interface (e.g., WIFI interface).
Those skilled in the art will appreciate that the wireless AP deployment optimization device structure shown in fig. 6 does not constitute a limitation of the wireless AP deployment optimization device, and may include more or fewer components than shown, or may combine certain components, or may be a different arrangement of components.
As shown in fig. 6, an operating system, a network communication module, and a wireless AP deployment optimization program may be included in a memory 1005, which is one type of computer storage medium. The operating system is a program that manages and controls the wireless AP deployment optimization device hardware and software resources, supporting the wireless AP deployment optimization program and the execution of other software and/or programs. The network communication module is used to enable communication between components within the memory 1005 and with other hardware and software in the wireless AP deployment optimization system.
In the wireless AP deployment optimization device shown in fig. 6, the processor 1001 is configured to execute a wireless AP deployment optimization program stored in the memory 1005, to implement the steps of the wireless AP deployment optimization method described in any one of the above.
The specific implementation manner of the wireless AP deployment optimization device in the present application is basically the same as the embodiments of the wireless AP deployment optimization method described above, and will not be described herein again.
In addition, referring to fig. 7, fig. 7 is a schematic functional block diagram of a wireless AP deployment optimization device of the present application, and the present application further provides a wireless AP deployment optimization system, where the wireless AP deployment optimization system includes:
the acquisition module is used for acquiring the space information of the three-dimensional space and the position information of each terminal in the three-dimensional space and determining the number of wireless APs;
the partition clustering module is used for performing partition clustering processing on the space information, the position information and the wireless AP quantity to obtain an initial solution of the wireless AP position;
the population generation module is used for generating a large-step-length population and a small-step-length population based on the initial solution of the wireless AP position;
the optimizing module is used for optimizing the initial solution of the wireless AP position through a pre-constructed large-step-size population optimizing algorithm and a small-step-size population optimizing algorithm to obtain an optimizing result of the large-step-size population and an optimizing result of the small-step-size population;
the optimal solution calculation module is used for respectively calculating an objective function value corresponding to the optimizing result of the large-step-length population and the optimizing result of the small-step-length population, and determining an optimal solution of the current iteration based on the objective function value;
the deployment position determining module is used for taking the optimal solution as a target deployment position of the wireless AP if the optimal solution is detected to be an effective solution and the current iteration number reaches the preset maximum iteration number.
Optionally, the wireless AP deployment optimization system is further configured to:
if the optimal solution is detected to be an invalid solution or the current iteration number does not reach the preset maximum iteration number, generating a new large-step-size population and a small-step-size population based on the optimal solution, and taking the optimal solution as an initial solution to return to the execution step: and carrying out optimizing treatment on the initial solution of the wireless AP position through a pre-constructed large-step-size population optimizing algorithm and a small-step-size population optimizing algorithm to obtain an optimizing result of the large-step-size population and an optimizing result of the small-step-size population.
Optionally, the partition clustering module is further configured to:
partitioning the space information based on the size proportion relation between the x-axis information and the y-axis information and the number of the wireless APs to obtain a plurality of area block information, wherein the area block information comprises the sizes of all area blocks in the x-direction and the y-direction;
calculating the central position of each regional block, and forming an initial population based on each central position, wherein the initial population comprises various target seeds;
respectively calculating the distance between the position of each terminal and each target seed;
generating a point group corresponding to each target seed based on the distance for each terminal;
calculating the mass centers of the point groups respectively, and taking each mass center as a new target seed;
judging whether the positions of each new target seed and the target seed corresponding to the point group are the same or not;
if yes, determining an initial solution of the wireless AP position based on the position of each new target seed;
if not, based on each new target seed, returning to the execution step: and respectively calculating the distance between the position of each terminal and each target seed.
Optionally, the population generation module is further configured to:
and copying the initial solutions of the wireless AP positions respectively to obtain the large-step-size population and the small-step-size population.
Optionally, the optimizing module is further configured to:
calculating optimization limits in x, y and z axis directions in a three-dimensional space based on z axis information of the space information and the size of each region block in the x, y axis directions;
for each wireless AP position in the initial solution of the wireless AP position, determining interval ranges in the x, y and z axis directions of the large-step-size population and the small-step-size population respectively based on optimizing limits in the x, y and z axis directions and the spatial information;
generating random numbers in the x, y and z axis directions respectively in the interval ranges in the x, y and z axis directions;
and overlapping the random numbers in the x, y and z axis directions on the wireless AP position to obtain the optimizing result of the large-step-size population and the optimizing result of the small-step-size population.
Optionally, the optimizing module is further configured to:
determining a range of the large-step population in the x, y and z axis directions based on the optimizing limit and the x axis information in the x axis direction, the optimizing limit and the y axis information in the y axis direction, and the optimizing limit and the z axis information in the z axis direction for each wireless AP position in the initial solution of the wireless AP positions;
for each wireless AP position in an initial solution of the wireless AP position, determining a section range of the small-step population in the x, y and z axis directions based on the wireless AP position and optimization limits in the x, y and z axis directions, wherein the section ranges of the small-step population in the x, y and z axis directions are respectively: [ -a) x ,b x ]、[-a y ,b y ]、[-a z ,b z ]，
a x ＝min(x,x STEP )
b x ＝min(g x -1-x,x STEP )
a y ＝min(y,y STEP )
b y ＝min(g y -1-y,y STEP )
a z ＝min(z,z STEP )
b z ＝min(g z -1-z,z STEP )
Where x, y, z represents the wireless AP location and min () represents the minimum, where x STEP Represents the optimization limit in the x-axis direction, y STEP Represents the optimization limit in the y-axis direction, z STEP Represents the optimization limit in the z-axis direction, g x Representing x-axis information, g y Representing y-axis information, g z Representing z-axis information.
Optionally, the optimal solution calculation module is further configured to:
calculating signal attenuation values between each wireless AP and each terminal of the optimizing result through a pre-constructed channel model aiming at the optimizing result of the large-step-size population and each optimizing result of the small-step-size population, wherein the channel model is constructed based on path loss and penetration loss of wireless signals in a geographic environment;
determining the received signal strength corresponding to each wireless AP received by each terminal based on the preset transmitted signal strength and the corresponding signal attenuation value of the wireless AP;
associating each terminal with the wireless AP corresponding to the strongest received signal strength so as to calculate the normalized throughput of each wireless AP in each optimizing result;
summing the normalized throughput of each wireless AP in each optimizing result to obtain the objective function value of each optimizing result;
and selecting an optimizing result corresponding to the maximum objective function value as an optimal solution of the current iteration.
The specific implementation manner of the wireless AP deployment optimization system is basically the same as the embodiments of the wireless AP deployment optimization method, and is not repeated here.
Embodiments of the present application provide a storage medium, which is a computer readable storage medium, and the computer readable storage medium stores one or more programs, where the one or more programs are further executable by one or more processors to implement the steps of the wireless AP deployment optimization method described in any one of the foregoing.
The specific implementation manner of the computer readable storage medium is basically the same as the above embodiments of the wireless AP deployment optimization method, and will not be repeated here.
The foregoing description is only of the preferred embodiments of the present application and is not intended to limit the scope of the claims, and all equivalent structures or equivalent processes using the descriptions and drawings of the present application, or direct or indirect application in other related technical fields are included in the scope of the claims.
Claims (9)
1. The wireless Access Point (AP) deployment optimization method is characterized by comprising the following steps of:
acquiring space information of a three-dimensional space and position information of each terminal in the three-dimensional space, and determining the number of wireless APs;
carrying out partition clustering processing on the space information, the position information and the wireless AP quantity to obtain an initial solution of the wireless AP position;
generating a large-step-length population and a small-step-length population based on the initial solution of the wireless AP position;
carrying out optimizing treatment on the initial solution of the wireless AP position through a pre-constructed large-step-size population optimizing algorithm and a small-step-size population optimizing algorithm to obtain an optimizing result of the large-step-size population and an optimizing result of the small-step-size population;
respectively calculating an objective function value corresponding to the optimizing result of the large-step-length population and the optimizing result of the small-step-length population, and determining an optimal solution of the current iteration based on the objective function value;
if the optimal solution is detected to be an effective solution and the current iteration number reaches the preset maximum iteration number, the optimal solution is used as a target deployment position of the wireless AP;
the step of calculating objective function values corresponding to the optimizing result of the large-step-size population and the optimizing result of the small-step-size population respectively, and determining an optimal solution of the current iteration based on the objective function values comprises the following steps:
calculating signal attenuation values between each wireless AP and each terminal of the optimizing result through a pre-constructed channel model aiming at the optimizing result of the large-step-size population and each optimizing result of the small-step-size population, wherein the channel model is constructed based on path loss and penetration loss of wireless signals in a geographic environment;
determining the received signal strength corresponding to each wireless AP received by each terminal based on the preset transmitted signal strength and the corresponding signal attenuation value of the wireless AP;
associating each terminal with the wireless AP corresponding to the strongest received signal strength so as to calculate the normalized throughput of each wireless AP in each optimizing result;
summing the normalized throughput of each wireless AP in each optimizing result to obtain the objective function value of each optimizing result;
and selecting an optimizing result corresponding to the maximum objective function value as an optimal solution of the current iteration.
2. The wireless AP deployment optimization method of claim 1, further comprising, after the steps of calculating objective function values based on the optimizing result of the large-step population and the optimizing result of the small-step population, and determining an optimal solution for a current iteration based on the objective function values:
if the optimal solution is detected to be an invalid solution or the current iteration number does not reach the preset maximum iteration number, generating a new large-step-size population and a small-step-size population based on the optimal solution, and taking the optimal solution as a new initial solution to return to the execution step: and carrying out optimizing treatment on the initial solution of the wireless AP position through a pre-constructed large-step-size population optimizing algorithm and a small-step-size population optimizing algorithm to obtain an optimizing result of the large-step-size population and an optimizing result of the small-step-size population.
3. The wireless AP deployment optimization method of claim 1, wherein the spatial information comprises x-axis information and y-axis information;
the step of performing partition clustering processing on the spatial information, the position information and the wireless AP quantity to obtain an initial solution of the wireless AP position comprises the following steps:
partitioning the space information based on the size proportion relation between the x-axis information and the y-axis information and the number of the wireless APs to obtain a plurality of area block information, wherein the area block information comprises the sizes of all area blocks in the x-direction and the y-direction;
calculating the central position of each regional block, and forming an initial population based on each central position, wherein the initial population comprises various target seeds;
calculating the distance between each terminal and each target seed based on the position information of each terminal;
generating a point group corresponding to each target seed based on the distance for each terminal;
calculating the mass centers of the point groups respectively, and taking each mass center as a new target seed;
judging whether the positions of each new target seed and the target seed corresponding to the point group are the same or not;
if yes, determining an initial solution of the wireless AP position based on the position of each new target seed;
if not, based on each new target seed, returning to the execution step: and respectively calculating the distance between the position of each terminal and each target seed.
4. The wireless AP deployment optimization method of claim 1, wherein the step of generating a large step population and a small step population based on an initial solution of the wireless AP location comprises:
and copying the initial solutions of the wireless AP positions respectively to form the large-step-size population and the small-step-size population.
5. The wireless AP deployment optimization method of claim 3, wherein the step of optimizing the initial solution of the wireless AP location by a pre-constructed large-step population optimizing algorithm and a small-step population optimizing algorithm to obtain the optimizing result of the large-step population and the optimizing result of the small-step population comprises:
calculating optimization limits in x, y and z axis directions in a three-dimensional space based on z axis information of the space information and the size of each region block in the x, y axis directions;
for each wireless AP position in the initial solution of the wireless AP position, determining interval ranges in the x, y and z axis directions of the large-step-size population and the small-step-size population respectively based on optimizing limits in the x, y and z axis directions and the spatial information;
generating random numbers in the x, y and z axis directions respectively in the interval ranges in the x, y and z axis directions;
and overlapping the random numbers in the x, y and z axis directions on the wireless AP position to obtain the optimizing result of the large-step-size population and the optimizing result of the small-step-size population.
6. The wireless AP deployment optimization method of claim 5, wherein the step of determining the range of intervals in the x, y and z axes of the large-step population and the small-step population, respectively, for each of the initial solutions of wireless AP locations, based on the optimization limits in the x, y and z axes and the spatial information, comprises:
determining a range of the large-step population in the x, y and z axis directions based on the optimizing limit and the x axis information in the x axis direction, the optimizing limit and the y axis information in the y axis direction, and the optimizing limit and the z axis information in the z axis direction for each wireless AP position in the initial solution of the wireless AP positions;
for each wireless AP position in an initial solution of the wireless AP position, determining a section range of the small-step population in the x, y and z axis directions based on the wireless AP position and optimization limits in the x, y and z axis directions, wherein the section ranges of the small-step population in the x, y and z axis directions are respectively: [ -a) x ,b x ]、[-a y ,b y ]、[-a z ,b z ]，
a x ＝min(x,x STEP )
b x ＝min(g x -1-x,x STEP )
a y ＝min(y,y STEP )
b y ＝min(g y -1-y,y STEP )
a z ＝min(z,z STEP )
b z ＝min(g z -1-z,z STEP )
Where x, y, z represents the wireless AP location and min () represents the minimum, where x STEP Represents the optimization limit in the x-axis direction, y STEP Represents the optimization limit in the y-axis direction, z STEP Represents the optimization limit in the z-axis direction, g x Representing x-axis information, g y Representing y-axis information, g z Representing z-axis information.
7. A wireless AP deployment optimization system, the wireless AP deployment optimization system comprising:
the acquisition module is used for acquiring the space information of the three-dimensional space and the position information of each terminal in the three-dimensional space and determining the number of wireless APs;
the partition clustering module is used for performing partition clustering processing on the space information, the position information and the wireless AP quantity to obtain an initial solution of the wireless AP position;
the population generation module is used for generating a large-step-length population and a small-step-length population based on the initial solution of the wireless AP position;
the optimizing module is used for optimizing the initial solution of the wireless AP position through a pre-constructed large-step-size population optimizing algorithm and a small-step-size population optimizing algorithm to obtain an optimizing result of the large-step-size population and an optimizing result of the small-step-size population;
the optimal solution calculation module is used for respectively calculating an objective function value corresponding to the optimizing result of the large-step-length population and the optimizing result of the small-step-length population, and determining an optimal solution of the current iteration based on the objective function value; the optimal solution calculation module is further configured to calculate, according to a pre-constructed channel model, a signal attenuation value between each wireless AP of the optimal result and each terminal, for each of the optimal result of the large-step population and each of the optimal result of the small-step population, where the channel model is constructed based on path loss and penetration loss of a wireless signal in a geographic environment; determining the received signal strength corresponding to each wireless AP received by each terminal based on the preset transmitted signal strength and the corresponding signal attenuation value of the wireless AP; associating each terminal with the wireless AP corresponding to the strongest received signal strength so as to calculate the normalized throughput of each wireless AP in each optimizing result; summing the normalized throughput of each wireless AP in each optimizing result to obtain the objective function value of each optimizing result; selecting an optimizing result corresponding to the maximum objective function value as an optimal solution of the current iteration;
the deployment position determining module is used for taking the optimal solution as a target deployment position of the wireless AP if the optimal solution is detected to be an effective solution and the current iteration number reaches the preset maximum iteration number.
8. A wireless AP deployment optimization device, the wireless AP deployment optimization device comprising: a memory, a processor and a wireless AP deployment optimization program stored on the memory,
the wireless AP deployment optimization program is executed by the processor to implement the steps of the wireless AP deployment optimization method according to any one of claims 1 to 6.
9. A storage medium, which is a computer-readable storage medium, wherein a wireless AP deployment optimization program is stored on the computer-readable storage medium, and the wireless AP deployment optimization program is executed by a processor to implement the steps of the wireless AP deployment optimization method according to any one of claims 1 to 6.
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