CN115134815A - Wireless AP deployment optimization method, system, equipment and storage medium - Google Patents
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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: the method comprises the steps of obtaining space information of a three-dimensional space and position information of each terminal, carrying out partition clustering processing on the space information, the position information and the number of wireless APs to obtain an initial solution of the positions of the wireless APs, generating a large-step-size population and a small-step-size population based on the initial solution, carrying out optimization processing on the initial solution through a large-step-size population optimization algorithm and a small-step-size population optimization algorithm which are constructed in advance to obtain an optimization result of the large-step-size population and an optimization result of the small-step-size population, respectively calculating target function values corresponding to the optimization results, determining the optimal solution of current iteration based on the target function values, and taking the optimal solution as the target deployment position of the wireless APs if the optimal solution is detected to be an effective solution and the current iteration number reaches the preset maximum iteration number. The method and the device solve the technical problems of long deployment time and low efficiency of the AP.
Description
Technical Field
The present application relates to the field of wireless local area network technologies, and in particular, to a method, a system, a device, and a storage medium for optimizing wireless AP deployment.
Background
With the rapid development of wireless technology, wlan is widely used in many fields due to its high efficiency, flexibility and low cost. In networking of an industrial internet of things, a Wireless Access Point (Wireless AP) is the most commonly used device for establishing a small Wireless local area network.
At present, a wireless AP deployment scheme is generally deployed through a traditional genetic algorithm, cross operation is needed when a population is updated in the genetic algorithm, two chromosomes need to be selected through a roulette algorithm to be spliced when each new chromosome is generated, the computational complexity is high in the process of generating the new population through iteration at last, and therefore the wireless AP deployment efficiency is low, and the time is long.
Disclosure of Invention
The present application mainly aims to provide a method, a system, a device and a storage medium for optimizing wireless AP deployment, and aims to solve the technical problems of long deployment time and low 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, where the wireless AP deployment optimization method 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;
performing partition clustering processing on the spatial information, the position information and the number of the wireless APs to obtain an initial solution of the positions of the wireless APs;
generating a large step size population and a small step size population based on the initial solution of the wireless AP position;
optimizing the initial solution of the wireless AP position through a pre-constructed large-step-length population optimization algorithm and a pre-constructed small-step-length population optimization algorithm to obtain an optimization result of the large-step-length population and an optimization result of the small-step-length population;
respectively calculating an optimization result of the large-step-size population and an objective function value corresponding to the optimization result of the small-step-size population, and determining an optimal solution of the current iteration based on the objective function values;
and if the optimal solution is detected to be an effective solution and the current iteration times reach the preset maximum iteration times, taking the optimal solution as the target deployment position of the wireless AP.
The present application further provides a wireless AP deployment optimization system, where the wireless AP deployment optimization system is a virtual system, and the wireless AP deployment optimization system includes:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for 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;
the partition clustering module is used for performing partition clustering processing on the spatial information, the position information and the number of the wireless APs to obtain an initial solution of the positions of the wireless APs;
the population generation module is used for generating a large step size population and a small step size 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-length population optimizing algorithm and a small-step-length population optimizing algorithm to obtain an optimizing result of the large-step-length population and an optimizing result of the small-step-length population;
the optimal solution calculation module is used for calculating an optimization result of the large-step-size population and an objective function value corresponding to the optimization result of the small-step-size population respectively and determining the optimal solution of the current iteration based on the objective function values;
and the deployment position determining module is used for taking the optimal solution as the target deployment position of the wireless AP if the optimal solution is detected to be an effective solution and the current iteration times reach the preset maximum iteration times.
The present application further provides a wireless AP deployment optimization device, where the wireless AP deployment optimization device is an entity device, and the wireless AP deployment optimization device includes: a memory, a processor, and a wireless AP deployment optimization program stored on the memory, the wireless AP deployment optimization program being executed by the processor to implement the steps of the wireless AP deployment optimization method as described above.
The present application further provides a storage medium, which is a computer-readable storage medium, on which a wireless AP deployment optimization program is stored, where the wireless AP deployment optimization program is executed by a processor to implement the steps of the wireless AP deployment optimization method as described above.
The application provides a wireless AP deployment optimization method, a wireless AP deployment optimization system, wireless AP deployment optimization equipment and a storage medium, 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 the positions of the wireless APs, further generating a large-step population and a small-step population based on the initial solution of the positions of the wireless APs, carrying out optimization processing on the initial solution of the positions of the wireless APs through a pre-constructed large-step population optimization algorithm and a small-step population optimization algorithm to obtain a step optimization result of the large-step population and an optimization result of the small-step population, and further respectively calculating a target function value corresponding to the optimization result of the large-step population and the optimization result of the small-step population, and determining an optimal solution of current iteration based on the objective function value, further, if the optimal solution is detected to be an effective solution and the current iteration number reaches a preset maximum iteration number, taking the optimal solution as a target deployment position of the wireless AP, so as to realize that the initial solution of the wireless AP is obtained based on partition clustering processing, thereby generating a population, further carrying out optimization processing on the initial solution of the wireless AP position through an optimization algorithm, carrying out random search through a simple mixed step random movement mode during iteration optimization, rapidly determining the optimal solution of wireless AP deployment based on an optimization result, and further improving the efficiency of AP deployment of the wireless access point.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present 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 needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious to those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a schematic flowchart of a first embodiment of a wireless AP deployment optimization method according to the present application;
fig. 2 is a schematic flowchart of a second embodiment of a wireless AP deployment optimization method according to the present application;
fig. 3 is a schematic flowchart of a third embodiment of a wireless AP deployment optimization method according to the present application;
fig. 4 is a schematic diagram illustrating comparison of time consumed by the wireless AP deployment optimization method of the present application and a conventional genetic algorithm for the same number of iterations;
FIG. 5 is a schematic diagram illustrating comparison between the wireless AP deployment optimization method and a conventional genetic algorithm to obtain objective function values calculated at the same iteration number;
fig. 6 is a schematic structural diagram of a wireless AP deployment optimization device in a hardware operating environment according to an embodiment of the present application;
fig. 7 is a functional module diagram of the wireless AP deployment optimization apparatus according to the present application.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
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, the three-dimensional space is 50m × 50m × 12m, the size in x-axis direction is 50m, the size in y-axis direction is 50m, and the size in z-axis direction is 12m, and further, the position information is a coordinate position of each terminal in the three-dimensional space.
Step S20, 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;
in this embodiment, it should be noted that the initial solution represents an initial position of each wireless AP.
Specifically, the size ratios of the x-axis information and the y-axis information corresponding to the x and y directions in the three-dimensional space are determined, and then the space information is partitioned based on the size ratio relationship and the number of the wireless APs to obtain a plurality of area blocks, where it should be noted that the number of the area blocks divided in the x and y directions is:
where ceil () denotes rounded 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 blocks in the x-axis direction, N y The number of blocks in the y-axis direction is shown, and the sizes of the blocks in the x and y directions are:
where Δ x represents the magnitude in the x direction, Δ y represents the magnitude in the y direction, and floor () represents a floor.
Further, the central position of each region block on the three-dimensional space is calculated, and the central position of each region block is used as a target seed, wherein the number of the target seeds in the initial population is equal to the number of the region blocks, an initial population is formed based on each target seed, further, the distance between each terminal and each target seed is calculated based on the position information of each terminal and the central position corresponding to each target seed, and then classifying the terminal into the corresponding point group in the target seeds with the closest distance to the terminal, thereby obtaining the point group corresponding to each target seed, wherein the point groups comprise at least one terminal, and further the mass center of each point group is calculated respectively, and the centroid corresponding to each point group is used as a new target seed, for example, the position of each terminal of the point group with size Ng is assumed to be { (x). 1 ,y 1 ,z 1 ),(x 2 ,y 2 ,z 2 ),…,(x Ng ,y Ng ,z Ng ) And the centroid of the point group is:
further, whether the positions of each new target seed and the target seed corresponding to the corresponding point group are the same or not is judged, if not, the execution steps are returned based on each new target seed: 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 distances are 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 size population and a small step size population based on the initial solution of the wireless AP position;
in this embodiment, specifically, the calculated initial solution is copied to obtain a large step size population and a small step size population, where the sizes of the large step size population and the small step size population may be different or the same, for example, the initial solution is copied to Nb parts and Ns parts, where Nb part and Ns part are the sizes of the large step size population and the small step size population, respectively.
Step S40, carrying out optimization processing on the initial solution of the wireless AP position through a large-step-length population optimization algorithm and a small-step-length population optimization algorithm which are constructed in advance to obtain an optimization result of the large-step-length population and an optimization result of the small-step-length population;
in the present embodiment, specifically, the optimization limits in the x, y, and z-axis directions are first determined. Further, the specific flow of large-step population optimization is as follows: aiming at each wireless AP position in the large-step-size population, determining the interval range of the large-step-size population in the x-axis direction based on the optimization limit and the x-axis information in the x-axis direction, determining the interval range of the large-step-size population in the y-axis direction based on the optimization limit and the y-axis information in the y-axis direction, determining the interval range of the large-step-size population in the z-axis direction based on the optimization limit and the z-axis information in the z-axis direction, dividing the interval range in the x-axis direction, the interval range in the y-axis direction and the interval range in the z-axis direction, respectively and randomly generating corresponding random numbers, and further superposing the corresponding random numbers to the three-dimensional coordinates of the wireless AP position in the corresponding direction, so as to obtain a new wireless AP position, and ensuring that the new wireless AP position does not exceed the boundary through modular operation.
The specific process of 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 directions of x, y and z axes based on the wireless AP position and the optimizing limit in the directions of the x, y and z axes, respectively and randomly generating random numbers corresponding to the directions of the x, y and z axes based on the interval range in the directions of the x, y and z axes, and then superposing the random numbers into the three-dimensional coordinate information corresponding to the wireless AP position to obtain a new wireless AP position, and ensuring that the new wireless AP position does not exceed the boundary through modular operation.
Step S50, respectively calculating the optimization results of the large step size population and the optimization results of the small step size population, and determining the optimal solution of the current iteration based on the objective function values;
step S60, if it is detected that the optimal solution is an effective solution and the current iteration number reaches a preset maximum iteration number, taking the optimal solution as the target deployment position of the wireless AP.
In this embodiment, it should be noted that the signals corresponding to the wireless AP whose effective solution represents 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 the target deployment position of the wireless AP, and additionally, if the optimal solution is detected to be an invalid 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 step is returned: and carrying out optimization processing on the initial solution of the position of the wireless AP through a pre-constructed large-step-size population optimization algorithm and a pre-constructed small-step-size population optimization algorithm to obtain an optimization result of the large-step-size population and an optimization result of the small-step-size population.
According to the scheme, the space information of the three-dimensional space and the position information of each terminal in the three-dimensional space are obtained, the number of wireless APs is determined, the space information, the position information and the number of the wireless APs are subjected to partition clustering processing to obtain the initial solution of the wireless AP position, furthermore, 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 optimization processing through a large-step population optimization algorithm and a small-step population optimization algorithm which are constructed in advance to obtain the optimization result of the large-step population and the optimization result of the small-step population, the optimization result of the large-step population and the target function value corresponding to the optimization result of the small-step population are calculated respectively, and 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 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 initial solution of the wireless AP is obtained based on partition clustering processing, so that a population with a mixed step length is generated, the initial solution of the wireless AP position is subjected to optimization processing through an optimization algorithm, the optimal solution for deployment of the wireless AP is rapidly determined based on an optimization result, namely, random search is performed in a simple mixed step length random movement mode during iteration optimization, the calculation complexity of each iteration operation is low, and the efficiency for deployment of the wireless access point AP is improved.
Further, referring to fig. 2, in another embodiment of the present application, based on the first embodiment of the present application, the step of obtaining the optimization result of the large-step population and the optimization result of the small-step population by performing optimization processing on the initial solution of the wireless AP position through a pre-constructed large-step population optimization algorithm and a small-step population optimization algorithm includes:
step A10, calculating optimizing limits in the directions of x, y and z axes in a three-dimensional space based on the z-axis information of the spatial information and the size of each area block in the directions of x and y axes;
step A20, determining the interval ranges in the directions of the x, y and z axes of the large step size population and the small step size population respectively according to the optimizing limits in the directions of the x, y and z axes and the space information aiming at 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 within the interval range in the x, y and z axis directions respectively;
step A40, superposing the random numbers in the x, y and z-axis directions to the wireless AP position, and obtaining the optimization result of the large step size population and the optimization result of the small step size population.
In this embodiment, specifically, the 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 Delay are the size of each region block in the x and y directions, g z Representing z-axis information, x STEP Indicating the limit of optimization in the x-axis direction, y STEP Indicating the limit of optimization in the y-axis direction, z STEP Indicating the limit of the optimization 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, respectively in [ x ] STEP ,g x ]、[y STEP ,g y ]、[z STEP ,g z ]And 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 exceed the boundary through modular operation, and taking each new wireless AP position as the optimizing result.
The specific method for optimizing the population with small step length comprises the following steps: for each AP position (x, y, z) of each solution, respectively 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 corresponding direction coordinate value of the wireless AP position, ensuring that the new wireless AP position does not exceed the boundary through modular operation, and further taking each new wireless AP position as the seeking positionThe advantages are that:
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 embodiment of the application, based on the optimization limit and the spatial information, optimization processing is carried out on each wireless AP position according to the optimization modes of different step lengths, namely random search is carried out in a simple mixed step length random moving mode during iterative optimization, so that the position after optimization is rapidly determined, iterative optimization is further carried out based on the position after optimization, and the deployment efficiency of the wireless AP is improved.
Further, referring to fig. 3, in another embodiment of the present application, based on the first embodiment of the present application, the step of calculating objective function values corresponding to the optimization result of the large-step population and the optimization result of the small-step population respectively, and determining an optimal solution for a current iteration based on the objective function values includes:
step B10, aiming at the optimization result of the large-step-size population and each optimization result of the small-step-size population, calculating the signal attenuation value between each wireless AP and each terminal of the optimization results through a pre-constructed channel model;
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 the optimization result of the large-step population and each optimization result of the small-step population, signal attenuation values between each wireless AP and each terminal of the optimization results are calculated through a channel model constructed in advance, where the channel model is as follows:
wherein d0 is the Euclidean distance between the transmitting end (wireless AP) and the reference point, 1m is usually taken, PL (d0) is the signal intensity attenuation value actually measured by the wireless AP and the reference point, n is the path loss coefficient, d is i,j Is the euclidean distance between the wireless AP and the terminal,
is the penetration loss of the obstacle, c i,j,k Representing slave wireless APs i If the link to the terminal j passes through the obstacle, the value is 1 if the link passes through the obstacle, and the value is 0 if the link does not pass through the obstacle.
Step B20, determining the received signal strength of each terminal corresponding to each wireless AP based on the preset sending 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 received signal strength, e.g., P, corresponding to each wireless AP received by each terminal is calculated based on the preset transmission signal strength of the wireless AP and the signal attenuation value corresponding to each terminal of the wireless AP, where the preset transmission signal strength of the wireless AP is known i,j ＝P t -PL i,j Wherein P is t For presetting 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 optimization searching result;
step B40, summing the normalized throughput of the wireless AP in each optimization result to obtain an objective function value of each optimization result;
and step B50, selecting the optimization result corresponding to the maximum objective function value as the optimal solution of the current iteration.
In this embodiment, specifically, each terminal is associated with the wireless AP corresponding to the strongest received signal strength,wherein the content of the first and second substances,
P i,j is AP i After each terminal is associated with a wireless AP, the signal strength of the terminal on the link to the terminal j may calculate the normalized throughput corresponding to each wireless AP, so as to obtain the normalized throughput of each wireless AP in each optimization 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 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 shown, tau and p respectively show the probability of a station transmitting packet and the probability of collision encountered during station transmission, and simultaneous solution is carried out through the following equation.
Wherein, σ is the time length of a standard time slot specified in the IEEE 802.11 protocol, T s And T c Respectively representing 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 optimization results: and summing the normalized throughput of each wireless AP in the optimization results to obtain an objective function value of the optimization results, and selecting the optimization result corresponding to the maximum objective function value from the objective function values of the optimization results as the optimal solution of the current iteration.
According to the embodiment of the application, through the scheme, a channel model constructed based on path loss and penetration loss of wireless signals in a geographic environment is realized, the signal attenuation value between each wireless AP and each terminal is calculated, each terminal is associated with the wireless AP corresponding to the strongest received signal strength based on the signal attenuation value, the normalized throughput of the wireless AP is calculated, the total throughput of the whole network is taken as a target, problems are quantified through establishing the channel model, optimizing a problem model and the like, the optimization result corresponding to the maximum total throughput is selected as the optimal solution of the current iteration, the energy consumption of the wireless AP is saved, and the network deployment cost is reduced.
Further, in the embodiment of the present application, consider the case where the three-dimensional space has a size of 50m × 50m × 12m, the positions of 80 terminals are randomly generated, and the number of APs is 8, when the population size is set to 60, deployment is performed by using the wireless AP deployment optimization method and deployment is performed by using a conventional genetic algorithm, referring to fig. 4, FIG. 4 is a schematic diagram showing the comparison between the time consumed by the wireless AP deployment optimization method and the conventional genetic algorithm in the same iteration number, and referring to fig. 5, fig. 5 is a schematic diagram illustrating a comparison between the wireless AP deployment optimization method and a conventional genetic algorithm to obtain an objective function value at the same iteration number, wherein the network throughput represents the objective function value, and based on fig. 4 and 5, under the same iteration number, the network throughput obtained by solving is closer to that of the genetic algorithm, and the running time is far shorter than that of the traditional genetic algorithm.
Referring to fig. 6, fig. 6 is a schematic structural diagram of a wireless AP deployment optimization device in a hardware operating 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, a memory 1005, and a communication bus 1002. The communication bus 1002 is used to realize connection and communication between the processor 1001 and the memory 1005. The memory 1005 may be a high-speed RAM memory or a non-volatile memory (e.g., a magnetic disk memory). The memory 1005 may alternatively be a memory 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, RF (Radio Frequency) circuits, a sensor, an audio circuit, a WiFi module, and the like. The rectangular user interface may comprise a Display screen (Display), an input sub-module such as a Keyboard (Keyboard), and the optional rectangular user interface may also comprise 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 architecture shown in fig. 6 does not constitute a limitation of the wireless AP deployment optimization device, and may include more or fewer components than those shown, or some components in combination, or a different arrangement of components.
As shown in fig. 6, a memory 1005, which is a type of computer storage medium, may include an operating system, a network communication module, and a wireless AP deployment optimization program. The operating system is a program for managing and controlling hardware and software resources of the wireless AP deployment optimization device, and supports the operation of the wireless AP deployment optimization program and other software and/or programs. The network communication module is used for communication among the components in the memory 1005 and with other hardware and software in the wireless AP deployment optimization system.
In the wireless AP deployment optimization apparatus shown in fig. 6, the processor 1001 is configured to execute the wireless AP deployment optimization program stored in the memory 1005, so as to implement the steps of the wireless AP deployment optimization method described in any one of the foregoing.
The specific implementation of the wireless AP deployment optimization device in the present application is substantially the same as the embodiments of the wireless AP deployment optimization method, and is not described herein again.
In addition, referring to fig. 7, fig. 7 is a schematic functional module diagram of a wireless AP deployment optimization apparatus according to the present application, and the present application further provides a wireless AP deployment optimization system, where the wireless AP deployment optimization system includes:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for 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;
the partition clustering module is used for performing partition clustering processing on the spatial information, the position information and the number of the wireless APs to obtain an initial solution of the positions of the wireless APs;
the population generation module is used for generating a large step size population and a small step size 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-length population optimizing algorithm and a small-step-length population optimizing algorithm to obtain an optimizing result of the large-step-length population and an optimizing result of the small-step-length population;
the optimal solution calculation module is used for calculating an optimization result of the large-step-size population and an objective function value corresponding to the optimization result of the small-step-size population respectively and determining the optimal solution of the current iteration based on the objective function values;
and the deployment position determining module is used for taking the optimal solution as the target deployment position of the wireless AP if the optimal solution is detected to be an effective solution and the current iteration times reach the preset maximum iteration times.
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 times do not reach the preset maximum iteration times, generating a new large step size population and a new 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 optimization processing on the initial solution of the wireless AP position through a large-step-length population optimization algorithm and a small-step-length population optimization algorithm which are constructed in advance to obtain an optimization result of the large-step-length population and an optimization result of the small-step-length population.
Optionally, the partition clustering module is further configured to:
partitioning the space information based on the size proportional 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 size of each area block in the x direction and the y direction;
calculating the central position of each region block, and forming an initial population based on each central position, wherein the initial population comprises each target seed;
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;
respectively calculating the centroids of the point groups, and taking each centroid 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 so, determining an initial solution of the wireless AP position based on the positions of the new target seeds;
if not, returning to the execution step based on each new target seed: and respectively calculating the distance between the position of each terminal and each target seed.
Optionally, the population generating module is further configured to:
and respectively copying the initial solutions of the wireless AP positions to obtain the large-step-size population and the small-step-size population.
Optionally, the optimizing module is further configured to:
calculating optimizing limits in the directions of the x axis, the y axis and the z axis in the three-dimensional space based on the z axis information of the space information and the size of each area block in the directions of the x axis and the y axis;
respectively determining the interval ranges in the directions of the x axis, the y axis and the z axis of the large step size population and the small step size population according to the optimization limits in the directions of the x axis, the y axis and the z axis and the spatial information aiming at each wireless AP position in the initial solution of the wireless AP position;
generating random numbers in the directions of the x axis, the y axis and the z axis in the interval ranges in the directions of the x axis, the y axis and the z axis respectively;
and superposing the random numbers in the directions of the x axis, the y axis and the z axis to the position of the wireless AP to obtain the optimization result of the large step size population and the optimization result of the small step size population.
Optionally, the optimizing module is further configured to:
for each wireless AP position in the initial solution of the wireless AP position, determining the interval range of the large-step-size population in the directions of x, y and z axes based on the information of the optimization limit and the x axis in the direction of the x axis, the information of the optimization limit and the y axis in the direction of the y axis and the information of the optimization limit and the z axis in the direction of the z axis;
for each wireless AP position in the initial solution of the wireless AP position, determining the interval range of the small step size population in the directions of the x axis, the y axis and the z axis based on the wireless AP position and the optimizing limits in the directions of the x axis, the y axis and the z axis, wherein the interval ranges of the small step size population in the directions of the x axis, the y axis and the z axis are respectively as follows: [ -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 )
Wherein x, y, z represent wireless AP position, min () represents minimum value, wherein x STEP Indicating the limit of optimization in the x-axis direction, y STEP Indicating the limit of optimization in the y-axis direction, z STEP Denotes the optimization limit in the z-axis direction, g x Representing x-axis information, g y Represents y-axis information, g z Representing z-axis information.
Optionally, the optimal solution calculation module is further configured to:
aiming at the optimization result of the large-step-size population and each optimization result of the small-step-size population, calculating a signal attenuation value between each wireless AP and each terminal of the optimization results through a pre-constructed channel model, wherein the channel model is constructed based on path loss and penetration loss of wireless signals in a geographic environment;
determining the strength of a received signal corresponding to each wireless AP received by each terminal based on the preset sending 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 to calculate the normalized throughput of each wireless AP in each optimization searching result;
summing the normalized throughput of each wireless AP in each optimization result to obtain an objective function value of each optimization result;
and selecting the optimization result corresponding to the maximum objective function value as the optimal solution of the current iteration.
The specific implementation of the wireless AP deployment optimization system of the present application is substantially the same as the embodiments of the wireless AP deployment optimization method, and is not described herein again.
The present application provides a storage medium, which is a computer-readable storage medium, and the computer-readable storage medium stores one or more programs, which can be further executed by one or more processors to implement the steps of the wireless AP deployment optimization method described in any one of the above.
The specific implementation manner of the computer-readable storage medium of the present application is substantially the same as that of each embodiment of the foregoing wireless AP deployment optimization method, and details are not described here again.
The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings, or which are directly or indirectly applied to other related technical fields, are included in the scope of the present application.
Claims (10)
1. A wireless Access Point (AP) deployment optimization method is characterized by comprising the following steps:
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 spatial information, the position information and the number of the wireless APs to obtain an initial solution of the positions of the wireless APs;
generating a large step size population and a small step size population based on the initial solution of the wireless AP position;
optimizing the initial solution of the wireless AP position through a pre-constructed large-step-length population optimization algorithm and a pre-constructed small-step-length population optimization algorithm to obtain an optimization result of the large-step-length population and an optimization result of the small-step-length population;
respectively calculating an optimization result of the large-step-size population and an objective function value corresponding to the optimization result of the small-step-size population, and determining an optimal solution of the current iteration based on the objective function values;
and if the optimal solution is detected to be an effective solution and the current iteration times reach the preset maximum iteration times, taking the optimal solution as the target deployment position of the wireless AP.
2. The method of claim 1, wherein after the steps of calculating objective function values based on the optimization results of the large-step population and the optimization results of the small-step population, respectively, and determining an optimal solution for a current iteration based on the objective function values, the method further comprises:
if the optimal solution is detected to be an invalid solution or the current iteration times does not reach the preset maximum iteration times, generating a new large step size population and a new 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 optimization processing on the initial solution of the wireless AP position through a large-step-length population optimization algorithm and a small-step-length population optimization algorithm which are constructed in advance to obtain an optimization result of the large-step-length population and an optimization result of the small-step-length 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 number of the wireless APs to obtain an initial solution of the wireless AP position includes:
partitioning the spatial information based on the size proportional 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 size of each area block in the x direction and the y direction;
calculating the central position of each region block, and forming an initial population based on each central position, wherein the initial population comprises each target seed;
respectively 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;
respectively calculating the centroids of the point groups, and taking each centroid 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 so, determining an initial solution of the wireless AP position based on the positions of the new target seeds;
if not, returning to the execution step based on each new target seed: 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 size population and a small step size population based on the initial solution of the wireless AP location comprises:
and respectively copying the initial solutions of the wireless AP positions to form the large step size population and the small step size population.
5. The method according to claim 3, wherein the step of obtaining the optimization result of the large-step population and the optimization result of the small-step population by performing optimization processing on the initial solution of the wireless AP position through a pre-constructed large-step population optimization algorithm and small-step population optimization algorithm comprises:
calculating optimizing limits in the directions of the x axis, the y axis and the z axis in the three-dimensional space based on the z axis information of the space information and the size of each area block in the directions of the x axis and the y axis;
respectively determining the interval ranges in the directions of the x axis, the y axis and the z axis of the large step size population and the small step size population according to the optimization limits in the directions of the x axis, the y axis and the z axis and the spatial information aiming at each wireless AP position in the initial solution of the wireless AP position;
generating random numbers in the directions of the x axis, the y axis and the z axis in the interval ranges in the directions of the x axis, the y axis and the z axis respectively;
and superposing the random numbers in the directions of the x axis, the y axis and the z axis to the position of the wireless AP to obtain the optimization result of the large step size population and the optimization result of the small step size population.
6. The wireless AP deployment optimization method of claim 5, wherein the step of determining the interval ranges in the x, y and z-axis directions of the large-step population and the small-step population, respectively, for each wireless AP position in the initial solution of wireless AP positions based on the optimization limits in the x, y and z-axis directions and the spatial information comprises:
for each wireless AP position in the initial solution of the wireless AP position, determining the interval range of the large-step-size population in the directions of x, y and z axes based on the information of the optimization limit and the x axis in the direction of the x axis, the information of the optimization limit and the y axis in the direction of the y axis and the information of the optimization limit and the z axis in the direction of the z axis;
for each wireless AP position in the initial solution of the wireless AP position, determining the interval range of the small step size population in the directions of the x axis, the y axis and the z axis based on the wireless AP position and the optimizing limits in the directions of the x axis, the y axis and the z axisThe interval ranges 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 )
Wherein x, y, z represent wireless AP position, min () represents minimum value, wherein x STEP Indicating the limit of optimization in the x-axis direction, y STEP Indicating the limit of optimization in the y-axis direction, z STEP Denotes the optimization limit in the z-axis direction, g x Representing x-axis information, g y Represents y-axis information, g z Representing z-axis information.
7. The method of claim 1, wherein the step of calculating objective function values corresponding to the optimization results of the large-step population and the small-step population, and determining the optimal solution for the current iteration based on the objective function values comprises:
aiming at the optimization result of the large-step-size population and each optimization result of the small-step-size population, calculating a signal attenuation value between each wireless AP and each terminal of the optimization results through a pre-constructed channel model, wherein the channel model is constructed based on path loss and penetration loss of wireless signals in a geographic environment;
determining the strength of a received signal corresponding to each wireless AP received by each terminal based on the preset sending 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 to calculate the normalized throughput of each wireless AP in each optimization searching result;
summing the normalized throughput of each wireless AP in each optimization result to obtain an objective function value of each optimization result;
and selecting the optimization result corresponding to the maximum objective function value as the optimal solution of the current iteration.
8. A wireless AP deployment optimization system, comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for 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;
the partition clustering module is used for performing partition clustering processing on the spatial information, the position information and the number of the wireless APs to obtain an initial solution of the positions of the wireless APs;
the population generation module is used for generating a large step size population and a small step size 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-length population optimizing algorithm and a small-step-length population optimizing algorithm to obtain an optimizing result of the large-step-length population and an optimizing result of the small-step-length population;
the optimal solution calculation module is used for calculating an optimization result of the large-step-size population and an objective function value corresponding to the optimization result of the small-step-size population respectively and determining the optimal solution of the current iteration based on the objective function values;
and the deployment position determining module is used for taking the optimal solution as the target deployment position of the wireless AP if the optimal solution is detected to be an effective solution and the current iteration times reach the preset maximum iteration times.
9. A 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 wireless AP deployment optimization method according to any one of claims 1 to 7.
10. A storage medium which is a computer-readable storage medium, wherein the computer-readable storage medium has a wireless AP deployment optimization program stored thereon, and the wireless AP deployment optimization program is executed by a processor to implement the wireless AP deployment optimization method according to any one of claims 1 to 7.
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