Network deployment method, apparatus and electronic equipment based on fast voting algorithm

Abstract

This invention provides a network deployment method, apparatus, and electronic device based on a fast voting algorithm, relating to the field of communication technology. During network deployment, this invention first obtains a 3D model and meshing results corresponding to the target scenario. The meshing results include multiple meshes obtained by meshing a planar map of the target scenario. Then, based on the 3D model, each network votes on candidate parameter combinations for candidate APs at multiple preset locations, obtaining the voting results. The channel capacity from the candidate AP under the candidate parameter combination to the corresponding mesh is maximized. Based on the voting results, the optimal network deployment result for the target scenario is determined. This approach, tailored to the characteristics of 2B scenarios, employs a simple and efficient voting algorithm based on channel capacity for network deployment, significantly reducing the complexity of optimization algorithms within a large parameter space while ensuring network coverage reliability.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to a network deployment method, apparatus, and electronic device based on a fast voting algorithm. Background Technology

[0002] Wireless communication technology is playing an increasingly important role not only in 2C scenarios, but also in a growing number of industrial applications. These include unmanned port control, real-time monitoring and video transmission in factories, machine tool control, robot control, full automation of large warehouses, and large office areas and live streaming platforms, all of which rely on the reliability, full coverage, and high-speed connectivity of wireless networks. Therefore, Wi-Fi and other wireless technologies are increasingly widely used in 2B scenarios within large-scale wireless local area networks. Here, 2C scenarios refer to scenarios using 2C networks, which are primarily for individual users; 2B scenarios refer to scenarios using 2B networks, which are primarily for non-personal applications in industries, organizations, and enterprises.

[0003] However, the 2B scenario involves a larger space and a more complex environment, resulting in a higher complexity of the optimization algorithm. Summary of the Invention

[0004] The purpose of this invention is to provide a network deployment method, apparatus, and electronic device based on a fast voting algorithm, so as to reduce the complexity of the optimization algorithm.

[0005] In a first aspect, embodiments of the present invention provide a network deployment method based on a fast voting algorithm, comprising:

[0006] Obtain the 3D model and meshing result corresponding to the target scene; wherein, the meshing result includes multiple meshes obtained by performing network processing on the planar map of the target scene;

[0007] Based on the three-dimensional model, each network votes on the candidate parameter combinations of candidate APs under multiple preset locations for each parameter combination, and obtains the voting result; among them, the channel capacity from the candidate AP under the candidate parameter combination to the corresponding grid is the largest;

[0008] Based on the voting results, the optimal network deployment result for the target scenario is determined; wherein, the optimal result includes the target parameter combination corresponding to each candidate AP.

[0009] Furthermore, based on the three-dimensional model, each network votes on candidate parameter combinations for candidate APs at multiple preset locations for various parameter combinations, obtaining voting results, including:

[0010] For each grid, based on the three-dimensional model, determine the maximum channel capacity under the optimal parameter combination for each candidate AP corresponding to that grid;

[0011] The candidate AP and the optimal parameter combination corresponding to the maximum value of each maximum channel capacity in the grid are determined as the candidate AP and candidate parameter combination for the grid.

[0012] Determine the candidate parameter combination for which the grid votes for the corresponding candidate AP.

[0013] Further, determining the maximum channel capacity under the optimal parameter combination for each candidate AP corresponding to the grid based on the three-dimensional model includes:

[0014] Based on the three-dimensional model, ray tracing technology is used to calculate the ray parameter information corresponding to each parameter combination of each candidate AP for the mesh; wherein, the ray parameter information includes the number of rays, the amplitude of each ray, and the time delay information;

[0015] Based on the ray parameter information corresponding to each parameter combination of each candidate AP in the grid, the channel capacity from each candidate AP to the grid under each parameter combination is calculated;

[0016] Based on the channel capacity of each candidate AP to the grid under each parameter combination, determine the maximum channel capacity of each candidate AP under the optimal parameter combination corresponding to the grid; wherein, the maximum channel capacity is the maximum value among the channel capacities of each candidate AP to the grid under each parameter combination, and the optimal parameter combination is the parameter combination corresponding to the maximum channel capacity.

[0017] Further, determining the optimal network deployment result for the target scenario based on the voting results includes:

[0018] Based on the voting results, the number of votes corresponding to each parameter combination of each candidate AP is calculated.

[0019] For each candidate AP, the target parameter combination corresponding to the candidate AP is determined based on the number of votes corresponding to each parameter combination of the candidate AP.

[0020] Further, determining the target parameter combination corresponding to the candidate AP based on the number of votes corresponding to each parameter combination of the candidate AP includes:

[0021] The parameter combination with the most votes is determined as the target parameter combination for the candidate AP; or...

[0022] Based on the number of votes corresponding to each parameter combination of the candidate AP, a weighted average is performed on each parameter combination of the candidate AP to obtain the target parameter combination corresponding to the candidate AP; or,

[0023] Cluster the number of votes corresponding to each parameter combination of the candidate AP, and use the obtained cluster center value as the target parameter combination corresponding to the candidate AP.

[0024] Furthermore, after determining the target parameter combination corresponding to each candidate AP based on the number of votes corresponding to each parameter combination of the candidate AP, the network deployment method based on the fast voting algorithm further includes:

[0025] Based on the voting results, the total number of votes for each candidate AP is calculated.

[0026] Based on the total number of votes for each candidate AP, a predetermined number of target APs are selected from the plurality of candidate APs;

[0027] The preset number of target APs are determined as APs to be deployed.

[0028] Furthermore, after determining the target parameter combination corresponding to each candidate AP based on the number of votes corresponding to each parameter combination of the candidate AP, the network deployment method based on the fast voting algorithm further includes:

[0029] Get the current value of the number of APs;

[0030] Based on the voting results, select the current number of target APs from the plurality of candidate APs;

[0031] The total network throughput corresponding to the current value and the target AP under the corresponding target parameter combination is calculated;

[0032] When the total network throughput does not reach the preset throughput threshold, the current value is updated according to a preset step size;

[0033] When the total network throughput reaches the preset throughput threshold, the current value and the target AP are determined as APs to be deployed.

[0034] Further, the calculation of the current value of the target AP under the corresponding target parameter combination includes:

[0035] Based on the three-dimensional model, the channel capacity from each target AP to each grid under the corresponding combination of target parameters is calculated;

[0036] The total network throughput is obtained by summing the channel capacities from each target AP to each grid.

[0037] Secondly, embodiments of the present invention also provide a network deployment apparatus based on a fast voting algorithm, comprising:

[0038] The acquisition module is used to acquire the 3D model and meshing result corresponding to the target scene; wherein, the meshing result includes multiple meshes obtained by performing network processing on the planar map of the target scene;

[0039] The voting module is used to vote on the candidate parameter combinations of candidate APs for each network under multiple preset locations based on the three-dimensional model, and obtain the voting results; wherein the channel capacity from the candidate AP under the candidate parameter combination to the corresponding grid is the largest;

[0040] The determining module is used to determine the optimization result of the network deployment under the target scenario based on the voting results; wherein the optimization result includes the target parameter combination corresponding to each candidate AP.

[0041] Thirdly, embodiments of the present invention also provide an electronic device, including a memory and a processor, wherein the memory stores a computer program that can run on the processor, and the processor executes the computer program to implement the network deployment method based on the fast voting algorithm described in the first aspect.

[0042] The network deployment method, apparatus, and electronic device based on a fast voting algorithm provided in this invention first acquire a 3D model and meshing results corresponding to the target scene during network deployment. The meshing results include multiple meshes obtained by meshing a planar map of the target scene. Then, based on the 3D model, each network votes on candidate parameter combinations for candidate APs at multiple preset locations, obtaining voting results. The channel capacity from the candidate AP under the candidate parameter combination to the corresponding mesh is maximized. Based on the voting results, the optimal network deployment result for the target scene is determined, where the optimal result includes the target parameter combination corresponding to each candidate AP. This approach, tailored to the characteristics of 2B scenarios, employs a simple and efficient voting algorithm based on channel capacity for network deployment, significantly reducing the complexity of the optimization algorithm within a large parameter space while ensuring network coverage reliability. Attached Figure Description

[0043] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0044] Figure 1 A flowchart illustrating a network deployment method based on a fast voting algorithm provided in an embodiment of the present invention;

[0045] Figure 2 A schematic diagram of three three-dimensional models provided in an embodiment of the present invention;

[0046] Figure 3 This is a schematic diagram of two meshing results provided in an embodiment of the present invention;

[0047] Figure 4 A schematic diagram of a network deployment device based on a fast voting algorithm provided in an embodiment of the present invention;

[0048] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0049] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] Currently, wireless technologies such as Wi-Fi are increasingly widely used in 2B scenarios of large-scale wireless LANs. Addressing the network deployment challenges in 2B scenarios, this invention provides a network deployment method, apparatus, and electronic device based on a fast voting algorithm. Employing a simple yet efficient voting algorithm—a network planning optimization technique for 2B scenarios—this method can be used for network deployment and planning optimization in 2B scenarios, quickly achieving large-scale AP (Access Point) deployment optimization. While enhancing network coverage reliability, it significantly reduces the complexity of optimization algorithms within a vast parameter space.

[0051] To facilitate understanding of this embodiment, a network deployment method based on a fast voting algorithm disclosed in this embodiment of the invention will first be described in detail.

[0052] This invention provides a network deployment method based on a fast voting algorithm, which can be executed by an electronic device with data processing capabilities. See also... Figure 1 The diagram shows a network deployment method based on a fast voting algorithm, which mainly includes the following steps S102 to S106:

[0053] Step S102: Obtain the 3D model and meshing result corresponding to the target scene; wherein, the meshing result includes multiple meshes obtained by meshing the planar map of the target scene.

[0054] The target scenario can be, but is not limited to, a B2B scenario. Examples include large office spaces, large factories / super factories, ports, steel mills, shopping malls, or coal mines. The 3D model is obtained by creating a 3D model of the target scenario, and the meshing result is obtained by processing the planar view of the target scenario into a network. Taking Wi-Fi AP deployment in an indoor scenario as an example, 3D modeling software can be used to create a 3D model of the indoor scene. The resulting 3D model includes walls, furniture, tables and chairs, lighting fixtures, greenery, and other objects that can reflect electromagnetic waves, such as... Figure 2 As shown; the interior floor plan is gridded, as follows. Figure 3 As shown, for example, the grid size is L×L, and there are a total of N grids.

[0055] It should be noted that the target scenarios mentioned above are not limited to indoor scenarios, and the network devices deployed are not limited to Wi-Fi APs. In other embodiments, they can also be outdoor scenarios, or network planning of cellular small base stations or cellular 4G / 5G base stations in urban scenarios, etc.

[0056] Step S104: Based on the three-dimensional model, each network votes on the candidate parameter combinations of candidate APs under multiple preset locations for each parameter combination, and obtains the voting results; among them, the channel capacity from the candidate AP under the candidate parameter combination to the corresponding grid is the largest.

[0057] The parameter combination includes the AP's altitude, antenna downtilt angle, and antenna azimuth angle. The AP's third-dimensional coordinates...<x,y,z> and the antenna's downtilt angle φ, and azimuth angle The parameters composed of the three These are the parameters to be optimized. When voting, each network only votes for the candidate parameter combination of the candidate AP with the largest channel capacity in that grid. Two different grids may vote for the same parameter combination of the same AP, or they may vote for different parameter combinations of the same AP, or they may vote for different parameter combinations of different APs.

[0058] This invention can utilize 3D reconstruction and ray tracing techniques to reconstruct wireless channels, calculate channel capacity based on channel parameters, and thus determine the candidate APs and candidate parameter combinations for each grid. Based on this, step S104 can be implemented through the following sub-steps 1 to 3:

[0059] Sub-step 1: For each grid, based on the 3D model, determine the maximum channel capacity under the optimal parameter combination for each candidate AP corresponding to that grid.

[0060] First, based on a 3D model, ray tracing technology can be used to calculate the ray parameter information corresponding to each parameter combination of each candidate AP for the grid. This ray parameter information includes the number of rays, the amplitude of each ray, and the delay information. Then, based on the ray parameter information corresponding to each parameter combination of each candidate AP for the grid, the channel capacity from each candidate AP to the grid under each parameter combination can be calculated. Finally, based on the channel capacity from each candidate AP to the grid under each parameter combination, the maximum channel capacity under the optimal parameter combination for each candidate AP corresponding to the grid can be determined. The maximum channel capacity is the maximum value among the channel capacities from each candidate AP to the grid under each parameter combination, and the optimal parameter combination is the parameter combination corresponding to the maximum channel capacity.

[0061] Sub-step 2 involves determining the candidate AP and candidate parameter combination corresponding to the maximum value among the maximum channel capacities of each grid as the candidate AP and candidate parameter combination for that grid.

[0062] Sub-step 3: Determine the candidate parameter combination for the grid to vote for the corresponding candidate AP.

[0063] Step S106: Based on the voting results, determine the optimization results for network deployment in the target scenario; wherein, the optimization results include the target parameter combination corresponding to each candidate AP.

[0064] In some possible embodiments, the number of votes corresponding to each parameter combination of each candidate AP can be counted based on the voting results; for each candidate AP, the target parameter combination corresponding to the candidate AP can be determined based on the number of votes corresponding to each parameter combination of the candidate AP.

[0065] The present invention provides three specific methods for determining the target parameter combination, as follows: (1) the parameter combination with the most votes is determined as the target parameter combination corresponding to the candidate AP; or, (2) according to the number of votes corresponding to each parameter combination of the candidate AP, the target parameter combination corresponding to the candidate AP is obtained by weighted averaging; or, (3) the number of votes corresponding to each parameter combination of the candidate AP is clustered, and the obtained cluster center value is used as the target parameter combination corresponding to the candidate AP.

[0066] In method (1) above, if there are two parameter combinations with the most votes, either one can be arbitrarily selected as the target parameter combination, or a weighted average of the two can be performed based on the number of votes to obtain the target parameter combination. In method (3) above, clustering algorithms such as K-means and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) can be used during clustering. This embodiment of the invention does not limit the specific clustering algorithm.

[0067] The network deployment method based on a fast voting algorithm provided in this invention first acquires a 3D model and meshing results corresponding to the target scene during network deployment. The meshing results include multiple meshes obtained by meshing the planar map of the target scene. Then, based on the 3D model, each network votes on candidate parameter combinations for candidate APs at multiple preset locations, obtaining the voting results. The channel capacity from the candidate AP under the candidate parameter combination to the corresponding mesh is maximized. Based on the voting results, the optimal network deployment result for the target scene is determined, where the optimal result includes the target parameter combination corresponding to each candidate AP. This method, tailored to the characteristics of 2B scenarios, employs a simple and efficient voting algorithm based on channel capacity for network deployment, significantly reducing the complexity of the optimization algorithm within a large parameter space while ensuring network coverage reliability.

[0068] Furthermore, a predetermined number of optimal APs can be selected from multiple candidate APs based on the voting results (i.e., K optimal AP positions can be selected from the positions of M candidate APs): First, the total number of votes for each candidate AP is calculated based on the voting results; then, based on the total number of votes for each candidate AP, a predetermined number of target APs are selected from multiple candidate APs; and finally, the predetermined number of target APs are determined as APs to be deployed.

[0069] This invention provides two specific methods for screening target APs, as follows: 1) Determine the preset number of candidate APs with the largest total number of votes as target APs; 2) Perform clustering processing on the total number of votes of each candidate AP, and use the preset number of cluster center values ​​as target APs.

[0070] In the specific implementation of method 1), the candidate APs can be sorted in descending order of total votes, and a predetermined number of candidate APs with the highest total votes are selected as the target APs. Alternatively, the candidate APs can be sorted in ascending order of total votes, and a predetermined number of candidate APs with the lowest total votes are selected as the target APs. In method 2), clustering algorithms such as K-means and DBSCAN can be used for clustering. This embodiment of the invention does not limit the specific clustering algorithm used.

[0071] Furthermore, the minimum number of APs required to meet the overall network throughput requirement can be found based on the voting results, i.e., the minimum K value required to meet the overall network throughput requirement can be found: obtain the current value of the number of APs; based on the voting results, select the current value of target APs from multiple candidate APs; calculate the overall network throughput corresponding to the current value of target APs under the corresponding target parameter combination; when the overall network throughput does not reach the preset throughput threshold, update the current value according to the preset step size; when the overall network throughput reaches the preset throughput threshold, determine the current value of target APs as APs to be deployed.

[0072] The specific implementation method for selecting the target AP from multiple candidate APs can refer to the aforementioned content on selecting a preset number of optimal APs from multiple candidate APs, and will not be repeated here. The overall network throughput can be calculated as follows: Based on the three-dimensional model, calculate the channel capacity from each target AP to each grid under the corresponding target parameter combination; sum the channel capacities from each target AP to each grid to obtain the overall network throughput. The preset step size can be set according to actual needs. For example, if the preset step size is 2, the sum of the current value and 2 is used as the updated current value.

[0073] This allows us to find the minimum number of APs needed to meet the overall network throughput requirements.

[0074] This invention provides a purely software-based simulation solution. Upon receiving the requirements for the target scene, a 3D model of the environment is created. Based on this 3D model, a 3D model is constructed, along with candidate AP locations (where candidate AP locations may only include APs within the model).<x,y,z> The first two coordinates (where height z is used as an optimization parameter) are used to calculate the changes in electromagnetic waves emitted by the AP in three-dimensional space, including direct, reflected, refracted, diffracted, and transmitted rays, as well as the influence of parameters such as the material, reflection coefficient, and refractive index of the surfaces of objects contacted during electromagnetic wave transmission. Ray parameters (including ray amplitude, time delay, and angle of arrival) within each meshed grid are calculated. Based on the rays received by each grid, time-domain channel information is obtained, and the spatial degrees of freedom of the channel are analyzed (note that this assumes a MIMO (multiple input multiple output) channel, i.e., the AP is a multi-antenna, two-dimensional antenna array), to calculate the channel capacity or throughput. Under the constraint of ensuring network coverage, the overall network throughput (the sum of the throughput of all grids) is maximized while minimizing the number of APs, thereby maximizing economy while ensuring overall network reliability. This invention proposes a simple and efficient voting algorithm, greatly reducing the complexity of optimization algorithms in a very large parameter space and providing a feasible implementation scheme that guarantees performance. Taking indoor Wi-Fi AP deployment as an example, the specific steps of the network deployment method based on the fast voting algorithm are as follows:

[0075] 1. Use 3D modeling software to create a 3D model of the interior scene, including walls, furniture, tables and chairs, lamps, green plants, and other objects that can reflect electromagnetic waves.

[0076] 2. Grid the interior floor plan with a grid size of L×L, assuming there are a total of N grids.

[0077] 3. Assuming there are M candidate AP locations, use ray-tracing technology to calculate the number of rays received on each grid, the amplitude of each ray, as well as information such as time delay and angle of arrival.

[0078] 4. Optimization strategy:

[0079] The optimal AP parameters (including the third-dimensional coordinates of the candidate APs) are selected from the positions of M candidate APs using an optimization algorithm.<x,y,z> and the antenna's downtilt angle φ, and azimuth angle The optimization objective is to maximize overall network coverage and throughput across the entire region. Parameters to be optimized:

[0080] a) Assuming the amplitude of the ray received by the i-th grid from the m-th AP, after time sorting according to the time delay information, is (padded with zeros to the J values): h i，m (j), j = 1, 2, ..., J, which is the time-domain impulse response CIR (Channel Impulse Response) from grid i to AP m.

[0081] b) Transform the CIR using IFFT (Inverse Fast Fourier Transform) to obtain the frequency domain channel response (CFR) of grid i: H i，m (j), j = 1, 2, ..., J.

[0082] c) Calculate the channel capacity C from AP m to grid i. i，m ,

[0083]

[0084] Where det represents the determinant, I represents the identity matrix, and E s Let α represent the transmit power of AP, α represent a preset constant, and H* represent the conjugate transpose of H.

[0085] d) Assume the range of adjustable downtilt angle φ for the AP m antenna is (φ dn φ up ), and azimuth angle variable range (For some APs, this can be equivalent to the AP body rotating within this angular range), the height z of the AP is within (z... low , z high Adjust within the range.

[0086] e) Using step size φ0 respectively And the adjustable range of downtilt angle, azimuth angle, and altitude of the antenna for AP m traversed by z0, calculate and record each Channel capacity below

[0087] f) Search among the M candidate APs that make The ID of the largest alternative AP, let's say it's k.

[0088] g) and for AP k, such that Largest parameter combination Assuming Then the maximum channel capacity is

[0089] h) A voting method is used: grid i votes for the parameter combination under the candidate AP with ID k. This means adding 1 to the vote count for this parameter combination.

[0090] i) All parameter combinations of each candidate AP will receive votes from the grid (vote count + 1); and the voting principle for each grid is the parameter combination of the candidate AP that maximizes the throughput of that grid.

[0091] j) Repeat steps f) to h) for all N grids.

[0092] k) Therefore, each parameter combination of each candidate AP will receive a vote from the grid.

[0093] l) Determine the optimization result using one of the following three methods:

[0094] i. The candidate AP sorts all parameter combinations by the number of votes received and selects the parameter combination with the most votes as the optimal result.

[0095] ii. Alternatively, a weighted average of the votes received by each parameter combination can be used to obtain a parameter combination as the optimal one. For example: Suppose there are three parameter combinations for a certain candidate AP. and If A, B, and C votes are received respectively, then the optimal parameter combination is:

[0096]

[0097] iii. Alternatively, cluster the number of votes obtained from each parameter combination (e.g., K-means, DBSCAN, etc.) and use the cluster center value as the optimal parameter combination.

[0098] m) In this way, each candidate AP selects an optimal parameter combination, and the optimization of the entire network is completed.

[0099] n) Additionally, suppose we need to select K optimal AP locations from M candidate AP locations.

[0100] There are two possible solutions:

[0101] i. Select the K APs that received the most votes (i.e., select the K APs with the most total votes);

[0102] ii. Use a clustering algorithm to find the K cluster center values ​​as the optimal AP location.

[0103] o) To find the minimum K value that satisfies the overall network throughput requirements, we can search for it using the above method with a certain step size.

[0104] In summary, this invention provides a network deployment optimization scheme for 2B scenarios, maximizing network speed and minimizing AP usage while ensuring network coverage. This guarantees network reliability and smoothness, as well as the economic efficiency of network deployment. A simple and efficient voting algorithm is proposed, significantly reducing the complexity of optimization algorithms within a large parameter space, and providing a feasible implementation scheme that guarantees performance.

[0105] Corresponding to the network deployment method based on the fast voting algorithm described above, this embodiment of the invention also provides a network deployment device based on the fast voting algorithm, see [link to relevant documentation]. Figure 4 The diagram shows a network deployment device based on a fast voting algorithm. The device includes:

[0106] The acquisition module 401 is used to acquire the 3D model and meshing result corresponding to the target scene; wherein, the meshing result includes multiple meshes obtained by performing network processing on the planar map of the target scene;

[0107] The voting module 402 is used to vote on the candidate parameter combinations of candidate APs for each network based on a three-dimensional model for multiple preset locations, and to obtain the voting results; among them, the channel capacity from the candidate AP under the candidate parameter combination to the corresponding grid is the largest.

[0108] The determination module 403 is used to determine the optimal network deployment result under the target scenario based on the voting results; wherein, the optimal result includes the target parameter combination corresponding to each candidate AP.

[0109] The network deployment device based on a fast voting algorithm provided in this invention first acquires a 3D model and meshing results corresponding to the target scene during network deployment. The meshing results include multiple meshes obtained by meshing the planar map of the target scene. Then, based on the 3D model, each network votes on candidate parameter combinations for candidate APs at multiple preset locations, obtaining voting results. The channel capacity from the candidate AP under the candidate parameter combination to the corresponding mesh is maximized. Based on the voting results, the optimal network deployment result for the target scene is determined, where the optimal result includes the target parameter combination corresponding to each candidate AP. This approach, tailored to the characteristics of 2B scenarios, employs a simple and efficient voting algorithm based on channel capacity for network deployment, significantly reducing the complexity of the optimization algorithm within a large parameter space while ensuring network coverage reliability.

[0110] Furthermore, the voting module 402 is specifically used for: for each grid, based on the three-dimensional model, determining the maximum channel capacity under the optimal parameter combination for each candidate AP corresponding to the grid; determining the candidate AP and optimal parameter combination corresponding to the maximum value among the maximum channel capacities corresponding to the grid as the candidate AP and candidate parameter combination corresponding to the grid; and determining the candidate parameter combination to vote for the corresponding candidate AP for the grid.

[0111] Furthermore, the voting module 402 is also used to: calculate the ray parameter information corresponding to each parameter combination of each candidate AP for the grid based on the three-dimensional model using ray tracing technology; wherein the ray parameter information includes the number of rays, the amplitude of each ray, and the time delay information; calculate the channel capacity from each candidate AP to the grid under each parameter combination based on the ray parameter information corresponding to each candidate AP for each parameter combination of the grid; determine the maximum channel capacity under the optimal parameter combination for each candidate AP corresponding to the grid based on the channel capacity from each candidate AP to the grid under each parameter combination; wherein the maximum channel capacity is the maximum value among the channel capacities from each candidate AP to the grid under each parameter combination, and the optimal parameter combination is the parameter combination corresponding to the maximum channel capacity.

[0112] Furthermore, the aforementioned determining module 403 is specifically used to: based on the voting results, calculate the number of votes corresponding to each parameter combination of each candidate AP; for each candidate AP, determine the target parameter combination corresponding to the candidate AP based on the number of votes corresponding to each parameter combination of the candidate AP.

[0113] Furthermore, the determining module 403 is also used to: determine the parameter combination with the most votes as the target parameter combination corresponding to the candidate AP; or, perform a weighted average of the parameter combinations of the candidate AP according to the number of votes corresponding to each parameter combination of the candidate AP to obtain the target parameter combination corresponding to the candidate AP; or, perform clustering processing on the number of votes corresponding to each parameter combination of the candidate AP and use the obtained cluster center value as the target parameter combination corresponding to the candidate AP.

[0114] Furthermore, the aforementioned determining module 403 is also used to: calculate the total number of votes for each candidate AP based on the voting results; select a preset number of target APs from multiple candidate APs based on the total number of votes for each candidate AP; and determine the preset number of target APs as APs to be deployed.

[0115] Furthermore, the aforementioned determining module 403 is also used to: obtain the current value of the number of APs; select the current value of target APs from multiple candidate APs based on the voting results; calculate the total network throughput corresponding to the current value of target APs under the corresponding target parameter combination; update the current value according to the preset step size when the total network throughput does not reach the preset throughput threshold; and determine the current value of target APs as APs to be deployed when the total network throughput reaches the preset throughput threshold.

[0116] Furthermore, the aforementioned determining module 403 is also used to: calculate the channel capacity from each target AP to each grid under the corresponding target parameter combination based on the three-dimensional model; and sum the channel capacities from each target AP to each grid to obtain the total network throughput.

[0117] The network deployment device based on the fast voting algorithm provided in this embodiment has the same implementation principle and technical effects as the aforementioned network deployment method embodiment based on the fast voting algorithm. For the sake of brevity, any parts not mentioned in the embodiment of the network deployment device based on the fast voting algorithm can be referred to the corresponding content in the aforementioned network deployment method embodiment based on the fast voting algorithm.

[0118] like Figure 5 As shown, an electronic device 500 provided in this embodiment of the invention includes: a processor 501, a memory 502 and a bus. The memory 502 stores a computer program that can run on the processor 501. When the electronic device 500 is running, the processor 501 and the memory 502 communicate through the bus, and the processor 501 executes the computer program to implement the above-mentioned network deployment method based on the fast voting algorithm.

[0119] Specifically, the memory 502 and processor 501 mentioned above can be general-purpose memory and processor, without any specific limitations here.

[0120] This invention also provides a computer-readable storage medium storing a computer program. When a processor runs this computer program, it executes the network deployment method based on the fast voting algorithm described in the preceding method embodiments. The computer-readable storage medium includes various media capable of storing program code, such as a USB flash drive, external hard drive, read-only memory (ROM), RAM, magnetic disk, or optical disk.

[0121] In all examples shown and described herein, any specific values ​​should be interpreted as merely exemplary and not as limitations; therefore, other examples of exemplary embodiments may have different values.

[0122] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0123] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A network deployment method based on a fast voting algorithm, characterized in that, include: Obtain the 3D model and meshing result corresponding to the target scene; wherein, the meshing result includes multiple meshes obtained by performing network processing on the planar map of the target scene; Based on the three-dimensional model, each network votes on candidate parameter combinations for candidate APs at multiple preset locations for each parameter combination, obtaining the voting result, including: for each grid, based on the three-dimensional model, determining the maximum channel capacity under the optimal parameter combination for each candidate AP corresponding to that grid; determining the candidate AP and optimal parameter combination corresponding to the maximum value among the maximum channel capacities for that grid as the candidate AP and candidate parameter combination for that grid; determining the candidate parameter combination that the grid votes for the corresponding candidate AP; wherein, the channel capacity from the candidate AP under the candidate parameter combination to the corresponding grid is the largest; Based on the voting results, the optimal network deployment result for the target scenario is determined; wherein, the optimal result includes the target parameter combination corresponding to each candidate AP.

2. The network deployment method based on the fast voting algorithm according to claim 1, characterized in that, The step of determining the maximum channel capacity under the optimal parameter combination for each candidate AP corresponding to the grid based on the three-dimensional model includes: Based on the three-dimensional model, ray tracing technology is used to calculate the ray parameter information corresponding to each parameter combination of each candidate AP for the mesh; wherein, the ray parameter information includes the number of rays, the amplitude of each ray, and the time delay information; Based on the ray parameter information corresponding to each parameter combination of each candidate AP in the grid, the channel capacity from each candidate AP to the grid under each parameter combination is calculated; Based on the channel capacity of each candidate AP to the grid under each parameter combination, determine the maximum channel capacity of each candidate AP under the optimal parameter combination corresponding to the grid; wherein, the maximum channel capacity is the maximum value among the channel capacities of each candidate AP to the grid under each parameter combination, and the optimal parameter combination is the parameter combination corresponding to the maximum channel capacity.

3. The network deployment method based on the fast voting algorithm according to claim 1, characterized in that, The step of determining the optimal network deployment result for the target scenario based on the voting results includes: Based on the voting results, the number of votes corresponding to each parameter combination of each candidate AP is calculated. For each candidate AP, the target parameter combination corresponding to the candidate AP is determined based on the number of votes corresponding to each parameter combination of the candidate AP.

4. The network deployment method based on the fast voting algorithm according to claim 3, characterized in that, The step of determining the target parameter combination corresponding to the candidate AP based on the number of votes corresponding to each parameter combination of the candidate AP includes: The parameter combination with the most votes is determined as the target parameter combination for the candidate AP; or... Based on the number of votes corresponding to each parameter combination of the candidate AP, a weighted average is performed on each parameter combination of the candidate AP to obtain the target parameter combination corresponding to the candidate AP; or, Cluster the number of votes corresponding to each parameter combination of the candidate AP, and use the obtained cluster center value as the target parameter combination corresponding to the candidate AP.

5. The network deployment method based on the fast voting algorithm according to claim 3, characterized in that, After determining the target parameter combination corresponding to each candidate AP based on the number of votes corresponding to each parameter combination of the candidate AP, the network deployment method based on the fast voting algorithm further includes: Based on the voting results, the total number of votes for each candidate AP is calculated. Based on the total number of votes for each candidate AP, a predetermined number of target APs are selected from the plurality of candidate APs; The preset number of target APs are determined as APs to be deployed.

6. The network deployment method based on the fast voting algorithm according to claim 3, characterized in that, After determining the target parameter combination corresponding to each candidate AP based on the number of votes corresponding to each parameter combination of the candidate AP, the network deployment method based on the fast voting algorithm further includes: Get the current value of the number of APs; Based on the voting results, select the current number of target APs from the plurality of candidate APs; The total network throughput corresponding to the current value and the target AP under the corresponding target parameter combination is calculated; When the total network throughput does not reach the preset throughput threshold, the current value is updated according to a preset step size; When the total network throughput reaches the preset throughput threshold, the current value and the target AP are determined as APs to be deployed.

7. The network deployment method based on the fast voting algorithm according to claim 6, characterized in that, The calculation of the current value of the target AP under the corresponding target parameter combination to obtain the total network throughput includes: Based on the three-dimensional model, the channel capacity from each target AP to each grid under the corresponding combination of target parameters is calculated; The total network throughput is obtained by summing the channel capacities from each target AP to each grid.

8. A network deployment device based on a fast voting algorithm, characterized in that, include: The acquisition module is used to acquire the 3D model and meshing result corresponding to the target scene; wherein, the meshing result includes multiple meshes obtained by performing network processing on the planar map of the target scene; The voting module is used to, based on the three-dimensional model, for each network to vote on candidate parameter combinations of candidate APs at multiple preset locations for each parameter combination, and obtain voting results, including: for each grid, based on the three-dimensional model, determining the maximum channel capacity under the optimal parameter combination of each candidate AP corresponding to that grid; determining the candidate AP and optimal parameter combination corresponding to the maximum value among the maximum channel capacities corresponding to that grid as the candidate AP and candidate parameter combination corresponding to that grid; determining the candidate parameter combination that the grid votes for the corresponding candidate AP; wherein, the channel capacity from the candidate AP under the candidate parameter combination to the corresponding grid is the largest; The determining module is used to determine the optimization result of the network deployment under the target scenario based on the voting results; wherein the optimization result includes the target parameter combination corresponding to each candidate AP.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1-7.

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