Electromagnetic map-assisted multi-UAV deployment method, system, equipment, and terminal

Abstract

The present invention belongs to the technical field of wireless communication networks for drones, and discloses a method, system, device, and terminal for deploying multiple drones assisted by electromagnetic maps. The method comprises the following steps: inputting user location information; randomly selecting k center points as the initial locations of drones; constructing an electromagnetic map based on the drone location information; performing user clustering and drone deployment iterations based on a division standard; determining whether the drone position no longer changes or reaches the number of iterations; and if not, returning to the electromagnetic map construction step; and selecting the optimal height within the drone altitude set. The present invention uses electromagnetic maps to make the channel system model more realistic, thereby improving the user received signal strength and effectively improving the rationality of drone layout. The present invention proposes an RM‑K‑means algorithm for user clustering and drone deployment in the context of drones acting as mobile base stations, which can effectively guarantee the communication experience of ground users and meet the communication needs of more users.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicle (UAV) wireless communication networks, and in particular relates to a method, system, device, and terminal for deploying multiple UAVs assisted by an electromagnetic map. Background Art

[0002] Currently, drone base stations have attracted significant research attention for their ability to provide wireless communication services. Drones can assist ground-based communications in a variety of situations. For example, when ground-based base stations are unable to communicate due to various incidents, drones can be deployed to act as mobile base stations to supplement existing communication infrastructure. Research on the deployment of unmanned aerial vehicles (UAVs) in wireless communication systems typically uses traditional air-to-ground channel models. In "Location Optimization for Unmanned Aerial Vehicles Assisted Mobile Networks," the authors address the issue of drone location and service area from a load balancing perspective. They propose an effective partitioning method to ensure that each drone serves nearly equal traffic demand. They then use a backtracking line search algorithm to perform a local search for drone locations, finding the optimal solution for both service sub-areas and drone positions. This algorithm achieves a more balanced traffic distribution among drones and a more balanced service area within each sub-area. The paper "Rapid Deployment of UAVs Based on Bandwidth Resources in Emergency Scenarios" considers the impact of factors such as user bandwidth, UAV bandwidth, and signal-to-noise ratio thresholds on 3D UAV deployment, and proposes a K-means-based algorithm to reduce deployment latency and the number of UAVs required. Most of the aforementioned studies use the classic air-to-ground model, which is overly idealized and merely describes the statistical laws of the propagation model through formulas. However, in real life, due to electromagnetic interference, building obstruction, and other factors, the actual path loss is significantly reduced. Therefore, to simulate a realistic environment, researchers have begun using electromagnetic maps to replace traditional channel models.

[0003] To make channel models more accurate, researchers are turning to the use of electromagnetic maps. The paper "Radio Map-Based UAV Placement Design for UAV-Assisted Relaying Networks" proposes an electromagnetic map-assisted method for placing drones as relays. The paper uses electromagnetic maps to dynamically adjust path loss parameters, resulting in a more rational distribution of drones. Currently, research on electromagnetic map-assisted UAV communication deployment is limited.

[0004] In summary, existing drone deployment methods each have their strengths and weaknesses. Methods based on traditional channel equations have explored various user clustering and drone deployment methods and demonstrated their effectiveness, but they fail to account for real-world electromagnetic influences and are overly idealistic. UAV communication systems based on electromagnetic maps do account for electromagnetic influences, but are limited to dynamically adjusting some parameters in traditional equations and cannot directly generate electromagnetic maps based on latitude and longitude coordinates.

[0005] Through the above analysis, the problems and defects of the existing technology are as follows:

[0006] (1) The classical air-to-ground channel model is too idealized, and only uses formulas to explain the statistical laws of the propagation model. However, in real life, due to electromagnetic interference, building obstruction and other reasons, the actual path loss is greatly reduced and is not only related to distance.

[0007] (2) In the existing UAV deployment methods, the UAV communication system based on electromagnetic maps is limited to dynamically adjusting some parameters of the traditional formula and cannot directly generate electromagnetic maps based on the real latitude and longitude coordinates. Summary of the Invention

[0008] In response to the problems existing in the prior art, the present invention provides a multi-UAV deployment method, system, device and terminal assisted by electromagnetic maps.

[0009] The present invention is implemented as follows: a multi-UAV deployment method assisted by an electromagnetic map comprises the following steps:

[0010] The first step is to establish a channel model for the target area under the condition that the drone acts as a mobile base station in the disaster scenario;

[0011] The second step is to establish a drone position arrangement model that maximizes the total received signal strength of the system;

[0012] The third step is to decouple the three-dimensional UAV deployment problem into two sub-problems: horizontal and vertical deployment. For the horizontal deployment sub-problem, given the UAV altitude, an improved K-means algorithm based on the electromagnetic map is used to cluster ground users. This yields the horizontal coordinates of the UAV deployment location and the user clustering results.

[0013] The fourth step is to adjust the drone's altitude. The drone's flight altitude is discretized within the drone's flight altitude range. The total received signal strength of users within the cluster is calculated at each altitude, and the altitude with the maximum total received signal strength of users within the cluster is selected as the final optimized altitude of the drone.

[0014] Furthermore, the establishment of the channel model of the target area under the condition of drones acting as mobile base stations in the disaster scenario in the first step includes: assuming a communication system in which the ground base station is destroyed or in other emergency communication situations, the drone acts as a mobile base station to provide services to ground users. When M users are randomly distributed in the area P and K drones provide services to them, the position of the mth user is ζ m =(x m ,y m ,h),x m ,y m are the x-axis and y-axis coordinates of the m-th user respectively; the z-axis coordinate is a fixed value, which is the average height h of the users; the position of the k-th drone is γ k =(x k ,y k , h k ), x k ,y k , h k are the x-, y-, and z-axis coordinates of the k-th UAV, respectively.

[0015] Furthermore, the establishment of the drone position arrangement model that maximizes the total received signal strength of the system in the second step includes:

[0016]

[0017]

[0018] Introducing the logical function indicator a m，k , when UAV k communicates with user m, a m，k is equal to 1, otherwise a m，k Equal to 0; the constraint states that a user can only be served by one drone.

[0019] r m，t (γ k ) represents the signal strength received by the mth user from the drone k, and the formula is as follows:

[0020] r m，t (γ k )=|g m，t (γ k )s k +n m，t |;

[0021] Where g m，t represents the channel gain between UAV k and ground user m at time t, s k is the signal power emitted by drone k, n m，t is the noise at the receiving end of the mth user at time t.

[0022] Furthermore, in the third step, the three-dimensional deployment problem is decoupled into two sub-problems: horizontal deployment and vertical deployment. If the drone height is fixed at a fixed value, the horizontal deployment solution is as follows:

[0023] (1) Randomly select the initial positions of K drones (x k ,y k , H0); define the maximum number of iterations N, and record the clustering results as (R1, R2, ..., R K );

[0024] (2) Generate an electromagnetic map of the target area based on the location of the drone and calculate the received signal strength r from each user m to drone k m，t (γ k );

[0025] (3) According to the principle of maximum received signal strength of users, users are assigned to corresponding drones, and users served by the same drone form a cluster;

[0026] (4) In each cluster, update the drone coordinates (x k ,y k , H0) is:

[0027]

[0028] (5) Determine whether the maximum number of iterations has been reached or the position of the UAV no longer changes. If the conditions are not met, return to step (2) to continue iterating; if the conditions are met, the algorithm ends.

[0029] Furthermore, in the fourth step, the altitude of the drone is adjusted to obtain the three-dimensional deployment coordinates, which specifically includes:

[0030] (1) Pre-set the minimum flight altitude H of the drone min and maximum height H max , discretize the height in the interval to obtain the height set H;

[0031] (2) The optimal height within the interval is obtained by solving the following equation

[0032]

[0033] For each height within the height range, calculate the total signal reception strength of users in each cluster at that height, and select the height with the maximum total user signal reception strength as the optimal height.

[0034] Another object of the present invention is to provide an electromagnetic map-assisted multi-UAV deployment system using the electromagnetic map-assisted multi-UAV deployment method. The electromagnetic map-assisted multi-UAV deployment system includes:

[0035] The model building module is used to establish the channel model of the target area under the condition that the drone acts as a mobile base station in the disaster scenario, and the drone position arrangement model that maximizes the total received signal strength of the system;

[0036] The horizontal deployment module is used to cluster ground users based on the improved K-means algorithm of the electromagnetic map given the drone altitude, and obtain the horizontal coordinates of the drone deployment location and the user clustering results;

[0037] The vertical deployment module is used to adjust the height of the drone. It discretizes the drone's flight altitude within the drone's flight altitude range, calculates the total received signal strength of users in the cluster at each altitude, and selects the altitude with the maximum total received signal strength of users in the cluster as the final optimized altitude of the drone.

[0038] Another object of the present invention is to provide a computer device, which includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor executes the steps of the multi-UAV deployment method assisted by electromagnetic maps.

[0039] Another object of the present invention is to provide a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of the multi-UAV deployment method assisted by an electromagnetic map.

[0040] Another object of the present invention is to provide an information data processing terminal, which is used to implement the multi-UAV deployment system assisted by the electromagnetic map.

[0041] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0042] First, in view of the technical problems existing in the above-mentioned prior art and the difficulty of solving these problems, we closely combine the technical solutions to be protected by the present invention and the results and data during the research and development process, and conduct a detailed and in-depth analysis of how the technical solutions of the present invention solve the technical problems and some creative technical effects brought about by solving the problems. The specific description is as follows:

[0043] This invention provides a multi-UAV deployment method assisted by electromagnetic maps. This method primarily addresses the deployment and user clustering issues of UAVs (Unmanned Aerial Vehicles) when acting as mobile base stations to provide communication services to ground users in situations where ground base stations are destroyed or in other emergency communication situations. The invention proposes an RM-K-means UAV clustering algorithm, which decouples the three-dimensional UAV deployment problem into two sub-problems: horizontal deployment and vertical deployment. For horizontal deployment, an electromagnetic map model is first constructed based on the UAVs' real-world location coordinates. The UAVs that serve each user are then selected based on the principle of maximum received signal strength. Ultimately, the horizontal deployment locations of the UAVs and the clustering of users served by the UAVs are obtained. For vertical deployment, specifically adjusting the UAVs' altitudes, the minimum and maximum flight altitudes of the UAVs are first defined. The altitude information within this range is discretized into a set of flightable altitudes. Within this set, the altitude that maximizes the received signal strength for users within the cluster is selected as the final deployment altitude. The proposed algorithm ensures that users receive greater received signal strength while effectively improving the rationality of UAV distribution.

[0044] This paper analyzes how to make channel modeling more reasonable. Considering the complex and changeable electromagnetic environment in real environments, it creates an electromagnetic map to simulate the real environment, providing more realistic channel information for subsequent drone deployment issues. It also provides an effective algorithm for drone deployment and user clustering, making drone location deployment more reasonable. It introduces an electromagnetic map to simulate the actual channel state between the transmitter and the receiver. It then proposes an improved K-means algorithm based on the electromagnetic map to solve the user clustering problem and the drone deployment location problem, thereby providing ground users with a better communication experience and making drone deployment more reasonable. This paper proposes an improved K-means algorithm based on the electromagnetic map to re-cluster ground users and deploy drones. It uses the received signal strength of the receiver in the electromagnetic map instead of distance as the clustering condition to increase the practicality of the algorithm. Subsequent simulation experiments also verify the correctness of this idea.

[0045] Second, considering the technical solution as a whole or from the perspective of the product, the technical effects and advantages of the technical solution to be protected by the present invention are described in detail as follows:

[0046] In the event of a ground base station being destroyed or other emergency communication situations, drones can serve as mobile base stations to provide services to ground users. This paper proposes a modified K-means (RM-K-means) clustering algorithm assisted by electromagnetic maps. By using electromagnetic maps to model the channel system more realistically, the signal strength received by users is enhanced, while also effectively improving the rationality of drone deployment.

[0047] This paper proposes an RM-K-means algorithm for user clustering and drone deployment in the context of drones serving as mobile base stations, which can effectively ensure the communication experience of ground users. At the same time, it can still ensure relatively more user access when the drone power drops, meeting the communication needs of more users. It can be used for drone communication deployment problems when the ground base station is destroyed or in other emergency communication situations.

[0048] Third, as auxiliary evidence for the inventiveness of the claims of the present invention, it is also reflected in the following important aspects:

[0049] (1) The expected benefits and commercial value of the technical solution of the present invention after transformation are:

[0050] none

[0051] (2) The technical solution of the present invention fills the technical gap in the industry at home and abroad:

[0052] This invention is the first system in China or abroad to utilize electromagnetic maps generated from terrain information to assist in optimizing the three-dimensional deployment of unmanned aerial vehicles (UAVs), filling a technological gap in this field. The application of electromagnetic maps significantly improves the performance of UAVs serving ground users as mobile base stations, providing a new solution for the three-dimensional deployment of UAVs in complex terrain. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0054] Figure 1 This is a flow chart of a method for deploying multiple UAVs assisted by electromagnetic maps provided by an embodiment of the present invention;

[0055] Figure 2 This is a schematic diagram of a multi-UAV deployment method assisted by an electromagnetic map provided by an embodiment of the present invention;

[0056] Figure 3 This is a model diagram of a UAV deployment urban communication system provided by an embodiment of the present invention;

[0057] Figure 4 This is an electromagnetic map generation effect diagram provided by an embodiment of the present invention;

[0058] Figure 5 (a) and Figure 5(b) are schematic diagrams of the two-dimensional projection and three-dimensional layout of drone positions and user clusters using the RM-K-means algorithm provided by an embodiment of the present invention;

[0059] Figure 6 (a) is a schematic diagram showing the location of UAV1 and the clustering of ground users served when the RM-K-means algorithm is used for deployment according to an embodiment of the present invention. Figure 6 (b) is a schematic diagram showing the specific situation of the UAV2 position and the clustering of the ground users served when the RM-K-means algorithm is used for deployment according to an embodiment of the present invention. Figure 6 (c) is a schematic diagram showing the specific situation of the UAV3 position and the clustering of the ground users served when the RM-K-means algorithm is used for deployment according to an embodiment of the present invention. Figure 6 (d) is a schematic diagram of the specific situation of the UAV4 position and the ground user clustering of the service when the RM-K-means algorithm is used for deployment according to an embodiment of the present invention. Figure 6 (e) is a schematic diagram showing the specific situation of the UAV5 position and the clustering of the ground users it serves when the RM-K-means algorithm is used for deployment according to an embodiment of the present invention;

[0060] Figure 7 (a) is a comparison diagram of the total signal reception strength of users when using the RM-K-mean and K-means algorithms under different numbers of drones provided by an embodiment of the present invention. Figure 7 (b) is a comparison diagram of the total signal reception strength of users when using the RM-K-mean and K-means algorithms with different numbers of users, provided by an embodiment of the present invention;

[0061] Figure 8 (a) is a diagram showing the simulation results of multi-user and multi-UAV position deployment using the RM-K-means algorithm provided by an embodiment of the present invention. Figure 8 (b) is a diagram showing the simulation results of multi-user and multi-UAV position deployment using the K-means algorithm provided by an embodiment of the present invention;

[0062] Figure 9 This is a comparison chart showing the number of users unable to communicate when using the RM-K-means algorithm and the K-means algorithm with different numbers of users and the same number of drones, with different percentages of drone power reduction, provided by an embodiment of the present invention.

[0063] Figure 10This is a graph comparing the number of users unable to communicate when using the RM-K-means algorithm and the K-means algorithm when the number of drones is different and the number of users is the same, provided by an embodiment of the present invention, and the drone power is reduced by different proportions. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0065] In response to the problems existing in the prior art, the present invention provides a multi-UAV deployment method, system, device and terminal assisted by an electromagnetic map. The present invention is described in detail below with reference to the accompanying drawings.

[0066] 1. Explanatory Examples In order to enable those skilled in the art to fully understand how to implement the present invention, this section provides an illustrative example that expands upon the technical solutions of the claims.

[0067] like Figure 1 As shown, the electromagnetic map-assisted multi-UAV deployment method provided by an embodiment of the present invention includes the following steps:

[0068] S101: Input the user's location information and randomly select k center points as the initial location of the drone;

[0069] S102: Build an electromagnetic map based on the drone location information, and perform user clustering and drone deployment iteration according to the division criteria;

[0070] S103, determining whether the drone position no longer changes or the number of iterations has been reached. If not, returning to the electromagnetic map construction step; if so, selecting the optimal height within the drone height set.

[0071] As a preferred embodiment, Figure 2 As shown, the electromagnetic map-assisted multi-UAV deployment method provided by the embodiment of the present invention specifically includes the following steps:

[0072] Step 1. Establish a channel model for the target area under the condition that the drone acts as a mobile base station in a disaster scenario.

[0073] The channel model of the target area provided by the embodiment of the present invention is described as follows:

[0074] Assume a communication system in which a ground base station is destroyed or in other emergency communication situations. In this case, drones act as mobile base stations to provide services to ground users. Assume that there are M users randomly distributed in area P, and K drones provide services to them. The position of the mth user is ζ m =(xm ,y m ,h),x m ,y m are the x-axis and y-axis coordinates of the m-th user respectively, and the z-axis coordinate is a fixed value, that is, the average height of the user h; the position of the k-th drone is γ k =(x k ,y k , h k ), x k ,y k , h k are the x-, y-, and z-axis coordinates of the k-th UAV, respectively.

[0075] Step 2. Establish the system total received signal strength R total The largest drone position arrangement model:

[0076]

[0077]

[0078] Where r m，t (γ k ) represents the signal strength received by the mth user from the drone k, and the formula is as follows:

[0079] r m，t (γ k )=|g m，t (γ k )s k +n m，t |

[0080] g m，t represents the channel gain between UAV k and ground user m at time t, s k is the signal power emitted by drone k, n m，t is the noise at the receiving end of the mth user at time t.

[0081] Here, the present invention introduces a logic function index a m，k , when UAV k communicates with user m, a m，k is equal to 1, otherwise a m，k Equal to 0. This constraint states that a user can only be served by one drone.

[0082] The embodiment of the present invention proposes an improved K-means clustering algorithm with electromagnetic map assistance to obtain the deployment location of drones and solve the user clustering problem.

[0083] Step 3. For the three-dimensional UAV deployment problem, this paper decouples it into two sub-problems: horizontal deployment and vertical deployment. For the horizontal deployment sub-problem, this paper assigns a UAV altitude and then clusters ground users using an improved K-means algorithm based on the electromagnetic map. Ultimately, the horizontal coordinates of the UAV deployment location and the user clustering results are obtained.

[0084] The improved K-means clustering algorithm provided in the embodiment of the present invention specifically includes the following steps:

[0085] The three-dimensional deployment problem is decoupled into two sub-problems: horizontal deployment and vertical deployment.

[0086] When solving the horizontal deployment problem, the present invention fixes the height of the drone to a fixed value. The horizontal deployment solution is as follows:

[0087] (3.1) Randomly select the initial positions of K drones (x k ,y k , H0); define the maximum number of iterations N, and record the clustering results as (R1, R2, ..., R K );

[0088] (3.2) Generate an electromagnetic map of the target area based on the location of the drone, and calculate the received signal strength r from each user m to drone k m，t (γ k );

[0089] (3.3) According to the principle of maximum received signal strength of users, users are assigned to corresponding drones, and users served by the same drone form a cluster;

[0090] (3.4) In each cluster, update the drone coordinates (x k ,y k , H0) is:

[0091]

[0092] (3.5) Determine whether the maximum number of iterations has been reached or the position of the drone no longer changes. If this condition is not met, return to step (3.2) and continue iterating; if this condition is met, the algorithm ends.

[0093] By improving the K-means clustering algorithm, the present invention can obtain the horizontal two-dimensional coordinates of the drone and the clustering results of the ground users. Then, the present invention adjusts the altitude of the drone to obtain a more reasonable three-dimensional deployment coordinate.

[0094] Step 4. Based on Step 3, deploy the drone vertically. This means adjusting the drone's altitude: discretize the drone's flight altitude within its flight range. Calculate the total received signal strength of users within the cluster at each altitude. Select the altitude with the maximum total received signal strength as the final optimized altitude.

[0095] (4.1) The present invention pre-sets the minimum altitude H at which the drone can fly. min and maximum height H max , within this interval, the present invention discretizes the height to obtain a height set H.

[0096] (4.2) The present invention obtains the optimal height within an interval by solving the following equation:

[0097]

[0098] That is, for each height within the height range, the present invention calculates the total signal reception strength of users in each cluster at this height, and selects the height with the maximum total user signal reception strength as the optimal height.

[0099] Finally, by solving the above two steps, the present invention can obtain a better result on the three-dimensional deployment position of UAVs and the clustering of ground users.

[0100] The electromagnetic map-assisted multi-UAV deployment system provided by an embodiment of the present invention includes:

[0101] The model building module is used to establish the channel model of the target area under the condition that the drone acts as a mobile base station in the disaster scenario, and the drone position arrangement model that maximizes the total received signal strength of the system;

[0102] The horizontal deployment module is used to cluster ground users based on the improved K-means algorithm of the electromagnetic map given the drone altitude, and obtain the horizontal coordinates of the drone deployment location and the user clustering results;

[0103] The vertical deployment module is used to adjust the height of the drone. It discretizes the drone's flight altitude within the drone's flight altitude range, calculates the total received signal strength of users in the cluster at each altitude, and selects the altitude with the maximum total received signal strength of users in the cluster as the final optimized altitude of the drone.

[0104] 2. Application Examples: In order to demonstrate the creativity and technical value of the technical solution of the present invention, this section provides application examples of the claimed technical solution on specific products or related technologies.

[0105] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware portion can be implemented using dedicated logic; the software portion can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. Those skilled in the art will appreciate that the above-mentioned devices and methods can be implemented using computer-executable instructions and / or contained in processor control code, for example, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, or can be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0106] 3. Evidence of the effects of the embodiments: The embodiments of the present invention have achieved some positive effects during the development or use process, and indeed have great advantages over the existing technology. The following content describes them with reference to the data, charts, etc. of the experimental process.

[0107] Figure 3 This is a model diagram of the drone deployment urban communication system provided by the example of the present invention: the drone acts as a mobile base station to provide communication services to ground users, each drone serves a part of the users on the ground, and one user can only be served by one drone.

[0108] Figure 4 This is an electromagnetic map generation effect diagram provided by an example of the present invention. In this invention, we selected Taiping National Forest Park with complex terrain as the research object. The figure shows the electromagnetic map model generated in the selected area.

[0109] The technical effects of the present invention are further described below in conjunction with simulation experiments.

[0110] Simulation experiment 1:

[0111] A. Simulation Conditions

[0112] A1) The target area is the Taiping National Forest Park with coordinates of [33.835°, 33.875°] × [108.57°, 108.63°];

[0113] A2) 100 users, randomly distributed in the target area, with known coordinates;

[0114] A3) Deploy 5 drones to provide communication services, with their initial 2D positions random and their initial altitude fixed at 100 meters.

[0115] B. Simulation content:

[0116] The RM-K-means algorithm is used to cluster 100 users and calculate the placement of drones. The placement results are as follows: Figure 5 (a) and 5(b), where Figure 5 (a) shows the two-dimensional projection results of the drone position and user clustering results. Figure 5 (b) shows the three-dimensional deployment position of the UAV.

[0117] C. Simulation results:

[0118] Figure 5 The final clustering and deployment results for 100 users and 5 drones using the RM-K-means algorithm are shown. The “+” represents the user’s location, and the solid diamond represents the drone’s placement. Figure 5 (a) shows the top-down projection effect of ground user clustering and UAV 3D position. Figure 5 (b) shows the stereoscopic results of ground user clustering and drone three-dimensional position deployment.

[0119] Figure 6 (a)~ Figure 6 (e) shows the deployment locations of UAV1, UAV2, UAV3, UAV4, and UAV5, as well as the user clustering results of specific services when the system uses the RM-K-means algorithm. It can be seen from the figure that the distribution load of drone service users is more balanced when using the improved algorithm.

[0120] Figure 7 (a) shows the comparison of the total signal reception strength of ground users under different numbers of drones; Figure 7 (b) shows a comparison of the total signal reception strength of ground users under different numbers of users. As can be seen from the figure, the clustering results using the RM-K-means algorithm can achieve greater received signal strength compared to those using the K-means algorithm, further proving that the improved algorithm can make user clustering more reasonable.

[0121] Simulation experiment 2:

[0122] A. Simulation Conditions

[0123] A1) The target area is the Taiping National Forest Park with coordinates of [33.835°, 33.875°] × [108.57°, 108.63°];

[0124] A2) 10 users are randomly distributed in the target area, where users 5, 6, and 7 are edge users and the coordinates of all users are known.

[0125] B. Simulation content:

[0126] The RM-K-means algorithm and K-means algorithm are used to cluster the 10 users and calculate the placement of the drones. The placement results are as follows: Figure 8 shown.

[0127] C. Simulation results:

[0128] Figure 8 (a) shows the UAV deployment location and user clustering results using the RM-K-means algorithm. Figure 8 (b) The UAV deployment location and user clustering results using the K-means algorithm are given. This simulation mainly verifies the rationality and effectiveness of the algorithm proposed in the present invention in the UAV deployment problem from the perspective of the marginal user affiliation problem. The present invention uses ten users (numbered 1 to 10) and two UAVs for simulation, where circles represent users and five-pointed stars represent UAVs. GU5, 6, and 7 represent three marginal users, and the yellow part in the figure represents the highest altitude in the entire figure. It can be seen from the figure that GU1 to 4 and GU5 to 10 are distributed on both sides of the mountain respectively. From the simulation results, the RM-K-means algorithm is used to take into account the mountain blocking problem. GU5 to 7 and GU8 to 10 are assigned an aircraft, and GU1 to 4 is assigned a UAV. The received signal strengths of GU5-7 were -67.4607dBm, -68.4216dBm, and -69.8877dBm, respectively. The traditional K-means algorithm only considers distance when clustering, so GU5-7 is clustered with GU1-4. The received signal strengths of GU5-7 are -127.6538dBm, -121.5749dBm, and -100.4792dBm, respectively. In this simulation, the algorithm proposed by this invention takes into account the influence of real-world electromagnetic maps, thereby making user clustering and drone deployment more rational.

[0129] Simulation experiment three:

[0130] A. Simulation Conditions

[0131] A1) The target area is the Taiping National Forest Park with coordinates of [33.835°, 33.875°] × [108.57°, 108.63°];

[0132] A2) The user sets them to 50, 150, and 250, randomly distributed in the target area, and the coordinate positions are known;

[0133] A3) Deploy 5 drones to provide communication services. The initial positions are random and the initial transmission power of the drones is 1W.

[0134] B. Simulation content:

[0135] When the UAV transmission power drops by 0%, 20%, 50%, 80%, and 90%, respectively, the RM-K-means algorithm proposed in this invention and the classic K-means algorithm are used to perform clustering iterative calculations on the experimental scene. The clustering results are as follows: Figure 9 shown.

[0136] C. Simulation results:

[0137] Figure 9 The results of clustering deployment using the RM-K-means algorithm proposed in this paper and the classic K-means algorithm under the condition of changing the number of users are given. Figure 9 As can be seen in the figure, after clustering, the RM-K-means algorithm produces better results than the K-means algorithm. When the drone's transmit power decreases, the number of users unable to communicate gradually increases. However, the RM-K-means algorithm allows more users to maintain contact with the drone, validating its effectiveness.

[0138] Simulation experiment 4:

[0139] A. Simulation Conditions

[0140] A1) The target area is the Taiping National Forest Park with coordinates of [33.835°, 33.875°] × [108.57°, 108.63°];

[0141] A2) 100 users, randomly distributed in the target area, with known coordinates;

[0142] A3) The number of drones is set to 3, 5, and 9 respectively, with their initial positions randomized and the initial transmission power of the drones set to 1W.

[0143] B. Simulation content:

[0144] When the UAV transmission power drops by 0%, 20%, 50%, 80%, and 90%, respectively, the RM-K-means algorithm proposed in this invention and the classic K-means algorithm are used to perform clustering iterative calculations on the experimental scene. The clustering results are as follows: Figure 10 shown.

[0145] C. Simulation results:

[0146] Figure 10 The results of clustering deployment using the RM-K-means algorithm proposed in this paper and the classic K-means algorithm under the condition of changing the number of drones are given; Figure 10As can be seen from the simulation, the RM-K-means algorithm achieves better results than the K-means algorithm. When the UAV transmit power decreases, the number of users unable to communicate gradually increases. However, as the number of UAVs increases, the number of users unable to communicate decreases. Furthermore, the RM-K-means algorithm can communicate with more ground users than the K-means algorithm. These simulation results indicate that increasing the number of UAVs can alleviate user communication issues to a certain extent and verify the effectiveness of the RM-K-means algorithm.

[0147] In summary, this paper proposes an RM-K-means algorithm for user clustering and drone deployment in the context of drones acting as mobile base stations. This algorithm effectively ensures the communication experience of ground users. Furthermore, it ensures a relatively high number of users can access the network even when drone power is reduced, meeting the communication needs of more users. This algorithm is particularly useful for drone deployment in situations where ground base stations are damaged or in other emergency communication scenarios.

[0148] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with this technical field within the technical scope disclosed by the present invention and within the spirit and principles of the present invention should be covered by the scope of protection of the present invention.

Claims

1. A multi-UAV deployment method assisted by electromagnetic maps, characterized in that: The electromagnetic map-assisted multi-UAV deployment method includes: inputting user location information; randomly selecting k center points as the initial UAV positions; constructing an electromagnetic map based on the UAV location information; clustering users and iterating UAV deployment according to a classification criterion; determining whether the UAV position has not changed or the number of iterations has been reached; if not, returning to the electromagnetic map construction step; and if so, selecting the optimal altitude within the UAV altitude set. The multi-UAV deployment method assisted by electromagnetic maps includes the following steps: Step 1: Establish a channel model for the target area under the condition that the drone acts as a mobile base station in the disaster scenario; Step 2: Establish a UAV position arrangement model that maximizes the total received signal strength of the system; Step 3: Decouple the three-dimensional UAV deployment problem into two sub-problems: horizontal and vertical deployment. For the horizontal deployment sub-problem, given the UAV altitude, an improved K-means algorithm based on the electromagnetic map is used to cluster ground users, obtaining the horizontal coordinates of the UAV deployment location and the user clustering results. Step 4: Adjust the drone's altitude. Discretize the drone's flight altitude within the drone's flight range. Calculate the total received signal strength of users within the cluster at each altitude. Select the altitude with the maximum total received signal strength as the final optimized altitude. The establishment of the channel model of the target area under the condition of drones as mobile base stations in the disaster scenario in step 1 includes: setting a communication system in which the ground base station is destroyed or in other emergency communication situations, and the drone acts as a mobile base station to provide services to ground users; when M users are randomly distributed in the area P, and K drones provide services for them, the position of the mth user is ζ m =(x m ,y m ,h),x m ,y m are the x-axis and y-axis coordinates of the m-th user respectively; the z-axis coordinate is a fixed value, which is the average height h of the users; the position of the k-th drone is γ k =(x k ,y k ,h k ), x k ,y k , h k are the x, y, and z axis coordinates of the kth UAV respectively.

2. The electromagnetic map-assisted multi-UAV deployment method according to claim 1, characterized in that: The establishment of the drone position arrangement model that maximizes the total received signal strength of the system in step 2 includes: Introducing the logical function indicator a m,k , when UAV k communicates with user m, a m,k is equal to 1, otherwise a m,k =0; the constraint states that a user can only be served by one drone; r m,t (γ k ) represents the signal strength received by the mth user from the drone k, and the formula is as follows: r m,t (c k )=|g m,t (c k )s k +n m,t |; Where g m,t represents the channel gain between UAV k and ground user m at time t, s k is the signal power emitted by drone k, n m,t is the noise at the receiving end of the mth user at time t.

3. The electromagnetic map-assisted multi-UAV deployment method according to claim 1, characterized in that: In step 3, the three-dimensional deployment problem is decoupled into two sub-problems: horizontal deployment and vertical deployment. If the drone height is fixed, the horizontal deployment solution is as follows: (1) Randomly select the initial positions of K drones (x k ,y k ,H0); define the maximum number of iterations N, and the clustering result is recorded as (R1,R2,...,R K ); (2) Generate an electromagnetic map of the target area based on the location of the drone and calculate the received signal strength r from each user m to drone k m,t (γ k ); (3) According to the principle of maximum received signal strength of users, users are assigned to corresponding drones, and users served by the same drone form a cluster; (4) In each cluster, update the drone coordinates (x k ,y k ,H0) is: (5) Determine whether the maximum number of iterations has been reached or the position of the UAV no longer changes. If the conditions are not met, return to step (2) to continue iterating; if the conditions are met, the algorithm ends.

4. The electromagnetic map-assisted multi-UAV deployment method according to claim 1, wherein: In step 4, the altitude of the drone is adjusted to obtain the three-dimensional deployment coordinates, which specifically includes: (1) Pre-set the minimum flight altitude H of the drone min and maximum height H max , discretize the height in the interval to obtain the height set Η; (2) The optimal height within the interval is obtained by solving the following equation For each height within the height range, calculate the total signal reception strength of users in each cluster at that height, and select the height with the maximum total user signal reception strength as the optimal height.

5. An electromagnetic map-assisted multi-UAV deployment system using the electromagnetic map-assisted multi-UAV deployment method according to any one of claims 1 to 4, characterized in that: The electromagnetic map-assisted multi-UAV deployment system includes: The model building module is used to establish the channel model of the target area under the condition that the drone acts as a mobile base station in the disaster scenario, and the drone position arrangement model that maximizes the total received signal strength of the system; The horizontal deployment module is used to cluster ground users based on the improved K-means algorithm of the electromagnetic map given the drone altitude, and obtain the horizontal coordinates of the drone deployment location and the user clustering results; The vertical deployment module is used to adjust the height of the drone. It discretizes the drone's flight altitude within the drone's flight altitude range, calculates the total received signal strength of users in the cluster at each altitude, and selects the altitude with the maximum total received signal strength of users in the cluster as the final optimized altitude of the drone.

6. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the multi-UAV deployment method assisted by an electromagnetic map as described in any one of claims 1 to 4.

7. A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the electromagnetic map-assisted multi-UAV deployment method according to any one of claims 1 to 4.

8. An information data processing terminal, characterized in that: The information data processing terminal is used to implement the multi-UAV deployment system assisted by the electromagnetic map as described in claim 5.

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