A communication coverage supplementing point method based on electromagnetic signal situation

Abstract

The application discloses a communication coverage supplementing point method based on electromagnetic signal situation, comprising the following steps: S1: determining a signal attenuation model by using electromagnetic signal strength collected by an unmanned system node; S2: generating an electromagnetic signal strength diagram of a target area by using the signal attenuation model; S3: determining the number of unmanned system nodes to be deployed in the target area by using the electromagnetic signal strength diagram; S4: determining the optimal deployment position of the unmanned system nodes to be deployed by using a particle swarm optimization algorithm, and deploying the unmanned system nodes at the optimal deployment position; S5: dividing all the deployed unmanned system nodes into corresponding point sets based on a Voronoi diagram algorithm and a depth-first search algorithm, and obtaining M point sets {C1, C2,..., C i ,…,C M} which are not connected with each other; and S6: merging all the point sets in S5 into one point set by dispatching the deployed unmanned system nodes or adding new unmanned system nodes. The application has low cost and can realize efficient and accurate coverage.

Description

Technical Field

[0001] This invention relates to the fields of distributed unmanned systems and communication technology, and more specifically to a communication coverage supplementation method based on electromagnetic signal situation. Background Technology

[0002] With the rapid development of technology and the acceleration of urbanization, the development of underground space is accelerating, and its usable area is steadily increasing, highlighting the growing demand for maintenance and emergency response measures. Simultaneously, the stability of underground space is directly related to social stability; the consequences of a safety accident would be unimaginable. The efficiency, precision, and safety of unmanned systems will greatly improve the maintenance and emergency management of underground space, providing strong support for urban safety, stability, and development. However, in complex underground environments, due to interference from terrain, obstacles, and electromagnetic shielding, unmanned ground system nodes such as unmanned vehicles often struggle to achieve complete coverage of target areas, resulting in blind spots in electromagnetic situational awareness. Traditional coverage optimization methods typically rely on the dense deployment of homogeneous sensor nodes, which is not only costly but also difficult to achieve efficient and accurate coverage.

[0003] Therefore, how to provide a communication coverage supplementation method that is not only low-cost but also achieves efficient and accurate coverage is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a communication coverage supplementation method based on electromagnetic signal situation.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A communication coverage supplementation method based on electromagnetic signal situation includes the following steps:

[0007] S1: Determine the signal attenuation model using the electromagnetic signal strength collected by the unmanned system nodes;

[0008] S2: Generate an electromagnetic signal intensity map of the target area using the aforementioned signal attenuation model;

[0009] S3: Use the electromagnetic signal intensity map to determine the number of unmanned system nodes to be deployed in the target area;

[0010] S4: Use the particle swarm optimization algorithm to determine the optimal deployment location of the unmanned system node to be deployed, and deploy the unmanned system node at the optimal deployment location;

[0011] S5: Based on the Voronoi diagram algorithm and the depth-first search algorithm, all deployed unmanned system nodes are divided into corresponding point sets, resulting in M ​​disconnected point sets. ;

[0012] S6: Combine all points in S5 into a single point set by scheduling deployed unmanned system nodes or adding new unmanned system nodes.

[0013] Preferably, S1 specifically includes the following steps:

[0014] S11: Deploy two unmanned system nodes in the target area; wherein, one of the two unmanned system nodes serves as a transmitting node and the other serves as a receiving node;

[0015] S12: Obtain different deployment distances The electromagnetic signal strength collected by the receiving node described below ;in, The distance between the transmitting node and the receiving node is represented by i = 1, 2, ..., m; j = 1, 2, ..., n; n represents the number of times the receiving node collects the electromagnetic signal strength at the same deployment distance; m and n are both positive integers.

[0016] S13: Utilize deployment distance and electromagnetic signal strength By fitting the parameters of the semi-empirical model, the signal attenuation model is obtained.

[0017] Preferably, S13 specifically includes the following steps:

[0018] S131: Calculate deployment distance The electromagnetic signal strength collected by the receiving node described below average and residual error ;

[0019] S132: Calculate deployment distance using Bessel's formula The electromagnetic signal strength collected by the receiving node described below Standard deviation ;

[0020] S133: Determine if there exist i and j such that... If true, then the corresponding electromagnetic signal strength is removed to obtain the filtered electromagnetic signal strength.

[0021] S134: Utilizing the filtered electromagnetic signal strength and the corresponding deployment distance By fitting the parameters in the semi-empirical model, the signal attenuation model is obtained.

[0022] Preferably, the expression of the semi-empirical model is:

[0023] ;

[0024] In the formula, This represents the electromagnetic signal strength attenuation value from the transmitting node to the receiving node; wherein, the electromagnetic signal strength attenuation value is the difference between the electromagnetic signal strength emitted by the transmitting node and the electromagnetic signal strength collected by the receiving node; d represents the distance between the transmitting node and the receiving node; γ represents the electromagnetic signal strength attenuation value at the reference distance d0; γ represents the electromagnetic signal strength attenuation index. This indicates that the mean is μ and the variance is σ. 2 Random variables that conform to a Gaussian distribution.

[0025] Preferably, S2 specifically includes the following steps:

[0026] S21: Deploy K1 unmanned system nodes within the target area; where K1 is a positive integer;

[0027] S22: Obtain the electromagnetic signal intensity map of the target area using the location information of K1 unmanned system nodes and the signal attenuation model.

[0028] Preferably, S3 specifically includes the following steps:

[0029] S31: Rasterize the electromagnetic signal intensity map;

[0030] S32: Determine the number of unmanned system nodes to be deployed in the target area based on the gridded electromagnetic signal intensity map.

[0031] Preferably, the number of unmanned system nodes to be deployed in the target area ;in, This indicates the number of grids in the electromagnetic signal strength graph where the electromagnetic signal strength is less than a preset threshold. This indicates the number of grid cells covered within the sensing radius of the unmanned system node to be deployed.

[0032] Preferably, S4 specifically includes the following steps:

[0033] S41: Initialize the position matrix and velocity matrix Where N represents the number of initial positions and velocities of a certain unmanned system node to be deployed;

[0034] S42: Initialize the individual optimal position matrix ;

[0035] Initialize the global optimal position ;in, express The fitness is based on the fitness function: get, RSSI represents the sum of the electromagnetic signal strengths of all grid cells within the sensing radius of an unmanned system node deployed at location x; r represents the total number of grid cells within the sensing radius of the deployed unmanned system node. i This represents the electromagnetic signal strength of the i-th grid.

[0036] S43: Update speed matrix ,in, w represents the inertia weight, c1 and c2 represent the learning factors, and t = 0, 1, 2, ..., T max ;T max Indicates the number of iterations;

[0037] S44: Update the position matrix ,in, Among them, if If the value exceeds the search space range, it is set to the corresponding search space range boundary value;

[0038] S45; Calculation fitness ;

[0039] like Then update ;

[0040] like ,but Finally, the updated individual optimal position matrix is ​​obtained. ;

[0041] S46: In the individual optimal position matrix In the case where there exists i such that Then update the global optimal position. If there is no i such that Then the global optimal position ;

[0042] S47: Repeat S43-S46 continuously until the maximum number of iterations T is reached. max The final globally optimal position obtained is the final deployment position of the unmanned system node to be deployed;

[0043] S48: Repeat S41-S47 continuously until the deployment locations of K2 unmanned system nodes to be deployed are obtained.

[0044] Preferably, S5 specifically includes the following steps:

[0045] S51: Based on each deployed unmanned system node Location information is used to generate location data for each deployed unmanned system node using the Voronoi diagram algorithm. Generate a Voronoi element; where, ; ;

[0046] S52: Obtain all nodes related to the selected unmanned system based on the depth-first search algorithm. Neighboring unmanned system nodes with potential coverage relationships From nearby unmanned system nodes Selecting nodes in the unmanned system Nearby unmanned system nodes within the perception radius will be selected and compared with the chosen unmanned system node. They are assigned to the same point set; among them, ; ;like and If they are adjacent, it is assumed that there is a potential covering relationship between them;

[0047] S53: Repeat S52 continuously until all deployed unmanned system nodes are connected. Divide the points into corresponding sets to obtain M disconnected point sets. ;in, ; .

[0048] Preferably, S6 specifically includes the following steps:

[0049] S61: From Select the set of connected points that contains the fewest nodes of the deployed unmanned system. ;in, ;

[0050] S62: Calculation Each of the deployed unmanned system nodes in China and The distances between each deployed unmanned system node are calculated, and the node corresponding to the shortest distance is obtained. , as well as To which the point set belongs ,in ; ; ; ;

[0051] S63: From Selected from Deployed unmanned system nodes within the perception radius obtain neighbor node set ;

[0052] from Selected from Deployed unmanned system nodes within the perception radius obtain neighbor node set ;

[0053] S64: Determine if... Are there any deployed unmanned system nodes? This enables scheduling Can

[0054] With connection and If it exists, then schedule. Will and Merge them into a new set of connected vertices; if it does not exist, then determine the connection for... Are there any deployed unmanned system nodes? This enables scheduling It can be connected and If it exists, then schedule. Will and Merge into a new set of connected vertices; if it does not exist, then... and A new unmanned system node will be deployed at the middle position;

[0055] S65: Repeat the above steps until you have M unconnected point sets. Merge them into a single set of connected points.

[0056] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a communication coverage supplementation method based on electromagnetic signal situation, which is not only low in cost, but also can achieve efficient and accurate coverage. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0058] Figure 1 A flowchart of a communication coverage supplementation method based on electromagnetic signal situation provided by the present invention;

[0059] Figure 2 This is a schematic diagram illustrating the generation of corresponding Voronoi units for deployed unmanned system nodes, as provided by the present invention. Detailed Implementation

[0060] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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.

[0061] like Figure 1 As shown in the figure, this invention discloses a communication coverage supplementation method based on electromagnetic signal situation, including the following steps:

[0062] S1: Determine the signal attenuation model using the electromagnetic signal strength collected by the unmanned system nodes;

[0063] In one embodiment, S1 specifically includes the following steps:

[0064] S11: Deploy two unmanned system nodes in the target area; wherein, one of the two unmanned system nodes serves as a transmitting node and the other serves as a receiving node;

[0065] It is understandable that the two deployed unmanned system nodes can be aerial unmanned system nodes, such as drones, and each aerial unmanned system node has the function of transmitting and receiving electromagnetic signals.

[0066] When deploying drones, ignore the drone's flight altitude and only consider signal attenuation in 2D space.

[0067] The two unmanned system nodes deployed are only used to fit the determination signal attenuation model and are not used as actual deployed nodes.

[0068] S12: Obtain different deployment distances The electromagnetic signal strength collected by the receiving node described below ;in, The distance between the transmitting node and the receiving node is represented by i = 1, 2, ..., m; j = 1, 2, ..., n; n represents the number of times the receiving node collects the electromagnetic signal strength at the same deployment distance; m and n are both positive integers.

[0069] Specifically: when the distance between the transmitting node and the receiving node is... At that time, the receiving node collects the electromagnetic signal strength n times to obtain the electromagnetic signal strength. ;

[0070] When the distance between the transmitting node and the receiving node is At that time, the receiving node collects the electromagnetic signal strength n times to obtain the electromagnetic signal strength. ; ...

[0072] When the distance between the transmitting node and the receiving node is At that time, the receiving node collects the electromagnetic signal strength n times to obtain the electromagnetic signal strength. ;

[0073] S13: Utilize deployment distance and electromagnetic signal strength By fitting the parameters of the semi-empirical model, the signal attenuation model is obtained.

[0074] In one embodiment, S13 specifically includes the following steps:

[0075] S131: Calculate deployment distance The electromagnetic signal strength collected by the receiving node described below average and residual error ;

[0076] S132: Calculate deployment distance using Bessel's formula The electromagnetic signal strength collected by the receiving node described below Standard deviation ;

[0077] S133: Determine if there exist i and j such that... If true, then the corresponding electromagnetic signal strength is removed to obtain the filtered electromagnetic signal strength.

[0078] Specifically: Suppose there exist i=2, j=1 such that If true, then remove. Corresponding electromagnetic signal strength ;

[0079] Suppose there exist i=3, j=2 such that If true, then remove. Corresponding electromagnetic signal strength .

[0080] S134: Utilizing the filtered electromagnetic signal strength and the corresponding deployment distance By fitting the parameters in the semi-empirical model, the signal attenuation model is obtained.

[0081] In one embodiment, the expression of the semi-empirical model is:

[0082] ;

[0083] In the formula, This represents the electromagnetic signal strength attenuation value from the transmitting node to the receiving node; wherein, the electromagnetic signal strength attenuation value is the difference between the electromagnetic signal strength emitted by the transmitting node and the electromagnetic signal strength collected by the receiving node; d represents the distance between the transmitting node and the receiving node; γ represents the electromagnetic signal strength attenuation value at the reference distance d0; γ represents the electromagnetic signal strength attenuation index. This indicates that the mean is μ and the variance is σ. 2 Random variables that conform to a Gaussian distribution.

[0084] Specifically: the attenuation value of the electromagnetic signal strength from the transmitting node to the receiving node. ( This indicates that the transmitting and receiving nodes are deployed at a distance of [distance missing]. When the electromagnetic signal strength emitted by the transmitting node is substituted into the semi-empirical model... ,Will Substituting d into the semi-empirical model, we fit γ, μ, and σ into the semi-empirical model. 2 γ, μ and σ can be obtained 2 The specific values; γ, μ and σ 2 Substitute the specific values ​​into the formula The signal attenuation model can then be obtained.

[0085] S2: Generate an electromagnetic signal intensity map of the target area using the aforementioned signal attenuation model;

[0086] In one embodiment, S2 specifically includes the following steps:

[0087] S21: Deploy K1 unmanned system nodes within the target area; where K1 is a positive integer;

[0088] It is understandable that K1 unmanned system nodes refer to the total number of unmanned system nodes in the target area after deployment.

[0089] The K1 unmanned system nodes can be selected from ground unmanned system nodes, such as unmanned vehicles, and each unmanned system node has the function of transmitting and receiving electromagnetic signals.

[0090] S22: Obtain the electromagnetic signal intensity map of the target area using the location information of K1 unmanned system nodes and the signal attenuation model.

[0091] S3: Use the electromagnetic signal intensity map to determine the number of unmanned system nodes to be deployed in the target area;

[0092] In one embodiment, S3 specifically includes the following steps:

[0093] S31: Rasterize the electromagnetic signal intensity map;

[0094] S32: Determine the number of unmanned system nodes to be deployed in the target area based on the gridded electromagnetic signal intensity map.

[0095] In one embodiment, the number of unmanned system nodes to be deployed in the target area ;in, This indicates the number of grids in the electromagnetic signal strength graph where the electromagnetic signal strength is less than a preset threshold. This indicates the number of grid cells covered within the sensing radius of the unmanned system node to be deployed.

[0096] S4: Use the particle swarm optimization algorithm to determine the optimal deployment location of the unmanned system node to be deployed, and deploy the unmanned system node at the optimal deployment location;

[0097] It is understood that the unmanned system nodes to be deployed in this invention can be aerial unmanned system nodes, such as drones, and each unmanned system node to be deployed has the function of transmitting and receiving electromagnetic signals.

[0098] When selecting drones for the unmanned system nodes to be deployed, the drone's flight altitude is ignored, and only the signal attenuation in 2D space is considered.

[0099] In one embodiment, S4 specifically includes the following steps:

[0100] S41: Initialize the position matrix and velocity matrix Where N represents the number of initial positions and velocities of a certain unmanned system node to be deployed; that is, this invention initializes N positions and N velocities for a node to be deployed.

[0101] It is understandable that: During initialization, it falls within the preset search space (i.e., The x-coordinate is within the horizontal search space [lbx, ubx] and the y-coordinate is within the vertical search space [lby, uby].

[0102] S42: Initialize the individual optimal position matrix ;

[0103] Initialize the global optimal position ;in, express fitness

[0104] Fitness is based on the fitness function: get, The value represents the coverage gain generated after the deployment of an unmanned system node, specifically the sum of the electromagnetic signal strengths of all grid cells within the sensing radius of the node when it is deployed at location x; r represents the total number of grid cells within the sensing radius of the deployed unmanned system node; RSSI i This represents the electromagnetic signal strength of the i-th grid.

[0105] therefore, This indicates the deployment location of the unmanned system node to be deployed. The sum of the electromagnetic signal strengths of all grids within its sensing radius;

[0106] S43: Update speed matrix ,in, w represents the inertia weight, c1 and c2 represent the learning factors, and t = 0, 1, 2, ..., T max ;T max Indicates the number of iterations;

[0107] It should be noted that: The initial value is ; The initial value is ; The initial value is .

[0108] S44: Update the position matrix ,in, Among them, if Exceeding the search space (i.e.) If the x-coordinate is not within the horizontal search space [lbx,ubx] and the y-coordinate is not within the vertical search space [lby,uby], then set it to the corresponding search space range boundary value (i.e., lbx,ubx, lby,uby).

[0109] It should be noted that: The initial value is ;

[0110] S45; Calculation fitness ;

[0111] like Then update ;

[0112] like ,but Finally, the updated individual optimal position matrix is ​​obtained. ;

[0113] It is understandable that:

[0114] express The fitness of the unmanned system node to be deployed specifically indicates its location within the deployment area. The sum of the electromagnetic signal strengths of all grids within its sensing radius;

[0115] express The fitness of the unmanned system node to be deployed specifically indicates its location within the deployment area. The sum of the electromagnetic signal strengths of all grids within its sensing radius;

[0116] S46: In the individual optimal position matrix In the case where there exists i such that Then update the global optimal position. If there is no i such that Then the global optimal position ;

[0117] It is understandable that:

[0118] express The fitness of the unmanned system node to be deployed specifically indicates its location within the deployment area. The sum of the electromagnetic signal strengths of all grids within its sensing radius;

[0119] express The fitness of the unmanned system node to be deployed specifically indicates its location within the deployment area. The sum of the electromagnetic signal strengths of all grids within its sensing radius;

[0120] S47: Repeat S43-S46 continuously until the maximum number of iterations T is reached. max The final globally optimal position obtained is the final deployment position of the unmanned system node to be deployed;

[0121] S48: Repeat S41-S47 continuously until the deployment locations of K2 unmanned system nodes to be deployed are obtained.

[0122] S5: Based on the Voronoi diagram algorithm and the depth-first search algorithm, all deployed unmanned system nodes are divided into corresponding point sets, resulting in M ​​disconnected point sets. ;

[0123] In one embodiment, S5 specifically includes the following steps:

[0124] S51: Based on each deployed unmanned system node Location information is used to generate location data for each deployed unmanned system node using the Voronoi diagram algorithm. Generate a Voronoi element; where, ; ;

[0125] like Figure 2 As shown in the figure, the red dots represent nodes of the deployed unmanned system. The polygon containing each red dot is the Voronoi element corresponding to the node of the unmanned system.

[0126] S52: Obtain all nodes related to the selected unmanned system based on the depth-first search algorithm. Neighboring unmanned system nodes with potential coverage relationships From nearby unmanned system nodes Selecting nodes in the unmanned system Nearby unmanned system nodes within the perception radius will be selected and compared with the chosen unmanned system node. They are assigned to the same point set; among them, ; ;like and If they are adjacent, it is assumed that there is a potential covering relationship between them;

[0127] S53: Repeat S52 continuously until all deployed unmanned system nodes are connected. Divide the points into corresponding sets to obtain M disconnected point sets. ;in, ; .

[0128] S6: Combine all points in S5 into a single point set by scheduling deployed unmanned system nodes or adding new unmanned system nodes.

[0129] In one embodiment, S6 specifically includes the following steps:

[0130] S61: From Select the set of connected points that contains the fewest nodes of the deployed unmanned system. (Right now );in, ;

[0131] S62: Calculation Each of the deployed unmanned system nodes in China and The distances between each deployed unmanned system node are calculated, and the node corresponding to the shortest distance is obtained. , as well as The set of points to which it belongs ,in ; ; ; ;

[0132] S63: From Selected from Deployed unmanned system nodes within the perception radius obtain neighbor node set ;

[0133] from Selected from Deployed unmanned system nodes within the perception radius obtain neighbor node set ;

[0134] S64: Determine if... Are there any deployed unmanned system nodes? This enables scheduling

[0135] (Specifically, it could be to Move to and The middle position can be connected and If it exists, then schedule. (Specifically, it could be to Move to and (the middle position) will and Merge them into a new set of connected vertices; if it does not exist, then determine the connection for... Are there any deployed unmanned system nodes? This enables scheduling (Specifically, it could be to Move to and The middle position can be connected and If it exists, then schedule. (Specifically, it could be to Move to and (the middle position) will and Merge into a new set of connected vertices; if it does not exist, then... and A new unmanned system node will be deployed at the middle position;

[0136] S65: Repeat the above steps until you have M unconnected point sets. Merge them into a single set of connected points.

[0137] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0138] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A communication coverage supplementation method based on electromagnetic signal situation, characterized in that, Includes the following steps: S1: Determine the signal attenuation model using the electromagnetic signal strength collected by the unmanned system nodes; S2: Generate an electromagnetic signal intensity map of the target area using the aforementioned signal attenuation model; S3: Use the electromagnetic signal intensity map to determine the number of unmanned system nodes to be deployed in the target area; S4: Use the particle swarm optimization algorithm to determine the optimal deployment location of the unmanned system node to be deployed, and deploy the unmanned system node at the optimal deployment location; S5: Based on the Voronoi diagram algorithm and the depth-first search algorithm, all deployed unmanned system nodes are divided into corresponding point sets, resulting in M ​​disconnected point sets. ; S6: Combine all points in S5 into a single point set by scheduling deployed unmanned system nodes or adding new unmanned system nodes.

2. The communication coverage supplementation method based on electromagnetic signal situation as described in claim 1, characterized in that, S1 specifically includes the following steps: S11: Deploy two unmanned system nodes in the target area; wherein, one of the two unmanned system nodes serves as a transmitting node and the other serves as a receiving node; S12: Obtain different deployment distances The electromagnetic signal strength collected by the receiving node described below ;in, The distance between the transmitting node and the receiving node is represented by i = 1, 2, ..., m; j = 1, 2, ..., n; n represents the number of times the receiving node collects the electromagnetic signal strength at the same deployment distance; m and n are both positive integers. S13: Utilize deployment distance and electromagnetic signal strength By fitting the parameters of the semi-empirical model, the signal attenuation model is obtained.

3. The communication coverage supplementation method based on electromagnetic signal situation as described in claim 2, characterized in that, S13 specifically includes the following steps: S131: Calculate deployment distance The electromagnetic signal strength collected by the receiving node described below average and residual error ; S132: Calculate deployment distance using Bessel's formula The electromagnetic signal strength collected by the receiving node described below Standard deviation ; S133: Determine if there exist i and j such that... If true, then the corresponding electromagnetic signal strength is removed to obtain the filtered electromagnetic signal strength. S134: Utilizing the filtered electromagnetic signal strength and the corresponding deployment distance By fitting the parameters in the semi-empirical model, the signal attenuation model is obtained.

4. The communication coverage supplementation method based on electromagnetic signal situation as described in claim 3, characterized in that, The expression for the semi-empirical model is: ； In the formula, This represents the electromagnetic signal strength attenuation value from the transmitting node to the receiving node; wherein, the electromagnetic signal strength attenuation value is the difference between the electromagnetic signal strength emitted by the transmitting node and the electromagnetic signal strength collected by the receiving node; d represents the distance between the transmitting node and the receiving node; γ represents the electromagnetic signal strength attenuation value at the reference distance d0; γ represents the electromagnetic signal strength attenuation index. This indicates that the mean is μ and the variance is σ. 2 Random variables that conform to a Gaussian distribution.

5. The communication coverage supplementation method based on electromagnetic signal situation as described in claim 1, characterized in that, S2 specifically includes the following steps: S21: Deploy K1 unmanned system nodes within the target area; where K1 is a positive integer; S22: Obtain the electromagnetic signal intensity map of the target area using the location information of K1 unmanned system nodes and the signal attenuation model.

6. The communication coverage supplementation method based on electromagnetic signal situation as described in claim 1, characterized in that, S3 specifically includes the following steps: S31: Rasterize the electromagnetic signal intensity map; S32: Determine the number of unmanned system nodes to be deployed in the target area based on the gridded electromagnetic signal intensity map.

7. The communication coverage supplementation method based on electromagnetic signal situation as described in claim 6, characterized in that, The number of unmanned system nodes to be deployed in the target area ;in, This indicates the number of grids in the electromagnetic signal strength graph where the electromagnetic signal strength is less than a preset threshold. This indicates the number of grid cells covered within the sensing radius of the unmanned system node to be deployed.

8. The communication coverage supplementation method based on electromagnetic signal situation as described in claim 1, characterized in that, S4 specifically includes the following steps: S41: Initialize the position matrix and velocity matrix Where N represents the number of initial positions and velocities of a certain unmanned system node to be deployed; S42: Initialize the individual optimal position matrix ; Initialize the global optimal position ;in, express The fitness is based on the fitness function: get, RSSI represents the sum of the electromagnetic signal strengths of all grid cells within the sensing radius of an unmanned system node deployed at location x; r represents the total number of grid cells within the sensing radius of the deployed unmanned system node. i This represents the electromagnetic signal strength of the i-th grid cell; S43: Update speed matrix ,in, w represents the inertia weight, c1 and c2 represent the learning factors, and t = 0, 1, 2, ..., T max ;T max Indicates the number of iterations; S44: Update the position matrix ,in, Among them, if If the value exceeds the search space range, it is set to the corresponding search space range boundary value; S45; Calculation fitness ; like Then update ; like ,but Finally, the updated individual optimal position matrix is ​​obtained. ; S46: In the individual optimal position matrix In the case where there exists i such that Then update the global optimal position. If there is no i such that Then the global optimal position ; S47: Repeat S43-S46 continuously until the maximum number of iterations T is reached. max The final globally optimal position obtained is the final deployment position of the unmanned system node to be deployed; S48: Repeat S41-S47 continuously until the deployment locations of K2 unmanned system nodes to be deployed are obtained.

9. The communication coverage supplementation method based on electromagnetic signal situation as described in claim 1, characterized in that, S5 specifically includes the following steps: S51: Based on each deployed unmanned system node Location information is used to generate location data for each deployed unmanned system node using the Voronoi diagram algorithm. Generate a Voronoi element; where, ; ; S52: Obtain all nodes related to the selected unmanned system based on the depth-first search algorithm. Neighboring unmanned system nodes with potential coverage relationships From nearby unmanned system nodes Selecting nodes in the unmanned system Nearby unmanned system nodes within the perception radius will be selected and compared with the chosen unmanned system node. They are assigned to the same point set; among them, ; ;like and If they are adjacent, it is assumed that there is a potential covering relationship between them; S53: Repeat S52 continuously until all deployed unmanned system nodes are connected. Divide the points into corresponding sets to obtain M disconnected point sets. ;in, ; .

10. A communication coverage supplementation method based on electromagnetic signal situation as described in claim 9, characterized in that, S6 specifically includes the following steps: S61: From Select the set of connected points that contains the fewest nodes of the deployed unmanned system. ;in, ; S62: Calculation Each of the deployed unmanned system nodes in China and The distances between each deployed unmanned system node are calculated, and the node corresponding to the shortest distance is obtained. , as well as The set of points to which it belongs ,in ; ; ; ; S63: From Selected from Deployed unmanned system nodes within the perception radius obtain neighbor node set ; from Selected from Deployed unmanned system nodes within the perception radius obtain neighbor node set ; S64: Determine if... Are there any deployed unmanned system nodes? This enables scheduling Can To connect and If it exists, then schedule. Will and Merge them into a new set of connected vertices; if it does not exist, then determine the connection for... Are there any deployed unmanned system nodes? This enables scheduling It can be connected and If it exists, then schedule. Will and Merge into a new set of connected vertices; if it does not exist, then... and A new unmanned system node will be deployed at the middle position; S65: Repeat the above steps until you have M unconnected point sets. Merge them into a single set of connected points.

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