Communication coverage point complementing method based on electromagnetic signal situation

Abstract

The invention discloses a communication coverage point complementing method based on an electromagnetic signal situation. The method comprises the following steps: S1, determining a signal attenuation model by using electromagnetic signal intensity collected by an unmanned system node; s2, generating an electromagnetic signal intensity graph 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 intensity graph; s4, determining the optimal deployment position of the unmanned system node to be deployed by using a particle swarm optimization algorithm, and deploying the unmanned system node at the optimal deployment position; s5, dividing all deployed unmanned system nodes into corresponding point sets based on a Voronoi graph algorithm and a depth-first search algorithm, and obtaining M point sets {C1, C2,..., Ci,..., CM} which are not communicated with one another; and S6, combining all the point sets in the S5 into a point set in a mode of scheduling deployed unmanned system nodes or newly adding unmanned system nodes. According to the invention, the cost is low, and efficient and accurate coverage can be realized.

Description

Technical Field

[0001] The present invention relates to the field of distributed unmanned systems and communication technology, and more specifically to a communication coverage point filling method based on electromagnetic signal situation. Background Art

[0002] With the rapid development of science and technology and the acceleration of urbanization, the development of underground space is accelerating, and its use area is also steadily increasing. The demand for its maintenance and emergency measures is becoming increasingly prominent. At the same time, the stability of underground space is also directly related to social stability. Once a safety accident occurs, the consequences will be disastrous. The efficiency, accuracy and safety of unmanned systems will greatly improve the maintenance and emergency management level of underground space, and provide strong guarantees for the safety, stability and development of cities. However, in complex underground environments, due to interference from factors such as terrain, obstacles, and electromagnetic shielding, ground unmanned system nodes such as unmanned vehicles often find it difficult to achieve complete coverage of the target area, resulting in blind spots in electromagnetic signal situational awareness. Traditional coverage optimization methods usually rely on 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 supplement method that is not only low-cost but also can achieve efficient and accurate coverage is a problem that technical personnel in this field urgently need to solve. Summary of the invention

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

[0005] In order to achieve the above object, the present invention adopts the following technical solution:

[0006] A communication coverage point filling 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 node;

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

[0009] S3: Determine the number of unmanned system nodes to be deployed in the target area using the electromagnetic signal strength map;

[0010] S4: using a particle swarm optimization algorithm to determine the optimal deployment location of the unmanned system node to be deployed, and deploying 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 to obtain M disconnected point sets {C 1,C 2 ,...,C i ,..,C M};

[0012] S6: All point sets in S5 are merged into one point set by scheduling deployed unmanned system nodes or adding new unmanned system nodes.

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

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

[0015] S12: Get different deployment distances d i The electromagnetic signal strength collected by the receiving node is Among them, d i represents the deployment distance between the transmitting node and the receiving node; 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: Using deployment distance d i and electromagnetic signal strength The signal attenuation model is obtained by fitting various parameters of the semi-empirical model.

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

[0018] S131: Calculate the deployment distance d i The electromagnetic signal strength collected by the receiving node is The average and the residual error

[0019] S132: Calculate the deployment distance d using the Bessel formula i The electromagnetic signal strength collected by the receiving node is Standard Deviation

[0020] S133: Determine whether i and j exist such that If it exists, the corresponding electromagnetic signal strength is eliminated to obtain the electromagnetic signal strength after screening;

[0021] S134: Using the Electromagnetic Signal Strength After Screening And the corresponding deployment distance d i The signal attenuation model is obtained by fitting various parameters in the semi-empirical model.

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

[0023]

[0024] Where PL(d) represents the electromagnetic signal strength attenuation value from the transmitting node to the receiving node; the electromagnetic signal strength attenuation value is the difference between the electromagnetic signal strength transmitted 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; PL(d 0 ) indicates that at the reference distance d 0 The electromagnetic signal strength attenuation value at the location; γ represents the electromagnetic signal strength attenuation index; N(μ,σ 2 ) means the mean is μ and the variance is σ 2 , a random variable that conforms to a Gaussian distribution.

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

[0026] S21: Deploy K in the target area 1 unmanned system nodes; among them, K 1 is a positive integer;

[0027] S22: Using K 1 The electromagnetic signal strength map of the target area is obtained by using the location information of the unmanned system nodes and the signal attenuation model.

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

[0029] S31: rasterizing the electromagnetic signal strength map;

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

[0031] Preferably, the number of unmanned system nodes to be deployed in the target area Among them, n no Indicates the number of grids in the electromagnetic signal strength map whose electromagnetic signal strength is less than a preset threshold; n r Indicates the number of grids covered within the perception radius of the unmanned system node to be deployed.

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

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

[0034] S42: Initialize individual optimal position moment Initialize the global optimal position in, express The fitness of is based on the fitness function: f(x) represents the sum of the electromagnetic signal strengths of all grids within the sensing radius when the unmanned system node is deployed at x; r represents the total number of grids within the sensing radius of the deployed unmanned system node, RSSI i Represents the electromagnetic signal strength of the i-th grid.

[0035] S43: Update speed matrix in, w represents the inertia weight, c 1 and c 2 represents the learning factor, t=0.1.2...T max ; T max Indicates the number of iterations;

[0036] S44: Update position matrix in, Among them, if If it exceeds the search space range, it is set to the corresponding search space range boundary value;

[0037] S45; Calculation Fitness

[0038] like Update

[0039] like but Finally, the updated individual optimal position matrix is ​​obtained

[0040] S46: In the individual optimal position matrix If there exists i such that Then update the global optimal position If there does not exist i such that The global optimal position

[0041] S47: Repeat S43-S46 until the maximum number of iterations T is reached max , the global optimal position finally obtained is the final deployment position of the unmanned system node to be deployed;

[0042] S48: Repeat S41-S47 until K is obtained. 2 The deployment location of the unmanned system node to be deployed.

[0043] Preferably, S5 specifically includes the following steps:

[0044] S51: Based on each deployed unmanned system node D i The Voronoi diagram algorithm is used to calculate the location information of each deployed unmanned system node D i Generate a Voronoi cell; where D i ∈D;

[0045]

[0046] S52: Obtain all nodes D related to the selected unmanned system based on the depth-first search algorithm j Adjacent unmanned system node D with potential coverage relationship k ; From the adjacent unmanned system node D k Filter out the nodes D in the selected unmanned system j The adjacent unmanned system nodes within the sensing radius are compared with the selected unmanned system nodes D j Divided into the same point set; among them, D j ∈D;D k ∈D; if D j With D k If they are adjacent, it is considered that there is a potential covering relationship between the two.

[0047] S53: Repeat S52 until all deployed unmanned system nodes D i Divide into corresponding point sets, and obtain M disconnected point sets {C 1 ,C 2 ,...,C i ,..,C M}; where C = {C 1 ,C 2 ,...,C i ,..,C M};

[0048]

[0049] Preferably, S6 specifically includes the following steps:

[0050] S61: From {C 1 ,C 2 ,...,C i ,..,C M} Select the connected point set C containing the least number of deployed unmanned system nodes min Among them, C min ∈C;

[0051] S62: Calculate C min Each deployed unmanned system node and C no-minThe distance between each deployed unmanned system node in the node, and obtain the deployed unmanned system node v corresponding to the shortest distance min 、u min and u min Point set C nearest , where C no-min ∪C min =C;v min ∈C min ;u min ∈C nearest ;

[0052] C nearest =C i And C nearest ≠C min ;

[0053] S63: From C min Filter out v min Detect deployed unmanned system nodes within the radius and obtain v min The neighbor node set N v ;

[0054] From C nearest Filter out u min The deployed unmanned system nodes within the sensing radius are obtained min The neighbor node set N u ;

[0055] S64: Determine whether v min Is there a deployed unmanned system node u∈N u , so that C can be connected by scheduling u min and C nearest , if it exists, then schedule u to C min and C nearest Merge into a new connected point set; if it does not exist, then determine for u min Is there a deployed unmanned system node v∈N v , so that C can be connected by scheduling v min and C nearest ; If it exists, schedule v to C min and C nearest Merge into a new connected point set; if it does not exist, then in v min and u min Deploy a new unmanned system node at the middle position;

[0056] S65: Repeat the above steps until M disconnected point sets {C 1 ,C 2 ,...,C i ,..,C M} are merged into a connected point set.

[0057] It can be seen from the above technical solution that, compared with the prior art, the present invention discloses a communication coverage point filling method based on electromagnetic signal status, which is not only low-cost but also can achieve efficient and accurate coverage. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0059] Figure 1 A flow chart of a communication coverage point filling method based on electromagnetic signal situation provided by the present invention;

[0060] Figure 2 A schematic diagram of generating corresponding Voronoi cells for deployed unmanned system nodes provided by the present invention. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] like Figure 1 As shown, an embodiment of the present invention discloses a communication coverage point filling method based on electromagnetic signal situation, comprising the following steps:

[0063] S1: Determine the signal attenuation model using the electromagnetic signal strength collected by the unmanned system node;

[0064] In one embodiment, S1 specifically includes the following steps:

[0065] S11: deploy two unmanned system nodes in the target area; wherein one of the two unmanned system nodes is used as a transmitting node and the other is used as a receiving node;

[0066] 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.

[0067] When a drone is deployed, the flight altitude of the drone is ignored and only the signal attenuation in the two-dimensional space is considered.

[0068] The two deployed unmanned system nodes are only used to fit the determined signal attenuation model and are not actually deployed nodes.

[0069] S12: Get different deployment distances d i The electromagnetic signal strength collected by the receiving node is Among them, d i represents the deployment distance between the transmitting node and the receiving node; 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;

[0070] Specifically: When the distance between the transmitting node and the receiving node is d 1 When the receiving node collects the electromagnetic signal strength n times, the electromagnetic signal strength is obtained

[0071] When the distance between the transmitting node and the receiving node is d 2 When the receiving node collects the electromagnetic signal strength n times, the electromagnetic signal strength is obtained ...

[0073] When the distance between the transmitting node and the receiving node is d m When the receiving node collects the electromagnetic signal strength n times, the electromagnetic signal strength is obtained

[0074] S13: Using deployment distance d i and electromagnetic signal strength The signal attenuation model is obtained by fitting various parameters of the semi-empirical model.

[0075] In one embodiment, S13 specifically includes the following steps:

[0076] S131: Calculate the deployment distance d i The electromagnetic signal strength collected by the receiving node is The average and the residual error

[0077] S132: Calculate the deployment distance d using the Bessel formula i The electromagnetic signal strength collected by the receiving node is Standard Deviation

[0078] S133: Determine whether i and j exist such that If it exists, the corresponding electromagnetic signal strength is eliminated to obtain the electromagnetic signal strength after screening;

[0079] Specifically: If there exists i=2, j=1 such that If established, then remove Corresponding electromagnetic signal strength

[0080] Suppose there exists i=3, j=2 such that If established, then remove Corresponding electromagnetic signal strength

[0081] S134: Using the Electromagnetic Signal Strength After Screening And the corresponding deployment distance d i The signal attenuation model is obtained by fitting various parameters in the semi-empirical model.

[0082] In one embodiment, the semi-empirical model is expressed as:

[0083]

[0084] Where PL(d) represents the electromagnetic signal strength attenuation value from the transmitting node to the receiving node; the electromagnetic signal strength attenuation value is the difference between the electromagnetic signal strength transmitted 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; PL(d 0 ) indicates that at the reference distance d 0 The electromagnetic signal strength attenuation value at the location; γ represents the electromagnetic signal strength attenuation index; N(μ,σ 2 ) means the mean is μ and the variance is σ 2 , a random variable that conforms to a Gaussian distribution.

[0085] Specifically: the electromagnetic signal strength attenuation value from the transmitting node to the receiving node ( Indicates that the transmitting node and the receiving node are deployed at a distance d i When the electromagnetic signal strength emitted by the transmitting node is substituted into PL(d) in the semi-empirical model, d i Substitute d in the semi-empirical model to fit γ, μ and σ in the semi-empirical model 2 , we can obtain γ, μ and σ 2 Specific values ​​of γ, μ and σ 2 Substitute the specific value of into the formula The signal attenuation model can be obtained.

[0086] S2: Generate an electromagnetic signal strength map of the target area using the signal attenuation model;

[0087] In one embodiment, S2 specifically includes the following steps:

[0088] S21: Deploy K in the target area 1 unmanned system nodes; among them, K 1 is a positive integer;

[0089] It is understandable that: K 1 The number of unmanned system nodes refers to the total number of K in the target area after deployment. 1 Unmanned system node.

[0090] The K 1 An unmanned system node can select a ground unmanned system node, such as an unmanned vehicle, and each unmanned system node has the function of transmitting and receiving electromagnetic signals.

[0091] S22: Using K 1 The electromagnetic signal strength map of the target area is obtained by using the location information of the unmanned system nodes and the signal attenuation model.

[0092] S3: Determine the number of unmanned system nodes to be deployed in the target area using the electromagnetic signal strength map;

[0093] In one embodiment, S3 specifically includes the following steps:

[0094] S31: rasterizing the electromagnetic signal strength map;

[0095] S32: Determine the number of unmanned system nodes to be deployed in the target area based on the gridded electromagnetic signal strength map.

[0096] In one embodiment, the number of unmanned system nodes to be deployed in the target area Among them, n no Indicates the number of grids in the electromagnetic signal strength map whose electromagnetic signal strength is less than a preset threshold; n r Indicates the number of grids covered within the perception radius of the unmanned system node to be deployed.

[0097] S4: using a particle swarm optimization algorithm to determine the optimal deployment location of the unmanned system node to be deployed, and deploying the unmanned system node at the optimal deployment location;

[0098] It can be understood that the unmanned system node to be deployed in the present invention can be an aerial unmanned system node, such as a drone, and each unmanned system node to be deployed has the function of transmitting and receiving electromagnetic signals.

[0099] When selecting a drone, the node of the unmanned system to be deployed ignores the flight altitude of the drone and only considers the signal attenuation in the two-dimensional space.

[0100] In one embodiment, S4 specifically includes the following steps:

[0101] S41: Initialize position matrix and the velocity matrix Wherein, N represents the number of positions and speeds initialized for a certain unmanned system node to be deployed; that is, the present invention initializes N positions and N speeds for a node to be deployed;

[0102] It is understandable that: Initialization falls within the preset search space range (i.e. The horizontal coordinate is within the horizontal search space [lbx,ubx], and the vertical coordinate is within the vertical search space [lby,uby]).

[0103] S42: Initialize individual optimal position moment Initialize the global optimal position in, express The fitness of is based on the fitness function: f(x) represents the coverage benefit after the deployment of the unmanned system node, specifically the sum of the electromagnetic signal strengths of all grids within the sensing radius when the unmanned system node is deployed at x; r represents the total number of grids within the sensing radius of the deployed unmanned system node, RSSI i Represents the electromagnetic signal strength of the i-th grid.

[0104] Therefore, f(P i (0) ) 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;

[0105] S43: Update speed matrix in,

[0106] w represents the inertia weight, c 1 and c 2 represents the learning factor, 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 position matrix in, Among them, if Beyond the search space (i.e. If the horizontal coordinate of is not within the horizontal search space [lbx, ubx], and the vertical coordinate is not within the vertical search space [lby, uby]), it is set 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 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 is 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 is The sum of the electromagnetic signal strengths of all grids within its sensing radius;

[0116] S46: In the individual optimal position matrix If there exists i such that Then update the global optimal position If there does not exist i such that The global optimal position

[0117] It is understandable that:

[0118] f(P i (t+1) ) indicates P i (t+1) The fitness of the unmanned system node to be deployed is 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 is The sum of the electromagnetic signal strengths of all grids within its sensing radius;

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

[0121] S48: Repeat S41-S47 until K is obtained. 2 The deployment location of the unmanned system node to be deployed.

[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 to obtain M disconnected point sets {C 1 ,C 2 ,...,C i ,..,C M};

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

[0124] S51: Based on each deployed unmanned system node D i The Voronoi diagram algorithm is used to calculate the location information of each deployed unmanned system node D i Generate a Voronoi cell; where D i ∈D;

[0125]

[0126] like Figure 2 As shown, the red dot in the figure indicates that the unmanned system node D has been deployed i The polygon where each red dot is located is the Voronoi cell corresponding to the unmanned system node.

[0127] S52: Obtain all nodes D related to the selected unmanned system based on the depth-first search algorithm j Adjacent unmanned system node D with potential coverage relationship k ; From the adjacent unmanned system node D k Filter out the nodes D in the selected unmanned system j The adjacent unmanned system nodes within the sensing radius are compared with the selected unmanned system nodes D j Divided into the same point set; among them, D j ∈D;D k ∈D; if D j With D kIf they are adjacent, it is considered that there is a potential covering relationship between the two.

[0128] S53: Repeat S52 until all deployed unmanned system nodes D i Divide into corresponding point sets, and obtain M disconnected point sets {C 1 ,C 2 ,...,C i ,..,C M}; where C = {C 1 ,C 2 ,...,C i ,..,C M};

[0129]

[0130] S6: All point sets in S5 are merged into one point set by scheduling deployed unmanned system nodes or adding new unmanned system nodes.

[0131] In one embodiment, S6 specifically includes the following steps:

[0132] S61: From {C 1 ,C 2 ,...,C i ,..,C M} Select the connected point set C containing the least number of deployed unmanned system nodes min (i.e. |C min |=min 1≤i≤M C i ), where C min ∈C;

[0133] S62: Calculate C min Each deployed unmanned system node and C no-min The distance between each deployed unmanned system node in the node, and obtain the deployed unmanned system node v corresponding to the shortest distance min 、u min and u min Point set C nearest , where C no-min ∪C min =C;v min ∈C min ;u min ∈C nearest ;

[0134] C nearest =C i And C nearest ≠C min ;

[0135] S63: From Cmin Filter out v min Detect deployed unmanned system nodes within the radius and obtain v min The neighbor node set N v ;

[0136] From C nearest Filter out u min The deployed unmanned system nodes within the sensing radius are obtained min The neighbor node set N u ;

[0137] S64: Determine whether v min Is there a deployed unmanned system node u∈N u , so that by scheduling u (specifically, u can be moved to v min and u min The middle position of C min and C nearest , if it exists, schedule u (specifically, move u to v min and u min The middle position of C min and C nearest Merge into a new connected point set; if it does not exist, then determine for u min Is there a deployed unmanned system node v∈N v , so that by scheduling v (specifically, v can be moved to v min and u min The middle position of C min and C nearest ; If it exists, schedule v (specifically, move v to v min and u min The middle position of C min and C nearest Merge into a new connected point set; if it does not exist, then in v min and u min Deploy a new unmanned system node at the middle position;

[0138] S65: Repeat the above steps until M disconnected point sets {C 1 ,C 2 ,...,C i ,..,C M} are merged into a connected point set.

[0139] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0140] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one 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 present invention. Therefore, the present invention is not limited to the embodiments shown herein.

[0141] Rather, it is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A communication coverage point filling method based on electromagnetic signal situation, characterized in that: The following steps are involved: S1: Determine the signal attenuation model using the electromagnetic signal strength collected by the unmanned system node; S2: Generate an electromagnetic signal strength map of the target area using the signal attenuation model; S3: Determine the number of unmanned system nodes to be deployed in the target area using the electromagnetic signal strength map; S4: using a particle swarm optimization algorithm to determine the optimal deployment location of the unmanned system node to be deployed, and deploying the unmanned system node at the optimal deployment location; S5: Based on the Voronoi diagram algorithm and depth-first search algorithm, all deployed unmanned system nodes are divided into corresponding point sets to obtain M disconnected point sets {C1, C2, ..., C i ,..,C M }; S6: All point sets in S5 are merged into one point set by scheduling deployed unmanned system nodes or adding new unmanned system nodes.

2. According to the method for filling communication coverage points based on electromagnetic signal situation of claim 1, it is 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 is used as a transmitting node and the other is used as a receiving node; S12: Get different deployment distances d i The electromagnetic signal strength collected by the receiving node is Among them, d i represents the deployment distance between the transmitting node and the receiving node; 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: Using deployment distance d i and electromagnetic signal strength The signal attenuation model is obtained by fitting various parameters of the semi-empirical model.

3. The communication coverage point filling method based on electromagnetic signal situation according to claim 2 is characterized in that: S13 specifically includes the following steps: S131: Calculate the deployment distance d i The electromagnetic signal strength collected by the receiving node is The average and the residual error S132: Calculate the deployment distance d using the Bessel formula i The electromagnetic signal strength collected by the receiving node is Standard Deviation S133: Determine whether i and j exist such that If it exists, the corresponding electromagnetic signal strength is eliminated to obtain the electromagnetic signal strength after screening; S134: Using the Electromagnetic Signal Strength After Screening And the corresponding deployment distance d i The signal attenuation model is obtained by fitting various parameters in the semi-empirical model.

4. The communication coverage point filling method based on electromagnetic signal situation according to claim 3 is characterized in that: The expression of the semi-empirical model is: Wherein, PL(d) 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 transmitted 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; PL(d0) represents the electromagnetic signal strength attenuation value at the reference distance d0; γ represents the electromagnetic signal strength attenuation index; N(μ,σ2) represents the mean μ and the variance σ 2 , a random variable that conforms to a Gaussian distribution.

5. The communication coverage point filling method based on electromagnetic signal situation according to claim 1 is characterized in that: S2 specifically includes the following steps: S21: deploy K1 unmanned system nodes in the target area; where K1 is a positive integer; S22: Using the location information of K1 unmanned system nodes and the signal attenuation model, an electromagnetic signal strength map of the target area is obtained.

6. The communication coverage point filling method based on electromagnetic signal situation according to claim 1 is characterized in that: S3 specifically includes the following steps: S31: rasterizing the electromagnetic signal strength map; S32: Determine the number of unmanned system nodes to be deployed in the target area based on the gridded electromagnetic signal strength map.

7. A communication coverage point filling method based on electromagnetic signal situation according to claim 6, characterized in that: The number of unmanned system nodes to be deployed in the target area Among them, n no Indicates the number of grids in the electromagnetic signal strength map whose electromagnetic signal strength is less than a preset threshold; n r Indicates the number of grids covered within the perception radius of the unmanned system node to be deployed.

8. The communication coverage point filling method based on electromagnetic signal situation according to claim 1 is characterized in that: S4 specifically includes the following steps: S41: Initialize position matrix and the velocity matrix Where N represents the number of positions and velocities initialized for a certain unmanned system node to be deployed; S42: Initialize individual optimal position moment Initialize the global optimal position in, express The fitness of is based on the fitness function: f(x) represents the sum of the electromagnetic signal strengths of all grids within the sensing radius when the unmanned system node is deployed at x; r represents the total number of grids within the sensing radius of the deployed unmanned system node, RSSI i Represents the electromagnetic signal strength of the i-th grid. S43: Update speed matrix in, w represents the inertia weight, c1 and c2 represent the learning factors, t=0.1.2...T max ; T max Indicates the number of iterations; S44: Update position matrix in, Among them, if If it exceeds the search space range, it is set to the corresponding search space range boundary value; S45; Calculation Fitness like Update like but Finally, the updated individual optimal position matrix is ​​obtained S46: In the individual optimal position matrix If there exists i such that Then update the global optimal position If there does not exist i such that The global optimal position S47: Repeat S43-S46 until the maximum number of iterations T is reached max , the global optimal position finally obtained is the final deployment position of the unmanned system node to be deployed; S48: Repeat S41-S47 continuously until the deployment positions of K2 unmanned system nodes to be deployed are obtained.

9. The communication coverage point filling method based on electromagnetic signal situation according to claim 1 is characterized in that: S5 specifically includes the following steps: S51: Based on each deployed unmanned system node D i The Voronoi diagram algorithm is used to calculate the location information of each deployed unmanned system node D i Generate a Voronoi cell; where D i ∈D; S52: Obtain all nodes D related to the selected unmanned system based on the depth-first search algorithm j Adjacent unmanned system node D with potential coverage relationship k ; From the adjacent unmanned system node D k Filter out the nodes D in the selected unmanned system j The adjacent unmanned system nodes within the sensing radius are compared with the selected unmanned system nodes D j Divided into the same point set; among them, D j ∈D;D k ∈D; if D j With D k If they are adjacent, it is considered that there is a potential covering relationship between the two. S53: Repeat S52 until all deployed unmanned system nodes D i Divide into corresponding point sets and obtain M disconnected point sets {C1,C2,...,C i ,..,C M }; where C={C1,C2,...,C i ,..,C M }; and C i ∩C j =Φ,C j ∈C,C i ∈C.

10. A communication coverage point filling method based on electromagnetic signal situation according to claim 9, characterized in that: S6 specifically includes the following steps: S61: From {C1,C2,...,C i ,..,C M } Select the connected point set C containing the least number of deployed unmanned system nodes min Among them, C min ∈C; S62: Calculate C min Each deployed unmanned system node and C no-min The distance between each deployed unmanned system node in the node, and obtain the deployed unmanned system node v corresponding to the shortest distance min 、u min and u min Point set C nearest , where C no-min ∪C min =C;v min ∈C min ;u min ∈C nearest ; C nearest =C i And C nearest ≠C min ; S63: From C min Filter out v min Detect deployed unmanned system nodes within the radius and obtain v min The neighbor node set N v ; From C nearest Filter out u min The deployed unmanned system nodes within the sensing radius are obtained min The neighbor node set N u ; S64: Determine whether v min Is there a deployed unmanned system node u∈N u , so that C can be connected by scheduling u min and C nearest , if it exists, then schedule u to C min and C nearest Merge into a new connected point set; if it does not exist, then determine for u min Is there a deployed unmanned system node v∈N v , so that C can be connected by scheduling v min and C nearest ; If it exists, schedule v to C min and C nearest Merge into a new connected point set; if it does not exist, then in v min and u min Deploy a new unmanned system node at the middle position; S65: Repeat the above steps until M unconnected point sets {C1, C2, ..., C i ,..,C M } are merged into a connected point set.

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