Unmanned aerial vehicle deployment method, device, equipment and storage medium

Abstract

The invention discloses an unmanned aerial vehicle deployment method and device, equipment and a storage medium. The method comprises the following steps: constructing an unmanned aerial vehicle signal attenuation model and a power consumption model; calculating the signal-to-noise ratio of the Internet of Things terminal, the ground coverage probability and the communication capacity of the unmanned aerial vehicle according to the unmanned aerial vehicle signal attenuation model; calculating the effective coverage radius of the unmanned aerial vehicle according to the ground coverage probability; calculating the total energy consumption of the unmanned aerial vehicle in the flying and hovering processes according to a power energy consumption model; constructing a target function according to the communication capacity and the effective coverage radius; constructing constraint conditions of unmanned aerial vehicle deployment according to the total energy consumption, the signal-to-noise ratio and the effective coverage radius; outputting an optimal deployment position by using a distributed particle swarm optimization algorithm; and controlling the unmanned aerial vehicle to operate according to the optimal deployment position. By considering the effective coverage area and the communication capacity under the condition of multi-unmanned aerial vehicle interference, the practicability of unmanned aerial vehicle auxiliary communication is improved, and the optimal deployment position of the unmanned aerial vehicle can be quickly solved through the distributed particle swarm optimization algorithm.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle (UAV) application technology, and in particular to a UAV deployment method, device, equipment and storage medium. Background Art

[0002] Due to their low cost and high flexibility, drones are currently used to provide flexible information coverage services. The use of multiple drones for dynamic deployment and collaborative connection coverage can significantly improve communication service capabilities.

[0003] When deploying drones in the existing technology, it is difficult for drones to cover a large area due to limitations in energy, communication distance, etc. How to find the best deployment position for multiple drones to fly under the interference constraints between multiple drones, and ensure that the drone coverage area and communication capacity are maximized, and improve the practicality of drone-assisted communication, is a key problem that needs to be solved. Summary of the invention

[0004] The purpose of the embodiment of the present invention is to provide a drone deployment method, which can realize the coordinated flight of multiple drones under the interference constraints between drones, ensure the maximization of the drone coverage area and the maximization of the communication capacity, and quickly solve the optimal deployment positions of multiple drone flights.

[0005] In order to solve the above technical problems, in a first aspect, an embodiment of the present invention provides a method for deploying a drone, comprising:

[0006] Construct a drone signal attenuation model and power energy consumption model involving at least two drones;

[0007] The signal-to-noise ratio, ground coverage probability and communication capacity of the Internet of Things terminal are calculated according to the UAV signal attenuation model; wherein the Internet of Things terminal is used for data transmission with the UAV;

[0008] Calculate the effective coverage radius of the drone based on the ground coverage probability;

[0009] Calculate the total energy consumption of the UAV during flight and hovering according to the power energy consumption model;

[0010] Constructing an objective function for drone deployment according to the communication capacity and the effective coverage radius; wherein the objective function aims to maximize the communication capacity and the coverage area;

[0011] Constructing constraints for drone deployment according to the total energy consumption, the signal-to-noise ratio, and the effective coverage radius;

[0012] Outputting optimal deployment positions of at least two UAVs using a distributed particle swarm optimization algorithm according to the objective function and the constraint conditions;

[0013] The operation of the UAV is controlled according to the optimal deployment position.

[0014] Furthermore, the signal-to-noise ratio, ground coverage probability and communication capacity of the Internet of Things terminal are calculated according to the UAV signal attenuation model; wherein the Internet of Things terminal is used to transmit data with the UAV, including:

[0015] Calculate the signal-to-noise ratio of the IoT terminal according to the total transmission attenuation value in the drone signal attenuation model, the drone operating power, the IoT terminal noise power, and the drone antenna gain;

[0016] Calculating the ground coverage probability of the drone according to the transmission probability of the line-of-sight channel and the transmission probability of the non-line-of-sight channel in the drone signal attenuation model;

[0017] Calculate the communication capacity of a single UAV according to the total transmission attenuation value in the signal attenuation model, the bandwidth of the UAV, the operating power of the UAV, and the Gaussian noise variance of the terminal node;

[0018] The communication capacity of the drone cluster is calculated based on the communication capacity of the single drone, the hovering time of the drone, the number of drones and the number of IoT terminal nodes.

[0019] Further, the calculating the effective coverage radius of the drone according to the ground coverage probability includes:

[0020] The effective coverage radius of the UAV is calculated according to the ground coverage probability and the ground coverage probability threshold.

[0021] Furthermore, the total energy consumption of the UAV during flight and hovering is calculated according to the power energy consumption model, including:

[0022] Calculate the total power of the UAV during uniform flight and the induced power during hovering; wherein the total power during uniform flight includes an induced power component, a vertical flight power component, and a wind resistance power component;

[0023] The total energy consumption of the UAV during the flight and hovering process is calculated according to the total power during the uniform speed flight process and the induced power during the hovering process.

[0024] Furthermore, outputting the optimal deployment positions of at least two UAVs using a distributed particle swarm optimization algorithm according to the objective function and the constraint conditions includes:

[0025] Using the distributed particle swarm optimization algorithm, the drone is abstracted into particles, and the position variables of each particle are iteratively optimized;

[0026] Substitute the iterative optimization result into the objective function, solve the optimal solution of the objective function while satisfying the constraint conditions, and output the optimal deployment position of the UAV.

[0027] In order to solve the above technical problems, in a second aspect, an embodiment of the present invention provides a drone deployment device, comprising:

[0028] A model building module, used to build a drone signal attenuation model and a power energy consumption model including at least two drones;

[0029] A communication capacity calculation module, used to calculate the signal-to-noise ratio, ground coverage probability and communication capacity of the Internet of Things terminal according to the UAV signal attenuation model; wherein the Internet of Things terminal is used to transmit data with the UAV;

[0030] An effective coverage radius calculation module, used to calculate the effective coverage radius of the drone according to the ground coverage probability;

[0031] A total energy consumption calculation module is used to calculate the total energy consumption of the UAV during flight and hovering according to the power energy consumption model;

[0032] An objective function construction module is used to construct an objective function for UAV deployment according to the communication capacity and the effective coverage radius, wherein the objective function aims at maximizing the communication capacity and the coverage area;

[0033] A constraint condition building module, used to build a constraint condition for UAV deployment according to the total energy consumption, the signal-to-noise ratio and the effective coverage radius;

[0034] An optimal deployment position output module, used to output the optimal deployment positions of at least two UAVs using a distributed particle swarm optimization algorithm according to the objective function and the constraint conditions;

[0035] The UAV control module is used to control the operation of the UAV according to the optimal deployment position.

[0036] In order to solve the above technical problems, in a third aspect, an embodiment of the present invention provides an electronic device, including:

[0037] Memory for storing computer programs;

[0038] A processor, configured to execute the computer program;

[0039] Wherein, when the processor executes the computer program, the drone deployment method described in any one of the first aspects above is implemented.

[0040] In order to solve the above technical problems, in a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, and when the computer program is executed, the drone deployment method described in any one of the above-mentioned first aspects is implemented.

[0041] Compared with the prior art, the method, device, equipment and storage medium for deploying drones provided by the embodiments of the present invention have the following beneficial effects: constructing a drone signal attenuation model and a power energy consumption model including at least two drones, calculating the signal-to-noise ratio of the IoT terminal, the ground coverage probability and the communication capacity of the drone according to the drone signal attenuation model, calculating the effective coverage radius of the drone according to the ground coverage probability, establishing an objective function and constraint conditions, and outputting the optimal deployment position of at least two drones using a distributed particle swarm optimization algorithm. By considering the effective coverage area and communication capacity of drones under multi-drone interference conditions, the practicality of drone-assisted communication is improved, and the optimal deployment position of the drone can be quickly solved by the distributed particle swarm optimization algorithm, thereby controlling the drone to fly to the optimal deployment position and completing the deployment of multiple drones. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical features of the embodiments of the present invention, the drawings required for use in the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0043] Figure 1 It is a flow chart of a preferred embodiment of a method for deploying a drone provided by the present invention;

[0044] Figure 2 It is a structural schematic diagram of a preferred embodiment of a UAV deployment device provided by the present invention;

[0045] Figure 3 It is a structural schematic diagram of a preferred embodiment of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0046] 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.

[0047] In a first aspect, an embodiment of the present invention provides a method for deploying a drone, see Figure 1 The figure is a flow chart of a preferred embodiment of a UAV deployment method provided by the present invention.

[0048] like Figure 1 As shown, the method comprises the following steps:

[0049] S1: Construct a drone signal attenuation model and power energy consumption model involving at least two drones;

[0050] S2: Calculate the signal-to-noise ratio, ground coverage probability and communication capacity of the Internet of Things terminal according to the UAV signal attenuation model; wherein the Internet of Things terminal is used to transmit data with the UAV;

[0051] S3: Calculating the effective coverage radius of the drone according to the ground coverage probability;

[0052] S4: Calculating the total energy consumption of the UAV during flight and hovering according to the power energy consumption model;

[0053] S5: constructing an objective function for drone deployment according to the communication capacity and the effective coverage radius; wherein the objective function aims to maximize the communication capacity and the coverage area;

[0054] S6: Constructing constraints for drone deployment according to the total energy consumption, the signal-to-noise ratio, and the effective coverage radius;

[0055] S7: Outputting optimal deployment positions of at least two UAVs using a distributed particle swarm optimization algorithm according to the objective function and the constraint conditions;

[0056] S8: Controlling the operation of the UAV according to the optimal deployment position.

[0057] Specifically, the process of constructing the drone signal attenuation model is as follows:

[0058] S101: Calculate the signal attenuation logarithm PL of the air-to-ground LOS channel between the drone and the ground IoT terminal los and the logarithmic value PL of the signal attenuation of the NLOS channel nlos :

[0059]

[0060]

[0061] Among them, LOS channel is line-of-sight channel, NLOS channel is non-line-of-sight channel, d represents the straight-line distance from the drone to the IoT terminal, which is related to the height h of the drone and the horizontal distance r from the drone to the IoT terminal. The specific calculation formula is: f represents the carrier frequency, c represents the speed of light, ηlos represents the attenuation coefficient of the corresponding LoS channel, η nlos Indicates the attenuation coefficient of the corresponding NLoS channel.

[0062] S102: Calculate the transmission probability P of the LoS channel los (θ) and the transmission probability P of the NLoS channel nlos (θ):

[0063]

[0064] P nlos (θ)=1-P los (θ);

[0065] Among them, θ represents the elevation angle of the communication link from the UAV to the IoT terminal, and a and b represent environment-related parameters.

[0066] S103: Calculate the total transmission attenuation value PL(h,r) of the air-ground channel:

[0067]

[0068] In summary, the implementation of the present invention improves the practicability of drone-assisted communication by considering the effective coverage area and communication capacity of drones under multi-drone interference conditions, and can quickly solve the optimal deployment position of drones through the distributed particle swarm optimization algorithm.

[0069] In a preferred embodiment, step S2 specifically includes:

[0070] S201: Calculating the signal-to-noise ratio of the IoT terminal according to the total transmission attenuation value in the drone signal attenuation model, the drone operating power, the IoT terminal noise power, and the drone antenna gain;

[0071] Specifically, the calculation process includes:

[0072] S2011: Calculating the signal-to-noise ratio of IoT terminalsγ 0 : γ 0 =P t -PL(h,r)-P n ;

[0073] Among them, P t Represents the operating power of the drone, P n Indicates the IoT terminal noise power.

[0074] S2012: Calculate the antenna gain G of the drone:

[0075]

[0076] Among them, θ BIndicates the half-beam width of the directional antenna mounted on the drone, expressed in degrees. g(θ) represents the gain of the communication link from the drone to the IoT terminal outside the elevation angle θ.

[0077] S2013: Calculate the signal-to-noise ratio γ of the IoT terminal taking into account the antenna gain: γ = P t +G-PL(h,r)-P n .

[0078] S202: Calculating the ground coverage probability of the drone according to the transmission probability of the line-of-sight channel and the transmission probability of the non-line-of-sight channel in the drone signal attenuation model;

[0079] Specifically, the calculation process includes:

[0080] S2021: Calculate the minimum power P required for the IoT terminal to receive the drone signal min :

[0081]

[0082] Among them, β represents the signal-to-noise ratio threshold, N represents the noise power, It indicates the average interference power of the drones affected by nearby drones.

[0083] S2022:

[0084]

[0085]

[0086] in, Function represents the standard normal distribution random variable P min +PL(h,r)-P t -G+μ los Greater than ψ LoS The probability of LoS It indicates that the communication signal has a normally distributed shadow fading on the dB scale on the LoS channel. Function represents the standard normal distribution random variable P min +PL(h,r)-P t -G+μ nlos Greater than ψ NLoS The probability of NLoS Indicates the shadow fading of the communication signal on the NLoS channel with normal distribution on the dB scale; μ los represents the average value of noise in the unobstructed transmission scenario, μ nlos represents the average value of the noise in the scenario with a small probability of occlusion transmission, σ los represents the standard deviation of noise in the unobstructed transmission scenario, σnlos Indicates the standard deviation of the noise in a scenario with a small probability of occlusion transmission.

[0087] S2023: Calculate the ground coverage probability P of the drone cov :

[0088]

[0089] S203: Calculate the communication capacity of a single UAV according to the total transmission attenuation value in the signal attenuation model, the bandwidth of the UAV, the transmission power of the UAV, and the Gaussian noise variance of the terminal node;

[0090] The communication capacity c of a single UAV is:

[0091] Among them, B represents the bandwidth of the drone, P tr represents the UAV transmission power, σ los 2 represents the Gaussian noise variance of the terminal node under unoccluded conditions.

[0092] S204: Calculating the communication capacity of the drone cluster according to the communication capacity of the single drone, the hovering time of the drone, the number of drones, and the number of IoT terminal nodes;

[0093] The communication capacity C of the drone cluster is:

[0094]

[0095] Among them, n represents the number of IoT terminal nodes, M represents the number of drones, and t represents the hovering time of the drones.

[0096] In a preferred embodiment, step S3 specifically includes:

[0097] The effective coverage radius of the UAV is calculated according to the ground coverage probability and the ground coverage probability threshold.

[0098] Specifically, the effective coverage radius r u The calculation formula is:

[0099] r u =max{r|P cov (r,P t ,θ B )≥ε};

[0100] Among them, r represents the horizontal distance between the drone and the IoT terminal. The horizontal distance is related to the height of the drone. The higher the height, the greater the horizontal distance of the drone. t represents the operating power of the UAV, θ Brepresents the half-beam width of the directional antenna mounted on the UAV, and ε represents the UAV ground coverage probability threshold.

[0101] In a preferred embodiment, step S4 specifically includes:

[0102] S401: Calculate the total power of the UAV during uniform flight and the induced power during hovering; wherein the total power during uniform flight includes an induced power component, a vertical flight power component, and a wind resistance power component;

[0103] Specifically, the calculation process includes:

[0104] S4011: Calculate the induced power component P of the UAV h (v h ):

[0105]

[0106] Among them, v h represents the horizontal speed of the drone, ||v h || represents the absolute value of the horizontal speed of the drone, G 0 Represents gravity, G 0 =mg, m represents the mass of the drone, g represents the acceleration due to gravity, represents the hovering power of the UAV, ρ represents the air density, and A represents the total area of ​​the powered propeller.

[0107] S4012: Calculate the vertical flight power component P of the UAV v (v v ):

[0108] P v (v v )=G 0 v v ;

[0109] Among them, v v Represents the vertical velocity component of the UAV.

[0110] S4013: Calculate wind resistance power component P p (v h ):

[0111]

[0112] in, Represents the drag coefficient of the UAV.

[0113] S4014: Calculate the total power P of the UAV during uniform flight based on the induced power component, vertical flight power component and wind resistance power component R (v):

[0114]

[0115] S4015: Calculate the induced power P of the drone in hovering state R (0):

[0116]

[0117] S402: Calculate the total energy consumption of the UAV during the flight and hovering process according to the total power during the uniform speed flight process and the induced power during the hovering process.

[0118] Specifically, the calculation process is:

[0119]

[0120] Where D is the distance flown by the drone, and t is the hovering time of the drone. The total process of flight and hovering is that the drone flies a distance D from the starting point, hovers for t time, and then flies back to the starting point.

[0121] In a preferred embodiment, step S5 specifically includes:

[0122] An objective function for drone deployment is constructed based on the communication capacity and the effective coverage radius; wherein the objective function aims to maximize the communication capacity and the coverage area.

[0123] In a preferred embodiment, step S6 specifically includes:

[0124] Constraints for drone deployment are constructed based on the total energy consumption, the signal-to-noise ratio, and the effective coverage radius.

[0125] Specifically, the objective function and constraints are:

[0126]

[0127] st

[0128]

[0129]

[0130]

[0131] γ≥β

[0132] In a preferred embodiment, step S7 specifically includes:

[0133] S701: Using the distributed particle swarm optimization algorithm, the drone is abstracted into particles, and the position variables of each particle are iteratively optimized;

[0134] S702: Substitute the iterative optimization result into the objective function, solve the optimal solution of the objective function while satisfying the constraint conditions, and output the optimal deployment position of the UAV.

[0135] Specifically, when the IoT terminal nodes are moving, the greedy algorithm cannot be used to solve the location of the drone deployment. The present invention uses a distributed particle swarm optimization algorithm to solve the location of the drone deployment. Assume that there are m particles (M drones) flying in an N 2 dimensional space, where the position of the mth particle at the lth iteration can be given by Indicates that the fitness function is the above objective function. In the lth iteration, multiple particles have an individual optimal position corresponding to the fitness function value, which is defined as After l iterations, all particles have a current global optimal position, which is defined as: In order to make the particle swarm converge to the global optimal position, the position is updated in combination with the constraint conditions. First, the position combination that satisfies the maximum value of the objective function is verified for the constraint conditions. If it does not meet the requirements of the constraint conditions, the position combination with the second largest objective function value is used for verification. If it meets the requirements, the position combination with the second largest objective function value is used for verification. Otherwise, the position combination with the third largest objective function value is used for verification. This process is repeated to find the optimal deployment positions of m drones.

[0136] In a second aspect, an embodiment of the present invention provides a drone deployment device, see Figure 2 Shown is a schematic structural diagram of a preferred embodiment of a UAV deployment device provided by the present invention.

[0137] like Figure 2 As shown, the device comprises:

[0138] A model building module 21, used to build a drone signal attenuation model and a power energy consumption model including at least two drones;

[0139] The communication capacity calculation module 22 is used to calculate the signal-to-noise ratio, ground coverage probability and communication capacity of the Internet of Things terminal according to the UAV signal attenuation model; wherein the Internet of Things terminal is used to transmit data with the UAV;

[0140] An effective coverage radius calculation module 23, used to calculate the effective coverage radius of the drone according to the ground coverage probability;

[0141] A total energy consumption calculation module 24 is used to calculate the total energy consumption of the UAV during flight and hovering according to the power energy consumption model;

[0142] An objective function construction module 25 is used to construct an objective function for UAV deployment according to the communication capacity and the effective coverage radius, wherein the objective function aims at maximizing the communication capacity and the coverage area;

[0143] A constraint condition building module 26, used to build a constraint condition for UAV deployment according to the total energy consumption, the signal-to-noise ratio and the effective coverage radius;

[0144] An optimal deployment position output module 27, used to output the optimal deployment positions of at least two UAVs using a distributed particle swarm optimization algorithm according to the objective function and the constraint conditions;

[0145] The UAV control module 28 is used to control the operation of the UAV according to the optimal deployment position.

[0146] In a preferred embodiment, the communication capacity calculation module 22 includes:

[0147] A signal-to-noise ratio calculation unit, used to calculate the signal-to-noise ratio of the Internet of Things terminal according to the total transmission attenuation value in the drone signal attenuation model, the drone operating power, the Internet of Things terminal noise power and the drone antenna gain;

[0148] A ground coverage probability calculation unit, used to calculate the ground coverage probability of the drone according to the transmission probability of the line-of-sight channel and the transmission probability of the non-line-of-sight channel in the drone signal attenuation model;

[0149] A single UAV communication capacity calculation unit, used to calculate the communication capacity of a single UAV according to the total transmission attenuation value in the signal attenuation model, the bandwidth of the UAV, the UAV transmission power and the Gaussian noise variance of the terminal node;

[0150] The communication capacity calculation unit of the drone cluster is used to calculate the communication capacity of the drone cluster according to the communication capacity of the single drone, the hovering time of the drone, the number of drones and the number of Internet of Things terminal nodes.

[0151] In a preferred embodiment, the effective coverage radius calculation module 23 is used to calculate the effective coverage radius of the drone according to the ground coverage probability.

[0152] In a preferred embodiment, the total energy consumption calculation module 24 includes:

[0153] A flight and hovering power calculation unit, used to calculate the total power of the UAV during uniform flight and the induced power during hovering; wherein the total power during uniform flight includes an induced power component, a vertical flight power component and a wind resistance power component;

[0154] The total energy consumption calculation unit is used to calculate the total energy consumption of the UAV during the flight and hovering process according to the total power during the uniform speed flight process and the induced power during the hovering process.

[0155] In a preferred embodiment, the optimal deployment position output module 27 includes:

[0156] The position iteration optimization unit is used to use the distributed particle swarm optimization algorithm to abstract the drone into particles and iteratively optimize the position variables of each particle;

[0157] The optimal deployment position output unit is used to substitute the iterative optimization result into the objective function, solve the optimal solution of the objective function while satisfying the constraint conditions, and output the optimal deployment position of the UAV.

[0158] It should be noted that the drone deployment device provided in an embodiment of the present invention can implement all the processes of the drone deployment method described in any of the above embodiments. The functions of each module and unit in the device and the technical effects achieved are respectively the same as the functions and technical effects achieved by the drone deployment method described in the above embodiments, and will not be repeated here.

[0159] In a third aspect, an embodiment of the present invention provides an electronic device, see Figure 3 Shown is a schematic structural diagram of a preferred embodiment of an electronic device provided by the present invention.

[0160] like Figure 3 As shown, the device includes:

[0161] A memory 31, used for storing computer programs;

[0162] A processor 32, configured to execute the computer program;

[0163] Wherein, when the processor 32 executes the computer program, the drone deployment method as described in any of the above embodiments is implemented.

[0164] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory 31 and executed by the processor 32 to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0165] The processor 32 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0166] The memory 31 can be used to store the computer program and / or module, and the processor 32 realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory 31, and calling the data stored in the memory 31. The memory 31 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory 31 can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0167] It should be noted that the above electronic device includes, but is not limited to, a processor and a memory. Those skilled in the art can understand that Figure 3 The structural diagram is merely an example of the electronic device described above and does not constitute a limitation on the electronic device, and may include more components than shown in the figure, or a combination of certain components, or different components.

[0168] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed, the drone deployment method described in any of the above embodiments is implemented.

[0169] It should be understood that the present invention implements all or part of the processes in the above-mentioned drone deployment method, and can also be completed by instructing related hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned drone deployment method can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electric carrier signals and telecommunication signals.

[0170] The above description is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto. It should be pointed out that for those skilled in the art, several equivalent obvious variations and / or equivalent substitutions can be made without departing from the technical principles of the present invention. These obvious variations and / or equivalent substitutions should also be regarded as the protection scope of the present invention.

Claims

1. A method for deploying a drone, characterized in that: include: Construct a drone signal attenuation model and power energy consumption model involving at least two drones; The signal-to-noise ratio, ground coverage probability and communication capacity of the Internet of Things terminal are calculated according to the UAV signal attenuation model; wherein the Internet of Things terminal is used for data transmission with the UAV; Calculate the effective coverage radius of the drone based on the ground coverage probability; Calculate the total energy consumption of the UAV during flight and hovering according to the power energy consumption model; Constructing an objective function for drone deployment according to the communication capacity and the effective coverage radius; wherein the objective function aims to maximize the communication capacity and the coverage area; Constructing constraints for drone deployment according to the total energy consumption, the signal-to-noise ratio, and the effective coverage radius; Outputting optimal deployment positions of at least two UAVs using a distributed particle swarm optimization algorithm according to the objective function and the constraint conditions; The operation of the UAV is controlled according to the optimal deployment position.

2. The method for deploying a drone as claimed in claim 1, characterized in that: The calculating the signal-to-noise ratio of the IoT terminal, the ground coverage probability and the communication capacity of the drone according to the drone signal attenuation model includes: Calculate the signal-to-noise ratio of the IoT terminal according to the total transmission attenuation value in the drone signal attenuation model, the drone operating power, the IoT terminal noise power, and the drone antenna gain; Calculating the ground coverage probability of the drone according to the transmission probability of the line-of-sight channel and the transmission probability of the non-line-of-sight channel in the drone signal attenuation model; Calculate the communication capacity of a single UAV based on the total transmission attenuation value in the signal attenuation model, the bandwidth of the UAV, the UAV transmission power, and the Gaussian noise variance of the terminal node; The communication capacity of the drone cluster is calculated based on the communication capacity of the single drone, the hovering time of the drone, the number of drones and the number of IoT terminal nodes.

3. The method for deploying a drone as claimed in claim 1, characterized in that: Calculating the effective coverage radius of the drone according to the ground coverage probability includes: The effective coverage radius of the UAV is calculated according to the ground coverage probability and the ground coverage probability threshold.

4. The method for deploying a drone according to claim 1, wherein: The calculating the total energy consumption of the UAV during flight and hovering according to the power energy consumption model includes: Calculate the total power of the UAV during uniform flight and the induced power during hovering; wherein the total power during uniform flight includes an induced power component, a vertical flight power component, and a wind resistance power component; The total energy consumption of the UAV during the flight and hovering process is calculated according to the total power during the uniform speed flight process and the induced power during the hovering process.

5. The method for deploying a drone according to claim 1, wherein: The method of outputting the optimal deployment positions of at least two UAVs using a distributed particle swarm optimization algorithm according to the objective function and the constraint conditions includes: Using the distributed particle swarm optimization algorithm, the drone is abstracted into particles, and the position variables of each particle are iteratively optimized; Substitute the iterative optimization result into the objective function, solve the optimal solution of the objective function while satisfying the constraint conditions, and output the optimal deployment position of the UAV.

6. A drone deployment device, characterized in that: include: A model building module, used to build a drone signal attenuation model and a power energy consumption model including at least two drones; A communication capacity calculation module, used to calculate the signal-to-noise ratio, ground coverage probability and communication capacity of the Internet of Things terminal according to the UAV signal attenuation model; wherein the Internet of Things terminal is used to transmit data with the UAV; An effective coverage radius calculation module, used to calculate the effective coverage radius of the drone according to the ground coverage probability; A total energy consumption calculation module is used to calculate the total energy consumption of the UAV during flight and hovering according to the power energy consumption model; An objective function construction module is used to construct an objective function for UAV deployment according to the communication capacity and the effective coverage radius, wherein the objective function aims at maximizing the communication capacity and the coverage area; A constraint condition building module, used to build a constraint condition for UAV deployment according to the total energy consumption, the signal-to-noise ratio and the effective coverage radius; An optimal deployment position output module, used to output the optimal deployment positions of at least two UAVs using a distributed particle swarm optimization algorithm according to the objective function and the constraint conditions; The UAV control module is used to control the operation of the UAV according to the optimal deployment position.

7. An electronic device, characterized in that: include: Memory for storing computer programs; A processor, configured to execute the computer program; Wherein, when the processor executes the computer program, the drone deployment method as described in any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed, the drone deployment method according to any one of claims 1 to 5 is implemented.

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