Unmanned aerial vehicle deployment method and device

Abstract

The invention discloses an unmanned aerial vehicle deployment method and device, and relates to the technical field of power field emergency planning. According to the main technical scheme, in a to-be-deployed area, the position coordinates of all users are determined firstly, then initial deployment information of the unmanned aerial vehicle is determined, and then four constraint conditions are constructed; from the four aspects of signal-to-noise ratio, preset transmitting power, constraint of unmanned aerial vehicle service user upper limit and constraint of communication link establishment between the unmanned aerial vehicle and the users, the method is equivalent to establishing constraints in four requirements of applying unmanned aerial vehicle networking, and finally, based on the position coordinates of each user and four constraint conditions, the unmanned aerial vehicle service user upper limit is determined. And adjusting the initial deployment information of the unmanned aerial vehicles to move and deploy each unmanned aerial vehicle to the target position. The method is applied to integrating the user position coordinates and multiple constraint conditions, mobile deployment of the unmanned aerial vehicle in a specific area is achieved, and large-scale emergency communication requirements are met through effective deployment networking of the unmanned aerial vehicle.

Description

Technical Field

[0001] The present application relates to the technical field of power field emergency planning, and in particular to a method and device for deploying unmanned aerial vehicles. Background Art

[0002] At present, the national power system has the characteristics of large-scale ultra-high voltage AC and DC hybrid connection and large-scale concentrated access of new energy, which makes operation and control more difficult. Faced with the current environment of frequent and frequent natural disasters, extreme abnormal climate will cause damage to field power facilities. For example, the risk of large-scale power outages still exists, posing a serious threat to the stable operation of the power grid.

[0003] Emergency on-site communication guarantee is a prerequisite for field power emergency response, but due to the wide range and complex terrain of field areas, it is very costly to deploy a dedicated network system in the field.

[0004] At present, in response to this high cost, air-to-ground communication based on drone base stations is an effective means of communication over obstacles. For example, portable rotary-wing / fixed-wing drones are the most suitable aerial platforms for field power emergency communication support. However, due to the communication distance and driver mode of the control link, it is impossible to flexibly meet the needs of large-scale emergency communications. How to achieve effective deployment and networking of drones while controlling costs is a technical problem that needs to be solved urgently. Summary of the invention

[0005] In view of this, the present application provides a drone deployment method and device, the main purpose of which is to integrate user location coordinates and multiple constraints to achieve mobile deployment of drones in a specific area, and utilize effective deployment and networking of drones to meet large-scale emergency communication needs.

[0006] In order to achieve the above objectives, this application mainly provides the following technical solutions:

[0007] The first aspect of the present application provides a method for deploying a drone, the method comprising:

[0008] Determine the location coordinates of each user in the area where the drone is to be deployed;

[0009] In the area, the initial deployment information corresponding to the drones is set to include at least: the initial number of drones, the initial position coordinates of the drones, and the initial position deployment vectors of the drones;

[0010] The first constraint condition is constructed as follows: constraining the sum of the signal-to-noise ratios of data communications on each user to reach a first preset threshold;

[0011] The second constraint condition is constructed as follows: the sum of the powers of the users served by each drone is constrained not to exceed the preset transmission power of the drone;

[0012] The third constraint condition is to constrain the upper limit of the number of users served by each drone to not exceed the preset number;

[0013] The fourth constraint condition is constructed as follows: constraining each user to have a corresponding communication connection with a unique drone, and the drone to have a corresponding communication connection with at least one user;

[0014] The initial deployment information is adjusted by using the position coordinates corresponding to each user, the first constraint condition, the second constraint condition, the third constraint condition and the fourth constraint condition to move and deploy each drone to obtain corresponding target deployment information.

[0015] A second aspect of the present application provides a drone deployment device, the device comprising:

[0016] A first determination unit is used to determine the position coordinates of each user in the area where the drone is to be deployed;

[0017] A setting unit, configured to set initial deployment information corresponding to the drones in the area, including at least: an initial number of drones, initial position coordinates of the drones, and initial position deployment vectors composed of the drones;

[0018] A construction unit, configured to construct a first constraint condition: constraining the sum of signal-to-noise ratios of data communications on each user to reach a first preset threshold;

[0019] The construction unit is further used to construct a second constraint condition: constraining the sum of the powers of various users served by each drone to not exceed the preset transmission power of the drone;

[0020] The construction unit is further used to construct a third constraint condition: constraining the upper limit of the number of users served by each drone to not exceed a preset number;

[0021] The construction unit is further used to construct a fourth constraint condition: constraining each user to have a corresponding communication connection with a unique drone, and the drone to have a corresponding communication connection with at least one user;

[0022] The adjustment unit is used to adjust the initial deployment information by using the position coordinates corresponding to each user, the first constraint condition, the second constraint condition, the third constraint condition and the fourth constraint condition to move and deploy each drone to obtain corresponding target deployment information.

[0023] A third aspect of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the drone deployment method as described above is implemented.

[0024] A fourth aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the drone deployment method as described above when executed by the processor.

[0025] By means of the above technical solution, the technical solution provided by this application has at least the following advantages:

[0026] The present application provides a method and device for deploying a drone. In the area to be deployed, the present application first determines the location coordinates of each user, then determines the initial deployment information of the drone, and then constructs four constraints, from the signal-to-noise ratio, preset transmission power, constraining the upper limit of drone service users, and constraining the relationship between the drone and the user to establish a communication link (these four aspects), which is equivalent to establishing constraints in the four requirements of applying drone networking. Finally, based on the location coordinates of each user and the four constraints, the initial deployment information of the drone is adjusted to move and deploy each drone to the target location.

[0027] Compared with the prior art that implements drone deployment due to the constraints of the communication distance of the control link and the driver mode, the present application integrates user location coordinates and multiple constraints to realize the mobile deployment of drones in a specific area, thereby realizing the dynamic deployment of drones "following the people". Without adding extra control costs, the effective deployment and networking of drones is utilized to meet large-scale emergency communication needs.

[0028] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0030] Figure 1 A flow chart of a method for deploying a drone provided in an embodiment of the present application;

[0031] Figure 2 A schematic diagram of a communication network composed of drone nodes and ground user nodes provided in an embodiment of the present application;

[0032] Figure 3A flow chart of a method for adjusting the three-dimensional position of a drone using a particle swarm algorithm provided in an embodiment of the present application;

[0033] Figure 4 A flow chart of a method for optimizing the transmission power of a drone provided in an embodiment of the present application;

[0034] Figure 5 A block diagram of a drone deployment device provided in an embodiment of the present application;

[0035] Figure 6 A block diagram of the composition of another drone deployment device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0036] The exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0037] Now, in some wild areas, a large number of transmission and distribution lines in the power grid are distributed in mountainous areas, with crisscrossing valleys, high mountains and dense forests, crossing the grasslands of national key forest areas, underdeveloped transportation and communications, and the overall structure has become complicated. There are risks of various geological natural disasters such as landslides, fires, and snowstorms. These risks will cause damage to field power facilities and are difficult to avoid.

[0038] The inventors found that emergency on-site communication guarantee is a prerequisite for field power emergency response, and it is adapted to factors such as the wide range of field maps and complex terrain. Taking into account these factors, the cost of deploying a dedicated network system will be very high. Therefore, in order to cope with this high cost, the use of drone base stations for air-to-ground communication is an effective means of obstacle-crossing communication.

[0039] However, the inventors have found that the strong dynamic environment of the emergency scene and the weak connection of the link are in significant conflict with the stable and efficient communication requirements of field emergencies. When using an aerial platform for large-scale emergency networking coverage, it is limited by the communication distance of the control link and the driver mode.

[0040] For example, the existing pilot mode requires the drone pilot to control the drone to fly and network to provide communication coverage for ground users, but the drone pilot does not know where to control the drone to fly. Therefore, it is impossible to flexibly meet large-scale emergency communication needs. Therefore, if the drone position can be effectively deployed and the drone can be effectively deployed and networked for users with different access needs without adding additional costs, the quality of emergency on-site communication support will be greatly improved.

[0041] Based on the above considerations, the present application embodiment provides a method for deploying a drone, such as Figure 1 As shown, the present application embodiment provides the following specific steps:

[0042] 101. In the area where the drone is to be deployed, determine the location coordinates of each user.

[0043] 102. In the area, the initial deployment information corresponding to the drones is set to include at least: the initial number of drones, the initial position coordinates of the drones, and the initial position deployment vectors composed of each drone.

[0044] In the embodiment of the present application, a communication network composed of drone nodes and ground user nodes is considered. Figure 2 As shown in the figure, the remote cloud cannot provide services to ground users due to the long distance and network congestion. Therefore, drone nodes are deployed at the edge close to the user side to act as temporary base stations to provide services to the users below.

[0045] Assume that there are N users in the rectangular area, and use the set Indicates that the coordinates of the Nth user are s n ={x n ,y n}.

[0046] The number of drones deployed in the area is K, and the set The heights of the UAVs may be different when they are deployed in the area, where the coordinates of the kth UAV can be represented as u k ={x k ,y k ,h k}, the position deployment vector of all drones is expressed as Consider the downlink scenario, and different drones and different users use different frequencies to communicate, that is, there is no mutual interference between users. The relationship between drones and ground users is one-to-many, that is, one drone can connect to multiple users, but one user can only connect to one drone. On this basis, the matching relationship matrix between all drones and all users is defined as X K×N , where X k，n Represents the connection relationship between the k-th drone and the n-th user.

[0047] 103. Construct a first constraint condition: constrain the sum of signal-to-noise ratios of data communications on each user to reach a first preset threshold.

[0048] 104. Construct the second constraint condition as follows: constrain the sum of the powers of the users served by each drone to not exceed the preset transmission power of the drone.

[0049] 105. The third constraint condition is constructed as follows: the upper limit of the number of users served by each drone shall not exceed the preset number.

[0050] 106. Construct a fourth constraint condition: constrain each user to have a communication connection with a unique drone, and constrain the drone to have a communication connection with at least one user.

[0051] As shown in 103-106 above, the embodiment of the present application uses the words "first", "second", "third" and "fourth" as identifiers to distinguish and refer to different constraints. Here, the above four words are only used as identifiers and there is no other ambiguity.

[0052] In the embodiment of the present application, the user's service quality (i.e., the power of sending content to the user), the maximum transmission power of the drone, and the upper limit of the number of users served by the drone are comprehensively considered, and the above four constraints are constructed with the goal of minimizing the number of drones. For example, the objective function is established as follows:

[0053]

[0054] The first constraint (a) indicates that the signal-to-noise ratio (SNR) of all users should meet a certain threshold; the second constraint (b) indicates that for the k-th UAV, the sum of the power of all users it serves cannot exceed the maximum transmit power P of the UAV. k，max , the third constraint (c) indicates that the upper limit of the number of users served by each drone cannot exceed its number threshold s; the fourth constraint (d) constrains the connection relationship between drones and users. The relationship between drones and ground users is one-to-many, that is, one drone can connect to multiple users, but one user can only connect to one drone, and different drones and different users communicate using different frequencies, that is, there is no mutual interference between users. The relationship matrix in the above example is X K*N , where any drone in the relationship matrix corresponds to at least one user.

[0055] 107. Using the position coordinates, the first constraint, the second constraint, the third constraint and the fourth constraint corresponding to each user, adjust the initial deployment information to move and deploy each drone to obtain corresponding target deployment information.

[0056] The embodiment of the present application is to adjust the initial deployment information corresponding to all drones based on the above four constraints and in combination with the location coordinates of each user in the area. This is equivalent to adjusting the initial deployment information to deploy mobile drones to move with users, taking into account the distribution locations of different users and the different requirements for accessing the network, thereby realizing dynamic deployment of drones that "the machine follows the user".

[0057] As described above, an embodiment of the present application provides a method for deploying a drone. In the area to be deployed, the embodiment of the present application first determines the location coordinates of each user, then determines the initial deployment information of the drone, and then constructs four constraints, from the signal-to-noise ratio, preset transmission power, constraining the upper limit of drone service users, and constraining the relationship between the drone and the user to establish a communication link (these four aspects), which is equivalent to establishing constraints in the four requirements of applying drone networking. Finally, based on the location coordinates of each user and the four constraints, the initial deployment information of the drone is adjusted to move and deploy each drone to the target location.

[0058] Compared with the prior art that implements drone deployment due to the constraints of the communication distance of the control link and the driver mode, the embodiment of the present application integrates the user location coordinates and multiple constraints to realize the mobile deployment of drones in a specific area, thereby realizing the dynamic deployment of drones "following the people". Without adding extra control costs, the effective deployment and networking of drones is utilized to meet large-scale emergency communication needs.

[0059] For more detailed description, in some specific embodiments, the embodiment of the present application refines 107, such as using a bionic algorithm to solve the adjustment of the drone position. Considering the superiority of the particle swarm algorithm in convergence speed and the simplicity of the algorithm structure, the embodiment of the present application uses a particle swarm algorithm to adjust and optimize the three-dimensional position of the drone. Specifically, Figure 3 As shown, the following specific steps are provided:

[0060] 201. Determine the starting position of the flight path of each UAV in the area according to the initial deployment information corresponding to the UAV.

[0061] For air-to-ground networking achieved by drones, the embodiment of the present application takes into account the use of drones carrying directional antennas to deliver content to users, while also carrying positioning base stations for positioning enhancement, and jointly optimizes the drone communication and navigation network deployment and antenna direction, thereby constructing the above four constraints.

[0062] In the air-ground networking that is implemented, user access requirements are different, and users are scattered. Although they do not move frequently, they are not always in one location. Therefore, it is necessary to consider the requirement of "the drone follows the user" when deploying drones. Therefore, the embodiment of the present application uses the deployment information of the drone at a certain moment as the initial deployment information, and takes this moment as the starting point. At a later time, the drone will be deployed according to the user's access requirements and location.

[0063] It should be noted that user access requirements will be affected by many factors, such as the transmission power of the drone and the power of the drone sending content to the user. Therefore, they are also covered within the scope of the above four constraints.

[0064] 202. Using the particle swarm algorithm, a group of particles are randomly generated in the area, and each particle is used to represent the corresponding allowed mobile deployment position of the drone in the area.

[0065] The basic idea of ​​Particle Swarm Optimization (PSO) is to solve optimization problems by simulating the foraging behavior of biological groups such as bird flocks or fish schools. In the algorithm, each particle represents a candidate solution, and they move and adjust in the solution space based on their own experience and group information to find the optimal solution. The position of the particle represents the feature vector of the candidate solution, and the velocity represents the direction and speed of the particle's movement in the search space.

[0066] The main process of the particle swarm algorithm includes the following steps:

[0067] (1) Initialization: Randomly generate the initial positions and velocities of a group of particles, and initialize the optimal individual position and the optimal group position.

[0068] (2) Evaluation: Calculate the fitness of each particle, that is, the objective function value.

[0069] (3) Update the best position: Compare the current position of each particle with its historical best position, and update the individual best position and the group best position.

[0070] (4) Update speed and position: Update the speed and position of the particle based on the individual optimal position and the group optimal position, as well as some weights and random factors.

[0071] (5) Termination condition judgment: Check whether the stopping condition is met, such as reaching the maximum number of iterations or the objective function value meets the requirements.

[0072] (6) Iteration: If the termination condition is not met, repeat steps 2 to 5 until the termination condition is met.

[0073] In an embodiment of the present application, a particle swarm algorithm is applied to the deployment of drones in a region, and a group of particles are randomly generated in the region, each particle being used to characterize the corresponding allowed mobile deployment position of the drone in the region.

[0074] 203. Based on the position coordinates corresponding to each user, the first constraint condition, the second constraint condition, the third constraint condition and the fourth constraint condition, an objective function is constructed, where the objective function is a key indicator for evaluating the quality of particles.

[0075] 204. Using the objective function, perform iterative optimization operations on a group of particles, output the global extreme value, and determine the target deployment information of the drone based on the global extreme value.

[0076] Exemplarily, the drone carries a directional antenna to deliver content to the user, and also carries a positioning base station to enhance positioning. For 203-204, the present application embodiment is explained as follows:

[0077] In the embodiment of the present application, according to the initial deployment information of the drone determined in step 102, the current upper limit number of drones allowed to be deployed into the area can be preliminarily determined. Within the current upper limit number range, the embodiment of the present application solves the multi-drone deployment problem by decomposing it into several single-drone optimal coverage problems. Each time a drone is added to the area, the drone position and antenna direction are optimized to maximize the communication performance and positioning performance of the network.

[0078] First, the user's coverage status is initialized to indicate whether the user has been served by a certain drone. Each time a drone is added, the drone is deployed iteratively based on the greedy idea to maximize the improvement of communication and positioning performance. The proposed PSO algorithm is used to solve the optimal deployment problem of the single drone, and the drone position and antenna direction are jointly optimized. After each drone is added, the status of the candidate location and the user status are updated to avoid repeated deployment or repeated coverage.

[0079] This means that in the proposed algorithm, the coverage of each user is only ensured once. These steps will be repeated until all drones have been deployed. When the number of drones is insufficient, limited drones can be used to maximize user coverage and average positioning accuracy, providing communication and positioning services with the highest quality possible; when the number of drones is sufficient, the minimum number of drones can be used to cover all users as much as possible, and there is a high achievable rate and high positioning accuracy between drones and users.

[0080] The PSO algorithm has excellent optimization performance and convergence speed, however, it also has problems such as being easily trapped in local extreme values ​​and slow convergence speed in the late iteration. The embodiment of the present application proposes an improved particle swarm algorithm (Improved PSO, IPSO) to solve the antenna direction optimization problem. In order to make the algorithm maintain fast convergence while improving local search capabilities, the following adjustments are made to the traditional PSO algorithm:

[0081] 1. Grouping strategy

[0082] The population is grouped according to the fitness value of the objective function. First, an initial population of N particles is generated. Then, the fitness values ​​of the objective function of all particles in the population are calculated and sorted. According to the sorting results, the first half of the particles with larger fitness values ​​are selected as the optimal solution group, and the other particles are selected as the inferior solution group. The particles in the optimal solution group are cross-operated, and a new excellent offspring population is generated using the following formula:

[0083]

[0084] Among them, x i represents the high-quality offspring individuals generated, x min 、x max It represents the best individual of any two different particles in the optimal solution group, r is a random number between [0,1], and η represents a random number between [0,1]. Its size is N / 2, and all solution values ​​of the group are added through the actual solution value. The following formula:

[0085]

[0086] Among them, N represents the area setting, which represents a single individual in the local effective part constraint area. Here, is an unconstrained optimization function. This selects an effective design domain. The mutation strategy is applicable to the crossover solution set. The calculation of r and η values ​​can be achieved through iteration. By setting G max Choose a fixed constraint vector x i .

[0087] 2. Information exchange between groups

[0088] In the traditional PSO algorithm, the speed and position update formulas of each particle are:

[0089]

[0090] Among them, ω is the inertia weight, c1 and c2 are the self-cognition and social cognition coefficients respectively, and r1 and r2 are random numbers between [0,1]. In order to prevent the algorithm from converging to the local optimum too early, the embodiment of the present application introduces a mechanism based on information exchange between groups after each iteration. Specifically, the individuals in the optimal solution group and the inferior solution group are cross-operated, and the new speed of each particle is calculated according to the following formula:

[0091]

[0092] in, It means that the individual with the smallest fitness value is the specific solution. The individual corresponding to the jth individual replaces the inferior solution individual.

[0093]

[0094] Through the above improvement measures, the improved particle swarm algorithm can improve the local search capability while maintaining fast convergence, thereby effectively solving the antenna direction optimization problem. The algorithm flow is as follows:

[0095] Algorithm 1: Improved Particle Swarm Optimization (IPSO)

[0096] 1): Initialization:

[0097] 2): Current candidate locations and ground user location distribution

[0098] 3): Iteration

[0099] 4): Calculate the fitness of all particles

[0100] 5): According to the fitness value, update the individual optimal pbesti and global optimal gbest of the particle

[0101] 6): Calculate the mean of elite particles

[0102] 7): Update the particle speed and position

[0103] 8): Update the fitness and update the particle position according to the grouping strategy

[0104] 9): Iteration ends

[0105] 10): Output the best individual in history

[0106] Based on the above analysis, the embodiment of the present application solves the problem of multi-drone deployment by breaking it down into several single-drone optimal coverage problems. Each time a drone is added, the drone position and antenna direction are optimized to maximize the communication performance and positioning performance of the network. The overall process of the solution is as follows:

[0107] 1) Input: each user’s location, line-of-sight direction set and viewing angle, drone deployment area, minimum signal-to-noise ratio constraint SNR0, and upper limit N of the number of drones u max

[0108] 2) Output: UAV position matrix U, UAV antenna direction matrix Du

[0109] 3): Initialization: user coverage status, user information matrix, etc.

[0110] 4): for 1 to N max

[0111] 5): Add drones and use the IPSO of algorithm 5-1 to optimize the deployment location and antenna direction, so as to maximize the communication performance and positioning performance of the network.

[0112] 6): Update each user's information matrix and user coverage status

[0113] 7):end for

[0114] 8): Get the number of deployed drones, drone position matrix U and antenna direction matrix Du

[0115] In some modified embodiments, after optimizing the deployment position of the drone, on this basis, the embodiment of the present application optimizes the transmission power of the drone, such as Figure 4 , the following implementation steps are adopted:

[0116] 301. Determine the target position coordinates of each UAV in the area according to the target deployment information.

[0117] 302. Circle a plurality of user areas in the area.

[0118] 303. Determine a user density level corresponding to the user area based on the target position coordinates of each UAV in the area.

[0119] 304. According to the preset positive correlation between the user density level and the signal-to-noise ratio, the transmission power of the drone is differentially allocated and adjusted to different user areas.

[0120] Considering that in the scenario of differentiated access requirements, ground users are widely distributed and emergency services are also different, users in different areas may have different requirements. Therefore, this scheme will further consider setting differentiated requirements for users in different areas. Specifically, the SNR of users will be divided into different levels for distinction. It is assumed that there are M levels in total, and they are represented as γ (1) , γ (2) ,…γ(M) . The first level SNR, namely γ (1) The corresponding user has the highest demand for SNR, level 2 SNR, i.e. γ (2) The corresponding user has the second highest demand for SNR, and so on. For each level of γ (m) The threshold conditions that need to be met are

[0121] Specifically, all deployed drones will be classified according to the user density area division of the entire area. It is assumed that all users connected to drones deployed in user-dense areas have the same SNR requirement, and the SNR requirement is the highest. It is assumed that all users connected to drones deployed in user-sparse areas have the same SNR requirement, and the SNR is the lowest. The SNR of the users connected to drones deployed in other areas is considered to decrease in sequence according to the user density of the area where they are located. Therefore, user power allocation under differential SNR constraints can be achieved, which is more in line with the diverse service needs of rescue users in actual emergency scenarios. In order to achieve low-complexity power allocation, this project will derive the SNR corresponding to the demand as γ (m) The closed-form solution of the power allocation scheme for users.

[0122] Further, based on the above 201-204 and 301-304, that is, after obtaining the three-dimensional position and power distribution under a given number of drones, the number of drones can also be checked to further achieve optimization, such as including the following:

[0123] In any user area, it is queried whether the corresponding signal-to-noise ratio of each user meets the second preset threshold; if so, it indicates that the air-ground networking realized at the current deployment position enables the quality of the content received by the user to be relatively high (i.e., the communication quality is relatively good) and meets the different access requirements of users; if not, it is determined that drones need to be added in the user area; according to the added drones, a particle swarm algorithm is used in the user area to perform an iterative optimization operation to redeploy the position of the drone in the user area.

[0124] For example, the SNR of all users at this time can be checked to determine whether the SNR requirements of all users are met. If so, it proves that the number of drones at this time is the minimum number. Otherwise, it means that even after position optimization and power allocation, the SNR requirements of users cannot be met, proving that the number of drones at this time is insufficient. It is necessary to increase the number of drones by one based on the number of drones at this time, and repeat 201-204 and 301-304 until the needs of all users are met.

[0125] Furthermore, as a response to the above Figure 1 , 3, 4, an embodiment of the present application provides a drone deployment device. This device embodiment corresponds to the aforementioned method embodiment. For ease of reading, this device embodiment will no longer repeat the details of the aforementioned method embodiment, but it should be clear that the device in this embodiment can correspond to all the contents of the aforementioned method embodiment. The device is used to integrate user location coordinates and multiple constraints to realize the mobile deployment of drones in a specific area, and utilize the effective deployment and networking of drones to meet large-scale emergency communication needs, specifically as Figure 5 As shown, the device comprises:

[0126] A first determining unit 41 is used to determine the position coordinates of each user in the area where the drone is to be deployed;

[0127] A setting unit 42, configured to set initial deployment information corresponding to the drones in the area, including at least: an initial number of drones, initial position coordinates of the drones, and initial position deployment vectors of the drones;

[0128] A construction unit 43 is used to construct a first constraint condition: constraining the sum of the signal-to-noise ratios of data communications on each user to reach a first preset threshold;

[0129] The construction unit 43 is further used to construct a second constraint condition: constraining the sum of the powers of each user served by each drone to not exceed the preset transmission power of the drone;

[0130] The construction unit 43 is further used to construct a third constraint condition: constraining the upper limit of the number of users served by each drone to not exceed a preset number;

[0131] The construction unit 43 is further used to construct a fourth constraint condition: constraining each user to have a corresponding communication connection with a unique drone, and the drone to have a corresponding communication connection with at least one user;

[0132] The adjustment unit 44 is used to adjust the initial deployment information to move and deploy each drone using the position coordinates corresponding to each user, the first constraint condition, the second constraint condition, the third constraint condition and the fourth constraint condition to obtain corresponding target deployment information.

[0133] In some specific embodiments, Figure 6 As shown, the adjustment unit 44 includes:

[0134] A first determination module 441 is used to determine the starting position of the flight path of each drone in the area according to the initial deployment information corresponding to the drone;

[0135] A generation module 442 is used to randomly generate a group of particles in the area using a particle swarm algorithm, each particle is used to represent a corresponding allowed mobile deployment position of the UAV in the area;

[0136] A construction module 443 is used to construct an objective function based on the position coordinates corresponding to each user, the first constraint condition, the second constraint condition, the third constraint condition and the fourth constraint condition, where the objective function is a key indicator for evaluating the quality of the particles;

[0137] An output module 444 is used to perform an iterative optimization operation on the group of particles using the objective function and output a global extreme value;

[0138] The second determination module 445 is used to determine the target deployment information of the UAV according to the global extreme value.

[0139] In some specific embodiments, Figure 6 As shown, the device also includes:

[0140] A second determining unit 45 is used to determine the target position coordinates of each UAV in the area according to the target deployment information;

[0141] A selection unit 46, configured to select a plurality of user areas in the area;

[0142] A third determining unit 47 is used to determine a user density level corresponding to the user area based on the target position coordinates of each drone in the area;

[0143] The allocation unit 48 is used to differentially allocate and adjust the transmission power of the drone to different user areas according to the preset positive correlation between the user density level and the signal-to-noise ratio.

[0144] In some specific embodiments, Figure 6 As shown, the adjustment unit 44 is also specifically used for:

[0145] In any one of the user areas, querying whether the corresponding signal-to-noise ratio of each user meets a second preset threshold;

[0146] If not, it is determined that additional drones are needed in the user area;

[0147] According to the added drones, a particle swarm algorithm is used to perform an iterative optimization operation in the user area to redeploy the positions of the drones in the user area.

[0148] In some specific embodiments, Figure 6 As shown, the construction unit 43, when applied to construct the fourth constraint condition, specifically includes:

[0149] The number of drones deployed in the area is K, where K is a positive integer;

[0150] The number of users in the area is N, where N is a positive integer;

[0151] The matching relationship between drones and users is expressed as: The relationship matrix is ​​X K*N , wherein any drone in the relationship matrix corresponds to at least one user.

[0152] In summary, the drone deployment device includes a processor and a memory. The above-mentioned first determination unit, setting unit, construction unit and adjustment unit are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize corresponding functions.

[0153] The processor contains a kernel, which calls the corresponding program unit from the memory. One or more kernels can be set, and the kernel parameters can be adjusted to integrate the user's location coordinates and multiple constraints to achieve mobile deployment of drones in a specific area, and to utilize the effective deployment and networking of drones to meet large-scale emergency communication needs.

[0154] An embodiment of the present application provides a storage medium on which a program is stored, and when the program is executed by a processor, the drone deployment method is implemented.

[0155] An embodiment of the present application provides a processor, which is used to run a program, wherein the drone deployment method is executed when the program is run.

[0156] The present application also provides a computer program product which, when executed on a data processing device, is suitable for executing a program for initializing the above drone deployment method steps.

[0157] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0158] In a typical configuration, the device includes one or more processors (CPU), memory and bus. The device may also include input / output interface, network interface and the like.

[0159] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip. The memory is an example of a computer-readable medium.

[0160] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0161] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0162] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0163] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for deploying a drone, characterized in that: The method comprises: Determine the location coordinates of each user in the area where the drone is to be deployed; In the area, the initial deployment information corresponding to the drones is set to include at least: the initial number of drones, the initial position coordinates of the drones, and the initial position deployment vectors of the drones; The first constraint condition is constructed as follows: constraining the sum of the signal-to-noise ratios of data communications on each user to reach a first preset threshold; The second constraint condition is constructed as follows: the sum of the powers of the users served by each drone is constrained not to exceed the preset transmission power of the drone; The third constraint condition is to constrain the upper limit of the number of users served by each drone to not exceed the preset number; The fourth constraint condition is constructed as follows: constraining each user to have a corresponding communication connection with a unique drone, and the drone to have a corresponding communication connection with at least one user; The initial deployment information is adjusted by using the position coordinates corresponding to each user, the first constraint condition, the second constraint condition, the third constraint condition and the fourth constraint condition to move and deploy each drone to obtain corresponding target deployment information.

2. The method according to claim 1, characterized in that The adjusting the initial deployment information to move and deploy each drone by using the position coordinates corresponding to each user, the first constraint condition, the second constraint condition, the third constraint condition, and the fourth constraint condition to obtain corresponding target deployment information includes: Determine the starting position of the flight path of each drone in the area according to the initial deployment information corresponding to the drone; Using a particle swarm algorithm, a group of particles are randomly generated in the area, and each particle is used to represent the corresponding allowed mobile deployment position of the UAV in the area; Based on the position coordinates corresponding to each user, the first constraint condition, the second constraint condition, the third constraint condition, and the fourth constraint condition, an objective function is constructed, where the objective function is a key indicator for evaluating the quality of the particles; Using the objective function, performing an iterative optimization operation on the group of particles and outputting a global extreme value; According to the global extreme value, the target deployment information of the UAV is determined.

3. The method according to claim 1 or 2, characterized in that: The method further comprises: Determining the target position coordinates of each UAV in the area according to the target deployment information; Circle a plurality of user areas in the area; Determine a user density level corresponding to the user area based on the target position coordinates of each drone in the area; According to the preset positive correlation between the user density level and the signal-to-noise ratio, the transmission power of the drone is differentially allocated and adjusted to different user areas.

4. The method according to claim 3, characterized in that The method further comprises: In any one of the user areas, query whether the corresponding signal-to-noise ratio of each user meets a second preset threshold; If not, it is determined that additional drones are needed in the user area; According to the added drones, a particle swarm algorithm is used to perform an iterative optimization operation in the user area to redeploy the positions of the drones in the user area.

5. The method according to any one of claims 1 to 4, characterized in that The fourth constraint condition is constructed as follows: constraining each user to have a communication connection with a unique drone, and the drone to have a communication connection with at least one user; including: The number of drones deployed in the area is K, where K is a positive integer; The number of users in the area is N, where N is a positive integer; The matching relationship between drones and users is expressed as: The relationship matrix is ​​X K*N , wherein any drone in the relationship matrix corresponds to at least one user.

6. A drone deployment device, characterized in that: The device comprises: A first determination unit is used to determine the position coordinates of each user in the area where the drone is to be deployed; A setting unit, configured to set initial deployment information corresponding to the drones in the area, including at least: an initial number of drones, initial position coordinates of the drones, and initial position deployment vectors composed of the drones; A construction unit, configured to construct a first constraint condition: constraining the sum of signal-to-noise ratios of data communications on each user to reach a first preset threshold; The construction unit is further used to construct a second constraint condition: constraining the sum of the powers of various users served by each drone to not exceed the preset transmission power of the drone; The construction unit is further used to construct a third constraint condition: constraining the upper limit of the number of users served by each drone to not exceed a preset number; The construction unit is further used to construct a fourth constraint condition: constraining each user to have a corresponding communication connection with a unique drone, and the drone to have a corresponding communication connection with at least one user; The adjustment unit is used to adjust the initial deployment information by using the position coordinates corresponding to each user, the first constraint condition, the second constraint condition, the third constraint condition and the fourth constraint condition to move and deploy each drone to obtain corresponding target deployment information.

7. The device according to claim 6, characterized in that The adjustment unit comprises: A first determination module is used to determine the starting position of the flight path of each drone in the area according to the initial deployment information corresponding to the drone; A generation module, used to randomly generate a group of particles in the area using a particle swarm algorithm, each particle being used to represent a corresponding allowed mobile deployment position of the UAV in the area; A construction module, used to construct an objective function based on the position coordinates corresponding to each user, the first constraint condition, the second constraint condition, the third constraint condition and the fourth constraint condition, wherein the objective function is a key indicator for evaluating the quality of the particles; An output module, used to perform an iterative optimization operation on the group of particles using the objective function and output a global extreme value; The second determination module is used to determine the target deployment information of the UAV according to the global extreme value.

8. The device according to claim 6 or 7, characterized in that The device also includes: A second determination unit is used to determine the target position coordinates of each UAV in the area according to the target deployment information; A selection unit, used to select a plurality of user areas in the area; A third determining unit is used to determine a user density level corresponding to the user area based on the target position coordinates of each drone in the area; The allocation unit is used to differentially allocate and adjust the transmission power of the drone to different user areas according to the preset positive correlation between the user density level and the signal-to-noise ratio.

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

10. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the drone deployment method as described in any one of claims 1 to 5.

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