Unmanned aerial vehicle flight control method and device supporting electric power emergency communication

Abstract

The invention discloses an unmanned aerial vehicle flight control method and device supporting electric power emergency communication, and belongs to the technical field of electric power communication, and the method comprises the steps: determining a target region of electric power emergency communication, considering the uncertainty of the flight height of an unmanned aerial vehicle, taking the maximum coverage rate as a target, and carrying out the deployment optimization through employing an ant colony optimization algorithm; taking a deployment position obtained by deployment optimization as an end point, considering flight height uncertainty of the unmanned aerial vehicle and taking path length, height change cost, path smoothness cost and collision penalty as constraint conditions, and adopting a particle swarm optimization algorithm to perform path optimization. The ant colony optimization algorithm and the particle swarm optimization algorithm are adopted, the complex three-dimensional environment can be dealt with, the optimal deployment point location and the flight track of the unmanned aerial vehicle can be obtained, the sufficient coverage rate is ensured on the premise that effective communication is met, and the unmanned aerial vehicle deployment efficiency is improved. And meanwhile, the task execution efficiency and the path planning robustness of the unmanned aerial vehicle in a complex electric power communication network environment are remarkably improved, and the method has a wide application prospect.

Description

Technical Field

[0001] The present invention relates to the technical field of power communication, and particularly to a method and device for controlling the flight of an unmanned aerial vehicle (UAV) to support power emergency communication. Background Art

[0002] Natural disasters, such as heavy snow and floods, are likely to cause serious damage to the power grid infrastructure. In order to restore power supply, the data collected by repair personnel needs to be transmitted back to the command center in a timely manner to obtain accurate information about casualties and damages. However, the existing ground network is vulnerable to disasters and lacks network reliability. The rapid development of mobile communication technology, UAVs, and satellites has led to a transformation of the communication network towards a new "space-air-ground integrated" model, where multiple UAVs can be integrated with satellites and ground networks to achieve collaborative fault inspection, long-distance relay, and enhanced wide-area air-ground coverage capabilities.

[0003] Resource scheduling is a key pillar for achieving space-air-ground integration. In particular, path planning finds the optimal deployment points and flight trajectories for UAVs, ensuring sufficient coverage while meeting the requirements of effective communication. However, there are still many deficiencies in the existing technologies to meet the above requirements. Summary of the Invention

[0004] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a method and device for controlling the flight of an unmanned aerial vehicle to support power emergency communication, which can cope with complex three-dimensional environments, obtain the optimal deployment points and flight trajectories of the UAV, and ensure sufficient coverage while meeting the requirements of effective communication.

[0005] To achieve the above purpose, the present invention is implemented by the following technical solutions:

[0006] In a first aspect, the present invention provides a method for controlling the flight of an unmanned aerial vehicle to support power emergency communication, including:

[0007] Determine the target area of power emergency communication, consider the uncertainty of the UAV flight altitude, and perform deployment optimization using the ant colony optimization algorithm with the goal of maximizing coverage.

[0008] Taking the deployment position obtained from the deployment optimization as the end point, consider the uncertainty of the UAV flight altitude, and perform path optimization using the particle swarm optimization algorithm with the path length, altitude change cost, path smoothness cost, and collision penalty as constraints.

[0009] Optionally, the performing deployment optimization using the ant colony optimization algorithm includes:

[0010] Determine the set of UAV deployment positions according to the target area;

[0011] Initialize the number of iterations , the The pheromone concentration corresponding to the th drone at the th deployment position in the th iteration, the maximum number of iterations , repeatedly execute the deployment optimization iteration step until , and output the final deployment positions of the drones;

[0012] Among them, the deployment optimization iteration step includes:

[0013] Based on the set of deployment positions, construct the heuristic information function corresponding to each drone in the th iteration;

[0014] According to the heuristic information function and pheromone concentration corresponding to each drone in the th iteration, calculate the selection probability of the deployment position of each drone by the ants in the th iteration; Each ant selects the deployment position of each drone in the

[0015] th iteration according to the selection probability of the deployment position of each drone in the th iteration; th iteration;

[0016] According to the deployment positions of each drone in the th iteration, considering the uncertainty of the drone flight altitude and aiming at maximizing the coverage rate, calculate the pheromone concentration corresponding to each drone in the th iteration ; let .

[0017] Optionally, the heuristic information function is:

[0018]

[0019] In the formula, is the value of the heuristic information function corresponding to the th iteration of the th drone at the th deployment position, is the task weight related to the th target point, is the position coordinate of the th target point, is the number of target points; is the position coordinate of the th deployment position selected by the th drone, is a constant term;

[0020] The selection probability is as follows:

[0021]

[0022] wherein is the selection probability of the th iteration of the th UAV for the th deployment location, is the pheromone concentration corresponding to the th iteration of the th UAV at the th deployment location, is the index value of the deployment location; are the weights of the pheromone concentration and the heuristic information function value, respectively.

[0023] Optionally, according to the deployment locations of each UAV in the th iteration, considering the uncertainty of the UAV flight altitude and aiming at maximizing the coverage rate, calculate the pheromone concentration corresponding to each UAV in the th iteration, which

[0024] includes:

[0025]

[0026]

[0027] wherein is the target flight altitude and the actual flight altitude of the th UAV, is the flight altitude perturbation of the th UAV, is the maximum flight altitude perturbation, is the uniform probability distribution in the interval from to ; is the UAV communication radiation adjustment parameter, is the minimum safe flight altitude of the UAV, is the effective signal coverage range of the th UAV;

[0028] Calculate the effective signal coverage status of the UAV for the target point based on the effective signal coverage range of the UAV:

[0029]

[0030] wherein is the The effective signal coverage status of the th drone at the th deployment location for the th target point, where is the three-dimensional Euclidean distance operator; is the position coordinate of the th target point, is the position coordinate of the th deployment location selected by the

[0031] Based on the effective signal coverage status, a coverage reward function of the drone for the target point is constructed with the goal of maximizing the coverage rate:

[0032]

[0033] Among them:

[0034]

[0035]

[0036] In the formula, is to maximize the coverage rate, is the number of drones, is the number of target points, is the position coordinate of the th deployment location selected by the th drone; is the deployment overlap penalty term, is the deployment overlap penalty coefficient, is the minimum distance threshold between drones, is the value of the coverage reward function;

[0037] Update the pheromone concentration based on the coverage reward function:

[0038]

[0039]

[0040] In the formula, is the pheromone concentration corresponding to the th iteration when the th drone is at the th deployment location, is the value of the coverage reward function of the drone for the target point in the th iteration, is the information enhancement coefficient, is the th iteration of the pheromone concentration volatilization rate, is the initial evaporation rate, is the decay constant.

[0041] Optionally, the path optimization using the particle swarm optimization algorithm includes:

[0042] Construct a fitness function considering the uncertainty of the UAV flight altitude and with the path length, altitude change cost, path smoothness cost, and collision penalty as constraints;

[0043] Set the number of particles, the initial position and initial velocity of each particle, and the maximum number of iterations , and the initial position of each particle is all waypoints on the path

[0044] Initialize the iteration count , initialize the global optimal position and the individual optimal position of each particle to the initial position, and repeat the path optimization iteration steps until , output the final global optimal position to obtain the optimal path;

[0045] Among them, the path optimization iteration steps include:

[0046] According to the velocity and position of each particle, the individual optimal position, and the global optimal position in the -th iteration, calculate the velocity and position of each particle in the -th iteration;

[0047] According to the velocity and position of each particle in the -th iteration, use the fitness function to calculate the fitness of each particle;

[0048] Update the global optimal position and the individual optimal position of each particle according to the fitness of each particle; let .

[0049] Optionally, the fitness function is:

[0050]

[0051] Where:

[0052]

[0053]

[0054]

[0055]

[0056]

[0057]

[0058] In the formula, is the path length, is the cost of altitude change, is the cost of path smoothness, is the collision penalty term, are the weight coefficients of the path length, the cost of altitude change, and the cost of path smoothness, respectively, are the position coordinates of the th waypoint on the path, is the number of waypoints on the path, is the z-axis coordinate value of the th waypoint on the path and the actual flight altitude of the UAV at the th waypoint, is the flight altitude perturbation of the UAV at the th waypoint, is the maximum flight altitude perturbation, is uniformly distributed between and , is the average value of the z-axis coordinate values of all waypoints on the path, is the preset altitude median, are all weight coefficients, is the angle between the line connecting the th waypoint and its adjacent waypoint on the path; is the xy-axis coordinate value of the th waypoint on the path, is the z-axis coordinate value of the th waypoint on the path, is the center coordinate of the th obstacle, is the minimum and maximum z-axis coordinate values of the th obstacle; is the minimum effective distance between the line connecting the th and th waypoints on the path and the th obstacle, is the safety radius of the th obstacle, is the penalty score, is the number of obstacles, is the interpolation coefficient between 0 and 1;

[0059]

[0060] In the formula, is the three-dimensional Euclidean distance operator, is the The position coordinates of a waypoint.

[0061] Optionally, calculating the velocity and position of each particle in the

[0062]

[0063]

[0064] In the formula, is the velocity of the th particle in the th iteration, is the inertia weight, is the learning factor, is a random number between 0 and 1, is the personal best position of the th particle, is the global best position, is the th iteration of the th particle position.

[0065] Optionally, the UAV flight control method further includes:

[0066] Smoothing the path obtained by path optimization using the cubic spline interpolation method.

[0067] In a second aspect, the present invention provides a UAV flight control device for supporting power emergency communication, including:

[0068] A deployment optimization module configured to determine the target area of power emergency communication, consider the uncertainty of UAV flight altitude, and use the ant colony optimization algorithm for deployment optimization with the goal of maximizing coverage;

[0069] A path optimization module configured to use the particle swarm optimization algorithm for path optimization with the deployment position obtained by deployment optimization as the end point, consider the uncertainty of UAV flight altitude, and use path length, altitude change cost, path smoothness cost, and collision penalty as constraint conditions.

[0070] Optionally, the UAV flight control device further includes:

[0071] A smoothing processing module configured to smooth the path obtained by path optimization using the cubic spline interpolation method.

[0072] Compared with the prior art, the beneficial effects achieved by the present invention:

[0073] A UAV flight control method and device for supporting power emergency communication provided by the present invention, in the positioning stage, adopts the ant colony optimization algorithm, takes the coverage reward function that simultaneously considers the coverage efficiency and deployment coordination as the optimization goal, and designs a pheromone evaporation mechanism that dynamically decays with the number of iterations to improve the algorithm search efficiency and deployment solution stability. In the path planning stage, selects the optimal suspension position in the positioning stage as the UAV planning end point, comprehensively considers constraint conditions such as path length, altitude change, smoothness, and collision detection, and generates a path through the particle swarm optimization algorithm. Finally, uses the cubic spline interpolation method for smoothing processing to further improve the executability of the path. In summary, the present invention can cope with complex three-dimensional environments, obtain the optimal deployment points and flight trajectories of UAVs, ensure sufficient coverage while meeting the requirements of effective communication, and significantly improve the task execution efficiency of UAVs in complex power communication network environments and the robustness of path planning, with broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 is a schematic flowchart of the UAV flight control method for supporting power emergency communication provided by an embodiment of the present invention;

[0075] Figure 2 is a schematic diagram of the simulation verification scenario of UAV flight control provided by an embodiment of the present invention;

[0076] Figure 3 is a schematic diagram of the three-dimensional path results of the method of the present invention, traditional PSO, and A* algorithm in one experiment provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0077] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and cannot be used to limit the protection scope of the present invention.

[0078] Embodiment 1:

[0079] As Figure 1 shown, an embodiment of the present invention provides a UAV flight control method for supporting power emergency communication, including the following steps:

[0080] Step S1. Determine the target area of power emergency communication, consider the uncertainty of the UAV flight altitude, and take maximizing the coverage rate as the goal, and adopt the ant colony optimization algorithm for deployment optimization.

[0081] Specifically, adopting the ant colony optimization algorithm for deployment optimization includes:

[0082] Step S1.1. Determine the UAV deployment position set according to the target area.

[0083] Specifically, in this embodiment, the space of the target area can be evenly divided into multiple sub-spaces in the shape of cubes, and the center point of each sub-space is used as the deployment position. In other alternative embodiments, those skilled in the art can set the deployment position according to actual needs.

[0084] Step S1.2: Initialize the number of iterations , the pheromone concentration corresponding to the th iteration and the th drone at the th deployment position, the maximum number of iterations , and repeatedly execute the deployment optimization iteration step until , and output the final deployment positions of the drones;

[0085] Among them, the deployment optimization iteration step includes:

[0086] Step S1.2.1: Construct the heuristic information function corresponding to each drone in the th iteration based on the set of deployment positions;

[0087] Step S1.2.2: Calculate the selection probability of each ant for the deployment position of each drone in the th iteration according to the heuristic information function and pheromone concentration corresponding to each drone in the th iteration; th iteration;

[0088] Step S1.2.3: Each ant selects the deployment position of each drone in the th iteration according to the selection probability of the deployment position of each drone in the th iteration;

[0089] Step S1.2.4: According to the deployment positions of each drone in the th iteration, considering the uncertainty of the flight altitude of the drones and aiming at maximizing the coverage rate, calculate the pheromone concentration corresponding to each drone in the th iteration ; Let .

[0090] Among them, the heuristic information function is:

[0091]

[0092] In the formula, is the value of the heuristic information function corresponding to the th iteration and the th drone at the th deployment position, is related to the The task weight related to a target point, is the position coordinate of the th target point; is the th selected position coordinate of the th deployment location of the drone;

[0093] The selection probability is:

[0094]

[0095] In the formula, is the th selection probability of the th drone for the th th th deployment location in the th iteration; is the index value of the deployment location; are the weights of the pheromone concentration and the heuristic information function value respectively.

[0096] In step S1.2.4, according to the deployment location of each drone in the th iteration, considering the uncertainty of the drone flight altitude and aiming at maximizing the coverage rate, calculate the pheromone concentration corresponding to each drone in the th iteration including:

[0097] Considering the uncertainty of the drone flight altitude, construct a drone flight altitude function and calculate the effective signal coverage range of the drone:

[0098]

[0099]

[0100] In the formula, is the th target flight altitude and the actual flight altitude of the drone; is the th flight altitude perturbation of the drone; is the maximum flight altitude perturbation; is the to equally probable distribution; is the drone communication radiation adjustment parameter; is the minimum safe flight altitude of the drone; is the effective signal coverage range of the drone;

[0101] Calculate the effective signal coverage status of the drone for the target point based on the effective signal coverage range of the drone:

[0102]

[0103] In the formula, is the th drone's effective signal coverage status for the th target point at the th deployment position, is the three-dimensional Euclidean distance operator; is the th target point's position coordinates, is the th drone's selected th deployment position's position coordinates;

[0104] Based on the effective signal coverage status, construct the coverage reward function of the drone for the target point with the goal of maximizing the coverage rate:

[0105]

[0106] Where:

[0107]

[0108]

[0109] In the formula, is to maximize the coverage rate, is the number of drones, is the number of target points, is the th drone's selected th deployment position's position coordinates; is the deployment overlap penalty term, is the deployment overlap penalty coefficient, is the minimum distance threshold between drones, is the value of the coverage reward function;

[0110] Update the pheromone concentration based on the coverage reward function:

[0111]

[0112]

[0113] In the formula, is the th iteration's The pheromone concentration corresponding to the th deployment position of the UAV, is the coverage reward function value of the UAV for the target point in the th iteration, is the information enhancement coefficient, is the th iteration, and is the evaporation rate of the pheromone concentration is the initial evaporation rate, and is the attenuation constant.

[0114] Step S2: Taking the deployment position obtained by deployment optimization as the end point, considering the uncertainty of the UAV flight altitude and using the path length, altitude change cost, path smoothness cost, and collision penalty as constraint conditions, a particle swarm optimization algorithm is used for path optimization.

[0115] Specifically, using the particle swarm optimization algorithm for path optimization includes:

[0116] Step S2.1: Considering the uncertainty of the UAV flight altitude and using the path length, altitude change cost, path smoothness cost, and collision penalty as constraint conditions to construct a fitness function;

[0117] Step S2.2: Set the number of particles, the initial position and initial velocity of each particle, and the maximum number of iterations , and the initial position of each particle is all the waypoints on the path

[0118] Step S2.3: Initialize the number of iterations , initialize the global optimal position and the individual optimal position of each particle to the initial position, and repeat the path optimization iteration step until , output the final global optimal position, and obtain the optimal path;

[0119] Among them, the path optimization iteration step includes:

[0120] Step S2.3.1: Calculate the velocity and position of each particle in the th iteration according to the velocity and position, individual optimal position, and global optimal position of each particle in the th iteration;

[0121] Step S2.3.2: According to the velocity and position of each particle in the th iteration, use the fitness function to calculate the fitness of each particle;

[0122] Step S2.3.3: Update the global optimal position and the individual optimal position of each particle according to the fitness of each particle; let .

[0123] Specifically, in this embodiment, the fitness function is:

[0124]

[0125] Where:

[0126]

[0127]

[0128]

[0129]

[0130]

[0131]

[0132] In the formula, is the path length, is the height change cost, is the path smoothness cost, is the collision penalty term, are the weight coefficients of the path length, height change cost, and path smoothness cost respectively, are the position coordinates of the th waypoint on the path respectively, is the number of waypoints on the path, is the z-axis coordinate value of the th waypoint on the path and the actual flight height of the UAV at the th waypoint, is the flight height perturbation of the UAV at the th waypoint, is the maximum flight height perturbation, is uniformly distributed with equal probability in the interval to , is the average value of the z-axis coordinate values of all waypoints on the path, is the preset height median, are all weight coefficients, is the angle between the line connecting the th waypoint and the adjacent waypoint on the path; is the xy-axis coordinate value of the th waypoint on the path, is the z-axis coordinate value of the th waypoint on the path, is the center coordinate of the th obstacle, is the The minimum and maximum z-axis coordinates of an obstacle; For the and th waypoint connection on the path and the th obstacle, the minimum effective distance, For the th obstacle, the safety radius, Is the penalty score, Is the number of obstacles, Is the interpolation coefficient between 0 and 1;

[0133]

[0134] In the formula, Is the three-dimensional Euclidean distance operator, For the th waypoint position coordinates on the path.

[0135] In step S2.3.1, calculating the velocity and position of each particle in the th iteration includes:

[0136]

[0137]

[0138] In the formula, For the th iteration, the th particle's velocity, Is the inertia weight, Is the learning factor, Is a random number between 0 and 1, For the th particle's individual optimal position, Is the global optimal position, For the th iteration, the th particle's position.

[0139] Step S3: For the path obtained by path optimization, use the cubic spline interpolation method for smoothing.

[0140] Cubic Spline Interpolation, abbreviated as Spline Interpolation, is a smooth curve passing through a series of shape value points. Mathematically, it is a process of obtaining a set of curve functions by solving a system of three-moment equations. Cubic Spline Interpolation constructs a path through piecewise cubic polynomials to ensure the continuity of function values, first derivatives (velocities), and second derivatives (accelerations) in adjacent intervals. This property avoids the "broken line effect" of traditional linear interpolation and the "Runge oscillation phenomenon" of high-order polynomial interpolation, and is particularly suitable for scenarios with high requirements for motion continuity such as unmanned vehicles and drones, which can reduce mechanical vibrations and energy losses caused by sudden changes in path curvature.

[0141] As Figure 2 shown, an embodiment of the present invention provides a simulation verification scenario for unmanned aerial vehicle flight control, which includes a power communication network control center, three fixed base stations (BS1, BS2, BS3), and five unmanned aerial vehicles (UAV1-UAV5). The control center obtains the deployment positions and flight trajectories of the unmanned aerial vehicles through the unmanned aerial vehicle flight control method proposed in the embodiment of the present invention according to the power grid inspection task requirements. The test area simulates mountainous terrain, including dynamic obstacles (such as high-voltage line towers, temporary construction areas) and random user demand distributions.

[0142] Parameter settings:

[0143] For the setting of environmental parameters, the regional range is 5km * 5km, the terrain undulation height difference is 0 - 200m, the number of dynamic obstacles is 10 (randomly distributed, with a radius of 10 - 50m), the positions change randomly with the experimental batches, and the number of user demand points is 20 (randomly generated, with high / medium / low three priority levels).

[0144] For the deployment and positioning stage, the ant colony optimization algorithm is used to search for the optimal suspension deployment points of the five unmanned aerial vehicles in three-dimensional space. The number of ants is 20, the maximum number of iterations is 100 times, the initial pheromone concentration is 1.0, and the evaporation factor adopts a dynamic attenuation form:

[0145]

[0146] The pheromone enhancement factor Q is 100, and the heuristic information function weight parameters 、 are 1.0 and 2.0 respectively. The flight altitude perturbation is modeled as:

[0147]

[0148] The communication coverage radius is:

[0149]

[0150] The coverage rate calculation is based on the perturbed coverage radius, and the deployment scheme is optimized according to the following objective function:

[0151]

[0152] Finally, the deployment result with the optimal coverage rate is selected as the candidate for the end point of path planning.

[0153] For the path planning stage, a feasible path from the communication center to the deployment point is planned. The maximum number of iterations of the particle swarm optimization algorithm is 200 times. The range of the inertia weight for speed update is (0.9~0.4). The weight coefficient w1 (path length) = 0.5, w2 (height change) = 0.3, w3 (smoothness) = 0.2, and the random perturbation intensity = 5m (standard deviation of height adjustment). In this embodiment, the method proposed in the embodiment of the present invention is considered to be compared with the traditional PSO algorithm and the typical A* algorithm in path planning. The experimental results are shown in Table 1.

[0154] Table 1 Comparison of results of three path planning methods

[0155] Index The present invention Traditional PSO A* algorithm Average path length (km) 8.7±0.3 9.4±0.5 11.2±0.8 Target coverage rate (%) 93.5±2.5 82.0±3.0 76.5±4.0 Number of collisions 1.2±0.5 4.8±1.2 6.5±1.5 Computation time (s) 52±8 28±5 145±20

[0156] Combined with the data results in Table 1, the path length generated by the method of the embodiment of the present invention is the shortest (8.7 km), which is about 7.4% shorter than the traditional PSO (only optimizing the path length) and 22.3% shorter than the A* algorithm. The traditional PSO ignores the height change and smoothness constraints, resulting in redundant detours in the generated path; the A* algorithm depends on the grid resolution, and the discretization degree of the generated path is high, resulting in a significant increase in length; at the same time, while the method of this patent combines multiple constraints such as path length and ensures path safety, it also combines the cubic spline interpolation technique, significantly shortening the path length and improving the path feasibility.

[0157] In terms of obstacle avoidance performance, the method of the embodiment of the present invention combines the collision detection and random perturbation mechanisms, and the number of collisions (1.2 times) is significantly lower than that of the traditional PSO (4.8 times) and the A* algorithm (6.5 times). The traditional PSO is prone to falling into a local optimal path and causing collisions due to the lack of dynamic adjustment ability; the A* algorithm has the highest collision risk due to the obstacle projection error and the principle of discrete path points in the three-dimensional environment, indicating that in this embodiment, the method of this patent has strong robustness in a complex dynamic environment.

[0158] In terms of computing performance, the computing time (52 s) of the method of this patent is slightly higher than that of the traditional PSO (28 s), but much lower than that of the A* algorithm (145 s). The traditional PSO has the shortest computing time due to simple single-objective optimization; the method of this patent increases the computing complexity due to multi-constraint optimization and dynamic perturbation; the A* algorithm has the lowest computing efficiency due to the explosion of the three-dimensional grid search space.

[0159] As shown Figure 3 in the figure, the three-dimensional path results of the method of the present invention and the traditional PSO and A* algorithms in one experiment are presented. It can be intuitively observed Figure 3 from the figure that in a relatively complex obstacle scenario, due to its poor obstacle avoidance performance, the A* algorithm generates an infeasible path that directly passes through the obstacle. At the same time, compared with the traditional PSO, the path of the method of the present invention is significantly shorter in length and better in smoothness, and the generated path has better feasibility.

[0160] Embodiment 2:

[0161] The embodiment of the present invention provides a drone flight control device for supporting power emergency communication, including:

[0162] A deployment optimization module configured to determine the target area of power emergency communication, consider the uncertainty of the drone flight altitude, and perform deployment optimization using the ant colony optimization algorithm with the goal of maximizing the coverage rate;

[0163] A path optimization module configured to take the deployment location obtained by the deployment optimization as the end point, consider the uncertainty of the drone flight altitude, and perform path optimization using the particle swarm optimization algorithm with constraints on path length, altitude change cost, path smoothness cost, and collision penalty;

[0164] A smoothing processing module configured to perform smoothing processing on the path obtained by the path optimization using the cubic spline interpolation method.

[0165] The final output result can be visualized in three dimensions using the MATLAB tool to intuitively display the whole process of path planning and the optimization effect.

[0166] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0167] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to produce a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0168] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0169] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0170] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the technical principles of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. A method for controlling the flight of an unmanned aerial vehicle to support emergency power communication, characterized in that, It includes: Determine the target area of power emergency communication. Considering the uncertainty of the UAV flight altitude and aiming at maximizing the coverage rate, use the ant colony optimization algorithm for deployment optimization; Taking the deployment positions obtained from the deployment optimization as the end points, considering the uncertainty of the UAV flight altitude and using the particle swarm optimization algorithm for path optimization with constraints of path length, altitude change cost, path smoothness cost and collision penalty.

2. The drone flight control method for supporting power emergency communication according to claim 1, wherein, The use of the ant colony optimization algorithm for deployment optimization includes: Determine the set of UAV deployment positions according to the target area; Initial number of iterations , the th iteration, the th drone at the th deployment location corresponding to the pheromone concentration , maximum number of iterations , repeatedly execute the deployment optimization iteration step until , output the final deployment locations of the drones; Among them, the deployment optimization iteration steps include: Construct the heuristic information function corresponding to each UAV in the ith iteration based on the set of deployment locations; According to the heuristic information function and pheromone concentration corresponding to each UAV in the ith iteration, calculate the selection probability of the deployment position of each UAV by the ant in the ith iteration; Each ant selects the deployment location of each drone in the th iteration according to the selection probability of the deployment location of each drone in the th iteration; According to the deployment positions of each UAV in the -th iteration, considering the uncertainty of the UAV flight altitude and aiming at maximizing the coverage rate, calculate the pheromone concentration corresponding to each UAV in the -th iteration ; Let .

3. The method for controlling the flight of an unmanned aerial vehicle supporting power emergency communication according to claim 2, wherein The heuristic information function is: ； In the formula, is the heuristic information function value corresponding to the -th iteration when the -th drone is at the -th deployment position, is the task weight related to the -th target point, is the position coordinate of the -th target point, is the number of target points; is the position coordinate of the -th deployment position selected by the -th drone, is a constant term; The selection probability is: ； Wherein, is the selection probability of the th iteration of the th UAV for the th deployment location, is the pheromone concentration corresponding to the th iteration of the th UAV at the th deployment location, is the heuristic information function value corresponding to the th iteration of the th UAV at the th deployment location, is the index value of the deployment location; are the weights of the pheromone concentration and the heuristic information function value, respectively.

4. The method for controlling the flight of an unmanned aerial vehicle for supporting power emergency communication according to claim 2, wherein According to the deployment positions of each UAV in the iteration, considering the uncertainty of UAV flight altitude and aiming at maximizing the coverage rate, calculate the pheromone concentration corresponding to each UAV in the iteration, including: Considering the uncertainty of the UAV flight altitude, construct the UAV flight altitude function and calculate the effective signal coverage range of the UAV: ； ； Wherein, is the target flight altitude and the actual flight altitude of the th drone, is the flight altitude perturbation of the th drone, is the maximum flight altitude perturbation, is a uniform distribution with equal probability in the range from to ; is the drone communication radiation adjustment parameter, is the minimum safe flight altitude of the drone, is the effective signal coverage range of the th drone; Based on the effective signal coverage range of the UAV, calculate the effective signal coverage status of the UAV for the target point: ； In the formula, is the effective signal coverage status of the th drone at the th deployment location for the th target point, is the three-dimensional Euclidean distance operator; is the position coordinate of the th target point, is the position coordinate of the th deployment location selected by the th drone; Based on the effective signal coverage status, construct the coverage rate reward function of the UAV for the target point with the goal of maximizing the coverage rate: ； Among them: ； ； In the formula, To maximize coverage, is the number of drones, is the number of target points, For the The drone selected The location coordinates of the deployment locations; To deploy the overlapping penalty term, is the deployment overlap penalty coefficient, is the minimum distance threshold between drones, is the coverage reward function value; Update the pheromone concentration based on the coverage rate reward function: ； ； Wherein, is the pheromone concentration corresponding to the -th drone at the -th deployment position in the -th iteration, is the coverage reward function value of the drone for the target point in the -th iteration, is the information enhancement coefficient, is the evaporation rate of the pheromone concentration in the -th iteration, is the initial evaporation rate, is the decay constant.

5. The method for controlling the flight of an unmanned aerial vehicle for supporting power emergency communication according to claim 1, wherein The use of the particle swarm optimization algorithm for path optimization includes: Considering the uncertainty of the UAV flight altitude and constructing the fitness function with constraints of path length, altitude change cost, path smoothness cost and collision penalty; Set the number of particles, the initial position and initial velocity of each particle, and the maximum number of iterations , and the initial position of each particle is all the waypoints on the path Initial number of iterations , initialize the global optimal position and the individual optimal position of each particle to the initial position, and repeat the path optimization iteration step until , output the final global optimal position to obtain the optimal path; Among them, the path optimization iteration steps include: According to the velocity and position of each particle, the individual best position, and the global best position in the iteration, calculate the velocity and position of each particle in the According to the velocity and position of each particle in the th iteration, the fitness of each particle is calculated using the fitness function; Update the global optimal position and the individual optimal position of each particle according to the fitness of each particle; Let .

6. The method for controlling the flight of a drone for supporting electric power emergency communication according to claim 5, characterized in that, The fitness function is: ； Among them: ； ； ； ； ； ； Wherein, is the path length, is the height change cost, is the path smoothness cost, is the collision penalty term, are the weight coefficients of the path length, height change cost, and path smoothness cost respectively, are the position coordinates of the th waypoint on the path respectively, is the number of waypoints on the path, is the z-axis coordinate value of the th waypoint on the path and the actual flight height of the UAV at the th waypoint, is the flight height perturbation of the UAV at the th waypoint, is the maximum flight height perturbation, is the uniform probability distribution in the interval from to , is the average value of the z-axis coordinate values of all waypoints on the path, is the preset height median, are all weight coefficients, is the angle between the line connecting the th waypoint and the adjacent waypoint on the path; are the xy-axis coordinate values of the th waypoint on the path, are the z-axis coordinate values of the th waypoint on the path, is the center coordinate of the th obstacle, is the minimum and maximum z-axis coordinate values of the th obstacle; is the minimum effective distance between the line connecting the th and th waypoints on the path and the th obstacle, is the safety radius of the th obstacle, is the penalty score, is the number of obstacles, is the interpolation coefficient between 0 and 1; ； In the formula, is the three-dimensional Euclidean distance operator, is the position coordinate of the th waypoint on the path.

7. The drone flight control method for supporting electric power emergency communication according to claim 5, characterized in that The calculation of the velocity and position of each particle in the ； ； Wherein, is the velocity of the -th particle in the -th iteration, is the inertia weight, is the learning factor, is a random number between 0 and 1, is the personal best position of the -th particle, is the global best position, is the -th iteration of the -th particle position.

8. The method for controlling the flight of an unmanned aerial vehicle for supporting power emergency communication according to claim 1, wherein The UAV flight control method further includes: For the path obtained from the path optimization, use the cubic spline interpolation method for smoothing processing.

9. An unmanned aerial vehicle flight control device for supporting electric power emergency communication, characterized in that, It includes: A deployment optimization module, configured to determine the target area of power emergency communication. Considering the uncertainty of the UAV flight altitude and aiming at maximizing the coverage rate, use the ant colony optimization algorithm for deployment optimization; A path optimization module, configured to take the deployment positions obtained from the deployment optimization as the end points. Considering the uncertainty of the UAV flight altitude and using the particle swarm optimization algorithm for path optimization with constraints of path length, altitude change cost, path smoothness cost and collision penalty.

10. The drone flight control device for supporting power emergency communication according to claim 9, characterized in that, The UAV flight control device further includes: A smoothing processing module, configured to use the cubic spline interpolation method for smoothing processing of the path obtained from the path optimization.

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