Unmanned aerial vehicle cluster laser energy transfer method in rainfall weather

Abstract

The invention provides an unmanned aerial vehicle cluster laser energy transfer method in rainfall weather, which comprises the following steps: acquiring unmanned aerial vehicle cluster position data, and calculating Euclidean distances from a laser transmitter to each unmanned aerial vehicle according to the unmanned aerial vehicle cluster position data; acquiring real-time rainfall data, and calculating a charging efficiency factor of each unmanned aerial vehicle according to the real-time rainfall data and the Euclidean distance; obtaining an unmanned aerial vehicle cluster execution task list, and obtaining an unmanned aerial vehicle charging sequence list according to the unmanned aerial vehicle cluster execution task list; calculating charging time according to the unmanned aerial vehicle charging sequence list and the charging efficiency factor; and charging the unmanned aerial vehicle cluster according to the charging time. According to the method, the attenuation characteristics of laser propagation in the rain are analyzed, the working priority and the charging efficiency of the single unmanned aerial vehicle are considered under the condition that the charging power is limited, and the charging time is reasonably distributed by taking the improvement of the overall hang time of the unmanned aerial vehicle cluster as a target.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle cluster charging, and in particular to a laser energy transmission method for unmanned aerial vehicle clusters in rainy weather. Background Art

[0002] Under the influence of natural disasters such as earthquakes, floods, and wildfires, the power supply and communication in the affected areas will be seriously affected, or even completely interrupted, which will greatly hinder disaster rescue. With its advantages of flexible deployment, high mobility and high reliability, drones provide effective solutions for important tasks such as establishing emergency communication links, data collection and mapping in post-disaster emergency rescue. However, due to the limitation of the battery capacity of (drone cluster) UAVS, the working radius and endurance of UAVS are limited to a certain range, and the efficiency of a single drone is low. The collaborative work of drone clusters can maximize their load capacity and information perception capabilities. Using laser wireless energy transmission technology to power UAVS can improve its endurance. For drone clusters, how to formulate a charging strategy is the key to improving the endurance and work efficiency of drone clusters. At present, there is little research on the charging strategy of laser-powered drone clusters. Summary of the invention

[0003] Based on this, it is necessary to provide a method for laser energy transmission of drone clusters in rainy weather to address the above technical problems.

[0004] A method for transmitting laser energy of a drone cluster in rainy weather comprises the following steps:

[0005] Obtaining the drone cluster position data, and calculating the Euclidean distance from the laser transmitter to each drone based on the drone cluster position data;

[0006] Acquire real-time rainfall data, and calculate a charging efficiency factor of each drone according to the real-time rainfall data and the Euclidean distance;

[0007] Obtain a list of tasks to be performed by the drone cluster, and obtain a list of charging sequences for the drones according to the list of tasks to be performed by the drone cluster;

[0008] Calculating charging time according to the drone charging sequence list and the charging efficiency factor;

[0009] The drone cluster is charged according to the charging time.

[0010] In one embodiment, obtaining real-time rainfall data, and calculating the charging efficiency factor of each drone according to the real-time rainfall data and the Euclidean distance includes:

[0011] The real-time rainfall data is obtained, and the received power of each UAV is calculated according to the Euclidean distance and the real-time rainfall data;

[0012] The charging efficiency factor of each UAV is calculated based on the received power.

[0013] In one embodiment, obtaining real-time rainfall data, and calculating the received power of each drone according to the Euclidean distance and the real-time rainfall data includes:

[0014] According to the real-time rainfall data, obtaining the attenuation coefficient of the laser;

[0015] The received power of each UAV is calculated according to the attenuation coefficient and the Euclidean distance using the following formula:

[0016]

[0017] Where Pr represents the received power, δ represents the power allocation factor used to separate the communication power, ω represents the efficiency of the photovoltaic converter, A represents the area of ​​the photovoltaic panel receiving the laser, η represents the photoelectric conversion efficiency of the photovoltaic panel, and P L represents the laser emission power, α represents the attenuation coefficient of the laser, d represents the Euclidean distance from the laser emission end to the receiving end, D represents the size of the initial laser beam, and Δθ represents the angular expansion of the laser beam.

[0018] In one embodiment, the charging efficiency factor of each drone calculated according to the received power includes:

[0019]

[0020] Among them, i represents the charging efficiency factor of the i-th UAV, P ri represents the received power of the i-th UAV, P h represents the power of the drone during hovering, takeoff and landing, T represents thrust, ρ represents air density, Indicates the windward area of ​​the drone.

[0021] In one embodiment, obtaining a list of tasks to be performed by a drone cluster, and obtaining a list of charging sequences of drones according to the list of tasks to be performed by the drone cluster includes:

[0022] Obtain the list of tasks to be performed by the drone cluster, and obtain the importance of the drone tasks according to the list of tasks to be performed by the drone cluster;

[0023] According to preset standards, the drone cluster execution task list is sorted according to the importance of the drone execution tasks to obtain a drone charging sequence list.

[0024] In one embodiment, calculating the charging time according to the drone charging sequence list and the charging efficiency factor includes:

[0025] According to the drone charging sequence list and the charging efficiency factor, the charging time required for each drone is calculated to determine whether each drone can get a charging opportunity before reaching the forced landing threshold;

[0026] In response to the drone not getting a chance to charge, recalculate the charging time;

[0027] In response to the fact that there is no drone and no charging opportunity, the charging time required for each drone is obtained.

[0028] In one embodiment, calculating the charging time required for each drone according to the drone charging sequence list and the charging efficiency factor includes:

[0029] The overall hovering time of the drone cluster satisfies the following formula:

[0030]

[0031] in, represents the overall hovering time of the drone cluster, n represents the total number of drones in the drone cluster, T0 represents the available flight time of the drone, and υ i represents the charging efficiency factor of the i-th UAV, t i represents the charging time of the i-th drone;

[0032] The charging time required for each drone is calculated using a meta-heuristic algorithm.

[0033] A UAV cluster laser energy transmission system in rainy weather, used to implement the UAV cluster laser energy transmission method in rainy weather as described above, comprising:

[0034] A position acquisition module is used to obtain the position data of the drone cluster and calculate the Euclidean distance from the laser transmitter to each drone based on the drone cluster position data;

[0035] An efficiency calculation module, used to obtain real-time rainfall data, and calculate the charging efficiency factor of each drone according to the real-time rainfall data and the Euclidean distance;

[0036] A sequence acquisition module is used to obtain a list of tasks to be executed by the drone cluster, and obtain a list of charging sequences of the drones according to the list of tasks to be executed by the drone cluster;

[0037] A time calculation module, used to calculate the charging time according to the charging sequence list of the drone and the charging efficiency factor;

[0038] A charging module is used to charge the drone cluster according to the charging time.

[0039] A device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of a method for laser energy transmission of a drone cluster in rainy weather described in the above-mentioned embodiments are implemented.

[0040] A storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for laser energy transmission of a drone cluster in rainy weather as described in the above-mentioned embodiments.

[0041] Compared with the prior art, the advantages and beneficial effects of the present invention are as follows: the present invention proposes a laser energy transmission method for drone clusters in rainy weather, which analyzes the attenuation characteristics of laser propagation in the rain, considers the work priority and charging efficiency of a single drone under limited charging power, and reasonably allocates charging time with the goal of increasing the overall hovering time of the drone cluster. The present invention calculates the charging time according to the rainfall conditions, ensures that the drones have the longest hovering time under different rainfall conditions, and has strong charging flexibility, so that the drones can be used efficiently in complex situations. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A schematic diagram of an application scenario of a laser energy transmission method for a drone cluster in rainy weather in one embodiment;

[0043] Figure 2 A schematic diagram of a flow chart of a method for laser energy transmission of a drone cluster in rainy weather in one embodiment;

[0044] Figure 3 A schematic diagram of the propagation of a laser beam in a dynamic scattering medium in one embodiment;

[0045] Figure 4 A schematic diagram of laser beam propagation loss in one embodiment;

[0046] Figure 5 A schematic diagram of performance differences of three charging methods under different rainfall amounts in one embodiment;

[0047] Figure 6 This is a schematic diagram of the structure of a UAV cluster laser energy transmission system in rainy weather in one embodiment;

[0048] Figure 7 Schematic diagram of the internal structure of a device in one embodiment. DETAILED DESCRIPTION

[0049] Before describing the specific embodiments of the present invention, the overall concept of the present invention is described as follows:

[0050] The present invention is mainly developed for the charging process of drone clusters. For drone clusters using laser wireless charging technology, most of the existing research is conducted under ideal weather conditions, and less consideration is given to the complex and changeable weather conditions after disasters. However, in practical applications, lasers will encounter various discrete suspended particles during transmission in the atmosphere, such as rain, fog, and dust, which will have significant scattering and absorption effects on the laser, especially in the near-ground atmosphere. These phenomena will reduce the performance and reliability of the laser wireless charging system, resulting in a reduction in the energy reaching the receiving end and a reduction in energy transfer efficiency. For UAVSs with laser wireless charging in rainy weather, the energy consumption of drones is accurately described, the attenuation characteristics of the laser under different rainfall amounts are analyzed, and the conversion efficiency of each part of the (laser wireless power transmission) LWPT system is combined to calculate the charging power of the drones. Considering the charging efficiency and work priority of each drone, a charging strategy is proposed to maximize the hovering time of the entire cluster, ensuring that the efficient operation of the drone cluster can be maintained even in rainy weather conditions.

[0051] Therefore, the present invention proposes a laser energy transmission method for drone clusters in rainy weather. Laser wireless energy transmission uses laser as an energy carrier, converts electrical energy into laser and transmits it through an optical system, transmits it in free space, and is finally received by photovoltaic cells installed on the drone and converted into electrical energy to power the drone.

[0052] After introducing the overall concept of the present invention, in order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below through specific implementation methods in conjunction with the accompanying drawings.

[0053] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in one or more embodiments of this specification should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in one or more embodiments of this specification do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0054] Figure 1 A schematic diagram of an application scenario of a method for laser energy transmission of a drone cluster in rainy weather in an embodiment disclosed in the present invention is shown.

[0055] like Figure 1 As shown, the drone cluster works in the air, and the ground obtains the location of the drone cluster and real-time rainfall conditions, calculates the charging time required for the drone, uses laser as an energy carrier, converts electrical energy into laser and emits it through an optical system, transmits it in free space, and is finally received by the photovoltaic cells installed on the drone and converted into electrical energy to power the drone.

[0056] In one embodiment, Figure 2 As shown, a method for laser energy transmission of a drone cluster in rainy weather is provided, comprising the following steps:

[0057] Step S201, obtaining the drone cluster position data, and calculating the Euclidean distance from the laser transmitter to each drone based on the drone cluster position data.

[0058] Specifically, when performing tasks such as relay communication, terrain scanning or disaster monitoring, drones often need to hover in the air for a long time. The drone cluster is equipped with a position acquisition structure that can obtain the position data of the drones in the drone cluster and calculate the Euclidean distance from the laser transmitter to each drone based on the position data. Given that GaAs photovoltaic cells have a high photoelectric conversion efficiency in the 808nm band, a laser with a wavelength of 808nm is selected as the laser light source. Figure 3 Schematic diagram of the propagation of a laser beam in a dynamic scattering medium.

[0059] Step S202, obtaining real-time rainfall data, and calculating the charging efficiency factor of each drone according to the real-time rainfall data and the Euclidean distance.

[0060] Specifically, when a laser beam is transmitted in the atmosphere, due to the various particles distributed in the atmosphere, scattering and absorption effects are produced with the particles, resulting in reduced laser transmittance, expanded light spot and decreased power at the receiving end.

[0061] According to the real-time rainfall data and the calculated Euclidean distance, the received power of each UAV is calculated, and the charging efficiency factor of each UAV is calculated.

[0062] On this basis, real-time rainfall data is obtained, and the charging efficiency factor of each drone is calculated according to the real-time rainfall data and the Euclidean distance, including:

[0063] The real-time rainfall data is obtained, and the received power of each UAV is calculated according to the Euclidean distance and the real-time rainfall data;

[0064] The charging efficiency factor of each UAV is calculated based on the received power.

[0065] On this basis, real-time rainfall data is obtained, and the received power of each UAV is calculated according to the Euclidean distance and the real-time rainfall data, including:

[0066] According to the real-time rainfall data, obtaining the attenuation coefficient of the laser;

[0067] The received power of each UAV is calculated according to the attenuation coefficient and the Euclidean distance using the following formula:

[0068]

[0069] Where Pr represents the received power, δ represents the power allocation factor used to separate the communication power, ω represents the efficiency of the photovoltaic converter, A represents the area of ​​the photovoltaic panel receiving the laser, η represents the photoelectric conversion efficiency of the photovoltaic panel, and P L represents the laser emission power, α represents the attenuation coefficient of the laser, d represents the Euclidean distance from the laser emission end to the receiving end, D represents the size of the initial laser beam, and Δθ represents the angular expansion of the laser beam.

[0070] Specifically, the transmission attenuation model of spherical and non-spherical raindrops to a laser beam with a wavelength of 808nm is obtained. According to numerical calculation and analysis, the attenuation coefficient of the 808nm laser in different rainfall amounts is obtained, as shown in Table 1 below:

[0071]

[0072] Table 1 Attenuation coefficient of 808nm laser under different rainfall conditions

[0073] The attenuation law of light in a medium is calculated by the following formula:

[0074] I=I0e -αx

[0075] I is the transmitted light intensity; I0 is the incident light intensity; α is the attenuation coefficient; x is the distance the light passes through the medium. The transmission efficiency of the LWPT system is not only affected by the atmospheric transmission loss, but also related to the electro-optical conversion efficiency and the photo-electric conversion efficiency of the photovoltaic array, as follows Figure 4 shown.

[0076] When a drone performs a mission, its energy consumption is mainly divided into two parts: flight energy consumption and communication energy consumption. A power distributor is used to distribute the received energy to ensure that it can provide energy for the drone and support information transmission. Based on this, the following model is constructed to calculate the received power of each drone:

[0077]

[0078] Where Pr is the power received by the UAVS for flight or hovering, in units of w; δ is the power allocation factor used to separate the communication power; ω is the efficiency of the photovoltaic converter; η is the photoelectric conversion efficiency of the photovoltaic panel; A is the area of ​​the photovoltaic panel receiving the laser, in units of m 2 ;P L is the laser emission power, in units of w; D is the size of the initial laser beam, in units of m; α is the attenuation coefficient of the laser transmission in the medium, in units of m -1 ; d is the Euclidean distance from the laser transmitter to the receiver, in meters, and Δθ is the angular expansion of the laser beam, in rad.

[0079] On this basis, the charging efficiency factors of each drone calculated according to the received power include:

[0080]

[0081] Among them, i represents the charging efficiency factor of the i-th UAV, P ri represents the received power of the i-th UAV, P h represents the power of the drone during hovering, takeoff and landing, T represents thrust, ρ represents air density, Indicates the windward area of ​​the drone.

[0082] Specifically, according to the drone hovering energy consumption model, the power consumed by the drone is approximately linearly proportional to the weight of the payload:

[0083]

[0084] P h is the power of the drone when hovering, taking off and landing, thrust T, unit Newton; ρ is the air density, unit kg / m 3 ; is the windward area of ​​the UAV, in m 2 When the UAVS is hovering, its thrust is equal to the total weight of the UAVS. Since air density is related to temperature, different temperatures correspond to air density, which in turn affects the hovering power of the UAVS.

[0085] The charging efficiency factor is:

[0086]

[0087] Step S203, obtaining a list of tasks to be performed by the drone cluster, and obtaining a list of charging sequences of the drones according to the list of tasks to be performed by the drone cluster.

[0088] Specifically, the drones in the drone cluster each perform different work tasks according to the drone cluster execution task list, obtain the drone cluster execution task list, and obtain the drone charging sequence list based on the drone cluster execution task list.

[0089] On this basis, a list of tasks to be performed by the drone cluster is obtained. According to the list of tasks to be performed by the drone cluster, a list of charging sequences of the drones is obtained, including:

[0090] Obtain the list of tasks to be performed by the drone cluster, and obtain the importance of the drone tasks according to the list of tasks to be performed by the drone cluster;

[0091] According to preset standards, the drone cluster execution task list is sorted according to the importance of the drone execution tasks to obtain a drone charging sequence list.

[0092] Specifically, a list of tasks to be performed by the drone cluster is obtained, the importance of the drone tasks is obtained according to the list of tasks to be performed by the drone cluster, and the list of tasks to be performed by the drone cluster is sorted from high to low according to the importance of the drone tasks to obtain a list of drone charging sequences.

[0093] Step S204, calculating the charging time according to the drone charging sequence list and the charging efficiency factor.

[0094] Specifically, when powering a cluster of drones performing missions, the charging power is limited by the maximum charging power and thermal management of the drones. The laser emission power cannot be increased arbitrarily to achieve the purpose of improving charging efficiency. The charging time of each drone needs to be calculated based on the drone charging sequence list and the charging efficiency factor.

[0095] On this basis, according to the drone charging sequence list and the charging efficiency factor, the charging time is calculated including:

[0096] According to the drone charging sequence list and the charging efficiency factor, the charging time required for each drone is calculated to determine whether each drone can get a charging opportunity before reaching the forced landing threshold;

[0097] In response to the drone not getting a chance to charge, recalculate the charging time;

[0098] In response to the fact that there is no drone and no charging opportunity, the charging time required for each drone is obtained.

[0099] Specifically, the charging time required for each drone is calculated according to the drone charging order list, and it is checked whether each drone can get a charging opportunity before the energy reaches the forced landing threshold. If a drone does not get a charging opportunity, the charging time is recalculated until all drones get a charging opportunity.

[0100] On this basis, according to the drone charging sequence list and the charging efficiency factor, the charging time required for each drone is calculated including:

[0101] The overall hovering time of the drone cluster satisfies the following formula:

[0102]

[0103] in, represents the overall hovering time of the drone cluster, n represents the total number of drones in the drone cluster, T0 represents the available flight time of the drone, and υ i represents the charging efficiency factor of the i-th UAV, t i represents the charging time of the i-th drone;

[0104] The charging time required for each drone is calculated using a meta-heuristic algorithm.

[0105] Specifically, for a UAV cluster, at any time j, the i-th UAV i The remaining hang time is:

[0106]

[0107] It is UAV i The remaining time in the air at time j, in minutes; It is UAV i The remaining energy at time j, in wh. When the remaining energy of the drone reaches the forced landing threshold When the drone is not performing the mission, it makes an emergency landing and returns. Then during the drone's mission, the remaining hovering time of any drone at any moment should meet the following requirements:

[0108]

[0109] The overall hovering time of the drone cluster is defined as

[0110]

[0111] For a single UAV iFor a battery, its flight time can be divided into two components: the flight time T0 under the available flight power, that is, the flight time after the battery energy minus the forced landing threshold; and the additional flight time T obtained by charging i .

[0112]

[0113] Among them, UAV i During the charging time t i The accumulated energy E ci for:

[0114] E Ci =P ri t i

[0115] P ri For UAV i The corresponding charging power is

[0116]

[0117] The overall hovering time of the drone cluster have:

[0118]

[0119] Charging efficiency factor Then the overall hovering time of the drone cluster satisfies the following formula:

[0120]

[0121] The charging strategy of drone clusters is a traveling salesman problem. The charging time required for each drone is calculated through a meta-heuristic algorithm. The meta-heuristic algorithms include but are not limited to: taboo search algorithm, simulated annealing algorithm, genetic algorithm, ant colony optimization algorithm, particle swarm optimization algorithm, artificial fish swarm algorithm, spider bee optimization algorithm, etc.

[0122] In this embodiment, the Spider Wasp Optimization (SWO) algorithm is used to determine the charging order by considering the priority of the drone cluster's tasks, and the charging time is determined by combining the charging efficiency factors of different drones.

[0123] SWO simulates the exploration, hunting, nesting and mating behaviors of female spider bees, where each individual represents a candidate solution, and performs selection, crossover and mutation operations in the population according to its fitness to generate new candidate solutions. Through continuous iterative updates, the spider bee population gradually approaches the global optimal solution.

[0124] First initialize:

[0125] t i =SW i t =L+r×(HL)

[0126] t i represents the charging time of the i-th drone, SW i t is the solution at time t, that is, the charging time of the drone cluster is stored, i is the number of spider bee populations, L and H represent the upper and lower bounds of the solution, which are used to control the charging time of a single drone; r∈[0,1] is a random vector.

[0127] In the search phase, the female bee randomly explores the search space with a constant step size to find its suitable offspring spider, thereby simulating exploration behavior. Performing a global search with a constant step size covers a larger search space, which is conducive to discovering potential global optimality or avoiding entering local optimality.

[0128]

[0129] SW i t+1 To solve the next generation, the spider bees in the population try to improve their current position to increase charging efficiency; and For two random individuals in the population, i.e., two random solutions, two reference points are selected in the solution set for improvement; μ1 is the parameter that controls the degree of improvement:

[0130] μ1=|r n |*r1

[0131] r1 is a random number between 0 and 1. n is a random number that follows a normal distribution. In order to simulate the search behavior of the female spider bee after losing the spider, a small step size is used for a more detailed search, in which the spider bee makes a small adjustment at the current position to finely optimize the quality of the current solution.

[0132]

[0133] is a random solution in the solution set. Each spider bee conducts a more local exploration based on the positions of other members in the population to further optimize the charging time allocation; r2 is a random vector with a value range of [0,1]. The search direction is determined according to the value of μ2, and the value of μ2 is:

[0134] μ2=B*cos(2πl)

[0135] Where B = 1 / (1-e l ).

[0136] The hunting phase is divided into capture state and escape state, which helps to explore new areas and avoid falling into local optimality.

[0137]

[0138] C is the distance control factor that determines the speed of the spider bee. When C>0.5, it means that the spider bee is faster than the prey; when C<0.5, it means that the spider bee is slower than the prey. r5 is a random vector with a value range of [0,1], where:

[0139]

[0140] t and t max They represent the current evaluation value and the maximum evaluation value respectively. The evaluation value corresponds to the charging efficiency factor of a single drone in the drone cluster. By comparing the current evaluation value and the maximum evaluation value, the spider bee can adjust its behavior according to the attractiveness of the prey. This mechanism helps the spider bee to better track the prey and avoid over-concentration on a certain prey and miss other targets that may be more valuable; r6 is a random number in the interval [0,1].

[0141] When prey flees from the spider wasp, hunting behavior transforms into exploratory behavior as the distance increases:

[0142] SW i t+1 =SW i t *vc

[0143] vc is a vector generated by normal distribution in the interval [-k, k], where k = 1-(t / t max ).

[0144] The nesting phase is also divided into two behaviors. The first behavior will simulate the spider bee dragging its prey into a nest of suitable size, that is, adjusting the charging time allocation plan to be closer to the current optimal solution:

[0145] SW i t+1 =SW * +cos(2πl)*(SW * -SW i t )

[0146] SW * represents the optimal solution, that is, the optimal charging time of the drone cluster. The second behavior is to randomly select the location of the female bees in the population and use an extra step to build a nest to avoid duplication in the nesting location. In one behavior, another drone cluster charging time allocation is selected as a reference, and some effective charging allocation schemes are tried to be learned from it.

[0147]

[0148] r3 is a random number in the range of [0,1]; γ is a number generated according to the Levy-fight model; U is a random binary vector of all 0s or 1s to avoid nesting in the same location.

[0149] During the mating phase, each spider bee represents a candidate solution and the spider bee egg represents a potential solution.

[0150]

[0151] Crossover means in SW i t and The uniform crossover operator applied between them, CR is the crossover rate; SW is the crossover rate. i t and The vectors represent the female and male spider bees respectively. This behavior mixes the two charging time allocation schemes to form a new charging time allocation scheme. The cross-breeding operation can avoid falling into the local optimal solution too early, allowing individuals in the population to learn from each other's advantages. By introducing a new allocation scheme, the population diversity is increased, and the global optimal solution is found to obtain the charging time.

[0152] Step S105: charging the drone cluster according to the charging time.

[0153] Specifically, charging the drone cluster according to the calculated charging time can ensure that the overall hovering time of the drone cluster is as long as possible.

[0154] The charging method provided by the present invention is simulated and verified by the MATLAB simulation platform, and compared with the traditional first-come-first-served (FCFS) and NJNP scheduling methods. In environments with rainfall of 5 mm / h, 12.5 mm / h and 25 mm / h, the performance of CSUCR is analyzed by comparing the improvement rate of the overall hovering time of the drone cluster by the three charging methods.

[0155] The experimental design is to set up the GCS at the center of the surface of the area in a three-dimensional space of 500m×500m×150m, and deploy the UAV within the range of 110m to 220m from the GCS. The setting parameters are shown in Table 2.

[0156]

[0157] Table 2 Simulation parameter settings

[0158] When other parameters are fixed, the proposed method is compared with the First Come First Served (FCFS) and NJNP algorithms under the conditions of rainfall of 5 mm / h, 12.5 mm / h and 25 mm / h. Figure 5 The performance differences of the three methods under different rainfall conditions are given respectively.

[0159] from Figure 5 (a) It can be seen that when R = 5 mm / h, the present invention performs better than other algorithms. Compared with NJNP, when multiple drones are close to each other, the present invention will distribute the charging time more evenly according to the task priority, thereby improving the overall hovering time of the drone cluster. Figure 5 (b) It can be seen that when the rainfall increases to 12.5 mm / h, the laser transmission efficiency is further reduced, and the present invention is still better than the other two methods. NJNP always selects the UAV closest to the GCS as the priority charging target and allocates more charging time. When the number of UAVs increases to 5 or more, the subsequent UAVs cannot get enough charging time to extend the endurance. The present invention can allocate a reasonable charging time according to the charging efficiency factor υ, so that the UAVs in the cluster can get enough energy to improve the endurance. Figure 5 (c) It can be seen that when the rainfall increases to 25 mm / h, the efficiency of laser propagation in the rain is greatly reduced, and the receiving power of the drone decreases seriously. As the number of drones increases, the overall improvement rate shows a downward trend. The present invention considers the charging opportunity of each drone and allocates the charging time according to the task priority and charging efficiency factor to avoid the situation where the drones ranked low get too little charging time and the endurance cannot be significantly increased, thereby improving the overall hovering rate of the drone cluster.

[0160] The present invention proposes a laser energy transmission method for drone clusters in rainy weather. By analyzing the attenuation characteristics of laser propagation in the rain, the working priority and charging efficiency of a single drone are considered under the condition of limited charging power, with the goal of increasing the overall hovering time of the drone cluster and reasonably allocating the charging time. The present invention calculates the charging time according to the rainfall conditions, ensuring that the drones have the longest hovering time under different rainfall conditions, and has strong charging flexibility, so that the drones can be used efficiently in complex situations.

[0161] It should be noted that the method of the embodiment of the present invention can be performed by a single device, such as a computer or a server. The method of this embodiment can also be applied in a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present invention, and the multiple devices will interact with each other to complete the described method.

[0162] It should be noted that some embodiments of the present invention are described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0163] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present invention also provides a method for laser energy transmission of a drone cluster in rainy weather.

[0164] refer to Figure 6 The method for transmitting energy by laser of a drone cluster in rainy weather comprises:

[0165] The position acquisition module 601 is used to acquire the position data of the drone cluster and calculate the Euclidean distance from the laser transmitter to each drone according to the position data of the drone cluster;

[0166] The efficiency calculation module 602 is used to obtain real-time rainfall data, and calculate the charging efficiency factor of each drone according to the real-time rainfall data and the Euclidean distance;

[0167] The sequence acquisition module 603 is used to acquire a list of tasks to be executed by the drone cluster, and obtain a list of charging sequences of the drones according to the list of tasks to be executed by the drone cluster;

[0168] A time calculation module 604 is used to calculate the charging time according to the charging sequence list of the drone and the charging efficiency factor;

[0169] The charging module 605 is used to charge the drone cluster according to the charging time.

[0170] For the convenience of description, the above system is described as being divided into various modules according to their functions. Of course, when implementing the present invention, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0171] The system of the above-mentioned embodiment is used to implement a corresponding drone cluster laser energy transmission method in rainy weather in any of the aforementioned embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0172] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a method for laser energy transmission of a drone cluster in rainy weather as described in any of the above embodiments is implemented.

[0173] Figure 7 A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are connected to each other through the bus 1050 in the device.

[0174] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0175] The memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0176] The input / output interface 1030 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0177] The communication interface 1040 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0178] The bus 1050 includes a path that transmits information between the various components of the device (eg, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040).

[0179] It should be noted that, although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040 and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of the present specification, and does not necessarily include all the components shown in the figure.

[0180] The electronic device of the above-mentioned embodiment is used to implement a corresponding drone cluster laser energy transmission method in rainy weather in any of the above-mentioned embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0181] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present invention also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute a method for laser energy transmission of a drone cluster in rainy weather as described in any of the above embodiments.

[0182] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, 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, read-only 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.

[0183] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute a method for laser energy transmission of a drone cluster in rainy weather as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0184] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present invention (including the claims) is limited to these examples. Under the concept of the present invention, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present invention as described above, which are not provided in detail for the sake of simplicity.

[0185] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0186] In addition, to simplify the description and discussion, and in order not to obscure the embodiments of the present invention, known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided figures. In addition, the devices may be shown in the form of block diagrams to avoid obscuring the embodiments of the present invention, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which the embodiments of the present invention will be implemented (i.e., these details should be fully within the scope of understanding of those skilled in the art). Where specific details (e.g., circuits) are set forth to describe exemplary embodiments of the present invention, it will be apparent to those skilled in the art that embodiments of the present invention may be implemented without these specific details or with variations in these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0187] Although the invention has been described in conjunction with specific embodiments of the invention, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

[0188] The embodiments of the present invention are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for laser energy transmission of drone clusters in rainy weather, characterized in that: include: Obtaining the drone cluster position data, and calculating the Euclidean distance from the laser transmitter to each drone based on the drone cluster position data; Acquire real-time rainfall data, and calculate a charging efficiency factor of each drone according to the real-time rainfall data and the Euclidean distance; Obtain a list of tasks to be performed by the drone cluster, and obtain a list of charging sequences for the drones according to the list of tasks to be performed by the drone cluster; Calculating charging time according to the drone charging sequence list and the charging efficiency factor; The drone cluster is charged according to the charging time.

2. According to the method of laser energy transmission of drone cluster in rainy weather as described in claim 1, it is characterized in that: The acquiring of real-time rainfall data and calculating the charging efficiency factor of each drone according to the real-time rainfall data and the Euclidean distance comprises: The real-time rainfall data is obtained, and the received power of each UAV is calculated according to the Euclidean distance and the real-time rainfall data; The charging efficiency factor of each UAV is calculated based on the received power.

3. According to the method of laser energy transmission of drone cluster in rainy weather as described in claim 2, it is characterized in that: The acquiring of real-time rainfall data and calculating the received power of each UAV according to the Euclidean distance and the real-time rainfall data comprises: According to the real-time rainfall data, obtaining the attenuation coefficient of the laser; The received power of each UAV is calculated according to the attenuation coefficient and the Euclidean distance using the following formula: Where Pr represents the received power, δ represents the power allocation factor used to separate the communication power, ω represents the efficiency of the photovoltaic converter, A represents the area of ​​the photovoltaic panel receiving the laser, η represents the photoelectric conversion efficiency of the photovoltaic panel, and P L represents the laser emission power, α represents the attenuation coefficient of the laser, d represents the Euclidean distance from the laser emission end to the receiving end, D represents the size of the initial laser beam, and Δθ represents the angular expansion of the laser beam.

4. According to the method of laser energy transmission of drone cluster in rainy weather in claim 2, it is characterized in that: The charging efficiency factor of each drone calculated according to the received power includes: Among them, i represents the charging efficiency factor of the i-th UAV, P ri represents the received power of the i-th UAV, P h represents the power of the drone during hovering, takeoff and landing, T represents thrust, ρ represents air density, Indicates the windward area of ​​the drone.

5. According to the method of laser energy transmission of drone cluster in rainy weather in claim 1, it is characterized in that: The obtaining of the drone cluster execution task list and obtaining the drone charging sequence list according to the drone cluster execution task list include: Obtain the list of tasks to be performed by the drone cluster, and obtain the importance of the drone tasks according to the list of tasks to be performed by the drone cluster; According to preset standards, the drone cluster execution task list is sorted according to the importance of the drone execution tasks to obtain a drone charging sequence list.

6. According to the method of laser energy transmission of drone cluster in rainy weather in claim 1, it is characterized in that: The calculating the charging time according to the drone charging sequence list and the charging efficiency factor comprises: According to the drone charging sequence list and the charging efficiency factor, the charging time required for each drone is calculated to determine whether each drone can get a charging opportunity before reaching the forced landing threshold; In response to the drone not getting a chance to charge, recalculate the charging time; In response to the fact that there is no drone and no charging opportunity, the charging time required for each drone is obtained.

7. The method for transmitting laser energy of drone clusters in rainy weather according to claim 6 is characterized in that: The calculating the charging time required for each drone according to the drone charging sequence list and the charging efficiency factor comprises: The overall hovering time of the drone cluster satisfies the following formula: in, represents the overall hovering time of the drone cluster, n represents the total number of drones in the drone cluster, T0 represents the available flight time of the drone, and υ i represents the charging efficiency factor of the i-th UAV, t i represents the charging time of the i-th drone; The charging time required for each drone is calculated using a meta-heuristic algorithm.

8. A UAV cluster laser energy transmission system in rainy weather, characterized in that: A method for transmitting laser energy of a drone cluster in rainy weather according to any one of claims 1 to 7, comprising: A position acquisition module is used to obtain the position data of the drone cluster and calculate the Euclidean distance from the laser transmitter to each drone based on the drone cluster position data; An efficiency calculation module, used to obtain real-time rainfall data, and calculate the charging efficiency factor of each drone according to the real-time rainfall data and the Euclidean distance; A sequence acquisition module is used to obtain a list of tasks to be executed by the drone cluster, and obtain a list of charging sequences of the drones according to the list of tasks to be executed by the drone cluster; A time calculation module, used to calculate the charging time according to the charging sequence list of the drone and the charging efficiency factor; A charging module is used to charge the drone cluster according to the charging time.

9. A device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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