A method for laser energy transmission of UAV clusters in rainy weather

Abstract

The present invention provides a method for laser energy transmission for a drone swarm in rainy weather, comprising: obtaining drone swarm location data, calculating the Euclidean distance from a laser transmitter to each drone based on the drone swarm location data; obtaining real-time rainfall data, calculating the charging efficiency factor of each drone based on the real-time rainfall data and the Euclidean distance; obtaining a list of drone swarm execution tasks, and obtaining a drone charging sequence list based on the drone swarm execution task list; calculating a charging time based on the drone charging sequence list and the charging efficiency factor; and charging the drone swarm based on the charging time. By analyzing the attenuation characteristics of laser propagation in rain, the present invention considers the work priority and charging efficiency of individual drones under limited charging power, and rationally allocates charging time with the goal of increasing the overall hovering time of the drone swarm.

Description

Technical Field

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

[0002] Under the influence of natural disasters such as earthquakes, floods, and wildfires, power and communications in affected areas can be severely impacted, or even completely disrupted, significantly hindering emergency rescue efforts. Drones, with their flexible deployment, high maneuverability, and high reliability, provide an effective solution for critical tasks such as establishing emergency communication links, collecting data, and mapping in post-disaster emergency rescue. However, due to the limited battery capacity of UAVS (drone swarms), the operating radius and flight time of UAVS are limited, and individual drones are less efficient. Drone swarms working together can maximize their payload capacity and information perception capabilities. Using laser wireless energy transmission technology to power UAVS can improve their flight time. For drone swarms, developing a charging strategy is key to improving their flight time and efficiency. Currently, little research has been conducted on charging strategies for laser-powered drone swarms. Summary of the Invention

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

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

[0005] Obtaining the position data of the drone cluster, 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 UAV based on the real-time rainfall data and the Euclidean distance;

[0007] Obtain a list of tasks to be executed by the drone cluster, and obtain a list of drone charging sequences based on the list of tasks to be executed 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 based on 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 based on 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 based on the Euclidean distance and the real-time rainfall data includes:

[0014] Obtaining a laser attenuation coefficient according to the real-time rainfall data;

[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 for separating 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 laser attenuation coefficient, 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 based on 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 frontal area of ​​the UAV.

[0021] In one embodiment, obtaining a list of tasks to be executed by a drone cluster, and obtaining a list of charging sequences of drones according to the list of tasks to be executed 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] Calculate the charging time required for each drone based on the drone charging sequence list and the charging efficiency factor, and 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, recalculating 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 based on 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 UAV cluster, n represents the total number of UAVs in the UAV cluster, T0 represents the available flight time of the UAV, 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 required charging time 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, configured to obtain real-time rainfall data and calculate a charging efficiency factor of each UAV based on 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 for the drones based on the list of tasks to be executed by the drone cluster;

[0037] a time calculation module, configured to calculate the charging time according to the UAV charging sequence list 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 each of the above 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 each of the above embodiments.

[0041] Compared to existing technologies, the advantages and benefits of the present invention are as follows: The present invention proposes a laser energy transmission method for drone clusters in rainy weather. By analyzing the attenuation characteristics of laser propagation in rain, and considering the work priority and charging efficiency of individual drones under limited charging power, the method aims to increase the overall hovering time of the drone cluster and rationally allocate charging time. The present invention calculates charging time based on rainfall conditions, ensuring that drones maintain their maximum hovering time under different rainfall conditions. This provides strong charging flexibility and ensures efficient use of drones in complex situations. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is 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 This is a flow chart of a method for laser energy transmission of a drone cluster in rainy weather according to an embodiment;

[0044] Figure 3 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 showing performance differences of three charging methods under different rainfall amounts in one embodiment;

[0047] Figure 6 This is a schematic structural diagram of a UAV cluster laser energy transmission system in rainy weather according to an 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] This invention primarily focuses on the charging process of drone swarms. Existing research on drone swarms using laser wireless charging technology has mostly been conducted under ideal weather conditions, with limited consideration given to the complex and changeable weather conditions after disasters. However, in practical applications, laser transmission in the atmosphere encounters various discrete suspended particles, such as rain, fog, and dust. These particles significantly scatter and absorb the laser light, especially in the near-Earth atmosphere. These phenomena can reduce the performance and reliability of laser wireless charging systems, resulting in reduced energy reaching the receiver and lowering energy transfer efficiency. For laser wireless charging of UAVs (UAVSs) in rainy weather, this paper precisely describes the energy consumption of the drones, analyzes the laser attenuation characteristics under different rainfall levels, and combines the conversion efficiency of each component of the (laser wireless power transmission) (LWPT) system to calculate the drone charging power. Taking into account the charging efficiency and work priority of each drone, a charging strategy is proposed to maximize the flight time of the entire swarm, ensuring efficient operation of the drone swarm even in rainy weather.

[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 solutions 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 have the usual meanings understood by people with ordinary skills in the field to which the invention belongs. The words "first", "second" and similar terms used in one or more implementations of this specification do not indicate any order, quantity or importance, but are only used to distinguish different components. Words such as "include" or "comprise" 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. Words such as "connect" or "connected" 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 object being described changes, the relative positional relationship may also change accordingly.

[0054] Figure 1 A schematic diagram of an application scenario of a UAV cluster laser energy transmission method in rainy weather according to an embodiment of 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 transmits 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 UAV cluster in rainy weather is provided, comprising the following steps:

[0057] Step S201: Obtain the drone cluster position data, and calculate 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: Acquire real-time rainfall data, and calculate the charging efficiency factor of each UAV based on the real-time rainfall data and the Euclidean distance.

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

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

[0062] On this basis, real-time rainfall data is obtained, and the charging efficiency factor of each drone is calculated based on 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 based on 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] Obtaining a laser attenuation coefficient according to the real-time rainfall data;

[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 for separating 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 laser attenuation coefficient, 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 808 nm was obtained. Based on numerical calculation and analysis, the attenuation coefficient of the 808 nm laser in different rainfall amounts was 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; and x is the distance the light travels through the medium. The transmission efficiency of the LWPT system is not only affected by atmospheric transmission losses, but also by the electro-optical conversion efficiency and the photo-electrical 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 divider 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, unit is w; D is the size of the initial laser beam, unit is m; α is the attenuation coefficient of the laser transmission in the medium, unit is m -1 ; d is the Euclidean distance from the laser transmitter to the receiver, in meters, and Δθ is the angular spread of the laser beam, in rad.

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

[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 frontal area of ​​the UAV.

[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 during hovering, takeoff and landing, thrust T, in Newton; ρ is the air density, in kg / m 3 ; is the frontal area of ​​the UAV, in m 2 When a 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: Obtain a list of tasks to be performed by the drone cluster, and obtain a list of charging sequences for the drones based on 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 executed by the drone cluster is obtained. According to the list of tasks to be executed by the drone cluster, a list of drone charging sequences 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, and the importance of the drone tasks is obtained according to the list of tasks to be performed by the drone cluster. According to the importance of the drone tasks, the list of tasks to be performed by the drone cluster is sorted from high to low to obtain a list of drone charging sequences.

[0093] Step S204: Calculate the charging time based on 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 drones' maximum charging power and thermal management. The laser emission power cannot be increased arbitrarily to improve charging efficiency. The charging time for 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] Calculate the charging time required for each drone based on the drone charging sequence list and the charging efficiency factor, and 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, recalculating 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 a check is made to see whether each drone can get a charging opportunity before its 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 UAV cluster, n represents the total number of UAVs in the UAV cluster, T0 represents the available flight time of the UAV, 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 required charging time 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 time in the air is:

[0106]

[0107] It's a UAV i The remaining time in the air at time j, in minutes; It's a 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 iIn terms of flight time, it 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 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 The overall hovering time of the drone cluster satisfies the following formula:

[0120]

[0121] The charging strategy for a drone swarm is a traveling salesman problem. The charging time required for each drone is calculated using a meta-heuristic algorithm. Meta-heuristic algorithms include but are not limited to: tabu 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 UAV cluster's tasks, and the charging time is determined by combining the charging efficiency factors of different UAVs.

[0123] SWO simulates the exploration, hunting, nesting, and mating behaviors of female spider bees. Each individual represents a candidate solution, and selection, crossover, and mutation operations are performed within the population based on their 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, which stores the charging time of the drone cluster, 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] During the search phase, the female bee randomly explores the search space with a constant step size, searching for suitable offspring spiders, thus simulating exploration behavior. Performing a global search with a constant step size covers a larger search space, which helps to discover potential global optima or avoid entering local optima.

[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, that is, 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 in [0,1], r n is a random number that follows a normal distribution. To simulate the search behavior of a female spider bee after losing a spider, a small step size is used for a more detailed search. At this time, the spider bee makes small adjustments to its 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 by the value of μ2, which 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 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 better track the prey and avoid over-concentration on a certain prey and missing other potentially more valuable targets; r6 is a random number in the interval [0,1].

[0141] When prey flees from the spider wasp, hunting behavior transitions to 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 simulates the spider wasp dragging its prey into a nest of appropriate size, adjusting the charging time allocation scheme 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, the charging time distribution of another drone cluster is selected as a reference, and some effective charging distribution schemes are tried to be learned from it.

[0147]

[0148] r3 is a random number in the range [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 wasp represents a candidate solution, and the spider wasp 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 i t and The vectors represent the number of female and male spider bees, respectively. This behavior combines the two charging time allocation schemes to form a new one. Cross-breeding prevents premature regression into local optima, allowing individuals in the population to learn from each other's strengths. By introducing new allocation schemes and increasing population diversity, the global optimal solution and charging time can be found.

[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 following simulations verify the proposed charging method using the MATLAB platform and compare it with traditional first-come, first-served (FCFS) and NJNP scheduling methods. The performance of CSUCR is analyzed by comparing the improvement in the overall hovering time of the drone cluster using the three charging methods under rainfall conditions of 5 mm / h, 12.5 mm / h, and 25 mm / h, respectively.

[0155] The experimental design is to establish a GCS at the center of the surface in a three-dimensional space of 500m×500m×150m, and deploy UAVs within a range of 110m to 220m from the GCS. The set 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 rainfall conditions 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) As can be seen, when R = 5 mm / h, the proposed method performs better than other algorithms. Compared with NJNP, when multiple drones are close to each other, the proposed method distributes charging time more evenly based on 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 12.5mm / h, the laser transmission efficiency further decreases, and the present invention is still better than the other two methods. NJNP always selects the drone closest to the GCS as the priority charging target and allocates more charging time. When the number of drones increases to 5 or more, the subsequent drones cannot get enough charging time to extend their endurance. The present invention can allocate a reasonable charging time according to the charging efficiency factor υ, so that the drones in the cluster can get enough energy to improve their endurance. Figure 5 (c) As can be seen, when rainfall increases to 25 mm / h, the efficiency of laser propagation in rain is greatly reduced, and the received power of the drones drops significantly. As the number of drones increases, the overall improvement rate shows a downward trend. This invention considers the charging opportunities of each drone and allocates charging time based on mission priority and charging efficiency factors. This prevents drones with lower rankings from receiving too little charging time and failing to significantly increase their flight range, thereby improving the overall hovering rate of the drone cluster.

[0160] This paper proposes a laser energy transfer method for drone swarms in rainy weather. By analyzing the attenuation characteristics of laser propagation in rain, and considering the work priority and charging efficiency of individual drones under limited charging power, this method aims to increase the overall hovering time of the drone swarm and rationally allocate charging time. The method calculates charging time based on rainfall conditions, ensuring that drones maintain their maximum hovering time under different rainfall conditions. This provides strong charging flexibility and ensures efficient use of drones 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 server. The method of this embodiment can also be applied in a distributed scenario, where multiple devices cooperate to perform the method. In such a distributed scenario, one of the multiple devices may only perform one or more steps of the method of the embodiment of the present invention, and the multiple devices will interact with each other to complete the method.

[0162] It should be noted that the above description is limited to some embodiments of the present invention. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in an order different from that described in the above embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain 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 drone clusters in rainy weather.

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

[0165] A position acquisition module 601 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;

[0166] an efficiency calculation module 602 for obtaining real-time rainfall data and calculating a charging efficiency factor of each UAV based on the real-time rainfall data and the Euclidean distance;

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

[0168] a time calculation module 604, configured to calculate the charging time based on the UAV charging sequence list 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 embodiment is used to implement a corresponding drone cluster laser energy transmission method in rainy weather in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0172] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the program, it implements the drone cluster laser energy transmission method in rainy weather described in any of the above embodiments.

[0173] Figure 7 10 is a schematic diagram showing a more specific hardware structure of an electronic device provided in this embodiment. 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 communicatively connected to each other within the device via the bus 1050.

[0174] The processor 1010 can be implemented using 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 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage devices, dynamic storage devices, etc. The memory 1020 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

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

[0177] The communication interface 1040 is used to connect to a communication module (not shown) to enable communication between the device and other devices. The communication module can communicate via a wired method (such as USB, network cable, etc.) or a wireless method (such as mobile network, WiFi, Bluetooth, etc.).

[0178] The bus 1050 comprises a path for transmitting 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 a specific implementation, the device may also include other components necessary for normal operation. In addition, it will be understood by those skilled in the art that the above device may only include the components necessary to implement the embodiments of this specification, and does not necessarily include all the components shown in the figure.

[0180] The electronic device of the above embodiment is used to implement a corresponding drone cluster laser energy transmission method in rainy weather in any of the above 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, which stores computer instructions, and the computer instructions are used to enable the computer to execute a drone cluster laser energy transmission method in rainy weather as described in any of the above embodiments.

[0182] The computer-readable media of this embodiment include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disc read-only memory (CD-ROM), digital versatile disc (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 drone cluster laser energy transmission method 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. Within the scope 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 comprising 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 be performed out of 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 pertain.

[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, 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 present invention has been described in conjunction with specific embodiments thereof, many alternatives, modifications and variations of these embodiments will be apparent to those skilled in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may utilize 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 scope of protection of the present invention.

Claims

1. A method for laser energy transmission of drone clusters in rainy weather, characterized in that: include: Obtaining the position data of the drone cluster, 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 UAV based on the real-time rainfall data and the Euclidean distance; Obtain a list of tasks to be executed by the drone cluster, and obtain a list of drone charging sequences based on the list of tasks to be executed by the drone cluster; Calculating charging time according to the drone charging sequence list and the charging efficiency factor; Charging the drone cluster according to the charging time; The acquiring of real-time rainfall data and calculating the charging efficiency factor of each UAV according to the real-time rainfall data and the Euclidean distance includes: The real-time rainfall data is obtained, and the received power of each UAV is calculated based on the Euclidean distance and the real-time rainfall data; Calculating the charging efficiency factor of each UAV based on the received power; The charging efficiency factor of each drone calculated based on the received power includes: ； ； in, Indicates the The charging efficiency factor of the drone, Indicates the The received power of the drone, Indicates the power of the drone when hovering, taking off and landing. Indicates thrust, represents the air density, represents the frontal area of ​​the UAV; The calculating of the charging time according to the drone charging sequence list and the charging efficiency factor includes: Calculate the charging time required for each drone based on the drone charging sequence list and the charging efficiency factor, and 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, recalculating the charging time; In response to the absence of a drone and the lack of a charging opportunity, obtaining a required charging time for each drone; Calculating the charging time required for each drone based on the drone charging sequence list and the charging efficiency factor includes: The overall hovering time of the drone cluster is calculated using the following formula: ； in, represents the overall hovering time of the drone cluster, represents the total number of drones in the drone cluster, Indicates the available flight time of the drone. Indicates the The charging efficiency factor of the drone, Indicates the Charging time of the drone; The meta-heuristic algorithm is used to optimize the charging time required for each drone with the goal of improving the overall hovering time of the drone cluster; wherein the meta-heuristic algorithm is a spider bee optimization algorithm.

2. The laser energy transmission method for drone clusters in rainy weather according to claim 1 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 includes: Obtaining a laser attenuation coefficient according to the real-time rainfall data; The received power of each UAV is calculated according to the attenuation coefficient and the Euclidean distance using the following formula: ； in, Indicates the received power. represents the power allocation factor used to separate the communication power, represents the photovoltaic converter efficiency, represents the area of ​​the photovoltaic panel that receives the laser, Represents the photoelectric conversion efficiency of photovoltaic panels, Indicates the laser emission power, represents the attenuation coefficient of the laser, Indicates the Euclidean distance from the laser transmitter to the receiver. represents the size of the initial laser beam, represents the angular spread of the laser beam.

3. The laser energy transmission method for drone clusters in rainy weather according to claim 1, 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 includes: 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.

4. A UAV cluster laser energy transmission system in rainy weather, characterized by: A method for implementing a laser energy transmission method for a drone cluster in rainy weather as described in any one of claims 1 to 3, 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, configured to obtain real-time rainfall data and calculate a charging efficiency factor of each UAV based on 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 for the drones based on the list of tasks to be executed by the drone cluster; a time calculation module, configured to calculate the charging time according to the UAV charging sequence list and the charging efficiency factor; A charging module, configured to charge the drone cluster according to the charging time; Wherein, the efficiency calculation module includes: The real-time rainfall data is obtained, and the received power of each UAV is calculated based on the Euclidean distance and the real-time rainfall data; Calculating the charging efficiency factor of each UAV based on the received power; The charging efficiency factor of each drone calculated based on the received power includes: ； ； in, Indicates the The charging efficiency factor of the drone, Indicates the The received power of the drone, Indicates the power of the drone when hovering, taking off and landing. Indicates thrust, represents the air density, represents the frontal area of ​​the UAV; Wherein, the time calculation module includes: Calculate the charging time required for each drone based on the drone charging sequence list and the charging efficiency factor, and 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, recalculating the charging time; In response to the absence of a drone and the lack of a charging opportunity, obtaining a required charging time for each drone; Calculating the charging time required for each drone based on the drone charging sequence list and the charging efficiency factor includes: The overall hovering time of the drone cluster is calculated using the following formula: ； in, represents the overall hovering time of the drone cluster, represents the total number of drones in the drone cluster, Indicates the available flight time of the drone. Indicates the The charging efficiency factor of the drone, Indicates the Charging time of the drone; The meta-heuristic algorithm is used to optimize the charging time required for each drone with the goal of improving the overall hovering time of the drone cluster; wherein the meta-heuristic algorithm is a spider bee optimization algorithm.

5. A device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 3 are implemented.

6. 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 3 are implemented.

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