Unmanned aerial vehicle charging allocation method and device for wireless sensor network

Abstract

The application discloses a kind of unmanned plane charging distribution methods and equipment for wireless sensor network, method includes: setting the selection preference between unmanned plane and sensor node, the preference of sensor node is to select the unmanned plane with maximum effective charging capacity, the first re-preference of unmanned plane is to select the sensor node charging closer, on the basis of first re-preference, the second re-preference of unmanned plane is to select the sensor node with minimum residual capacity charging;Sensor node needing charging initiates request to the unmanned plane that can provide maximum effective charging capacity in priority, and the unmanned plane received more than one charging request selects sensor node according to double preference.The application sets double preference for unmanned plane, considers charging total amount and sensor node residual capacity to prolong the service life of wireless sensor network, and completes the design of distributed resource allocation algorithm by the bidirectional selection of unmanned plane and sensor node, to realize the reasonable allocation of power resource.

Description

Technical Field

[0001] This invention belongs to the field of wireless network communication technology, and specifically relates to a method and device for charging distribution of unmanned aerial vehicles (UAVs) for wireless sensor networks. Background Technology

[0002] Wireless sensor networks (WSNs) can collect data and monitor the environment by randomly deploying or scattering sensor nodes in harsh or complex environments. The low cost and small size of sensor nodes severely limit their available resources, especially energy. If a sensor node runs out of power, it becomes a dead node; if most of the sensor nodes in the network become dead, the network cannot function properly. To extend the lifespan of sensor networks and enable long-term effective environmental monitoring, drones carrying wireless charging devices can be used to charge the sensor nodes. Currently, drone wireless charging technology is a hot research topic in wireless sensor networks.

[0003] Current research on wireless charging technology for drones mainly falls into two categories: one combines data collection with wireless charging, where the drone simultaneously collects data and performs wireless charging, using mathematical optimization methods to solve for the optimal parameters. However, this approach is unsuitable for scenarios with a large number of sensors, as the increased number of sensors and drones leads to a surge in optimization complexity. The other approach focuses on drone path planning, designing flight paths to meet the power demands of more drone nodes. However, this type of research does not fully consider the limited battery capacity of drones in real-world systems. Drones consume significant energy during flight and hovering, resulting in a very limited time for a single drone to perform tasks, making it difficult to implement wireless charging for multiple nodes with a single drone in practical applications. Summary of the Invention

[0004] To address the aforementioned problems, this invention provides a method and device for allocating charging power to drones in wireless sensor networks. This method is applicable to wireless sensor networks where multiple drones assist in wireless charging. The method considers the total wireless charging capacity of the system while also taking into account the remaining power of each sensor node to extend the network's lifespan.

[0005] According to an embodiment of this disclosure, a method for allocating charging power to a drone in a wireless sensor network is provided, the method comprising the following steps:

[0006] Information exchange phase: The distance between the UAV and the charging sensor node is determined based on the UAV's position coordinates and the charging sensor node's position coordinates. Based on the condition that the UAV must return to the control center before its battery runs out, the effective charging time of the UAV at a certain sensor node and the effective charging amount proportional to the effective charging time are determined based on the distance between the UAV and the charging sensor node, the UAV's flight parameters, and the charging parameters.

[0007] Preference establishment phase: Set the selection preference between the drone and the sensor node. The sensor node's preference is to select the drone with the largest effective charging capacity. The drone's first preference is to select the sensor node that is closer to it for charging. Based on the first preference, the drone's second preference is to select the sensor node with the lowest remaining power for charging.

[0008] During the charging allocation phase: Sensor nodes that need charging will prioritize sending a request to the drone that can provide the maximum effective charging amount. If a drone receives more than one charging request, it will select a sensor node based on dual preferences.

[0009] In one embodiment, determining the effective charging time of the UAV at a certain sensor node specifically includes: the UAV's flight power is not less than the sum of the power consumed between the sensor node and the control center and the power consumed by the UAV during its hovering during the effective charging time.

[0010] In one embodiment, the effective charging amount is proportional to the effective charging time, the charging power of the charging device carried by the drone, and the wireless charging efficiency.

[0011] In one embodiment, a drone that receives more than one charging request selects a sensor node based on dual preferences, specifically including:

[0012] Based on the first preference, the drone sorts the sensor nodes that have received charging requests in ascending order of distance and selects the top-ranked sensor nodes. Based on the second preference, the drone selects the sensor node with the lowest remaining battery power from the sensor nodes selected by the first preference and rejects other sensor nodes that have initiated charging requests.

[0013] In one embodiment, if a sensor node’s charging request is rejected by a drone, the sensor node removes the rejected drone from its preferences and continues to send requests to other drones with the largest effective charging capacity. The selected drone is also selected based on its dual preferences.

[0014] The sensor node repeats the request until it is assigned to a drone or has made requests to all drones but all of them have been rejected, at which point the request ends.

[0015] In one embodiment, during the first preference selection by the UAV, the first half of the sensor nodes are selected from the sensor nodes sorted in ascending order of distance. If half is not an integer, it is rounded down to the next position in the sorting.

[0016] According to another embodiment of this disclosure, an electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the drone charging distribution method for wireless sensor networks as described above.

[0017] According to another embodiment of this disclosure, a non-transitory computer-readable storage medium stores computer instructions, characterized in that, when executed by a processor, the instructions implement the steps of the UAV charging distribution method for wireless sensor networks as described above.

[0018] The technical solution provided in this disclosure is a method and device for allocating charging power to unmanned aerial vehicles (UAVs) in a wireless sensor network. This method extends the lifespan of the wireless sensor network by setting dual preferences for UAVs to balance the total charging amount with the remaining power of sensor nodes. Furthermore, it achieves a reasonable allocation of power resources by designing a distributed resource allocation algorithm through bidirectional selection between UAVs and sensor nodes.

[0019] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0020] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0021] Figure 1 This is a flowchart of a drone charging allocation method for wireless sensor networks in an embodiment of the present invention;

[0022] Figure 2 This is a structural diagram of an electronic device for charging distribution of drones in a wireless sensor network according to an embodiment of the present invention;

[0023] Figure 3 This is a schematic diagram of a drone charging distribution method for wireless sensor networks in an embodiment of the present invention. Detailed Implementation

[0024] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be noted that, for ease of description, only the parts relevant to the present invention are shown in the drawings, not the entire structure.

[0025] Before discussing the exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the steps as sequential processes, many of these steps can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the steps can be rearranged. The process can be terminated when its operation is complete, but may also have additional steps not included in the figures. The process can correspond to a method, function, procedure, subroutine, subroutine, etc.

[0026] This invention provides a method and device for charging distribution of unmanned aerial vehicles (UAVs) in the context of wireless sensor networks, and offers the following embodiments:

[0027] Example 1 illustrates a method for allocating charging resources for drones in wireless sensor networks. The method includes the following steps:

[0028] Information exchange phase: The distance between the UAV and the charging sensor node is determined based on the UAV's position coordinates and the charging sensor node's position coordinates. Based on the condition that the UAV must return to the control center before its battery runs out, the effective charging time of the UAV at a certain sensor node and the effective charging amount proportional to the effective charging time are determined based on the distance between the UAV and the charging sensor node, the UAV's flight parameters, and the charging parameters.

[0029] Preference establishment phase: Set the selection preference between the drone and the sensor node. The sensor node's preference is to select the drone with the largest effective charging capacity. The drone's first preference is to select the sensor node that is closer to it for charging. Based on the first preference, the drone's second preference is to select the sensor node with the lowest remaining power for charging.

[0030] During the charging allocation phase: Sensor nodes that need charging will prioritize sending a request to the drone that can provide the maximum effective charging amount. If a drone receives more than one charging request, it will select a sensor node based on dual preferences.

[0031] The determination of the effective charging time of a drone at a certain sensor node specifically includes: the drone's flight power is not less than the sum of the power consumed between the sensor node and the control center and the power consumed by the drone during its hovering within the effective charging time.

[0032] Effective charging capacity is directly proportional to effective charging time, the charging power of the charging equipment carried by the drone, and wireless charging efficiency.

[0033] When a drone receives more than one charging request, it selects sensor nodes based on dual preferences, specifically including:

[0034] Based on the first preference, the drone sorts the sensor nodes that have received charging requests in ascending order of distance and selects the top-ranked sensor nodes. Based on the second preference, the drone selects the sensor node with the lowest remaining battery power from the sensor nodes selected by the first preference and rejects other sensor nodes that have initiated charging requests.

[0035] If a sensor node's charging request is rejected by a drone, the sensor node will remove the rejected drone from its preferences and continue to send requests to other drones with the largest effective charging capacity. The selected drone will also be chosen based on its dual preferences.

[0036] The sensor node repeats the request until it is assigned to a drone or has made requests to all drones but all of them have been rejected, at which point the request ends.

[0037] When selecting the first preference for the drone, the first half of the sensor nodes are selected from the sensor nodes sorted in ascending order of distance. If half is not an integer, it is rounded up to the next position in the sorting.

[0038] It should be noted that the embodiment describes a wireless charging allocation method for drones applicable to wireless sensor networks. Sensor nodes in the wireless sensor network are randomly scattered across the monitoring area. Due to limitations in cost and size, the computing, storage, and energy resources of sensor nodes are very limited. Once their energy resources are exhausted, the sensor nodes will become inactive and dead. To extend the lifespan of the sensor network, this embodiment designs a new wireless charging allocation algorithm for drones, taking into account factors such as charging amount, remaining battery power, and charging distance.

[0039] Specifically, by setting dual preferences for drones to balance total charging capacity and remaining battery power of sensor nodes, the lifetime of the wireless sensor network is extended. A distributed resource allocation algorithm is designed through bidirectional selection between drones and sensor nodes, as shown in the flowchart below. Figure 1 As shown. The specific steps of the entire plan are as follows:

[0040] Step 1: Information Exchange Phase. Assume there are M drones equipped with wireless charging devices, and the set of drones is represented as U = {U1, ..., U...}. i ,...,U M Meanwhile, the wireless sensor network has N sensor nodes that need to be charged, and the set of sensor nodes is represented as S = {S1,...,S...}. j ,...,S N The coordinates of all drones are represented as follows:

[0041]

[0042] The coordinates of all sensor nodes are represented as follows: Because the drone charges wirelessly, it needs to hover above the sensor node being charged. i To sensor node S j Flight distance When allocating drones, their own battery level must be fully considered. Throughout the allocation process, the drone's battery is primarily used for round-trip flights and hovering around sensor nodes during wireless charging. Wireless charging is handled by the drone's onboard wireless charging device; the charging process itself does not consume the drone's battery. To ensure the drone can return to the control center before its battery runs out, the drone U... i At sensor node S j The longest time t hovering above ij Energy constraints must be met Where v represents the drone's flight speed, p f Represents the power consumption of drone flight, t ij p represents the effective charging time (also known as the drone's hovering time). h This represents the power consumption during the drone's hovering process. If the drone U... i Assigned to sensor node S j The effective charging amount can be expressed as in U-shaped drone i The charging power of the charging device it carries, where η represents the wireless charging efficiency.

[0043] Step Two: Preference Establishment Phase. This example, considering factors such as the number of drones and sensor nodes, their respective battery levels, and distances, reflects the selection criteria for both parties during the allocation process through preference settings for the drones and sensor nodes, aiming to maximize the total charging amount. At the same time, it also takes into account the energy balance of each sensor node. Among them, α ij This indicates the wireless charging allocation result, if α ij =1 indicates U drone i Assigned to sensor node S j α ij =0 indicates that the drone U i No sensor node S was assigned j .for limit This means that a sensor node can be assigned to at most one drone. limit This indicates that a drone can be assigned a maximum of one sensor node. Preference establishment needs to consider both the drone and the sensor nodes. For the sensor node, the preference is defined as the effective charge amount, i.e., sensor node S. j For drones U i Preference is defined as Furthermore, all sensor nodes tend to select the drone with the largest effective charge; for the drone, a dual preference is set, with the first preference being the distance between the two parties, i.e., the drone's U... i For sensor node S j The first preference setting Furthermore, the drone tends to choose sensor nodes closer to it for charging. Building upon this first preference setting, to accommodate sensor nodes with low battery levels and prevent them from running out of power and becoming dead nodes, the embodiment employs a dual preference. The second preference is based on the remaining battery power of the sensor node, i.e., the drone's U... i For sensor node S j The second preference is set Where R j Indicates sensor node S j Given the remaining battery power, the drone will select the sensor node with the lowest remaining battery power, based on the first level of preference filtering.

[0044] Step 3: Charging Allocation Phase. Sensor nodes requiring charging are sorted in descending order based on their preference for the drone, prioritizing requests to the drone that can provide the maximum effective charging amount. If a drone receives more than one request, it will select a sensor node based on a dual preference mechanism. According to the first preference, the drone will first determine the distance d between itself and all sensors that have requested charging from it. ij The sensor nodes are sorted in ascending order, and half of the closest sensor nodes are selected (if half are not integers, they are rounded up). Based on the second preference, the drone selects the sensor node with the lowest remaining battery power, based on the first preference selection, and rejects other sensor nodes that have initiated charging requests. The first round of bidirectional selection ends. If a sensor node's charging request is rejected by the drone, the sensor node will remove that drone from its preference list and continue to initiate requests to other drones with the largest effective charging capacity. This drone will also select based on its own dual preferences. The algorithm iterates until all sensor nodes have been assigned drones or the sensor nodes that have not been assigned drones have initiated requests to all drones but have been rejected by all of them. The iteration ends then.

[0045] Another embodiment illustrating that the system of the present invention can also be achieved by means of... Figure 2 The architecture of the computing device shown is used to implement this. Figure 2 The architecture of the computing device is shown. For example... Figure 2As shown, the computer system 210 includes a system bus 230, one or more CPUs 240, input / output 220, and memory 250. Memory 250 can store various data or files used for computer processing and / or communication, as well as program instructions executed by the CPU, including the UAV charging distribution method for wireless sensor networks described in the embodiment. Figure 2 The architecture shown is merely exemplary and should be adjusted according to actual needs when implementing different devices. Figure 2 One or more components in the system. The memory 250, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules. One or more CPUs 240 execute various functional applications and data processing of the system of the present invention by running the software programs, instructions, and modules stored in the memory 250, thereby implementing the above-described method for charging and distributing data for a drone in a wireless sensor network, the method comprising:

[0046] Information exchange phase: The distance between the UAV and the charging sensor node is determined based on the UAV's position coordinates and the charging sensor node's position coordinates. Based on the condition that the UAV must return to the control center before its battery runs out, the effective charging time of the UAV at a certain sensor node and the effective charging amount proportional to the effective charging time are determined based on the distance between the UAV and the charging sensor node, the UAV's flight parameters, and the charging parameters.

[0047] Preference establishment phase: Set the selection preference between the drone and the sensor node. The sensor node's preference is to select the drone with the largest effective charging capacity. The drone's first preference is to select the sensor node that is closer to it for charging. Based on the first preference, the drone's second preference is to select the sensor node with the lowest remaining power for charging.

[0048] During the charging allocation phase: Sensor nodes that need charging will prioritize sending a request to the drone that can provide the maximum effective charging amount. If a drone receives more than one charging request, it will select a sensor node based on dual preferences.

[0049] Of course, the processor of the server provided in the embodiments of the present invention is not limited to performing the method operations described above, but can also perform related operations in the drone charging distribution method for wireless sensor networks provided in any embodiment of the present invention.

[0050] The memory 250 may primarily include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on terminal usage. Furthermore, the memory 250 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 250 may further include memory remotely configured relative to one or more CPUs 240, which can be connected to the device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0051] Input / output 220 can be used to receive input digital or character information, and to generate key signal inputs related to user settings and function control of the device. Input / output 220 may also include a display device such as a display screen.

[0052] This invention also provides a non-transitory computer-readable storage medium storing a computer program that, when executed by a processor, implements the UAV charging distribution method for wireless sensor networks described in the above embodiments. The computer-readable storage medium of this invention can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. For example, a computer-readable storage medium can be—but is not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0053] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0054] The program code contained on the storage medium can be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, RF (Radio Frequency), or any suitable combination thereof.

[0055] Furthermore, other specific operational processes of a non-transitory computer-readable storage medium are described in the above-described embodiment of the UAV charging distribution method for wireless sensor networks, and will not be repeated here.

[0056] To further demonstrate the effectiveness of the invention, the inventors conducted simulation experiments, such as... Figure 3 As shown, assuming M=3 and N=3, the sensor network consists of 3 drones and 3 sensor nodes. It is also specified that on a two-dimensional plane coordinate system, in meters, the coordinates of the 3 drones are (0, 0), (-720, 0), and (-360, 0), respectively, and the coordinates of the 3 sensor nodes are (360, 0), (720, 0), and (-180, 0), respectively. The flight speed of each drone is 10 m / s.

[0057] exist Figure 3 Based on the combination Figure 1 The flowchart and all steps of the specific implementation method are shown below:

[0058] Step 1: Information Exchange Phase. Drones in the sensor network exchange information, calculating the distance between them (d) based on their respective coordinates. 11 =360m, d 12 =720m,d 13 =180m, d 21 =1080m, d 22 =1440m,d 23 =540m,d 31 =720m,d 32 =1080m, d 33 =180m. Maximum battery capacity of the three drones. Flight power is p f =120W, swirling power p h =100W, the charging power p of the equipment carried by the drone UAV =100W, charging efficiency η=0.3. From the formula... Deformation can be obtained Therefore t 11 =633.6s,t 12 =547.2s,t 13 =676.8s,t 21 =460.8s,t22 =374.4s,t 23 =590.4s,t 31 =187.2s,t 32 =100.8s,t 33 =316.8s. Then, from the formula... The amount of charge that three drones can provide for three sensor nodes can be calculated as: c 11 =5.28Wh, c 12 = 4.56Wh, c 13 =5.64Wh, c 21 =3.84Wh, c 22 =3.12Wh, c 23 =4.92Wh, c 31 =1.56Wh, c 32 =0.84Wh, c 33 =2.64Wh.

[0059] Step 2: Preference Establishment Phase. For sensor nodes, their preferences are established based on the effective charging amount. According to the results of Step 1, the preference matrix of the sensor node can be expressed as follows:

[0060]

[0061] The three sensors each establish a corresponding matrix based on the effective power the drone can provide, and then sort the elements in the matrix to obtain... Where > indicates preference ranking. Similarly, a dual preference system is established for the three drones: a first preference is established based on the distance between the sensor nodes that initiated the requests and the drones themselves; a second preference is established based on the remaining battery power of the three sensor nodes. The distance d between the two drones is then considered. ij and remaining power R j Sort them.

[0062] Step 3: Charging Allocation Phase. Assume the remaining battery power of the three sensor nodes is 0.2Wh, 0.3Wh, and 0.4Wh respectively. All three sensor nodes are at low battery levels and need to be charged using a drone equipped with charging devices. The charging allocation process is described below:

[0063] First round of allocation: For the three sensor nodes, drone 1 has the highest effective charging capacity, so they all send charging requests to drone 1. Drone 1 rejects the request from the farthest sensor node, drone 2, based on the first level of preference, and temporarily retains the requests from sensor nodes 1 and 3. Then, based on the second level of preference, drone 1 rejects the charging request from sensor node 3, which has a relatively high remaining battery capacity, and temporarily accepts the charging request from sensor node 1.

[0064] In the second round of allocation: the rejected sensor nodes 2 and 3 remove drone 1 from their preferences and then send a request to drone 2, which can provide them with the maximum effective charging amount. Drone 2 directly rejects the request from the relatively far sensor node 2 based on the first preference and temporarily accepts the charging request from sensor node 3.

[0065] In the third round of allocation, the remaining sensor node 2 sends a request to drone 3. Since drone 3 only received a request from sensor node 2, it temporarily accepts the request. At this point, all sensor nodes have been assigned drones: drone 1 is assigned to sensor node 1, drone 2 is assigned to sensor node 3, and drone 3 is assigned to sensor node 2. The iteration ends, completing the wireless charging allocation process.

[0066] Based on the technical solutions provided in the above embodiments, a method and device for charging distribution of unmanned aerial vehicles (UAVs) for wireless sensor networks extends the lifespan of the wireless sensor network by setting dual preferences for UAVs to balance the total charging amount with the remaining power of sensor nodes. Furthermore, the distributed resource allocation algorithm is designed through bidirectional selection between UAVs and sensor nodes to achieve reasonable allocation of power resources.

[0067] In this document, the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a step or method that comprises a list of elements includes not only those elements but also other elements not expressly listed or inherent to such a step or method.

[0068] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A method for allocating charging power to unmanned aerial vehicles (UAVs) in wireless sensor networks, characterized in that: The method includes the following steps: Information exchange phase: Based on the condition that the UAV must return to the control center before its battery runs out, the effective charging time of the UAV at a certain sensor node and the effective charging amount proportional to the effective charging time are determined according to the distance between the UAV and the charging sensor node, the UAV's flight parameters and charging parameters. Preference establishment phase: Set the selection preference between the drone and the sensor node. The sensor node's preference is to select the drone with the largest effective charging capacity. The drone's first preference is to select the sensor node that is closer to it for charging. Based on the first preference, the drone's second preference is to select the sensor node with the lowest remaining power for charging. During the charging allocation phase: Sensor nodes that need charging will prioritize sending a request to the drone that can provide the maximum effective charging amount. If a drone receives more than one charging request, it will select a sensor node based on dual preferences. When a drone receives more than one charging request, it selects sensor nodes based on dual preferences, specifically including: Based on the first preference, the drone sorts the sensor nodes that have received charging requests in ascending order of distance and selects the top-ranked sensor nodes. Based on the second preference, the drone selects the sensor node with the lowest remaining battery power from the sensor nodes selected by the first preference and rejects other sensor nodes that have initiated charging requests. If a sensor node's charging request is rejected by a drone, the sensor node will remove the rejected drone from its preferences and continue to send requests to other drones with the largest available charging capacity. The selected drone will also be chosen based on the sensor node's dual preferences. The sensor node repeats the request until it is assigned a drone or has sent requests to all drones but all of them have been rejected, at which point the request process ends.

2. The UAV charging distribution method for wireless sensor networks according to claim 1, characterized in that, The determination of the effective charging time of the UAV at a certain sensor node specifically includes: the flight power of the UAV is not less than the sum of the power consumed between the sensor node and the control center and the power consumed by the UAV during the effective charging time.

3. The UAV charging distribution method for wireless sensor networks according to claim 1, characterized in that, The effective charging amount is directly proportional to the effective charging time, the charging power of the charging equipment carried by the drone, and the wireless charging efficiency.

4. The UAV charging distribution method for wireless sensor networks according to claim 1, characterized in that, When selecting the first preference for the drone, the first half of the sensor nodes are selected from the sensor nodes sorted in ascending order of distance. If half is not an integer, it is rounded up to the next position in the sorting.

5. An electronic 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 program, it implements the steps of the drone charging distribution method for wireless sensor networks as described in any one of claims 1 to 4.

6. A non-transitory computer-readable storage medium, wherein computer instructions are stored on the storage medium, characterized in that, When the instructions are executed by the processor, they implement the steps of the drone charging distribution method for wireless sensor networks as described in any one of claims 1 to 4.

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