A UAV airport charging method and system

Abstract

The present invention relates to the technical field of charging stations, and specifically to a method and system for charging drones at airports, including: the charging amount required for each drone to be fully charged is recorded as a first charging amount, and the optimal charging scheme obtained by the first charging amount is recorded as a first scheme; the total amount of electricity consumed by the drone during each inspection is obtained, and the charging amount required for each drone to be charged to the total amount is recorded as a second charging amount, and the optimal charging scheme obtained by the second charging amount is recorded as a second scheme; according to the first charging amount, the second charging amount, and the difference in emergency handling capabilities between the first scheme and the second scheme, the final charging amount required for each drone is obtained, and the optimal charging scheme obtained by the final charging amount is used as the final charging scheme when the drone is charged. The present invention ensures the responsiveness and scheduling flexibility of the drone when facing emergency inspection tasks, while further avoiding the problem of a significant decrease in the service life of the battery.

Description

Technical Field

[0001] The present invention relates to the technical field of charging stations, and in particular to a method and system for charging an unmanned aerial vehicle at an airport. Background Art

[0002] Charging stations and charging equipment play a vital role in drone inspection scenarios. Remote inspections and emergency missions of drones require stable and reliable charging stations and charging equipment. Charging stations and charging equipment in existing drone airports often require a certain amount of charging time to charge drones. When charging stations and charging equipment lack reasonable charging solutions, if drones face some emergency inspection tasks, there will be problems such as the drones being unable to respond in time when they are in the charging process, which will lead to the inability to reasonably dispatch drones or drone groups, affecting the emergency inspection tasks. Summary of the invention

[0003] In order to solve the above problems, the present invention provides a method and system for charging a drone at an airport.

[0004] The present invention provides a method and system for charging a drone at an airport, which adopts the following technical solutions:

[0005] An embodiment of the present invention provides a method for charging a drone at an airport, the method comprising the following steps:

[0006] The energy storage device of the drone airport includes a plurality of battery packs, each battery pack corresponds to a discharge curve; all battery packs are matched with all drones one by one, and each matching result is recorded as a charging scheme; each battery pack in each charging scheme charges the matched drone;

[0007] The optimal charging scheme is obtained according to the charging amount required by each drone, including: obtaining the charging time of each drone according to the charging amount required by each drone and the discharge curve of each battery pack, and obtaining the charging stability of each drone during charging according to the historical change characteristics of the discharge curve of the battery pack; the emergency handling capacity of each charging scheme is obtained according to the charging time of all drones and the charging stability of the drones, and the charging scheme with the maximum emergency handling capacity is taken as the optimal charging scheme;

[0008] The charging amount required for each drone to be fully charged is recorded as the first charging amount, and the optimal charging solution obtained by the first charging amount is recorded as the first solution; the total power consumed by the drones during each inspection is obtained, and the charging amount required for each drone to be charged to the total power is recorded as the second charging amount, and the optimal charging solution obtained by the second charging amount is recorded as the second solution;

[0009] According to the first charging amount, the second charging amount, and the difference in emergency handling capabilities between the first plan and the second plan, the final charging amount required for each drone is obtained, and the optimal charging plan obtained by the final charging amount is used as the final charging plan when the drone is charged.

[0010] Preferably, the step of obtaining the charging time of each drone according to the charging amount required by each drone and the discharge curve of each battery pack includes the following specific steps:

[0011] The abscissa of the discharge curve is time, and the ordinate is the remaining power of the battery pack;

[0012] For the battery pack that charges each drone, obtain the discharge curves of all the charging processes of the battery pack in history; the vertical coordinate corresponding to any time t on each discharge curve is marked as f(t), and the vertical coordinate of the discharge curve at time 0 is marked as f0. (f0, f(t)) is used as a sample, and t is used as the label of the sample. The samples and labels composed of the discharge curves of all charging processes are used as a data set, and a fully connected neural network is trained using the data set;

[0013] The charging amount required for each drone is recorded as P; the remaining power Pf of each battery pack in each charging scheme is obtained, Pf and Pf-P are input into the fully connected neural network, and the output result of the fully connected neural network is recorded as the charging time T of the drone.

[0014] Preferably, the step of obtaining the charging stability of each drone during charging according to the historical variation characteristics of the discharge curve of the battery pack includes the following specific steps:

[0015] For the battery pack that charges each drone, obtain the discharge curve of the battery pack during the historical charging process, which is recorded as the historical discharge curve of the battery pack. For any historical discharge curve, the remaining power corresponding to time 0 is recorded as q1, and q2=q1-P is set. The horizontal coordinate t corresponding to the vertical coordinate q2 on the historical discharge curve is obtained, and the curve segment in the interval [0, t] is obtained on the historical discharge curve, which is recorded as the reference curve of each historical discharge curve;

[0016] For the reference curve of all historical discharge curves of each drone, the average remaining power of the reference curve is obtained, the absolute value of the difference between the average remaining power of any two reference curves is obtained, and the absolute value of the difference between the average remaining power of all reference curves is averaged, which is recorded as the average discharge difference f of the battery pack. The ratio of exp(-f) to t is recorded as the charging stability of the drone during charging; exp() represents an exponential function with a natural constant as the base.

[0017] Preferably, the step of obtaining the emergency handling capability of each charging solution according to the charging time of all drones and the charging stability of the drones includes the following specific steps:

[0018] Under each charging scheme, the response degree of each UAV is obtained, and the response degree is negatively correlated with the charging time;

[0019] The charging stability of each UAV is normalized, and the normalized result is used as the weight of each UAV. The weight is used to perform weighted summation on the response degree of the UAV to obtain the emergency handling capability of each charging scheme.

[0020] Preferably, the method of obtaining the final charging capacity required for each drone according to the first charging capacity, the second charging capacity, and the difference in emergency handling capabilities between the first solution and the second solution includes the following specific steps:

[0021] Obtain the difference in emergency handling capabilities between the first plan and the second plan;

[0022] The final charging amount required for each drone MP=m2×d2+m1×d1, d1 represents the first charging amount, d2 represents the second charging amount, m1 and m2 respectively represent the battery life concern and emergency capability concern of each drone; the battery life concern is negatively correlated with the difference in the emergency handling capability, and the emergency capability concern is positively correlated with the difference in the emergency handling capability.

[0023] Preferably, the step of obtaining the difference between the emergency handling capabilities of the first solution and the second solution includes the following specific steps:

[0024] The absolute value of the difference between the emergency processing capabilities of the first and second solutions is obtained, recorded as W1, the maximum value of the emergency processing capabilities of the first and second solutions is recorded as W2, and the ratio of W1 to W2 is taken as the difference in emergency processing capabilities of the first and second solutions.

[0025] Preferably, the specific steps of obtaining the battery life concern and the emergency capability concern are as follows:

[0026] For any of the charging schemes in the first and second schemes, all drones in the charging schemes are divided into two categories: one category is drones that are in the charging process, recorded as category one drones; the other category is drones that need to be charged after completing the inspection mission but have not yet been charged, recorded as category two drones;

[0027] The mean charging time of the first type of drones in the first scheme is recorded as a1, and the mean charging time of the second type of drones is recorded as a2; the mean charging time of the first type of drones in the second scheme is recorded as b1, and the mean charging time of the second type of drones is recorded as b2;

[0028] According to the difference between b2 and a1, the difference between a2 and b1, and the difference between the emergency handling capabilities of the first solution and the second solution, the battery life concern and the emergency capability concern corresponding to each drone in the second category of drones are obtained;

[0029] The battery life concern and emergency capability concern corresponding to each drone in a category of drones are obtained by the difference in emergency handling capabilities between the first solution and the second solution.

[0030] Preferably, the battery life concern and emergency capability concern corresponding to each drone in the second category of drones are obtained according to the difference between b2 and a1, the difference between a2 and b1, and the difference between the emergency handling capabilities of the first solution and the second solution, including the calculation formula:

[0031] Let w1 = (b2-a1) / b2, w2 = (a2-b1) / a2; let X1 = (w1+1-W) / (w2+1), X2 = (W+w2) / (w1+1); normalize X1 and X2 to obtain m1 and m2 respectively; where W represents the difference in emergency handling capabilities between the first and second solutions.

[0032] Preferably, the specific calculation method of the battery life concern and emergency capability concern corresponding to each drone in the first category of drones is as follows:

[0033] Let 1-W, W be m1, m2 respectively, where W represents the difference in emergency handling capabilities between the first solution and the second solution.

[0034] In another embodiment of the present invention, a drone airport charging system is provided, comprising a memory, a processor, and a computer program stored in the memory and executable in the processor, wherein the computer program in the processor implements the steps of the above-mentioned drone airport charging method when executed.

[0035] The beneficial effects of the technical solution of the present invention are:

[0036] The present invention obtains the optimal charging plan according to the charging time of all drones and the charging stability of the drones. Under the optimal charging plan, when facing an emergency, the drone or drone group has higher scheduling flexibility, and also avoids the impact of charging instability during the charging process to a certain extent.

[0037] Furthermore, the charging amount required for each drone to be fully charged is recorded as the first charging amount, and the optimal charging solution obtained by the first charging amount is recorded as the first solution; the total amount of electricity consumed by the drone during each inspection is obtained, and the charging amount required for each drone to be charged to the total amount is recorded as the second charging amount, and the optimal charging solution obtained by the second charging amount is recorded as the second solution. The second solution is based on the purpose of completing at least one normal inspection task. The advantage of the second solution is that the drone takes relatively less charging time and has a stronger ability to respond to emergencies. The first solution is based on the purpose of the drone having sufficient power. Its advantage is that deep discharge is not likely to occur during inspections (including normal inspection tasks and emergency inspection tasks), and has less impact on the service life of the drone battery.

[0038] Further, according to the first charging amount, the second charging amount, and the difference in emergency handling capabilities between the first solution and the second solution, the final charging amount required for each drone is obtained, and the optimal charging solution obtained by the final charging amount is used as the final charging solution when the drone is charged. The final charging solution can ensure a certain emergency response capability as much as possible without significantly affecting the service life of the drone battery, so that the charging station and charging equipment at the drone airport face an emergency, and the drone or drone group has a higher scheduling flexibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 A flowchart of the steps of a method for charging a drone at an airport provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0041] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation, structure, features and effects of a drone airport charging method and system proposed by the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0042] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0043] The specific scheme of the UAV airport charging method and system provided by the present invention is described in detail below with reference to the accompanying drawings.

[0044] Embodiment 1:

[0045] This embodiment provides a drone airport charging system, which includes a charging station, a drone, and a computer module; the charging station uses photovoltaic panels to generate electricity, the photovoltaic panels are connected to an energy storage device, and the electricity generated by the photovoltaic panels is stored in the energy storage device. The energy storage device of this embodiment is composed of a number of battery packs (this embodiment is described by taking 8 battery packs as an example), and the battery in each battery pack is a lithium iron phosphate battery. Other batteries can be used in other embodiments, and this embodiment does not specifically limit it. Each battery pack also provides a discharge interface, and each drone is connected to the discharge interface of each battery pack to realize the charging of each drone by each battery pack. This embodiment uses wireless charging technology for charging, and other embodiments can use direct plug-in charging. This embodiment does not limit the specific charging connection method.

[0046] In this embodiment, there are multiple drones in the drone airport. This embodiment is described by taking 8 drones as an example. These drones are used to perform inspection tasks. This embodiment is described by taking power inspection as an example. During the inspection, the drones perform inspections according to the pre-set inspection tracks. The process of performing inspections according to the pre-set inspection tracks is recorded as a regular inspection task, which means an inspection task that needs to be repeated multiple times, such as inspecting pole towers, transmission lines, substations, etc. in power lines. In this embodiment, each drone performs a corresponding regular inspection task.

[0047] In addition to routine inspection tasks, emergency inspection tasks may also be faced, such as tower tilting and collapse accidents, substation cable line fires, abnormal operation of power equipment and other dangerous or emergency tasks, as well as other tasks that require urgent inspections in addition to routine inspection tasks; emergency inspection tasks are mainly aimed at emergencies in the inspection trajectory. The difference between emergency inspection tasks and regular inspection tasks is that the occurrence time of emergency inspection tasks is uncertain and needs to be executed as promptly as possible.

[0048] The computer module includes a memory, a processor, and a computer program stored in the memory and executable in the processor. The storage device is also used to record the discharge curve of each battery pack when charging the drone. The horizontal axis of the discharge curve is the time (one moment per second in this embodiment, and the moment when charging starts is recorded as time 0), and the vertical axis is the remaining power at each moment. Each battery pack records a discharge curve every time it is charged.

[0049] Furthermore, the storage device is also used to record the total power consumed by each drone when performing routine inspection tasks, that is, the total power consumed by the drone from takeoff to landing after the inspection is completed.

[0050] Furthermore, the computer program of the processor in the computer module executes a drone airport charging method, which controls the charging process of the drone airport according to the above-mentioned discharge curve and the total power consumed by routine inspection tasks, so that when some emergency inspection tasks occur, there are dispatchable drones or drone groups to avoid the problem of emergency inspection tasks not being responded to and processed in a timely manner.

[0051] Embodiment 2:

[0052] See also Figure 1 , which shows a flowchart of a method for charging a drone at an airport in Embodiment 1, the method comprising the following steps:

[0053] Step S201: All battery packs are matched with all drones one by one to obtain several charging solutions.

[0054] This embodiment takes into account that the performance of the battery pack will decline during long-term use and when charging in an unstable photovoltaic power grid, such as energy attenuation and increased internal resistance, reduced discharge capacity, unstable discharge power and other problems; when charging a drone, the discharge curves corresponding to different battery packs may also be different, and the corresponding discharge curve of the same battery pack when charging a drone in different time periods will also change.

[0055] In addition, after completing routine inspection tasks, different drones have different remaining power when returning home. In this embodiment, the remaining power is recorded as the initial power. When the initial power of the drone is different, the charging time and charging efficiency of the battery pack for the drone are also different;

[0056] In this embodiment, all battery packs are matched with all drones one by one to obtain several matching results, each of which represents a charging scheme. For example, in one charging scheme, a drone A1 is matched with a battery pack B1, indicating that the drone A1 is charged with the battery pack B1 in the charging scheme. If in another charging scheme, the drone A1 is matched with another battery pack B2, indicating that the drone A1 is charged with the battery pack B2 in the other charging scheme.

[0057] In some embodiments, if the number of drones is greater than the number of battery packs, some drones do not have matching battery packs in some charging solutions, and these drones are not charged. If the number of drones is less than the number of battery packs, some battery packs do not have matching drones in some charging solutions, and these battery packs are not charged.

[0058] It should be noted that several charging solutions are obtained above, but considering that some drones are currently being charged, that is, these drones have been matched with battery packs, then these drones maintain the matching relationship unchanged until these drones fly away from the battery pack, and the battery pack can be matched with other drones.

[0059] Step S202: obtain the charging time of each drone according to the charging amount required by each drone and the discharge curve of each battery pack, and obtain the charging stability of each drone during charging according to the historical change characteristics of the discharge curve of the battery pack; obtain the emergency handling capacity of each charging scheme according to the charging time of all drones and the charging stability of the drones, and use the charging scheme with the maximum emergency handling capacity as the optimal charging scheme.

[0060] In any charging scheme, the charging amount required for each drone is recorded as P, where P represents the amount of electricity that needs to be charged into the drone.

[0061] For the battery pack that charges each drone, the discharge curves of all the charging processes of the battery pack in history are obtained. The ordinate (i.e., the remaining power) corresponding to any time t on each discharge curve is recorded as f(t), and the initial remaining power of the discharge curve is recorded as f0 (i.e., the ordinate at time 0). (f0, f(t)) is used as a sample, and t is used as the label of the sample. The samples and labels composed of the discharge curves of all charging processes are used as a data set. A fully connected neural network model is trained using the data set. The input of the fully connected neural network model is the initial remaining power and the ordinate f(t), and the output is the time t corresponding to the ordinate f(t). The fully connected neural network model has three hidden layers, each with 4 neurons. The loss function used in training is the mean square error loss function, and the neuron update method is the stochastic gradient descent method.

[0062] The remaining power of each battery pack in the charging scheme is obtained, denoted as Pf, which represents the remaining power at time 0 when the drone is charged. Pf and Pf-P are input into the fully connected neural network, and the output result is recorded as the charging time T of the drone, where T represents the time required for the battery pack to charge P power to the drone.

[0063] It should be noted that when Pf-P is less than or equal to 0, it indicates that the current of the battery pack is insufficient, and the battery pack is considered non-existent at this time, that is, the battery pack does not exist in the calculation of all steps of this embodiment. In other embodiments, Pf-P can be considered non-existent when it is less than 20% of the maximum capacity of the battery pack, which also ensures that the battery pack will not continue to discharge when the power is too low.

[0064] The purpose of this embodiment is to enable drones to respond to emergency inspection tasks quickly and promptly, but the release time of emergency inspection tasks is uncertain. Therefore, a feasible method is to enable drones to have strong emergency response capabilities at all times. The charging solution required by this embodiment (that is, the optimal charging solution) at least meets the following requirements: the charging time of all drones in the charging solution is as short as possible, thereby ensuring that at each moment after the current moment, when an emergency inspection task occurs, there are as many drones as possible that can respond quickly; when facing an emergency, the drone or drone group has a higher scheduling flexibility.

[0065] Furthermore, when the battery pack is charged and discharged in the photovoltaic power grid, the battery performance may be reduced and the charging process may be unstable. For example, when charging the drone, the discharge current and voltage may be unstable, which may cause the discharge power to be unstable. This instability may further affect the service life of the battery pack (and the drone battery) and make the charging time unreliable and inaccurate. Therefore, when obtaining the optimal charging solution, this embodiment also needs to consider the charging stability of the drone during charging.

[0066] As an example, a method for obtaining the charging stability of a drone during charging includes:

[0067] For the battery pack that charges each drone, the discharge curve of the battery pack during the historical charging process is obtained, which is recorded as the historical discharge curve of the battery pack. For any historical discharge curve, the remaining power corresponding to time 0 is recorded as q1, and q2=q1-P is set. The horizontal coordinate t corresponding to the vertical coordinate q2 on the historical discharge curve is obtained, and the curve segment in the interval [0, t] is obtained, which is recorded as the reference curve of each historical discharge curve.

[0068] It should be noted that, for any historical discharge curve, if q2 is less than or equal to 0 or there is no horizontal coordinate t corresponding to the vertical coordinate q2, then the historical discharge curve no longer participates in the above-mentioned charging stability acquisition process.

[0069] The reference curve on any one of the historical discharge curves represents the change in the battery pack power during the charging process of the drone when the discharge curve of the battery pack is the historical discharge curve.

[0070] If the reference curves of all historical discharge curves have a large change, it means that the battery pack has a large instability during multiple charging processes. If the reference curves of all historical discharge curves have a small change, it means that the battery pack is relatively stable during multiple charging processes.

[0071] The ordinate of the reference curve on each historical discharge curve is linearly normalized to remove the interference of order of magnitude and dimension; the mean of the ordinate of the reference curve after linear normalization is obtained, which is recorded as the average remaining power of the reference curve; the absolute value of the difference between the average remaining power of any two reference curves is obtained, and the absolute value of the difference between the average remaining power of all reference curves is averaged, which is recorded as the average discharge difference f of the battery pack; the ratio of exp(-f) to t is recorded as the charging stability of the drone during charging. The larger the value, the smaller the difference in discharge per unit time during multiple charging processes of the drone (that is, the historical charging process), and the more stable the discharge process. The smaller the charging stability, the larger the difference in discharge per unit time during multiple charging processes of the drone (that is, the historical charging process), and the more unstable the discharge process.

[0072] So far, the charging time and charging stability of each drone in each charging scheme have been obtained.

[0073] Under each charging scheme, the response degree of each drone is obtained as F=exp(-T), where T represents the charging time of each drone, and exp() represents an exponential function with a natural constant as the base.

[0074] The charging stability of each drone is normalized. In this embodiment, the softmax algorithm is used for normalization, so that the sum of the normalized results of the charging stability of all drones is equal to 1.0. The normalized charging stability is used as the weight of each drone, and the weight is used to perform weighted summation on the response degree of the drone to obtain the emergency handling capacity of each charging scheme. In this process, the greater the charging stability of the drone, the more attention is paid to its corresponding charging time, so that the obtained emergency handling capacity is more reliable. The charging scheme with the maximum emergency handling capacity is taken as the optimal charging scheme.

[0075] The optimal charging solution obtained above enables the drone or drone swarm to have higher scheduling flexibility in the face of emergency situations, while also avoiding to a certain extent the impact of unstable charging during the charging process, such as reduced battery life (battery pack and drone battery), and unreliable charging time and low accuracy.

[0076] Step S203, record the charging amount required for each drone to be fully charged as the first charging amount, and record the optimal charging plan obtained by the first charging amount as the first plan; obtain the power A consumed during the drone inspection, record the charging amount required for each drone to be charged to the power A as the second charging amount, and record the optimal charging plan obtained by the second charging amount as the second plan.

[0077] The optimal charging solution is obtained above; it should be noted that the optimal charging solution obtained above is obtained based on the charging capacity P required by each drone. When P takes different values, the optimal charging solution obtained may also be different.

[0078] In this embodiment, the charging amount required when each drone is fully charged is obtained, which is recorded as the first charging amount. The specific method for obtaining the first charging amount is:

[0079] The remaining power of the drone when it returns is recorded as P0, and the total power of the drone's battery is recorded as P1, then the first charging amount is equal to P1-P0.

[0080] Further, let P be equal to the first charge amount, and the optimal charging scheme obtained according to the method in step S202 is recorded as the first scheme. The first scheme ensures that there are as many drones as possible that can respond quickly when an emergency inspection task occurs, while ensuring that the drone has enough power to charge.

[0081] Furthermore, the power consumed by each drone during inspection is obtained, recorded as P2, and P2-P0 is recorded as the second charging amount.

[0082] Let P be equal to the second charge amount, and again obtain the optimal charging solution according to the method in step S002, and record it as the second solution. The first solution ensures that there are as many drones with fast response as possible when an emergency inspection task occurs, and ensures that the drone can perform at least one regular inspection task.

[0083] It should be noted that if the value of P is less than the second charging amount, the optimal charging solution obtained can ensure that there are drones with fast responses as much as possible, but there may be a situation where the emergency inspection task cannot be completed smoothly due to insufficient power of the drone.

[0084] Comparing the first and second options, it can be seen that the second option is based on the purpose of completing at least one normal inspection task. The advantage of the second option is that the drone takes relatively less time to charge and has a stronger ability to respond to emergencies. The disadvantage is that the drone's power is relatively low, and it is prone to deep discharge, which affects the service life of the drone's battery. The first option is based on the purpose of the drone having sufficient power. Its advantage is that it is not easy to have deep discharge during inspections (including normal inspection tasks and emergency inspection tasks), which has little impact on the service life of the drone's battery; its disadvantage is that the drone takes a long time to charge and has a weaker ability to respond to emergencies.

[0085] As an example, the method for obtaining the power P2 consumed by each drone during inspection is:

[0086] Get the total power consumed by each drone when it last performed a routine inspection mission and use it as P2.

[0087] As another example, the method for obtaining the power P2 consumed by each drone during inspection is as follows:

[0088] Obtain the total power consumed during the last five routine inspection tasks, calculate the average of these total power consumption, and use it as P2.

[0089] In other examples, the total power consumed by each drone when performing all routine inspection tasks is obtained, and the maximum value of these total powers is used as P2.

[0090] Step S204: According to the first charging capacity, the second charging capacity, and the difference in emergency processing capabilities between the first solution and the second solution, the final charging capacity required for each drone is obtained, and each drone is used as a drone required for emergency inspection tasks when it is charged to the final charging capacity.

[0091] The absolute value of the difference between the emergency processing capabilities of the first and second solutions is obtained, recorded as W1, the maximum value of the emergency processing capabilities of the first and second solutions is recorded as W2, and the ratio W of W1 to W2 represents the difference in emergency processing capabilities between the first and second solutions.

[0092] The smaller W is, the more it means that the emergency handling capabilities of the drones when they are charged to P2 and when they are fully charged are similar. At this time, it is more necessary to fully charge all drones directly to ensure strong emergency response capabilities while avoiding the situation where the drone batteries have a significant reduction in battery life due to deep discharge. When W is larger, it means that the emergency handling capabilities of the drones when they are charged to P2 and when they are fully charged are quite different. At this time, the drones need to be charged to P2 to ensure the emergency response capabilities of the drones.

[0093] In this embodiment, the final charge amount required for each drone is MP = W × d2 + (1-W) × d1. d1 represents the first charge amount, and d2 represents the second charge amount. d1 means that the drone is fully charged after charging the drone with d1; d2 means that when charging the drone with d2, it can ensure that the drone can perform at least one routine inspection task. MP means that when charging the drone with MP, a certain emergency response capability can be guaranteed as much as possible without significantly affecting the service life of the drone battery.

[0094] Further, P in step S202 is set equal to MP, and the optimal charging plan is re-derived using step S202, which is recorded as the final charging plan. At the same time, the charging time of each drone obtained in step S202 is recorded as the final charging time of each drone. After the final charging plan is obtained, charging is performed according to the final charging plan.

[0095] When an emergency inspection task occurs, the drone that has reached the final charging time is used as the drone to perform the emergency inspection task. If there is no drone that has reached the final charging time when an emergency inspection task occurs, then among several (for example, 3) drones with the shortest final charging time, the drone with the largest battery power is selected as the drone to perform the emergency inspection task. If there is no emergency inspection task, the drone can continue to perform the regular inspection task, and in other embodiments, the drone can also be allowed to continue charging.

[0096] So far, this embodiment provides a suitable charging method for the drone airport, which includes the battery pack used when charging the drone (that is, the drone and battery pack that match in the final charging plan), and also includes the final charging time of the drone. This ensures that the drone airport can promptly handle and respond to emergency inspection tasks (that is, respond to emergencies), while avoiding as much as possible the problem of the service life of the battery pack and the drone battery being significantly affected. It should be noted that the final charging time of the drone is the estimated time for charging to be completed. When dispatching drones, other embodiments can end charging in advance according to specific circumstances.

[0097] It should be further explained that in this embodiment, the above steps S201 to S204 are performed once every minute, and the purpose is to update the final charging plan to ensure the reliability of the final charging plan. In other embodiments, all the above steps can be performed once every second, and in other embodiments, all the above steps can be performed once when a drone returns.

[0098] Embodiment three:

[0099] This embodiment further provides a method for obtaining the final charge capacity MP, the method comprising:

[0100] For any charging scheme in the first scheme and the second scheme of the second embodiment, all drones involved in the charging scheme can be divided into two categories, one category is drones that are being charged, and the drones of this category have been matched with corresponding battery packs, which are recorded as category one drones; the other category is drones that need to be charged after completing the inspection task but have not been charged, which are recorded as category two drones;

[0101] The charging scheme of a type of drone (that is, the matching relationship between a type of drone and a battery pack) has been determined and cannot be changed; therefore, the matching relationship between a type of drone and a battery pack is the same in both the first scheme and the second scheme.

[0102] The charging scheme for Category II drones has not yet been determined, which means that its charging scheme can still be adjusted.

[0103] The mean charging time of the first type of drones in the first scheme is denoted as a1, and the mean charging time of the second type of drones is denoted as a2;

[0104] In the second scheme, the mean charging time of the first type of drones is denoted as b1, and the mean charging time of the second type of drones is denoted as b2.

[0105] Let w1 = (b2-a1) / b2. The larger w1 is, the larger b2 is and the smaller a1 is. This means that compared with the Class II UAV, the Class I UAV is more suitable for a charging scheme that can respond to emergency inspection tasks in a timely manner. The charging scheme for the Class II UAV can be adjusted to avoid affecting the battery life (that is, making the required charging amount of the Class II UAV closer to d1). This is beneficial for all UAVs to respond to emergency inspection tasks in a timely manner as a whole, while further avoiding affecting the battery life.

[0106] Let w2 = (a2-b1) / a2. When w2 is larger, it means a2 is relatively larger and b1 is relatively smaller, which means that compared with Class II UAVs, Class I UAVs are more suitable for charging solutions that avoid affecting the battery life. The charging solution for Class II UAVs can be adjusted to a charging solution that responds to emergency inspection tasks in a timely manner (that is, the required charging amount for Class II UAVs is closer to d2). This is also beneficial for all UAVs as a whole to respond to emergency inspection tasks in a timely manner while further avoiding affecting the battery life.

[0107] The final charge required for each drone in the second category is MP = m2 × d2 + m1 × d1. m1 and m2 represent the battery life concern and emergency capability concern respectively.

[0108] Let X1 = (w1+1-W) / (w2+1) and X2 = (W+w2) / (w1+1). Use the softmax algorithm to normalize X1 and X2 to obtain m1 and m2 respectively.

[0109] When m1 is larger, w1 is larger, thereby ensuring that the required charging amount of the second type of drone is closer to d1. When m2 is larger, w2 is larger, thereby ensuring that the required charging amount of the second type of drone is closer to d2.

[0110] When w1 or w2 is less than or equal to 0, set w1 or w2 equal to 0.

[0111] Then, the optimal charging solution is obtained again according to the final charge amount MP as the final charging solution. The final charging solution obtained in this embodiment can further enable all drones as a whole to respond to emergency inspection tasks in a timely manner while avoiding affecting the battery life.

[0112] The final charge MP required for each drone in a class of drones is calculated according to the method in the first embodiment, and m1=1-W, m2=W.

[0113] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for charging a drone at an airport, characterized in that: The method comprises the following steps: The energy storage device of the drone airport includes a plurality of battery packs, each battery pack corresponds to a discharge curve; all battery packs are matched with all drones one by one, and each matching result is recorded as a charging scheme; each battery pack in each charging scheme charges the matched drone; The optimal charging scheme is obtained according to the charging amount required by each drone, including: obtaining the charging time of each drone according to the charging amount required by each drone and the discharge curve of each battery pack, and obtaining the charging stability of each drone during charging according to the historical change characteristics of the discharge curve of the battery pack; the emergency handling capacity of each charging scheme is obtained according to the charging time of all drones and the charging stability of the drones, and the charging scheme with the maximum emergency handling capacity is taken as the optimal charging scheme; The charging amount required for each drone to be fully charged is recorded as the first charging amount, and the optimal charging solution obtained by the first charging amount is recorded as the first solution; the total power consumed by the drones during each inspection is obtained, and the charging amount required for each drone to be charged to the total power is recorded as the second charging amount, and the optimal charging solution obtained by the second charging amount is recorded as the second solution; According to the first charging amount, the second charging amount, and the difference in emergency handling capabilities between the first plan and the second plan, the final charging amount required for each drone is obtained, and the optimal charging plan obtained by the final charging amount is used as the final charging plan when the drone is charged.

2. A drone airport charging method according to claim 1, characterized in that: The specific steps of obtaining the charging time of each drone according to the charging amount required by each drone and the discharge curve of each battery pack are as follows: The abscissa of the discharge curve is time, and the ordinate is the remaining power of the battery pack; For the battery pack that charges each drone, obtain the discharge curves of all the charging processes of the battery pack in history; the vertical coordinate corresponding to any time t on each discharge curve is marked as f(t), and the vertical coordinate of the discharge curve at time 0 is marked as f0. (f0, f(t)) is used as a sample, and t is used as the label of the sample. The samples and labels composed of the discharge curves of all charging processes are used as a data set, and a fully connected neural network is trained using the data set; The charging amount required for each drone is recorded as P; the remaining power Pf of each battery pack in each charging scheme is obtained, Pf and Pf-P are input into the fully connected neural network, and the output result of the fully connected neural network is recorded as the charging time T of the drone.

3. A drone airport charging method according to claim 2, characterized in that: The specific steps of obtaining the charging stability of each drone during charging according to the historical variation characteristics of the discharge curve of the battery pack are as follows: For the battery pack that charges each drone, obtain the discharge curve of the battery pack during the historical charging process, which is recorded as the historical discharge curve of the battery pack. For any historical discharge curve, the remaining power corresponding to time 0 is recorded as q1, and q2=q1-P is set. The horizontal coordinate t corresponding to the vertical coordinate q2 on the historical discharge curve is obtained, and the curve segment in the interval [0, t] is obtained on the historical discharge curve, which is recorded as the reference curve of each historical discharge curve; For the reference curve of all historical discharge curves of each drone, the average remaining power of the reference curve is obtained, the absolute value of the difference between the average remaining power of any two reference curves is obtained, and the absolute value of the difference between the average remaining power of all reference curves is averaged, which is recorded as the average discharge difference f of the battery pack. The ratio of exp(-f) to t is recorded as the charging stability of the drone during charging; exp() represents an exponential function with a natural constant as the base.

4. A drone airport charging method according to claim 1, characterized in that: The specific steps of obtaining the emergency handling capability of each charging solution according to the charging time of all drones and the charging stability of the drones are as follows: Under each charging scheme, the response degree of each UAV is obtained, and the response degree is negatively correlated with the charging time; The charging stability of each UAV is normalized, and the normalized result is used as the weight of each UAV. The weight is used to perform weighted summation on the response degree of the UAV to obtain the emergency handling capability of each charging scheme.

5. The method for charging a drone at an airport according to claim 1, characterized in that: The method of obtaining the final charging capacity required for each drone according to the first charging capacity, the second charging capacity, and the difference in emergency handling capabilities between the first solution and the second solution includes the following specific steps: Obtain the difference in emergency handling capabilities between the first plan and the second plan; The final charging amount required for each drone MP=m2×d2+m1×d1, d1 represents the first charging amount, d2 represents the second charging amount, m1 and m2 respectively represent the battery life concern and emergency capability concern of each drone; the battery life concern is negatively correlated with the difference in the emergency handling capability, and the emergency capability concern is positively correlated with the difference in the emergency handling capability.

6. A drone airport charging method according to claim 5, characterized in that: The step of obtaining the difference between the emergency handling capabilities of the first solution and the second solution includes the following specific steps: The absolute value of the difference between the emergency processing capabilities of the first and second solutions is obtained, recorded as W1, the maximum value of the emergency processing capabilities of the first and second solutions is recorded as W2, and the ratio of W1 to W2 is taken as the difference in emergency processing capabilities of the first and second solutions.

7. A drone airport charging method according to claim 5, characterized in that: The specific steps for obtaining the battery life concern and the emergency capability concern are as follows: For any of the charging schemes in the first and second schemes, all drones in the charging schemes are divided into two categories: one category is drones that are in the charging process, recorded as category one drones; the other category is drones that need to be charged after completing the inspection mission but have not yet been charged, recorded as category two drones; The mean charging time of the first type of drones in the first scheme is recorded as a1, and the mean charging time of the second type of drones is recorded as a2; the mean charging time of the first type of drones in the second scheme is recorded as b1, and the mean charging time of the second type of drones is recorded as b2; According to the difference between b2 and a1, the difference between a2 and b1, and the difference between the emergency handling capabilities of the first solution and the second solution, the battery life concern and the emergency capability concern corresponding to each drone in the second category of drones are obtained; The battery life concern and emergency capability concern corresponding to each drone in a category of drones are obtained by the difference in emergency handling capabilities between the first solution and the second solution.

8. A method for charging a drone at an airport according to claim 7, characterized in that: The battery life concern and emergency capability concern corresponding to each drone in the second category of drones are obtained according to the difference between b2 and a1, the difference between a2 and b1, and the difference between the emergency handling capabilities of the first solution and the second solution, including the calculation formula: Let w1 = (b2-a1) / b2, w2 = (a2-b1) / a2; let X1 = (w1+1-W) / (w2+1), X2 = (W+w2) / (w1+1); normalize X1 and X2 to obtain m1 and m2 respectively; where W represents the difference in emergency handling capabilities between the first and second solutions.

9. A method for charging a drone at an airport according to claim 7, characterized in that: The specific calculation method of the battery life concern and emergency capability concern corresponding to each drone in the first category of drones is as follows: Let 1-W, W be m1, m2 respectively, where W represents the difference in emergency handling capabilities between the first solution and the second solution.

10. A drone airport charging system, comprising a memory, a processor, and a computer program stored in the memory and executable in the processor, characterized in that: When the computer program in the processor is executed, the steps of the drone airport charging method as described in any one of claims 1 to 9 are implemented.

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