Unmanned aerial vehicle intelligent base station battery replacement control method and system, terminal and medium

Abstract

The embodiment of the invention relates to the field of data processing, and provides an unmanned aerial vehicle intelligent base station battery replacement control method and system, a terminal and a medium, and the method comprises the steps: obtaining the state information corresponding to a battery currently carried by a to-be-replaced unmanned aerial vehicle, and obtaining the first state information; according to the first state information and the current first position information of the to-be-replaced unmanned aerial vehicle, determining an optimal intelligent base station of the to-be-replaced unmanned aerial vehicle, and obtaining a target intelligent base station; according to the first position information, the second position information of the target intelligent base station and the first state information, generating a battery replacement strategy corresponding to the unmanned aerial vehicle to be subjected to battery replacement, and obtaining a first battery replacement strategy; generating a power conversion strategy of the target intelligent base station according to the first power conversion strategy to obtain a second power conversion strategy; controlling the target intelligent base station to complete the battery replacement operation of the unmanned aerial vehicle to be subjected to battery replacement according to the second battery replacement strategy; and the accuracy of carrying out battery replacement control on the intelligent base station of the unmanned aerial vehicle is improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and specifically to a method, system, terminal and medium for controlling battery replacement of an intelligent base station for unmanned aerial vehicles. Background Art

[0002] The drone's smart base station is an energy station that provides quick replacement for the drone's power battery. It replenishes the drone's energy by directly replacing the drone's power battery pack, thereby increasing the drone's range or working time.

[0003] When a drone is performing a long-term flight mission, such as at a fire rescue scene, it is necessary for the drone to hover over the fire scene for a long time to monitor the changes at the fire scene in real time. At this time, the battery replacement control system needs to monitor the battery power of the drone in real time. When the battery power of the drone is low, it is necessary to rely on the battery replacement control system to guide the drone to the smart base station in a safe area outside the fire scene for battery replacement. After the smart base station receives the battery replaced by the drone, the battery replacement control system will also control the smart base station to charge the replaced battery to ensure that the replaced battery can be fully charged and replaced with the next drone that needs battery replacement.

[0004] When the battery swap control system controls the charging base station to charge the replaced battery, it usually uses a standard charging voltage and charging current. When the drone is operating in extreme environments, such as at a fire rescue site, the drone is exposed to high temperatures for a long time, which can cause the performance of the drone's battery to change. If the smart base station uses the standard charging voltage and charging current to charge the replaced battery, it will cause the replaced battery to overcharge, causing the battery to catch fire or explode, etc., which in turn leads to low accuracy when controlling the battery swap of the drone's smart base station. Summary of the Invention

[0005] The embodiments of the present application provide a method, system, terminal and medium for controlling battery replacement of an intelligent base station of a drone, which can improve the accuracy of battery replacement control of the intelligent base station of a drone.

[0006] A first aspect of an embodiment of the present application provides a method for controlling battery replacement of a drone intelligent base station, the method comprising: obtaining status information corresponding to a battery currently carried by a drone to be replaced, to obtain first status information;

[0007] Determine the best smart base station for the drone to be battery-swapped based on the first state information and the current first location information of the drone to be battery-swapped, and obtain a target smart base station;

[0008] Generate a battery swap strategy corresponding to the battery-swapped drone based on the first location information, the second location information of the target intelligent base station, and the first state information to obtain a first battery swap strategy;

[0009] Generate a battery swap strategy for the target smart base station according to the first battery swap strategy to obtain a second battery swap strategy;

[0010] According to the second battery exchange strategy, the target intelligent base station is controlled to complete the battery exchange operation for the drone to be battery exchanged.

[0011] In this example, by obtaining the status information corresponding to the battery currently carried by the drone to be replaced, the first status information is obtained, and the optimal intelligent base station for the drone to be replaced is determined according to the first status information and the current first position information of the drone to be replaced, and the target intelligent base station is obtained. The battery replacement strategy corresponding to the drone to be replaced is generated according to the first position information, the second position information of the target intelligent base station and the first status information, and the first battery replacement strategy is obtained. The battery replacement strategy of the target intelligent base station is generated according to the first battery replacement strategy, and the second battery replacement strategy is obtained. According to the second battery replacement strategy, the target intelligent base station is controlled to complete the battery replacement operation for the drone to be replaced, thereby improving the accuracy of battery replacement control of the drone's intelligent base station.

[0012] In one possible implementation, a method for obtaining status information corresponding to a battery currently carried by a drone to be battery-replaced to obtain first status information includes:

[0013] Obtain historical operating data of the battery currently carried by the drone to be battery-replaced, to obtain a first historical operating data set;

[0014] Acquire historical environmental data of the battery currently carried by the drone to be battery-replaced to obtain a first environmental data set;

[0015] Obtaining basic parameter information of the battery currently carried by the drone to be battery-replaced, to obtain first basic parameter information;

[0016] The status information corresponding to the battery currently carried by the drone to be replaced is determined based on the first historical working data set, the first environmental data set and the first basic parameter information to obtain the first status information.

[0017] In one possible implementation, a method for determining status information corresponding to a battery currently carried by a drone to be battery-replaced based on the first historical working data set, the first environmental data set, and the first basic parameter information to obtain first status information includes:

[0018] Determine a life prediction curve corresponding to the battery currently carried by the drone to be replaced based on the first historical working data and the first basic parameter information in the first historical working data set, to obtain a first life prediction curve;

[0019] Determine the life prediction curve corresponding to the battery currently carried by the drone to be replaced based on the first environmental data and the first basic parameter information in the first environmental data set, and obtain a second life prediction curve;

[0020] Fitting the first life prediction curve and the second life prediction curve to obtain a third life prediction curve;

[0021] Predicting the remaining life of the battery currently carried by the drone to be replaced according to the third life prediction curve to obtain first life information;

[0022] Obtaining the remaining power information corresponding to the battery currently carried by the drone to be replaced, and obtaining the first power information;

[0023] The status information corresponding to the battery currently carried by the drone to be replaced is determined according to the first life information and the first power information to obtain the first status information.

[0024] In one possible implementation, a method for fitting the first life prediction curve and the second life prediction curve to obtain a third life prediction curve includes:

[0025] Obtaining the life decay rate corresponding to the battery currently carried by the battery-replacement drone at each moment according to the first life prediction curve, and obtaining a first life decay rate set;

[0026] Obtaining the life decay rate corresponding to the battery currently carried by the battery-replacement drone at each moment according to the second life prediction curve, and obtaining a second life decay rate set;

[0027] determining a fitting polynomial used for fitting the first life prediction curve and the second life prediction curve according to a first life decay rate in the first life decay rate set and a second life decay rate in the second life decay rate set, to obtain a target polynomial;

[0028] The first life prediction curve and the second life prediction curve are fitted according to the target polynomial to obtain a third life prediction curve.

[0029] In one possible implementation, a method for determining the best smart base station for the drone to be battery swapped based on the first state information and the current first location information of the drone to be battery swapped, and obtaining a target smart base station, includes:

[0030] Determine the maximum remaining cruising range information of the battery to be replaced without anyone according to the first state information, and obtain target cruising range information;

[0031] According to the current first location information of the UAV to be replaced with a battery, all smart base stations within a circular range with the target cruising range information as a radius are obtained to obtain a set of smart base stations to be determined;

[0032] Acquire status information corresponding to each intelligent base station in the set of intelligent base stations to be determined to obtain a second state information set;

[0033] According to the first state information and the second state information in the second state information set, the best smart base station for the drone to be replaced is determined in the set of smart base stations to be determined, and the target smart base station is obtained.

[0034] A second aspect of the embodiments of the present application provides a drone intelligent base station battery replacement control system, the system comprising:

[0035] An acquiring unit, configured to acquire status information corresponding to a battery currently carried by the drone to be battery-replaced, and obtain first status information;

[0036] a determination unit, configured to determine an optimal intelligent base station for the drone to be battery-swapped based on the first state information and the current first location information of the drone to be battery-swapped, and obtain a target intelligent base station;

[0037] A first generating unit is configured to generate a battery swap strategy corresponding to the battery-swapped drone according to the first location information, the second location information of the target intelligent base station, and the first state information, to obtain a first battery swap strategy;

[0038] A second generating unit is configured to generate a battery swap strategy for the target smart base station according to the first battery swap strategy to obtain a second battery swap strategy;

[0039] The control unit is used to control the target intelligent base station to complete the battery replacement operation for the UAV to be replaced according to the second battery replacement strategy.

[0040] In one possible implementation, the acquiring unit is specifically configured to:

[0041] Obtain historical operating data of the battery currently carried by the drone to be battery-replaced, to obtain a first historical operating data set;

[0042] Acquire historical environmental data of the battery currently carried by the drone to be battery-replaced to obtain a first environmental data set;

[0043] Obtaining basic parameter information of the battery currently carried by the drone to be battery-replaced, to obtain first basic parameter information;

[0044] The status information corresponding to the battery currently carried by the drone to be replaced is determined based on the first historical working data set, the first environmental data set and the first basic parameter information to obtain the first status information.

[0045] In one possible implementation, the acquiring unit is specifically configured to:

[0046] Determine a life prediction curve corresponding to the battery currently carried by the drone to be replaced based on the first historical working data and the first basic parameter information in the first historical working data set, to obtain a first life prediction curve;

[0047] Determine the life prediction curve corresponding to the battery currently carried by the drone to be replaced based on the first environmental data and the first basic parameter information in the first environmental data set, and obtain a second life prediction curve;

[0048] Fitting the first life prediction curve and the second life prediction curve to obtain a third life prediction curve;

[0049] Predicting the remaining life of the battery currently carried by the drone to be replaced according to the third life prediction curve to obtain first life information;

[0050] Obtaining the remaining power information corresponding to the battery currently carried by the drone to be replaced, and obtaining the first power information;

[0051] The status information corresponding to the battery currently carried by the drone to be replaced is determined according to the first life information and the first power information to obtain the first status information.

[0052] In one possible implementation, the acquiring unit is specifically configured to:

[0053] Obtaining the life decay rate corresponding to the battery currently carried by the battery-replacement drone at each moment according to the first life prediction curve, and obtaining a first life decay rate set;

[0054] Obtaining the life decay rate corresponding to the battery currently carried by the battery-replacement drone at each moment according to the second life prediction curve, and obtaining a second life decay rate set;

[0055] determining a fitting polynomial used for fitting the first life prediction curve and the second life prediction curve according to a first life decay rate in the first life decay rate set and a second life decay rate in the second life decay rate set, to obtain a target polynomial;

[0056] The first life prediction curve and the second life prediction curve are fitted according to the target polynomial to obtain a third life prediction curve.

[0057] In one possible implementation, the determining unit is specifically configured to:

[0058] Determine the maximum remaining cruising range information of the battery to be replaced without anyone according to the first state information, and obtain target cruising range information;

[0059] According to the current first location information of the UAV to be replaced with a battery, all smart base stations within a circular range with the target cruising range information as a radius are obtained to obtain a set of smart base stations to be determined;

[0060] Acquire status information corresponding to each intelligent base station in the set of intelligent base stations to be determined to obtain a second state information set;

[0061] According to the first state information and the second state information in the second state information set, the best smart base station for the drone to be replaced is determined in the set of smart base stations to be determined, and the target smart base station is obtained.

[0062] A third aspect of an embodiment of the present application provides a terminal, comprising a processor, an input device, an output device, and a memory, wherein the processor, input device, output device, and memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions and execute the step instructions in the first aspect of the embodiment of the present application.

[0063] The fourth aspect of the embodiments of the present application provides a computer-readable storage medium, wherein the above-mentioned computer-readable storage medium stores a computer program for electronic data exchange, wherein the above-mentioned computer program enables a computer to execute some or all of the steps described in the first aspect of the embodiments of the present application.

[0064] A fifth aspect of the embodiments of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, wherein the computer program is operable to cause a computer to perform some or all of the steps described in the first aspect of the embodiments of the present application. The computer program product may be a software installation package. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0066] Figure 1A schematic diagram of the network architecture of a UAV intelligent base station battery replacement control system is provided for an embodiment of the present application;

[0067] Figure 2 A flowchart of a method for controlling battery replacement of a UAV intelligent base station is provided for an embodiment of the present application;

[0068] Figure 3 A schematic diagram of the structure of a terminal provided in an embodiment of the present application;

[0069] Figure 4 A structural schematic diagram of a drone intelligent base station battery replacement control system is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0070] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0071] The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0072] References to "embodiments" in this application mean that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described in this application may be combined with other embodiments.

[0073] See also Figure 1 , Figure 1A network architecture diagram of a drone intelligent base station battery swap control system is provided for an embodiment of the present application. The drone intelligent base station battery swap control system includes a drone 1, an intelligent base station 2, and a server 3. The drone can perform preset flight missions, such as inspection missions and aerial photography missions. While performing a mission, to ensure the continuous operation of a single drone, the drone can perform battery swapping and charging at the intelligent base station to increase the drone's range.

[0074] The drone can report its own battery level and the progress of the current flight mission to the server at certain time intervals. After receiving the battery level and mission completion progress reported by the drone, the server can determine the drone's endurance. If the battery level is insufficient to support the completion of the entire flight mission, the drone's battery replacement task is triggered. Based on the drone's current position and battery level, the optimal target smart base station is determined, and the drone's battery replacement strategy and the target smart base station's battery replacement strategy are determined, and the two strategies are sent to the corresponding devices respectively. After receiving the battery replacement strategy, the drone flies to the target smart base station for battery replacement. After receiving the battery replacement strategy, the target smart base station can monitor whether the drone has arrived and perform battery replacement after the drone arrives. Therefore, the smart base station can assist the drone in completing the battery replacement operation according to the corresponding battery replacement strategy, and perform charging operations on the drone's replaced battery.

[0075] In order to better understand the battery replacement control method for a drone intelligent base station provided in an embodiment of the present application, the following first briefly introduces the battery replacement control method for a drone intelligent base station in the existing solution. In the existing solution, a standard charging voltage and charging current are usually used to perform charging operations on the replaced battery. When a drone is operating in some extreme environments, such as at a fire rescue site, the drone will be in a high temperature environment for a long time, which will cause the performance of the drone's battery to change. If the intelligent base station uses a standard charging voltage and charging current to charge the replaced battery at this time, it will cause the replaced battery to be overcharged, causing the battery to catch fire, explode, etc., which will lead to lower accuracy in the battery replacement control of the drone's intelligent base station.

[0076] In order to solve the above technical problems, an embodiment of the present application provides a method for controlling battery replacement of an intelligent base station of a drone, which obtains the status information of the battery currently carried by the drone to be replaced, obtains first status information, and determines the best intelligent base station for the drone to be replaced based on the first status information, obtains the target intelligent base station, obtains the status information of the target intelligent base station, obtains second status information, and generates a battery replacement strategy corresponding to the target intelligent base station based on the first status information and the second status information, obtains the second battery replacement strategy, thereby improving the accuracy of battery replacement control of the drone's intelligent base station.

[0077] See also Figure 2 , Figure 2 The present invention provides a flow chart of a method for controlling battery replacement of a UAV intelligent base station. Figure 2 As shown, the method includes:

[0078] 101. Obtain status information corresponding to the battery currently carried by the drone to be replaced, and obtain first status information.

[0079] The method may be performed by obtaining historical working data corresponding to the battery currently carried by the drone to be replaced, obtaining a first historical working data set; obtaining historical environmental data corresponding to the battery currently carried by the drone to be replaced, obtaining a first environmental data set; obtaining a life prediction curve corresponding to the battery currently carried by the drone to be replaced based on the first historical working data in the first historical working data set and the first environmental data in the first environmental data set and in combination with the basic parameter information corresponding to the battery currently carried by the drone to be replaced, and obtaining the status information corresponding to the battery currently carried by the drone to be replaced based on the life prediction curve to obtain the first status information. The basic parameter information corresponding to the battery currently carried by the drone to be replaced includes the maximum charging time of the battery in an empty state, the minimum charging time of the battery in an empty state, the maximum discharge time of the battery in a fully charged state, the minimum discharge time of the battery in a fully charged state, the maximum operating environment temperature when the battery is discharged in a fully charged state, the minimum operating environment temperature when the battery is discharged in a fully charged state, the maximum operating environment temperature when the battery is charged in an empty state, and the minimum operating environment temperature when the battery is charged in an empty state.

[0080] 102. Determine the best smart base station for the drone to be battery-replaced based on the first status information and the current first location information of the drone to be battery-replaced, and obtain the target smart base station.

[0081] Specifically, the current location information of the drone to be replaced can be obtained by obtaining a universal positioning device carried by the drone to be replaced, and the first location information can be obtained; the maximum remaining cruising range information of the drone to be replaced can be determined based on the first status information, and the target cruising range information can be obtained; the intelligent base station that can meet the battery replacement needs of the drone to be replaced can be obtained based on the target cruising range information and the first location information, and the set of intelligent base stations to be determined can be obtained; and the best intelligent base station for the drone to be replaced can be determined in the set of intelligent base stations to be determined based on the second status information corresponding to each intelligent base station to be determined in the intelligent base stations to be determined, and the target intelligent base station can be obtained. The second status information includes the queue sequence of the drones that need to perform battery replacement operations at the intelligent base station to be determined, the number of fully charged batteries in the battery compartment of the intelligent base station to be determined, and the model corresponding to the battery in the battery compartment of the intelligent base station to be determined.

[0082] 103. Generate a battery replacement strategy corresponding to the drone to be battery-replaced based on the first location information, the second location information of the target intelligent base station, and the first status information to obtain a first battery replacement strategy.

[0083] Specifically, the second location information can be obtained by obtaining the positioning information corresponding to the target intelligent base station transmitted back from the universal positioning device carried in the target intelligent base station, and determining the positioning information corresponding to the target intelligent base station as the location information of the target intelligent base station; generating the shortest path for the UAV to be battery-replaced to complete the battery replacement operation based on the first location information and the second location information, and obtaining the target battery replacement path information; determining the cruising speed of the UAV to be battery-replaced in the process of heading to the target intelligent base station based on the first status information, and obtaining the target cruising speed; determining the target battery replacement path information and the target cruising speed as the battery replacement strategy corresponding to the UAV to be battery-replaced, and obtaining the first battery replacement strategy.

[0084] 104. Generate a battery swap strategy for the target smart base station based on the first battery swap strategy to obtain a second battery swap strategy.

[0085] Among them, it can be that the first battery replacement strategy is sent to the UAV to be replaced by a universal wireless transmission device, and the model information corresponding to the UAV to be replaced is obtained to obtain the target model information; based on the target model information; the position information of the fully charged battery matching the target model information in the battery compartment of the target intelligent base station is obtained to obtain the third position information; based on the first state information, the charging information corresponding to the battery currently carried by the UAV to be replaced is determined to obtain the target charging information; the third position information and the target charging information are determined as the battery replacement strategy of the target intelligent base station to obtain the second battery replacement strategy. Among them, the target charging information includes the optimal charging voltage, optimal charging current and optimal charging time corresponding to the battery currently carried by the UAV to be replaced.

[0086] 105. Control the target intelligent base station according to the second battery swap strategy to complete the battery swap operation for the UAV to be battery swapped.

[0087] Among them, the second battery exchange strategy can be transmitted to the target intelligent base station through a universal wireless transmission module; when the target intelligent base station receives the second battery exchange strategy uploaded by the server and executes the battery exchange operation for the drone to be battery exchanged, the target intelligent base station replaces the battery currently carried by the drone to be battery exchanged with the battery corresponding to the third position information in the battery compartment, and places the battery currently carried by the drone to be battery exchanged in the charging slot corresponding to the third position information, and charges the battery currently carried by the drone to be battery exchanged according to the target charging information in the second battery exchange strategy.

[0088] In this example, by obtaining the status information corresponding to the battery currently carried by the drone to be replaced, the first status information is obtained, and the optimal intelligent base station for the drone to be replaced is determined according to the first status information and the current first position information of the drone to be replaced, and the target intelligent base station is obtained. The battery replacement strategy corresponding to the drone to be replaced is generated according to the first position information, the second position information of the target intelligent base station and the first status information, and the first battery replacement strategy is obtained. The battery replacement strategy of the target intelligent base station is generated according to the first battery replacement strategy, and the second battery replacement strategy is obtained. According to the second battery replacement strategy, the target intelligent base station is controlled to complete the battery replacement operation for the drone to be replaced, thereby improving the accuracy of battery replacement control of the drone's intelligent base station.

[0089] In one possible implementation, a method for obtaining status information corresponding to a battery currently carried by a drone to be battery-replaced to obtain first status information includes:

[0090] A1. Obtain historical operating data of the battery currently carried by the drone to be battery-replaced to obtain a first historical operating data set;

[0091] A2. Obtain historical environmental data of the battery currently carried by the drone to be battery-replaced to obtain a first environmental data set;

[0092] A3. Obtain basic parameter information of the battery currently carried by the drone to be battery-replaced, obtaining first basic parameter information;

[0093] A4. Determine the status information corresponding to the battery currently carried by the drone to be replaced based on the first historical work data set, the first environmental data set and the first basic parameter information to obtain first status information.

[0094] During the process of the drone to be replaced with a battery performing a flight mission, the server can monitor the power information, location information, etc. of the drone to be replaced with a battery in real time. When it is monitored that the remaining power of the drone to be replaced with a battery is lower than the preset power threshold, the server can automatically obtain the status information corresponding to the battery currently carried by the drone to be replaced with a battery, and obtain the first status information.

[0095] Specifically, when the power level of the battery currently carried by the drone to be replaced is lower than a preset power threshold, the target battery number information can be obtained by reading the battery number of the battery currently carried by the drone to be replaced, and according to the target battery number information, the work log corresponding to the battery currently carried by the drone to be replaced is obtained from the database preset in the server, and the historical work data of the battery currently carried by the drone to be replaced is extracted from the work log to obtain the first historical work data set. The work log can be a sensor built into the battery that collects and records the daily charging time, discharge time, number of charges, number of discharges, average temperature of the charging working environment, and average temperature of the discharging working environment of the corresponding battery; the first historical work data includes the daily charging time, discharge time, number of charges, and number of discharges of the battery currently carried by the drone to be replaced, and the work log can be transmitted to the preset database of the server through a universal wireless transmission module.

[0096] While obtaining the first historical operating data set, the first environmental data set may be obtained by extracting environmental data from the operating log of the battery currently carried by the drone to be replaced. The first environmental data includes the average daily charging operating environment temperature and the average daily discharging operating environment temperature of the battery currently carried by the drone to be replaced.

[0097] After obtaining the first historical working data set and the first environmental data set, the product description of the battery currently carried by the drone to be replaced can be consulted in the preset database of the server according to the target battery number information, and the maximum charging time of the battery in an empty state, the minimum charging time of the battery in an empty state, the maximum discharge time of the battery in a fully charged state, the minimum discharge time of the battery in a fully charged state, the maximum working environment temperature of the battery when discharging in a fully charged state, the minimum working environment temperature of the battery when discharging in a fully charged state, the maximum working environment temperature of the battery when charging in an empty state, and the minimum working environment temperature of the battery when charging in an empty state can be extracted from the product description to obtain the first basic parameter information.

[0098] After obtaining the first basic parameter information, a life prediction curve corresponding to the battery currently carried by the drone to be replaced can be constructed through the first historical working data set, the first environmental data set and the first basic parameter information, and the status information corresponding to the battery currently carried by the drone to be replaced can be determined based on the constructed life prediction curve to obtain the first status information.

[0099] In one possible implementation, a method for determining status information corresponding to a battery currently carried by a drone to be battery-replaced based on the first historical working data set, the first environmental data set, and the first basic parameter information to obtain first status information includes:

[0100] B1. Determine a life prediction curve corresponding to the battery currently carried by the drone to be battery-replaced based on the first historical operating data and first basic parameter information in the first historical operating data set to obtain a first life prediction curve;

[0101] B2. Determine a life prediction curve corresponding to the battery currently carried by the battery-replacement drone based on the first environmental data and the first basic parameter information in the first environmental data set to obtain a second life prediction curve;

[0102] B3. Fitting the first life prediction curve and the second life prediction curve to obtain a third life prediction curve;

[0103] B4. Predicting the remaining life of the battery currently carried by the drone to be replaced based on the third life prediction curve to obtain first life information;

[0104] B5. Obtain the remaining power information corresponding to the battery currently carried by the drone to be replaced, and obtain the first power information;

[0105] B6. Determine the status information corresponding to the battery currently carried by the drone to be replaced based on the first lifespan information and the first power information to obtain first status information.

[0106] Specifically, the first historical working data in the first historical working data set can be sorted in chronological order to obtain a first historical working data sequence; a preset first correction parameter is obtained, and the life of the battery currently carried by the drone to be replaced at each moment is predicted based on the first correction parameter and the first historical working data in the first historical working data sequence to obtain a first predicted life information set; the first predicted life information in the first predicted life information set is plotted into a curve in chronological order to obtain a first life prediction curve.

[0107] Specifically, the lifespan of the battery currently carried by the battery-replacement drone at each moment may be predicted based on the first correction parameter and the first historical working data in the first historical working data sequence by the method shown in the following formula to obtain a first predicted lifespan set:

[0108]

[0109] D in the formula k1 represents the first predicted life information in the first predicted life information set corresponding to the battery; ai represents the discharge time of the battery on day i; t ai-1 Indicates the discharge time of the battery on day i-1; t amax Indicates the maximum discharge time of the battery in the fully charged state in the first basic parameter information of the battery; t amin represents the minimum discharge time of the battery in the fully charged state in the first basic parameter information of the battery; δ1 represents a preset first correction parameter, which is used to prevent erroneous small positive numbers other than zero, and can be obtained by conducting a simulation experiment on the battery currently carried by the drone to be replaced; t bi represents the charging time of the battery on day i; t bi-1 Indicates the charging time of the battery on day i-1; t bmax Indicates the maximum charging time of the battery in the empty state in the first basic parameter information of the battery; t bmin It indicates the minimum charging time of the battery in the empty state in the first basic parameter information of the battery; n indicates the number of charge and discharge times of the battery.

[0110] Since the battery life depends on the density of the dielectric material of the battery itself, the ambient temperature is one of the external factors that affect the battery life. Therefore, the battery life cannot be directly estimated based on the temperature alone; unsuitable ambient temperature will cause damage to the battery, thereby affecting the battery life. The greater the damage to the battery, the shorter the battery life. Therefore, the battery life is linearly negatively correlated with the battery damage value. Therefore, the battery life can be estimated by calculating the damage value corresponding to the battery currently carried by the drone to be replaced, and then based on the damage value corresponding to the battery.

[0111] While obtaining the first life prediction curve, the first environmental data in the first environmental data set can be sorted in chronological order to obtain a first environmental data sequence; a preset second correction parameter is obtained, and the life corresponding to the battery currently carried by the drone to be replaced is predicted based on the first environmental data in the first environmental data sequence and the preset second correction parameter to obtain a second predicted life information set; the second predicted life information in the second predicted life information set is plotted into a curve in chronological order to obtain a second life prediction curve.

[0112] Specifically, the life span of the battery currently carried by the battery-replacement drone may be predicted based on the first environmental data in the first environmental data sequence and the preset second correction parameter using the method shown in the following formula:

[0113]

[0114] D in the formula k2 represents the second predicted life information in the second predicted life information set corresponding to the battery; D s Indicates the damage value of the battery; n indicates the number of times the battery is charged and discharged; T ai represents the average discharge working environment temperature of the battery on day i; T ai-1 T represents the average discharge working environment temperature of the battery on day i-1; amax Indicates the maximum operating ambient temperature of the battery when discharging in a fully charged state in the first basic parameter information of the battery; T amin represents the minimum operating ambient temperature of the battery when it is discharged in a fully charged state in the first basic parameter information of the battery; δ2 represents a preset second correction parameter used to prevent erroneous small positive numbers other than zero, which can be obtained by conducting a simulation experiment on the battery currently carried by the battery-swappable drone; T bi represents the average temperature of the charging working environment corresponding to the battery on the i-th day; T bi-1 T represents the average temperature of the charging working environment corresponding to the battery on day i-1; bmaxIndicates the maximum operating ambient temperature of the battery when charging in an empty state in the first basic parameter information of the battery; T bmin Indicates the minimum operating environment temperature of the battery when charging in an empty state in the first basic parameter information of the battery.

[0115] After obtaining the first life prediction curve and the second life prediction curve, a polynomial fitting method can be used to determine a fitting polynomial for polynomial fitting based on the first life prediction curve and the second life prediction curve to obtain a target polynomial; and polynomial fitting is performed on the first life prediction curve and the second life prediction curve based on the target polynomial to obtain a third life prediction curve.

[0116] After obtaining the third life prediction curve, the first life information can be obtained by substituting the current moment as an input parameter into the curve function corresponding to the third prediction curve and solving it.

[0117] After obtaining the first lifespan information, the first power information can be obtained by reading the remaining power of the drone to be replaced displayed on a real-time monitoring device of the drone to be replaced.

[0118] After obtaining the first power information, the maximum energy intensity that can be released by the battery currently carried by the drone to be replaced under the state of the first life information and the first power information can be calculated according to the first power information and the first life information using a general battery energy calculation method to obtain the first state information.

[0119] In this example, the life of the battery currently carried by the battery-swapped drone is predicted based on the first historical working data in the first historical working data set and the first environmental data in the first environmental data set, to obtain a first life prediction curve and a second life prediction curve; the first life prediction curve and the second life prediction curve are curve-fitted to obtain a third life prediction curve, which improves the accuracy of the third life prediction curve, thereby improving the accuracy of predicting the life of the battery currently carried by the battery-swapped drone, and thereby improving.

[0120] In one possible implementation, a method for fitting the first life prediction curve and the second life prediction curve to obtain a third life prediction curve includes:

[0121] C1. Obtaining the life decay rate corresponding to each moment of the battery currently carried by the battery-replacement drone based on the first life prediction curve to obtain a first life decay rate set;

[0122] C2. Obtaining the life decay rate corresponding to each moment of the battery currently carried by the battery-replacement drone according to the second life prediction curve to obtain a second life decay rate set;

[0123] C3. Determining a fitting polynomial used for fitting the first life prediction curve and the second life prediction curve according to the first life decay rate in the first life decay rate set and the second life decay rate in the second life decay rate set, to obtain a target polynomial;

[0124] C4. Fitting the first life prediction curve and the second life prediction curve according to the target polynomial to obtain a third life prediction curve.

[0125] In the function curve, the first-order derivative of the function expression at any point on the function curve can be used to express the changing speed of the data at that point, and the first-order derivative of the function expression at any point on the function curve can be used to express the changing trend of the data at that point, that is, the rate of change. Therefore, the life decay rate corresponding to the battery currently carried by the drone to be replaced at each moment can be obtained by taking the first-order derivative of the function expression corresponding to the first life prediction curve or the second life prediction curve.

[0126] Specifically, the first-order derivative corresponding to each point on the first life prediction curve can be solved by a general first-order derivative method, and the first-order derivative corresponding to each point on the first life prediction curve can be determined as the life decay rate corresponding to the battery currently carried by the drone to be replaced at each moment, thereby obtaining a first life decay rate set.

[0127] While obtaining the first life decay rate set, the second life decay rate set can be obtained by using the same method as that used to obtain the first life decay rate set to obtain the first-order derivative corresponding to each point on the second life prediction curve.

[0128] After obtaining the second life decay rate set, the target degree can be obtained by determining the maximum degree of the fitting polynomial used for polynomial fitting; a general mathematical expression corresponding to the fitting polynomial used for polynomial fitting is constructed according to the target degree to obtain a reference polynomial; and the specific values ​​of the coefficients corresponding to each degree in the reference polynomial are determined according to the first life decay rate in the first life decay rate set and the second life decay rate in the second life decay rate set to obtain the target polynomial.

[0129] Specifically, the target polynomial may be obtained by determining the specific value of the coefficient corresponding to each degree in the reference polynomial according to the first lifetime decay rate in the first lifetime decay rate set and the second lifetime decay rate in the second lifetime decay rate set using the method shown in the following formula:

[0130]

[0131] In the formula, y represents the target polynomial; a0 represents the coefficient corresponding to the degree 0 in the polynomial; q0 represents the first predicted life, which can be used to represent a point on the first life prediction curve; a1 represents the coefficient corresponding to the degree 1 in the polynomial; a2 represents the coefficient corresponding to the degree 2 in the polynomial; a3 represents the coefficient corresponding to the degree 3 in the polynomial; q1 represents the second predicted life, which can be used to represent a point on the second life prediction curve; a4 represents the coefficient corresponding to the degree 4 in the polynomial; a5 represents the coefficient corresponding to the degree 5 in the polynomial; v1 represents the second life decay rate in the second life decay rate set; v0 represents the first life decay rate in the first life decay rate set; T d represents the time interval corresponding to two adjacent second predicted lifespans, which can be determined by user input or system default; T a It represents the time interval corresponding to two adjacent first predicted lifespans, which can be determined by user input or system default; T represents the current moment, which can be understood as the moment when the drone to be battery-replaced triggers the drone intelligent base station battery replacement control method for the first time; h represents the theoretical maximum service life of the battery, which can be obtained by conducting a simulation experiment on the battery to simulate the battery's usage time under the conditions of the best working environment and the best charging and discharging time.

[0132] After obtaining the target polynomial, the first goodness of fit can be obtained by calculating the goodness of fit between the target polynomial and the first life prediction curve; the second goodness of fit can be obtained by calculating the goodness of fit between the target polynomial and the second life prediction curve; it is determined whether the first goodness of fit and the second goodness of fit are both greater than or equal to a preset goodness of fit threshold, and if both are greater than or equal to the preset goodness of fit threshold, the target polynomial is determined as the third life prediction curve; if any one of the first goodness of fit and the second goodness of fit is or are both less than the preset goodness of fit threshold, the degree of the reference polynomial is adjusted to obtain a new reference polynomial; the coefficient corresponding to each degree of the new reference polynomial is recalculated to obtain the target polynomial, and the goodness of fit between the target polynomial and the first life prediction curve and the second life prediction curve is repeatedly calculated to obtain the first goodness of fit and the second goodness of fit, until the first goodness of fit and the second goodness of fit are both greater than or equal to the preset goodness of fit threshold.

[0133] Specifically, the goodness of fit between the target polynomial and the first life prediction curve or the second life prediction curve may be calculated by the method shown in the following formula to obtain the first goodness of fit or the second goodness of fit:

[0134]

[0135] In the formula, L represents the first goodness of fit or the second goodness of fit; M represents the mean square error between the first life prediction curve or the second life prediction curve and the target polynomial; n represents the number of data points extracted from the curves corresponding to the first life prediction curve, the second life prediction curve, and the target polynomial when calculating the first goodness of fit and the second goodness of fit, which can be determined by user input or system default; y i represents the data corresponding to the i-th data point among the n data points extracted from the first life prediction curve or the second life prediction curve; represents the data corresponding to the i-th data point among the n data points extracted from the curve corresponding to the target polynomial; M r Represents the root mean square error between the first life prediction curve or the second life prediction curve and the target polynomial.

[0136] In this example, the life decay rate of each point on the first life prediction curve and the second life prediction curve is obtained respectively to obtain the first life decay rate set and the second life decay rate set, and the fitting polynomial used for polynomial fitting is determined according to the first life decay rate in the first life decay rate set and the second life decay rate in the second life decay rate set to obtain the target polynomial, thereby improving the accuracy of the obtained target polynomial, and further improving the accuracy of battery replacement control of the intelligent base station of the drone.

[0137] In one possible implementation, a method for determining the best smart base station for the drone to be battery swapped based on the first state information and the current first location information of the drone to be battery swapped, and obtaining a target smart base station, includes:

[0138] D1. Determine the maximum remaining cruising range of the battery to be replaced without any human intervention based on the first status information, and obtain target cruising range information;

[0139] D2. Obtain all smart base stations within a circular range with the target cruising range as a radius based on the current first location information of the drone to be battery-swapped, to obtain a set of smart base stations to be determined;

[0140] D3. Obtain status information corresponding to each intelligent base station in the set of intelligent base stations to be determined, to obtain a second state information set;

[0141] D4. Determine the best smart base station for the drone to be battery-replaced in the set of smart base stations to be determined based on the first state information and the second state information in the second state information set, and obtain the target smart base station.

[0142] After obtaining the status information corresponding to the battery currently carried by the drone to be replaced, in order to ensure that the drone to be replaced can complete the battery replacement operation immediately, so as to avoid the drone to be replaced from losing power due to low power during the battery replacement process and crashing, and thus safely continue to perform subsequent flight missions, it is also necessary to determine an intelligent base station among the many intelligent base stations that can meet the battery replacement needs of the drone to be replaced, which can enable the drone to be replaced to complete the battery replacement operation in the shortest time, and obtain the target intelligent base station.

[0143] Specifically, the target flight speed can be obtained by obtaining the optimal flight speed of the UAV to be replaced; the target power consumption information of the UAV to be replaced is obtained; the target flight time is obtained by calculating the maximum flight time corresponding to the first state information of the UAV to be replaced based on the target power consumption information and the first state information; the target flight time is obtained by calculating the remaining maximum cruising range information of the UAV to be replaced based on the target flight speed and the target flight time. Among them, the optimal flight speed of the UAV to be replaced can be understood as the cruising speed corresponding to the lowest power consumption of the UAV to be replaced in the cruising state, which can be obtained by conducting simulation experiments on the UAV to be replaced.

[0144] Specifically, the maximum flight time corresponding to the first state information of the battery-replacement drone may be calculated by the following formula to obtain the target flight time:

[0145]

[0146] In the formula, S represents the target flight time of the UAV to be replaced with a battery; J represents the first state information; V represents the volume of the battery currently carried by the UAV to be replaced with a battery; and W represents the target power consumption information of the UAV to be replaced with a battery, which can be obtained by obtaining the average power consumption information of the UAV to be replaced with a battery during the flight.

[0147] After obtaining the target cruising range information, a circular area can be determined with the current first position of the drone to be replaced as the origin and the target cruising range information as the radius to obtain the target area; the smart base stations in the target area are determined as the smart base stations to be determined, and a set of smart base stations to be determined is obtained.

[0148] After obtaining the set of intelligent base stations to be determined, the number of fully charged batteries in the battery compartment of each intelligent base station to be determined in the set of intelligent base stations to be determined can be obtained through the wireless communication module carried by the intelligent base station to be determined, and a first battery quantity set can be obtained; the queue sequence of drones that need to perform battery replacement operations at the intelligent base station to be determined can be obtained, and a first queue sequence set can be obtained; the first battery quantity and first queue sequence corresponding to each intelligent base station to be determined in the set of intelligent base stations to be determined can be determined as the status information of the intelligent base station to be determined, and a second status information set can be obtained.

[0149] After obtaining the second state information set, the time required for the battery-changing drone to go to each of the to-be-determined smart base stations to complete the battery-changing operation in the first state can be predicted by a preset time prediction model based on the second state information in the second state information set to obtain the first time information set; the to-be-determined smart base station corresponding to the first time information with the smallest value in the first time information set is obtained to obtain the target time information, and the to-be-determined smart base station corresponding to the target time information is determined as the target smart base station. The preset time prediction model can be obtained by selecting a general machine learning model and inputting a training data set into the selected machine learning model for training; the general machine learning model includes but is not limited to a random forest model and a gradient boosting tree model; the training data set used for machine learning model training includes the time required for the battery-changing drone to go to different second state information under different first state information to complete the battery-changing operation.

[0150] In this example, the smart base station that can meet the battery replacement needs of the drone to be replaced is determined based on the first status information and the first location information, and a set of smart base stations to be determined is obtained. The status information corresponding to each smart base station to be determined in the set of smart base stations to be determined is obtained to obtain a second status information set. Based on the first status information and the second status information in the set of second status information, the smart base station to be determined that can meet the battery replacement needs of the drone to be replaced in the shortest time is determined in the set of smart base stations to be determined, and the target smart base station is obtained, thereby improving the accuracy of battery replacement control of the smart base station of the drone.

[0151] For the same example as above, please refer to Figure 3 , Figure 3 A schematic diagram of the structure of a terminal provided in an embodiment of the present application is shown in FIG. Figure 3 As shown, the system comprises a processor, an input device, an output device and a memory, which are connected to each other, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions, and the program includes instructions for executing the following steps;

[0152] Obtaining status information corresponding to the battery currently carried by the drone to be replaced, and obtaining first status information;

[0153] Determine the best smart base station for the drone to be battery-swapped based on the first state information and the current first location information of the drone to be battery-swapped, and obtain a target smart base station;

[0154] Generate a battery swap strategy corresponding to the battery-swapped drone based on the first location information, the second location information of the target intelligent base station, and the first state information to obtain a first battery swap strategy;

[0155] Generate a battery swap strategy for the target smart base station according to the first battery swap strategy to obtain a second battery swap strategy;

[0156] According to the second battery exchange strategy, the target intelligent base station is controlled to complete the battery exchange operation for the drone to be battery exchanged.

[0157] The above mainly introduces the scheme of the embodiment of the present application from the perspective of the execution process on the method side. It is understandable that, in order to implement the above functions, the terminal includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of the various examples described in the embodiments provided herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in a hardware or computer software driven hardware manner depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0158] The embodiment of the present application can divide the terminal into functional units according to the above method example. For example, each functional unit can be divided according to each function, or two or more functions can be integrated into one processing unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. It should be noted that the division of units in the embodiment of the present application is schematic and is only a logical functional division. In actual implementation, there may be other division methods.

[0159] In line with the above, please see Figure 4 , Figure 4 The present application embodiment provides a schematic diagram of the structure of a UAV intelligent base station battery replacement control system. Figure 4 As shown, the system includes:

[0160] The acquisition unit 301 is configured to acquire status information corresponding to the battery currently carried by the drone to be battery-replaced, and obtain first status information;

[0161] A determination unit 302 is configured to determine an optimal smart base station for the drone to be battery-swapped based on the first state information and the current first location information of the drone to be battery-swapped, thereby obtaining a target smart base station;

[0162] The first generating unit 303 is configured to generate a battery swap strategy corresponding to the battery swapping drone according to the first location information, the second location information of the target intelligent base station, and the first state information, to obtain a first battery swap strategy;

[0163] The second generating unit 304 is configured to generate a battery swap strategy for the target smart base station according to the first battery swap strategy to obtain a second battery swap strategy;

[0164] The control unit 305 is used to control the target intelligent base station to complete the battery replacement operation for the UAV to be battery-replaced according to the second battery replacement strategy.

[0165] In a possible implementation, the acquiring unit 301 is specifically configured to:

[0166] Obtain historical operating data of the battery currently carried by the drone to be battery-replaced, to obtain a first historical operating data set;

[0167] Acquire historical environmental data of the battery currently carried by the drone to be battery-replaced to obtain a first environmental data set;

[0168] Obtaining basic parameter information of the battery currently carried by the drone to be battery-replaced, to obtain first basic parameter information;

[0169] The status information corresponding to the battery currently carried by the drone to be replaced is determined based on the first historical working data set, the first environmental data set and the first basic parameter information to obtain the first status information.

[0170] In a possible implementation, the first acquiring unit 301 is specifically configured to:

[0171] Determine a life prediction curve corresponding to the battery currently carried by the drone to be replaced based on the first historical working data and the first basic parameter information in the first historical working data set, to obtain a first life prediction curve;

[0172] Determine the life prediction curve corresponding to the battery currently carried by the drone to be replaced based on the first environmental data and the first basic parameter information in the first environmental data set, and obtain a second life prediction curve;

[0173] Fitting the first life prediction curve and the second life prediction curve to obtain a third life prediction curve;

[0174] Predicting the remaining life of the battery currently carried by the drone to be replaced according to the third life prediction curve to obtain first life information;

[0175] Obtaining the remaining power information corresponding to the battery currently carried by the drone to be replaced, and obtaining the first power information;

[0176] The status information corresponding to the battery currently carried by the drone to be replaced is determined according to the first life information and the first power information to obtain the first status information.

[0177] In a possible implementation, the acquiring unit 301 is specifically configured to:

[0178] Obtaining the life decay rate corresponding to the battery currently carried by the battery-replacement drone at each moment according to the first life prediction curve, and obtaining a first life decay rate set;

[0179] Obtaining the life decay rate corresponding to the battery currently carried by the battery-replacement drone at each moment according to the second life prediction curve, and obtaining a second life decay rate set;

[0180] determining a fitting polynomial used for fitting the first life prediction curve and the second life prediction curve according to a first life decay rate in the first life decay rate set and a second life decay rate in the second life decay rate set, to obtain a target polynomial;

[0181] The first life prediction curve and the second life prediction curve are fitted according to the target polynomial to obtain a third life prediction curve.

[0182] In a possible implementation, the determining unit 302 is specifically configured to:

[0183] Determine the maximum remaining cruising range information of the battery to be replaced without anyone according to the first state information, and obtain target cruising range information;

[0184] According to the current first location information of the UAV to be replaced with a battery, all smart base stations within a circular range with the target cruising range information as a radius are obtained to obtain a set of smart base stations to be determined;

[0185] Acquire status information corresponding to each intelligent base station in the set of intelligent base stations to be determined to obtain a second state information set;

[0186] According to the first state information and the second state information in the second state information set, the best smart base station for the drone to be replaced is determined in the set of smart base stations to be determined, and the target smart base station is obtained.

[0187] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program enables the computer to execute part or all of the steps of any one of the drone intelligent base station battery replacement control methods recorded in the above method embodiments.

[0188] An embodiment of the present application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program enables a computer to execute part or all of the steps of any one of the drone intelligent base station battery replacement control methods recorded in the above method embodiments.

[0189] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0190] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0191] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0192] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0193] In addition, the functional units in the various embodiments of the application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software program modules.

[0194] If the integrated unit is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, including a number of instructions for enabling a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: various media that can store program codes, such as a U disk, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0195] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, which may include: a flash drive, a read-only memory, a random access memory, a magnetic disk or an optical disk, etc.

[0196] The above is a detailed introduction to the embodiments of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for those skilled in the art, according to the idea of ​​the present application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for controlling battery replacement of an intelligent base station of a drone, characterized in that: The method comprises: Obtaining status information corresponding to the battery currently carried by the drone to be replaced, and obtaining first status information; Determine the best smart base station for the drone to be battery-swapped based on the first state information and the current first location information of the drone to be battery-swapped, and obtain a target smart base station; Generate a battery swap strategy corresponding to the battery-swapped drone based on the first location information, the second location information of the target intelligent base station, and the first state information to obtain a first battery swap strategy; Generate a battery swap strategy for the target smart base station according to the first battery swap strategy to obtain a second battery swap strategy; According to the second battery exchange strategy, the target intelligent base station is controlled to complete the battery exchange operation for the drone to be battery exchanged.

2. The UAV intelligent base station battery replacement control method according to claim 1 is characterized in that: The step of obtaining the status information corresponding to the battery currently carried by the drone to be battery-replaced to obtain the first status information includes: Obtain historical operating data of the battery currently carried by the drone to be battery-replaced, to obtain a first historical operating data set; Acquire historical environmental data of the battery currently carried by the drone to be battery-replaced to obtain a first environmental data set; Obtaining basic parameter information of the battery currently carried by the drone to be battery-replaced, to obtain first basic parameter information; The status information corresponding to the battery currently carried by the drone to be replaced is determined based on the first historical working data set, the first environmental data set and the first basic parameter information to obtain the first status information.

3. The UAV intelligent base station battery replacement control method according to claim 2 is characterized in that: The determining, based on the first historical working data set, the first environmental data set, and the first basic parameter information, the status information corresponding to the battery currently carried by the drone to be battery-replaced, and obtaining the first status information includes: Determine a life prediction curve corresponding to the battery currently carried by the drone to be replaced based on the first historical working data and the first basic parameter information in the first historical working data set, to obtain a first life prediction curve; Determine the life prediction curve corresponding to the battery currently carried by the drone to be replaced based on the first environmental data and the first basic parameter information in the first environmental data set, and obtain a second life prediction curve; Fitting the first life prediction curve and the second life prediction curve to obtain a third life prediction curve; Predicting the remaining life of the battery currently carried by the drone to be replaced according to the third life prediction curve to obtain first life information; Obtaining the remaining power information corresponding to the battery currently carried by the drone to be replaced, and obtaining the first power information; The status information corresponding to the battery currently carried by the drone to be replaced is determined according to the first life information and the first power information to obtain the first status information.

4. The UAV intelligent base station battery replacement control method according to claim 3 is characterized in that: The fitting of the first life prediction curve and the second life prediction curve to obtain a third life prediction curve includes: Obtaining the life decay rate corresponding to the battery currently carried by the battery-replacement drone at each moment according to the first life prediction curve, and obtaining a first life decay rate set; Obtaining the life decay rate corresponding to the battery currently carried by the battery-replacement drone at each moment according to the second life prediction curve, and obtaining a second life decay rate set; determining a fitting polynomial used for fitting the first life prediction curve and the second life prediction curve according to a first life decay rate in the first life decay rate set and a second life decay rate in the second life decay rate set, to obtain a target polynomial; The first life prediction curve and the second life prediction curve are fitted according to the target polynomial to obtain a third life prediction curve.

5. The UAV intelligent base station battery replacement control method according to claim 4 is characterized in that: The step of determining the optimal smart base station for the drone to be battery-swapped based on the first state information and the current first location information of the drone to be battery-swapped, and obtaining a target smart base station, includes: Determine the maximum remaining cruising range information of the battery to be replaced without anyone according to the first state information, and obtain target cruising range information; According to the current first location information of the UAV to be replaced with a battery, all smart base stations within a circular range with the target cruising range information as a radius are obtained to obtain a set of smart base stations to be determined; Acquire status information corresponding to each intelligent base station in the set of intelligent base stations to be determined to obtain a second state information set; According to the first state information and the second state information in the second state information set, the best smart base station for the drone to be replaced is determined in the set of smart base stations to be determined, and the target smart base station is obtained.

6. A UAV intelligent base station battery replacement control system, characterized in that: The system comprises: An acquiring unit, configured to acquire status information corresponding to a battery currently carried by the drone to be battery-replaced, and obtain first status information; a determination unit, configured to determine an optimal intelligent base station for the drone to be battery-swapped based on the first state information and the current first location information of the drone to be battery-swapped, and obtain a target intelligent base station; A first generating unit is configured to generate a battery swap strategy corresponding to the battery-swapped drone according to the first location information, the second location information of the target intelligent base station, and the first state information, to obtain a first battery swap strategy; A second generating unit is configured to generate a battery swap strategy for the target smart base station according to the first battery swap strategy to obtain a second battery swap strategy; The control unit is used to control the target intelligent base station to complete the battery replacement operation for the UAV to be replaced according to the second battery replacement strategy.

7. The UAV intelligent base station battery replacement control system according to claim 6 is characterized in that: In terms of obtaining the status information corresponding to the battery currently carried by the drone to be battery-replaced and obtaining the first status information, the first obtaining unit is specifically configured to: Obtain historical operating data of the battery currently carried by the drone to be battery-replaced, to obtain a first historical operating data set; Acquire historical environmental data of the battery currently carried by the drone to be battery-replaced to obtain a first environmental data set; Obtaining basic parameter information of the battery currently carried by the drone to be battery-replaced, to obtain first basic parameter information; The status information corresponding to the battery currently carried by the drone to be replaced is determined based on the first historical working data set, the first environmental data set and the first basic parameter information to obtain the first status information.

8. The UAV intelligent base station battery replacement control system according to claim 7 is characterized in that: In determining the status information corresponding to the battery currently carried by the drone to be battery-replaced based on the first historical working data set, the first environmental data set, and the first basic parameter information, and obtaining the first status information, the first acquiring unit is specifically configured to: Determine a life prediction curve corresponding to the battery currently carried by the drone to be replaced based on the first historical working data and the first basic parameter information in the first historical working data set, to obtain a first life prediction curve; Determine the life prediction curve corresponding to the battery currently carried by the drone to be replaced based on the first environmental data and the first basic parameter information in the first environmental data set, and obtain a second life prediction curve; Fitting the first life prediction curve and the second life prediction curve to obtain a third life prediction curve; Predicting the remaining life of the battery currently carried by the drone to be replaced according to the third life prediction curve to obtain first life information; Obtaining the remaining power information corresponding to the battery currently carried by the drone to be replaced, and obtaining the first power information; The status information corresponding to the battery currently carried by the drone to be replaced is determined according to the first life information and the first power information to obtain the first status information.

9. A terminal, characterized in that: It includes a processor, an input device, an output device and a memory, and the processor, input device, output device and memory are interconnected, wherein the memory is used to store a computer program, and the computer program includes program instructions. The processor is configured to call the program instructions and execute the drone intelligent base station battery replacement control method as described in any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which includes program instructions. When the program instructions are executed by the processor, the processor executes the drone intelligent base station battery replacement control method according to any one of claims 1 to 5.

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