CN113954664B - A wireless charging method and system for a vehicle-mounted drone - Google Patents

Abstract

The application discloses a wireless charging method and system for a vehicle-mounted unmanned aerial vehicle. Firstly, accurately sampling state characteristics of uncertainty of an unmanned aerial vehicle, and evaluating and classifying sampling information; secondly, a plurality of unmanned aerial vehicle-electric vehicle transaction modes are provided by combining environmental information, path planning and the like, and the maximum reciprocal win-win of the unmanned aerial vehicle and the electric vehicle is realized through energy distribution and income estimation; finally, the unmanned aerial vehicle can select the matched charging-end electric automobile information through the system screening, and the matched charging-end electric automobile is selected automatically. The vehicle-mounted unmanned aerial vehicle wireless charging method and system provided by the application can fully utilize the flexibility of the electric vehicle to supply power for the unmanned aerial vehicle, and can conveniently and quickly screen an appropriate energy provider for the unmanned aerial vehicle through an energy calculation and price estimation algorithm, so that energy interaction between the two can be completed, and the maneuverability and endurance mileage of the unmanned aerial vehicle can be improved.

Description

A wireless charging method and system for a vehicle-mounted drone

technical field

The invention relates to the technical field of unmanned aerial vehicles, in particular to a wireless charging method and system for a vehicle-mounted unmanned aerial vehicle.

Background technique

In recent years, the global drone industry has maintained rapid growth, and the application scenarios and major markets are transforming from military use to commercial and consumer drones. my country's UAV market has experienced more than 50 years of development and cultivation, and has accumulated a large number of core technologies and application scenarios. In 2016, the market size of my country's drone industry was about 9.1 billion yuan, and by 2019, the figure had reached 29 billion yuan. With the full promotion of 5G and the gradual maturity of 5G+ drone technology, the drone market will expand rapidly. It is estimated that by 2025, the total size will exceed 75 billion. The application field of drones is expanding and extending at a high speed. The UAV application industry has developed from aerial photography entertainment to energy and power inspection, agricultural plant protection, UAV logistics, security rescue and other livelihood fields. Especially under the new crown pneumonia epidemic, the demand in these fields has increased significantly.

With the increasing market demand for drones, the bottleneck of drone endurance is gradually becoming prominent. At present, the endurance of commercial UAVs is generally about half an hour, and the endurance cannot meet the requirements of UAVs that operate for a long time. Therefore, improving the endurance of UAVs is an important technical guarantee for the development of UAVs.

There are generally two ways to increase the endurance of UAVs: carrying more batteries or recharging power multiple times. The structure and size of the UAV limit the volume of its battery, which in turn limits the battery capacity, making it difficult to increase the battery capacity and prolong the flight time. The traditional multiple round trips for wired charging at a fixed location require more manpower management. Wireless charging technology is an important way to improve the endurance of UAVs. It can supply power for UAVs multiple times, eliminating the need for additional manpower wiring operations. It is expected to fundamentally solve the problem of flight time, The short-range performance bottleneck can effectively improve the UAV's maneuverability and continuous combat capability.

However, there are still obvious technical barriers to the application of wireless charging technology in drones: how to maximize the mobility of wireless charging for drones, that is, how to place wireless chargers can reduce the It is one of the urgent research directions of UAV wireless charging technology to really optimize the maneuverability of UAVs and facilitate equipment maintenance. Therefore, the present invention proposes a design based on an electric vehicle-unmanned aerial vehicle wireless charging system.

As a provider of electric energy, electric vehicles have strong mobility, which can reduce the time and energy loss of drones going to and from the charging location, so that drones can be charged anytime and anywhere. Owners of electric vehicles can earn a certain amount of income from it, and both parties can achieve mutual benefit and win-win results.

In addition, vehicle-mounted drone wireless charging can improve the accuracy of drone wireless charging. The positioning system of drones is divided into GPS system and visual positioning system. The accuracy of the two is quite different. Taking DJI Phantom4ADVANCED as an example, the visual positioning accuracy of horizontal hovering is 0.3m, and the accuracy of GPS positioning is 0.3m. It is ±1.5m. When UAVs work outdoors, they mainly use the GPS positioning system, mainly because the visual positioning system in open places cannot collect effective data, so they do not participate in the work. If vehicle-mounted drones are used for wireless charging, electric vehicles can be used as effective visual positioning data, so that high-precision visual positioning can be used to find wireless chargers for drones.

Contents of the invention

The purpose of the present invention is to provide a wireless charging method and system for vehicle-mounted drones. Through dynamic sampling and analysis of the charging behavior of drones, a series of energy distribution and income prediction such as energy numerical calculation and price calculation are performed, and the above The goal is to optimize the system algorithm, accurately match and identify electric vehicles at the charging end, greatly saving time and reducing additional consumption.

In order to achieve the above object, the present invention provides the following technical solutions:

The present invention firstly provides a wireless charging method for a vehicle-mounted drone, comprising the following steps:

S1. Information collection: including the real-time information collection of the unmanned aerial vehicle to be charged and the electric vehicle at the charging end in the energy pool, as well as the collection of environmental information, traffic information, and path planning. Electric vehicles powered by humans and machines;

S2. Trading mode selection: divided into three scenarios for trading mode:

Scenario 1: Taking the location of the electric vehicle as the transaction location: In this scenario, the electric vehicle is relatively stationary, and the drone flies to the location of the electric vehicle to complete the charging process;

Scenario 2: The location of the UAV is used as the transaction location: In this scenario, the UAV is relatively stationary, and the electric vehicle drives to the location of the UAV to complete the charging process;

Scenario 3: The best place selected by the system is the transaction location: the transaction location in this scenario is a certain location between the drone and the electric vehicle, and the electric vehicle and the drone drive to the location to complete the charging process;

S3. Energy calculation and price estimation: respectively calculate the total power required by the drone and the energy required by the electric vehicle in the three scenarios, and estimate the charging price Price by formula (1) or (2):

Price＝EEV_Supply*Charging_price(1)

Price＝EEV_Supply*Charging_price-EEV_Supply(P_initial+Cost_d)(2)

EEV_Supply is the energy that the electric vehicle needs to provide, Charging_price is the charging price, P_initial is the purchase price of the vehicle, and Cost_d is the battery loss of the vehicle;

S4. Screening and matching: Through energy calculation and price estimation, the charging-end electric vehicle with the lowest price is the first choice, and one to three charging-end electric vehicles that meet the requirements are selected, and the information of these charging-end electric vehicles is sent to no one On the machine side, the UAV makes the final selection through the controller, and then sends the acceptance information of the UAV to the selected electric vehicle at the charging end to complete the whole process of screening and matching.

Further, in step S1, the real-time information of the unmanned aerial vehicle to be charged includes the unmanned aerial vehicle number N, the real-time geographic location of the unmanned aerial vehicle, the total battery power EUAV_Battery of the unmanned aerial vehicle, the current electric quantity EUAV_Current of the unmanned aerial vehicle battery and the required The initial power

EUAV_0 = EUAV_Battery - EUAV_Current.

Further, in step S1, the real-time information of the electric vehicle at the charging end in the energy pool includes the license plate number L of the electric vehicle in the energy pool, the geographical location of the electric vehicle in the energy pool, battery information and the purchase price of electricity.

Further, in the first scenario of step S2, the flight mileage efficiency of the UAV is: η_UAV, and the extra flight distance of the UAV is: D_UAV; the extra power required by the UAV is EUAV_Extra=D_UAV/η_UAV; in this scenario, the UAV The current power of the battery meets the extra power for the drone to fly to the location of the electric vehicle, that is, EUAV_Current>EUAV_Extra.

Further, in the second scenario of step S2, the driving distance of the electric vehicle is: D_EV, the mileage efficiency of the electric vehicle is: η_EV, and the additional electricity that the electric vehicle needs to provide is EEV_Extra=D_EV/η_EV.

Further, in the third scenario of step S2, on the premise that the current battery power of the drone is sufficient to support the extra power for flying to this location, the electric vehicle will also provide additional power to travel to this location.

Further, in step S3, the total power required by the UAV is carried out based on three scenarios:

The formula for calculating the total power required by the UAV in Scenario 1 is:

EUAV_Total = EUAV_0 + EUAV_Extra;

The formula for calculating the total power required by the UAV in Scenario 2 is:

EUAV_Total = EUAV_0;

The formula for calculating the total power required by the UAV in Scenario 3 is:

EUAV_Total = EUAV_0 + EUAV_Extra;

Among them, EUAV_Total is the total power required by the UAV, EUAV_0 is the initial power required by the UAV, and EUAV_Extra is the extra power required by the UAV.

Further, in step S3, the energy that the electric vehicle needs to provide is performed based on three scenarios:

The energy calculation formula that electric vehicles need to provide in Scenario 1 is:

EEV_Supply=EUAV_Total/charger efficiency;

The energy calculation formula that electric vehicles need to provide in Scenario 2 is:

EEV_Supply=EUAV_0/charger efficiency+EEV_Extra;

The formula for calculating the energy that electric vehicles need to provide in Scenario 3 is:

EEV_Supply=EUAV_Total/charger efficiency+EEV_Extra;

Among them, EEV_Supply is the energy that the electric vehicle needs to provide, EUAV_Total is the total power required by the drone, EUAV_0 is the initial power required by the drone, EEV_Extra is the additional power that the electric vehicle needs to provide, EEV_Extra=D_EV/η_EV, D_EV is the electric Vehicle driving distance, η_EV is the mileage efficiency of electric vehicles.

Further, in step S4, the information sent to the electric vehicle at the charging end of the drone includes charging location, charging price, charging time and license plate number.

The present invention also provides a wireless charging system for a vehicle-mounted UAV, including an electric vehicle for wireless charging of a vehicle-mounted UAV and a cloud controller, so as to realize the above-mentioned charging method, and the cloud controller includes:

The signal acquisition module is used for real-time information collection of the unmanned aerial vehicle to be charged and the electric vehicle at the charging end after the unmanned aerial vehicle to be charged sends a charging request, and sends the sampled information to the transaction mode selection module;

The transaction mode selection module is used to select the transaction mode of drone electric vehicles in combination with environmental information and path planning, and screen out the transaction location;

The energy calculation and price estimation module is used to calculate the total power required by drones under different transaction modes, the energy that electric vehicles need to provide, and the charging price;

The screening and matching module, based on energy calculation and price estimation, is used to select the most matching charging-end electric vehicle in the charging-end energy pool, and send the relevant information of the charging-end electric vehicle to the drone; the drone passes the controller Make the final selection, and then send the received information of the drone to the selected electric vehicle at the charging end to complete the whole process of screening and matching.

Compared with prior art, the beneficial effect of the present invention is:

The vehicle-mounted UAV wireless charging method and system proposed by the present invention can make full use of the flexibility of electric vehicles to power UAVs, and through energy calculation and price estimation algorithms, it can conveniently and quickly select suitable energy supplies for UAVs. side, complete the energy interaction between the two, and improve the maneuverability and cruising range of the UAV.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings that are required in the embodiments. Obviously, the accompanying drawings in the following description are only described in the present invention For some embodiments of the present invention, those skilled in the art can also obtain other drawings according to these drawings.

Fig. 1 is the process flow of the wireless charging method for vehicle-mounted drone provided by the embodiment of the present invention;

Fig. 2 is a model of a wireless charging system for a vehicle-mounted UAV provided by an embodiment of the present invention.

Detailed ways

The invention provides a wireless charging method and system for a vehicle-mounted UAV. Through dynamic sampling and analysis of the charging behavior of the UAV, a series of energy distribution and income prediction such as energy numerical calculation and price calculation are performed, and the above objectives are systematically Algorithm optimization, accurate matching and identification of electric vehicles at the charging end, greatly saving time and reducing additional consumption.

The charging behavior of UAVs is significantly affected by random events such as target tasks and weather, and it is impossible for UAVs to be completely stationary in the air. The charging system is a system with dynamic load characteristics.

Therefore, first of all, it is necessary to accurately sample the uncertain state characteristics of UAVs, evaluate and classify the sampled information; secondly, combine environmental information, path planning, etc., to propose a variety of UAV-electric vehicle transaction modes, through energy Distribution and revenue estimation, to achieve the maximum mutual benefit and win-win between drones and electric vehicles; finally, drones can choose matching charging-end electric vehicles by themselves through the information of matching charging-end electric vehicles screened out by the system.

In order to better understand the technical solution, the method of the present invention will be described in detail below in conjunction with the accompanying drawings.

The present invention provides a wireless charging method for a vehicle-mounted unmanned aerial vehicle, as shown in Figure 1, comprising the following steps:

(1) Information collection

The information collection of the UAV is mainly the UAV number (N), the real-time geographic location (UAV_x, UAV_y, UAV_z), the total power of the UAV battery (EUAV_Battery), the current power of the UAV battery (EUAV_Current), and the unmanned The initial power required by the battery EUAV_0=EUAV_Battery-EUAV_Current.

The information collection of electric vehicles at the charging end is mainly based on the electric vehicle license plate number (L) in the energy pool, geographical location (EV_x, EV_y, EV_z), battery information, and electricity purchase price.

The electric vehicle in the energy pool is an electric vehicle that provides energy for the drone by agreement, which can ensure that the system can search for the electric vehicle at the charging end in the area at any time.

The information collection in the optimization part also includes environmental information, traffic information, etc.

(2) Trading mode selection

Since both drones and electric vehicles have strong mobility, the selection of trading locations is highly diverse, thereby extending the diversity of trading models. In this part of the research, the transaction mode is divided into three scenarios:

Scenario 1: Taking the location of the electric vehicle as the transaction location:

In this scenario, the electric car is relatively stationary (if the driver is not in the car, you can choose to turn on the remote control charging mode), and the drone flies to the location of the electric car to complete the charging process. Considering that drone flight is not affected by ground traffic conditions, and the time required for flight may be shorter than the driving time of vehicles, the system will first use this scenario as the preferred transaction mode.

UAV flight mileage efficiency: η_UAV, UAV extra flight distance: D_UAV, UAV needs extra power as EUAV_Extra=D_UAV/η_UAV;

The condition for this scenario to be established is that the current power of the drone’s battery must meet the additional power required for the drone to fly to the location of the electric vehicle, that is, EUAV_Current>EUAV_Extra;

Scenario 2: Take the location of the drone as the trading location:

In this scenario, the drone is stationary, and the electric vehicle drives to the location of the drone to complete the charging process. Electric vehicle driving distance: D_EV, electric vehicle mileage efficiency: η_EV, electric vehicle needs to provide additional electricity as EEV_Extra=D_EV/η_EV;

Scenario 3: Use the best location selected by the system as the trading location:

The transaction location in this scenario is a certain location between the UAV and the electric vehicle, and the transaction location is screened out through the system's path planning and optimization. On the premise that the current power of the UAV battery is sufficient to support the extra power for flying to this location, the electric vehicle will also provide extra power to drive to this location. The advantage is that compared with Scenario 2, it can effectively shorten the waiting time for charging.

(3) Energy calculation and price estimation

Energy calculations are performed separately based on three scenarios.

Calculation of the total power required by the UAV in Scenario 1:

EUAV_Total = EUAV_0 + EUAV_Extra;

Electric vehicles need to provide energy:

EEV_Supply=EUAV_Total/charger efficiency;

Calculation of the total power required by the UAV in Scenario 2:

EUAV_Total = EUAV_0;

Electric vehicles need to provide energy:

EEV_Supply=EUAV_0/charger efficiency+EEV_Extra;

Calculation of the total power required by the UAV in Scenario 3:

EUAV_Total = EUAV_0 + EUAV_Extra;

Electric vehicles need to provide energy:

EEV_Supply=EUAV_Total/charger efficiency+EEV_Extra.

Price estimate:

The charging price (Charging_price) is a value that can be optimized, and there are many ways to set the charging price. Determine the target charging end vehicle from the evaluation dimension of the charging cost paid by the drone, then the charging end vehicle with the smallest charging cost can be determined as the target charging end vehicle, and the charging cost can be determined by Price (Price) = EEV_Supply*Charging_price Calculated, that is, multiplying the electric energy to be consumed by the vehicle at the charging end by the preset selling price can obtain the charging cost for the client vehicle to call the vehicle at the charging end.

Of course, according to different application scenarios, the income of charging-end vehicles can also be used as the screening target. The price estimation can be obtained from the vehicle parameters reported by each charging-end vehicle (P_initial) and battery information, and from the battery information The battery loss (Cost_d) of each charging-end vehicle is obtained from the vehicle, and then based on the purchase price of each charging-end vehicle, battery loss, power to be consumed and the preset selling price of the electric vehicle energy management system, the preset price The calculation formula calculates the expected price for each charging end vehicle to charge the client UAV. The price calculation formula can be:

Price＝EEV_Supply*Charging_price-EEV_Supply(P_initial+Cost_d)

In order to create the maximum benefit for the driver of the charging-end vehicle, the calculated charging-end vehicle with the largest expected benefit can be determined as the final target charging-end vehicle. In this way, when the target charging vehicle provides charging services for the client UAV, the client UAV can get convenient charging services, and the target charging vehicle can get a certain reward, achieving mutual benefit and win-win for both parties.

(4) Screening and matching

Through energy calculation and price estimation, the cloud controller will screen out one to three charging-end electric vehicles that meet the requirements. The selection criteria in this system is from the perspective of drones, and the charging-end electric vehicle with the lowest price is the first choice. , After the cloud controller sends the information (charging location, charging price, charging time, license plate) of these electric vehicles at the charging end to the UAV, the UAV can make the final choice through the controller. Then send the acceptance information of the drone to the selected electric vehicle at the charging end to complete the whole process of screening and matching.

The present invention provides a wireless charging system for a vehicle-mounted UAV, as shown in Figure 2, comprising an electric vehicle and a cloud controller for wireless charging of a vehicle-mounted UAV, so as to realize the above-mentioned charging method, and the cloud controller includes:

The signal acquisition module is used for real-time information collection of the unmanned aerial vehicle to be charged and the electric vehicle at the charging end after the unmanned aerial vehicle to be charged sends a charging request, and sends the sampled information to the transaction mode selection module;

The transaction mode selection module is used to select the transaction mode of drone electric vehicles in combination with environmental information and path planning, and screen out the transaction location;

The energy calculation and price estimation module is used to calculate the total power required by drones under different transaction modes, the energy that electric vehicles need to provide, and the charging price;

The screening and matching module, based on energy calculation and price estimation, is used to select the most matching charging-end electric vehicle in the charging-end energy pool, and send the relevant information of the charging-end electric vehicle to the drone; the drone passes the controller Make the final selection, and then send the received information of the drone to the selected electric vehicle at the charging end to complete the whole process of screening and matching.

The precise matching process of electric vehicles at the charging end is that after the UAV sends a charging request to the cloud controller, the system signal acquisition module first collects real-time information (geographic location information, battery energy information, etc.) of the UAV to be charged, and then trades The mode selection module selects the trading mode of UAV electric vehicles, and screens out the trading locations. Within the preset range, through the calculation of the energy value in the system and the calculation of the price estimation module, and the selection and matching module based on the energy calculation and price estimation. Screening, select the most matching electric vehicle at the charging end from the energy pool at the charging end, and send the relevant information about the electric vehicle at the charging end to the drone. According to the system instructions, the UAV can find the designated electric vehicle at the charging end, and confirm the electric vehicle at the charging end through license plate recognition.

The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: it can still be described in the foregoing embodiments The recorded technical solutions are modified, or some of the technical features are equivalently replaced, but these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (4)

1. A vehicle-mounted unmanned aerial vehicle wireless charging method, is characterized in that, comprises the following steps: S1. Information collection: including the real-time information collection of the unmanned aerial vehicle to be charged and the electric vehicle at the charging end in the energy pool, as well as the collection of environmental information, traffic information, and path planning. An electric vehicle with energy provided by a human-machine; the real-time information of the drone to be charged includes the drone number N, the real-time geographic location of the drone, the total battery power of the drone EUAV_Battery, the current battery power of the drone EUAV_Current and the drone The required initial power EUAV_0=EUAV_Battery-EUAV_Current; S2. Trading mode selection: divided into three scenarios for trading mode: Scenario 1: Taking the location of the electric vehicle as the transaction location: In this scenario, the electric vehicle is relatively stationary, and the drone flies to the location of the electric vehicle to complete the charging process; In scenario 1, the flight mileage efficiency of the UAV is: η_UAV, and the extra flight distance of the UAV is: D_UAV; the extra power required by the UAV is EUAV_Extra=D_UAV/η_UAV; The extra power for the man-machine to fly to the location of the electric vehicle, that is, EUAV_Current>EUAV_Extra; Scenario 2: The location of the UAV is used as the transaction location: In this scenario, the UAV is relatively stationary, and the electric vehicle drives to the location of the UAV to complete the charging process; In Scenario 2, the driving distance of the electric vehicle is: D_EV, the mileage efficiency of the electric vehicle is: η_EV, and the additional power that the electric vehicle needs to provide is EEV_Extra=D_EV/η_EV; Scenario 3: The best place selected by the system is the transaction location: the transaction location in this scenario is a certain location between the drone and the electric vehicle, and the electric vehicle and the drone drive to the location to complete the charging process; In Scenario 3, on the premise that the current power of the drone’s battery is sufficient to support the extra power to fly to this location, the electric car will also provide extra power to drive to this location; S3. Energy calculation and price estimation: respectively calculate the total power required by the drone and the energy required by the electric vehicle in the three scenarios, and use the formula (1) - estimate the charging price Price1, and calculate it through the formula (2) The expected price Price2 of each charging terminal vehicle charging the client drone: Price1=EEV_Supply*Charging_price(1) Price2=EEV_Supply*Charging_price-EEV_Supply (P_initial +Cost_d) (2) EEV_Supply is the energy that electric vehicles need to provide, Charging_price is the charging price, P_initial is the purchase price of the vehicle, and Cost_d is the battery loss of the vehicle; the formula for calculating the total power required by the drone in scene 1 is: EUAV_Total=EUAV_0+EUAV_Extra; The formula for calculating the total power required by the UAV in Scenario 2 is: EUAV_Total = EUAV_0; The formula for calculating the total power required by the UAV in Scenario 3 is: EUAV_Total=EUAV_0+EUAV_Extra; Among them, EUAV_Total is the total power required by the UAV, EUAV_0 is the initial power required by the UAV, and EUAV_Extra is the extra power required by the UAV; The energy calculation formula that electric vehicles need to provide in Scenario 1 is: EEV_Supply=EUAV_Total/charger efficiency; The energy calculation formula that electric vehicles need to provide in Scenario 2 is: EEV_Supply=EUAV_0/charger efficiency+EEV_Extra; The formula for calculating the energy that electric vehicles need to provide in Scenario 3 is: EEV_Supply=EUAV_Total/charger efficiency+EEV_Extra; Among them, EEV_Supply is the energy that electric vehicles need to provide, EUAV_Total is the total power required by drones, EUAV_0 is the initial power required by drones, EEV_Extra is the additional power that electric vehicles need to provide, EEV_Extra=D_EV/η_EV, D_EV is electric Vehicle driving distance, η_EV is the mileage efficiency of electric vehicles; S4. Screening and matching: Through energy calculation and price estimation, the charging-end electric vehicle with the lowest charging cost is the first choice, and one to three charging-end electric vehicles that meet the requirements are selected, and the information of these charging-end electric vehicles is sent To the UAV side, the UAV makes the final selection through the controller, and then sends the acceptance information of the UAV to the selected electric vehicle at the charging end to complete the whole process of screening and matching. 2. The vehicle-mounted UAV wireless charging method according to claim 1, characterized in that, in step S1, the real-time information of the electric vehicle at the charging end in the energy pool includes the license plate number L of the electric vehicle in the energy pool, Geographical location of electric vehicles, battery information and purchase price of electric vehicles. 3. The wireless charging method for vehicle-mounted drones according to claim 1, wherein in step S4, the information sent to the electric vehicle at the charging end of the drone includes charging location, charging price, charging time and license plate number. 4. A vehicle-mounted unmanned aerial vehicle wireless charging system, is characterized in that, comprises the electric vehicle of vehicle-mounted UAV wireless charging and cloud controller, to realize the charging method described in claim 1-3, described cloud controller comprises: The signal acquisition module is used for real-time information collection of the unmanned aerial vehicle to be charged and the electric vehicle at the charging end after the unmanned aerial vehicle to be charged sends a charging request, and sends the sampled information to the transaction mode selection module; The transaction mode selection module is used to select the transaction mode of drone electric vehicles in combination with environmental information and path planning, and screen out the transaction location; The energy calculation and price estimation module is used to calculate the total power required by drones under different transaction modes, the energy that electric vehicles need to provide, and the charging price; The screening and matching module, based on energy calculation and price estimation, is used to select the most matching charging-end electric vehicle in the charging-end energy pool, and send the relevant information of the charging-end electric vehicle to the drone; the drone passes the controller Make the final selection, and then send the received information of the drone to the selected electric vehicle at the charging end to complete the whole process of screening and matching.